Efficiency of Bark, Activated Charcoal, Foam and

Water Air Soil Pollut DOI 10.1007/s11270-012-1139-z

Efficiency of Bark, Activated Charcoal, Foam and Sand Filters in Reducing Pollutants from Greywater Sahar S Dalahmeh & Mikael Pell & Björn Vinnerås & Lars D Hylander & Ingrid Öborn & Håkan Jönsson

Received: 22 September 2011 / Accepted: 8 March 2012 # Springer Science+Business Media B.V. 2012

Abstract Greywater is a potential resource of water that can be improved to meet the quality needed for irrigation. This study evaluated the performance of bark, activated charcoal, polyurethane foam and sand filters in removing biochemical oxygen demand (BOD5), surfactants, phosphorus, nitrogen and microbial indicators from greywater during start-up and steady state. In column experiments, 0.6 m high filters (diameter 20 cm) were fed for 113 days with artificial greywater at a hydraulic loading rate of 0.032 m3 m−2 S. S. Dalahmeh (*) : B. Vinnerås : H. Jönsson Department of Energy and Technology, Swedish University of Agricultural Sciences (SLU), Box 7032, 750 07 Uppsala, Sweden e-mail: [email protected] M. Pell Department of Microbiology, Swedish University of Agricultural Sciences (SLU), Box 7025, 750 07 Uppsala, Sweden B. Vinnerås National Veterinary Institute, 751 89 Uppsala, Sweden L. D. Hylander Department of Earth Sciences, Uppsala University, Villavägen 16, 752 36 Uppsala, Sweden I. Öborn Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Box 7043, 750 07 Uppsala, Sweden

day−1 and an organic loading rate of 0.014 kg BOD5 m−2 day−1. Bark and activated charcoal efficiently reduced the concentrations of organics (BOD5), surfactants (methylene blue active substances—MBAS), total phosphorus (Tot-P) and total thermotolerant coliform numbers, while sand and foam were less efficient. Bark, activated charcoal, foam and sand reduced influent BOD5 by 98, 97, 37 and 75 %; MBAS by >99, >99, 73 and 96 %; Tot-P by 97, 91, 36 and 78 %; and total nitrogen by 19, 98, 13 and 5 %, respectively. BOD5 and MBAS were efficiently reduced directly from start-up by bark and activated charcoal, while foam needed 30 days to achieve about 50 % reduction in BOD5. Bark was the most efficient filter in reducing thermotolerant faecal coliforms (2.4 log10), while foam achieved the lowest reduction (0.5 log10). Overall, bark and activated charcoal filters appeared to be the most suitable filters for improving greywater quality to reach irrigation quality in terms of organic matter reduction. Performance of these filters under higher and fluctuating loadings and the longterm sustainability of the filter materials need further investigation. Keywords Adsorption . BOD5 reduction . Coliforms . Residence time . Irrigation . Nitrate . Organic matter Abbreviations BOD5 Biochemical oxygen demand COD Chemical oxygen demand CFU Colony forming unit

Water Air Soil Pollut

DOC EC MBAS SU TOC Tot-N Tot-P TTFC

Dissolved organic carbon Electrical conductivity Methylene blue active substances Standard unit Total organic carbon Total nitrogen Total phosphorus Thermotolerant faecal coliforms

1 Introduction Greywater accounts for 50–80 % of household wastewater (Al-Jayyousi 2002). Treated greywater that fulfils the quality requirements can be viewed as a sustainable source of irrigation or service water, especially in water-scarce countries. For example, Jordan generates about 55×106 m3 of greywater per year (Morel and Diener 2006), which corresponds to 18 % of its total water supply (MWI 2007). The health risks relating to greywater are well known, but are considered low compared with those associated with sewage water (WHO 2006). The main microbial hazards in greywater stem from faecal cross contamination which according to WHO (2006) is limited and related to washing diapers, anal cleansing and/or showering. The concentrations of salts, solids, organic matter, nitrogen, phosphorus and pathogens in greywater vary widely (Gross et al. 2005) and depend on the volume of water used (Morel and Diener 2006). Recent studies have reported organic matter including fat and surfactants as problematic pollutants in greywater and impact soil and plants (Travis et al. 2010; Dalahmeh et al. 2011), which triggers the necessity for greywater treatment before use for irrigation. The most commonly applied method for treating greywater is sand infiltration. Clogging problems (Spychala and Ejewski 2003), scarcity of wellgraded sand in some regions and high transportation costs due to the high bulk density (Roy et al. 1998) are obstacles in using sand filters. Thus, alternative materials such as bark, activated charcoal and polyurethane foam with suitable physicochemical characteristics, low bulk density allowing easy transportation and handling might be attractive alternative but must investigated for reduction of biochemical oxygen demand (BOD5), chemical oxygen demand (COD), surfactants, nutrients and

microbial pollutants and compared to sand before large-scale use. Only a limited number of studies on the reduction of BOD5, COD and surfactants in filters using bark or charcoal or light weight polyurethane foam as biocarrier have been reported, and none of them have focussed on producing irrigation water. Bark (Lens et al. 1993), charcoal (Ahsan et al. 2001) and polyurethane foam (Guo et al. 2010) seem to have a high capacity to reduce BOD5 and COD, making them promising for greywater treatment. In addition, bark and charcoal have been studied for the adsorption of other pollutants, such as heavy metals (Babel and Kurniawan 2004; Argun et al. 2009) and aromatic hydrocarbons (Mukherjee et al. 2007; Li et al. 2010). Nonetheless, the potential of these materials for the reduction of organic matter from greywater with the aim to produce water for irrigation needs more attention. The behaviour of filters during start-up and steady state needs to be studied in view of their physical characteristics (effective size and specific surface area). This is needed to understand the pollutant reduction in the filter and consequently establish (at later stages) a design model and criteria for the filters. The objective of this study was to analyse and compare the reduction of BOD5, COD, methylene blue active substances (MBAS), phosphorus, nitrogen and certain microorganisms from artificial greywater during the start-up and initial steady state phase for four different filters, namely bark, charcoal, polyurethane foam and sand. The overall aim was to evaluate suitable filters for small-scale greywater treatment to produce water to be used for irrigation of crops.

2 Materials and Methods 2.1 Filter Materials Four filter materials were studied: pine bark (bark), activated charcoal (charcoal), polyurethane foam (foam) and sand. The latter was included as a reference due to its common use as filter material. Sand and bark were obtained from Rimbo Jord (Rimbo, Sweden). The sand was sieve-analysed according to ASTM (1998). The grain size distribution ranged from 0.8 to 6.3 mm, the effective grain size D10 was 1.4 mm, D60 was 3.1 mm and the uniformity coefficient was 2.2. The bark originated from an undefined mixture of pine bark and was


air-dried before being sieved through 7, 5, 3, 2, 1 and 0.8 mm screens. Bark retained on the 5-, 3- and 1-mm screens was remixed in a 3:5:2 ration to obtain uniformity coefficient and effective size corresponding to that of the sand. Two different sizes of pelleted charcoal (diameter 1.5 mm, length 1.5 or 3–5 mm) were obtained from VWR International AB (Stockholm, Sweden). Some charcoal pellets were also crushed to obtain particle size less than 1.5 mm, and the 1.5-mm, 3–5-mm and crushed fractions were mixed in a 1:3:1 ratio, to obtain a uniformity coefficient and effective size similar to that of the sand. Polyurethane-based polyole of ether type, (FiltrenTM foam), with 1.0–1.6 mm pore opening size was obtained from B. Åkesson (Mönsterås, Sweden). The dry, remixed materials were manually packed to a depth of 60 cm in 100 cm high acrylate plastic columns with a diameter of 20 cm (Fig. 1). Before filling, a 10-cm layer of gravel (10–25 mm) was placed in the bottom of the columns to facilitate drainage. During filling with filter material, the columns were levelled after every 10 cm by manual shaking. The foam was provided by the supplier in a 0.6 m× 1.0 m×1.5 m block that was precision cut into cylinders (60 cm height and 20 cm diameter) to exactly fit the acrylate plastic columns. At each of 0, 20, 40 and 60 cm depths below the top surface of the filter materials, four sampling holes (diameter 20 mm) were made around the circumference of the columns (Fig. 1). The pH, loss on ignition, bulk density, particle density, porosity, constant head hydraulic conductivity

sprinkler course gravel 20 cm sampling ports 20 cm

20 cm course gravel

10 cm of course gravel (Ø10 mm)

outlet 20 cm

Fig. 1 Set-up of filter columns used for infiltration of artificial greywater

and specific surface area were determined for the filter materials (Table 1). For pH measurement, samples were prepared by mixing filter material with deionised water with ratios of 1:10 by weight for bark and foam and 1:5 for charcoal and sand. Materials were stirred for 1 h and then left to rest for 10 min before pH was measured with Ag/AgCl SympHony pH electrode and SympHony pH/DO meter (VWR, Stockholm, Sweden). Different mixing ratios corresponded to the different volumes of the materials. Loss on ignition was determined at 550°C for 4 h (Wright et al. 2008). Bulk density was determined by dividing the dry weight of the filter media by the volume occupied by the media (60 cm deep cylinder of 20 cm diameter). Particle densities of bark, charcoal and sand were determined by dividing 25 g sample by the corresponding volume of particles excluding pores. Volume of particles excluding pores was determined using the liquid immersion method where the volume of deionised water displaced by the particles was measured. Air-filled pores were eliminated by gentle boiling of the mixture. The submerged particles were left for saturation for 24 h. Porosity of bark, charcoal and sand was determined by Eq. 1. ! ρB p ¼ 100 1 ð1Þ ρp Where p is the porosity in percent of the total volume, ρB is the bulk density and ρP the particle density. Constant head hydraulic conductivity was determined according to Jacob et al. (2002). The specific surface areas of bark, charcoal and sand was determined using the Brunauer–Emmett–Teller (BET) method (Brunauer et al. 1938). BET equation was used to calculate the specific surface area of bark, charcoal and sand based on measurements at 99,834 Pa and 20°C (Flowsorb II 2300, 1996), where 1 mL N 2 gas corresponded to 2.86 m 2 (Brunauer et al. 1938). A kaolinite sample with a defined area of 15,900 m2 kg−1 was used as the control (Brunauer et al. 1938). Specific surface area of foam was provided by the manufacturer. Residence time was determined after 10 days from start-up by adding a 1-L pulse of NaCl tracer solution (10 gL−1) to the filters and subsequently monitoring the electrical conductivity (EC) of the effluent as a function of time (Lens et al. 1993). Residence time in bark and foam was also determined before the start of the experiments by

Water Air Soil Pollut Table 1 Characteristics of the bark, charcoal, foam and sand filter materials used in the study

Parameter

Bark

Charcoal

Foam

Sand

pH (SU)

5.1

10.4

5.6

7.9

Loss on ignition (%)

90

90

100

1,000

0.05c

0.136

Hydraulic conductivity (cm h−1)

330

500

500

360

−3

a

Pore opening size

b

Characteristic not applicable for the material c

Specific surface of foam as obtained from the supplier

2

−1

adding a 200-mL pulse of a fluorescent organic tracer (uranin) (2 g L−1), and subsequently, the uranin concentration in the filtered effluent was measured spectrophotometrically (Thermoaquamate, Thermo Electron Ltd, Cambridge, UK) at 490 nm wavelength (Schmid and Barczewski 1995). The mean, longest and shortest residence times in the filters were determined. Mean residence time was defined as the time when 50 % of the total tracer recovery had been eluted, the shortest residence time as the time lapse between addition of water to the drained filter and the start of water outflow and the longest residence time as the time when no more tracer was recovered. 2.2 Artificial Greywater Artificial greywater solution was prepared by mixing 1.25 g standard nutrient broth (Oxiod, Sollentuna, Sweden), 0.16 g Yes dishwashing gel (Procter and Gamble, Stockholm, Sweden), 0.16 g hair shampoo (PalmoliveColgate, Italy), 0.16 g washing powder (Ariel, Procter and Gamble, Geneva, Switzerland), 0.1 g maize oil (El Nada, Al-Asher for products, 10th Ramadan City, Egypt) and 1 L tap water (softened groundwater, with calcium but not magnesium removed) from Uppsala water plant (Uppsala, Sweden). To establish a composition similar to that of a natural greywater (Morel and Diener 2006), different proportions of the ingredients were mixed and analysed for BOD5 and MBAS before deciding the final recipe (Table 3). The artificial greywater was mixed with 4 % (v/v) wastewater from the primary sedimentation tank at the Kungsängen municipal sewage treatment plant (Uppsala, Sweden) to inoculate it with an indigenous bacterial flora. The proportion of inoculums (4 %) was estimated by

calculating the volume of wastewater needed to dilute the Escherichia coli concentration from 5.3×106 CFU 100 mL−1 as reported by Vattenlaboratoriet (2000) to 2.0×105 CFU 100 mL−1, which has been reported for greywater (Halalsheh et al. 2008). The artificial greywater was prepared once a week and stored at 2–4°C under continuous mixing. 2.3 Experimental Set-up The columns containing bark, charcoal, foam and sand were studied in duplicate. However, to measure the release of organic substances from bark, an additional bark filter column was prepared and loaded at the same rate as the other columns, but with tap water. Prior to the start of the experiment, each filter was fed with 0.33 L tap water day−1 for 75 days to wash the filter materials, after which the filters were operated with artificial greywater for 113 days. The greywater was added three times a day, in amounts of 0.7, 0.1 and 0.2 L, respectively, based on a hydrograph for greywater generation in a typical household in a rural community in Jordan (Abu Ghunmi et al. 2008). The hydraulic loading rate on the filters was 0.032 m3 m−2 day−1, and the organic loading rate was 0.014 kg BOD5 m−2 day−1. The columns were kept at room temperature around 25°C. At each loading, greywater from the cooling tank was pumped to a heating tank and heated to 25°C to simulate field conditions (Morel and Diener 2006) before being distributed over the filters using solid cone sprinklers (Fig. 2). The surplus was recycled to the cooling tank. Greywater pumping, heating and distribution to columns were controlled by a computer, using the software Labview 2009 (National Instrument Sweden AB, Stockholm, Sweden).

Water Air Soil Pollut Fig. 2 Schematic diagram of greywater infiltration system: storage, pumping, heating and dosing

compressed air sprinklers

recirculation pipe

cooling tank with stirrer

2.4 Sampling and Analyses Samples of influent and effluent greywater from the different filters were collected at regular intervals during the experimental period and analysed, with the parameters most in focus sampled and analysed more frequently. Thus in total, between 26 and 52 effluent samples per filter material were analysed for pH, EC, BOD5 and MBAS, while fewer samples (2–10) were used for analysis of COD, total organic carbon (TOC), nitrate nitrogen (NO3-N), ammonium-nitrogen (NH4N), total nitrogen (Tot-N), phosphate phosphorus (PO4-P), total phosphorus (Tot-P), Enterococcus spp. and thermotolerant faecal coliforms (TTFC). The tests were performed according to standard methods presented in Table 2. The efficiency of reduction for the various parameters analysed was calculated using Eq. 2):

pump

pressure tank & heater

column filters

MBAS L−1 (n02), which means an overall reduction of about 60 %. Hence, during the course of the experiment, both the influent and effluent from the filters were routinely analysed once a week, 2 days after the greywater preparation. Based on these results, the actual reductions achieved by the different filters were calculated (Table 3). After 113 days of operation, the cumulative BOD5, MBAS, Tot-N and Tot-P loads into the filters were 48, 3.4, 8.5 and 0.5 g, respectively. 3.2 Residence Time and Tracer Recovery

where E is the efficiency (percent), Cin the influent concentration (milligram per litre) and Cout the effluent concentration (milligram per litre). When Cout was less than the detection limit, the detection limit is used in the calculation and E is reported as more than the result of this calculation.

Over the 9 days of tracer monitoring for determination of residence time, around 60 % of NaCl was recovered from the bark, charcoal and foam filters, while 94 % was recovered from the sand filter (Fig. 3). In comparison, 7 % of uranin was recovered from the bark filter and 94 % from the foam during 11 days of monitoring. Using data on the EC of the effluents (Fig. 3) and the hydraulic load of 0.032 m3 m−2 day−1, the mean residence time of NaCl was estimated to 43 h for bark, 16 h for charcoal, 4 h for sand and less than 30 min for the foam filter. The shortest residence time also differed between the filters and was more than 2.5 times longer for bark and charcoal than for the sand and foam filters (Table 4). The longest residence times did not differ greatly between the filters.

3 Results

3.3 Filter Performance

3.1 Feed Characteristics

In bark filters, the pH of the treated greywater decreased, while in the charcoal, foam and sand filters, the pH remained similar to that in the influent (Table 3). Compared to EC in influent greywater, initially EC decreased when greywater passed the bark and charcoal filters (Fig. 3). Thereafter, the EC

E¼

Cin Cout 100 Cin

ð2Þ

The freshly prepared artificial greywater contained 495±44 mg BOD5 L−1 (n02) and 39 mg MBAS L−1 (n01), while artificial greywater stored refrigerated for 7 days contained 204±73 mg BOD5 L−1 and 15±2 mg

Water Air Soil Pollut Table 2 Parameters analysed, frequency of analysis, instruments, kits and media used for analysis, sources of materials and methods Parameter

Frequency of testing

Kit/instrument/media

Source of kit/instrument and/or country of origin

pH

Weekly

VWR, Stockholm, Sweden

EC

Weekly

BOD5

Weekly

MBAS

Weekly

Ag/AgCl SympHony pH electrode and SympHony pH/DO meter Condi 330i conductivity meter SympHony dissolved oxygen electrode and SympHony pH/DO meter Spectroquant kit no. 14697

CODa

Twice during experiment Once during experiment Weeklyb

TOCa PO4 Tot-Pa NO3

Twice during experiment Weeklyb

NH4

Weeklyb

Tot-Na

Twice during experiment Three times during experiment Three times during experiment

TTFCc Enterococcusc a

WTW, Weilheim Germany VWR, Stockholm, Sweden

Merck KGaA, Darmstadt, Germany

Method

APHA 5210-BOD5 B

US Standard Methods 5540 C Dr Lange LCK814, SS EN 1484

Spectroquant kit no. 14848

Merck KGaA, Darmstadt, Germany

US Standard Methods 4500-P E SS EN 15681-2:2005

Spectroquant kit no. 14773 Spectroquant kit no. 14752

Merck KGaA, Darmstadt, Germany Merck KGaA, Darmstadt, Germany

APHA 4500-NH3 D Previous. SS 028131-1

Violet red bile agar

SVA, Sweden

Slantez-Bartley agar

Oxoid, Sollentuna, Sweden

Analysed by the water laboratory at Uppsala Water and Waste Company (Sweden)

b

Analysed weekly during the last 40 days of the experiment

c

National Veterinary Institute, SVA (Uppsala, Sweden)

increased, but kept at a lower level than in the influent, until the onset of the residence time test at day 10. During the residence time test, the effluent EC greatly increased as a response to the NaCl tracer pulse, but then declined gradually and by day 22 reached a level similar to that at the influent greywater. EC increased throughout the experiment in greywater passing the foam and sand filters compared to EC in influent greywater (Table 3). The bark and charcoal filters were characterised by a high initial reduction of BOD5 (Fig. 4), which increased further to >99 % and then remained at this level throughout the experiment. The mean BOD5 reduction over the experimental period was 98 and 97 % for the bark and charcoal filters, respectively

(Table 3). The sand filters displayed a similar pattern to bark and charcoal but achieved a lower level of reduction (Fig. 4). Initially, the efficiency of BOD5 reduction by sand was 62 %. The highest reduction efficiency (82 %) was observed on day 36, after which it declined somewhat and then remained steady. The foam filters achieved no reduction of BOD5 at startup, but then gradually improved to reach more than 50 % reduction after 30–40 days, then declined to a steady-state reduction of 40 %. The steady-state value of BOD5 in the effluents was 6±5 from bark, 6±2 from charcoal, 268±41 from foam and 112±16 mg BOD5 L−1 from sand filters, respectively. The COD reduction rates estimated in all filters were about the same as that for BOD5 except in the

Water Air Soil Pollut Table 3 Influent characteristics and treatment performance (mean ± standard deviation) of the different filter materials (bark, charcoal, foam and sand) used for treating artificial greywater Parameter

Limit of detection

pH (SU) −2

EC (μS cm )

Concentration in influenta

No. data points of influent (n)

Percentage reduction in effluent, except for pH and ECb

7.8±0.3b

18

6.1±0.4

7.8±0.4

7.7±0.2

7.7±0.3

38

1,960±140b

26

1,820±400

1,730±310

2,050±110

2,200±140

52

Bark

Charcoal

Foam

No. of data points for each of the filter effluents (n)

Sand

BOD5c

2

425±56

12

98±2

97±3

37±13

75±6

24

COD

30

890±130

2

74±12

94±2

37±9

72±2

4

TOC

0.5

304

1

74±4

97±0

46±16

75±2

2

MBASc

0.05

30±10

13

>99±0

>99±0

73±9

96±1

26

Tot-Pc

0.02

4.2±0.2

2

97±2

91±8

36±34

78±7

4

PO4-P

0.01

2.1±0.4

5

97±2

98±1

50±5

83±3

10

0.3

75±10

2

19±9

98±1

13±1

5±7

4 (2 for bark)

4

99±1

91±11

74±14

91±11

8

Tot-Nc −1

TTFC (CFU mL ) a

1

5

1.73±3.3×10

All units are in milligram per litre unless stated otherwise

b

As percentage reduction is not valid for pH and EC parameters, concentrations measured in the effluent from filters are shown in the table c After 113 days of operation, the cumulative BOD5, MBAS, Tot-N and Tot-P loads into the filters were 48, 3.4, 8.5 and 0.5 g, respectively

bark filters, which was less efficient at reducing COD than BOD5 (Table 3). The mean COD concentration in effluent from bark, charcoal, foam and sand was 200± 7, 48±10, 540±205 and 245±106 mg L−1, respectively. The reduction efficiency of TOC was fairly similar to the COD reduction for all filters, leaving 80, 11, 165 and 75 mg TOC L−1 in the effluent from bark, charcoal, foam and sand, respectively. The 95±14 mg COD L−1 (n01) measured in the effluent from the bark filter fed

11

Electrical conductivity (mS/cm)

Fig. 3 Electrical conductivity vs time of effluent from different filter units after adding a pulse of NaCl to influent artificial greywater. The filters, bark (diamond), charcoal (triangle), foam (cross mark) and sand (square), loaded at a rate of 0.032 m3 m−2 day−1

with tap water dropped during 85 days to 55±8.3 mg COD L−1 (n01). The reduction of MBAS in the bark and charcoal filters was >99 % immediately at the start and continued at this level over the 113-day experimental period (Table 3 and Fig. 5). The reduction rate of MBAS in the foam filters was 66 % at the start and then fluctuated between >80 % and approximately 65 % until day 64, when it was stabilised at around 70 %, leaving

10 9 8 7 6 5 4 3 2 1 0

0

20

40

60

80

100

120

Time (h)

140

160

180

200

220

Water Air Soil Pollut Table 4 Recovery of tracer substance (NaCl) and hydraulic characteristics of the different filter materials used for treating artificial greywater (mean ± standard deviation; n02) Filter material

Tracer recoverya (%)

Mean residence time (h)

Shortest residence time (min)

Longest residence time (h)

Bark

54±3

43±4

2.5±1

163±4

Charcoal

52±1

16±1

2.5±1

190±7

Foam

64±14

1,000 m2 g−1); nonetheless, it showed as high adsorption of the tracer as charcoal. This is explained by the active surface of the bark attributed to the polyhydroxy polyphenol groups (Bailey et al. 1999), in which H+ ions are exchanged by cationic metals (Randall et al. 1974). In addition, the diffusion of tracer from the mobile water to immobile water fractions and the dilution effects of the tracer in the

Table 5 Nitrogen characteristics (milligram per litre) of influent and effluents from the bark, charcoal, foam and sand filters used for treating artificial greywater (mean ± standard deviation)

Nitrogen characteristics (milligram per litre) of influent and effluents from the bark, charcoal, foam and sand filters used for treating artificial greywater (mean ± standard deviation)

0 0

20

40

60

80

100

120

Time (days)

Fig. 5 Reduction of MBAS of artificial greywater treated in bark (diamond), charcoal (triangle), foam (cross mark) and sand (square) filters loaded at a rate of 0.032 m3 m−2 day−1

Filter material

Tot-Na

NO3-Nb

NH4-Nc

Greywater inflow

75±10

1.1±0.6

0.5±0.2

Bark

64±7

51±10

0.05±0.03

Charcoal

1.3±0.4

0.2±0.1

0.07±0.06

Foam

66±3

16±3

14.76±1.92

Sand

72±10

57±7

3.76±0.06

No. of data points for each filter

4 (2 for bark)

10

10

a

Limit of detection is 0.3 mg L−1

b


c



immobile water (Schwager and Boller 1997) leads to slowing down and retention of the tracer. Visual observation of bark filters showed that bark has noticeably high water holding capacity. This observation is in agreement with 58 % water holding capacity reported for pine bark with particle size 1,000 m2 g−1) explains the high adsorption of Na (Marzal et al. 1996). Tracer diluted/ held by the immobile water suggested by Schwager and Boller (1997) is also thought to contribute to tracer retention in charcoal. Sand marginally adsorbed the tracers which might be attributed to the small surface area (0.136 m2 g−1). Low retention capacity of tracer in sand has also been reported by Stevens et al. (1986) using a dye tracer. The mean residence time in the filters tested varied between less than 30 min for foam up to 43 h for bark at the hydraulic loading of 0.032 m3 m−2 day−1. Lens et al. (1993) found a similar mean residence time (2 days) for a 0.5-m-deep bark filter (30 mm average particle size) loaded at 0.01 m3 m−2 day−1 using NaCl as tracer. However, Rodgers et al. (2005) reported a considerably longer residence time of 6 days for 0.9 m deep stratified sand filters (0.11–0.45 mm effective size) loaded at 0.02 m3 m−2 day−1 using sodium bromide as tracer. The partial recovery of tracer led to uncertain estimates of mean and longest residence times. Nonetheless, the approximate results are still helpful to understand the performance of the filters when loaded with greywater, i.e. with bark and charcoal removing more organic matter at start-up than the foam and sand, especially in combination with the recovery of the tracer. Bark and charcoal both had a low recovery of tracer (54 and 52 %, respectively) and long mean residence times (43 and 16 h, respectively), suggesting that their high initial removal of organics might be due to binding of a significant part of the organic load and delaying the remaining organics sufficiently long for most of them to be degraded. 4.3 Filter Performance The decline observed in pH of the greywater passing through the bark filter is in agreement with GençFuhrman et al. (2007), who attributed the change in pH to the release of organic acids from the bark. In the present experiment, release of substances was observed as a yellow tint to the effluent from the control

bark filter fed with tap water. Another contributor to the pH decline is probably the production of hydrogen ions during nitrification of NH4-N to NO3-N (Table 5). The decrease in EC in the bark and charcoal filter effluent at the beginning of the experiment may be attributed to adsorption, as indicated by the tracer measurements discussed above. In the bark and charcoal filters, the reduction rate of BOD5 reached a steady state of 99 % early in the experiment, probably as a result of adsorption. The high percentage retention of NaCl (Table 4) and 93 % of uranin tracer in the bark confirmed its high adsorption/retention capacity. The initial effective reduction of organic matter (BOD5 and COD) in bark was also attributed to adsorption (Ratola et al. 2003). After the initial period, dominated by physical and chemical filtration processes, biological activities gradually took over due to the development of a biofilm (Lens et al. 1993). Respiration activity in the bark filter materials increased after start-up (data not shown), which indicated an increasing biological activity. Microorganisms available on the bark, as indicted by the respiration activity in the bark fed with tap water (data not shown), may have contributed to organic matter reduction. In addition, the presence of larvae was observed in the upper 20-cm of the bark filter, which might have contributed to the high turnover of organic matter. The larvae were about 5 mm in length and white in colour and were probably fly larvae. The observed reduction capacity of organic matter for the bark filter was in agreement with Lens et al. (1993), who reported >90 % for BOD5 and >60 % for COD in Pinus spp. bark loaded at 0.01 m3 m−2 day−1. However, the start-up efficiency of the bark filter in the present experiment differed from that reported by Lens et al. (1993), who observed a 30–40-day start-up period with limited organic matter reduction. The low reduction of COD in the bark as compared with BOD5 was not observed with the other filters and could be explained by washout and release of organic acids from the bark (Genç-Fuhrman et al. 2007; Ribé et al. 2009). This is in agreement with the 55–95-mg COD L−1 in the effluent from bark fed with tap water. When the COD in the tap water effluent was subtracted from the COD in effluent from the bark filter fed with artificial greywater, the mean net COD reduction was 86 %. Pine bark contains low cellulose and high lignin content (Cunha-Queda et al. 2007) and has C/N ratios 300:1 to 723:1 as reviewed by Trois and Polster


(2007). In spite of its complex structure, bark might be attacked by lignocellulolytic microbial communities if soluble, and easily degraded carbon sources are exhausted (Liu et al. 2011). More recalcitrant material will probably leach from the filter and can be viewed as a yellow tint of the effluent (Lens et al. 1993). Measurements of respiration (data not shown) and leachate indicated that the bark filter fed with tap water lost less than 12 % of its dry matter per year. However, adding a simpler carbon source than lignin and cellulose (such as greywater) is expected to reduce the dry matter loss as microorganisms tend to utilise the easily degraded substrate before attacking the bark (Liu et al. 2011). The COD reduction by the charcoal filter (94 %) was close to the 85–88 % reported by Ahsan et al. (2001). They used charcoal made from coconut shells and explained the high COD reduction by adsorption. The activated charcoal used in the present study originated from black coal and had a large specific surface area which was available for adsorption, as confirmed by the low recovery in the tracer measurements (Tables 1 and 4). Foam performed as a typical biofilter, i.e. it needed a long start-up period (35 days) to develop a biofilm with sufficient capacity to degrade and consume the organic matter. Nonetheless, the foam material reached a much lower BOD5 steady-state reduction than bark, charcoal and sand. The structure of the foam, which is characterised by closely connected pores (1–1.6 mm pore opening size) that form continuous flow path/channels (Fig. 6), led to a rapid flow of liquid through the foam immediately after feeding filters with greywater. This in turn led to a limited

1-1.6 mm

Fig. 6 Pores shape and structure of the foam filter

biofilm formation, which was observed to be present mainly in the lower part of the filter. The limited distribution of the biofilm most likely restricted the degradation of organic matter. Moreover, high BOD5 levels (300 mg L−1) were measured towards the end of the experiment in the effluent from the foam filter, probably due to sloughing of biofilm material from the lower surface of filter (visual observation) and breakthrough of organic matter. The reduction rate of organic matter achieved by the foam filter was lower than that reported by Guo et al. (2010) for an aerobic moving sponge bioreactor filled with 1 cm3 polyurethane foam cubes. The foam cubes achieved 92 % reduction in DOC during 8 h of retention time and continuous mixing in the reactor. The reactor conditions of Guo et al. (2010), with upflow regime and long retention time with continuously submerged foam surface, probably enabled larger biofilm coverage resulting in higher degradation capacity of organic matter. The performance of the sand filters tested was less efficient than that of a sand filter loaded at 0.067 m3 m−2 day−1 by Pell and Nyberg (1989), which achieved a mean COD reduction of 91 %. Compared with the 0.21-mm effective particle size used in that study, the large effective size of sand used in the present experiment (1.4 mm) provided much less specific surface area to host a biofilm, which might explain the lower capacity for reducing organic matter. Moreover, the sand filter used by Pell and Nyberg (1989) was deeper (0.75 m) than that used here (0.60 m), which may also have contributed to the difference. The performance of bark and charcoal filters in reducing MBAS agreed with the BOD5 reduction as expected. MBAS, ionic surfactants with negatively charged organic complex of hydrocarbons containing 10 to 20 carbon atoms adhering to sulfonate (SO3)−Na+ ion (APHA 1995), are biodegradable and should thus be included in the BOD5 analysis. The MBAS reduction in the charcoal filter agreed well with that reported by Purakayastha et al. (2005), who found 96 % anionic surfactant reduction using granular activated charcoal. The foam and sand filters reduced MBAS better than BOD5 at the start of the experiment, 45 and 20 % higher MBAS reduction, respectively, compared with BOD5, probably because of the high degradability of MBAS. However, foam was less efficient and gave more variable effects than the other filters. The successive development of the biofilm in


foam filters was probably the reason why MBAS reduction stabilised at later stages. Reduction of MBAS by the foam filter (73 %) was less efficient than the 85 % reduction reported by Baldez et al. (2008), whereas reduction of MBAS by the sand filter (96 %) was more efficient than the 69 % reported by Suleiman et al. (2010). The charcoal filter was more efficient in reducing PO4-P than Tot-P. This might be due to higher adsorption of mineral PO4-P compared with organic forms of phosphate on charcoal. The charcoal used in this study was more efficient in PO4-P reduction than the coconut charcoal used by Ahsan et al. (2001): 98 % reduction compared with 11–17 %. Charcoal used in this study is activated carbon; activated carbons have both high specific surface and porosity area (Downie et al. 2009), thus enabling higher adsorption. The performance of the foam filter was close to that reported for the polyurethane foam used by Guo et al. (2010), which achieved about 60 % Tot-P. The high mean and standard deviation of Tot-P found in foam effluent in the present study could be attributed to sloughing of biofilm material containing phosphorus. The PO4-P reduction (83 %) and Tot-P reduction by the sand filter (78 %) was higher than the 58 % Tot-P as reported by Suleiman et al. (2010). Adsorption is the principal mechanism for PO4-P reduction in sand filter (Pell and Nyberg 1989), and the capacity of the sand to bind phosphorus depends on pH and the Ca, Fe and Al content in the sand (Arias et al. 2001). However, the analysis done in this study was not enough to explain the mechanisms of phosphorus reduction. Aerobic conditions prevailed in the bark filter, as indicated by nitrification of most of the mineralised nitrogen, in combination with low Tot-N reduction capacity (19 %). Relatively low Tot-N reduction in bark (35 %) was also reported by Lens et al. (1993). In contrast, the reduction of Tot-N in charcoal was high (98 %). Different hypotheses can be suggested to explain the low Tot-N in the effluent from charcoal filter, such as adsorption of organic nitrogen, mineralization of organic nitrogen into NH4/NH3 followed by adsorption of gaseous ammonia or ammonium ions (Rodrigues et al. 2007) or emission of NH3 gas. However, none of these hypotheses were tested during this experiment. Effluent from the foam filter contained both mineralised and non-mineralised forms of nitrogen. The presence of the non-mineralised form of nitrogen in the effluent from foam was expected and was probably due to the short retention time and the

low reduction capacity of BOD5 and COD. The short retention time also resulted in low nitrification efficiency, as indicated by the high effluent NH4-N level of all the filters. Similarly, Guo et al. (2010) reported low reduction of Tot-N (11 %) in low density polyurethane foam. Nitrification of nitrogen occurred in sand filters as well; 76 % of the Tot-N fed into sand filter transformed into NO3-N by nitrification activities. Although not as distinct as in the bark filter, the pH in the greywater dropped slightly when passing the sand filters which indicated nitrification activity. Originally, pH in sand material was 7.9 due to the presence of 2 % lime in the sand which buffered the pH decrease due to nitrification. Denitrification did not seem to occur in sand filters which can be confirmed by the low reduction of Tot-N (5 %). No anaerobic condition in sand or the other filters was expected to occur due to the fact the all columns were exposed to air from the upper and lower ends and that air was compressed in the pipes feeding the filters to discharge any remaining wastewater. The TTFC reduction in the bark filter was higher than the faecal coliform reduction reported by Lens et al. (1993). The reduction of microorganisms in bark may be explained by adsorption (Lewis et al. 1995), release of phenols (Ribé et al. 2009), which might be toxic to microorganisms, and competition by other microorganisms. A similar rate of TTFC reduction to that reported by Torrens et al. (2009) was obtained in our sand filter (91 %) which can be explained by the low hydraulic loading rates in both studies, 0.03 and 0.04 m3 m−2 day−1, respectively. The effluent from charcoal, foam and sand contained about 2 log10 Enterococcus, which thus must be explained by growth within the filters from non-detectable levels (10 CFU/ mL) to levels that could be analysed by the method used, as Enterococcus sp. were not detected in the greywater inflow samples analysed. The seed organisms for this regrowth probably originated from the wastewater inflow, as regrowth occurred in all filter materials except bark, but might also have come from non-sterile handling of the filter materials. Also, Desmarais et al. (2002) and Yamahara et al. (2009) have reported regrowth of Enterococcus sp. under similar conditions, i.e. in unseeded sand and soil wetted intermittently and supplied with nutrients at temperature range 20–28°C. In terms of the different performance parameters, especially the reduction of BOD5, MBAS and pathogen indicators, the bark and charcoal materials seem to


be suitable for use in filters for treating greywater. Before they are implemented in full-scale systems, the performance of bark and charcoal filters subjected to varying hydraulic and organic loads needs to be verified, as well as their long-term performance. 4.4 Suitability of Treated Greywater for Irrigation The use of urban wastewater for irrigation is gaining attention due to increasing scarcity in freshwater sources (Scott et al. 2004). Different factors determine the usability of wastewater for irrigation: salinity, nutrient (P and N), Na, Cl, B and trace metal concentrations (Ayers and Westcot 1994) and microbial quality (WHO 2006) as well as aesthetic issues such as emission of offensive odours related to anaerobic degradation of organic matters. Salinity represented by EC of greywater effluent from all filters lies in the range 0– 3,000 μS cm−1 which is usually found in irrigation water (Ayers and Westcot 1994). However, salinity issue in agricultural systems does not only depend on irrigation water, but also on other factors such as pH, EC and organic content of the soil as well as type of crop to be irrigated (Ayers and Westcot 1994). The Tot-N and NO3-N of the effluent from bark, foam and sand were higher compared to 0–10 mg L−1 usually found in irrigation water (Ayers and Westcot 1994). However, farmers appreciate the nutrient value of the wastewater (Scott et al. 2004) as the nitrogen will replace the chemical fertiliser, thus minimising the need for fertiliser and thereby reducing costs of fertiliser and energy required for its application. However, if much of nitrogen-rich water is applied, a risk of pollution or eutrophication might arise if the surplus water flows to surface or ground water, but with a well-managed water saving irrigation system such as drip irrigation, the risk of nitrogen leaching to ground water should be low and the nitrogen be a pure asset. Normally, irrigation water is scarce and therefore not applied in larger amounts than necessary to compensate for evapotranspiration. Evaluation of sodium, sodium adsorption rate, chloride, boron and trace metal concentrations of the effluent water from the filters was not tackled in this study. The TTCF analyses indicated a 1–2 log10 reduction in bacteria in all filters except for the foam. As E. coli was included in the group of organisms analysed, the results indicate that at least some reduction can be expected for E. coli in the filters. E. coli is probably

the most commonly used indicator for the presence of pathogenic bacteria, which under environmental conditions is expected to reflect feacal pathogens (WHO 2006). Drip irrigation of crops, mulch covering of soil, crop selection and food processing (WHO 2006) can also compliment greywater treatment to reduce health risks associated with pathogenic contamination by greywater. Bark and charcoal filters showed the best performance of the four filter materials tested in terms of BOD5 and MBAS reduction. Therefore, emission of offensive odours is not likely which makes possible the storage of the treated greywater.

5 Conclusions 1. Immediately at start-up of the bark and charcoal filters, BOD5 and MBAS reductions were high. The start-up reduction of sand filter was somewhat lower, while that of the polyurethane foam filter was poor. 2. The bark and charcoal filters were more efficient than the sand filter in reducing Tot-P, NH4-N and TTFC, while the foam filter was the least efficient. 3. Bark and charcoal seem to be suitable materials for use in small-scale filter systems for treating greywater to irrigation water quality with respect to BOD5 and MBAS. High concentrations of nitrogen in effluent from bark filters can be an advantage if the effluent is used for irrigation, as it can replace chemical fertiliser. Acknowledgments We gratefully acknowledge the Islamic Development Bank, Jeddah, Saudi Arabia, the Swedish International Development Cooperation Agency (Sida), the Swedish Research Council Formas and the Swedish University of Agricultural Sciences (SLU). Special thanks to Sven Smårs, Dick Gustafsson and Carl Westberg at the Department of Energy and Technology for their technical assistance, and Emelie Kjellberg at the National Veterinary Institute for performing the microbial analyses.

References Abu Ghunmi, L., Zeeman, G., Lier, J. V., & Fayyed, F. (2008). Quantitative and qualitative characteristics of grey water for reuse requirements and treatment alternatives: the case of Jordan. Water Science & Technology, 58(7), 1385–1396.

Water Air Soil Pollut Ahsan, S., Kaneco, S., Ohta, K., Mizuno, T., & Kani, K. (2001). Use of some natural and waste materials for waste water treatment. Water Research, 35(15), 3738–3742. Al-Jayyousi, O. R. (2002). Focused environmental assessment of greywater reuse in Jordan. Environmental Engineering Policy, 3, 67–73. APHA. (1995). Standard methods for the examination of water and wastewater (19th ed.). Washington: American Public Health Association. Argun, M. E., Dursun, S., & Karatas, M. (2009). Removal of Cd (II), Pb(II), Cu(II) and Ni(II) from water using modified pine bark. Desalination, 249(2), 519–527. Arias, C. A., Del, B. M., & Brix, H. (2001). Phosphorus removal by sands for use as media in subsurface flow constructed reed beds. Water Research, 35(5), 1159–1168. ASTM. (1998). Manual on test sieving methods: guidelines for establishing sieve analysis procedures. Lawrence R. P., Charles, W. W. (Ed.), West Conshohocken: American Society for Testing and Materials. Ayers, R.S., Westcot, D.W. (1994). Water quality for agriculture. Food and Agriculture Organization of the United Nations Rome, 1985 © FAO. Report nr ISBN 92-5102263-1. Babel, S., & Kurniawan, T. A. (2004). Cr(VI) removal from synthetic wastewater using coconut shell charcoal and commercial activated carbon modified with oxidizing agents and/or chitosan. Chemosphere, 54, 951–967. Bailey, S. E., Olin, T. J., Bricka, R. M., & Adrian, D. D. (1999). A review of potentially low-cost sorbents for heavy metals. Water Research, 33(11), 2469–2479. Baldez, E. E., Robaina, N. F., & Cassella, R. J. (2008). Employment of polyurethane foam for the adsorption of methylene blue in aqueous medium. Journal of Hazardous Materials, 159(2–3), 580–586. Brunauer, S., Emmett, P. H., & Teller, E. J. (1938). Adsorption of gases in multimolecular layers. American Chemical Society, 60(309A), 309–319. Cunha-Queda, A. C., Ribeiro, H. M., Ramos, A., & Cabral, F. (2007). Study of biochemical and microbiological parameters during composting of pine and eucalyptus bark. Bioresource technology, 98(17), 3213–3220. Dalahmeh, S. S., Hylander, L. D., Vinnerås, B., Pell, M., Öborn, I., & Jönsson, H. (2011). Potential of organic filter materials for treating greywater to achieve irrigation quality: a review. Water Science and Technology, 63(9), 1832–1840. Desmarais, T. R., Solo-Gabriele, H. M., & Palmer, C. J. (2002). Influence of soil on fecal indicator organisms in a tidally influenced subtropical environment. Applied and Environmental Microbiology, 68(3), 1165–1172. Downie, A., Crosky, A., & Munroe, P. (2009). Physical properties of biochar. In J. Lehmann & S. Joseph (Eds.), Biochar for environmental management: Science and technology (pp. 13–32). London: Earthscan. Genç-Fuhrman, H., Mikkelsen, P. S., & Ledin, A. (2007). Simultaneous removal of As, Cd, Cr, Cu, Ni and Zn from stormwater: experimental comparison of 11 different sorbents. Water Research, 41(3), 591–602. Gross, A., Azulai, N., Oron, G., Ronen, Z., Arnold, M., & Nejidat, A. (2005). Environmental impacts and health risks associated with greywater irrigation: a case study. Water Science & Technology, 52(8), 161–169.

Guo, W., Ngo, H.-H., Dharmawan, F., & Palmer, C. G. (2010). Roles of polyurethane foam in aerobic moving and fixed bed bioreactors. Bioresource Technology, 101(5), 1435–1439. Halalsheh, M., Dalahmeh, S., Sayed, M., Suleiman, W., Shareef, M., Mansour, M., & Safi, M. (2008). Grey water characteristics and treatment options for rural areas in Jordan. Bioresource Technology, 99, 6635–6641. Jacob, H., Dance, G., Clarke, T. (2002). Methods of soil analysis, part 4: physical methods. G. S.Campbell, Horton R., Jury W. A., Nielsen D. R., Es H. Mv, Wieregna P. J., Dane J. H., Topp G. C. (Ed.), Madison: Soil Science Society of America, Inc. Lens, P. N., Vochten, P. M., Speleers, L., & Verstraete, W. H. (1993). Direct treatment of domestic wastewater by percolation over peat, bark and woodchips. Water Research, 28 (1), 17–26. Lewis, G. D., Lomax, T. D., & Kimberley, M. (1995). Removal of virus particles, bacteria and bovine serum albumin from water by steam-exploded Pinus radiata bark. Water Research, 29(7), 1689–1693. Li, Y., Chen, B., & Zhu, L. (2010). Enhanced sorption of polycyclic aromatic hydrocarbons from aqueous solution by modified pine bark. Bioresource Technology, 101(19), 7307–7313. Liu, D., Zhang, R., Wu, H., Xu, D., Tang, Z., Yu, G., Xu, Z., & Shen, Q. (2011). Changes in biochemical and microbiological parameters during the period of rapid composting of dairy manure with rice chaff. Bioresource technology, 102 (19), 9040–9049. Luo, J., & Lindsey, S. (2006). The use of pine bark and natural zeolite as biofilter media to remove animal rendering process odours. Bioresource Technology, 97(13), 1461– 1469. Marzal, P., Seco, A., Gabaldón, C., & Ferrer, J. (1996). Cadmium and zinc adsorption onto activated carbon: influence of temperature, pH and metal/carbon ratio. Journal of Chemical Technology & Biotechnology, 66(3), 279–285. Morel, A., & Diener, S. (2006). Greywater management in low and middle-income countries, review of different treatment systems for households or neighbourhoods. Dübendorf: Swiss Federal Institute of Aquatic Science and Technology (Eawag). Mukherjee, S., Kumar, S., Misra, A. K., & Fan, M. (2007). Removal of phenols from water environment by activated carbon, bagasse ash and wood charcoal. Chemical Engineering Journal, 129(1–3), 133–142. MWI. (2007). Annual report (p. 202). Amman: Ministry of Water and Irrigation. Pell, M., & Nyberg, F. (1989). Infiltration of wastewater in a newly started pilot sand-filter system: I. Reduction of organic matter and phosphorus. Journal of Environmental Quality, 18, 451–457. Purakayastha, P. D., Pal, A., & Bandyopadhyay, M. (2005). Adsorbent selection for anionic surfactant removal from water. Indian Journal of Chemical Technology, 12, 281– 284. Randall, J. M., Garrett, V., Bermann, R. L., & Waiss, A. C., Jr. (1974). Use of bark to remove heavy metal ions from waste solutions. Forest Products Journal, 24(9), 80–84. Ratola, N., Botelho, C., & Alves, A. (2003). The use of pine bark as a natural adsorbent for persistent organic pollutants—

Water Air Soil Pollut study of lindane and heptachlor adsorption. Journal of Chemical Technology & Biotechnology, 78(2–3), 347–351. Ribé, V., Nehrenheim, E., Odlare, M., & Waara, S. (2009). Leaching of contaminants from untreated pine bark in a batch study: chemical analysis and ecotoxicological evaluation. Journal of Hazardous Materials, 163(2–3), 1096–1100. Rodgers, M., Healy, M. G., & Mulqueen, J. (2005). Organic carbon removal and nitrification of high strength wastewaters using stratified sand filters. Water Research, 39(14), 3279–3286. Rodrigues, C. C., de Moraes, J. D., da Nóbrega, S. W., & Barboza, M. G. (2007). Ammonia adsorption in a fixed bed of activated carbon. Bioresource Technology, 98(4), 886–891. Roy, C., Auger, R., & Chenier, R. (1998). Use of non woven textile in intermittent filters. Water Science & Technology, 38(3), 159–166. Schmid, G., Barczewski, B. (1995). Development and application of a fibre optic fluorimeter for in situ tracer concentration measurements in groundwater and soil. Tracer Technologies for Hydrological Systems((Proceedings of a Boulder Symposium, July 1995). IAHS Publ. no. 229, 1995:13-20. Schwager, A., & Boller, M. (1997). Transport phenomena in intermittent filters. Water Science and Technology, 35(6), 13–20. Scott C. A, Faruqui, N. I., Raschid-Sally L. 2004. Wastewater use in irrigated agriculture: management challenges in developing countries. In: C. A. Scott, Faruqui, N. I., RaschidSally L. (Ed.), Wastewater use in irrigated agriculture: confronting the livelihood and environmental realities. IWMI and IDRC. Wallingford: CABI. p 1-24. Spychala, M., & Ejewski, R. B. (2003). Sand filter clogging by septic tank effluent. Water Science & Technology, 48(11– 12), 153–159. Stevens, D. K., Berthouex, P. M., & Chapman, T. W. (1986). The effect of tracer diffusion in biofilm on residence time distributions. Water Research, 20(3), 369–375.

Suleiman, W., Al-Hayek, B., Assayed, M., Dalahmeh, S. and Al-Hmoud, N. (2010). Greywater management in the northeastern Badia of Jordan. McIlwaine S., Redwood, M. (Ed.), Practical Action Publishing/CSBE/IDRC 2010. 200p. Torrens, A., Molle, P., Boutin, C., & Salgot, M. (2009). Removal of bacterial and viral indicator in vertical flow constructed wetlands and intermittent sand filters. Desalination, 246(1– 3), 169–178. Travis, M. J., Wiel-Shafran, A., Weisbrod, N., Adar, E., & Gross, A. (2010). Greywater reuse for irrigation: effect on soil properties. Science of The Total Environment, 408(12), 2501–2508. Trois, C., & Polster, A. (2007). Effective pine bark composting with the dome aeration technology. Waste Management, 27 (1), 96–105. Vattenlaboratoriet. (2000). Provtagningar på obehandlat avloppsvatten: Bakteriehalter i avloppsvatten. (Sampling untreated sewage water: concentrations of bacteria in sewage water) Uppsala, Uppsala Vatten och Avfall AB. Vinnerås, B., Palmquist, H., Balmer, P., & Jönsson, H. (2006). The characteristics of household wastewater and biodegradable solid waste—a proposal for new Swedish design values. Urban Water, 3(1), 3–11. WHO. (2006). Guidelines for the safe use of wastewater, excreta and greywater (Vol. IV. Excreta and greywater use in agriculture). Geneva: World Health Organization. Wright, A. L., Wang, Y., & Reddy, K. R. (2008). Loss-onignition method to assess soil organic carbon in calcareous Everglades wetlands. Communications in Soil Science and Plant Analysis, 39(19), 3074–3083. Yamahara, K. M., Walters, S. P., & Boehm, A. B. (2009). Growth of enterococci in unaltered, unseeded beach sands subjected to tidal wetting (vol 75, pg 1517, 2009). Applied and Environmental Microbiology, 75(9), 2997–2997.