Journal of Environmental Science and Water Resources Vol. 1(4), pp. 80 - 84, May 2012 Available online at http://www.wudpeckerresearchjournals.org/JESWR 2012 Wudpecker Research Journals
Full Length Research Paper
A system dynamics modeling approach for dumpsite waste generation and its attendant challenges in Ogo Oluwa Local Government Area, Nigeria S.O. Ojoawo*1, O.A. Agbede2 and A.Y Sangodoyin3 1
Department of Civil Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. 2 Department of Civil Engineering, University of Ibadan, Ibadan, Nigeria. 3 Department of Agriculture and Environmental Engineering, University of Ibadan, Ibadan, Nigeria. Accepted 27 April 2012
The paper focuses on the application of system dynamics computer model in the waste generation and its attendant challenges from the dumpsites of Ogo Oluwa Local Government Area (LGA), Nigeria. Model equations were formulated to project the population of the study area. Pre-determined percent organic contents of the wastes and temperature were the key variables employed in developing the causal loops linking the population, economic status, and waste generation per capita in a STELLA flow diagram. Simulated results show that population in the urban region increases sharply with time in the first 25 years as against steady increase in the rural region. The community witnesses a continuous increase in wastes, leachate and gas generation over the 100 years projection. The rate of gas generation exceeds that of solid wastes at most given simulated times. Key words: System dynamics, dumpsite, wastes, leachate, gas.
INTRODUCTION System dynamics was introduced by Jay Forrester in the 1960s at the Massachusetts Institute of Technology as a modeling and simulation methodology for the longterm decision-making analysis of industrial management problems (Forrester, 1961; Randers, 1980; Chaerul et al., 2008). Forrester’s system dynamics methodology provides a foundation for constructing computer models to do what the human mind cannot do (Forrester, 1968), i.e. to rationally analyse the structure, the interactions and mode of behavior of complex technological and environmental systems. As a modeling method, system dynamics is particularly suited to the simulation of complex systems such as wastes/leachate generation and its management. The method is capable of dealing with assumptions about system structures in a stringent fashion, and particularly for monitoring the effects of
*Corresponding author. E-mail:
[email protected]. Tel; +234-803-391-6883.
changes in sub-systems and their relationships. System dynamics method is based on a feedback concept of control theory and the feedback loops simulate dynamic behaviors (Bala, 1999). Hence the system dynamics approach is one of the most appropriate techniques to handle complex problems like wastes/leachate generation and i t s management (Sufian and Bala, 2007). System dynamics model has therefore been used in many areas including global environmental sustainability for pollution control/ abatement (Forrester, 1971; Meadows et al., 1992; Saysel et al., 2002); waste management challenges and their solutions for safe living of humankind (Ulli-Beer, 2003; Dyson and Chang, 2005) and environmental management in developing countries (Mashayekhi,1990; Sudhir et al., 1997). This paper attempts to develop a model for the waste generation of residents of Ogo Oluwa Local Government Area (LGA), Nigeria and applying the same for simulating the quantities of wastes and corresponding gases over a period of 100 years. In order to proffer a holistic solution to the pollution menace, the
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work therefore also looks briefly into the attendant leachate and gas generation challenges of solid wastes. Ogo Oluwa Local Government Area has an average population of 65,184 going by NPC (2006) cencus. It lies 0 0 approximately on Longitude 4 18’ East, Latitude 8 04’ North and situated in the transitional zone between rain forest and savannah region (Edward and Joel, 1978). The study area it the suburb of Ogbomosoland and is thereforecategorized as rural/low income LGA. The predominant occupations of the residents of the urban LGAs are commerce and manufacturing while those of the rural are agriculture.
I = Immigration E = Emmigration
The amount and composition of wastes generated comprise the basic information needed for the planning, operation and optimization of waste management systems (Beigl et al., 2008). Waste generation has been predicted on a per capita basis (Dennison et al., 1996, Parizeau et al., 2006). If the average waste generated by an urban community is j ton/yr and that of a rural community is k ton/yr. Then, the total waste generated, Gn; Gn = j α kβ Pn (t)………………………………….…(5) For gas generation, the equation given by Erhig (1996) expressing the total amount of gas expected to be produced was adopted as:
METHODOLOGY Gp = 1.868 C (0.014 T + 0.28) ………………….. (6) Model Equations The principal governing equations for the system are: Gn = j α kβPn(t)
(Chaerul et al., 2008)………………………(1)
Where; Gp = gas produced (m3/ton of MSW) C = the total organic content (TOC) kg/ton of MSW (Note that C = 86%, according to Ojoawo, 2009) T = temperature in Centigrade
Where; Gn = Total wastes generated Computer programming and simulation α and β = Boolean notations for j and k respectively (At any given point α and β are mutually exclusive, and are assigned values of 0 and 1respectively) j = Average waste generated by urban community k = Average waste generated by rural community Pn (t)= Population. Waste generation depends largely on two factors (Ulli-Beer, 2003; Dyson and Chang, 2005): (i) Population size per time (ii)Social status of the people (classified as urban/rural centre or high/low income) According to Sage (1980), the relationship between the Birth rate (B), Death rate (D) and the Population Pn (t) is expressed as: B = B’ . Pn (t) ln [ Pn (t) / Pn max (t)]…………………………….. (2)
The model equations were coded using Visual Basic Language. All the key elements of the model were defined and quantified as variables. The main variable is the population figures of the residents in the study area. Other minor variables are precipitation, moisture content, pan evaporation, temperature and percent organic content. Relationships between these were formulated mathematically and the source codes were developed using system dynamics structures. In designing the stock flow diagram of the system, the STELLA 9.0 software and simulation package was employed. The principles of system dynamics were applied in determining the interrelationships of wastes and leachate components by connecting the variables and expressing their correlations. These were simulated to predict the results for the next 100years using year 1991 census population figure as baseline values in the stocks of the flow diagram. Causal loops indicating the linkage of population, economic status, waste generation per capita and weather conditions to wastes and leachate generation were developed. The STELLA flow diagram of the model is shown in Figure 1.
D = D’ . Pn (t) ln [ Pn (t) / Pn max (t)]……………………………...(3) Where; B = Birth rate at time (t) B’ = Initial Birth rate D = Death rate at time (t) D’ = Initial Death rate Pn (t) = Population at time (t) and Pn max = Maximum population.
Model validation Validation of the model is considered necessary so as to compare the model results with historical data, and to check whether the model demonstrates plausible behavior. The developed model was validated by applying for in solving the practical problems of leachate pollution in the LGA. As shown in Table 1, the key validation data are population, precipitation, temperature, percent organic content, evapotranspiration and moisture content.
Therefore the Population at a given time, t is: dPn (t)/ dt = [ B – D + I – E ] Pn (t)
RESULTS AND DISCUSSION
and the estimated Population at the time t + 1 is thus given as:
Simulated population and its related factors
Pn (t+1) = Pn (t) e( B – D + I – E )t………..……………… (4) Where;
The computer projection on the respective population and its related factors in Ogo Oluwa LGA is as shown in
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Figure 1: The Stella flow diagram of the system.
Table 1. Validation data for the wastes, gas and leachate generation.
Parameter Population(NPC, 1991) %Organic Content Total Precipitation(mm) %Moisture Content Runoff Coefficient 2 Surface Area of wastes (m )
Values 36,255 85.69 1296.65 40.6 0.25 136.72
Source: Ojoawo (2009); IITA (2001); NPC (2006).
Figure 2. It was observed that both the population and estimated population increase with time through the simulation period. The sharp increase was however th noticeable from the 20 year. This may be attributable to the prevailing growth factors. Conversely, the birth and death rates decreased drastically after about 40 years. The simulated death rate pattern in t he st u dy are a was found to exceed the birth rate at the early years but later sharply decline with time. The pattern could be attributed to the rural nature of the LGA where most residents are low-income earners. When this population pattern is compared with the per capita waste generation it was noticed to be of a direct relationship. The waste generation increased with increasing th population up till about the 90 year when further increase in population does not translate to significant amount of additional wastes. This could be due to expected decomposition of wastes which would reduce its accumulated volume over such a long period of time. Consider the population situation in year 2006, the 15th
year of the model’s simulation as portrayed in Table 2. In the LGA, it was observed that the simulated population when statistically compared with the Census figure set as benchmark (NPC, 2006) exhibited significant difference (p, 0.05). The average population growth rate of the community (2.76%) also exceeded the one obtained from the simulation (2.28%). This trend is traceable to the fact that the model kept the Immigration factor as a constant whereas the academic activities of higher institutions like the Ladoke Akintola University of Technology, The Nigerian Baptist Theological Seminary and the Baptist Medical Centre’s School of Nursing and Midwifery attract well over 15,000 students and staff yearly into the community (Ojoawo, 2009). Simulated solid wastes and gas generation The simulated trend of both solid wastes and gas
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1: BirthRate 1: 2: 3: 4:
2: Death Rate
3: Population
4: Estimated Population
0 0 550000 1
2
2
3
1
2
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4
1 1: 2: 3: 4:
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Figure 2. The Population related factors over time in Ogo Oluwa LGA.
Table 2. Observations on the simulated population and the Benchmarks of the National Population Commission of Nigeria.
Initial Population in Year 1991 (NPC, 1991) 36,255
Year 2006 (NPC, 2006) 65,184
Cencus
generation of Ogo Oluwa LGA is presented in Figure 3. The LGAs witnessed continuous increase in solid wastes and the gas over 100-year projection. More population translates to more wastes being generated and thereby more gas production. This was confirmed by trends in the graphs of the population, wastes and gas generation. As shown in Figure 3 after 100 years, the total amount of wastes produced from the LGAs is 90,000 ton/yr. The corresponding quantity of gas produced is 11,500m3/ton annually. These results corroborate the fact that more gas is produced from organic-related wastes as the biodegradation processes of rural wastes components increase over time. It has been reported that gas generation begins only after elapse of several months of wastes deposition (Cossu et al.,1996). The rate of gas generation in the LGAs exceeded that of waste at most given times of the simulation. This may be linked to high proportion of organic/putrescible components of the total waste in the study area, which is about 86% (Ojoawo, 2009). The organic component of waste decomposes and degenerates with time, thereby reducing the quantity of
Simulated (Year 2006) 45,824
Population
Observed difference Population 19,360
in
wastes available. High amount of gas generation was recorded in this rural area, thus buttressing the hypothesis that biodegradable wastes of the rural areas generate more gas than the non-putrescible components common in urban areas. Similar trend was observed for the quantity of wastes generated. This is in line with the field survey data that the wastes in rural areas are of higher densities than those of urban dumpsites (Ojoawo, 2009). The total simulated waste volume after 25 year period was about 19,663 tonnes whereas after 50 years, the average waste volume stood at 38,831 tonnes this being twice the 25 year value. Waste decomposition and degeneration processes could be responsible for this indirect relationship. Conclusion The wastes and gas generation in the system increase continuously for the study area over the 100 year projection period. The total amount of wastes generated
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1: Total Amount of Gas to be Generated 1: 2:
2: Total Waste Generated
11500 90000 1
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Figure 3. Simulated amounts of solid wastes and gas generated over time in Ogo Oluwa LGA
is perpetually below those of gas. The developed model serves as a general predictive tool for policy making in the Solid Waste Management Systems as long as the population involved does not exceed 300,000 people. ACKNOWLEDGEMENT The authors wish to acknowledge the franctic efforts of Mrs. O.T Ojoawo and Mr. J. Adenekan on some computer aspects of this research. REFERENCES Bala BK (1999). Principles of System Dynamics. Agrotech Publishing Academy, Udaipur, India. 45-82. Beigl P, Lebersorger S, Salhofer S (2008). Modeling municipal solid waste generation: A review. Science Direct. Waste Manage., 28: 200-214. Chaerul M, Tanaka M., Shekdar AV (2008). A system dynamics approach for hospital waste management, Science Direct, Waste Management, 28: 442-449. Cossu R, Andreottola G, Muntoni A (1996). Modeling landfill gas production, Landfilling of waste: Biogas, E & FN SPON. 237-268. Dennison CJ, Dodd VA, Whelan B (1996). A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland, Waste Quantities Resources, Conservation and Recycling, 17(3): 227-244. Dyson B, Chang NB (2005). Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling, Waste Management. 25(7): 669-679. Edward B and Joel LM (1978). Rand McNally Goode’s World Atlas. th 16 ed., USA Erhig, HJ (1996) Prediction of landfill gas production from laboratoryscale tests. Landfilling of Wastes: Biogas, E & FN SPON, 269-291. Forrester JW (1961). Industrial Dynamics. The MIT Press. Cambridge,
Massachusetts. Forrester JW (1968). Principles of Systems. WrightAllenPress. MIT, Massachusetts. Forrester JW (1971). World Dynamics. Wright-Allen Press. MIT, Massachusetts. IITA (2001). Metrological Data. International Institute of Tropical Agriculture, Ibadan. Nigeria. Mashayekhi AN (1990). Rangeland destruction under population growth, the case of Iran, System Dynamics Rev., 6: 167-193. Meadows DH, Meadows DL, Randers J (1992). Beyond Limits, Chelsea Green Publisher, Vermont. Pp. 23-67. NPC (2001). Official gazette for 2006 population cencus. National Population Commision. Nigeria. NPC (2006). Official gazette for 2006 population cencus. National Population Commision. Nigeria. Ojoawo SO (2009). Management of Leachate Pollution from Dumpsites in Ogbomosoland, Nigeria. Ph.D. Thesis, Faculty of Technology, University of Ibadan, Pp. 113-116. Parizeau K, Maclaren V, Chanthy L (2006). Waste characterization as an element of waste management planning: lessons learned from a study in Siem Reap. Cambodi., Resources, Conservation and Recycling, 49(2):110-128. Randers J (1980). Elements of the system dynamics methods. Cambridge. Productivity Press. M.A. 25-29. Sage AP (1980). Methodology of large scale systems. Beverly Hill Sage Publications. London, 25-65. Saysel AK, Barlas Y, Yengun O (2002). Environmental sustainability in an agricultural development project: a system dynamics approach. J. Environ. Manage., 64: 247-260. Sudhir V, Srinivasan G, Moraleeharan VR (1997). Planning for sustainable solid waste management in urban India, System Dynamics Rev., 13(3): 223-246. Sufian MA, Bala BK (2007). Modeling of urban solid waste management system: The case of Darka city. Waste Manage., 27: 858-868. Ulli-Beer S (2003). Dynamic interactions between citizen choice and preferences and public initiatives- A system dynamics model of recycling dynamics in a typical Swiss locality. In: Proceedings of the 2003 International Conference of the System Dynamics Society. New York City. USA. 20-24 July, Pp. 25-34.
