the journal of solid waste technology and management

THE JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT Formerly The Journal of Resource Management and Technology (Volumes 12-22) Formerly NCRR Bulletin (Volumes 1-11) May 2015

Volume 41

Number 2

THE JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT ISSN: 1088-1697

FOUNDER: Iraj Zandi University of Pennsylvania U.S.A.

EDITOR: Ronald L. Mersky Widener University U.S.A.

SENIOR ASSOCIATE EDITOR: Wen K. Shieh University of Pennsylvania U.S.A.

ASSOCIATE EDITORS Haluk Akgun Department of Geological Engineering Middle East Technical University Ankara 06531, Turkey Email: [email protected]

Adam Read Knowledge Leader – Waste & Resources Management AEA The Gemini Building, Harwell IBC Didcot Oxon OX11 0QR, U.K.

Ofira Ayalon Samuel Neaman Institute, Technion, Haifa; Natural Resources & Environmental Research Center, Faculty of Management University of Haifa Haifa, 3498838 , Israel Email: [email protected]

Email: [email protected]

Cristina Braga Universidade Federal do Paraná Setor de Tecnologia Departamento de Engenharia Química Centro Politécnico Cx.P. 19011 Curitiba - PR - 81531-990, Brasil

EDITORIAL BOARD


Shoou-Yuh Chang Department of Civil Engineering North Carolina A&T State University Greensboro, NC 27411, U.S.A. Email: [email protected]

David Smith The Regional Municipality of Niagara (retired) Canada Email: [email protected]

Magdy Abdelrahman North Dakota State University U.S.A. Amimul Ahsan Universiti Putra Malaysia (UPM) Malaysia

Sarvesh Chandra Indian Institute of Technology Kanpur India


Jess Everett Rowan University U.S.A


Ilona Sárvári Horváth Swedish Centre for Resource Recovery University of Borås SE-501 90 Borås, Sweden Email: [email protected] Chukwu Onu Department of Civil Engineering Southern University Southern Branch Post Office Baton Rouge, LA 70813, U.S.A.

Terry Tudor University College Northampton U.K. N.C. Vasuki Delaware Solid Waste Authority (retired) U.S.A. Ming-Yen Wey National Chung Hsing University Republic of China Keith P Williams Cardiff University U.K. Anita Závodská Barry University U.S.A.

Steve Bloomer University of Teesside U.K.

Mervat El-Hoz Department of Civil Engineering University of Balamand P.O.Box 100, Tripoli, Lebanon Noah I. Galil Department Civil Engineering Technion—Israel Institute of Technology Haifa 32000, Israel

Ilan Nissim Israel Ministry of Energy and Water Resources Israel

Patrick Hettiaratchi University of Calgary Canada Isam Janajreh Masdar Institute Abu Dhabi Gennaro J. Maffia Manhattan College U.S.A. Richard Marsh Cardiff University U.K.


Paul Phillips School of Environmental Science University College Northampton Boughton Green Road Northampton, NN2 7AL, U.K. Email: [email protected]

Franco Medici University of Rome “La Sapienza” Italy Yusuf Mehta Rowan University U.S.A.

The Journal of Solid Waste Technology and Management, is published by Widener University School of Engineering. The responsibility for contents rests upon the authors and not upon the University. This journal is available by subscription and may be purchased at the rates posted at solidwaste.metapress.com. Editorial and subscription address is: Department of Civil Engineering, Widener University, One University Place, Chester, PA 19013-5792, U.S.A.; Telephone (610) 499-4042; Fax (610) 499-4461. Email: [email protected]. Web site: solid-waste.org. Copyright © 2015 by Widener University. Printed in U.S.A.


Volume 41

109

Number 2

A SYSTEM DYNAMICS MODEL TO PREDICT MUNICIPAL WASTE GENERATION AND MANAGEMENT COSTS IN DEVELOPING AREAS

Issam A. Al-Khatib, Derar Eleyan, Joy Garfield 121

THE APPLICABILITY OF NIMBY AND NIMTO SYNDROMES, WILLINGNESS AND ABILITY TO PAY FOR IMPROVED SOLID WASTE MANAGEMENT AMONG NAIROBI HOUSEHOLDS

Augustine Otieno Afullo 136

REDUCTION OF CORROSION OF REINFORCING STEEL IN CONCRETE USING ALKALI ASH MATERIAL

Hossein Rostami, Fernando Tovia, Reza Masoodi, Mozhgan Bahadory 146

POULTRY WASTE GENERATION, MANAGEMENT AND THE ENVIRONMENT: A CASE OF MINNA, NORTH CENTRAL NIGERIA

Peter Aderemi Adeoye, Hasfalina Che Man, Mohd. Amin Soom, Ahmad Mohammed Thamer, Akinbile Christopher Oluwakunmi 157

ENHANCING BIOGAS YIELD FROM COW DUNG BY CO-DIGESTING WITH CHICKEN AND SWINE MANURES AT DIFFERENT PROPORTIONS

G. A. Ogunwande, O. A. Adeagbo, S. O. Ojo 165

MATERIAL FLOW ANALYSIS OF ABATTOIR SOLID WASTE MANAGEMENT SYSTEM IN MINNA, NIGERIA

I.E. Ahaneku, C.F. Njemanze


Volume 41

173

Number 2

THE EU WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT DIRECTIVE: THE IMPLEMENTATION OF PRODUCER RESPONSIBILITY ACROSS THE EU-27

Susanna Paleari 189

CO-PYROLYSIS STUDY OF POLYLACTIC ACID AND POLYETHYLENE TEREPHTHALATE PLASTIC WASTES

Hua-Shan Tai, Jui-LanYeh 203

DESIGN OF VERTICAL WELLS FOR LEACHATE RECIRCULATION IN BIOREACTOR LANDFILLS USING TWO-PHASE MODELING

Krishna R. Reddy, Rajiv K. Giri, Hanumanth S. Kulkarni

A SYSTEM DYNAMICS MODEL TO PREDICT MUNICIPAL WASTE GENERATION AND MANAGEMENT COSTS IN DEVELOPING AREAS Issam A. Al-Khatib1*, Derar Eleyan2, and Joy Garfield3 1

Institute of Environmental and Water Studies, Birzeit University 2

Information Systems, Birzeit University

3

Computer Science and Mathematics Department, Faculty of Science and Engineering University of Wolverhampton Wolverhampton, United Kingdom Email: [email protected]; Fax: 009722-2982120

ABSTRACT This paper utilized system dynamics modeling as a new analytical approach to predict both the municipal waste generated and the associated disposal costs in developing areas. This approach facilitates the decomposition of general waste into its main components to enable municipalities to manage recyclables and find out the feasibility of performing recycling better rather than disposal by performing comparative disposal cost analysis. This study is different from previous work as it only considers population as a factor to predict the total waste generated and recycled, together with the associated expenditure and disposal cost savings. The approach is verified by applying it to a case study in Nablus and demonstrates the evaluation of the quantity and composition of generated waste by considering population as the main influencing factor. The quantity and composition of municipal solid waste was evaluated to identify opportunities for waste recycling in the Nablus municipality. Municipal solid waste was collected and classified into eight main physical categories. The system dynamics model enable the quantity of each generated component such as plastic and metals to be anticipated together with the cost of recycling or disposal. Keywords: System dynamic model; solid waste; waste characterization; economy; developing areas

INTRODUCTION This paper presents a new analytical approach using system dynamics modeling to predict municipal solid waste (MSW) generation and disposal costs with a focus on developing areas and uses Nablus as a case study example. The approach evaluates the quantity and composition of

generated waste by considering population and quantities of each generated waste component, such as metal and plastics together with the cost of recycling or disposing of the waste. A variety of data must be collected and analyzed before a community adopts and implements any waste management approach or combination of approaches. Community’s waste

______________________________________________________ *Correspondence author


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profile, types and quantities of waste generated and how much can be prevented realistically through source reduction and recycling are a prerequisite to develop a successful waste management program. This program will help determine the degree of detail needed in the waste characterization study. Modeling techniques are inexpensive and use generic waste generation rates and other information to provide only a general idea of waste volumes and types. Each management approach carries a price tag. Estimating costs before acting is essential to long-term success (USEPA, 1995). The following municipal solid waste management (MSWM) practices have been observed in the Nabulus area. There is very limited segregation of MSW into different components. All types of MSW are collected, including hazardous household and infectious waste from hospitals, which are disposed of together. Various issues such as the safety of cleaning workers, public health For example, source reduction and landfill projects require only gross waste volume from estimates and recycling and waste-to-energy projects require accurate predictions of waste quantities and composition. and environmental protection, are often not considered by management. For example, MSW is disposed in many randomly distributed dumping sites, causing pollution to surface and ground water together with the spread of litter in streets and public places. Moreover, scavenging at disposal dumping sites often worsens the problems (Al-Khatib et al., 2007; Arafat et al., 2007; AlKhatib et al., 2010). A partnership was established among three organizations: Applied Research Institute-Jerusalem; PADICO, a major local company; and Nablus municipality. The new recycling plant signed a contract with the municipality giving the company exclusive rights to utilize solid waste in Nablus. The company will eliminate solid waste in an environmentally friendly manner; use organic solid waste to produce compost; recycle other components that include metals, glass, and plastics; create new jobs; and reduce the municipality’s solid waste disposal costs. As of 2013, the new recycling plant is under construction, and it is expected to be operational by the end of the 2013 year. Linear programming, input–output analysis, expert systems (a methodology that uses expert knowledge to solve problems of a complex system) and system dynamics have been applied to aid decision makers in the planning and management of solid waste management systems (Everett and Modak, 1996; Barsi, 2000; Ming et al., 2000; Heikki, 2000). More recently emphasis has been placed on the capability of system dynamics for the prediction of solid waste generation (Saysel, 2002; Themelis et al., 2002; Kum et al., 2005; Dyson and Chang, 2005; Sufian and Bala, 2007). However consideration was not given to separating the general waste into its main components, as the proposed model does. This paper does not compare and contrast these different tools but utilizes the efficiency of the system dynamics methodology to construct a stock and flow model. The proposed system dynamics model considers the population as a main waste generating factor and decomposes the generated waste into different components to provide a clearer picture about the generated quantities of each

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component. This could help decision makers to plan for the recycling and utilization of these components.

SYSTEM DYNAMICS APPROACH This paper considers a system dynamics methodology as a computer-assisted decision making approach. Indeed computer-assisted decision making in the public policy field has become more common in recent years as policymakers have faced increasing demands for accountability (Rubenstein-Montano and Zandi, 2000). System dynamics was founded as a new modeling approach in the 1960’s by Jay Forrester. It takes feedback into consideration, which is a fundamental concept of systems analysis and is widely used as a modeling and simulation methodology for long-term decision-making analysis of industrial management problems. System dynamics also helps modelers and decision makers to conceptualize and rationally analyze the structure, interactions and mode of behavior of complex systems and sub-systems to explore, assess, and prognosticate their impacts in an integrated, holistic manner. System dynamics is also differentiated from simple spreadsheet programs as it facilitates a more sophisticated, quantitative simulation and is capable of more robust and reliable outcomes (Kollikkathar et al., 2010). As a method, system dynamics is particularly suited to the simulation of complex systems, such as a waste management system. It has the capability of dealing with assumptions about system structures in a stringent fashion, and is, in particular, a way of monitoring the effects of changes in subsystems and their relationships. Furthermore, it is also capable of representing these changes and rendering them communicable. The structure of system dynamics is exhibited by causal loop (influence) diagrams which capture the major feedback mechanisms, as shown simply in Figure 1. The diagram includes elements and arrows (which are called causal links) linking these elements together in the same manner and a sign (either + or -) on each link to indicate the relation between the two successive variables. If the relation is positive, it means that the two variables are moving in the same direction. An increase in one variable leads to an increase in the other. If

+

+

+ Birth

Population

-

Death

-

+

FIGURE 1 Population causal loop diagram

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the relation is negative, it means that the two variables are moving in opposite directions as if one is increasing the other is decreasing and vice versa. These signs have the following meanings:  The causal link between birth and population is positive (+), which means that the birth is added to population and an increase in births will lead to an increase in population.  The causal link between death and population is negative (-), which means that the death is subtracted from population and an increase in death will lead to a decrease in population. In addition to the sign of each causal link between any successive variable, the whole loop is given a sign. If the sum of negative signs in a loop is even, the whole loop is given a positive sign, which means the loop is reinforcing and the system is in unstable equilibrium. In contrast, if the sum of negative signs is odd, the whole loop is assigned with a negative sign, which means the loop is balancing and the system seeks to return to an equilibrium situation. The next step in using system dynamics modeling is to convert the causal loop diagram into a process model, called a stock and flow diagram. Figure 2 shows a system dynamics model: the stock and flow model. It shows a convertor used to hold a value of a variable, for example death fraction. Another icon is used to represent the frequent flow of birth in a time unit (e.g. year). It is therefore a time related variable. Another icon represents a stock, otherwise known as a repository or accumulator. This accumulates quantities of a variable over a period of time, such as the number of people in a stock population in ten years’ time. The model is built using the ithink simulation tool, which is a famous simulation modeling tool used in system dynamics. The mathematical mapping of a system dynamics stock-flow diagram occurs via a system of differential equations, which is solved numerically via simulation as shown in Appendix A. The model is used to simulate different scenarios to find out the optimal situation. All the parameters leading to this situation are recorded and a real model is built by switching the relevant parameters to the optimal values. Currently, highlevel graphical simulation programs (such as ithink®, Stella®, Vensim®, and Powersim®) support the analysis and study of these systems. System dynamics modeling has been used to address

Death Fraction

Birth Fraction Population

Birth Rate

Death Rate

FIGURE 2 Population stock and flow diagram

practically every sort of feedback system, including business systems, ecological systems, social-economic systems, agricultural systems, political decision making systems and environmental systems (Dyson and Chang, 2005). In terms of environmental concerns, the application has covered many issues. These vary from salt accumulation in lowlands under continuous irrigation practice (Saysel and Barlas, 2001);the value of water conservation (Stave, 2003); the consequences of dioxins to the supply chain of the chicken industry (Minegishi and Thiel, 2000); the eutrophication problem in shallow freshwater lakes (Guneralp and Barlas, 2003); the impact of environmental issues on long-term behavior of a single product supply chain with product recovery (Georgiadis and Vlachos, 2004); sustainability of ecological agricultural development at a county level (Shi and Gill 2005); estimation of methane emissions from rice welds (Anand et al., 2005), basin’s environmental management system (Guo et al., 2001) and waste management (Dyson and Chang, 2005; UlliBeer, 2003; Karavezyris et al., 2002; Sudhir et al., 1997).

MATERIALS AND METHODS Waste characterization The determination of waste composition is not straightforward and requires a small amount of training, as the nature of waste in general is heterogeneous. Therefore, common sense and random sampling techniques have evolved as generalized field procedures (Tchobanoglous et al., 1993). Sampling was conducted according to Standard test method at Al-Serafi Transfer Station that is at the north east Nablus city, for determining the composition of unprocessed municipal solid waste (World Health Organization (WHO), 1988). The determined mean composition of MSW was based on the collection and manual sorting of 100 samples of waste during June – August 2010. Vehicle loads of waste were designated for sampling, and a sorting sample was collected from the discharged vehicle load and sorted manually into the following waste components (1) Organic waste (compostable, including food waste), (2) Plastics, (3) Paper and cardboard, (4) Glass, (5) Metals, (6) Textiles, (7) Other waste (leather, wood, ashes, etc.) and (8) Waste less than 10 mm size (passing through the mesh and termed as inert). The weight fraction of each component in the sorting sample was calculated by the weights of the components. The mean waste composition was calculated using the results of the composition of each of the sorting samples. Vehicles for sampling were randomly selected during the sampling period to be representative of the waste stream. To apply the WHO method, a tank of 0.5 m3was filled with solid waste and shaken three times without applying any additional force. The tank contents were then disposed of on screening equipment (1.5 x 3m) with a (10 x 10mm) mesh surface size, specifically designed and fabricated for dealing with the heterogeneity of solid waste. The waste that did not


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pass through the mesh surface was then separated manually. The ‘potential use’ categorization was used to sort the waste instead of the traditional material-based categorization, as it was a preferable method for examining the feasibility of waste separation for composting and recycling (Al-Khatib et al., 2010). Eight dustbins, each with a capacity of 80 litres, were used for the separation of solid waste into the abovementioned components. A scale was used to weigh the dustbins at the different sampling locations. The percentage of the solid waste components and the total sample weight was computed. The average disposal cost was computed by dividing the total annual disposal cost by the total weight of waste generated in tons. The disposal cost was estimated on a monthly basis so that it was fluctuating, and based on that the cost range was determined.

System dynamics waste generation and disposal cost model The consideration and planning of MSWM helps to address several interrelated issues, such as public health, the environment, solid waste generated and present the future costs incurred to society. The MSWM is a complex, dynamic and multi-faceted system, depending not only on available technology but also upon economic and social factors. Experimentation with an existing MSWM system containing economic, social, technological, environmental and political

elements may be costly and time consuming or totally unrealistic. By simulating MSWM with a computer model, a series of computer experiments can be conducted to find out the best situation for the MSWM by considering all of the interrelated variables. Computer models enable the understanding of the dynamic behavior of such complex systems (Bala, 1999). Owing to the intrinsically complex nature of MSWM problems, it is advantageous to implement MSWM policy options only after careful modeling analyses which can lead to an optimal situation. The analysis involves the use of different modeling techniques, such as optimization, econometrics, input–output analysis, multiobjective analysis and system dynamics simulation. Forrester’s system dynamics methodology provides a foundation for constructing computer models to do what the human mind cannot do because of its complexity (Forrester 1968). The methodology can rationally analyze the structure, the interactions and mode of behavior of complex socioeconomic, technological, and environmental systems. Hence, the system dynamics approach is the most appropriate technique to handle this type of complex problem, as it offers the opportunity to handle all the interrelated variables which can affect system behavior. The proposed system dynamics model defines the key elements which have to be quantified as variables and their influences are formulated mathematically as shown in Appendix A. The model is definitively determined when the parameters and the initial values for the state variables (stocks) have been specified. Figure 3 shows a stock-flow

FIGURE 3 System dynamics waste management model

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diagram for the waste disposal cost, which is designed using the ithink® 8.0 software package. It is a collection of different variables, such as stocks, flows and converters which generate and influence the behavior of the whole system. Any changes to variables will affect and result in changes to others. To run the model, the data available from the Nablus municipality is used as sample data to generalize a representation. The data was obtained from examining and testing different daily samples of waste and calculating the average disposal cost. Table 1 shows the data used as values in the model. It shows the population as a primary source of generating waste as household people, the waste generated per capita on an annual basis and the cost of disposal. The model also shows the main components of waste, which Table 1 uses samples generated throughout different subsequent years in the Nablus municipality (2002-2005). Data from years 2002-2005 were used to fit the model and calibrate its parameters, then the model was used to predict

includes items such as paper and plastics. The model offers the ability to anticipate the quantity of each component generated in a period of time and the cost to recycle or dispose the waste. This model also provides an understanding of the future and could help with planning the best possible ways of disposing or recycling of such waste. In addition, it provides the volume estimation of accumulated wastes in the landfill (UsedVolume). UsedVolume equals the amount of waste sent to the landfill divided by the compacted density of solid waste in the landfill. According to the United Nations Environment Programme (2012) the compacted densities of solid waste in landfills go up to 700-1000 kg/m3 after compaction on-site. 800 kg/m3is recommended in this case study. the outcome for other years (not used in the initial fitting) and the latter is compared to real data for the years 2006-2011 for model verification (Table 2). Each year the population of the municipality generated a

TABLE 1 Solid waste quantities and their disposal cost for the years 2002-2005 Nablus municipality. Year

Quantity (tons/year)

Population

Mean generation rate (kg/cap/day)

Mean generation rate (kg/cap/year)

Annual Disposal Cost (NIS)*

Cost Range (NIS/ ton)

Average cost

2002

42,153

154,649

0.75

270

1,321,200

20-45

31.3

2003

59,284

159,753

1.02

367.2

1,901,100

20-49

32.1

2004

40,716

164,864

0.68

244.8

2,492,000

60-62.5

61.2

2005

51,160

169,975

0.82

295.2

3,137,000

30-62.5

61.3

(NIS / ton)

*New Israeli Shekels, 1 NIS equals 3.8 $US

TABLE 2 Solid waste quantities and their disposal cost for the years 2002-2011 Nablus municipality. Year

Quantity (tons/year)

Population

Mean generation rate (kg/cap/day)

Mean generation rate (kg/cap/year)

Annual Disposal Cost (NIS)*

Cost Range (NIS/ ton)

Average cost

2002

42,200

154,649

0.75

270

1,321,200

20-45

31.3

2003

59,300

159,753

1.02

367.2

1,901,100

20-49

32.1

2004

40,700

164,864

0.68

244.8

2,492,000

60-62.5

61.2

2005

51,200

169,975

0.82

295.2

3,137,000

30-62.5

61.3

2006

52,700

170,211

0.85

309.6

10,604,100

45-67.3

62.3

2007

53,300

179,659

0.81

296.6

9,953,100

41-58.6

55.4

2008

51,500

185,834

0.76

277

10,927,000

38-64.3

58.8

2009

55,900

189,893

0.81

294.4

12,039,200

45-68.7

63.4

2010

56,100

195,457

0.79

287.2

12,763,300

54-67.4

65.3

2011

56,000

198,267

0.77

282.5

12,728,700

51-65.3

64.2

(NIS / ton)

*New Israeli Shekels, 1 NIS equals 3.8 $US


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certain amount of waste. Table 1 also shows the cost for disposing of the waste generated on an annual basis in local currency (New Israeli Shekels which is equivalent to 3.8 $ US dollars).

RESULTS AND DISCUSSION The data for 2002 provided the initial values (shown in Table 1) in the stocks that were used to predict results yearly until 2011 (shown in Table 2). Most of the core elements contained in this model are now discussed below. Population: the population in 2002 was 154,649. For each 1000 the birth and death rates were 32.7 and 4.3 respectively. The difference between births and deaths generates the net population, which is used to calculate waste generation. Table 3 shows the population predicted until 2011 and how much waste will be generated. The predicted population from the model (Figure 3) was compared to the population and total waste generated from Table 2 for verification purposes. These two numbers are quite close as Table 2 shows the population in 2006 was 170,211, while predicted population from the model (Figure 3) was 178,381. This suggests the model is 95% accurate. Additionally, the simulated population and waste generation until 2011 are shown in Table 3. The population increases from a base year data to 213,981 at the end of the simulation. If the constant rate of birth and death in year 2002 is considered, the population would be 213,981 by the end of 2011. Waste generation: The total quantity of waste generated is calculated by multiplying the waste generation rate

(kg/day/capita) and total population. Finally, the model shows the total amount of waste generated by the total population as shown in Table 4. The amounts of waste generated are accumulated amounts, which means each amount is composed of that generated in a particular year added to the amount of waste generated in the previous year. For example, in 2002 the amount of waste generated was 37,920 ton and that in 2003 was75,840 ton. This being the accumulated amount of waste generated in 2002 and 2003.The difference of 37,920 ton is therefore the generated amount of waste in 2003. Table 3 shows the population on a yearly basis and the representative estimated values of all the waste fractions considered that are likely to be generated in the assessment year. The tonnage continues to increase with increasing population and changing socio-economic conditions, as expected. For example, in a study conducted in Ghana, the results showed wide variation in levels of association between the socioeconomic variables and environmental conditions, with strong evidence of a real difference in environmental quality across socioeconomic classes with respect to total waste generation (p < 0.001) and waste collection rate (Fobil et al., 2010). In this study, for example in 2002 the population was 154,648, generating 18,890 kg of organic waste while generating 720 kg of glass waste. Hence the glass waste in 2011 will be 720 kg while organic waste will be 188,860 kg. The model studies the different types of waste (i.e. cardboard, glass, inert (less than 10 mm in diameter), metal, organic, paper, plastic, others). The model also shows the quantity of each type generated as shown in Table 3. The model considers the possibility of adopting some kind of recycling, concluded from the nature of the region as agricultural where there is scope for different types

TABLE 3 Prediction of population and all the waste components Years

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Population

Cardboard

Glass

Inert

Metal

Organic

Paper

Plastic

Others

(kg)

(kg)

(Kg)

(Kg)

(Kg)

(Kg)

(Kg)

(Kg)

2002

154,648

2,130

720

2,160

1,820

18,890

750

5,060

6,400

2003

160,267

4,260

1,440

4,320

3,630

37,770

1,500

10,110

12,800

2004

166,091

6,390

2,160

6,480

5,440

56,660

2,250

15,160

19,200

2005

172,126

8,530

2,880

8,640

7,260

75,540

3,000

20,220

25,600

2006

178,381

10,660

3,600

10,800

9,070

94,430

3,750

25,270

32,000

2007

184,862

12,790

4,320

12,960

10,890

113,310

4,500

30,330

38,400

2008

191,580

14,920

5,040

15,120

12,700

132,200

5,240

35,380

44,810

2009

198,541

17,050

5,760

17,280

14,520

151,090

5,990

40,440

51,210

2010

205,756

19,180

6,480

19,440

16,330

169,970

6,740

45,490

57,610

2011

213,981

21,310

7,201.0

21,600

18,145.0

188,860

7,490

50,550

64,010


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TABLE 4 Prediction of population, total waste generated and used volume (the no recycling scenario) Years

Population

Total Waste Generated

Waste Disposal Expenditure

(ton)

(ton)

Used Volume (m3)

2002

154,648

37,920

2,320,800

74.4

2003

160,267

75,840

4,648,800

94.8

2004

166,091

113,750

6,973,000

147.19

2005

172,126

151,670

9,297,200

189.58

2006

178,381

189,580

11,621,400

236.98

2007

184,862

227,500

13,945,600

284.37

2008

191,580

265,410

16,269,800

331.77

2009

198,541

303,330

18,594,000

379.16

2010

205,756

341,240

20,918,200

426.55

2011

213,981

379,160

23,242,400

473.95

of recycling. When using the model for the purpose of considering recycling, it is shown that large savings can be achieved. Solid waste contains significant amounts of valuable materials such as steel, aluminum, copper and other metals which, if they are recovered and recycled or reused, could reduce the volume of waste to be collected and occupied in the landfill, and at the same time yield significant salvage and resale incomes. In addition, better reclamation techniques could help to save valuable natural resources and turn waste, which could be dangerous, into useful products. Some important solid wastes that have been successfully reclaimed are paper, plastics, glass and metals. This study is undertaken to evaluate the quantity and composition of MSW to identify opportunities for waste recycling in Nablus municipality. MSW solid waste was collected and classified into 8 main physical categories. The system dynamics model was utilized for the estimation of the yearly average MSW solid waste generation rate. The model (Figure 3) was compared to the population and total waste generated from Table 1 for verification purposes. The system dynamics model generated three simulated scenarios showing the amount of waste generated, the amount recycled and the disposal expenditure.

Partially recycling scenario Concerning the quality of service provided, Nablus municipality has begun to work on plans to tackle the waste problem. These plans include improving waste collection to make streets cleaner and setting up a system to manage waste disposal in a way that is cost efficient as well as environmentally safe. In the area of waste recycling, the waste management department at Nablus municipality is

undertaking some sorting of garbage. Thus, on a small scale the garbage at ‘Al-Serafi Transfer Station’ is classified into plastic, iron, and paper. Paper material is sold for Israeli industries and Nablus factories are buying the plastic and the iron. UsedVolume (m3) = (total waste – recycled waste)/800. The results of this scenario are summarized in Table 5.

No recycling scenario This scenario represents closing the Al-Serafi Transfer Station and stopping the segregation process as it is operated mainly manually. In this case all wastes will be disposed of in the landfill, and the used volume will be maximized as UsedVolume (m3) = total waste (kg)/800.The results of this scenario are presented in Table 4. The total waste generation (ton/year) increases with time, as population increases. Consequently, the total waste disposal expenditure will increase. Therefore, by the end of 2011 the total waste disposal expenditure would be 23,242,450 NIS.

Recycling all recyclables scenario Table 6 provides estimates on the change in disposal costs associated with recycling all recyclables waste at the end of each year, rather than the use of landfill. The table shows that total disposal expenditure will be reduced and the total waste recycled saving will be 17,513,410 NIS (New Israeli Shekel) as shown in Table 6. This expenditure is reduced if recycling procedures have taken place. The recycled saving of 17,513,410 NIS is based on an assumption that the Nablus municipality adopt recycling procedures. If recycling procedures do not occur, the resultant large expenditure should prompt the authority to investigate measures to reduce costs and even gain an income by possible recycling methods.


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TABLE 5 Prediction of population and total accumulated waste generated and the partially waste recycled scenario Years

Population

Total Waste Generated (ton)

Partial Recycled Waste (ton)

Waste Disposal Expenditure (NIS)

Partial Waste Recycled Savings

Used Volume (m3)

(NIS) 2002

154,648

37,920

8,970

2,320,800

548,700

11.69

2003

160,267

75,840

17,930

4,648,800

1,099,100

23.37

2004

166,091

113,750

26,890

6,973,000

1,648,600

35.05

2005

172,126

151,670

35,860

9,297,200

2,198,100

46.73

2006

178,381

189,580

44,800

11,621,400

2,747,600

58.41

2007

184,862

227,500

53,790

13,945,600

3,297,100

70.10

2008

191,580

265,400

62,750

16,269,800

3,846,600

81.78

2009

198,541

303,330

71,710

18,594,000

4,396100

93.46

2010

205,756

341,240

80,680

20,918,200

4,945,600

105.14

2011

213,981

379,160

89,640

23,242,400

5,495,100

116.82

TABLE 6 Prediction of population, total waste and disposal expenditure and recycling saving ($US) (recycling all recyclables scenario). Years

Population

Total Waste Generated (ton)

Recyclables Waste (ton)

Waste Disposal Expenditure (NIS)

Total Waste Recycled Savings

Used Volume (m3)

(NIS) 2002

154,648

37,920

28,570

2,320,830

1,748,700

11.69

2003

160,267

75,840

57,140

4,648,830

3,502,880

23.37

2004

166,091

113,750

85,710

6,973,030

5,254,190

35.05

2005

172,126

151,670

114,280

9,297,240

7,005,510

46.73

2006

178,381

189,580

142,850

11,621,440

8,756,830

58.41

2007

184,862

227,500

171,420

13,945,640

10,508,140

70.10

2008

191,580

265,400

199,990

16,269,840

12,259.460

81.78

2009

198,541

303,330

228,560

18,594,040

14,010,780

93.46

2010

205,756

341,240

257,130

20,918,240

15,762,090

105.14

2011

213,981

379,160

285,700

23,242,450

17,513,410

116.82

Sensitivity analysis Model sensitivity analysis would need to take into consideration key influencing factors, such as population, generated waste components and costs together with boundary and policy changes. The growth and decline of a population would be directly influenced by changes in birth

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and death rate fractions where both of them assumed to be constant throughout the period between 2002-2011. The model can be enhanced by considering different factors which affect the birth and death rates which make the model more comprehensive. This will be a proposal for future consideration to perform further development to the model. However it does not take other factors such as immigration


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and emigration into consideration. Such figures could be considered to be not significant enough to largely alter population figures, although this may become more of an influential factor in cases of war and famine. Used volume depends on both the waste generated and waste recycled (fully, or partial) while the compacted density is constant. Which means, if the generated waste increases due to an increase in population, the volume used will increase. Equilibrium will be achieved between the waste generated and the waste recycled. An increase in waste generated stimulates the municipality to increase recycling and reduce disposal costs and landfills. Indeed the model is not fully comprehensive but built in a way that would enable the addition of such factors as further converters without altering the structure of the model. Fluctuations in disposal costs can be directly reflected in the results of the model.

CONCLUSION Prediction analysis The paper has demonstrated an initial inquiry into the possibility of using systems dynamics to solve complex waste-management problems and reduce future uncertainty of the impact of waste generation on the economy, environment and socio-economic environment in developing areas. It is different from previous studies in that it only considers population as a factor to predict the total waste generated and recycled, together with the associated expenditure and disposal cost savings. It demonstrates that system dynamic modeling provides a more comprehensive and sophisticated simulation method for integrated assessment of complex waste-management processes. The results from the simulation process show that the generation of MSW undergoes a general increase during the period of forecast, due to the increase in the dimensions of influencing socioeconomic and population variables. Similarly, MSW disposal costs were shown to follow an increasing trend during the period of forecast. The simulated recycling scenario demonstrates that huge savings can be gained by reducing the cost generated from disposing of waste. Recycling is highly recommended to save money, utilize waste in beneficial ways and assist in reducing environmental pollution.

Supporting decision making in developing areas The system's model presents a more practical and realistic picture of the next decade in solid waste-disposal for cities like Nablus, as compared with traditional approaches. The model can be used as a decision support tool to anticipate the future situation and the volume of different types of waste generated in the area of Nablus. Indeed the model is especially useful for local and national decision makers as it is able to determine the future potential economic impact of recycling or resource recovery activities. The model can also provide an alert for waste generation decision makers,

enabling confrontation of the situation either by proposing proactive and preventative procedures or by inventing creative solutions relating to recycling different types of waste.

Possible future research Future enhancement to the model could involve consideration of additional sources of waste apart from the population, such as factories, industries and hospitals and medical centers. Further development of the model is warranted for the solution of complex problems requiring more refined results for waste generation, such as landfill siting capacity, waste prevention and associated budget allocations. This study concentrated primarily on the general economic impacts, and certain limitations remain in the lack of other associated economic, environmental and social impact modeling. There exists a tremendous scope to extend and further its utility by introducing new sub-component ranges and by integrating various natural and anthropogenic system components, thereby facilitating the system actors across boundaries to come together for developing integrated solutions. It should also be noted that other factors that influence waste generation such as the monthly income of the family, education levels, consumption habits, family size and locality type could be taken into consideration.

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management.” Environmental Technology, Volume 21(2), 157–166. Deaton, ML, J.J. Winebrake, (2000) Dynamic modeling of environmental systems. Springer Science-Business Media, New York, USA. Dyson, B., N.B. Chang, (2005) “Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling,” Waste Management, Volume 25, pp. 669-679. Everett, J.W, A.R. Modak, (1996) “Optimal regional scheduling of solid waste systems,” Journal of Environmental Engineering, Volume 122(9), pp. 785-792. Fobil, J., J. May, A. Kraemer, (2010) “Assessing the relationship between socioeconomic conditions and urban environmental quality in Accra, Ghana,” International Journal of Environmental Research and Public Health, Volume 7, pp. 125-145. Forrester J.W. (1968) Principles of systems. Wright-Allen Press MIT Massachusetts. Georgiadis, P., D. Vlachos (2004) “The effect of environmental parameters on product recovery,” European Journal of Operational Research, Volume 157, pp. 449-464. Guneralp, B., Y. Barlas, (2003) “Dynamic modeling of a shallow freshwater lake for ecological and economic sustainability,” Ecological Modeling, Volume 167, pp. 115-138. Guo, H.C., L. Liu, G.H. Huang, G.A. Fuller, R. Zou, Y.Y. Yin (2001) “A system dynamics approach for regional environmental planning and management: A study for the Lake Erhai Basin,” Journal of Environmental Management, Volume 61, pp. 93-111. Hannon, B., M. Ruth, (2001) Dynamic modeling. Second Ed. Springer Science-Business Media New York USA. Heikki, T.J., (2000) “Strategic planning of municipal solid waste management,” Resources Conservation and Recycling, Volume 30(2), pp. 111-133. Karavezyris, V., K.P. Timpe, R. Marzi, (2002) “Application of system dynamics and fuzzy logic to forecasting of municipal solid waste,” Mathematics and Computers in Simulation, Volume 60, pp. 149–158. Kollikkathara, N., H. Feng, D. Yu, (2010) “A system dynamic modeling approach for evaluating municipal solid waste generation landfill capacity and related cost management issues,” Waste Management, Volume 30, pp. 2194-2203. Kum, V., A. Sharp, N. Harnpornchai, (2005) “Improving the solid waste management in Phnom Penh city: A strategic approach,” Waste Management, Volume 25, pp. 101-109. Minegishi, S., D. Thiel, (2000) “System dynamics modeling and simulation of a particular food supply chain Simulation,” Practice and Theory, Volume 8, pp. 321339. Ming, Z.G., Y.X. Zhong, Z.P. Yue, G.H. Cheg, H.G.G. He, L. Hemelaar, (2000) “Environmental input–output model

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and its analysis with a focus on the waste management sectors,” Journal of Environmental Science, Volume 12(2), pp. 178-183. Rubenstein-Montano, B., I. Zandi, (2000) “An evaluative tool for solid waste management,” Journal of Urban Planning and Development, ASCE, Volume 1263, pp. 119-135. Saysel, A.K., Y. Barlas, (2001) “A dynamic model of Stalinization on irrigated lands,” Ecological Modeling, Volume 139, 177-199. Saysel, A.K., Y. Barlas, O. Yenigun, (2002) “Environmental sustainability in an agricultural development project: A system dynamics approach,” Journal of Environmental Management, Volume 64, pp. 247–260. Shi, T., R. Gill, (2005) “Developing effective policies for the sustainable development of ecological agriculture in China: The case study of Jinshan County with a systems dynamics model,” Ecological Economics, Volume 53, pp. 223-246. Stave, K.A., (2003) “A system dynamics model to facilitate public understanding of water management options in Las Vegas Nevada,” Journal of Environmental Management, Volume 67, pp. 303-313. Sudhir, V., G. Srinivasan, V.R. Muraleedharan, (1997) “Planning for sustainable solid waste management in urban India,” System Dynamics Review, Volume 13, pp. 223-246. Sufian, M.A., B.K. Bala, (2007) “Modeling of urban solid waste management system: The case of Dhaka city,” Waste Management, Volume 27, pp. 858–868. Tchobanoglous, G., H. Theisen, S. Vigil, (1993) Integrated Solid Waste Management: Engineering Principles and Management Issues. McGraw-Hill Book Co Singapore. Themelis, N.J., Y.H. Kim, M.H. Brady, (2002) “Energy recovery from New York City solid wastes,” Waste Management & Research, Volume 20, pp. 223–233. Ulli-Beer, S., (2003) Dynamic interactions between citizen choice and preferences and public policy initiatives - A system dynamics model of recycling dynamics in a typical Swiss locality. Proceedings of the 2003 International Conference of the System Dynamics Society, New York City USA 20–24 July 2003. United Nations Environment Programme (2012) Municipal solid waste management. [online] Available from: http://www.unep.or.jp/ietc/estdir/pub/msw/ro/Latin_A/To pic_g.asp [Date accessed: 18/7/2012]. United States Environmental Protection Agency (USEPA) (1995) Decision-makers’ guide to solid waste management, Volume II. Solid Waste and Emergency Response 5305W, EPA530-R-95-023. [online] Available from: http://www.smallbizenviroweb.org/Resources/sbopubs/cdocs/c70.pdf [Date accessed: 8/10/2012]. World Health Organization (WHO) (1988) Handling solid wastes in developing countries. Alexandria, Egypt.


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Appendix A: System Dynamics Model Equations Cardboard (t) = Cardboard (t - dt) + (Cardboard Rate) * dt INIT Cardboard = 1 INFLOWS: Cardboard Rate = (Cardboard Density*Cardboard Volume) Glass (t) = Glass (t - dt) + (Glass Rate) * dt INIT Glass = 1 INFLOWS: Glass Rate = (Glass Density*Glass Volume) Inert (t) = Inert (t - dt) + (Inert Rate) * dt INIT Inert = 1 INFLOWS: Inert Rate = (Inert Density*Inert Volume) Metal (t) = Metal (t - dt) + (Metal Rate) * dt INIT Metal = 1 INFLOWS: Metal Rate = (Metal Density*Metal Volume) Organic (t) = Organic (t - dt) + (Organic Rate) * dt INIT Organic = 1 INFLOWS: Organic Rate = Organic Density*Organic Volume Others (t) = Others (t - dt) + (Others Rate) * dt INIT Others = 1 INFLOWS: Others Rate = (Others Density*Others Volume) Paper (t) = Paper (t - dt) + (Paper Rate) * dt INIT Paper = 1 INFLOWS: Paper Rate = (Paper Density*Paper Volume) Plastic (t) = Plastic (t - dt) + (Plastic Rate) * dt INIT Plastic = 0 INFLOWS: Plastic Rate = (Plastic Density*Plastic Volume) Population (t) = Population (t - dt) + (Birth Rate – Death Rate) * dt INIT Population = 154648 INFLOWS: Birth Rate = Population*Birth Fraction OUTFLOWS: Death Rate = Population*Death Fraction Total Waste Ton (t) = Total Waste Ton (t - dt) + (Waste Rate - Organic Rate - Plastic Rate - Metal Rate - Glass Rate - Cardboard Rate - Others Rate - Paper Rate - Inert Rate) * dt INIT Total Waste Ton = 1 INFLOWS: Waste Rate = (Population*Average waste per cap) OUTFLOWS: Organic Rate = Organic Density*Organic Volume Plastic Rate = (Plastic Density*Plastic Volume) Metal Rate = (Metal Density*Metal Volume)


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Glass Rate = (Glass Density*Glass Volume) Cardboard Rate = (Cardboard Density*Cardboard Volume) Others Rate = (Others Density*Others Volume) Paper Rate = (Paper Density*Paper Volume) Inert Rate = (Inert Density*Inert Volume) Average waste per cap = .275 Birth Fraction = .04 Cardboard Density = .08 Cardboard Percentage by Weight = Cardboard/Total Waste Generated Cardboard Volume = 74*360 Compacted Density = 800 Death Fraction = .0043 Glass Density = 1 Glass Percentage by Weight = Glass/Total Waste Generated Glass Volume = 2*360 Inert Density = .50 Inert Percentage by Weight = Inert/Total Waste Generated Inert Volume = 12*360 Metal Density = .36 Metal Percentage by Weight = (Metal/Cardboard Percentage by Weight) Metal Volume = 14*360 Organic Density = .43 Organic Percentage by Weight = Organic/Total Waste Generated Organic Volume = 122*360 Others Density = .27 Others Percentage by Weight = Others/Total Waste Generated Others Volume = 52*360 Paper Density = .08 Paper Percentage by Weight = Paper/Total Waste Generated Paper Volume = 26*360 Plastic Density = .07 Plastic Percentage by Weight = Plastic/Total Waste Generated Plastic Volume = 254*360 Recycle recyclables Waste Ton = Glass+Metal+Organic+Plastic Total disposal Expenditure = Total Waste recycled saving + Waste disposal Expenditure Total Waste Generated = Cardboard+Glass+Inert+Metal+Organic+Others+Paper+Plastic Total Waste recycled saving = Recycle recyclables Waste Ton*Average cost per ton UsedVolume m3 = (Total Waste Generated-Recycle recyclables Waste Ton)/Compacted Density Waste disposal Expenditure = Total Waste Generated*Average cost per ton Average cost per ton = GRAPH (time) (2002, 31.3), (2003, 32.1), (2004, 61.2), (2005, 61.3)

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THE APPLICABILITY OF NIMBY AND NIMTO SYNDROMES, WILLINGNESS AND ABILITY TO PAY FOR IMPROVED SOLID WASTE MANAGEMENT AMONG NAIROBI HOUSEHOLDS Augustine Otieno Afullo Maasai Mara University, P.O. BOX 861-20500, Narok, Kenya Tel: +254722690956; Email: [email protected]; [email protected] Formerly: Assistant Professor and Fulbright Scholar-In-Residence (SIR), North Central College, 40N Brainard Street, Naperville 60540, Illinois / 213N Brainard Street, APR 2R, Naperville 60540, Illinois, USA Tel: 1-630-802-9759; 1-630-637-5370; Email: [email protected]

ABSTRACT Nairobi’s residential areas are chocked with garbage. It was hypothesized that residents exhibit a “Not in My Backyard” (NIMBY) and “Not in my terms of office” (NIMTO) syndromes, with challenges in willingness and ability to pay (WATP) for improved solid waste management (SWM) services. 30 key informant interviews, 20 Focus group discussions, and pre-tested HH questionnaires were administered in two phases in 2007 and 2010, using sample sizes of 430 and 600 Households respectively. At most 39% residents were able to pay for improved SWM services. In 2007, 78% of Nairobi HHs had no SWM service, and by 2010, 70% had it. The effective demand is exhibited by the US$ 1.53 they are WATP for monthly garbage collection, maintained at statistically the same level in 2010. Open dumping as the proxy indicator of NIMBY had a prevalence of under 30% down from over 70% in 2007. There was also evidence HHs exhibited the NIMTO syndrome, with 54% proposing the government, NCC or a sponsor purchases them a household bin. There is need for intensive public education on SWM, so that households, through CBOs, directly participate in urban neighbourhood cleaning, and venture into waste for wealth through informal sector incorporation into environmental management. Keywords: Solid waste management, Nairobi Kenya, willingness to pay (WTP), ability to pay (ATP), willingness and ability to pay (WATP), not in my backyard (NIMBY), not in my terms of office (NIMTO).

INTRODUCTION Nairobi County was the area of study in the first study, with its largest and most populous sub-county, Embakasi being the study are in the second phase of the study. Nairobi is the administrative and commercial capital city of Kenya, which is one of the East African Countries. It is located at the equator at 6000ft above sea level. It covers an area of 684 km2, and is the smallest of Kenya’s six provinces. Other Kenyan provinces are central, Rift Valley, Nyanza, Western, Eastern, Coast and North Eastern. Nairobi province is bounded by Rift valley to the West and South, Eastern province to THE APPLICABILITY OF NIMBY AND NIMTO SYNDROMES

the East, Central province to the North and North East (GoK, 2009, Afullo, 2014 and Afullo, 2014b). As per the 2009 population census, the total Kenyan population was 38,610,097 of whom Males were 19,192,458 and Females were 19,417,639 in 8,767,954 households. Of these, Nairobi had 3,238,369 people comprising 1,605,230 males and 1,533,139 females, distributed in 985,016 households. Embakasi district, the largest and the most populous district of Nairobi, had a total population of 925,775, of whom 468, 097 were males and 457,678 females distributed in 296,942 households (Table 1).

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TABLE 1 Demographic characteristics of Kenya and Nairobi Districts (Source: GoK (2009)) Constituency

Male

Female

Total

Households

Area in Sq. Km.

Density

National

19,192,378

19,417,719

38,610,097

8,767,954

581,309.00

66.42

Nairobi

1,605,219

1,533,150

3,138,369

985,016

695.10

4,514.96

Embakasi

468,093

457,682

925,775

296,942

204.00

4,546.27

Kasarani

266,679

258,945

525,624

164,354

86.00

6,081.56

Lang'ata

185,832

169,356

355,188

108,477

223.00

1,591.63

Dagoretti

166,394

163,183

329,577

103,818

39.00

8,533.89

Starehe

142,097

132,510

274,607

87,519

11.00

25,640.24

Kamukunji

136,919

124,936

261,855

75,555

12.00

21,604.66

Westlands

124,748

122,354

247,102

75,427

97.00

2,537.52

Makadara

114,457

104,184

218,641

72,924

23.00

9,484.69

Nairobi is administratively divided into districts or administrative units of Mathare, Westlands, Starehe, Dagoreti, Langata, Makadara, Kamkunji and Embakasi (Figure 1). Nairobi is a varied city, with rapid urbanization amidst deteriorating economic, environmental and health conditions, with

features and facilities of a modern city on one hand, and extreme pockets of poverty and destitution on the other hand (Afullo and Odhiambo, 2009). For instance, it has Kibera, Mathare and Korokocho as major slums, among others, where about 2 million Nairobi residents live yet occupying

FIGURE 1 Distribution of informal settlements in Nairobi Administrative Divisions) (source: Mutisya and Yarime, 2011)

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robi residents able to pay for (ii) To determine the prevalence of HHs exhibiting the NIMBY and NIMTO syndromes; and (iii) To evaluate the Nairobi households’ willingness to pay for the improved SWM service

only 5% of the municipal residential land (JICA, 1998; GoK, 1994). Kibera prides in being the largest slum in Kenya and sub-Saharan Africa, with more than 25% of the Nairobi population confined in only250 hectares of land (Afullo, 2014, Afullo 2014b, GoK, 2003 and WSP, 2005).

Classification of socio-economic status of HH by estate as a source of NIMTO

Research question

The UN-Habitat (2009, cited in Mutisya and Yarime (2011)) observed that than 34% of Kenya’s total population lives in urban areas, with more than 71% of this confined in informal settlements. Kenya’s annual informal settlements growth rate of 5%, is the highest in the world and it is likely to double in the next 30 years if positive intervention measures are not put in place (UNDP, 2007, cited in Mutisya and Yarime, 2011). This number will therefore continue to increase unless a serious and concerted action by all relevant stakeholders is undertaken. This unfortunately is the population not served by the city councils, the one with mounds of garbage in their neighborhoods, and the ones who have least access to health care services- due to distance and money demands.

2.

Thesis statement Nairobi residents contribute at least 70% of all solid wastes, most of which largely remains uncollected causing wanton littering. The control of how much of this solid waste is collected depends on the cooperation from the HHs, which is in turn dependent on the mindset of the waste generators. Those with the NIMBY and NIMTO syndromes can be difficult to work with and may need a lot of education. But understanding who is in which category is critical in making an inclusive integrated SWM program. The waste management service is generally poor, with most never receiving it, and for the few who do, many are dissatisfied with it. Instead they seek alternative services. How much they are willing and able to pay for these services, and their ability to do so, remain largely unknown. Yet the potential of the households’ role in improving solid waste management is generally assumed, and services continue to be provided on an assumption that SWM service is a good which behaves in the market like any other, and assume an equilibrium price which has been misleading. There has been little effort, if any, to do a proper assessment of effective demand. This is partly due to the service providers’ unawareness of the type of service the HHs require, and effective demand for various aspects of the service among them. This study strives to establish the type of service demanded by Nairobi households, as well as the amount of money they are willing and able to pay for the desired services.

Aim and objectives This research aims at studying the potential of households in improving SWM in Nairobi. The objectives are: (i) To determine the improved SWM services that Nai-

THE APPLICABILITY OF NIMBY AND NIMTO SYNDROMES

1.

3.

What is the prevalence of NIMBY and NIMTO syndromes among the Nairobi residents? What services are Nairobi residents willing to pay for improved SWM? How much are Nairobi residents willing to pay for improved SWM?

Literature review Determinants of WTP. Generally, HHs not satisfied with an existing SWM scheme may be more willing to pay more than what they are currently incurring in more promising SWM scheme such as a private one. A positive WTP implies that households demand SWM in which the improvement in the solid waste management will directly improve their welfare also. Theoretically, a low-income household is willing to pay less than the higher income household. This is as incomes increase, households tend to have more “discretionary income “and hence more scope of choice about the disposition is a luxurious good. On the hand, it is expected the coefficient of the environment ethics dummy will be negative. Households that take sanitational precaution such as proper solid waste disposal are less willing to pay for the service via this arrangement. On the other hand, as income increases, many households tend to shy away from communal arrangements. For low income groups, willingness to pay ay not necessarily translate to effective demand; others cannot afford the services or are simply acting strategically or exhibiting free riding behavior. Khattak et al. (2009) work identified HH size, Income of HH and Higher education as important determinants of HH’s willingness to pay for better SWM services. Income is another very important determinant of HH demand for any service. With the increase in family income people can spare money for improvement in their living standards. The environment is considered to be normal with income elasticity of 0.13 using the contingent valuation of the environmental impact of SWM in San Pedro Cholula-Mexico (Viniegra et al., 2001).

Literature review of method WTP methodology. Contingent valuation methodology: approach asks people directly what they are willing to pay for a good, or what they are willing to accept to give it up, rather than inferring this from observed behavior” (World Bank, 2002, cited in Khattak et al., 2009). Contingent valuation method is based on the stated valuation of a consumer for any good or service which is not marketable. CVM is widely used in surveys and research globally, and can also be defined as a method which measures how an individual be compensated or charged for a good or service which he sells or takes as the

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case may be (Khattak et al., 2009). Kimenju, et al. (2005, cited in Khattak et al., 2009) has compared various methods and found the contingent valuation method to be easy and fast in use. Bateman et al. (2006) contrast applications of both the contingent valuation (CV) and contingent ranking (CR) methods as applied to a common issue, the valuation of improvements to the water quality of an urban river. Building upon earlier experimental work, the CV design ensures that respondents are fully aware of all impending valuation tasks prior to undertaking any one of those tasks.

Literature review on NIMBY and NIMTO To address NIMBY and NIMTO syndromes among producers and managers of waste, a wide variety of ideas to overcome obstacles, some of which are very innovative, some of which draw upon tradition; some are firmly embedded in local culture and habits, some aim at changing habits and attitudes (Rodic et al., 2010, cited in Wilson et al., 2012). Behaviors towards SWM may sometimes be associated with expectations from a service provider. For instance, Khattak et al. (2009) observes that one concern was that the proposed services would not be provided consistently and thus, the new system would not be reliable, while the least observed concern was that public was not satisfied with the quality of the services provided by the TMAs. This gives us a clear picture into the issue zero WTP (Khattak et al., 2009), partly an aspect of NIMTO as it can be related to economic sabotage. Mutisya and Yarime (2011) observe that unfortunately, residents of informal settlements are not empowered to allow them to make any significant contribution to community building, pushing Nairobi city to the verge of sinking into abyss as the weight of mushrooming slums takes its toll. NIMBY, NIMTO or governance? According to Oteng-Ababio (2011) a critical analysis of the SWM challenges reveals a fundamental cause which is skewed towards a governance crisis rather than attitudinal challenges. For example, policies relating to the adaptation of institutional arrangements and the purchasing of transportation equipment are developed in the absence of both the private sector and public participations. Oteng-Ababio, 2011 observes that unilateral decisions ignore the realities of local conditions, as in the case of the failure to acknowledge the operations of the Kaya bola. The authorities have also failed to implement the necessary bylaws to make compliance with policies enforceable (OtengAbabio, 2011). This can make citizens in poor neighborhoods (70% of the population) to simply refuse to pay for waste services and begin to dump waste indiscriminately, creating financial challenges for service providers who will then be compelled to downgrade the quality of service. This can in turn possibly frustrate the fee paying residents in the middle and high-income areas. Sandra and Adrian (2000) opine that the public is generally united in its view on waste management - “Pick the waste up, but don’t put it down.” ‘Everyone wants a waste collection service in their neighborhood, but no one wants a disposal site near their house. NIMBY (Not in My Back Yard) has become a common slogan. But the waste must be deposited somewhere’. This is the concept called

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NIMBY.

Literature gap There is a lot on the economic, social, entrepreneurial and engineering aspects of solid waste in Nairobi. Most of the researches deal with supply driven aspects. The households’ willingness and ability to pay for this desired level of SWM service as well as the exact behavioral motivations to the prevailing and observed behaviours of the households remain as glaring research gaps. These are what this research aimed at filling.

APPROACHES/ METHODOLOGY A combination of methods, including the following were used: These included (i) 12 key informant interviews (ii) 6 Focus group discussions(iii) Transect walk through different estates where the questionnaire survey took place- to compare the intensity of waste problem in each, as well as to confirm some of the data already collected in questionnaires and interviews; (iv) 430 and 600 questionnaires administered to households distributed through stratified sampling as per the socio-economic grouping and in proportionate to estate population in stages I and II respectively .

The sampling procedure The sampling frame includes all households within Nairobi’s eight administrative divisions. The sampling unit is the household, while the sample populations are the households in Nairobi estates under study. The study population is the actual study population, whose unit of measurement is the household. The number for this is calculated as follows [Mugenda, and Mugenda, 1999]

n=

Z 2 pq d2

Where n= the desired sample size (if target population exceeds 10,000) z = the standard normal deviate at the required confidence level (Thus 1.96 used) p= the proportion in the target population estimated to have characteristics being measured (garbage collection efficiency in Nairobi; 25% used) q= 1-p (75% used) d= is the level of statistical significance set (95%) (Thus 0.05 used) Going by this formula and assuming 95% Confidence Interval and a p figure of 50% our calculated sample population was at least 410 and at least 600 for studies I and II respectively. This was either distributed across the eight administrative divisions of Nairobi, distributed as described in the sampling plan, or among the estates within Embakasi / Njiru


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districts (both carved out of the original Embakasi division).

Eastlands based district, the most populous, the second largest, and typically represents the face of Nairobi, with all the socio-economic classes represented in various estates. The inclusion criterion was those who had stayed in Embakasi for at least 1 year. However some questions were meant for comparison, and needed those who had been there for at least 4 years, or during both surveys for the responses to be comparable. The 600 sample size in 2010 was based on the premise that about 70%, or 380-430 households were expected to have been in Embakasi during both studies. An estimate of 6-8% emigration rate from the estate was assumed, giving 30% over the 4 years between the surveys. Further sampling was done on estates as shown in Table 2 and Figure 2.

Household sampling. To distribute the numbers per division above among estates, Stratified random sampling method was used because it can help achieve desired representation from various sub groups in the population. The identified sub groups include the medium to high income, medium income and, low-income groups and those living in slums. Normally, these groups can be identified from the socio-economic status as represented by housing development- in the Kenyan case, residential estates. A total of 430 and 600 HHs in 2007 and 2010 respectively were selected through stratified sampling from dwellings with different social status (as represented by estate). The first level of sampling in 2007 was the division; all the 8 divisions in Nairobi were represented, in a ratio proportionate to HHs numbers. In 2010, only Embakasi- an

TABLE 2 Household sampling plan 2007 and 2010 Income of estates

Sample size

Estate 2007

% 2007

Middle-high income

60

Langata

20.9%

Lower middle

100

Riruta / satellite, Kangemi,

23.2%

Sample 2010

121

% 2010

Estates represented

0

None

20.2%

Embakasi, Umoja,

Easily Low income

100

Kayole, Makongeni

Donhom: 23.2%

329

39.8%

Ruai, Ngundu and Kayole

Slums

140

Kibera, Mathare, Korokocho

430

32.6%

249

40%

Mukuru, Maili Saba and Soweto:

100

600

100

FIGURE 2 Household sampling plan in 2007 and 2010


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RESULTS AND DISCUSION Demographics. The mean household size was 4.6 + 0.2 in 2007 and 4.4+0.18 in 2010. The households interviewed had stayed in the area for a mean of 6.5+0.4 years, with a range between 1 year and more than 10 years, with the majority having stayed for over 10 years (22.9%), followed by 1-4 years (21.5%), and then followed by 1-2 years. At least 60% of the respondents (360 households) had stayed in Embakasi for at least 4 years, and were able to give a clear comparison between the 2010 and 2007. A comparison of the number of households who were interviewed in 2010, who were also there in 2007 were 360, compared with sample size of 410 in 2007. This was the reason the sample size was increased to 600 in 2010. Ability to pay. The concept of ability is exhibited in different ways. This is because services can be accessed in kind and cash payments. These can be in terms of economic activity of HH, the wages / salary earned by the HH and the other com-

munity activities / volunteer service activities a HH does. Therefore the household economic activity and income were used as the first proxy indicators of ability to pay. Source of income. There were at least 15 sources of income for the households, summarized in Figure 3. The main ones were casual labour (31.8%); formal employment (26.5%); food related enterprises (12.4%); selling of various wares (7.1%); farming (3.6%); sell water / hawk (2.2%) and building contractor (2%). Of these, all the specified economic activities earn fairly good and reliable income source. It is the group of others which may include other non income earners such as students / or school leavers who are struggling to place themselves in the city whose source of income is still shaky. Income as an indicator of ability to pay. Mean monthly household income is Kshs 16,070.80 + 1072 (US$ 214.3+ 14.3), with the distribution shown in Figure 4 above and Table 3. The cut off point for per capita dollar per day is 11,088

FIGURE 3 The main economic activity of HHs in the last 2 years

FIGURE 4 Distribution of Household income across income groups (in Kshs)

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TABLE 3 The household income Statistics (Kshs per month in Eastlands, 2010) N Mean Std. Error of Mean Median Mode Std. Deviation Variance Skewness Std. Error of Skewness Kurtosis Std. Error of Kurtosis Range Minimum Maximum Sum Percentiles

Valid Missing

5 10 20 25 30 40 50 60 70 75 80 90 95 99

per household per month (assuming a HH size of 4.4), 61.045% earn less than a dollar per person per day, earning only 38.8% are considered as able to pay for SWM services. Those who pay weekly spend Kshs 122 (US$ 1.63) (0.76%) of their monthly income, whereas those who do monthly payment spend Kshs 94 (US$ 1.26) (0.59%) of their income on SWM, indicating disparities. However, all pay less than 12% of the HH income, rendering the rates as affordable. The threshold (1-2%) for affordability for weekly payment is 160320(US$ 2.14- 4.28) per month (or Kshs 40-80 per week) (US$ 0.54-1.08), while the threshold for those who pay monthly is Kshs 159-318 (US$ 2.12- 4.24). Kasozi and Blottnitz (2010) argues that the average household charge for the disposal of all generated waste at Dandora would be about Kshs 310 (US$ 4.14) per month using weight based charging, which seems reasonable in light of current private charges in all income level areas. Lower income household would pay on average about KShs. 205 (US$ 2.74) per month. Whereas Nairobi expresses a willingness and ability to pay for services, results of such a study in Uganda suggested little chance of success if solid waste collection service charges are introduced (Niringiye and Omortor 2010). Khattak et al. (2009) found out that low income and the rich were not unwilling to pay, but there is no association with willing either. Results exhibit that HHs which are in the third income quartile (Q3 or middle income and Q1- slum) are WTP to get better SWM services. Thus people who fall in the upper middle class are willing to have better manage solid


546 0 16073.53 1072.207 10000.00 10000 25053.876 627696715.739 8.766 .105 112.749 .209 399800 200 400000 8776150 2000.00 3000.00 5000.00 6000.00 6000.00 8000.00 10000.00 12000.00 15000.00 18000.00 20000.00 30000.00 43950.00 101200.00

waste and want to contribute monetarily for this purpose. Khattak et al. (2009) found out that the other two income categories i.e. Q2 and Q4 failed to represent any significant relationship with WTP. Nevertheless, it is still understandable because those HH which falls in the second income quartiles might be left with no money after fulfilling their basic necessities. Thus they will find it hard to spare money for other social issues. On the other side, HHs that come under the fourth income quartile (the rich) have often careless attitude or they are already spending a fair amount of money to get their home and surroundings clean. Thus if proper motivation and persuasion is achieved, there is great possibility that they will become willing to contribute in monetary terms for better SWM services (Njagi et al., 2013 and Khattak et al., 2009). According to the regression results education, income and HH size are the important determinants of HH demand and consequently their WTP for better SWM services. There is lacking awareness among the HHs as it failed to establish any significant statistical relationship with HH Willingness to pay, despite the huge claim of being aware. These types of claims are normally observed in the surveyed analysis as respondents feel social pressure while exhibiting some information. Willingness to pay for improved SWM services. Primary SWM services vary, ranging from storage bin, to household collection services, to secondary and tertiary services. An environment with an array of all these services is likely to be

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fairly good in terms of SWM and the state of its environment in general. The services can be available to the consumer through self-doing or self-purchase of goods and services required. Willingness to buy household waste bins / container(s). There has been a marked improvement in willingness to purchase household storage bins from 72% in 2007 to 83% in 2010. In 2007, 72.3% of Nairobi households were willing to purchase waste bins. These figures compare favorably with those of JICA (1998) which found out that 76% and 58% of high and low income residents respectively were willing to pay for improved services. This is a significant change which has been capitalized on for improvement in SWM in Nairobi. By 2010, 82.7% HHs were actually purchasing waste bins either individually (11.4%), or through a privately engaged company (71.3%). Who supplies HH waste storage bins vs WTP. The containers are largely supplied by a waste collector (71.3%), improvised (6.1%), and bought by HH (5.3%). Another 14-17% doesn’t use any HH storage container. The high % that are supplied by waste collector implies an arrangement / some contract exists for solid waste collection, and HHs are getting the waste bin (a good) and the collection service from a service provider. This over 71% with HH storage containers supplied by private provider, in addition to the 5.3% who buy HH storage containers, represents the households willing to pay for improved SWM services. Thus it can be inferred that about 76.8% are willing to pay for improved SW services in the Eastlands of Nairobi. In 2007, in terms of number of bins HHs state they can buy, 20.7% were unwilling to buy any; 47.9% are willing to buy one bin; 25.3% are willing to buy two bins; 3.7% are willing to buy three; 1.9% is willing to buy 4 bins; and 0.5% is willing to buy 5. On the average,

78.2% of households are willing to purchase at least one waste bin (Figure 5). The bulk of those who are unwilling cite poverty as their main reason for not being able. Given the critical mass is willing, a program can be designed that supports the 20% very poor households, while utilizing the goodwill of the rest of the households willingness to purchase bins. This is bound to significantly improve household storage service. The households have one waste bin, meaning the 72% who were willing to purchase at least a waste bin went ahead and implemented their wish. As shown in Figure 5, the HH willingness to purchase waste bins increased sharply from 72.3% in 2007 to 95% in 2010 (Figure 5). Statistically, since the sampling in this research was pegged on 95% confidence level, the current 95% willing to purchase a bin statistically implies all are willing to purchase bins. This augers well for the future of the city.

Amount of money to spend on HH waste bins Willingness and ability to purchase Household waste bins (WTP for bins). In 2007, the majority of the Nairobi HHs were willing and able to spend US$ 0.74 on bins. By Kenyan standards, this can purchase a plastic bin of 5-10 litres, rendering it fairly reasonable. There is therefore a high effective demand for bins. In 2010, the households not only maintained their willingness to buy a bin, but went ahead and acquired some means however temporary, which they were using. Only less than 20% were not using any bin at all. This again indicates a positive development on the mindset of Nairobi residents; they are willing to purchase a bin commensurate with their level of income; less than 2% feel they are constrained by ability.

FIGURE 5 % Of Nairobi households willing to buy at least a household waste bin

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The 2010 situation on waste bins. In 2010, the household containers are largely supplied by a waste collector (71.3%), improvised bin (6.1%), and bought by HH (5.3%). Another 14-17% doesn’t use any HH storage container. The HHs actually looks for and engages the private collector at a fee. This indicated a great deal of change within the 4 years. The high % of HHs who are supplied by private waste collector implies an arrangement / some contract exists for solid waste collection, and HHs are getting the waste bin (a good) and the collection service from a service provider. This over 70% in addition to the 5.3% who buy, represents the household willing to pay for improved SWM services. Thus it can be inferred that about 76.8% are willing to pay for improved SW services in the Eastlands of Nairobi. In terms of distribution by socio-economic grouping, the majority of slum, low income, lower middle and middle-high

income groupings are willing and able to spend Kshs 20 (US$ 0.3), Kshs 50 (US$ 0.74), Kshs 20 (US$ 0.3) and Kshs 200 (US$ 3) respectively for waste bin purchase. These represent the modal amounts. Thus 24.8% of slum dwellers are willing to spend Kshs 20 (US$ 0.3); 47.1% of low income estates are willing and able to spend Kshs 50 (US$ 0.74); 37.1% of lower middle income is willing and able to spend Kshs 20 (US$ 0.27) on waste bins; and 22.4% of middle-high income households are willing and able to spend Kshs 200 (US$ 2.7) on bins. The thin plastic (Jwala) is still the most prevalence at 74.1%, followed by Plastic bin (8.8%) and carton box at 1.8%. Another 14.5% have no HH storage bin (Figure 6), indicating plastic HH container is still the most popular having increased from 59.6% to 82.9% in 2007 and 2010 respectively. Figure 7 also shows primary waste collection, with a focus on how households dispose of garbage.

FIGURE 6: % Distribution of materials of which waste bins used by Nairobi Households are made

FIGURE 7 Primary waste collection: How does your HH dispose of garbage?


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Unlike Accra and Kumasi, 100% and 65% of respondents in the high and middle-income areas of Tema, respectively, used the standard plastic containers. This is primarily due to the planned nature of Tema. Additionally, the authorities in Tema, through the Waste Management Department (WMD), have been supplying plastic containers to residents at a fee, thus providing motivation and impetus for the use of the standard containers. Consequently, littering in these areas is relatively minimal and thus the city has a relatively clean environment (Oteng-Ababio, 2011). In Bulawayo City Council (Zimbabwe) used to supply metal bins to its residents but due to high manufacturing costs, plastics bins became common as temporary waste storage facility as 48% of the households store their refuse in plastic bags. 44% were still using metal bins while 8% neither have metal bins nor plastic bins and this is caused by the introduction of a fee of US$2 per plastic bin which some cannot afford. Mangizo (2007) also highlighted the same challenges in her study, which was done in Gweru the provincial town of Midlands Province in Zimbabwe. She recommended the city councils to make sure refuse bins are readily available to residents for sustainable waste management. Refuse bins must be charged at a nominal fee so that members of the community can afford them (Njagi et al., 2013; Fungai and Chigwenya, 2012)).

Disposal of garbage- current practice 2010 In the Nairobi Eastland’s, about 22% (20.8% - 25.2) of the HHs don’t have a garbage collection service. The households dispose of garbage by engaging private company (62.9%); burning (15.6%); throwing outside dwelling (9.6%); collected by CBO (4.9%); collected by NCC (3.7%) (Fig 7). Of these, the 71.5% HHs with collection services by private, community organization (CBO) or NCC constitute the proper disposal mechanisms. Whereas those who burn garbage (15.6%) do not fit into the not in my backyard syndrome (NIMBY) group, they practice a seriously polluting waste management approach whereby the waste is transferred from one physical

state (solid) to gaseous, and from one geographical state (the estate) to another geographical location, with the gaseous waste causing wanton respiratory and environmental impacts as it moves as smoke. In his case, everybody- including the HH which does the burning suffers the consequences of the resulting air pollutant. The HH suffers without knowing. It has no intention of anybody suffering. Therefore because they also suffer albeit without knowing, they are not clearly in the NIMBY syndrome category of HHs. However, the last group- constituting 9.6% do suffer from not in my backyard (NIMBY) syndrome. Their approach is to remove the waste from their backyard where it is offensive to them, to the next point where perhaps it offends nobody. If the HH happens to throw the waste where it happens to offend anybody, the dumper hopes that the offended person / HH will push it to the next point, and the pushing continues until it reaches appoint where it clearly does not offend anybody- at which time it rests there. These final resting points for wastes happen to be pathways, hinds of residential houses, public plots and private plots without an active owner. The Nairobi households are highly willing and able to pay those collecting garbage from their households. The weighted effective demand in Nairobi, i.e. what they are willing and able to pay for monthly garbage collection is Kshs 107.65 (US$ 1.44) (Figure 8). This figure seemingly has been maintained at 107 in 2010, with the lower income residents who pay per week spending more (Kshs 122) (US$1.63) per month, compared with those with more regular monthly salary, who pay Kshs 94.4 (US$1.26) per month. This willingness should be capitalized on by the service providers to improve the garbage situation of the estates.

Willingness and ability to pay for garbage collection In 2010, households are willing to pay at most an average to Kshs 108.52 (US$ US$1.45) the Nairobi households are willing and able to pay for garbage collection per month (Fig

FIGURE 8 Secondary collection: Who collect the garbage where you have disposed of it

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9). This, however, is a weighted mean from all estates, whose socio-economic grouping are willing and able to pay as follows for garbage collection: Slums- Kshs 23.13 (US$ 0.31); Low income- Kshs 105.48 (US$1.41); Lower middle income group- Kshs 131.43 (US$ 1.75); and Middle-high income group- Kshs 173.75 (US$2.32). This figure has been maintained at around 107 four years down the lane 20072010. In 2007, households were willing and able to pay Kshs 107.65 (US$1.44) per month. By socio-economic class; the mean amounts for slum (Kshs 23.13) (US$0.31); Low income (105.48) (US$1.41); Lower middle income (Kshs 131.43)(US$1.75) and Middle-high income (Kshs 173.75) (US$ 2.32). This gives a weighted mean of Kshs 107.65 (US$ 1.44). The low income and slum estates are struggling to eke a living, and are understandably not able to ay any more than Kshs 50 (US$ 0.67) per month.

Are current charges reasonable? (Affordability) Only 178, representing 29.6% of the 600 surveyed did their payment for SWM services per week, while 290 paid per month. Of those who pay per week, the mean was Kshs 40.67 (US$ 0.54). The modal weekly fee was of Kshs 50 (US$ 0.67), Q1 of Kshs 20 (US$ 0.27; Q2 (median) of Kshs 40 (US$ 0.54); and Q3 of Kshs 60 (US$ 0.81). This translates to a mean monthly pay of Kshs 122.01 (US$ 1.63), mode of Kshs 200 (US$ 2.7); Q1 of Kshs 80 (US$ 1.08); median of Kshs 200 (US$ 2.7); and Q3 of Kshs 240 (US$ 3.2). The low income and slum estates are struggling to eke a living, and are understandably not able to pay any more than Kshs 50 (US$0.67) per month. For these, Nairobi HHs are willing and able to pay Kshs 66.59 (US$ 0.89) for garbage containers and Kshs 107.65 (US$ 1.44) for garbage collection per month. The households are willing to pay for improved solid waste management services as follows: slum Kshs 64 (US$ 0.85) (Kshs 23or US$ 0.31 in 2007); low income Kshs 130 (1.73)

(Kshs 105 or US$ 1.4 in 2007); and lower middle income Kshs 190 (US$ 2.53) from Kshs 131 (US$ 1.75) in 2007. This gives a net WTP of Kshs 108.52 (US$ 1.45) up from Kshs 107 (US$ 1.43) in 2007. This compare closely with what they are already paying as follows: mean of Kshs 107 (US$ 1.43) in 2010, with those paying weekly in at a rate of Kshs 122 (US$ 1.67) and those paying monthly incurring a mean of Kshs 94 (US$ 1.26).

Willingness to pay (WTP) by various income groups Q2 (low income) and Q4 (the rich) not unwilling, but there are no association with willing either. Results exhibit that HHs which are in the third income quartile (Q3 or middle income and Q1- slum) are WTP to get better SWM services. Thus people who fall in the upper middle class are willing to have better manage solid waste and want to contribute monetarily for this purpose. Khattak et al. (2009) found out that the other two income categories i.e. Q2 and Q4 failed to represent any significant relationship with WTP. Nevertheless, it is still understandable because those HH which falls in the second income quartiles might be left with no money after fulfilling their basic necessities. Thus they will find it hard to spare money for other social issues. On the other side, HHs that come under the fourth income quartile (the rich) have often careless attitude or they are already spending a fair amount of money to get their home and surroundings clean. Thus if proper motivation and persuasion is achieved, there is great possibility that they will become willing to contribute in monetary terms for better SWM services (Khattak et al., 2009). According to the regression results education, income and HH size are the important determinants of HH demand and consequently their WTP for better SWM services. There is lacking awareness among the HHs as it failed to establish any significant statistical relationship with HH Willingness to pay, despite the huge claim

FIGURE 9 How much maximum are you willing and able to pay for household garbage collection per month


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of being aware. These types of claims are normally observed in the surveyed analysis as respondents feel social pressure while exhibiting some information.

Exhibition of NIMBY and NIMTO syndromes Where the waste is deposited. Whereas those who burn garbage (15.6%) do not fit into the not in my backyard syndrome (NIMBY) group, they engage in a seriously polluting waste management practice whereby the waste is transferred from one physical state (solid) to gaseous, and from one geographical state (the estate) to another geographical location, with the gaseous waste causing wanton respiratory and environmental impacts as it moves as smoke. In this case, everybody- including the HH which does the burning -suffers the consequences of the resulting air pollutant. The HH suffers without knowing, and has no intention of anybody suffering as it engages in burning- it’s all done in good faith. Therefore because they also suffer albeit without knowing, they are not clearly in the NIMBY syndrome category of HHs. Sanjay (2011) observed that the Pune city (India) households are expected to deposit their solid waste in bins located at street corners and at specific intervals. Even though the storage arrangements are conveniently located in city, solid waste tends to be thrown around the storage area, roadside gutters etc. It happens partly because of indiscipline among people and partly by rag pickers. Here the households behave with the NIMTO syndrome- as they deposit wastes carelessly into the communal bin area. According to Khattak et al. (2009), the second most observed reason for avoiding monetary contribution for better SWM services was that 28% of the HHs thought that government was responsible to ensure the availability of basic amenities to its masses. In Nairobi’s 2010 research, of the households, 54% require government/ NCC or sponsor to purchase HH waste bins,

thereby exhibiting the NIMTO syndrome. These are clear cases of NIMTO (Figures 10 and 11). The last group- constituting 9.6% who simply dump waste out of the house do suffer from not in my backyard (NIMBY) syndrome. Their approach is to remove the waste from their backyard where it is offensive to them, to the next point where perhaps it offends nobody. If the HH happens to throw the waste where it happens to offend anybody, the dumper hopes that the offended person / HH will push it to the next point, and the pushing continues until it reaches appoint where it clearly does not offend anybody- at which time it rests there. These final resting points for wastes happen to be pathways, hinds of residential houses, public plots and private plots without an active owner. About 17.3% HHs do not collect waste, but are aware where it goes after leaving their household. Of the 83% who are not aware, 28.8% think they need to know where their wastes are taken after collection. This puts a total of those who care to 40.9 (83% of 28.8%) plus the 17.3% (Figure 12). NIMBY in HH waste storage. Those with some form of HH storage container are likely to store sizeable enough quantities to feel ashamed of dumping them in the immediate neighbourhood, and are most unlikely to exhibit NIMBY syndrome. About 14% use no household container, meaning they simply dump outside their door as the wastes are generated. Lack of HH container implies they use hand, again implying they cannot take the wastes far. They are likely to dump them out of the immediate household space, perhaps at the nearest next point where they are not offended as a HH. The neighbours who find this waste- because it’s in small quantity- eg a ball of plastic, paper, etc, nobody takes offence and simply remove the waste from their area of control and interest. This waste may be pushed to the point where nobody makes a claim. This is a group which may fall in the not in my backyard syndrome.

FIGURE 10 % Distribution of who Nairobi households feel should purchase household waste bins

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FIGURE 11 % Distribution of feelings of Nairobi households who feel they should know where their household waste is taken after removal from their residences.

FIGURE 12 % Distribution of reasons why the Nairobi households feel they should know the destination of their household wastes even after collection by non-household agent. Over three in five (61.3%) households believe the household has a role to play in SWM, and to improve the state of their environment. This group is liberated, and take responsibility, and are not ready to blame other authorities; they are free from the NIMTO syndrome, meaning the rest of about 38% of HHs may , though not necessarily exhibit the NIMTO THE APPLICABILITY OF NIMBY AND NIMTO SYNDROMES

syndrome. However, 7.7% completely disagree, and can be classified as the group with the NIMTO syndrome. Another 3.1% don’t know, but 27.5% are already fully involved and chose to be silent on this question. Those who are not happy cite the following reasons: littering (29.4%); blocked drains (18.5%); cause diseases (9.5%); human and livestock scav133

enging (10.8%); polluting water (5.8%); and to 23.2% this was not applicable because they were happy with the state of the environment in their estate (Figure 12). In terms of optimism, 65.3% believe the state can be improved, while 3.2% believe it cannot, and the rest were happy with the environment the way it is. This would mean that those who think there is room for improvement (65.3%) neither exhibit the syndromes of NIMTO and NIMBY, but also would be willing to pay for improvement services, regardless of their ability to pay. The optimistic propose the following for improvement in state of the environment in the estates: Collect wastes regularly (23.3%); more resident participation on SWM (18.8%); clear the waste heaps around the estates (16.3%); organize regular waste collection and cleaning days (13.3%); teach sanitation in schools and community (9.4%); supply HH waste storage containers (7.5%); incorporate SWN as part of kazi kwa vijana (5.5%); privatize waste collection (3.1%); and pay CBOs to remove wastes from narrow corridors 91.1%). Already, at least 25.6% of the HHs are aware of involvement of the local community CBO in SWM in income generation. These do a wide range of activities as follows: recycle (25%); CBO (20%); private company (10%); making some product (reuse 7%); dumping (7%); among others. At least 67% believe wastes can be used to generate wealth. This would imply the 67% recognition waste as a resource would be willing to commercially engage in its collection as a resource or raw material for another activity or process, and therefore can never exhibit NIMTO or NIMBY syndromes. They would, however, be willing to pay to collect and process the garbage.

waste collection. This effective demand is exhibited by the amounts they are willing and able to pay, that is, Kshs 107.4 (USD 1.5) for garbage collection per month for 2007 and 2010. 72.3% are willing to purchase waste bins. Even though at least 30% of the households are able to pay, given they are either landlords (28%) or live above poverty line (40%), there are prospects of at least 95% being progressively able to pay given the latrine use as an indicator of abject poverty indicates they are not abjectly poor, and have positive prospects. The evidence shows that Kenyans exhibit NIMBY syndrome through burning of waste and open dumping of waste. This constitutes less than 30% of Nairobi residents, down from almost 70% in 2007 when the weight of filth in the neighborhoods had not sunk deep into the community. After 2007, an increased level of awareness and responsibility has been exhibited by the residents of Nairobi, leading to the majority willing to engage private collection of waste however low their income is. They have also gone ahead and forming CBOs to specifically deal with solid waste, which is no longer seen as a liability but as a resource. Kenyans exhibit NIMTO syndrome through their feeling government, CBO or NGO should give them free SWM services even without paying, improper disposal of waste into communal bins and / or sending children to dispose of households wastes into full communal bins. Other indications of NIMTO include household lack of interest in knowing the fate of their wastes after collection. These habits are exhibited by at least 15% of Nairobi residents in 2010, down from almost 50% in 2007. This indicates a remarkable improvement in attitude towards SWM.

Burning wastes- NIMBY in disguise. Burning of waste leads to air pollution in terms of increased total suspended solids (TSP) and especially particles less than 10 micrometers in diameter (PM10) emissions, which is equivalent to vehicular emissions at times Gupta et al. (1998). Ladu et al. (2012) revealed that 36% household said that public health department burned solid waste on-site, 30% said that they collected domestic solid waste, 20% said they transported solid wastes, 14% said they burn solid waste at main dumps and no wastes containers were provided to them. Ladu et al., 2012. Similarly, 74.6% of respondents in Adisababa, Ethiopia, indicated that they burn organic waste together with the other solid waste (Nigatu et al., 2011).

Recommendations Facilitate a process to allow the willing and able households to pay the amounts they feel feasible for their desired level of solid waste collection service. In addition, waste bins of the appropriate design options (size, shape, durability and material) should be designed and availed in the market that matches the amount households in various socioeconomic statuses are willing and able to pay. This will help match the demand with supply of bins, rendering their use more popular.

REFERENCES CONCLUSIONS AND RECOMMENDATIONS Conclusions From the discussion, it is evident there is high demand for improved SWM service at the household level in Nairobi. In 2007, 30% get no service and would like to get it. Of those already with a collection service, 50% would like to have a higher level of service. By 2010, 78% are getting the service. In addition, Nairobi households show a very high consistent effective demand for waste bins and improved household

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Afullo, A (2014) Integrated Solid Waste Management Handbook: For Policy Makers, Engineers, Environmentalists and Students. Wamra Techoprises, Nairobi, Kenya and Createspace Online Edition. Afullo, A. (2014b) Advances in Water Supply, Sanitation and Environmental Management: A Water, Sanitation and Hygiene (Wash) Perspective. Wamra Techoprises, Nairobi, Kenya and Createspace Online Edition. Njagi, J.M., Ireri, A.M., Njagi, E.N.M., Akunga, D.L, Afullo, A.T., Ngugi, M.P., Mwanzo, I. and Njagi I. K. (2013) “Knowledge, attitude and perceptions of village residents on the health Risks posed by Kadhodeki dumpsite in Nai-


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robi, Kenya.” Ethiopian Journal of Environmental Studies and Management. EJESM: Volume 6. No.4 2013:427434. http://dx.doi.org/10.4314/ejesm.v6i4.12. Afullo, A and Odhiambo F (2009) “Primary Solid Waste Storage Gaps Experienced By Nairobi Households.” Ethiopian Journal of Environmental Studies And Management. EJESM VOL.2.NO.3 2009:34-43. Aggrey, Niringiye and Douglason Omortor G. (2010). “Determinants of Willingness to Pay for Solid Waste Management in Kampala City.” Current Research Journal of Economic Theory, Volume 2(3): 119-122, 2010. ISSN: 2042-485x. © Maxwell Scientific Organization, 2010. Allison, Kasozi and Harro Von Blottnitz (2010). Solid Waste Management in Nairobi: A Situation Analysis. Technical Document Accompanying The Integrated Solid Waste Management Plan Prepared By: Environmental & Process Systems Engineering Group, University of Cape Town. For The City Council of Nairobi on Contract for the United Nations Environment Programme Draft: 17 February 2010. Bateman, J. J., Cole M.A, Georgioua S., and Hadley D.J. (2006) Comparing contingent valuation and contingent ranking: A case study considering the benefits of urban river water quality improvements. Journal of Environmental Management Elsevier Science Direct http://www.ScienceDirect.com. Volume 79, Issue 3; May 2006; pp. 221-231. Baud, I. & Post J. (Undated). “Between Market and Partnerships: Urban Solid Waste Management and Contributions to Sustainable Development?” Gber, Volume 3 No. 1, pp. 46-65. Mutisyaa, Emmanuel, Masaru Yarime, (2011). “Understanding the Grassroots Dynamics of Slums in Nairobi: The Dilemma of Kibera Informal Settlements.” International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.2. ISSN 2228-9860 Eissn 1906-9642. Online Available At http://Tuengr.Com/V02/197-213.Pdf. Fungai, Hamilton Mudzengerere and Average Chigwenya (2012) Waste Management in Bulawayo City Council in Zimbabwe: In Search of Sustainable Waste Management. In the City ISSN: 1520-5509. GoK (1994) Institutionalization of urban environment management, training and awareness creation. Environment and urban development training project Phase II Nairobi, Kenya. GoK (2009). Population and household census, (2009) MPND, Republic of Kenya, Nairobi. GoK (2003) Kenya Demographic and health Survey report, Central bureau of statistics, Nairobi. Gupta, S., K. Mohan, R. Prasad, S. Gupta S. and A. Kansal (1998), “Solid Waste Management in India: Options and Opportunities,” Resources, Conservation and Recycling, Volume 24(2): pp. 137–154. JICA (1998). “The Study on Solid Waste management in


Nairobi City in the Republic of Kenya,” Japan International Cooperation Agency. Nairobi. John, Leju Celestino Ladu; Mohammed Ahmed Osman (2012). Scholarly Journals of Biotechnology Vol. 1(2), Pp. 28-38, September 2012. Available Online At http:// www.Scholarly-Journals.Com/Sjb. ISSN 2315-6171 Solid Waste Management and Its Environmental Impacts on Human Health in Juba Town - South Sudan. Khattak, Naeem-Ur-Rehman., J. Khan and I. Ahmad. (2009). “An Analysis of Willingness to Pay for Better Solid Waste Management Services In Urban Areas of District Peshawar.” Sarhad J. Agric. Volume 25(3): pp. 529-535. Mangizvo, V. Remigios (2010). “An Overview of the Management Practices at Solid Waste Disposal Sites in African Cities and Towns.” Journal of Sustainable Development in Africa (Volume 12, No.7, 2010). ISSN: 15205509. Clarion University of Pennsylvania, Clarion, Pennsylvania. Mugenda, O and Mugenda A (1999). Research methods: A quantitative and qualitative approach. ACTS Press, Nairobi. Nigatu, Regassa, Rajan D. Sundaraa and Bizunesh Bogale Seboka. (2011). “Challenges and Opportunities in Municipal Solid Waste Management: The Case of Addis Ababa City, Central Ethiopia.” J Hum Ecol., 33(3): pp. 179-190. Nissim, I., T. Shohat And Y. Inbar (2005), “From Dumping To Sanitary Landfills – Solid Waste Management In Israel,” Waste Management, Volume 25 (3): pp. 323-328. Oteng-Ababio, Martin (2011). Governance Crisis or Attitudinal Challenges? Generation, Collection, Storage and Transportation of Solid Waste in Ghana, Integrated Waste Management - Volume I, ISBN: 978-953-307-469-6, Intech, Available From: http://www.Intechopen.Com/Books/IntegratedWastemanagement- Volume-I/. Sandra, Cointreau-Levine and Adrian Coad. 2000. Guidance Pack Private Sector Participation in Municipal Solid Waste Management: ISBN: 3-908001-90-0. Copyright: © Skat, 2000. Swiss Centre for Development Cooperation in Technology and Management and Intermediate Technology Publications, Ltd. Sanjay, Rode 2011. “Integrated Approach to Solid Waste Management in Pune City.” Journal of Geography and Regional Planning, Vol. 4(8), Pp. 492-497, August 2011. Available Online At http://www.Academicjournals.Org/Jgrp. ISSN 2070-1845 ©2011 Academic Journals. Wilson, D. C., Rodic, L., Scheinberg, A., Velis, C. A., & Alabaster, G. (2012). “Comparative Analysis of Solid Waste Management in 20 Cities.” Waste Management & Research, Volume 30(3), pp. 237-254. WSP (2005). Understanding small scale providers of sanitation services: A case of Kibera, field note, Water and sanitation program-Africa, Nairobi.

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REDUCTION OF CORROSION OF REINFORCING STEEL IN CONCRETE USING ALKALI ASH MATERIAL Hossein Rostami1, Fernando Tovia2 Reza Masoodi3 and Mozhgan Bahadory4 1

Professor of Science and Engineering, Philadelphia University, Philadelphia PA, 19144 Phone: (215) 951-2877, Fax: (215) 951-6812, Email: [email protected] 2

Associate Professor of Engineering, Philadelphia University, Philadelphia PA, 19144 Phone: (215) 951-5652, Email: [email protected]

3

Associate Professor of Engineering, Philadelphia University, Philadelphia PA, 19144 Phone: (215) 951-5630, Email: [email protected]

4

Assistant Faculty, Department of Chemistry, Community College of Philadelphia, Philadelphia PA, 19130 Phone: (215)751-8616, Email: [email protected]

ABSTRACT Approximately 850 million tons of coal are consumed for electric generation and industrial use in the United States each year. This generates about 100 million tons of by-products including bottom ash, fly ash, flue gas desulfurization sludge, and boiler slag. One of these by-products, fly ash, has a potential to reduce the corrosion of reinforcing steel in concert. In this paper, the effect of Alkali activated Ash Material (AAM) on mechanical properties and corrosion protection are investigated. Our experiments showed that coating the rebar with AAM reduces the corrosion rate significantly. The most important advantage of using AAM coated rebar over epoxy coated rebar is their corrosion rate when the coating is damaged. The corrosion rate in a rebar coated with AAM remained almost the same after damaging the coating, while the corrosion rate of a rebar with damaged epoxy coating was the same as an uncoated rebar. Keywords: Fly ash, Alkali Activated Ash Material (AAM), Corrosion, Rebar, Concrete, Coating

INTRODUCTION Corrosion is the destructive attack of a material by reaction with its environment. In the United States, the cost of corrosion is about $500 billion/year. The annual economical loss due to corrosion of reinforcement in concrete is estimated around $200 billion/year. Concrete alkalinity should provide for protection of reinforcing steel against corrosion. This desirable environment should not allow deterioration of reinforcing steel during its design life. However, the steel-friendly environment does not always remain intact, thus resulting in the problem of deterioration of reinforcing steel by means of corrosion. The two

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main causes of steel rebar corrosion are chloride induced corrosion and carbonations attack. In this paper, mechanical properties and corrosion protection of Alkali activated Ash Material (AAM) are investigated.

Alkali Activated Ash Materials Approximately 1050 million tons of coal is consumed annually for electric generation and industrial use in the United States. This generates about 130 million tons of byproducts including bottom ash, fly ash, flue gas desulfurization sludge, and boiler slag. The majority of these materials are landfilled [1]. The disposal of huge quantities of these


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materials poses a potentially significant environmental problem. Coal combustion ash contains concentrations of Arsenic, Barium, Cadmium, Chromium, Lead, Mercury, Selenium, and Silver, which can be leached and pollute groundwater supplies [2, 3].

sion resistance of AAM for the creation of more durable concrete structures in marine applications and structures exposed to aggressive environments. This technology is based on the use of fly ash as a raw material. Fly ash includes those particles collected from coal combustion flue gases to meet the Clean Air Act particulate standards. Fly ash particles are very small in size, over 50% have diameter less than 45 microns and present a more significant danger to the environment than the heavy metals would. The large surface area of the fly ash and its fine particle size makes it likely to leach its hazardous components to the environment, if it is not handled with utmost care. This means there is a greater risk of hazardous materials leaching from fly ash and contaminating groundwater supplies. Superfund sites have resulted from improper disposal of these materials, and studies have shown higher levels of certain metals in groundwater down gradient from ash disposal sites [12]. Superfund sites are toxic site in the United States which requiring cleanup and has been placed on the National Priorities List (NPL). The Environmental Protection Agency (EPA) ensures the clean-up and the maintenance of superfund sites to protect the environment and the health of all in the United States. The addition of fly ash to cement mixtures is well established and a great body of research on the subject is available [13-18]. There are significant benefits to the use of fly ash in concrete including better economics, increased ultimate strength, improved chemical resistance, reduced alkali-silica reactivity, and a number of other property improvements [2, 3]. The greatest shortcoming of utilizing fly ash is that there is a limited amount which can be used in this application. Inclusion of higher levels of fly ash in the concrete (about 20% replacement of Portland cement) would create severe adverse effects in the properties of the final products (i.e.

Composition of Fly Ash There are two primary types of fly ash according to ASTM C-618: Class F, low calcium fly ash and Class C, high calcium fly ash. Table 1 gives the chemical composition of Class F and Class C fly ash, Portland cement, and the fly ash used in this work. The same oxides appear in fly ash and Portland cement concrete, but in different amounts. Fly ash has higher SiO2 content while Portland cement contains more CaO. Typically, Class F fly ash has less than 15% CaO content and Class C fly ash has greater than 20% CaO. More than 70% of the Class F fly ash consists of the oxides of silicon, aluminum and iron. Its particles are classified as an aluminosilicate glass which exhibit pozzolanic reactivity in the presence of alkali, but do not themselves exhibit cementitious properties when mixed with water. Class C fly ash has a combined silicon oxide, aluminum oxide, and iron oxide content greater than 50%. The material is a calcium aluminosilicate and exhibits cementitious properties when exposed to water along with pozzolanic reactivity [4]. A new technology has been developed which transforms fly ash into a high performance cementitious material. In this technology, fly ash replaces Portland cement and a part of fine aggregate in concrete. Fly ash is chemically activated and as a result, Alkali activated Ash Material (AAM) is produced [5-11]. This technique creates a high performance concrete material which resists chemical attacks and corrosion better than Portland cement. This work describes the corro-

TABLE 1 Composition of Class F and C Fly Ash and Portland Cement Oxides

Class F Fly Ash

Class C Fly Ash

Portland Cement

Fly Ash (this work)

SiO2

45-65

48-68

20

61.3

Al2O3

20-45

18-34

6

22.7

Fe2O3

3-12

2-8

3

4.8

CaO

3-10

15-39

63

4.1

MgO

1-3

3-6

1.5

1.3

Alkali

0.05) correlation with the pH in any of the treatments (Table 5).

pH The initial pH of the substrates (6.25-7.66) was within the range of 6-8 considered suitable for bacteria involved in anaerobic digestion. The results of the ANOVA showed that codigestion had significant (p ≤ 0.05) effect on pH in CD:SM mixtures (Table 3). CD:CM and CD:SM mixtures had means and standard deviations of 7.08 and 7.00, and 0.340 and 0.438, respectively. This showed higher pH fluctuations in the latter treatments. The lowest and highest pH values in the CD:SM mixtures translated to lower biogas production (Table 4). Within a week of the digestion process, the pH of the

ENHANCING BIOGAS YIELD FROM COW DUNG BY CO-DIGESTING WITH CHICKEN AND SWINE MANURES

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TABLE 3 ANOVA results showing the effect of co-digestion on the parameters MP

Parameter

Source

Df

SS

MS

F-value

Pr>F

CD:CM

Temperature

Treatment

3

0.182

0.061

2.644

0.121

Error

8

0.184

0.023

Treatment

3

0.027

0.009

3.587

0.066

Error

8

0.020

0.003

Treatment

3

4.509

1.503

5.174

0.028

Error

8

2.324

0.290

Treatment

3

3.043

1.014

291.150

CM>SM. The study showed that co-digesting CD with CM was better for biogas production with the optimum mix proportion of CD:CM (50:50). Furthermore, the sizes of the digesters will be reduced since one needs less quantity of mixtures to produce the same quantity of biogas as compared to individual samples of either CD or CM.

REFERENCES Adewumi, I.K., 1995. Determination of parameters for the design of biogas digesters. Ife Journal of Technology 5 (1), 9-15. Alatriste-Mondragon, F., Samer, P., Cox, H., Ahring, B., Iranpour, R., 2006. Anaerobic co-digestion of municipal, farm, and industrial wastes; a survey of recent literature. Water Environmental Research 78 (6), 607-636. Benabdallah, El-Hadj T., Astals, S., Gali, A., Mace, S., MataAlvarez, J., 2009. Ammonia influence in anaerobic digestion of OFMSW. Water Science and Technology 59 (6), 1153-1158. Callaghan, F.J., Wase, D.A.G., Thayanithy, K., Forster, C.F.,

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1999. Co-digestion of waste organic solids: batch studies. Bioresource Technology 67, 117-122. Chen, S., Liao, W., Liu, C., Wen, Z., Kincaid, R.L., Harrison, J.H., Elliott, D.C., Brown, M.D., Solana, A.E., Stevens, D.J., 2003. Value-added chemicals from animal manure. National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Road., Springfield, VA 22161. Comino, E., Rosso, M., Riggio, V., 2009. Development of a pilot scale anaerobic digester for biogas production from cow manure and whey mix. Bioresource Technology 100, 5072-5078. Coombs, J., 1990. The present and future of anaerobic digestion. In: Anaerobic Digestion- A Waste Treatment Technology, 1-42, Elsevier Applied Science, London. Cuzin, N., Farinet, J.L., Segretain, C., Labat, M., 1992. Methanogenic fermentation of cassava peel using a pilot plug flow digester. Bioresource Technology 41, 259-264. El-Mashad, H.M., Zhang, R., 2010. Biogas production from co-digestion of dairy manure and food waste. Bioresource Technology 101, 4021-4028. Fantozzi, F., Buratti, C., 2009. Biogas production from different substrates in an experimental continuously stirred tank reactor anaerobic digester. Bioresource Technology 100, 5783-5789. Hartmann, H., Angelidaki, I., Arhing, B.K., 2003. Codigestion of the organic fraction of municipal waste with other waste types. In: Mata-Alvarez, J. (Ed.), Biomethanization of the Organic Fraction of Municipal Solid Wastes. IWA Publishing, UK. Itodo, I.N., Awulu, J.O., 1999. Effects of total solids concentrations of poultry, cattle, and piggery waste slurries on biogas yield. Transactions of ASAE 42 (6), 1853-1855. Jain, S.R., Maattiasson, B., 1998. Acclimatization of methanogenic consortia for low pH biomethanation process. Biotech Letter 20 (8), 771-772. Lalitha, k., Swaminathan, K.R., Bai, R.P., 1994. Kinetics of

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biomethanation of solid tannery waste and the concept of interactive metabolic control. Applied Biochemistry and Biotechnology 47, 73-87. Lasaridi K., Protopapa I., Kotsou M., Pilidis G., Manios T., Kyriacou A., 2006. Quality assessment of composts in the Greek market: the need for standards and quality assurance. Journal of Environmental Management 80, 58-65. Macias-Corral, M., Samani, Z., Hanson, A., Smith, G., Funk, P., Yu, H., Longworth, M., 2008. Anaerobicdigestion of municipal solid waste and agricultural waste and the effect of co-digestion with dairy cow manure. Bioresource Technology 99, 8288-8293. Muyiiya, N.D., Kasisira, L.L., 2009. Assessment of the effect of mixing pig and cow dung on biogas yield. Agricultural Engineering International: the CIGR Ejournal. Manuscript PM 1329, Vol. XI. Odeyemi, O., 1982. Relative biogas generation from animal manures in Nigeria. University of Regina, Saskatchewan, Canada: Energex ’82. Ojolo, S.J., Dinrifo, R.R., Adesuyi, K.B., 2007. Comparative study of biogas production from five substrates. Advanced Materials Research 18-19, 519-525. SAS, 2002. Statistical Analysis Software Guide for Personal Computers. Release 9.1 SAS Institute Inc., Cary, NC 27513, USA. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater engineering: treatment and reuse. Fourth ed. New Delhi: Tata McGraw-Hill Publishing Company Limited, p. 1819. Yusuf, M.O.L., Ify, N., 2011. The effect of waste paper on the kinetics of biogas yield from the co-digestion of cow dung and water hyacinth. Biomass and Bioenergy 35, 1345-1351. Zennaki, B.Z., Zadi, A., Lamini, H., Aubinear, M., Boulif, M., 1996. Methane fermentation of cattle manure: effects of HRT, temperature & substrate concentration. Tropicultural 14 (4), 134-140.


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MATERIAL FLOW ANALYSIS OF ABATTOIR SOLID WASTE MANAGEMENT SYSTEM IN MINNA, NIGERIA I.E. Ahaneku1 and C.F. Njemanze2 Department of Agricultural and Bioresources Engineering, Federal University of Technology, Minna, Nigeria P.M.B 65, Minna, Niger State Email: [email protected]; 2 [email protected]

ABSTRACT Material flow analysis (MFA) is an excellent tool that describes the static situation of different materials flows between different subsystems in a defined system. This study estimated the annual amount of the total waste generation in Minna main abattoir; it calculated abattoir waste flow and employed MFA using the waste cube model to illustrate the flows of abattoir waste from waste generators to waste disposers. Results indicated that a total of 66,630 cows and 13,884 goats were slaughtered in Minna main abattoir. This generated 849.54 tons of blood, 550.39 tons of intestinal content, 814.83 tons of bone and 437.55 tons of waste tissues between 2010 and 2012. The abattoir waste flow indicated that the dominant waste treatment methods of Minna main abattoir was re-use and recycling, accounting for 72.60% of waste disposal on the average from 2010 to 2012, whereas blood accounted for 32.03% of the total abattoir waste for the same period. The study has shown how solid wastes from Minna abattoir can be managed and converted into value-added products for effective utilization. The solid wastes generated can be re-used for land application as manure or recycled for other income generating activities like animal feed and aquaculture. Keywords: Material flow analysis, abattoir, waste generation, re-use and recycling, Minna.

INTRODUCTION To achieve sustainable development of urban regions it is essential to manage the resource consumption and to leave unpolluted ecosystems for future generations. Agricultural waste is waste produced in agricultural premises as a result of agricultural activities. Globally, 140 billion metric tons of biomass is generated every year from agriculture (UNEP, 2007). These wastes, if not properly handled will lead to environmental hazard, hence the need for effective waste management. One type of waste that is of great concern in both urban and rural areas in Nigeria is abattoir waste. No doubt, abat-

toirs or slaughterhouses, in this clime, are foremost sources of water and air pollution. What often constitutes waste in abattoirs includes condemned organs, carcasses, bones, blood, faeces, hides, horns, hoofs, animal hair among others. Environmentalists would say waste is not waste until one wastes it. Each of these wastes or by-products is a potential wealth waiting to be taped. Almost every day in all the urban and rural markets in Nigeria, animals are slaughtered and the meat sold to the public for consumption. Meat wastes originate from killing, hide removal or dehairing, paunch handling, rendering, trimming, processing and clean-up operations. Abattoir wastes often contain blood, fat, organic and inorganic solids, and

____________________________________ 12

Corresponding authors


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salts and chemicals added during processing operations (ESRC, 2011). Abattoir effluent (waste water) has a complex composition and can be very harmful to the environment (Polprasert and Tran, 1992). Despite advancements in technology, waste management in Nigeria is characterized by inadequate disposal technology, high cost of management and adverse effect of waste on the environment. The most important issue in all meat-processing plants is maintenance of proper hygiene and adequate sanitary conditions. The continuous drive to increase meat production for the protein needs of the ever increasing world population has some pollution problems attached to it. Pollution arises from activities in meat production as a result of failure in adhering to Good Manufacturing Practices (GMP) and Good Hygiene Practices (GHP) (Akinroet al., 2009). An abattoir has been defined as a premise approved and registered by the controlling authority for hygienic slaughtering and inspection of animals, processing and effective preservation and storage of meat products for human consumption (Alonge, 1991). Abattoir waste just like any other waste can be detrimental to humans and the environment if definite precautions are not taken. Abattoir waste consists of both solid waste and wastewater. The solid wastes from abattoir are varied depending on the kind and scale of operations. Usually the quantity of wastes per animal is large in small scale operations where the recovery of offal is ineffective. In simple operations, animals are slaughtered and have a very limited amount of by-product processing. Its main products are fresh meat in the form of whole, half or quarter carcasses or in smaller meat cuts. Modern complex abattoir does extensive processing of by-products. In such plants at least three additional operations; rendering, paunch and viscera handling, blood processing, and hide and hair processing take place. By these operations, maximum recovery of edible and inedible materials from the offal is done and that results in less production of wastes. Effluent from abattoir waste has also been known to contaminate both surface and groundwater because during abattoir processing, blood, fat, manure, urine and meat tissue are lost to the wastewater streams (Bello and Oyedemi, 2009). In Nigeria, many abattoirs dispose their effluents directly into streams and rivers without any form of treatment and the slaughtered meat is washed by the same water. Leaching into groundwater is a major source of concern, especially due to the recalcitrant nature of some contaminants (Muhirwa et al., 2010). According to Adeyemo et al. (2009) facilities for waste recovery, treatment, and reuse are either inadequate or nonexistent in most Nigerian abattoirs. Thus, wastes are indiscriminately and improperly discharged and constitute environmental hazards. Leachates from their serial decomposition processes have the potential to pollute nearby surface water, with enteric pathogens and excess nutrients which may percolate into the underlying aquifers and contaminate shallow wells. Blood constitutes the highest pollution load of all the components of abattoir effluents, followed by fat. Blood, one of the major dissolved pollutants in abattoir wastewater, has

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the highest chemical oxygen demand (COD) of any effluent from abattoir operations. If the blood from a single cow carcass is allowed to discharge directly into a sewer line, the effluent load would be equivalent to the total sewage produced by 50 people on average day (Aniebo et al., 2009). From the viewpoint of environmental management, unreliable records has made it difficult for proper assessment of waste load generated at each slaughter house; and thus, difficulty in planning for waste containment. Data would also not elicit government interest in addressing the problems at slaughter houses if the proper records of huge amount of abattoir wastes generated and its yearly increase are not kept. For adequate management, it is important to know the quantity being generated daily, weekly and yearly, their characteristics and existing management facilities. It is also important to note that for one to understand the methods of handling and disposing waste there is need for a vivid knowledge of the basic characteristics of the waste in question and its quantity (Chukwu et al., 2011). Determining the amount and type of waste generated within an area is very vital for waste management planning and implementation. In Minna city, no comprehensive study has been done with respect to abattoir waste management. Material flow analysis (MFA) is an effective analysis tool in this regard. Material Flow Analysis (MFA) is a systematic approach aimed at presenting an overview of the materials used in a company/industry; identifying the point of origin; the volumes as well as the causes of waste and emissions; creating a basis for an evaluation and forecast of future developments; and defining strategies to improve the overall situation. A MFA describes the static situation of different materials flows between different subsystems in a defined system. It is a quantitative procedure for determining the flow of material and energy through the economy. It is generally based on methodically organized account in physical unit and uses the principle of mass balancing to analyze the relationships between material flows, human activities and environmental changes (OECD, 2008). The advantage of the MFA is the possibility for reducing complex systems to the key goods and processes relevant for the study objectives. This way, the base is created for deriving necessary measures or for calculating scenarios aiming at system optimization. MFA models have been developed in carrying out material flow analysis of various materials, ranging from waste materials to other materials of good use. In this study, the waste cube model developed by Plubcharoensuk et al. (2008) was used. It is a matrix-type model, which is a basic and systematic tool. The model consists of three parameters: type of industry, type of waste generated and type of waste treatment facility. These three parameters represent the industrial waste system of an area as a cube, thus the model was referred to as a “waste cube model.” The objectives of this study are: to assess the various types of wastes generated from Minna abattoir in north central Nigeria; and the application of material flow analysis (MFA) to determine inputs-outputs of the waste treatment


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processes and disposal of abattoir waste using the waste-cube model.

bones, horns, hoofs, urinary bladder, gall bladder, uterus, rectum, udder, fetes, snout, ear, penis, meat trimmings, hide and skin trimmings, condemned meat, condemned carcass, esophagus, hair and poultry offal (feathers, head). Only few of these by-products can be used directly. Relevant data was collected for monthly records of slaughtered animals from the Zonal Livestock Office Veterinary Public Health (VPH), Minna for Minna main abattoir for the period covering 2010 to 2012 as shown in Table 1. The average number of cows and goats that were slaughtered monthly were assessed from the data collected. The waste generated was calculated based on the data reported by Aniebo et al. (2009) as shown in Table 2.

METHODOLOGY The method implemented in dealing with the various abattoir wastes is a combination of material flow analysis (MFA). The MFA is a scientific method considering counting, describing and interpreting the metabolism processes. By means of the MFA, goods and substances turnover and their stocks or changes in an exactly defined system can be described both quantitatively and qualitatively within a given time period. The results allow for identifying the most important goods sources, sinks and transfers as well as for their hierarchical weighing according to their importance. The relevance of the MFA is given by its capacity for creating an overview over an entire system. Its greatest advantage is the possibility it offers for reducing and depicting of highly complex systems down to their most significant processes, goods and substance fluxes, this way, such systems are distorted into ones of manageable size. The main wastes of small scale slaughterhouses in Minna, Niger state includes hides, skins, blood, rumen contents,

Waste Disposal Methods in Slaughterhouse Currently there is no organized system for the disposal of solid wastes in most abattoirs in Nigeria. The entire solid waste is collected and dumped along with Municipal Solid Waste or disposed off in unplanned land fill. In few abattoirs, dung and rumen digested are collected separately for composting. Almost all by-product of slaughter house can be utilized.

TABLE 1 Monthly records of slaughtered cows and goats in Minna main abattoir for year 2010 – 2012 2010 Month

2011

2012

No. of Cow

No. of Goat

No. of Cow

No. of Goat

No. of Cow

No. of Goat

January

1327

1239

1520

1125

1908

101

February

1591

1207

1420

15

2250

89

March

1439

1608

1308

6

2435

43

April

1460

1229

1502

1229

2503

1925

May

1614

813

1641

9

2500

72

June

1548

31

1496

45

2247

122

July

1800

25

1577

41

2434

96

August

1662

9

1910

108

2175

91

September

1700

7

1855

84

1946

98

October

1811

14

2461

65

2520

44

November

1410

1229

2365

75

1464

48

December

2451

5

1177

872

2315

61

Total

19741

7416

20232

3678

26657

2790

Source: Zonal Livestock Office of Minna Veterinary Public Health (V.P.H), 2012.


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TABLE 2 Waste generated per cow and goat in abattoirs Cow

Goat

Blood/head (kg)

12.6

0.72

Intestinal content/head (kg)

8.0

1.25

Waste tissue/head (kg)

6.4

0.8

Bone/head (kg)

11.8

2.06

Source: Aniebo et al. (2009).

However, various circumstances do not always permit byproduct recovery. The reasons may be inadequate quantity of materials, lack of markets, cost of processing etc. In such instances, they simply form part of waste lot for which different methods of processing and disposal have to be considered.

Classification of solid wastes The solid waste from abattoirs can be broadly classified into two categories i.e. vegetable matter such as rumen, stomach and intestine contents, dung, agriculture residues, etc., and animal matter like inedible offal, tissues, meat trimmings, waste and condemned meat, bones etc. These waste streams

can be segregated and treated separately. The Solid Wastes are classified based on their constituents. The classifications are detailed in Table 3.

Analysis of Abattoir Waste Flow The waste-cube model Plubcharoensuk et al. (2008) was used to determine the amounts and types of waste generated by the main abattoir in Minna. The cube model consists of three parameters: type of industry/waste source (farm animals), type of waste generated (blood, intestinal content, waste tissues and bone); and type of waste treatment facility/method (composting, biomethenation and rendering). These three parameters can represent the abattoir waste system of

TABLE 3 Classification of solid wastes based on constituents Category

Constituents of waste

Type-I waste

Vegetable matter such as rumen, stomach and intestine contents, dung, agriculture residues etc.

Type II waste

Animal matter such as inedible offals, tissues, meat trimmings, waste and condemned meat, bones etc.

TABLE 4 Nodes in the waste flow system Node No.

Node name

1

Waste generator

2

Intermediate treatment

3

Reuse and recycling

4

Final disposal

5

Export

Source: Plubcharoensuk et al. (2008)

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Minna main abattoir. The details of the Waste-cube model are as follows: = amount of abattoir waste in kg of waste type j Let: generated by abattoir ί, = amount of abattoir waste in kg of waste type j generated by abattoir ί and disposed using abattoir waste treatment k Then, (1)

= Amount of waste flow from node m to node n, Let Where m, n = 1, 2,…, 5 The situation at each node is as follows: Node 1: Amount of waste generated by the waste generator = Node 2: Amount of waste disposed by the intermediate treatment = Node 3: Amount of waste disposed by reuse and recycling treatment =

Waste-cube flow model The abattoir management system of Minna was analyzed using the waste cube model as follows.

Node 4: Amount of waste disposed by final disposal treatment =

Let there be five nodes in the waste flow system as shown in Table 4.

Node 5: Amount of waste disposed by export treatment =

The waste flow system is shown in Figure 1 as a network model.

Using the waste-cube model, lows:

can be calculated as fol-

TABLE 5 Total abattoir wastes generated from Minna main abattoir (2010 – 2012) Year

Cow/yr (No.)

Goat/yr (No.)

Total waste generated x 1,000kg Blood/yr

Intestinal content/yr

Bone/yr

Waste tissue/yr

2010

19,741

7,416

254.08

167.20

248.22

132.28

2011

20,232

3,678

257.57

166.45

246.31

132.43

2012

26,657

2,790

337.89

216.74

320.30

172.84

Total

66,630

13,884

849.54

550.39

814.83

437.55

FIGURE 1 Network model of the waste flow system


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= where i ϵ waste generators and k ϵ waste generators and k ϵ intermediate treatments; = where i ϵ waste generators and k ϵ recycle and use treatments; = where i ϵ waste generators and k ϵ final disposal treatments; = where i є waste generators and k є export treatments; = where i є intermediate treatment and k є recycle and reuse treatments; = where i є intermediate treatment and k є final disposal treatments; = where i є recycle and reuse and k є final disposal treatments.

RESULTS AND DISCUSSION Results Total abattoir waste generated from Minna were computed based on data in Tables 1 and 2 and are presented in Table 5. Estimation of the abattoir waste generation and waste flow.

The waste treatment/disposal methods emanating from the waste flow analysis in this study were used for the estimation of the abattoir waste generated. These amounts of waste generated were summed according to the waste type and the disposal treatment method. The results for the waste flows are shown in Table 6. The abattoir waste flow network for Minna main abattoir (2010 – 2012) is shown in Fig.2. Amount of waste generation by waste type. Waste generation by waste type from 2010 to 2012 is shown in Figure 3.

Discussion Table 5 indicates that a total of 66,630 cows and 13,884 goats were slaughtered between 2010 to 2013 in Minna main abattoir. Juxtaposing Table 5 and Table 2, we can infer that about 849.54 metric tons of blood, 550.39 metric tons of intestinal contents, 814.83 metric tons of bone and 437.55 metric tons of waste tissues were generated. The waste flow network (Figure 2) shows that waste reuse and recycling was the dominant waste disposal method, accounting for 72.60% or 2916.92 metric tons of abattoir waste. Figure 3 indicates that there was an abrupt increase in the amount of waste generated in Minna main abattoir (from 801.78 metric tons in 2010 to 1047.77 metric tons in 2012). Table 6 shows that composting was the intermediate treatment method adopted in Minna abattoir, giving a total of 1356 metric tons of waste. Bone and intestinal content are majorly used for composting. Reuse and Recycling amounted to 2101.92 metric tons of waste which includes the three methods of waste treatment namely, Composting, Biomethenation and Rendering of bone, blood and waste tissues, respectively.

FIGURE 2 Abattoir waste flow network for Minna main abattoir, 2010 – 2012

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FIGURE 3 Amount of abattoir waste generated by waste type

TABLE 6 Calculated abattoir waste flows in Minna, (2010-2012) x 1,000 kg Source

Intermediate

Reuse and recycle

Final disposal

Export

Total output

1365.22

2101.92

550.39

0

4017.53

Intermediate

0

814.83

550.39

0

1365.22

Reuse and recycle

0

0

0

0

0

Final disposal

0

0

0

0

0

1365.22

2916.75

1100.78

0

5382.75

0

2916.75

1100.78

-

-

Waste generator

Total input Net flow output

The intermediate waste that can be reused and recycled from the total waste generated comes from the bone (814.83 metric tons), while that finally disposed for land application as manure comes for the intestinal content (550.39 metric tons).

CONCLUSION The main findings from the study are as follows: • The waste-cube model used estimated the total abattoir waste generation as well as the total abattoir waste flow. The model can be used as an effective tool in managing the waste system in Minna and the country at large. • The waste flow shows the abattoir waste treatment situation which can be used for Minna abattoir. It also illustrates the distribution of the usage of abattoir waste treatment methods. Reuse and recycling were

the most dominant waste treatment method in Minna abattoirs. This paper shows that with sufficient available abattoir waste data, the abattoir as well as other industrial waste flow can be determined and an analysis can be made efficiently using the waste-cube model. Difficulty is not in relating the observed and collected data into the method to be used, but the waste data and economic data management, which would manage all the data effectively because a waste database is an essential tool that needs to be developed to store all required data. Furthermore, a computer application can be developed to calculate and construct the abattoir waste flow and systematically analyze the abattoir waste flow.

REFERENCES 1.

Adeyemo, O., I. Adeyemi, E. Awosanya (2009). “Cattle cruelty and risks of meat contamination at Akinyele cat-


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tle market and slaughter slab in Oyo State, Nigeria.” Tropical Animal Health and Production, Volume 41, pp. 1715- 1721. Akinro A.O., I.B. Ologunagba and Y. Olotu (2009). “Environmental implications of unhygienic operation of a city abattoir in Akure, Western Nigeria.” ARPN Journal of Engineering and Applied Sciences, Volume 4, pp. 311-315. Alonge, D.O. (1991).Textbook of Meat Hygiene in the Tropics Farmcoe Press, Ibadan, Nigeria.58pp. Aniebo, A.O., Wekhe S.N., and Okoli I.C. (2009). “Abattoir blood waste generation in River state and its environmental implications in the Niger Delta.” International Journal of Toxicological and Environmental Chemistry, Volume 91:4, pp. 619-625. Bello, Y.O. and D.T.A. Oyedemi, (2009). “Impact of abattoir activities and management in residential neighborhoods: A case study of Ogbomoso, Nigeria.” Journal of Social Science,” Volume 19, pp. 121-127. Chukwu, O., P.A. Adeoye, I. Chidiebere (2011). “Abattoir waste generation, management and the environment: A case study of Minna, North Central Nigeria.” International Journal of Biosciences (IJB), Volume 1(6), pp. 100-109. http://www.innspub.net Environmental Sustainability Resource Centre (ESRC), (2011). Meat Processing: Environmental Impacts, http://wrrc.p2pays.org/p2rx/index.cfm?page=subsection &hub id=44. Accessed 10th April, 2013. Muhirwa, D., I. Nhapi, U. Wali, N. Banadda, J. Kashaigili, and R. Kimwaga (2010). “Characterization of

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wastewater from an abattoir in Rwanda and the impact on downstream water quality.” International Journal of Ecology and Development, Volume 16, pp. 30-46. Organization for Economic Co-operation and Development (OECD), (2008). Measuring Material Flow Analysis and Resource Productivity. Synthesis Report, OECD, 2008. Plubcharoensuk, P., H. Nakayama and T. Shimaoka (2008). “Material Flow Analysis for Industrial Waste Management in Thailand.” Memoirs of the Faculty of Engineering, Kyushu University, Volume 68 (2), June 2008, pp. 107 – 127. Polprasert, C.P.K. and F.T. Tran, (1992). “Anaerobic Baffle Reactor (ABR) Process for Treating a Slaughterhouse Waste.” Environmental Technology, Volume 13, 1992, pp. 857 865. Roberts, H. (2011). Waste handling practices at red meat abattoirs in South Africa. http://www.sagepub. com/content/27/1/25. Abstract retrieved 25th April, 2013. United Nation Environmental Programme (UNEP) (2007). Using Agricultural Biomass Waste for Energy and Materials: Resource Conservation and GHG Emission Reduction, a Biomass Assessment and Compendium of Technologies Project. UNEP, August, 2007. http/www.un.org/apps/news/story.asp? News ID=15483 &Cr=development&Cr1. Accessed 28th April, 2013. Zonal Livestock Office Minna Veterinary Public Health (V.P.H). Meat Inspection and Hygiene report for the year 2010 – 2012 of Minna main abattoir.


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THE EU WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT DIRECTIVE: THE IMPLEMENTATION OF PRODUCER RESPONSIBILITY ACROSS THE EU-27 Susanna Paleari Research Institute on Sustainable Economic Growth (IRCrES) National Research Council of Italy (CNR), Milan Tel. +39 – 02 23699515 Email: [email protected]

ABSTRACT This article explores how the producer responsibility (PR) principle of the EU Directive on Waste Electrical and Electronic Equipment (WEEE) 2002/96/EC has been transposed and implemented by the EU-27 Member States, focusing in particular on business to consumer WEEE. It adopts a systematic approach in identifying the conceptual, legal, and practical elements that need to be evaluated with this regard and offers an empirical overview of how they have been shaped at the national level. The article analyses the characteristics (both similarities and discrepancies) of the systems actually in place in the EU Member States to collect and manage WEEE. Some of them can be explained based on the discretion that the WEEE Directive allows to the Member States on many aspects which are relevant for the implementation of PR. Others, instead, are the result of an incorrect transposition/implementation of the WEEE Directive, fostered by the uncertainties surrounding the PR concept and the practical obstacles to its implementation. Keywords: WEEE, producer responsibility, collective compliance schemes, eco-design

INTRODUCTION The rationale behind producer responsibility (PR) is to make producers internalize the end-of-life costs of their products. Producers are deemed responsible for their products exactly because they have the capacity to make changes at source, so that the environmental impacts of the products are reduced throughout their life cycles (Lifeset, 1993; Lindhqvist, 1992). Other aspirations that are usually associated with the advocacy of the principle include: increased collection and reuse/recycling rates of the targeted products and materials; consumers education, as product prices could reflect the producers’ relative success in meeting their end-oflife products obligations; and the shaping of competitive and dynamic schemes, which are able to adapt to changing conditions (relative to product mix, production and processing THE EU WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT DIRECTIVE

technologies, market and societal factors, etc.) and to drive innovation (Lifeset and Lindhqvist, 2008). The emergence of the concept of PR in the EU reflects three main trends in environmental policy-making: the prioritization of preventive measures over end-of-life approaches; the enhancement of life cycle thinking and the shift from the command and control approach to a market-based, nonprescriptive and goal-oriented approach (INSEAD IPR Network, 2010; Kalimo et al., 2012; Tojo, 2004; Van Rossem et al., 2006). In particular, the principle is considered as an instrument in support of the implementation of the waste hierarchy (see Art. 8 of Directive 2008/98/EC), as it encourages waste management options in the upper part of the hierarchy (prevention, reuse, and recycling). The role of PR in promoting resource efficiency and as a vehicle in moving towards a more circular economy, by minimizing the impact of prod-

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ucts on the environment and using resources in a sustainable way, has been recently often emphasized by the European Commission (EC, 2011 and 2013). Moreover, as a result of a market-based, non prescriptive and goal oriented approach, PR should provide the Member States room for creative and flexible solutions which, taking into account the diversity of their situations, allow them to achieve certain objectives in a cost-effective manner. PR comprises, at least, a physical and financial dimension (Linhqvist, 1992; Tojo, 2004; Van Rossem et al., 2006) 1, which can be implemented through several administrative and economic instruments, as well as variants of those instruments. With this regard, we can mention e.g. voluntary/mandatory product take back and recycling rates; advanced recycling fees (i.e. taxes assessed on product sales to cover the costs of recycling); tradable recycling credits; “pay as you throw” pricing of waste collection/disposal; and landfill bans (Tojo, 2004; Van Rossem at al., 2006; Walls, 2006). Building on previous experience gained in the packaging waste (Directive 94/62/EC, as amended by Directive 2004/12/EC) and end-of-life vehicles (Directive 2000/52/EC) sectors, the PR principle has been later extended by the EU legislator to waste electrical and electronic equipment (WEEE), a complex and relevant waste stream in terms of association of different materials and components, their potentially hazardous nature, and growth pattern. To this end, in 2002, the Directive on Waste Electrical and Electronic Equipment (WEEE Directive, 2002/96/EC 2) was adopted, which had to be transposed by the Member States by 13 August 2004. PR is seen by the EU legislator as a mean for encouraging the design and production of electrical and electronic equipment (EEE) which takes into full account and facilitate the repair, reuse, disassembly and recycling (Recital 12 of the WEEE Directive). The WEEE Directive aims at harmonizing national applications of the principle, in order to avoid internal market problems and enhance environmental protection (Recital 8 of the WEEE Directive). At the same time, however, harmonization is informed by flexibility, so that considerable scope is left in national implementation response. Indeed, directives are flexible legislative instruments that, following the subsidiarity principle, oblige the Member States to achieve certain results, leaving them free to choose how to do so. For this reason, they are not self-executing, but they need to be transposed, as national measures are to be adopted to enable the achievement of the stipulated results. The WEEE Directive allows Member States discretion on many aspects which are relevant for the implementation of PR. In particular, it is up to the Member States to decide on the organization and financing of the collection of WEEE from private households (business to consumers or B2C), the shaping of

1 Lindhqvist (1992), Tojo (2004), and Van Rossem et al. (2006) also speak of an informational responsibility, which requires producers to provide information on the environmental properties of their products to consumers and to recyclers, so that the latter can optimise treatment processes accordingly. 2 Directive 2002/96/EC will be replaced by the new WEEE Directive 2012/19/EU of the European Parliament and the Council of 4 July 2012 on Waste Electrical and Electronic Equipment by 15 February 2014.

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the collective schemes, and how to implement individual PR in practice. The implementation of PR is expected to generate a wide range of effects related to WEEE management (e.g. effectiveness and efficiency in the achievement of the WEEE Directive targets) and broader consequences (e.g. in terms of product-making and eco-design). After more than ten years the WEEE Directive has been introduced, however, its outcomes are still contested (Lambert, 2012). On the one hand, it is often recognized that experience with the implementation of the WEEE Directive has indicated “technical, legal and administrative problems that result in unintentionally costly efforts from market actors and administrations, continuing environmental harm, low levels of innovation in waste collection and treatment, a lack of level playing field or even distortion of competition and unnecessary administrative burden” (European Commission, 2008), so that alternative approaches to PR have sometimes been suggested (Sachs, 2006). On the contrary, other scholars point out that, although poorly understood and implemented by the EU Member States, the PR principle still remains valid and could be expanded further (Kalimo et al. 2012; Lifeset and Lindhqvist, 2008; Van Rossem et al., 2006). The preliminary condition, in order to study the impacts of PR, is represented by the evaluation of whether and how it has been transposed and implemented at the national level. Have the mandatory provisions of the WEEE Directive on PR been transposed and implemented by the Member States? How have the provisions of the WEEE Directive on PR, which allow the Member States discretion on relevant aspects, been transposed and implemented by the Member States? Which are the main similarities and differences among the PR systems actually in place across Europe? Which are the most innovative organizational settings resulting from the application of the WEEE Directive? This article aims at contributing to the evaluation of the status of implementation of PR for B2C WEEE management across the EU-27 (while, apart from a few notes concerning the effectiveness of PR systems in reaching the WEEE Directive targets, it is out of its scope to analyse the effects generated by PR implementation). To this end, the following steps have been taken: • In first place, we identify the main elements that need to be investigated to evaluate the status of implementation of the WEEE Directive in the Member States. In particular, we argue that a comprehensive evaluation of PR implementation cannot focus on isolated issues, but has to be structured based on a systematic approach which distinguishes among the conceptual, legislative, and practical levels (Atasu and Van Wassenhove, 2012; Gui et al., 2013; Kalimo et al. 2012). Given the complexity of the PR concept, as provided by the WEEE Directive and of the waste stream it is applied to, the identification of the relevant elements to be examined is a challenging task. Under the former respect, PR includes a physical and a financial responsibility for WEEE management, which can be individually or collectively allocated and combined in different ways. Under the latter re-


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•

spect, WEEE is a disomogeneous waste stream generated by different sources (consumers and business) and divided by the WEEE Directive into ten broad categories. B2C e-waste is further distinguished into new and historical WEEE. In second place, we provide for an empirical overview of how all the relevant elements have been shaped by national legislations and applied in practice across the EU-27, so that we are able to highlight the most important similarities and differences among the existing PR systems. With this regard, it has to be underlined that the empirical research on PR implementation by the Member States, although recently stimulated by the revision of the WEEE Directive (see in particular Ökopol et al., 2007), is still limited. This part of the article is drawn from a broad background research on the implementation of the WEEE Directive, covering all the EU-27 Member States, which has been prepared for the European research project EMInInn (Environmental Macro Indicator of Innovation), under the 7th Framework Programme for research.

The article is organized as follows: Section 2 examines the provisions of the WEEE Directive on PR, distinguishing between mandatory and non-mandatory ones. Since the WEEE Directive does not define either PR or individual/collective financial responsibilities and does not indicate how to implement them, Section 3 tries to clarify, at the theoretical level, the abovementioned concepts and illustrates the most challenging aspects related to their application. Section 4 analyses the status of legislative transposition and practical implementation of the PR principle of the WEEE Directive across the EU, taking into account that it depends not only on legislation, but also on how B2C WEEE collection and management work in practice. This, in turn, involves numerous interactions among multiple stakeholders, such as producers, distributors, municipalities, individual and collective compliance schemes, etc. (Gui et al., 2013). Section 5 concludes.

Producer responsibility within the WEEE Directive and results achieved The WEEE Directive applies to 10 categories of EEE and to the related waste 3. WEEE is divided into two broad

3

1. Large household appliances; 2. Small household appliances; 3. IT and telecommunications equipment; 4. Consumer equipment; 5. Lighting equipment; 6. Electrical and electronic tools (with the exception of large-scale stationary industrial tools); 7. Toys, leisure and sports equipment; 8. Medical devices (with the exception of all implanted and infected products); 9. Monitoring and control instruments; 10. Automatic dispensers.

THE EU WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT DIRECTIVE

groups: WEEE from private households (business to consumers or B2C) and WEEE not from private households (business to business or B2B). WEEE from private households is defined by the WEEE Directive (Art. 3k) as “WEEE which comes from private households and from commercial, industrial, institutional, and other sources which, because of its nature and quantity is similar to that from private households”. There are a number of obligations concerning B2C WEEE that the actors identified as producers must fulfill, according to the Directive. “Producers” include any person who, irrespective of the selling technique used, also by means of distance communication: (i) manufactures and sells EEE under his own brand, (ii) resells under his own brand equipment produced by other suppliers, or (iii) imports or exports EEE on a professional basis into a Member State (Art. 3i). With regard to collection, Member States shall achieve, by 31 December 2006 at the latest, a rate of separate collection of at least 4 kg on average per inhabitant per year of B2C WEEE (derogation periods have been granted to Slovenia until 31 December 2007 and to all other new Member States, as well as to Ireland, until 31 December 2008). To this end Member States shall ensure that (Art. 5 par. 2): • Distributors of new products ensure that waste of the same type of equipment can be returned to them free of charge on a one-to-one basis (1:1), as long as the equipment is of equivalent type and has fulfilled the same functions as the supplied equipment (Member States may depart from this provision provided that returning the WEEE is not thereby made more difficult for the final holder and remain free of charge for him). • Systems are set up allowing final holders and distributors to return such waste free of charge (Member States shall ensure the availability and accessibility of the necessary collection facilities taking into account the population density). • Producers are allowed to set up and operate individual or collective take-back systems. The WEEE Directive, hence, does not explicitly identify who shall be physically responsible for setting up the collection points for B2C WEEE and, also with regard to financial responsibility for collection, Art. 8 par. 1 (see below) only makes producers responsible at least for financing of the collection, treatment, recovery and environmentally sound disposal of WEEE from private households deposited at collection facilities (i.e. producers shall be financially responsible for B2C WEEE management from collection points onwards). The WEEE Directive leaves, therefore, broad scope for EU Member States to transpose its provisions on B2C WEEE collection. Once B2C WEEE has been returned to a collection point, producers are required to set up systems either on an individual or on a collective basis, for the recovery of (both B2C and B2B) WEEE collected separately, to meet the following targets by 31 December 2006 (Art. 7 par. 1 and 2): 1. the rate of recovery by an average weight per appliance shall be at least: • 80% in the case of large domestic appliances and automatic dispensers (cat. 1 and 10),

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75% in the case of IT and telecommunications equipment and consumer equipment (cat. 3 and 4), and • 70% in the case of small domestic appliances, lighting equipment, electrical and electronic tools, toys, leisure and sports equipment and monitoring and control instruments (cat. 2, 5, 6, 7, and 9). 2. the rate of component, material and substance reuse and recycling by an average weight per appliance shall be at least: • 80% in the case of gas discharge lamps; • 75% in the case of large domestic appliances and automatic dispensers (cat. 1 and 10); • 65% in the case of IT and telecommunications equipment and consumer equipment (cat. 3 and 4); • 50% in the case of small domestic appliances, lighting equipment, electrical and electronic tools, toys, leisure and sports equipment and monitoring and control instruments (cat. 2, 5, 6, 7, and 9). Derogation periods have been granted to Slovenia until 31 December 2007 and all other new Member States, as well as to Ireland, until 31 December 2008. Moreover, the WEEE Directive makes producers responsible for financing B2C WEEE collection, treatment, recovery and environmentally sound disposal (Art. 8. par. 1). In particular: • For products placed on the market later than 13 August 2005 (“new waste”), each producer is responsible for financing the above-mentioned operations in respect of his own products (individual financial responsibility). The producer may choose to fulfill this obligation either individually or by joining a collective scheme (Art. 8 par. 2). • Since it cannot be assumed that all producers that are on the market today will remain active on the market when their products are collected as WEEE, Member States shall ensure that each producer provides a guarantee when placing a product on the market showing that the management of all WEEE will be financed. The guarantee may take the form of participation by the producer in appropriate schemes for the financing of the management of WEEE, a recycling insurance or a blocked bank account (Art. 8 par. 2). • For products placed on the market before 13 August 2005 (“historical waste”) the financial responsibility shall be provided by one or more systems to which all producers, existing on the market when the respective costs occur, contribute proportionately, e.g. in proportion to their respective share of the market by type of equipment (collective financial responsibility, Art. 8 par. 3). • For a transitional period of eight years (10 years for category 1 of Annex IA, i.e. large household appliances) after entry into force of the WEEE Directive, producers are allowed to show purchasers, at the time of sale of new products, the costs of collection, treatment and disposal in an environmentally sound

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way (so called “visible fee”). The abovementioned costs shall not exceed the actual costs incurred. What results have been achieved so far in terms of WEEE collection, reuse, recycling and recovery? With regard to the B2C WEEE collection target set by the WEEE Directive (4 kg per capita per year), in 2010, according to Eurostat, B2C WEEE collected per country in the EU27 ranged from 1,1 kg per capita of Romania to 15,9 kg per capita of Sweden, with an average of 6 kg per capita. Ten countries did not reach the 4kg per capita target, namely Cyprus, Greece, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, and Spain. This data suggests that, while the improvement of reuse and recycling of the WEEE collected is of great importance and should be built upon, even stronger attention should be given to collection, as it does not seem to be adequately developed in all the Member States (ETC/SCP,2011). Overall collection percentages were observed to be roughly 25% for medium-sized appliance to 40% for larger appliances, while, in most countries, small appliances pose the biggest challenge with collection rates of almost 0%, indicating much room for improvement (Khetriwal et al., 2011). In 2010, according to Eurostat, the best performing Member States in terms of total (B2C+B2B) WEEE collection relative to EEE put on the market were the Netherlands (208%), Bulgaria (88%), Sweden (69%), and Denmark (56%). When comparing Member States total (B2C+B2B) collection rates in 2008 and 2010 to the amounts of EEE put on the market in those years, the following trends can be observed: • Italy (32%-23%), Luxembourg (36%-28%), Spain (38%-21%), and the UK (33%-31%) show a decreasing collection trend. • Poland (10%-23%), Estonia (17%-43%), and Slovakia (31%-44%) show an increasing trend of between 10%-30%; • Bulgaria (39%-88%) and the Netherlands (131%208%) show an increasing trend of over 30%. • All the other countries remained stable or were characterized by increasing trends equal or below 10%. The last available Eurostat data (2009 or 2010 depending on the country) confirm that WEEE reuse, recycling, and recovery rates based on total WEEE collected have generally been in line with the targets set by the WEEE Directive. Fifteen Member States met both the reuse-recycling and the recovery targets for all the WEEE categories (Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Hungary, Ireland, Latvia, Poland, Portugal, Romania, Slovenia, and Sweden). With reference to the nine recovery targets, six countries did not achieve one of them (cat. 10 by the Czech Republic, Estonia, and Luxembourg; cat. 7 by Greece, cat. 9 by Spain and cat. 1 by the UK), while Lithuania did not reach five of them (cat. 1, 3, 4, 7, and 10). With reference to the 10 reuse-recycling targets, four countries did not meet one of them (cat. 10 by Estonia, Luxembourg, and the Netherlands; cat. 5 by Spain), while Lithuania did not reach two of them (cat. 1 and 10). Apart from some problems with category 10


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(automatic dispenser) and a few specific cases, results achieved across the EU-27 are, therefore, good.

Producer responsibility: a conceptual analysis As we have seen, the WEEE Directive allows the Member States discretion on many aspects which are relevant for the implementation of PR. Such discretionality has been amplified by the uncertainties surrounding both the definition of the concept and its application, as the Directive does not either define it or establish how to implement it. In particular, there is ambiguity: 1) in the difference between producer physical and financial responsibilities, 2) in what constitutes individual responsibility; 3) in the difference between producer (physical and financial) responsibilities on the one hand and (individual and collective) compliance schemes on the other. PR for e-waste management comprises, at least, a physical and financial dimension (Linhqvist, 1992, Tojo, 2004; Van Rossem et al., 2006). In theory, both physical and financial responsibilities can be set at the individual (IPR) or collective (CPR) level, so that they can be combined in different ways, bringing to pure (IPR or CPR) systems, as well as to mixed or hybrid systems. Pure systems are those which give producers an individual responsibility (or, as an alternative, a collective responsibility) for both the physical management of e-waste and its financing. When physical responsibility is set at the individual level and financial responsibility at the collective one (or viceversa physical responsibility at the collective level and financial responsibility at the individual one), we have a mixed or hybrid system. According to the WEEE Directive, producers are physically responsible for the management of B2C WEEE that has been returned to a collection point and, to this end, they are required to set up systems to provide for its recovery, either on an individual or on a collective basis. At a legal level, producers can choose to individually organize the collection and recovery of their own end-of-life products. As an alternative, the tasks associated with their physical responsibility, can also be contractually delegated to a third party (Atasu and Van Wassenhove, 2012; INSEAD IPR Network, 2010). Practical obstacles to the implementation of individual solutions, as well as legal, administrative, and financial requirements applying, at the national level, to individual compliers have usually make collective physical responsibility less expensive and time-consuming than the individual one. The delegation of physical responsibility by producers has enabled the flourishing, across Europe, of a multitude of collective compliance schemes, which, although diverse under several respects (legal requirements, structure, performed functions, etc.), through shared infrastructures, take advantage of the economies of scale in collection and treatment. Individual solutions, instead, although difficult for the Governments to monitor and enforce, may provide more direct incentives to eco-design (Walls, 2006). The key issue of PR is how e-waste management costs are allocated among producers (financial responsibility). Also in


this case, we can distinguish between an individual and a collective responsibility. The WEEE Directive assigns producers an individual responsibility for the financing of new B2C WEEE management. The financing of historical B2C WEEE management, instead, shall be provided by one or more systems to which all producers, existing on the market when the respective costs occur, contribute proportionately, e.g. in proportion to their respective share of the market by type of equipment (i.e. for historical waste, a CPR applies). The Directive does not further specify how to implement the abovementioned provisions. Collective financial responsibility entails that producers jointly meet their responsibilities as groups of producers. As a typical way of CPR implementation, the costs related to the management of mixed brand WEEE are shared between producers currently existing on the market, based on their market share. Market shares are usually calculated based on weight or number of products sold. Producers currently active on the market pay for mixed brand WEEE arising using a standard cost per tonne (or per unit) for all products within the same product category. Eco-design incentives are limited (e.g. market shares based on weight of products could result in more lightweight EEE). On the contrary, IPR as a financial mechanism entails that the costs covered by the producer should be equal to the costs of dealing with that producer’s own products at end of life (Tojo, 2004; Van Rossem, 2006). Indeed, the idea behind IPR is to foster eco-design: if a product has been designed to reduce its end-of-life impacts and this results in lower end-of-life costs, this cost reduction should be passed back to the individual producer who invested in eco-design. Based on individual financial responsibility, costs associated to WEEE management shall be individually covered also in the situation where a producer withdraws from the market, leaving orphan WEEE for which the other producers are not legally responsible. For this reason, the WEEE Directive requires producers to provide for a guarantee, when placing a new product on the market, which may take the form of participation by the producer in appropriate schemes for the financing of the management of WEEE, a recycling insurance or a blocked bank account. The extent to which individual financial responsibility, as defined above, is practically achievable depends, of course, on several factors. A relevant aspect, which brings us back to the possible combinations between the different types of physical and financial responsibilities, is how WEEE is physically collected and managed: separate collection of different brands or brand identification after WEEE is collectively collected (through, e.g., sampling or full brand counting) are good starting points. It is often argued that costs associated with sorting/sampling of WEEE by brand are very high. However, these costs could be overstated, as many collective systems have already sampling or full sorting processes in place for a number of reasons, such as the request of members in collective systems to ensure that no cross subsidisation takes place within collection categories and to meet the

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reporting requirements of national authorities for WEEE collected and managed by producers (Ökopol et al., 2007). Moreover, new technologies have been developed, which may make sorting and segregation of WEEE according to brand more cost efficient. These include systems based upon bar codes and radio frequency identification (RFID) tags. Most products on the market today, however, do not carry RFID yet, not is clear how long the tags would remain serviceable for (Kalimo et al., 2012). Another obstacle to the practical implementation of IPR as a financial mechanism is that, at present, recycling technologies, process WEEE en masse, so that there is little opportunity to differentiate recycling costs for different types of products. At the same time, the elements of eco-design which have a significant impact on end-of-life management costs are evolving. With regard to dismantling, e.g., as technologies have been improved, certain processes are capable of treating complete WEEE fractions (without relevant components being removed) or even complete products, whilst achieving high recovery rates for valuable materials and ensuring that hazardous substances are controlled, at a lower cost compared to disassembly. For example, treating cellular phones (without batteries) in a modern copper smelter is environmentally preferred option, as loss of valuable precious metals through separation is avoided (Magalini, 2011 and UNU, 2007). An interesting issue concerning producer financial responsibility is how the financial burden for WEEE management is finally allocated between producers and consumers. The issue is related, even if not strictly, to the use of the visible fees, which enables producers to pass to the consumer, at the time of sale of new products, the costs of managing WEEE, as a separate element visible on the top of the product price. According to the WEEE Directive, producers shall be allowed by the Member States to show purchasers, at the time of sale of new products, the costs of managing historical WEEE, for a period of 8-10 years, depending on the EEE (transitional measure), provided that the costs shown do not exceed the actual costs incurred (Art. 8 par. 3). The use of the visible fee has been extended by the recast WEEE Directive (Art. 14 par. 1). In theory, visible fees can be seen as a delegation of the financial burden for WEEE management from producers to consumers, so that they can be considered as running contrary to IPR as a financial mechanism (Magalini and Huisman, 2007). But visible fees could also increase consumer awareness, facilitate comparisons between the eco-efficiencies of the producers, and are in line with the “polluter pays” principle. Also when visible fees are not used, producers can choose to absorb end-of-life management costs into their profit margins and/or raising the related prices. Costs for WEEE management are, hence, generally reflected on both the producers and the consumers, independently from the use of the visible fee. The key question is, instead, whether or not economic incentives for eco-design are created (Atasu and Van Wassenhove, 2012; Kalimo et al., 2012). For example, where the product demand is inelastic to product prices and the cost

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of recycling is fully passed on to consumer through increased prices or fees, producers may not find incentives to ecodesign. The same can be said when, as it happens in most Member States using it, the visible fee is fixed for all EEE belonging to the same category, so that it does not reflect either the actual costs of a product at end-of-life and its environmental impact. Finally, it is important to distinguish between producer (physical and financial) responsibility on the one hand and (individual and collective) compliance schemes on the other. The latter practically implement the former, combining them in various ways, which do not always exactly reflect the four models described above (pure and hybrid systems). European collective compliance schemes usually embody pure CPR systems, but also a collective system, managed by several producers, can operate under/approximate to IPR as a financial mechanism and can require or enable its members to take individual physical responsibility for treatment. For example, the members of ICT Milieu (Netherlands), between 1999 and 2003, paid for the costs of WEEE treatment and recycling based on the weight of each brand of product collected, identified via full brand counting, manually performed (return share approach, see below). Moreover, producers could also opt-out with regard to physical responsibility, specifying that their own products should be separated out and delivered to their appointed recycling facilities. Viceversa, individual compliance schemes are expected to embody pure IPR systems, but, in practice, this is not always the case. In Germany, through individual non-selective takeback schemes, which are the prevailing system, producers do not take individually back their own brand products, but the share of e-waste falling under their responsibility within each collection group stored at municipal collection points. Then, they directly contract with end of life service providers to arrange WEEE management after collection (UNU, 2011). Most deviations of the existing individual and collective compliance schemes from the four PR models can be explained by the difficulties in the practical implementation of IPR, especially with regard to its financial component. According to most sources, complete and fully effective IPR systems in Europe have not been established yet (JRC, 2006; INSEAD IPR Network, 2010; Kalimo et al., 2012) and there are not, to date, any blueprints for the “ideal IPR system” which meets the requirements of the WEEE Directive, provides significant incentives/rewards for eco-design, is easy and practical to implement, and can be transferred to other markets, transcending cultural differences in consumer behavior (BIS IPR Working Group, 2012). Some solutions for the approximation of collective schemes to individual financial responsibility have been developed so far, including the return share approach and the differentiation of the fees applied to producers (or consumers) based on EEE eco-design characteristics. Return share approach: the path towards a pure IPR WEEE system has traditionally been considered as a progression starting with market share allocation, moving through return share approaches, and resulting in systems whereby producers organize their own recycling of own brand products. Alt-


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hough such a view has been recently criticized, this kind of financial mechanism is generally deemed to be fairer than market share. Under the return share approach, producers pay for a proportion of WEEE arising based on the number or weight of own brand products within that WEEE arising. It is currently adopted in Japan (Specific Japanese Home Appliances Recycling Law and PC), Washington, and Maine. It was also used in the Netherlands by ICT Milieu between 1999 and 2003. The number or weight of own brand products can be identified either via: a) brand sampling, undertaken in accordance with agreed protocols (as in Washington DC); b) full brand counting (where brands are identified in all WEEE arising on a continuous basis, as in the Netherlands in the 1999-2003 period and in Maine). When return share systems are based on WEEE weight, they give producers an incentive to reduce individual product weight and increase product longevity. Apart from this, ecodesign incentives are limited, since producers generally pay a fee per tonne of a specific product category. Further common arguments brought forth against this financing model are the following: added costs associated with sorting/sampling of WEEE by brand could not yield enough environmental gain to justify them; problem (arising in Member States defining importers based on intra-community trade) of parallel imports: parallel importers, identified as producers, should in theory re-label their products to distinguish themselves as the producers responsible for their returned WEEE, but this practice never happens; variations in the market shares of producers over time may bring resistance to the return share model, since recovery costs cannot easily be predicted by producers (Ökopol et al., 2007). Differentiation of the fees applied to producers/consumers: as we have seen, a main obstacle to the practical implementation of IPR as a financial mechanism is represented by the differentiation of products’ recycling costs (which is not facilitated by treatment in large scale shredder). Such differentiation can be extremely challenging in practice, with varying costs associated with the level of differentiation (individual products, categories of product types like mobile phones, broad product categories like small appliances). According to Atasu and Van Wassenhove (2012), a reasonable compromise solution could be achieved if e-waste would be categorized based on the recycling technology required, with all products that can be recycled with the same technology belonging to the same category. In practice, it has to be noted that, although the fees charged to producers by collective compliance schemes (directly or via visible fees) do not reflect one-to-one take back costs, they are usually differentiated based on the 10 WEEE Directive categories or on alternative collection groups. Such categories/groups are often further divided in subcategories/groups, depending, e.g., on the product weight/dimension, sale prices, and product type. In France, where the visible fee is mandatory from the legal point of view since 2010, it is differentiated for six products groups, based on specific design for dismantling, recovery and reuse criteria.


As the fee structure is often decided by collective compliance schemes and it is not usually regulated at the national level, this clarifies that not only the EU WEEE Directive and national laws play a role in the implementation of PR, but also local actors (collective compliance schemes and producers in primis).

The implementation of the WEEE Directive in the EU-27 Prior to the introduction of the WEEE Directive, a few Member States, namely Belgium, Denmark, the Netherlands, and Sweden (as well as, to a lesser extent, Germany), had already established a national WEEE legislation and/or a national system for WEEE collection and management. These countries were naturally influential in shaping the WEEE Directive (JRC, 2006) and, apart from Denmark and Germany, did not have to substantially change their systems after its adoption. The WEEE Directive had to be transposed by Member States by 13 August 2004. For Bulgaria and Romania this requirement was obviously postponed to the date of their accession (1st January 2007). All the Member States have transposed the WEEE Directive, even if, in most cases, after the deadline. Transposition approaches taken by Member States include the breaking of the WEEE Directive into several parts, implemented in stages through different pieces of legislation and the use of secondary regulations to enact parts of the primary legislation (JRC, 2006). Some Member States (e.g. Italy) have not adopted some of the secondary regulations yet. The original implementing legislation of many Member States has been later amended, revised, or replaced by new one. There is huge discrepancy across the EU-27 in how the provisions of the WEEE Directive related to PR have been transposed and implemented. In first place, the EU Member States have made different choices in transposing the definitions of “WEEE from private households” and “producer”, which both play a relevant role with regard to PR. The most common criteria used by the Member States to distinguish between B2C and B2B WEEE can be summarized as follows: • use of various selection criteria such as sales channels (France), typical use of the products (Poland), EEE which can be expected to form part of private household waste (Sweden), weight and sizes parameters (Luxembourg), etc.; • predetermined lists of B2C/B2B products established by national legislation (Austria, Hungary, Spain, Slovenia, Slovakia, etc.); • distinction made by collective schemes through predetermined lists (Belgium) or based on own criteria (the Netherlands); • self-declaration of B2C/B2B split by producers (Bulgaria, the Czech Republic, Lithuania, etc.). As far as the definition of “producer” is concerned, one interesting issue is whether import and export are defined on the national level (intra-community trade), or they only refer

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to the trade with countries outside the EU (European approach). Although the European Commission has adopted the latter solution, an European approach has been applied only by a few Member States (Finland, France, Spain, UK) and often suffers from limitations (e.g. in Finland, foreign producers are not able to register directly to the national register, effectively putting onus on Finnish importers to register as obligated producers, in the absence of a local manufacturer or brand owner; JRC, 2006 and Őkopol et al., 2007). The national approach facilitates Member States in identifying, within their national territory, the actor who shall be responsible for WEEE financing/management. However, where imports are defined on the national level, the first importer is considered the producer if there is no manufacturer of that brand on the national market and this poses two problems (Őkopol et al., 2007): • When products are subsequently shipped to another Member State for distribution through intraCommunity trading, there exists a potential that the same products will have one producer in one Member State and one producer in the other Member State (with problems on potential product re-marking, change of visible fee, product traceability, etc. and related extra-administrative/financial burdens). • When there is no manufacturer or brand-owner in a Member State, also the “wholesaler” or “distributor” who brings EEE on the national market for the first time could be qualified as “producer”. It is open to question whether these actors can meet all the obligations of a designed producer established by the WEEE Directive. Hereafter we analyze the transposition and implementation by the Member States of the obligations, established by the WEEE Directive, related to B2C WEEE collection and management B2C WEEE collection. The WEEE Directive offers the Member States great flexibility in identifying who shall be physically and financially responsible for B2C WEEE collection. PR, as shaped by the WEEE Directive, only covers WEEE management, once it has been deposited at collection facilities. However, the way in which B2C WEEE collection has been organized by the Member States play a relevant role, at least, under the following respects: • WEEE collection shall be organized by the Member States, so that they can reach the B2C WEEE collection target (4 kg on average per inhabitant per year, by 31 December 2006 at the latest). The reuse, recycling, and recovery targets, in their turn, are also to be applied to (both B2C and B2B) WEEE separately collected. • WEEE management is conditional on WEEE collection and the extent to which PR (and in particular individual financial responsibility) is practically achievable also depends on how WEEE is collected. According to most national legislations, producers and/or municipalities are generally identified as the main responsible actors for setting up the collection network (physical responsibility). Specific requirements have been introduced by some

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countries in order to ensure the availability and accessibility of the necessary collection facilities. In Estonia, e.g., producers are required to provide at least one B2C WEEE collection point on the territory of a local government of over 3.500 inhabitants. Moreover, in the majority of the Member States, distributors are also physically involved in the take back of B2C WEEE on a one-to-one basis (1:1), as long as the equipment is of equivalent type and has fulfilled the same functions as the supplied equipment. In some countries, their involvement is subject to conditions/exceptions (Austria, Finland, Luxembourg, Slovenia) or is voluntary (Denmark and Germany). For example, in Austria, only distributors with a selling area over 150m² are required to take back B2C WEEE on a 1:1 basis, while in Luxembourg distributors can refuse to take back B2C WEEE because of insufficient storage capacity and, in this case, shall inform their clients of the possibilities available for return of the WEEE. In Cyprus and Sweden, distributors are exempted from the 1:1 take back obligation, while in the UK, they can decide to fulfill such an obligation by joining a distributor take back scheme, so that they do not have to offer in-store take back of WEEE, but can direct consumers to the nearest Designated Collection Facility. Some Member States, going beyond the WEEE Directive requirements, make producer the main financially responsible actor for B2C WEEE collection (e.g., Cyprus and the Netherlands), eventually along with distributors’ involvement on a 1:1 basis. (e.g., Estonia, Finland, France, Hungary, Malta, Portugal, Slovakia, Spain). In these cases, compensation mechanisms can be established by law or operate in practice, in favour of municipalities and/or distributors. For example, in Finland, Perchards (2011) estimates that the framework agreement between collective compliance systems and waste management companies envisages a payment of around 100€ per tonne of collected WEEE. The relationship between municipalities (collecting WEEE) and producers (financing it) appears sometimes unbalanced: producers often complain about municipalities seeing e-waste collection as a revenue generator, charging them excessive fees for access to their waste (Atasu and Van Wassenhove, 2012), but the strong market power gained by some collective compliance schemes can also be exploited against municipalities (Massarutto, 2007). In other countries, municipalities are identified as the main financially responsible actor for B2C WEEE collection. For example, in Denmark municipalities bear both the physical and financial responsibility for B2C WEEE collection, while producers are not involved, and 1:1 take back by distributors is voluntary. In Luxembourg and Romania the financial responsibility for B2C WEEE collection is borne by both municipalities and distributors on a 1:1 basis. In Germany, municipalities play an important role, as producers only finance the provision of containers and 1:1 take back by distributors is voluntary. A similar situation can be found in Ireland (where producers only finance the provision of containers and compensate distributors for 1:1 take back, as the latter can retain 20% of the visible fee) and Sweden (where


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distributors are not involved and producers only pay for containers). In the UK, the financial responsibility for B2C WEEE collection is mainly allocated to distributors. Distributors joining the only existing take-back scheme (Valpak Retail WEEE Services), who account for over 75% of EEE sales by value, are required to contribute to a fund that pays local authorities to upgrade civic amenity sites put forward as Designated Collection Facilities. Finally, it has to be underlined that roles and responsibilities are not always clearly defined by national legislations. Poland, for example, is struggling to resolve the issue of financial responsibility for local collection facilities. Producers, on the one hand, regard these activities as a municipal responsibility and are refusing to provide additional resource and infrastructures. On the other hand, however, there is no funding mechanism, nor obligation for municipalities to provide collection points and services (JRC, 2006). Also in Ireland, there is an ongoing dispute between local authorities and compliance schemes over the provision of financing to cover the operational costs associated with handling WEEE at civic amenity facilities (Ökopol et al., 2007). WEEE management after collection: transposition and legal requirements. We will now illustrate how PR, as shaped by the WEEE Directive, has been transposed by national legislations. Differences among Member States can be explained partly as the result of an incorrect/incomplete transposition of the Directive, partly as the result of the flexibility offered by the Directive to the Member States, amplified by the uncertainties surrounding the PR concept. At the legal level, only a few Member States have fully transposed the provisions of the WEEE Directive on producer financial responsibility for the management of new (IPR) and historical (CPR) WEEE. While, apart from Greece and Latvia 4, market share has been identified by all the Member States as the financial mechanism for covering the costs associated with historical B2C WEEE management, with regard to individual financial responsibility for new B2C WEEE, the following can be highlighted: Full transposition: in Belgium (Brussels and Flemish Regions), Cyprus, the Czech Republic, Estonia, Luxembourg, Malta, the Netherlands, Romania, and Slovakia producers are individually responsible for new waste; Delays/exceptions to IPR: other countries introduced an individual financial responsibility for new e-waste, but its implementation has been postponed (Italy) or suffers from exceptions (Austria, Germany, and Ireland). In particular, in Austria, with regard to new WEEE, producers bear an individual financial responsibility only when they are not members of a collective scheme (otherwise their responsibility is

according to their market share). In Germany producers are given the choice to decide of whether or not they are individually or collectively financially responsible for new WEEE. In Ireland, members of an “approved body” are exempted from the provisions on financing (new and historical) WEEE from private households. Unclear transposition: in Belgium (Wallonian Region), Hungary, Poland, Portugal, Spain, and Sweden producer individual financial responsibility for new waste is not clearly formulated by legislation (e.g. as responsibility is assigned to “producers” in the plural form and/or no reference is made to own products); Market share: in Bulgaria, Denmark, Finland, France, Lithuania, Slovenia, and UK there is no legal or practical distinction between new and historical waste and financing is always based on market share. No financial method specified: in Greece and Latvia, as for historical WEEE, national legislation does not specify any financial method to be applied to implement producer financial responsibility for new waste. In most Member States, producers shall provide for a financial guarantee when placing a B2C product on the market, except for producers joining a collective scheme. Countries which do not consider collective scheme membership as a financial guarantee include Belgium (Brussels Region), Denmark, Germany, Italy, and UK, as well as, in practice, Sweden. In the Brussels Region, e.g., a financial guarantee is required for both individual and collective schemes, but is only needed for 6 months contingency. In Denmark, an aggregate obligation is calculated for all producers in a collective scheme and DPA-System 5, which determines the magnitude of the financial guarantee, can grant exemption to collective schemes, based on their dimension (members’ market share or number of members). In Sweden, the financial guarantee is indirectly shaped to encourage producers to manufacture easily recoverable products. Indeed, producers who can show that the costs of dealing with their products are lower than for others, should be required to provide the relevant guarantee only at that lower level. In some other countries (Bulgaria, Latvia, as well as Slovakia), producers failing to meet their obligations shall pay a product tax which acts as a de facto guarantee. In most Member States, the use of a visible fee for financing the management of historical WEEE by producers is optional, at a legal level. On the contrary, it is legally binding in France and Spain. Unless rigorous monitoring is provided by the authorities, the mandatory use of visible fees can foster free-riding, as any producer who does not comply with his obligations would be profiting any fee collected at the point of sale. Member States give EEE producers the choice to fulfill their obligations related to new B2C recovery either individu-

4

In Greece and Latvia national legislation does not specify any financial method to be applied to implement producer financial responsibility both for new and historical waste.


5

Danish Producer Responsibility System

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ally or by joining a collective scheme, as required by the WEEE Directive. However, several legal, administrative, and financial requirements applying to individual compliers are often defined at the national level. For example, apart from the mandatory provision of a financial guarantee for B2C EEE, in Austria producers opting for individual compliance should sort out their own products at collective sites where they may be returned (concluding contracts with all the related operators) and in Finland, B2C EEE producers shall ensure that the network of collection facilities is of such extent that the last holders have a reasonable opportunity to deliver discarded products in all parts of country. Also financial guarantees, when only applied to individual compliers, can be used by Governments to encourage collective compliance. Member States have generally established, at the legal level, the status, conditions of approval and approval procedure, duration of accreditation, and operation requirements of collective compliance schemes. Beside WEEE management, national legislations often allow collective compliance schemes to assume some of the practical tasks associated with PR for their members (such as registration, reporting, provision of financial guarantee, etc.). The fees charged by collective compliance schemes to their members are not extensively regulated by national legislation, even if some provisions are sometimes established (e.g. in Austria fees are to be set by collection group and cross-subsiding is prohibited). Where two or more competing collective compliance systems exist in the country, Member States usually provide, through the Government or a specific clearing house, for a certain degree of coordination, in order to define and allocate, as a minimum, the collection obligation of each collective compliance scheme. When the same collection points are used by different schemes, the latter can be prevented by national legislations from “cherry picking”, based on the location of the former. WEEE management after collection: practical implementation of national legislations. Although Member States give producers the choice to fulfill their obligations related to the recovery of new B2C WEEE either individually or by joining a collective scheme, national requirements applying to individual compliers make this option rarely used for B2C WEEE management (e.g., based on available information, no individual management system exists in Austria, Greece, Hungary, and UK). As the most relevant exception to the above considerations, individual non-selective take-back schemes are the prevailing system in Germany, where collective compliance is, on the contrary, quite limited. The implementation of the WEEE Directive in Germany was influenced by the experience gained with another PR scheme, the “Duales System Deutschland”, responsible for the management of packaging waste which, from the time of its introduction in 1990 until recently, was a monopoly. In shaping the WEEE management system, the promotion of competition was, hence, recognized as a priority (avoidance of monopolies and maximum freedom for producers to decide how to comply with their responsibilities). To maintain complete competition, the market

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shares of collective schemes are limited 6. However, German individual non-selective take-back schemes do not reflect a pure IPR model. Indeed, producers do not take back their own brand products, but the share of e-waste falling under their responsibility within each collection group stored at municipal collection points. Then, they directly contract with end of life service providers to arrange WEEE management after collection (UNU, 2011). At present, European EEE producers mainly comply with their obligations related to B2C WEEE management through collective compliance schemes. In the EU-27, there are more than 160 collective schemes addressing B2C and/or B2B WEEE. Based on the number of their collective schemes, EU Member States can be classified as follows: • 1 collective scheme: Belgium, Cyprus, Lithuania, Luxembourg, Malta; • 2-5 collective schemes: Greece, Ireland, the Netherlands, Portugal, and Sweden (2); Germany (2 at least); Estonia, Finland 7, and Slovenia (3); Denmark (4); Austria and France (5); • 6-10 collective schemes: Hungary (6 at least); Latvia and Romania (7); the Czech Republic (8); Poland and Spain (about 9); Bulgaria (about 10); • 11-20 collective schemes: Slovakia (about 15); Italy (16); • >20 collective schemes: UK (37). Independently from how the provisions of the WEEE Directive on individual financial responsibility have been transposed at the national level, all existing collective compliance schemes apply a market share approach for financing the management of both new and historical B2C WEEE (we have not found any information about “return share models”). The fact that new waste has been locked in the same system as historical waste discloses the difficulties of the Member States in establishing a system of collective responsibility only for historical waste, while simultaneously maintaining a parallel system for new waste on the basis of individual responsibility. One of the main problems with this regard is represented by the efficient separation of the two waste fractions (Kalimo et al., 2012). The “locked-in effect” is reflected in the fact that, when charging producers, collective compliance schemes do not operate any fee split into cots for historical and new WEEE. A similar situation can be observed in relation to the use of the visible fee (Perchard, 2011). At the legal level, visible fees are optional in all the Member States, except for France and Spain where, in breach of current WEEE Directive, they are binding. In practice, they are also mandatory in other countries where all existing collective schemes use them (e.g. Belgium and Luxembourg). Apart from these cases, the use of the visible fee greatly varies across the EU-27 (not used,

6

The German “Bundeskartellamt” (Federal Cartel Authority) advised, e.g., the producers of large white goods (category 1) not to set up a collective scheme covering more than 25% of market share of EEE in collection group 1 (large household appliances, automatic dispensers). 7 In Finland one of the three collective schemes is an umbrella organization comprising 3 collective schemes.


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used by some collective schemes, used for some WEEE categories 8, etc.). Despite the WEEE Directive introduces the use of the visible fee for financing the costs of historical B2C WEEE management, the visible fee was often used/is currently used to cover the costs of both historical and new B2C WEEE, as no differentiation of flows is generally in place in collective schemes (Ökopol et al., 2007; UNU, 2007). For example, in Belgium, Recupel “all-in contribution” (i.e. visible fee) seems to apply both to new and historical B2C WEEE. As an exception, the collective compliance schemes SEWA and Envidom (Slovakia) declare that their visible fees serve to compensate for the costs related to handling historical B2C WEEE (see related websites). Moreover, Envidom specifies that, for the management of new WEEE, members pay an additional fee (although since April of 2011, this is not applied to small household appliances any more). There are two main models of collective compliance schemes (JRC, 2006; Ökopol et al., 2007): monopolistic systems (MS) and competitive systems (CS). MS are characterized by one scheme in operation in the country or by more schemes covering different product categories, so that there is no competition between them in the same category. This approach prevails in some of the countries that established their WEEE systems prior to the implementation of the WEEE Directive, such as Belgium (Recupel) and the Netherlands (NVMP and ICT-Milieu) and in small countries (Cyprus, Malta, and Luxembourg), where collected volumes cannot create a viable market for multiple systems. In Sweden, Greece, and Ireland there are two collective schemes, but competition is limited. Sweden has traditionally operated under the MS model (with El-Kretsen) but, since 2007, a new collective scheme (EÅF) was licensed. However, the new system plays a minor role compared to the old one in terms of participation (either number of members and market shares). Moreover, EÅF uses part of El-Kretsen’s collection network. Competition is also limited in Greece, where one the two existing collective schemes only covers one WEEE category. In Ireland, the two existing collective schemes cover different geographical areas of the country. CS, on the other hand, are characterized by two or more collective systems in competition, as they cover WEEE in the same category. In this case, the Government or a coordinating body shall provide for allocation mechanisms, to ensure that the WEEE Directive re-use, recycling and recovery targets are reached, and for monitoring. In particular, the collection obligation of each producer has to be defined and assigned to the related compliance scheme and when the same collection points are used by different schemes, the latter should be prevented from “cherry picking”, based on the location of the former. The European Recycling Platform, created in 2002 as the first ever pan-European take back scheme and its founding

8

With regard to the use of the “visible fee” for specific EEE categories (or by the collective schemes addressing them), it has to be noted that EEE categories are characterized by a different historical waste burden. White and brown goods companies, e.g., bear a significant historical waste responsibility compared to ICT ones.


members (Electrolux, HP, Sony and Procter and Gamble) strongly support multiple collective systems. Most Member States, especially big countries, also opt for this model which has been developed under different approaches. For example, in Germany (where there are different collective compliance schemes, as well as individual non-selective take back schemes, representing the prevailing option), a fully centralized and coordinated system has been created (BIS IPR Working Group, 2012). Collection points communicate container and pick up requests to EAR (“Elektro-Altgeräteregister”), which calculates the mass of WEEE for which a single producer has to finance and organize treatment. Based on individual obligations, EAR allocates pick up requests to individual producers, so that, over time, each producer has to pick up containers from municipal collection points all over Germany (from the countryside as well as from big cities). On the contrary, in Estonia, there is no clearing house system. Producers have to divide the costs themselves and communicate with each other. If a producer has collected more WEEE than (s)he places to the market, (s)he presents a bill for payment to a producer who collected less. If they do not get an agreement, then the Court solves the problem. An intermediate approach has been developed by other countries. In Austria, e.g., the clearing house ensures that every collective system is collecting according to its market share. If it has not collected enough, it gives order to the collective scheme to collect at those municipalities, where collection is expensive. Collective schemes have contracts with the Austrian Federal Associations for Waste Management or with collection points. If there is no such contract, collection points have the legal possibility to send an on-line pick-up order to the Austrian Coordination and Clearing House. A web-based tool has been implemented. The pick-up order gets announced on the web-site of the Austrian Coordination and Clearing House for 24 hours. During this period, collective schemes can apply to the announced pick-up order. If not, it is forwarded to the collective system with the highest obligation for taking back (Twinning, 2011). Producers supporting MS argue that market based systems are designed to meet the minimum levels of collection and recycling in the most cost-efficient manner, without any pressure to exceed them. Moreover, they identify the additional costs of managing a national clearing house, separate collection containers, extra logistics, etc. and they point out that MS are useful for historical waste, where there is little competitive advantage in running a CS. CS, on the contrary, seem to have continuous “cost-down pressure” and perform well especially where the market is large and the potential cost savings are substantial (JRC, 2006; Perchard, 2011). A drawback of the consolidation of prices for WEEE treatment could be, however, the tendency to its low quality (Twinning, 2011). Apart from the monopolistic and competitive approaches, collective schemes across the EU-27 show both similar characteristics and significant differences under other respects. Collection networks established by collective schemes may include, to different degrees, various “collecting operators”, such as distributors, municipal collection points, collec-

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tion points owned by private waste management companies, producers, charity shops, etc. In some cases (Austria, Denmark, etc.), collective schemes are also required to provide for regional reception centers. WEEE is often collected in separate groups, which are different from the ten WEEE categories defined by the WEEE Directive. Such groups can be established by national legislation (as it happens, e.g., in Portugal) or at collective scheme level (as it happens, e.g., in Ireland). The collection by collective schemes of WEEE deposited at collection points is organized in different ways across the EU-27. To this end, collective schemes often contract with “collecting operators” using framework agreements which are concluded between the associations of the latter, on the one hand and collective schemes, on the other. Collective schemes can take directly part in such agreements (as in Finland) or can be represented by their coordinating bodies (as in Italy and France). Sometimes, agreements on collection are also concluded between collective schemes: in Ireland the two existing collective schemes (WEEE Ireland and ERP Ireland) agreed on a geographical split of municipal and retail collection points, which is representative of the relative total market shares of the members in each system; in Sweden, one of the two operating collective schemes, EÅF, uses its members’ shops as collection points, but since those shops are not located in all municipalities, it has reached an agreement with the other collective scheme (El-Kretsen), according to which EÅF pays the same fee as other members of El-Kretsen for the part of their electric waste that is collected by El-Kretsen. WEEE management is outsourced to logistic and treatment partners which are generally selected by collective schemes through competitive tenders. Fees charged by collective compliance schemes to their members (directly or via visible fees) differ, across Europe, in structure and level. They are usually differentiated based on the 10 WEEE Directive categories or on alternative collection groups, which are sometimes further divided in subcategories/groups, depending, e.g., on the product weight/dimension, sale prices, and product type. They can be unit based, weight based or lump sum (i.e. flat fee on a yearly basis, regardless of sales volume or market-share) and they can reflect either the actual or the projected costs of recycling (e.g. EES-Ringlus in Estonia and Retela in the Czech Republic deduct the recycling fee from real-costs). Multiple financing systems can co-exist under the same scheme (as for ElKretsen in Sweden). The more complicated the fee structure, the more demanding it is in collection and administration. There is a challenge to balance administrative efficiency against the wish to relate real costs of recycling a given product to the fee charged (JRC, 2006). The fee structure can approximate to individual financial responsibility and foster eco-design to a different extent. For example, in some countries, cross-subsidizing is prohibited by law (e.g. in Austria) or it is excluded at a collective scheme level (e.g. by El Kretsen in Sweden) and in France, where the visible fee is and in France, where the visible fee is mandatory from the legal point of view, since 2010, it is differentiated for six

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products groups, based on specific design for dismantling, recovery and reuse criteria.. Fee levels could reflect different degrees of competition within a national market between recyclers and logistic partners, but they can also be caused by other factors, such as economies of scale, raising funds as future guarantees, service and treatment level, R&D costs, information and educational costs, etc. (Magalini and Huisman, 2007). There are significant disparities in fees levels depending on the scheme/country9. For example, the ecological tax applied in Portugal to refrigerators is 70€/t, while the fee applied to refrigerators by the Elker Group in Finland amounts to 400€/t. The fee applied by Retela to TVs (in the Czech Republic) is 98€/t; the one of Eesti Eelktroonikaromu (Estonia) is 192€/t, while the Elker Group, in Finland, applies a fee of 660€/t. Some collective schemes exempt specific products from the payment of the fee (e.g. NVMP in the Netherlands and Ecotic in Romania). A particular case, however, is the one of Ecodom (Italy). Ecodom chose to suspend, starting from April 1th 2012, the application of any WEEE recycling fee, retaining the right to revise this decision in case of modification of the current market conditions of secondary raw materials and the current financial situation of the Consortium which coordinates all the Italian collective schemes. Although it is not within the scope of this article to analyze the effects generated by different national PR systems, with regard to their effectiveness in reaching the WEEE Directive targets, we can observe that not all the WEEE systems have performed at the same level as far as B2C WEEE collection is concerned, while almost all the Member States have reached the reuse, recycling, and recovery targets. In light of the collection and management results illustrated by section 2, we have selected, among the EU-27 Member States, five best performing countries: Denmark, Finland, Germany, Ireland, and Sweden. These countries, based on Eurostat data, met all of the following criteria: B2C WEEE recycling rate equal or higher than 8 kg/per capita in 2010; total (B2C+B2B) WEEE collected in 2008 and in 2010, relative to EEE put on the market, higher than 30% and showing an increasing trend; all reuse, recycling, and recovery targets set by the WEEE Directive reached, according to the last available data (2009 or 2010). When considering the characteristics of the selected countries in terms of organizational settings and IPR implementation, they do not seem to shape any blueprints for an “ideal WEEE management system”. Only some of these countries (Denmark, Sweden and, to a minor extent, Germany) had already established a national WEEE legislation and/or a na-

9

We have compared the fees currently applied by six collective schemes operating in six different countries (Electrocyclosis in Cyprus, Retela in the Czech Republic, Eesti Eelktroonikaromu in Estonia, the Elker Group in Finland, Appliance recycling SA in Greece, and ERP Portugal in Portugal) for EEE belonging to cat. 1 (Large Household Appliances), 2 (Small Household Appliances), 3 (Information and Communication Technology), and 4 (Consumer Equipment), excluding VAT. For the Czech Republic and Estonia, fees have been converted in Euro, based on the following exchange rates: 1€=25,60CZK and 1€=15.6466EEK.


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tional system for WEEE collection and management prior to the adoption of the WEEE Directive and none of them has fully transposed and implemented the provisions related to individual financial responsibility. The physical and financial responsibility for WEEE collection has been assigned to different actors: municipalities (Denmark, Germany, Ireland, and Sweden), producers (Finland, Germany, Ireland, and Sweden), and distributors on a 1:1 basis (Ireland and Finland, as well as, on a voluntary basis, Denmark and Germany). Individual compliance is not used or rarely used in all the selected countries, apart from Germany, where it is the prevailing system. All the selected countries have developed collective compliance systems, although limited in number (not more than 4 per country, according to available information). Denmark, Finland, and Germany have CS, with a different degree of centralization and coordination (maximum in Denmark and Germany; intermediate in Finland). In Sweden and Ireland, instead, there are two collective schemes, but competition is limited. Visible fees are used only in Ireland where they are applied to some WEEE categories (1, 2, 4, 5, 6). Some collective schemes have adopted fee structures in line with IPR: cross-subsidising, e.g., has been prohibited by agreement by the members of El Kretsen (Sweden) and Rene AG (Denmark) calculates its fees, based on the real take-back volumes.

CONCLUSIONS The WEEE Directive applies PR to the management of ewaste. It makes producers: a) physically responsible for the recovery of separately collected WEEE, through individual or collective systems and b) financially responsible for B2C WEEE management on an individual basis for new waste and on a collective basis for historical waste. The Directive leaves it up to the Member States to decide on the organization and financing of B2C WEEE collection, the shaping of the collective schemes, and how to implement PR. The flexibility offered by the WEEE Directive to the Member States in articulating their implementation responses has been amplified by the uncertainties surrounding the PR concept (which is not defined by the Directive) and its practical application. Moreover, Member States had little over 18 months to transpose the WEEE Directive into their national legislation and only a few of them had already established a national WEEE legislation and/or a national system for WEEE collection and management. There is huge discrepancy across Europe in current systems in place to collect B2C WEEE and the interaction between the subjects who are responsible for WEEE collection and the ones responsible for its management. PR does not cover collection, but WEEE management and the practical implementation of PR also depend on how collection is organized. Most Member States have designed, to a different extent, municipalities, producers and distributors (on a 1:1 basis) as responsible actors for B2C WEEE collection, both at physical and financial level. The WEEE Directive has stimulated the flourishing of a multitude of collective compliance schemes (about 160, in-


cluding those addressing B2B WEEE). The legal, administrative, and financial requirements applied to individual compliers at the national level make collective solutions less expensive and time-consuming than individual ones. Another advantage offered by collective compliance schemes is that, beside WEEE management, they often assume some of the practical tasks associated with PR for their members (such as registration, reporting, provision of financial guarantee, etc.). Although diverse under several respects, based on the available information, all the collective compliance schemes across the EU-27 currently apply both producer physical and financial responsibilities for WEEE management at the collective level (pure CPR systems). In particular, the provisions of the WEEE Directive on individual financial responsibility have been fully transposed only by a few Member States, which, however, have not implemented them so far. Therefore, at a practical level, a “locked-in” effect can be observed, as all the Member States make producers financially responsible for both new and historical waste based on market shares (collective responsibility). Harmonization has been attained, but in breach of the WEEE Directive and Member States have not been able to develop innovative solutions. With this regard, the uncertainties surrounding the concept of individual financial responsibility do not help, but the main obstacles faced by the Member States concern its practical implementation. The most relevant difficulties, with this regard, include the following: • Separation of historical and new waste (for collective compliance schemes covering both of them); • For new WEEE: a) separate collection of different brands or brand identification after WEEE is collectively collected and b) further distinction of each producer’s own e-waste in categories/types and definition of the related recycling costs (not facilitated by technologies that process WEEE en masse). The “locked-in effect” is reflected in the fact that, when charging producers, collective compliance schemes do not operate any fee split into costs for historical and new WEEE. A similar situation can be observed in relation to the use of the visible fee. The application of a collective financial responsibility for new waste means that there is no clear feedback to individual producers on the end-of-life costs of their own products and this weakens the drivers of eco-design. Significantly, the existing literature on the impacts of the WEEE Directive on innovation in EEE products has generated a mixed analysis of the direction of the impacts and whether indeed such impacts exist (ARCADIS ECOLAS & RPA, 2008; Gottberg et al., 2006). However, the establishment of the abovementioned feedback loop from the downstream to the upstream can be stimulated by the way the recycling or visible fees are structured and regulated, favoring a departure from a pure collective financial responsibility model. For example, the prohibition of cross-subsidizing (as it happens in Austria and in Sweden for El Kretsen) and the differentiation of the fees for products groups, based on specific environmental criteria (as it happens for the visible fees in France) can be useful instruments with this regard. The de-

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velopment of fee-setting mechanisms which reward ecodesigned products could be further improved, as the experience gained by the Member States and the collective schemes in this respect is still limited. Major challenges to be faced include the technical definition of specific criteria which need to be accepted by producers and do not pose excessive burden at the administrative level. Under other respects, collective compliance schemes and PR implementation across the EU-27 are very diverse. Such differences can be explained partly as the result of an incorrect/incomplete transposition of the Directive, partly as the result of the flexibility offered by the Directive to the Member States. In first place, collective compliance schemes can operate under a MS or a CS model. MS models prevail in some of the countries that established their WEEE systems prior to the implementation of the WEEE Directive (Belgium and the Netherlands) and in small countries (Cyprus, Malta, and Luxembourg), where collected volumes cannot create a viable market for multiple systems. Most Member States, especially big countries, instead opt for CS models, even if they apply such models according to different degrees of centralization/coordination. Secondly, the ways collective compliance schemes work in practice and interact with WEEE collectors are not homogeneous. For example, collection networks established by collective schemes may include, to a different extent, various “collecting operators”, such as distributors, municipal collection points, collection points owned by private waste management companies, producers, charity shops, etc. WEEE is sometimes collected in separate groups, different from the ten WEEE categories defined by the WEEE Directive. Collective schemes often contract with “collecting operators” using framework agreements which are concluded between the associations of the latter, on the one hand and collective schemes, on the other. Sometimes, agreements on collection are also concluded between collective schemes (such as in Ireland and Sweden). The shaping of integrated WEEE management systems is, in any case, pivotal to well functioning PR schemes. Indeed, only the establishment of adequate infrastructure for separate collection and recovery of discarded products can accomplish the manufacturers’ efforts towards design for reusability and recyclability. In particular, results achieved by the Member States show that collection is not adequately developed everywhere across the EU-27 and still represents a bottleneck in many WEEE management systems. Beside producers and their collective schemes, the improvement of WEEE collection and recycling demands further efforts by several actors such as municipalities, consumers, waste management operators, and the national competent authorities for the implementation of the WEEE Directive. The high number of stakeholders involved in the waste management system and the complexity the waste stream in question pose relevant problems in terms of coordination and reconciliation of different interests. Third. Although all the collective schemes use CPR, as a financial mechanism, through the market share approach, relevant differences among them can be observed relative to other financial issues. As required by the WEEE Directive,

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pursuant to national legislations, producers shall provide for a financial guarantee, when placing a product on the market. Only a few countries, however, do not consider collective scheme membership as a financial guarantee (Belgium Brussels Region-, Denmark, Germany, Italy, UK, as well as, in practice, Sweden), while in some others (Bulgaria, Latvia, Slovakia), producers failing to meet their obligations shall pay a product tax which acts as a de facto guarantee. In most Member States, the use of a visible fee for financing the management of historical WEEE by producers is optional, at a legal level. On the contrary, it is legally binding in France and Spain and it is mandatory, in practice, in countries, such as Belgium and Luxembourg, where all existing collective compliance schemes use it. Contrary to what provided by the WEEE Directive, the visible fees were used/are currently used to cover the costs of both historical and new B2C WEEE, as no differentiation of flows is generally in place in collective schemes (exceptions being SEWA and Envidom in Slovakia). Finally, also the structure and level of the (recycling and visible) fees applied by collective compliance schemes vary greatly across the EU. Some of the differences among the European WEEE systems in place, especially when in breach of the WEEE Directive, could lead to a lack of harmonization and undermine the positive effects PR is expected to generate. However, diversity, when associated with flexibility and innovative organizational settings, can also support the implementation of the Directive, as Member States can learn a lot from each other. Since there is no single best WEEE system suitable for all, different experiences can help Member States in shaping their systems, according to their specific conditions.

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Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste and repealing certain Directives. Directive 2004/12/EC of the European Parliament and the Council of 11 February 2004 amending Directive 94/62/EC on packaging and packaging waste. Directive 2002/96/EC of the European Parliament and the Council of 27 January 2003 on waste electrical and electronic equipment (WEEE). Directive 2000/53/EC of the European Parliament and the Council of 18 September 2000 on end-of-life vehicles. Directive 94/62/EC of the European Parliament and the Council of 20 December 1994 on packaging and packaging waste. Ecodom: accessed 20 May 2013. ECO LOGIC and IEEP (2009), A Report on the Implementation of Directive 2002/96/EC on Waste Electrical and Electronic Equipment. EcoTic: accessed 1 July 2013. Ecotrel: accessed 20 May 2013. EES-Ringlus: accessed 20 May 2013. Eesti Eelktroonikaromu: accessed 20 May 2013. Electro-coord Magyarország Nonprofit Kft.: accessed 20 May 2013. Electrocyclosis Cyprus Ltd: accessed 20 May 2013. Elker Ltd Group: accessed 20 May 2013. El-Kresten: accessed 20 May 2013. Envidom: accessed 20 May 2013. European Recycling Platform (ERP): accessed 20 May 2013. ERP Ireland: accessed 20 May 2013. ERP Portugal: accessed 20 May 2013. ETC/SCP (2011), Europe as a Recycling Society: European Recycling Policies in Relation to the Actual Recycling Achieved, Working Paper 2/2011. European Commission (2011), Roadmap to a Resource Efficient Europe, COM(2011)571 final. European Commission (2008), Proposal for a Directive of the European Parliament and of the Council on waste electrical and electronic equipment (WEEE) (Recast), COM(2008)810 final. Eurostat: accessed 20 May 2013. Gottberg A., Morris J., Pollard S., Mark-Herbert C., Cook M. (2006), “Producer Responsibility, Waste Minimisation and the WEEE Directive: Case Studies in Eco-Design from the European Lighting Sector,” Sci Total Environ., 2006 Apr 15, Volume 359(1-3), pp. 38-56.


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United Nations University (2011), E-Waste Management in Germany, Deutsche Gesellschaft fur International Zusammenarbeit GmbH. United Nations University – Solving the e-waste problem (StEP), (2009), E-waste Take Back System Design and Policy Approaches, White Paper. Valpak UK: accessed 20 May 2013. Van Rossem C., Tojo N., and Lindhqvist T. (2006), Extended Producer Responsibility. An Examination of its Impact on Innovation and Greening Products, The International Institute for Industrial Environmental economics, A report Commissioned by the European Environmental Bureau, Friends of the Earth Europe, Greenpeace International. Walls M. (2006), Extended Producer Responsibility and Product Design. Economic Theory and selected Case Studies, Discussion Paper, OECD, Paris. WEEE Forum (European Association of WEEE Take-Back Systems): accessed 20 May 2013. WEEE Ireland: accessed 20 May 2013.


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MAY 2015

CO-PYROLYSIS STUDY OF POLYLACTIC ACID AND POLYETHYLENE TEREPHTHALATE PLASTIC WASTES Hua-Shan Tai1*, Jui-LanYeh2 1

Hua-Shan Tai, Department of Safety, Health, and Environmental Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Jhuoyue Rd., Nanzih, Kaohsiung City, 811, Taiwan, R.O.C. Email: [email protected], Fax: 886-7-6011608; Telephone: 886-7-6011540 2

Jui-LanYeh, Graduate Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Jhuoyue Rd., Nanzih, Kaohsiung City, 811, Taiwan, R.O.C. Email: [email protected]

ABSTRACT Separating plastics made of polyactic acid (PLA) and polyethylene terephthalate (PET) can be difficult; consequently, their recycling values are affected. The purpose of this study is to investigate the feasibility of recycling mixed PLA and PET wastes by co-pyrolysis. Specimens prepared from different ratios of preprocessed PLA and PET wastes were subjected to relevant property studies followed by thermogravimetric (TG) and reaction kinetic analyses. Subsequently, pyrolytic studies were conducted based on the obtained TG reaction conditions to investigate energy yields of pyrolytic reactions. Results indicated that the HHV of PLA and PET were approximately 18.26 and 22.85 MJ/kg, respectively and those of the mixtures were between these two values. Each specimen has a combustible portion of greater than 96% and a maximum decomposition temperature between 618K and 736K. Greater PET ratios were found to result in higher activation energies and pre-exponential factors. Additionally, PLA ratios were positively correlated to the mass yield of gaseous products, whereas PET ratios were positively correlated to the yields of solid and condensation products. Unless energy yield is a major concern, co-pyrolysing PLA and PET wastes may avoid the need to separate PLA and PET and may effectively reduce the volume of plastic wastes. Keywords: Polylactic acid, polyethylene terephthalate, pyrolysis, resource recycling, renewable energy

INTRODUCTION The invention and evolution of synthetic polymers has greatly improved living standards. However, limited petroleum resources and environmental pollution resulting from

polymer wastes are worldwide concerns.1 Development of biodegradable polymers and technologies for recycling and reuse of plastics become major issues in sustainable development. Numerous biodegradable plastics are invented in recent years to overcome plastic’s resilience against degradation. According to the American Society for Testing and Ma-

_____________________________________________________ *Corresponding author


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terials (ASTM), biodegradable plastics are plastics that are capable of being decomposed by naturally occurring microorganisms (bacteria, fungi, and algae) in the natural environment.2 According to Taiwan’s Environmental Protection Administration, biodegradable plastics are plastics that generate carbon dioxide, water, inorganic compounds, and biomass through biodegradation in a composting process, at rates comparable to other known compostable materials and without leaving observable, distinguishable, or toxic residuals.3 Polyactic acid (PLA), a thermoplastic aliphatic polyester, is regarded as a plastic material that has high developmental potentials among various biodegradable polymers.4-6 Raw materials required for the synthesis of PLA, such as agricultural products,7 forestry and agricultural residues, and lignocellulosic materials,8 are isolated and enzymatically hydrolyzed to glucose. Glucose thus obtained is fermented into lactate acid which is then polymerized to make bioplastic.9 Bioplastics made from PLA have comparatively low melting temperatures and good processability; they are widely used for manufacturing daily plastic products. Furthermore, PLA products are made of plant resources and can be degraded by microorganisms. Carbon dioxide and water generated from degradation processes return to the atmosphere which allows the maintenance of carbon balance. Therefore, degradation problems associated with petrochemical plastics do not occur in PLA products. In summary, PLA plastics have smaller impact on the environment as compare to traditional plastics have.5,10-11 Recycling plastic wastes is a relatively easy, economical, and environmentally friendly approach to effectively realizing green living and carbon reduction.12,13 According to Recycling Fund Management Board of Environmental Protection Administration, Taiwanese use approximately 167 tons of biomass plastics every month with the majority of which being PLA plastics. However, the amount of plastics recycled is low. Only approximately 913 kg of plastics are recycled each month, which less is far below 10%.14 The appearances and applications of PLA are similar to that of traditional plastics, particularly to polyethylene terephthalate (PET). PET is the most commonly used traditional plastic in Taiwan and its recycling rate is also the highest (50.8% of the total amount of plastics recycled). In 2012 alone, 96,133 tons of PET was recycled in Taiwan.14 Existing technologies have difficulties separating shredded PLA and PET, resulting in low recycling values.12,15-16 Consequently, recyclable waste-collectors are less willing to recycle PLA and the recycling rate of PLA is less than ideal. At the moment, recycling PET could possibly be contaminated by PLA, resulting in quality drops among recycled materials, making it difficult for product manufacturing applications. Most contaminated recycle materials are being incinerated at the incinerators. Some literature focused on exploring the thermal and mechanical properties of PLA/PET mixture, evaluating the practicality of future application on research and development for environmental products. Studies reported better thermal stability in the PET when mixed with PLA than with naturally renewable components (such as cellulose), as well as better crystallinity, but the mechanical properties appeared to dete-

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riorate after adding PLA to PET. 17-18 Furthermore, researches added small amount of recycled PLA into recycled PET materials, and investigated the degree of influence in rheological, mechanical and thermogravimetric properties of the plastic fiber to evaluate its reusable value. Results showed that rheological and mechanical properties of the recycled PET materials are notably affected by mixing with small amounts of PLA, which led to the decrease in reusable value, but the influence on thermal stability is limited; 19 there’re also studies which conducted the chemical modification on PLA/PET, to explore the increase in copolymer degradability, revealing the degradation activation energies in PLA/PET mixture to be lower than pure PET materials, and the increase in PLA content promotes degradation rate within the mixture.20 Although only limited numbers of literatures devoted to the discussion of PLA/PET mixing and thermal co-processing, they all shared a point in common: the mixing of PET and PLA affects its mechanical properties and creates material deterioration, which leads to reduction in reusable value. On the other hand, there were little affects on thermal stability, and PLA is capable of decreasing the degradation activation energies during the PET degradation process. Therefore, thermal treatment technologies should be feasible to apply to the mixing of PET and PLA. 21 The use of thermal treatment technologies on investigating pyrolysis behavior and reaction kinetics of the polymer during thermochemical processing, to which reactions generated by the processing of mixed materials are capable of providing analytic data as reference.15 Pyrolysis is one of the thermal treatment technologies that decompose organic materials by heat in the absence of oxygen. Pyrolysis is a potential approach to avoid the need to separate PLA and PET once they enter the recovery routes.22,23 Additionally, pyrolytic reactions may generate valuable energy or chemicals;24-26 thereby, endowing this approach an added value. Moreover, current literature related to co-pyrolysis of the plastic material mixtures rarely studies the co-pyrolysis on PLA/PET using laboratory-scale pyrolysis reactor, analyzes its pyrolysis reaction kinetics, and discuss the analytic results in an energyrecovery perspective. Consequently, the purpose of this study is to explore the feasibility of recycling mixed PLA and PET wastes by pyrolysis, and to analyze pyrolysis kinetic parameters and the energy efficiency of and criteria for co-pyrolysis, then compares with heating values yield from incineration, in hope to improve the waste-to-resource efficiency of mixed recycling of PLA and PET.

MATERIALS AND METHODS Materials The source of waste PLA in this study were disposed drinking cups from a café, the waste PET were disposed polyethylene terephthalate bottles for commercial mineral water (the resin identification code for PLA is [7], and [1] for PET, both shown in Figure 1 & 2). PLA and PET wastes were washed, dried, and cut into 0.3 cm in both the length and


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FIGURE 1 The disposed PLA cup used in this study.

FIGURE 2 The disposed PET bottle used in this study. width for experimental use. Seven specimens S1 to S7 were prepared by mixing preprocessed PLA and PET at different ratios. The composition ratio of PLA and PET in each specimen is as shown in Table 1.

Experiments and Analyses Property analyses. The specimens were subjected to property analyses including proximate analysis, HHV determination, and ultimate analysis.

TABLE 1 Specimen ID. and composition ratio Specimen ID.

Composition ratio (w.t%) PLA

PET

S1

100

0

S2

90

10

S3

70

30

S4

50

50

S5

30

70

S6

10

90

S7

0

100


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Proximate analysis was conducted according to the standard detection methods NIEA R212.01C and NIEA R205.01C announced by Environmental Analysis Laboratory in Taiwan. The amount of moisture, volatile, fixed carbon, and ash in each specimen was determined using a drying oven (DengYng, Drying OvenDOS30) and a rapid heating chamber (CarbLite, RWF1100). HHV of the specimens were determined according to the standard detection methods NIEA R214.01C announced by Environmental Analysis Laboratory in Taiwan using a calorimeter (Parr/USA, Parr 1266). Ultimate analysis was conducted according to the standard detection method NIEA M403.00C announced by Environmental Analysis Laboratory in Taiwan. The amount of carbon, hydrogen, oxygen, and nitrogen in each specimen was analyzed using an elemental analyzer (ThermoQuest, EA 1110). Thermogravimetric analysis. Thermogravimetric analysis (TGA) was conducted using a thermogravimetric analyzer (METTLER, TGA/SDTA851e). Both the reactive and protective gases used in the experiment were nitrogen. The flow rates of the reactive and protective gases were 50 ml/min and 20 ml/min, respectively. The heating rates were 10℃/min, 15℃/min, and 20℃/min, and the temperatures were set at between 25 and 800℃. The weight loss condition of each specimen was analyzed by TGA, and the weight, temperature, and reaction time of each specimen was recorded by computer software till the end of reaction. Pyrolysis kinetics. Pyrolysis kinetics is a kinetic model established based on changes of reaction rate with time, with rate of weight loss, and with temperature. Experimental temperature could be controlled by either constant or non-constant methods. By referring to literature;27 we adopted a nonconstant temperature control method in combination with Friedman method28 to obtain pyrolysis kinetic parameters such as activation energy and pre-exponential factor. Relevant parameters were derived from TGA data. The pyrolytic reaction equation obtained based on Friedman method was as follows:

dx  − Ea  n = A exp (1 − x ) dt RT  

(1)

A represents pre-exponential factor (sec-1) Ea represents the activation energy of the reaction (kJ/mol) R represents the universal gas constant (J/mol K) T represents an absolute temperature (K) t represents time (sec) x represents weight loss fraction or pyrolytic conversion rate, and is presented by

x=

Wi − Wt Wi − W f

Wi represents the initial specimen mass

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(2)

Wt represents specimen mass at time t (sec) or T (K) Wf represents the calculated specimen mass after pyrolysis Friedman method states that residual reactants have fixed chemical compositions at a constant conversion rate; it also proposes the kinetic parameters may be analyzed using TGA curves derived from different heating rates. The following equation was derived by taking natural logarithm on both sides of Equation (1).

ln

dx Ea =− + ln A(1 − x) n dt RT

(3)

According to Friedman method, kinetic parameters are calculated in two stages. First, 1/T was graphed against ln(dx/dt) at a fixed conversion rate and different heating rates, and a series of line slopes (-Ea/R) was derived by liner regression analysis to obtain the activation energy and mean activation energy at the conversion rate. The following equation was obtained by reorganizing Equation (3):

ln

dx Ea + = ln A(1 − x) n dt RT

 dx  − Ea  ln  / exp  = ln A + n ln(1 − x)  RT   dt

(4)

(5)

Second, it is learned from Equation (5) that reaction order n and pre-exponential factor A could be obtained by plotting ln(1－x) at a fixed heating rate, wherein the slope was reaction order n, and the intercept was ln(A). Subsequently, the reaction rate of each specimen dx/dt at different heating rates were obtained by substituting n, A, and Ea into Equation (1) Laboratory scale pyrolysis. Figure 3 shows a schematic diagram of a laboratory-scale pyrolysis apparatus. The pyrolytic reaction conditions were set based on data obtained from TGA. Approximately 20 g of one specimen was weighed and placed in a quartz tube, and the tube was positioned on the built-in electronic scale. Subsequently, a steel pipe was slipped over the quartz tube and the setting was placed inside the apparatus. Thermometers were secured both on the inside and outside of the steel tube to measure internal and external temperatures. Nitrogen gas was ventilated under the electronic scale at 1L/min. Changes in temperature and weight of the specimen were recorded by a computer throughout the experiment. A condensing system (with the condensing temperature set at -10℃) was also installed at the gas outlet to condense and collect reaction products for analysis. Estimation of the mass yields of pyrolytic products. Based on results of pyrolytic reactions conducted at different heating rates, regression analyses were performed on specimens versus their yields of pyrolytic products to estimate the mass yields of pyrolytic products from PLE/PET mixtures with different component ratios and to establish the relationship between mixing ratios and the mass yield of pyrolytic prod-


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FIGURE 3 A schematic diagram of a laboratory-scale pyrolysis apparatus ucts. The relationship among mixing ratios, heating rates, and mass yields. Based on results of pyrolytic reactions conducted at different heating rates, multiple regression analyses were performed to investigate the effect of PLA/PET mixtures with different component ratios and heating rates on the mass yields of pyrolytic products and to establish the relationships among mixing ratios, heating rates, and product yields. Analyses of mass yields and energy yields of pyrolytic products. Using the weight and the heating value of raw materials prior to pyrolysis to represent the amount of energy being produced by incineration, combined with results from the pyrolysis study to analyze mass yields, energy densification ratios, and energy yields of pyrolytic products. To discuss the energy yields for incineration and pyrolysis processing, these factors served as indicators of energy efficiency. The equation for calculating the indicators is as follows:29 (6) in which Mintial represents the weight of specimen before pyrolysis Mpyrolyzed represents the weight of pyrolytic product

(7) HHVintial represents the HHV of specimen before pyrolysis. HHVpyrolyzed represents the HHV of pyrolytic product.

(8) Data and statistical analysis. Equations were established on Microsoft Office Excel 2007 to allow TGA analysis in order

to obtain the dynamic model of the study. The mass yields of pyrolytic products were estimated by conducting regression analyses on data acquired from pyrolytic studies using statistical software SPSS Statistics 17.0 and by establishing multiple regression with one predictor variable. The relationship among mixing ratios, heating rates, and mass yields of pyrolytic products was determined by conducting multiple regression analyses using statistical software SPSS Statistics 17.0; the multivariate multiple regression equation was also established.

RESULTS AND DISCUSSION Property Analyses of Each Specimen Proximate analysis. Results of proximate analysis (Table 2) indicate that the contents of fixed carbon and ash increase with increasing PET ratios in specimens, and that volatile contents increase with increasing PLA ratios. All of the specimens have combustible contents greater than 96%, ash contents less than 3%, and a negligible amount of moisture contents. Therefore, processing PLA and PET wastes by pyrolysis is feasible, and pyrolysis does not only greatly reduce the volume of plastic wastes but also lower the cost for disposal processing. Analysis of higher heating values. Results of HHV analyses (Table 3) show that the HHV for PLA and PET are approximately 18.26 MJ/kg and 22.85 MJ/kg; respectively, and the HHV are observed to increase with increasing PET ratios in specimens. The HHV of PLA and PET mixtures are between those of brown coal and subbituminous coal. PLA and PET mixture can be used to produce refuse plastic fuel (RPF) which is one of the applicable renewable energy today.30,31 If recycled, RPF may reduce the dependence of modern societies on fossil fuels. Developing heat-recycling route in addition to material recycling also increases the diversity of recy-


193

TABLE 2 Results of proximate analysis Specimen ID. (PLA:PET)

Fixed carbon

Volatile

Moisture

Ash

S1 (100：0)

0.82

98.75

0.42

0.01

S2 (90：10)

1.16

98.22

0.35

0.27

S3 (70：30)

1.96

96.76

0.33

0.95

S4 (50：50)

2.74

95.57

0.38

1.31

S5 (30：70)

3.77

94.13

0.41

1.69

S6 (10：90)

4.64

92.43

0.39

2.54

S7 (0：100)

4.93

91.82

0.41

2.84

Unit: w.t%

TABLE 3 Higher heating value of each specimen Specimen ID.

HHV

S1 (100：0)

18.26

S2 (90：10)

19.36

S3 (70：30)

20.25

S4 (50：50)

20.98

S5 (30：70)

21.51

S6 (10：90)

22.50

S7 (0：100)

22.85

Unit: MJ/kg

cle routes and adds value to plastic recycling by pyrolysis.

greater amounts of hydrogen and oxygen, whereas nitrogen is not detected in all of the specimens. Generally, substances with high carbon and hydrogen contents have comparatively greater HHV. According to results of ultimate analysis and HHV tests, PET contains a greater amount of carbon but a

Ultimate analysis. Results of ultimate analysis (Table 4) indicate that specimens with greater PET ratios contain greater amounts of carbon, those with greater PLA ratios contain

TABLE 4 Results of ultimate analysis Specimen ID.

Carbon

Hydrogen

Oxygen

Nitrogen

S1 (100：0)

53.96

5.81

40.23

N.D.

S2 (90：10)

54.02

5.47

40.51

N.D.

S3 (70：30)

55.64

4.98

39.38

N.D.

S4 (50：50)

57.78

4.73

37.49

N.D.

S5 (30：70)

59.80

4.56

35.64

N.D.

S6 (10：90)

65.87

4.55

29.58

N.D.

S7 (0：100)

67.09

4.47

28.44

N.D.

(PLA:PET)

Unit: w.t%

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slightly lower amount of hydrogen as compared to PLA. However, the overall carbon-hydrogen contents in PET are greater than that in PLA. Therefore, consistent with the result of HHV analysis, the HHV of PET is greater than that of PLA.

Thermogravimetric Analysis TGA was conducted at 3 heating rates (10, 15, and 20℃/min) to determine the maximum decomposition temperature of each specimen (Table 5). A TG curve and a derivative thermogravimetric (DTG) curve of weight loss against time derivative was plotted based on TGA results. Figures 4 to 6 show the results of TGA for S1, S4, and S7. Results of TGA indicate that at a heating rate of 10℃/min, the maximum decomposition temperature of S1 is 618K and that of S7 is 716K. The temperature of each specimen at trough 1 (the first trough, which marks the lowest point on

DTG curve) is approximately between 625 and 635K, and that of trough 2(the second trough) is approximately between 698 and 715K. At a heating rate of 15℃/min, the maximum decomposition temperature of S1 is 626K and that of S7 is 730K. The temperature of each specimen at trough 1 is approximately between 637 and 647K, and that of trough 2 is approximately between 702 and 728K. Finally, at a heating rate of 20℃/min, the maximum decomposition temperature of S1 is 658K and that of S7 is 736K. The temperature of each specimen at trough 1 is approximately between 646 and 657K, and that of trough 2 is approximately between 718 and 735K.Consistent with literature,4,23,32 all of the specimens complete pyrolysis at temperatures no greater than 773K (500℃). The increase of maximum decomposition temperatures in specimens with increasing heating rates is caused by uneven heating at high heating rates. This observation agrees with the theory of thermo analytical kinetics.4,33

TABLE 5 The maximum decomposition temperature of each specimen at different heating rates 10℃/min

15℃/min

20℃/min

Specimen ID. (PLA:PET) Trough 1

Trough 2

Trough 1

Trough 2

Trough 1

Trough 2

S1 (100：0)

618

--

626

--

658

--

S2 (90：10)

635

698

647

702

657

718

S3 (70：30)

633

703

645

718

651

728

S4 (50：50)

630

712

646

725

653

734

S5 (30：70)

625

715

641

728

646

735

S6 (10：90)

626

711

637

726

650

735

S7 (0：100)

--

716

--

730

--

736

Unit: K

FIGURE 4 Results of TGA on S1 (heating rate = 10℃/min)


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Analysis of Pyrolysis Kinetics Data obtained from TGA (with the temperature set at 500℃ for both PLA and PET completed pyrolysis at temperatures no greater than 500℃) were subjected to pyrolysis kinetics analysis by applying Friedman model to calculate the three main factors in pyrolysis kinetics: activation energy (Ea), pre-exponential factor (A), and reaction order (n). Friedman method calculates pyrolysis kinetic parameters in two stages. First, a series of line slopes (-Ea/R) is derived by plotting 1/T against ln(dx/dt) at a fixed conversion rate and different heating rates followed by linear regression analysis to obtain the activation energy and mean activation energy at the conversion rate. Second, n and A are acquired by plotting ln(1－x) at a fixed heating rate, wherein the slope is n and the intercept is ln(A). For example, the activation energy and mean activation energy of PET are obtained by plotting 1/T

196

against ln(dx/dt) at a conversion rate of 0.5 followed by deriving a series of line slopes at different conversion rates by applying least squares regression. Figure 7 illustrates the relationship between the conversion rate (x) and temperature. Figure 8 shows the relationship between ln(dx/dt) and 1/T at a conversion rate of 0.5 and different heating rates for PET. The activation energy and mean activation energy of PLA and PET mixtures at different ratios at a fixed conversion rate and different heating rates can also be calculated by plotting 1/T against ln(dx/dt) followed by deriving a series of line slopes (-Ea/R) by linear regression analysis. Similarly, n and A are obtained according to Equation (5) by determining the slope (n) and intercept (lnA) of the line after applying linear regression analysis, and the kinetic parameters of the specimens are obtained by applying Friedman model. As shown in Table 6, greater PET ratios result in greater the activation energy and pre-exponential factor. This phenomenon may be attributed to that the majority of bonds within PLA are single


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FIGURE 7 The relationship between the conversion rate and temperature at different heating rates for PET

FIGURE 8 The relationship between ln(dx/dt) and 1/T at a conversion rate of 0.5 and different heating rates for PET.

TABLE 6 The kinetic parameters of each specimen at different heating rates derived from Friedman method Specimen ID. (PLA:PET)

10℃/min

15℃/min

20℃/min

Ea (kJ/mol)

n

A

n

A

n

A

S1 (100：0)

3.61

2.55E+08

3.63

2.58E+08

3.60

3.28E+08

115.56

S2 (90：10)

2.21

3.00E+07

2.14

2.70E+07

2.24

3.10E+07

126.65

S3 (70：30)

1.52

1.62E+09

1.40

1.63E+09

1.54

1.62E+09

152.13

S4 (50：50)

3.29

1.41E+09

3.02

9.01E+08

3.00

9.00E+08

168.11

S5 (30：70)

3.10

2.71E+11

3.28

2.85E+11

2.73

1.61E+11

179.03

S6 (10：90)

4.05

4.67E+10

4.06

3.85E+10

4.06

3.31E+10

208.74

S7 (0：100)

1.79

2.14E+11

1.70

2.09E+11

1.79

2.54E+11

226.56

bonds, thus the energy required for bond breaking is relatively low. In comparison, PET contains a greater number of benzene and double bonds and a greater amount of energy is required to break these bonds. Consequently, the activation

energy of PLA and PET mixtures is lower than that of pure PET during pyrolysis, and PLA and PET mixtures require less energy to achieve pyrolysis, which are consistent with literature.20


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The Mass Yields and Energy Yields of Pyrolytic Products The mass yields of pyrolytic products. Table 7 and Figures 9 to 11 show the mass yields of solid, gaseous, and condensation products of pyrolytic reactions in each specimen at different heating rates. Results of pyrolysis study indicate that greater PET ratios in specimens result in greater mass yields of solid and condensation products, and greater PLA ratios result in greater mass yields of gaseous products. Greater heating rates are also found to result in greater mass yields of condensation and gaseous products and smaller mass yields of solid products, and the converse is true for lower heating rates. Heating rates have been demonstrated to affect pyrolytic reactions in two aspects.34 At low heating rates, reactants are unable to promptly reach the target decomposition temperature; the prolonged low internal temperature phase is beneficial for the generation of solid products. Conversely, the time required for reactants to reach decomposition temperature is shortened at high heating rates, and this

condition is beneficial for pyrolytic reactions. However, heat transfer effect can influence internal pyrolytic reactions when a great difference exists between the internal and surface temperatures of reactants. As heating rate increases the amount and rate of reactant weight loss, the pyrolytic rate, and the decomposition temperature (the initial, maximum, and terminal decomposition temperatures) shift towards the high temperature zone. The mass yield of gaseous products is also higher because prolonged retention time of volatiles intensifies the secondary pyrolysis. The reported observations of this study are consistent with results of this study. Estimations of product yields. The specimens in the study are mixtures of PLA and PET with defined ratios. However, PLA/PET ratios in recovered plastic wastes in practice may not be identical to the defined ratios. Therefore, regression analyses were used to estimate the mass yields of pyrolytic products of PLA/PET mixtures with different component ratios and to establish the relationship between the mixing ratios and product yields.

TABLE 7 The mass yields of pyrolytic products

Specimen ID. (PLA:PET)

10°C/min

15°C/min

20°C/min

Solid

Condensate

Gaseous

Solid

Condensate

Gaseous

Solid

Condensate

Gaseous

S1 (100：0)

29.38

2.13

68.49

28.45

2.18

69.37

24.58

2.59

72.83

S2 (90：10)

30.70

2.17

67.13

32.43

2.69

64.88

17.90

4.54

77.57

S3 (70：30)

43.12

3.26

53.62

44.77

3.87

51.36

32.10

4.79

63.11

S4 (50：50)

55.28

3.68

41.04

51.84

4.94

43.22

46.28

5.39

48.33

S5 (30：70)

67.49

3.75

28.76

64.55

4.82

30.63

63.54

5.79

30.67

S6 (10：90)

78.78

3.84

17.38

75.47

5.27

19.26

72.86

6.32

20.82

S7 (0：100)

85.64

4.02

10.34

81.36

5.33

13.31

77.83

6.46

15.71

Unit: w.t%

FIGURE 9 The mass yields of solid products of each specimen at different heating rates

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FIGURE 10 The mass yields of condensation products of each specimen at different heating rates

FIGURE 11 The mass yield of gaseous products of each specimen at different heating rates

The relationship between the proportion of PLA in a mixture and the mass yields of solid, condensation, and gaseous products at a pyrolytic temperature of 500°C and a heating rate of 10°C/min are as follows: Y1＝85.03－50.36X－34.54X2＋28.3X3 (R2＝0.99) Y2＝3.95－0.28X－0.13X2－1.57X3 (R2＝0.96) Y3＝11.02＋50.64X＋34.67X2－26.72X3 (R2＝0.99) Their relationship at a pyrolytic temperature of 500°C and a heating rate of 15°C/min are as follows: Y1＝81.52－63.6X＋18.88X2－8.64X3 (R2＝0.99) Y2＝5.31－0.59X－0.29X2－2.35X3 (R2＝0.98) Y3＝13.17＋64.19X－18.59X2＋10.98X3 (R2＝0.99) Finally, their relationship at a pyrolytic temperature of 500°C and a heating rate of 20°C/min are as follows: Y1＝76.58＋0.21X－190.19X2＋135.87X3 (R2＝0.99)

Y2＝6.6－5.7X＋12.05X2－10.02X3 (R2＝0.94) Y3＝16.82＋5.47X＋178.21X2－125.89X3 (R2＝0.99) Wherein, X represents the proportion of PLA in a PLA/PET mixture, Y1 represents yields of solid products, Y2 represents the yields of condensation, and Y3 represents the yield of gaseous products. The above regression relations can be used to estimate the mass yields of pyrolytic products generated from PLA/PET mixtures with different component ratios. The mixing ratios may also be back-estimated from the mass yields of pyrolytic products using the equations. The relationship among effect mixing ratios, heating rates, and product yields. Multiple regression analyses were conducted based on experimental results of pyrolytic studies to investigate the relationship among PLA/PET ratios, heating rates, and the mass yields of pyrolytic products, and to establish their relationships. The relationship at a pyrolytic temperature of 500°C are


199

as follows: Y1＝－57.32X1－0.79X2＋93.1 (R2＝0.98, X1 and X2 are both statistically significant) Y2＝－2.72X1＋0.19X2＋2.75 (R2＝0.89, X1 and X2 are both statistically significant) Y3＝60.04X1＋0.6X2＋4.15 (R2＝0.98, X1 and X2 are both statistically significant) Wherein, X1 represents the proportion of PLA in a PLA/PET mixture, X2 represents the heating rate, Y1 represents the yields of solid products, Y2 represents the yields of condensation products, and Y3 represents the yield of gaseous products. Three conclusions are derived based on the above equations: 1. Both the PLA ratios and heating rates are negatively correlated to the mass yields of solid products. In other words, the mass yields of solid pyrolytic products may be increased by reducing both PLA ratios and heating rates. 2. PLA ratios are negatively correlated to the mass yields of condensation products, whereas heating rates are positively correlated to the yields of condensation products. In other words, the yields of condensation products may be increased by reducing PLA ratios and increasing heating rates. 3. Both the PLA ratios and heating rates are positively correlated to the mass yields of gaseous products. In other words, the mass yields of gaseous pyrolytic products may be increased by increasing both PLA contents and heating rates. The energy yields of pyrolytic products. The energy densifi-

cation ratios and energy yields of solid and condensation products generated from pyrolytic reactions were calculated by applying Equations (7) and (8). Analyses on the energy densification ratio and energy yield of gaseous products were omitted in this study due to its challenge level and higher costs. Results of pyrolytic experiments indicate that heating rates have limited effects on the HHV of pyrolytic products. Thus energy densification ratios and energy yields of solid and condensation products were calculated based on the HHV of products at a heating rate of 15°C/min (Tables 8 and 9). The HHV of the solid and condensation products of the specimens are similar before and after pyrolytic reactions; consequently, variation in energy densification ratios before and after pyrolytic reactions is insignificant (between 1.00 and 1.08). Evidently, energy densification ratios have little effects on energy yields. Variations in energy yields are mainly affected by the mass yields of products; that is, greater PET ratios in specimens and lower heating rates lead to greater energy yields from solid products, and greater PET ratios and the higher heating rates lead to greater energy yields from condensates. Because variations in energy yield are more significant in solid products than in condensates, the trend of total energy yield is mainly affected by and is consistent with the energy yield from solid products. That is, greater PET ratios in specimens and lower heating rates lead to greater energy yield from solid and condensation products, with a maximum yield of 92.31%. Therefore, pyrolytic reactions may be performed at greater PET ratios and lower heating rates in the future if the energy yields of solid and condensation products are the first priority. By summarizing the results of energy yields, the energy densification ratio (HHV of products/HHV of reactants) is slightly greater than 1. Therefore, without accounting for energy yield from gaseous products, the total energy yield of pyrolytic solid and condensation products is lower than the energy yield of reactants before pyrolysis.

TABLE 8 The energy densification ratio of pyrolytic reaction products Solid products after pyrolysis Specimen ID. (PLA:PET)

HHV before pyrolysis

S1 (100：0)

Condensates after pyrolysis

HHV

Energy densification ratio

HHV

Energy densification ratio

18.26

18.69

1.02

18.73

1.03

S2 (90：10)

19.36

19.33

1.00

19.85

1.03

S3 (70：30)

20.25

20.16

1.00

20.44

1.01

S4 (50：50)

20.98

21.46

1.02

21.85

1.04

S5 (30：70)

21.51

22.20

1.03

22.84

1.06

S6 (10：90)

22.50

23.20

1.03

24.22

1.08

S7 (0：100)

22.85

23.47

1.03

24.73

1.08

Unit of HHV: MJ/kg Heating rate: 15℃/min

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MAY 2015

TABLE 9 The energy yield of pyrolytic reaction products Solid products Specimen ID. (PLA:PET)

Condensates

Total yields

10

15

20

10

15

20

10

15

20

°C/min

°C/min

°C/min

°C/min

°C/min

°C/min

°C/min

°C/min

°C/min

S1 (100：0)

30.06

29.11

25.15

2.18

2.24

2.66

32.24

31.35

27.81

S2 (90：10)

30.65

32.38

17.87

2.22

2.76

4.65

32.87

35.14

22.52

S3 (70：30)

42.93

44.58

31.96

3.29

3.91

4.83

46.22

48.49

36.79

S4 (50：50)

56.55

53.03

47.34

3.83

5.15

5.61

60.38

58.18

52.95

S5 (30：70)

69.65

66.62

65.58

3.98

5.12

6.15

73.63

71.74

71.73

S6 (10：90)

81.22

77.80

75.11

4.13

5.67

6.80

85.35

83.47

81.91

S7 (0：100)

87.96

83.56

79.94

4.35

5.77

6.99

92.31

89.33

86.93

Unit: %

CONCLUSION

ACKNOWLEDGEMENTS

Results of this study demonstrate that using co-pyrolysis technology to process PLA and PET is a feasible approach to avoid the need of separating one from another. Furthermore, pyrolysis may effectively reduce the volume of PLA and PET wastes. Future considerations for practical applications may adopt the regression equation derived in this study to estimate the relationship between PLA/PET mixtures with different component ratios and the yields of pyrolytic products. The equation also allows the setting of relevant conditions according to required products to achieve optimal yields. Despite this, due to the fact that once PLA and PET are being shredded, the mixture would be difficult to identify, which decreases its reusable value. Material recycling is hard to achieve with current technology, therefore this study looks at recycling from the perspective of usable source of energy, evaluating results based on heating value and energy yield analysis of the PLA/PET mixture. Energy yields generated from incineration of the PLA/PET mixture, is relatively higher than the yields from pyrolysis processing (energy yields for pyrolytic products are less than 1; and this study did not include the energy needed to perform pyrolysis). Thus, incineration is a desirable approach for managing waste PET contaminated by PLA if proper countermeasures are developed to effectively reduce associated impact on the environment in the future. Yet, it should be noted that the condensate generated from pyrolytic reactions are condensed from gaseous products. The condensing temperature in this study was set at -10℃, consequently, some products and gas were not effectively condensed and collected. In-depth studies may be conducted in this aspect in the future to improve the overall energy yields.

Special thanks to Chun-Yu Chen and Yung-Tai Wang for their dedication in assisting with data analysis and integration for this research.

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VOLUME 41, NO. 2

MAY 2015

DESIGN OF VERTICAL WELLS FOR LEACHATE RECIRCULATION IN BIOREACTOR LANDFILLS USING TWO-PHASE MODELING Krishna R. Reddy1*, Professor Email: [email protected]

Rajiv K. Giri*, Graduate Research Assistant Email: [email protected]

Hanumanth S. Kulkarni*, Graduate Research Assistant Email: [email protected]

*University of Illinois at Chicago, Department of Civil & Materials Engineering 842 West Taylor Street, Chicago, IL 60607

ABSTRACT Vertical wells (VW), commonly used for leachate recirculation in bioreactor landfills, have an advantage over other recirculation systems as they can be installed either during the construction of a landfill or after its closure. Currently, the design of VWs is based on very limited field and laboratory investigations and modeling studies that has resulted in a large variation in their performance in the field. The main objective of this paper is to perform a detailed parametric study using two-phase flow model and develop design charts for the rational design and operation of VWs. Effects of the leachate recirculation rate, hydraulic properties of the MSW, and depth of VW on MSW wetted diameter, wetted area, developed pore pressures (water and landfill gas), and the length of time to reach the steady-state condition are studied. Modeling was performed for both homogeneous and isotropic, and heterogeneous and anisotropic MSW (most representatives of field conditions). An example of the application of VWs is presented through the design charts for this field system that are developed as part of the study. Overall, the design charts provide useful guidance to the design and operation of VWs during leachate recirculation. Keywords: Vertical well, two-phase flow, moisture distribution, pore water pressure, pore gas pressure, design chart

INTRODUCTION Vertical injection wells drilled into a landfill at the desired locations and depths allow the recirculation of leachate into the compacted municipal solid waste (MSW). The construction of a VW is similar to that of a gas extraction well (Reinhart and Townsend, 1997). The main advantage of a VW is

that it can be installed in a closed or active conventional landfill to increase the moisture distribution, thus converting an existing landfill into a bioreactor landfill. Moreover, the installation is simple, essentially drilling a well to required depth at a desired location. Depending upon the volume of the MSW and other parameters, such as the initial moisture content, density of compacted fill, and hydraulic properties of

___________________________________ 1

Corresponding author: University of Illinois at Chicago, Department of Civil & Materials Engineering; Email: [email protected]


203

the MSW, VWs should be designed to achieve increased uniform distribution of moisture throughout the landfill mass (Reinhart and Townsend, 1997; Jain et al., 2010; Reddy et al., 2013). The studies that document this moisture distribution in a bioreactor landfill using VWs as the leachate distribution system are limited in number. Khire and Mukherjee (2007) presented a study on moisture distribution in a bioreactor landfill by implementing a mathematical model of VWs as a leachate recirculation system (LRS). That study used a model of a landfill assumed to be 100 m wide and 20 m high and also assumed the MSW to be homogeneous and isotropic. The authors assumed hydraulic properties for that homogeneous and isotropic MSW similar to those of silt loam. They presented the maximum influence diameter around the VW, injection pressure and pressure head on the liner with respect to leachate injection rate, and saturated hydraulic conductivity of the MSW. However, the characteristics of silt loam are not a realistic representation of the MSW samples taken in the field at operational landfills. Reinhart et al. (1998) presented a study of an LRS that used a single phase flow analysis to simulate the flow pattern in the bioreactor landfill cell and presented results on the lateral and upward movement of leachate injected in VWs. Jain et al. (2010) studied the moisture distribution in a landfill cell using VWs, but the unsaturated hydraulic properties of sand were assumed to simulate the MSW, which is again not a realistic representation of the filed observations. To date, VWs are being designed for use in the field and operated based on empirical assumptions and rules of thumb that originate with the very limited collection of laboratory and field observations. This has led to variations in their performance. The main reason for this is that those modeling scenarios are constructed based on improper assumptions. The studies presented either assume the saturated MSW condition is isotropic or homogeneous MSW and/or a single phase flow process or improperly assume the unsaturated hydraulic properties of the MSW by considering the soils that do not represent real MSW. Thus, it is of utmost importance to develop a rational method for understanding the functions of and designing VWs based on observations and sampling that reflects the real-life environment of a landfill. The main objective of this study is to perform a parametric analysis developed to investigate the effects of the saturated hydraulic conductivity of the MSW, leachate injection rate, dimension and location of VWs with respect to leachate collection and removal system (LCRS) on moisture distribution indicating the maximum wetted diameter, maximum wetted area, and pore water and gas pressures developed. A validated two-phase flow mathematical model was implemented to simulate the different scenarios and to understand the moisture distribution in a landfill cell. This paper presents the two-phase flow model development and procedure for this parametric analysis. The design charts developed are based on these results and their use is explained with an example of a field application.

MODELING METHODOLOGY

204

Numerical two-phase flow model The two-phase flow model considers the flow of the two immiscible fluids (leachate as wetting fluid and landfill gas as non-wetting fluid) that fill the pore spaces of the MSW. The flow of each fluid is described by Darcy's law with the unsaturated hydraulic conductivity represented by van Genuchten function (Peaceman 1977; Lu and Likos, 2004; ICGI, 2011; Reddy et al., 2013). The pore fluid pressure difference between gaseous phase and liquid phase is also known as capillary pressure, and it is a function of degree of leachate saturation. In the numerical two-phase flow model, the governing equations of unsaturated MSW are given by the linear momentum balance and the fluid balance laws (based on mass balance) and are given as:

ρ = ρ d + n( S L ρ L + S G ρ G )

(1)

 ∂qiL   S ∂PL ∂S L  + n L   = − ∂t   K L ∂t  ∂xi 

(2)

 ∂q iG   S ∂PG ∂S G  n G + = −    ∂t   K G ∂t  ∂x i 

(3)

Where: n = porosity, SL = leachate (liquid) saturation, SG =gas saturation, PL = pore liquid pressure, PG = pore gas pressure, ρL, ρG = fluid densities of liquid and gaseous phases, ρd = matrix dry density, KL and KG= liquid and gas bulk modulus, respectively, q iL and q iG = flow rate of wetting liquid and non-wetting gas given by Darcy’s law. The above governing Eqn. (1) through Eqn. (3) for the two-phase unsaturated flow are solved numerically with the Fast Lagrangian Analysis of Continua (FLAC) program using the finite difference method (ICGI, 2011). The use of the FLAC model is validated by reported laboratory and field studies as well as previous modeling studies by Kulkarni (2012). The FLAC model can predict the laboratory, field and previous modeling results reasonably well (Kulkarni, 2012).

Model implementation Conceptual model. In this study, the properties of the MSW are considered as (a) homogeneous-isotropic (HIW) and (b) heterogeneous-anisotropic waste (HTAW) and those conditions are the basis for the many simulations and results that are compared. A bioreactor landfill cell with 100 m wide and 30 m high was selected for the two-phase flow numerical model (Figure 1). A single vertical well (VW) of 0.3 m in diameter was located at the center of the model. The VW was assumed to be backfilled with clay having saturated hydraulic conductivity of 10-8 cm/s and the injection screen of 3.0 m was considered to have gravel as backfill material with saturated hydraulic conductivity of 10-2 cm/s. The depth between the leachate injection screen and the leachate collection and removal system (LCRS) was varied as 3, 5, 7 and 9, representing a non-dimensional parameter called the ratio of depth


VOLUME 41, NO. 2

MAY 2015

of LCRS to the height of leachate injection screen. Modeling of moisture distribution greatly depends on the size of the grid that is assumed in the analysis. Therefore, different square grid sizes ranging from 1.0 m to 0.1 m that represent the model to grid size ratios of 40, 30, 20 and 10, were evaluated to understand the effect of the grid size ratio on moisture distribution. Based on those results and the length of time required to conduct the computations, a square grid size of 0.3 m was deemed optimal. All the boundaries are assumed to be impermeable and no external infiltration was considered since only the subsurface hydraulics was significant in this study. The LCRS was represented in the two-phase flow model by fixing the pore pressures in the bottom most grids. This results in zero pore pressures, allowing the amount of leachate entering the LCRS to be computed. Further, it is assumed that there is no leachate accumulation above the LCRS at any time. Losses in the pipe networks and pumping system are purposefully not considered in the model.

the effect of heterogeneous waste was ignored. The effect of heterogeneous and anisotropic waste on moisture distribution was considered by varying the hydraulic properties with depth in each compacted layer of the MSW based on heterogeneous factor “B.” Based on the laboratory investigation conducted, Reddy et al. (2009) found the value for “B” in Eq. 4 is equal to 5.3. However, “B” can vary depending on the composition of the MSW. This leads to the “B” value being varied from 0 to 6 during the simulations to represent the potential heterogeneity of the MSW. The initial saturated hydraulic conductivity with respect to zero normal pressure (kv0) was varied from 10-2 to 10-6 cm/s. In addition, the saturated hydraulic conductivity in horizontal direction was varied in each layer during the simulations. The horizontal saturated hydraulic conductivity is related to vertical hydraulic conductivity by:

Material properties. Initially, the flow problem was simplified by considering the MSW as homogeneous-isotropic waste with MSW unit weight equals to 11 kN/m3 (Reddy et al. 2009; Reddy et al. 2011). Reddy et al. (2009) conducted a laboratory study to determine the saturated hydraulic conductivity of the MSW under applied normal pressure to represent the waste compaction in layers in a field operation, and this data can be expressed by the following relationship:

Where: A = anisotropy factor It is well known fact that the anisotropy of soils is approximately 10 (Tchobanoglous, 1993). Anisotropy factor “A” was thus defined and the saturated hydraulic conductivity of the MSW in each layer incorporating the anisotropy factor “A” to vary as 1, 2, 4, 6, 8 and 10, and the heterogeneity factor “B” was defined as 0, 2, 4 and 6 in Eq. 5 and Eq. 4, respectively, to represent heterogeneous and anisotropic condition of the waste. Flow in unsaturated MSW depends on the unsaturated hydraulic parameters of the MSW. The van Genuchten function represents the unsaturated hydraulic conductivity of the MSW that involves different constant parameters: residual saturation, saturated moisture content, matric suction, fitting parameters "a,” "b,” "c,” and "P0.” These parameters were kept constant in all of the model simulations and their values were selected from the published literature according to the values for a sample of fresh MSW extracted from a French bioreactor landfill as given by Stoltz et al. (2012) having the MSW dry unit weight of 6.08 kN/m3. Based on the typical leachate injection rate adopted in field, the leachate injection rate (Qi) was varied between 5 and 55 m3/d (Table 1). The maximum saturated width of the MSW and area were measured with respect to saturation greater than 60% (initial degree of saturation of the MSW was 40%). Pore water and gas pressures during the transient condition and the maximum

(4)

Where: σ' is the effective normal stress, Pa is the atmospheric pressure, and kv0 is the initial saturated hydraulic conductivity at zero normal stress. Based on the laboratory investigation, Reddy et al. (2009) presented a large variation of saturated hydraulic conductivity ranging from 10-2 to 10-6 cm/s for low and high applied normal pressure, respectively. Considering this fact, the value for "kv0" in Eqn.4 was assumed to vary from 10-2 to 10-6 cm/s. The unit weight of the MSW, for all waste conditions, was assumed to be equal to 11 kN/m3 (Reddy et al. 2009; Reddy et al. 2011). To represent homogeneous and isotropic waste, the value for “B” in Eqn. 4 was assumed as zero; as a result,

k h = Ak v

(5)

TABLE 1 Modeling scenarios for parametric study with vertical wells as leachate recirculation system Variables MSW Condition Qi (m3/d)

DVW/HS

kv0 (cm/s)

Anisotropy Factor “A”

Heterogeneous Factor “B”

Homogeneous and Isotropic MSW

5, 15, 25, 35, 45, and 55

3, 5, 7, and 9

10-2, 10-3, 10-4, 10-5, and 10-6

1

0

Heterogeneous and Anisotropic MSW

5, 15, 25, 35, 45, and 55

3, 5, 7, and 9

10-2, 10-3, 10-4, 10-5, and 10-6

1, 2, 4, 6, 8, and 10

0, 2, 4, and 6


205

pore water pressure at steady-state condition were also computed and compared. Initial saturation, residual saturation and the initial porosity of the MSW are assumed as 25%, 40% and 45%, respectively, in all of the simulations in this study. The other hydraulic and mechanical properties assumed and their respective sources from the published literature are documented in Table 2.

and 55 m3/d in VW Saturated hydraulic conductivity for homogeneous and isotropic waste is computed based on Eq. 4, with the value for "kv0" as 10-2, 10-3, 10-4, 10-5, and 10-6 cm/s. To represent homogeneous and isotropic waste, the value of "B" in Eqn. 4 is assumed as zero. Varying the depth of VW (DVW) to the LCRS, measured from the center of leachate injection screen (Figure 1) to the LCRS, corresponds to the different VW dimensions and locations and is represented by the non-dimensional parameter called depth to screen height ratio (DVW/HS), which is varied as 27, 21, 15, and 9.

•

•

Modeling scenarios. The wetted diameter, wetted and pore water and gas pressures developed due to leachate recirculation were compared when the VW was assumed as a LRS. The following variables were considered for each of the MSW conditions, namely homogeneous and isotropic, and heterogeneous and anisotropic waste. These simulations were performed until the steady-state conditions were achieved: • Different leachate injection rates of 5, 15, 25, 35, 45,

TABLE 2 Constant hydraulic and material properties used for parametric study Parameter

Value

Remarks

Source

Hydraulic Properties Residual moisture content (θr) (%): MSW

20

Gravel

2

van Genuchten parameter (α) (/kPa): MSW

2.9 – 5.7 (4.55)

Gravel

5.7

1. Laboratory experiments conducted on fresh MSW collected from French Bioreactor Landfill 2. Laboratory experiments conducted on gravel

van Genuchten parameter (a): MSW

0.318 – 0.88 (0.65)

Gravel

0.88

van Genuchten parameter (b)

0.50

van Genuchten parameter (c)

0.50

1. MSW based on Stoltz et al. (2012) - γd = 6.08 kN/m3 2. Gravel based on Haydar and Khire (2005)

Porosity (n) (%): MSW

63

40% to 80%

gravel

47

Typical

Saturated hydraulic conductivity (k ) (cm/s): sat

MSW

-3

1.0x10 -2

Gravel

-2

Variable for (MSW)

-5

1.0x10 to 1.0x10

Variable

Laboratory tests conducted on gravel

Haydar and Khire (2005)

Varied between 1.0x105 to 4.5 x105

---

1.0x10

Mechanical Properties 1.5x105

Bulk modulus of the MSW (Pa) Shear modulus of the MSW (Pa) 3

Unit weight of the MSW (kN/m )

206

1.0x10 11.5

5

5

5

Varied between 1.0 x10 to 2.0 x10

---

Laboratory experiments conducted on fresh MSW collected from Bioreactor Landfill in USA

Reddy et al. (2011)


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FIGURE 1 Bioreactor landfill cell model with vertical well for parametric study

RESULTS AND DISCUSSION

Saturation contours

In the study, the leachate injection rate, saturated hydraulic conductivity and dimensions and location of the VW in the landfill model were varied. The wetted MSW width, area, maximum pore water pressure were compared for the different leachate injection rates and saturated hydraulic conductivities of the MSW, assuming different dimensions and location of the injection screen in the VW in the landfill cell and considering the three different MSW conditions. Finally, the design charts were developed based on the initial parameters used in the model simulations.

A typical saturation contour showing the moisture distribution in a landfill cell using a VW as LRS is shown in Figure 2, and indicates the maximum saturated width and area measurements with respect to the initial saturation of the MSW. The maximum pore water and gas pressures developed in the landfill cell and wetted diameter and area corresponding to the saturation greater than 60% (initial saturation of the MSW was 40%) were compared for these scenarios. The leachate recirculation was performed until the steadystate condition was reached. This is a significant considera-

FIGURE 2 Typical saturation contour for injection rate of 25 m3/d vertical well with saturated hydraulic conductivity of 10-4 cm/s and DVW/HS = 5


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tion given that the effects of the MSW condition significantly impact the moisture distribution and, that the design of LRS therefore depends on the MSW condition and properties. Moisture saturation with respect to the initial degree of saturation (40%) was compared for the different leachate injection rates and the results of the study of the saturated hydraulic conductivity of the MSW indicates that when the hydraulic conductivity is found to be lower, then the area covered within the MSW is greater.

Wetted diameter and area Leachate injection rate, saturated hydraulic conductivity and the location of the VW were varied for different VW depths and the resulting wetted diameter and wetted area were compared for saturation greater than 60% (initial MSW saturation was 40%). The wetted diameter (DWmax) (normalized to the diameter of VW (dVW)) is plotted in Figure 3 for all considered MSW conditions. As shown in Figure 3(a), the maximum diameter of the wetted area of influence occurs in the homogeneous-isotropic waste, as measured around the VW for varying leachate injection rates and saturated hydraulic conductivity of the MSW. It is observed to be more than 200 for lower saturated hydraulic conductivity of 10-6 cm/s with high leachate injection rate of 55 m3/d and DVW/HS = 3. The results are quite different for the same situation with

respect to high saturated hydraulic conductivity of 10-2 cm/s; the value was 70 when the DVW/HS = 9. The higher saturated hydraulic conductivity caused the leachate injected in the MSW to migrate towards the LCRS located at the bottom of the landfill. Figure 3(b) illustrates the effects on the maximum influenced diameter around VW for the different DVW/HS ratios when the heterogeneous factor B = 6 and the anisotropy factor A = 6 (i.e., HTAW condition). The normalized maximum influenced diameter around the VW, when the results for the homogeneous-isotropic are compared (Figure 3a), shows that the WWmax/dVW values increase when the waste is heterogeneous-anisotropic. For example, the maximum WWmax/dVW value for A = 1, B = 0 (homogeneous-isotropic waste) and DVW/HS ratio of 5 was 80 with saturated hydraulic conductivity of 10-5 cm/s and the same has increased to a value of 100 when A = 6 and B = 6 with saturated hydraulic conductivity of 10-5 cm/s (Figure 3(b)). This indicates the effect of assuming the MSW as heterogeneous; it increases the maximum influenced diameter around VW. Further, low saturated hydraulic conductivity below the VW with the anisotropic condition resists the vertical migration of injected leachate and this results in the lateral spread increasing the WWmax/dVW values. The maximum wetted area (WAmax) (normalized with respect to the initial total area of the landfill cell (CellArea)) for

FIGURE 3(a) Maximum influence diameter of MSW around VW for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in homogeneous and isotropic waste

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FIGURE 3(b) Maximum influence diameter of MSW around VW for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in heterogeneous and anisotropic waste with A = 6, B = 6

the different normalized DVW/HS ratios using homogeneousisotropic MSW are presented in Figure 4(a), with varying leachate recirculation rate and saturated hydraulic conductivity. The total MSW area considered in the model was 3000 m2. It is clear that the WAmax/CellArea value for the lower DVW/HS values has created a wetted area that is about 30% higher than the initial area in the landfill (Figure 4a). Further, the increase in the DVW/HS ratio has also increased the VW depth into the landfill; therefore, it has decreased the MSW wetted area caused by migration of leachate into the LCRS. The wetted influenced area, when compared for heterogeneous-anisotropic MSW in Figure 4(b) for different DVW/HS ratios with respect to the saturation greater than 40%, showed that the WAmax/CellArea value with greater DVW/HS ratios increased to about 55% of the initial MSW area in the landfill (was 30% in homogeneous-isotropic waste) when that landfill is saturated with the highest leachate injection rate of 55 m3/d and low saturated hydraulic conductivity of the MSW of 10-6 cm/s. In addition to the heterogeneous waste condition, the anisotropic property of the MSW causes the lateral spread of the injected leachate, resulting in a higher wetted area in this waste condition than was seen in the homogeneous-isotropic waste conditions at the steady-state condition.

Pore water and gas pressures The increase in the MSW moisture enhances the biodegradation of the MSW (Barlaz and Ham, 1993; Bayard et al. 2005; and Bogner et al. 2001). However, excess leachate injection can induce high pore pressures in the MSW and endanger the physical stability of the landfill. Therefore, it is important to prevent the excess pore pressure built up while increasing the moisture content in MSW. The two-phase flow implemented in this study estimated the pore water and gas pressures with time due to leachate recirculation. Figures 5(a) and 5(b) show, for HIW and HTAW conditions, respectively, the distribution of the pore water and gas pressures with the height of the landfill for a leachate injection rate of 25 m3/day during one week flow, four weeks flow and at the steady-state condition for a DVW/HS ratio of 3 and saturated hydraulic conductivity of the MSW as 10-4 cm/s. The evolution of pore water and gas pressures with time (one week flow, four weeks flow and at steady-state condition) is illustrated in these figures. During the initial stage of leachate injection or first week flow, the pore gas pressure is higher than the pore water pressure because of the unsaturated MSW properties found in the homogeneous-isotropic waste. Maximum pressures of about 55 and 73 kPa for pore water and gas


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FIGURE 4(a) Maximum influenced area around VW for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in homogeneous and isotropic waste

pressures, respectively, were observed but only near the injection point during that initial week flow. Over time, the successive leachate injection results in lower gas pressure as the pore water pressure builds during the injection and the leachate displaces the gas once located in the pores between the MSW solids. Leachate injection continued for four weeks further increased the degree of saturation and, therefore, the pore water pressure was higher than the gas pressure (about 88 and 75 kPa for pore water and gas pressures, respectively). Finally, when the steady-state condition was attained, the pore pressure was due exclusively to the injected leachate since all the voids between the solids are filled with the leachate. The maximum pore water pressure at steady-state condition was 117 kPa near the leachate injection screen. When the MSW is considered heterogeneous-anisotropic with decreasing saturated hydraulic conductivity with depth and relatively higher saturated hydraulic conductivity in lateral direction due to anisotropy, the pore water and gas pressures are higher during the initial stages (one week flow) of leachate recirculation (Figure 5b). Continued recirculation of leachate with time increases the pore water pressure compared to the gas pressure first, and then the pore water pres-

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sure will eventually become dominant due to the saturation of the voids between the solids. It is important to note that the pore pressure values observed for both fluids (water and gas) were higher for HTAW condition than in HIW due to the lower hydraulic conductivity values found in the deeper layers of the landfill. Since the leachate was recirculated until the steady-state condition was attained in the landfill cell, the normalized maximum pore water pressure (respect to atmospheric pressure of 101.32 kPa) developed in accordance with the different leachate injection rates and varying saturated hydraulic conductivity of the MSW when the VW injection screen was located in homogeneous-isotropic waste at different depths as measured from the ground surface. Figure 6(a) shows the normalized maximum pore water pressure (Pwmax) for the different leachate injection rates and saturated hydraulic conductivity of that MSW. Note that the maximum pore pressure values plotted here were observed only near the vicinity of leachate injection screen in the VW. For the high saturated hydraulic conductivity of 10-2 cm/s, the developed pore water pressure is observed to be very low; nearly equal to zero for low leachate injection rate. The injected leachate migrates to


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FIGURE 4(b) Maximum influenced area around VW for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in heterogeneous and anisotropic waste with A = 6, B = 6 the LCRS at faster rate and, therefore, the leachate pressure developed is very low for every injection rate. For example, the values for Pwmax/pa are 0.12 and 0.25, respectively, for Qi = 5 and 55 m3/d in VW with saturated hydraulic conductivity of the MSW as 10-4 cm/s. Furthermore, the decrease in the saturated hydraulic conductivity increased the pore water pressure developed in the landfill mass. For instance, the Pwmax/pa value for Qi = 25 m3/d/m and the DVW/HS ratio of 5, are 0.002, 0.005, 0.04, 0.15 and 0.35 for the saturated hydraulic conductivity of 10-2, 10-3, 10-4, 10-5 and 10-6 cm/s, respectively. Figure 6(b) shows the normalized maximum pore water pressure for different leachate injection rates, saturated hydraulic conductivity and location of the leachate injection screen in the VW placed in heterogeneous-anisotropic waste. The Pwmax/pa values are low for lower leachate injection rate and higher saturated hydraulic conductivity, with values of 0.03 and 0.14, respectively, for Qi = 5 and 15 m3/d. It is interesting to note that when the leachate injection rates increased in the heterogeneous-anisotropic MSW, the pore pressures were higher than those rates found in the homogeneousisotropic waste under a similar circumstance. In contrast, the study shows that due to a decrease in the saturated hydraulic conductivity of the MSW in the deeper compacted layers, the

pore water pressure will be greater than recorded in the homogeneous-isotropic waste. Recent studies conducted by Reddy and Kulkarni (2010); Kulkarni and Reddy (2010); and Giri and Reddy (2013) show that intermittent leachate recirculation, when used from the outset, reduces these developed pore pressures effectively.

DEVELOPMENT OF DESIGN CHARTS Design charts are developed for the use of a VW as a leachate recirculation system (LRS) based on the simulations of the moisture distribution in the MSW that were performed by varying the hydraulic properties of the MSW, leachate injection rate, and location of VW screen. These charts rely on steady-state condition results and are specifically based on simulations of the wetted diameter, wetted area, and pore water pressure developed. The design charts are defined in terms of non-dimensional variables. Leachate recirculation rate is normalized as a function of depth of VW measured from ground surface of the landfill and the saturated hydraulic conductivity of the MSW is given by:


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FIGURE 5(a) Pore water and gas pressure distributions in the landfill cell for DVW/HS ratio of 3, leachate injection rate of 25 m3/d and saturated hydraulic conductivity of MSW as 10-4 cm/s in homogeneous and isotropic waste

Qs =

Qi DVW k v 0

(6)

The maximum wetted diameter around the VW in the MSW mass is normalized based on the VW diameter (dVW) implemented in the landfill (Eqn. 7) as:

DW =

WW max dVW

the values presented in the literature and the values generally observed in the field (Haydar and Khire, 2007; Reddy et al., 2009a and b). Similar to the maximum wetted diameter of the MSW, the maximum wetted MSW area is computed with respect to the degree of saturation greater than the initial degree of saturation of the MSW. Maximum MSW wetted area (WAmax) is normalized as a function of total initial landfill area (AreaTotal) (Eqn. 8):

(7)

AW =

In this study, maximum wetted influential diameter (WWmax) is computed with respect to the saturation greater than 40%, which is the initial saturation of the MSW defined in the model. That initial degree of saturation is assumed based on 212

FIGURE 5(b) Pore water and gas pressure distributions in the landfill cell for DVW/HS ratio of 3, leachate injection rate of 25 m3/d and saturated hydraulic conductivity of MSW as 10-4 cm/s in heterogeneous-anisotropic waste

W A max AreaTotal

(8)

Pore water and gas pressures developed in the landfill are computed by the two-phase flow program, but the maximum


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FIGURE 6(a) Maximum pore water pressure developed in the landfill cell for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in homogeneous and isotropic waste

pore water pressure developed under the steady-state condition is used. The maximum pore water pressure is normalized with respect to the atmospheric pressure (101.3 kPa):

NPW =

Pwmax pa

(9)

Design Chart for Wetted Diameter in MSW The steady-state maximum wetted influential diameter of the MSW (DW) is measured with respected to the initial saturation of the MSW in the landfill and is analyzed as a function of leachate injection rate, saturated hydraulic conductivity of the MSW and geometry of the LRS. As defined earlier, the initial degree of saturation was 40% and the wetted diameter is measured as the zone of impact having saturation greater than 60%. Figure 7(a) shows the design chart for maximum wetted diameter DW as a function of QS and saturated hydraulic conductivity values considering the MSW as homogeneous-isotropic waste. In Figure 7(a), a logarithmic correlation is obtained when plotted on a semi-logarithmic

plot. The respective maximum wetted influential diameters of the MSW (DW) due to leachate recirculation can be predicted at steady-state condition for a given set of leachate injection rates, saturated hydraulic conductivity of the MSW and VW depth. In the case of the low DVW/HS ratio of 3, the maximum wetted influential diameter of the MSW observed was 60 m, and the same was 10 m for DVW/HS ratio of 9 for a leachate injection rate of 55 m3/d. The maximum wetted influential diameter of the MSW can be used as the basis for the selection of the appropriate spacing of the VWs when they are installed in the landfill. When the waste is considered as heterogeneous and anisotropic and WW is plotted with log of QS , it follows a sigmoid function with an initial increase in the wetted diameter unlike the situation for the homogeneous isotropic MSW (Figure 7(b)). This means that if a bioreactor landfill section is considered to have an impervious boundary, the injected leachate reaches the boundary when there is high leachate injection rate and low saturated hydraulic conductivity, and then it migrates to the leachate collection system to the bottom of the landfill. This behavior was observed for all DVW/HS ratios. However, when the MSW is homogeneous and isotropic, because of the identical material properties throughout the


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FIGURE 6(b) Maximum pore water pressure developed in the landfill cell for different DVW/HS ratios with varying leachate injection rate and saturated hydraulic conductivity of MSW in heterogeneous and anisotropic waste with A = 6, B = 6

entire waste mass, the injected leachate reaches the leachate collection system at the bottom of the cell first, before reaching the boundaries (if it ever does). In this situation, the logarithmic nature of the relation was observed for all of the DVW/HS ratios.

Design Chart for Wetted Area of the MSW The wetted MSW area due to the leachate recirculation in a VW was computed by varying the leachate injection rates, saturated hydraulic conductivity and dimension of the LRS in the landfill cell. Values for the wetted area are referred to in the context of the degree of saturation of the MSW that is greater than 60% (initial degree of saturation of the MSW was 40%). Figure 8(a) refers to homogeneous isotropic waste. As illustrated in this figure, the maximum wetted area when normalized with respect to the initial total area of the landfill, and the values, when are plotted against normalized leachate injection rate (QS), follow a power function shown on semilogarithmic scale of QS. These results show that the maximum wetted area will decrease with an increase in the DVW/HS ratio. The maximum area of influence can be predict-

214

ed for any given saturated hydraulic conductivity of the MSW and leachate injection rate. On the other hand, for a desired maximum influence area and the saturated hydraulic conductivity (which represents different biodegradation stages of the MSW), the required leachate injection rate and mode of leachate recirculation in the field can also be determined by using these charts. Leachate recirculation in heterogeneous and anisotropic waste increased the wetted MSW area more than in the homogeneous and isotropic waste. Since the saturated hydraulic conductivity decreases with depth, the lateral spread increases and the time needed for the leachate to migrate to leachate collection system and the wetted area of the MSW both increase. The normalized values of wetted area (AW) with respect to the total MSW area in landfill are plotted with QS by varying the leachate injection rate, initial saturated hydraulic conductivity (kv0 in Eqn. 4), heterogeneity factor B in Eqn. 4, and the dimension of VW (representing the location of the injection screen in the landfill mass), with respect to ground surface in Figure 8(b) and follows a sigmoid relation with respect to the QS.


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FIGURE 7 Normalized lateral extent of injected leachate as a function of dimensionless leachate injection rate (QS) for different DVW/HS ratios in (a) homogeneous isotropic waste; and (b) heterogeneous anisotropic waste

Design Chart for Maximum Pore Water Pressure The design charts for maximum pore water pressure for the different DVW/HS ratios when the MSW is homogeneousisotropic are shown in Figure 9(a). The maximum pore water pressure determined is normalized with atmospheric pressure to obtain normalized pore water (NPW) parameter as a function of leachate injection rate and saturated hydraulic conductivity, as plotted in Figure 9(a). The maximum NPW in the landfill cell follows a power function with respect to the increase in the leachate injection rate and decrease in the saturated hydraulic conductivity of the MSW. A fair correlation was obtained with the R2 value ranging from 0.855 to 0.913. The NPW can be estimated for a given leachate injection rate and MSW saturated hydraulic conductivity. Based on that estimated rate, the leachate recirculation can be controlled to avoid building up excess pore water pressures in a bioreactor landfill cell. Furthermore, in a given landfill, saturated hydraulic conductivity varies with depth as a function of overburden pressure (Reddy et al. 2009a); therefore, the leachate recirculation rates in the leachate recirculation system located in the deeper layers of the landfill can be regulated based on the hydraulic properties of the MSW at a given

FIGURE 8 Normalized lateral extent of injected leachate as a function of dimensionless leachate injection rate (QS) for different DVW/HS ratios in (a) homogeneous isotropic waste; and (b) heterogeneous anisotropic waste

depth. To simulate the actual variation of the saturated hydraulic conductivity to vary with the depth and account for anisotropy, the detailed parametric study was conducted by varying the anisotropic constant "A" in Eqn. 5 and heterogeneous factor "B" in Eqn. 4. Similar to the homogeneous and isotropic MSW, the pore water pressure that developed due to leachate injection into the VW was monitored at the steady-state condition in heterogeneous-anisotropic waste, as seen in this section of Figure 9(b). Interestingly, the pore pressures variation when the waste is heterogeneous-anisotropic follows a power function with respect to the QS (when QS is on logarithmic scale) that is similar to that of homogeneous-isotropic waste, but produces higher magnitudes. This increase in pore pressures is due to the decrease in saturated hydraulic conductivity of the MSW with depth, which is to say that the pore size between the MSW solids reduces due to the low saturated hydraulic conductivity and therefore, the pressure will build up under these circumstances.

APPLICATION OF DESIGN CHARTS


215

FIGURE 9 Normalized pore water pressure developed as a function of normalized leachate injection rate (QS) for different DVW/HS ratios (a) homogeneous isotropic waste; (b) heterogeneous anisotropic waste

FIGURE 10 Landfill model used for the field implementation Figure 10 shows a bioreactor landfill cell of 150 m wide and 40 m height considered as a field application problem to use the design charts. A single VW of 0.3 m in diameter, extending 20 m in the landfill from ground surface with a leachate injection screen of 3.0 m located at the end of VW in the landfill. Wetted diameter and maximum pore water pressure developed in the MSW assumed as homogeneousisotropic, and heterogeneous-anisotropic waste conditions are determined with the leachate injection rate of 12 m3/d in the VW. For homogeneous-isotropic waste condition, uniform saturated hydraulic conductivity of 10-3 cm/s was used in the entire landfill. In the case of heterogeneous-anisotropic waste, the vertical saturated hydraulic conductivity in the layers of compacted MSW was determined with the heterogeneous factor “B” as 6 in Eq. 4, while the corresponding horizontal saturated hydraulic conductivity was calculated

216

with the anisotropy factor "A" of 10 in Eq. 5. In the case of homogeneous-isotropic waste, the normalized leachate injection rate is determined using Eq. 6 as:

Qs =

Qi 12 = = 4.0 x106 dVW kv 0 0.3 x(1x10 −5 )

The ratio of depth of VW from ground surface to the injection screen height is determined as:

DVW 20 = =5 HS 4 From the design chart for wetted diameter (Figure 7a), the normalized wetted diameter in case of homogeneous-


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TABLE 3 Results comparison for the application of the developed design charts with the published study MSW Condition Homogeneous Isotropic MSW

Wetted Diameter (m)

Pore Water Pressur (kPa)

9.6 (This study)

8.6

5.2 (Jain et al. 2010)

-—

15.0

18.2

Heterogeneous Anisotropic MSW

isotropic waste under steady-state condition is determined as:

dW =

WW max = 50 DVW

⇒ WW max = 50 x0.3 = 15.0m Similarly, from the design chart for maximum pore water pressure (Figure 9), the normalized pore water pressure can be determined as:

PPmax = 0.18 pa = 0.18 x101.32 = 18.2kPa

NPW =

⇒ PPmax

Similarly the wetted width and maximum pore water pressure developed for the case of heterogeneous-anisotropic waste conditions is determined and is summarized in Table 3. Jain et al. (2010) presented the design charts for the wetted width at steady-state condition assuming the hydraulic properties of sand. Table 3 also summarized the comparison of the values with the published studies.

CONCLUSIONS Effects of saturated hydraulic conductivity, leachate injection rates, the depth of the VW in the landfill cell as measured from ground surface, and VW dimensions were analyzed by performing the parametric study using a two-phase flow model. Based on the results of these simulations results, design charts for wetted diameter of the MSW, wetted area of the MSW and maximum pore water pressure were developed. The MSW in the study was considered as homogeneous and isotropic, and heterogeneous and anisotropic and the results are compared. The main findings of this study follow: • Design charts were developed for homogeneous and isotropic MSW, the simplest definition of a most uniform waste used in the study, shows the logarithmic correlations for the maximum wetted MSW width and wetted MSW area and power relation for the pore water pressure. • Most representatives of field waste conditions, heterogeneous and anisotropic MSW was considered by varying the hydraulic properties with depth and also varying the hydraulic properties within the compacted layer in the

vertical and horizontal directions. The variation of maximum MSW wetted diameter, wetted area follows power function, and pore water pressure developed, follow a sigmoidal variation when plotted against the leachate injection rate, saturated hydraulic conductivity and location of VW with respect to ground surface for different DVW/HS ratios. The design charts developed are useful for the design of optimal vertical well (VW) system in a bioreactor landfill.

REFERENCES Barlaz, M.A., Ham, R.K. (1993). “Leachate and gas generation.” Chapter in Geotechnical Practice for Waste Disposal, Ed. by David. E. Daniel, Chapman & Hall, London. Bayard, J.R., Gachet, C., Achour, F., de Brauer, C., Gourdon, R. (2005). “Effect of leachate recirculation on the stabilization of organic matter in municipal solid waste.” Proc., International workshop on Hydro Physico-Mechanics of landfills, Grenoble, France. Bogner, J., Reddy, K., Spokas, K. (2001). “Dynamic water balance aspects of bioreactor landfills.” Proc., Sardinia '2001, Eighth International Waste Management and Landfill Symposium, S. Margherita di Pula, Cagliari, Italy. ITASCA Consulting Group Inc. (ICGI) (2011). “Fast Lagrangian Analysis of Continua (FLAC) Version 7.0: Fluid-Mechanical Interaction.” User’s Manual, 4th eds, Minneapolis, MN, pp 2.1-2.7 ITRC (2006). “Characterization, Design, Construction and Monitoring of Bioreactor Landfills.” Interstate Technology and Regulatory Council Alternative Landfill Technologies Team, Washington, D.C., U.S.A. Jain, P., Townsend, T.G. and Tolaymat, T.H. (2010). “Steady-state design of vertical wells for liquids addition at bioreactor landfills.” Waste Management, Volume 30, pp. 2022-2029. Khire, M.V. and Mukherjee, M., (2007) “Leachate injection using vertical wells in bioreactor landfills.” Waste Management, Volume 27, pp. 1233-1247. Kulkarni, H.S. (2012). “Optimization of Leachate Recirculation Systems in Bioreactor Landfill.” Ph.D. Thesis, Dept. of Civil and Materials Engineering, University of Illinois at Chicago, Chicago, IL, U.S.A. Landva, A.O. and Clark, J.I., (1986). “Geotechnical testing of


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waste fill.” Proceedings of 39th Canadian Geotechnical Conference, Canadian Geotechnical Society, Ottawa, Ontario, Canada, pp. 371-385. Lu, N., and Likos, W.J. (2004). “Unsaturated Soil Mechanics.” John Wiley & Sons Inc., Hoboken, NJ, U.S.A. McCreanor, P.T. (1998). “Landfill leachate recirculation systems: Mathematical modeling and validation.” Ph.D. Thesis, Dept. of Civil and Environmental Engineering, University of Central Florida, Orlando, FL, U.S.A., 231p. McCreanor, P.T., Reinhart, D.R. (1999). “Hydrodynamic modeling of leachate recirculating landfills.” Waste Management Research, Volume 17, pp. 465-469. Peaceman, D.W. (1977). “Fundamentals of Numerical Reservoir Simulation.” Elsevier Scientific Publishing Company, New York, NY, U.S.A. Reddy, K.R. (2006). “Geotechnical aspects of bioreactor landfills.” Proceedings of Indian Geotechnical Conference 2006 - Geotechnical Engineering - Indian Experience, Indian Institute of Technology, India, pp. 79-94. Reddy, K.R., Hettiarachchi, H., Parakalla, N., Gangathulasi, J., Bogner, J. and Lagier, T. (2009). “Hydraulic conductivity of the MSW in landfills.” Journal of Environmental Engineering, Volume 135 (8), pp. 1-7. Reddy, K.R., Kulkarni, H.S., and Khire, M.V. (2013). “Twophase modeling of leachate recirculation using vertical wells in bioreactor landfills. J. of Hazardous, Toxic, and

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Radioactive Waste, ASCE. 17(1), pp. 272-284. Reinhart, D.R., and Townsend, T.G. (1997). “Landfill Bioreactor Design and Operation.” Lewis Publishers, Washington D.C., U.S.A. Reinhart, D.R., McCreanor, P.T. and Townsend, T. (2002). “The bioreactor landfill: Its status and future.” Waste Management Research, Volume 20, pp. 172-186. Sharma, H.D. and Reddy, K.R. (2004). “Geoenvironmental Engineering: Site Remediation, Waste Containment, and Emerging Waste Management Technologies.” John Wiley & Sons Inc., Hoboken, NJ, U.S.A. Stoltz, G., and Tinet, A. J., Staub, M. J., Oxarango, L., and Gourc, J.P. (2012). “Moisture Retention Properties of Municipal Solid Waste in Relation to Compression.” Journal of Geotechnical and Geoenvironmental Engineering, Volume 138(4), pp. 535-543. Tchobanoglous, G., Theisen, H., and Vigil, S.A. (1993). “Integrated solid waste management, engineering principles and management issues.” McGraw-Hill, New York, USA. van Genuchten, M. Th. (1980). “A closed-form equation for predicting the hydraulic conductivity of unsaturated soils.” Soil. Sci. Soc. Am. J., Volume 44, pp. 892-898. Vilar, O.M., and Carvalho, M.F. (2004). “Mechanical Properties of Municipal Solid Waste.” Journal of Testing and Evaluation, ASTM, Volume 32 (6), pp. 1-12.


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