In this study, the performance of four different pre-treatment alternatives for granular media filtration, namely, settling, aeration, coarse media filtration and chemical coagulation were compared experimentally. Further, analytical hierarchy process (AHP) was used to compare their performance based on economic, environmental, technical and performance criteria. Performance of settling and aeration were evaluated up to 24 h duration. The coarse media filter was intermittently operated with 10 L of greywater in downflow mode while alum was used for chemical coagulation. Experimental results showed that settling up to 6 h did not show significant removal of different pollutants whereas 24 h settling resulted in moderate removal of turbidity and organic content but was not efficient in the removal of nutrients and faecal coliforms. Chemical coagulation reduced 93, 66, 48 and 97% of turbidity, COD, NH4-N and faecal coliforms, respectively from greywater but resulted in excessive sludge generation and is difficult to adopt on-site and requires skilled supervision. Coarse filtration of greywater resulted in 61, 41, 36 and 35% removal of turbidity, COD, PO4-P and faecal coliforms, respectively. Considering different criteria AHP gave coarse filtration as the best pre-treatment option to the granular media filters treating greywater.

  • The performance of four pre-treatment options namely, settling, aeration, coarse media filtration and chemical coagulation was compared experimentally.

  • Economic, environmental, technical and performance criteria were considered.

  • AHP gave coarse media filtration as the best pre-treatment alternative to the granular media filters treating greywater.

Urban densification makes implementation of nature-based solutions difficult, and solutions that are integrated onsite are receiving increased interest (Pucher et al. 2022). Onsite treatment of greywater is one such alternative (Quispe et al. 2022). Greywater contains less organic content, nutrients, and pathogens compared to wastewater since it excludes water from toilets and urinals. However, the reuse of greywater without proper treatment is a concern for human health and the environment. Biological systems such as membrane bioreactor, rotating biological contactor, and upflow anaerobic sludge blanket are mechanized, and their performance is affected with variations in quantity and quality of greywater (Garg et al. 2021; Quispe et al. 2022), hence more suitable as a centralized treatment alternative.

The use of locally available materials makes granular media filtration a low-cost option. Granular media filters are energy efficient and result in less harmful by-products (Gupta et al. 2023). However, early clogging limits the use of filtration systems for the onsite treatment of greywater. Clogging in filters is mainly due to the accumulation of suspended solids with repeated use of the filters. Clogging in filters is directly proportional to the hydraulic loading rate and is inversely proportional to the media size (Shaikh & Ahammed 2022). Reduction in pollutants more specifically suspended solids in the influent to the filtration system, which helps to increase the days of filter operation (Raj et al. 2023).

Different pre-treatments to the filters including settling, equalization, screening, aeration, coagulation, and coarse filtration have been used in the literature. Abdel-Shafy et al. (2014a) settled mixed greywater (MGW) for 1 h and reported 56, 11, and 14% removals of turbidity, BOD5, and chemical oxygen demand (COD), respectively. Katukiza et al. (2014) in the settling of dark greywater (DGW) from households in Uganda reported 65% of total suspended solids (TSS) and 48% of COD removal for 1 h.

Abdel-Shafy et al. (2014a) reported 4, 8, and 10% removal of TSS, BOD5, and COD, respectively, after 15 min of aeration of MGW, which was significantly increased to 68, 36, and 38% after 2 h of aeration. Younus (2014) in the aeration of MGW from Iran reported a COD removal of 73% after 23 h of aeration. A great variation in the treatment performance of aeration systems has been reported in the literature. However, no studies have been reported on the performance of aeration systems treating greywater in the removal of turbidity and NH4-N.

Shaikh & Ahammed (2021a) while treating MGW by alum coagulation reported 94, 65, and 51% removal of turbidity, COD, and NH4-N, respectively, while PO4-P and FC were reduced by 99% and 1.38 log, respectively. Vinitha et al. (2018) reported 91, 83, and 73% removal of turbidity, TSS, and COD in alum coagulation of MGW. Patil & Munavalli (2016) in coarse filtration of MGW reported 66, 27, and 24% removal of turbidity, BOD5, and PO4-P, respectively. No studies have been reported on the selection of a pre-treatment alternative to granular media filtration.

Analytical hierarchy process (AHP), a multi-criteria decision-making (MCDM) method, has been used for a wide variety of applications including the selection of the best wastewater treatment alternative (Karimi et al. 2011; Srdjevic et al. 2012), location of water treatment plant (Skoczko & Oszczapińska 2019), wastewater treatment plant selection (Mansouri et al. 2013), sustainable solid waste management system (Azahari et al. 2021), sewage sludge thermal treatment (Đurđević et al. 2020), and best leachate treatment method (Nabavi et al. 2021). However, no study has been reported on the selection of pre-treatment alternatives to the granular media filters treating greywater.

Pre-treatment for greywater filtration is crucial to remove large particles, sediment, and contaminants from water before it enters the main filtration system. This helps enhance the efficiency and lifespan of the filtration system and ensure that the system can effectively target smaller particles and impurities. In addition, pre-treatment minimizes clogging and improves overall quality in greywater treatment (Shaikh & Ahammed 2022). However, no study has been reported in the literature on comparing the different pre-treatment alternatives.

In the present study, performance of four pre-treatment options, namely, settling, aeration, coarse media filtration, and chemical coagulation, was compared experimentally. Further, an MCDM method, AHP was used to select the best pre-treatment alternative among the four pre-treatment alternatives using economic, environmental, technical, and performance criteria.

Materials

Raw greywater (RGW) was collected from Mother Teresa Bhavan (girls' hostel), Sardar Vallabhbhai National Institute of Technology, Surat, India, during the morning peak hours of 6–8 am. Greywater sources included hand basin, bathroom, shower, washing machines, and laundry. The hostel employs separate systems for discharging blackwater and greywater. Before daily collection, the RGW, accumulated in a 500-L container, was thoroughly mixed for homogeneity. All samples of RGW collected were transferred to the environmental engineering laboratory and analysed within 24 h of collection. Six identical plastic containers of 20 L capacity were used for settling 10 L of RGW. A plastic drum of 50 L capacity was used for alum coagulation of real greywater, and alum contained 4.75% aluminium. Aquarium aerators and six identical plastic buckets of 20 L capacity were used for aeration. The aquarium aerator with a power rating of 2.5 W could deliver an airflow of 5 L/min.

The coarse filter was set up in a plastic drum of 50 L capacity, and coarse sand of 1.18–4.75 mm with a depth of 50 cm was used as filter media above a 10 cm support layer of gravel with 4.75–12.00 mm size (Figure 1). Both coarse sand and gravel were washed with tap water and sun dried before being used in the coarse filter. A total of 52 RGW samples were collected between June 2022 and August 2022. All 52 collected samples were treated by all four treatment systems.
Figure 1

Experimental setup of coarse filter.

Figure 1

Experimental setup of coarse filter.

Close modal

Methodology

Samples aerated for 1, 2, 3, 4, 6, 12, and 24 h were named as A1, A2, A3, A4, A6, A12, and A24, respectively, while samples settled for 1, 2, 3, 4, 6, 12, and 24 h were named as S1, S2, S3, S4, S6, S12, and S24, respectively. Aerated samples were settled for 30 min before being analysed for different parameters.

In the chemical coagulation of real greywater, the optimum coagulant dose was determined using jar test apparatus. Six jars of 1,000 mL capacity were dosed with increasing concentrations of alum. The samples were rapidly mixed at 100 rpm for 2 min and then slowly mixed at 30 rpm for 15 min followed by settling for 30 min. The turbidity of the supernatant was used to determine the optimum coagulant dose.

The coarse filter was intermittently operated in downflow mode and fed daily once with 10 L of RGW. Water was kept in the filter before the manual packing of the media to avoid short-circuiting. The raw and treated greywater samples were analysed for different physicochemical and microbiological parameters as per Standard Methods (2012). In the present study, the BOD5 and total COD were examined, and they are referred to as BOD5 and COD, respectively.

Ranking of pre-treatment options by analytical hierarchy process

MCDM methods help decision-makers to select the best option from several alternatives. The AHP is a robust and flexible MCDM system in which the goal is at the top of the hierarchy, while criteria and sub-criteria are at subsequent levels and a set of alternatives are at the lowest level of the hierarchy (Saaty 1980). The techniques rely on a multi-criteria measurement theory to establish relative priority scales that remain consistent across different scales, derived from both discrete and continuous paired comparisons within multilevel hierarchies or network structures. These comparisons can originate from either actual measurements or a foundational scale representing preferences and sentiments (Saaty 1980). They enable the integration of tangible and intangible factors concurrently in evaluations, utilizing both empirical data and experts' subjective judgements.

Preference for alternatives was determined in AHP by pairwise comparison of the elements at different levels using a preferential scale given by Saaty (1980). Saaty's Fundamental Scale as shown in Table S1 was used to compare the two elements simultaneously with respect to their contribution to a specific upper-level criterion on a scale of 1–9. Criteria and alternatives were compared and scored by experts on a scale of 1–9, where 1 being assigned when both elements are equally important, while absolute importance is indicated by a score of 9.

The ladder includes various levels such as equally to moderately preferred (2), moderately preferred (3), moderately to strongly preferred (4), strongly preferred (5), strongly to very strongly preferred (6), very strongly preferred (7), and very strongly to extremely preferred (8). However, the interpretation of this scale depends on personal background, expertise, and attitude towards the issue at hand. Typically, a panel of experts evaluates different approaches, each contributing their own ideas and perspectives based on their life experiences (Azahari et al. 2021). The technique breaks down the problem into smaller components, requiring only simple pairwise comparisons to create a hierarchy. This hierarchy is then analysed to generate a final matrix, indicating the priorities of alternatives relative to each other. By systematically comparing alternatives and criteria, managers can make logical decisions. This method facilitates straightforward comparisons of decision factors, leading to a structured hierarchy of alternatives (Mansouri et al. 2013). It equips managers with analytical tools for rational decision-making, reducing the uncertainty often associated with decisions. In addition, it allows consideration of both tangible and intangible factors, ensuring decisions are not solely based on financial or measurable characteristics (Jan et al. 2020).

Figure 2 presents the hierarchy structure for ranking the best pre-treatment alternative to granular media filters. Settling, aeration, chemical coagulation, and coarse filtration were selected as pre-treatment alternatives to the granular media filter since these were the most commonly used pre-treatment methods in the literature. To quantitatively and qualitatively discriminate the alternatives, four main criteria and 16 sub-criteria were selected. Economic, environmental, technical, and performance were the four major criteria considered. Environmental and performance were qualitative criteria, while economic and technical were quantitative criteria (Suryavanshi et al. 2023). The criteria denote the broad aspects deemed relevant to achieving the objective (Bottero et al. 2011).
Figure 2

Hierarchy structure for ranking the best pre-treatment alternative to granular media filters.

Figure 2

Hierarchy structure for ranking the best pre-treatment alternative to granular media filters.

Close modal

The capital costs, operation and maintenance costs, land costs, sludge disposal costs, and savings were considered in economic criteria. Pre-treatment to the granular media filters helps not only to increase the days of filter operation but also to reduce the pollutant loads on the filter. Different pre-treatment alternatives require different materials and hence have different capital costs. Aeration involves the use of aerators and associated operation and maintenance costs, while chemical coagulation involved sludge disposal costs.

Energy savings and disposal of sludge and residuals were considered in the environmental criteria. As regulatory standards tighten and as public awareness regarding environmental protection and the effects of hazardous substances on water, soil, and air increases, environmental considerations are emerging as the paramount criteria in the selection of technologies for waste and wastewater management (Đurđević et al. 2020). Chemical coagulation leads to harmful sludge generation, while aeration is not only energy intensive but also leads to the generation of noise. To minimize the direct and indirect health impacts, for any treatment, minimal contact is preferred. A pre-treatment alternative should not lead to the generation of odour, noise, and mosquitos. Reuse of untreated greywater or use of partly treated greywater not meeting standards is associated with several health hazards as reported in the literature (Pucher et al. 2022; Patel et al. 2023).

Flexibility, adaptability, scalability, and requirement of skilled supervision were considered in the technical criteria. A pre-treatment alternative should meet the standards under different scalability of treatment. A pre-treatment should be flexible with variations in the quantity and quality of RGW. The adaptability of the treatment system is an important factor. A treatment system should be easy to operate and maintain and should not require skilled supervision.

A pre-treatment alternative should have a high pollutant removal potential. The efficiency of a pre-treatment alternative should not deteriorate significantly with variations in the quantity and quality of RGW. A pre-treatment alternative combined with granular media filters should meet different reuse standards.

The best alternative was selected considering all criteria. The judgments of decision-makers may vary in consistency. AHP assesses this consistency by computing the matrix's consistency index (CI). The reliability of the results was verified using the CI, which is the measure of consistency and CI was determined using Equation (1).
formula
(1)
where CI is the consistency index, λmax is the maximum eigen value of the matrix, and n is the number of factors in the judgement matrix.
Consistency ratio (CR), which is a measure of how a given matrix compares to a purely random matrix, is determined using Equation (2):
formula
(2)
where RI is the random CI provided by Saaty (1980), and RI is related to the dimensions of the matrix in other words related to several criteria (Skoczko & Oszczapińska 2019). A CR value of less than 0.1 or a 10% indicates that the expert's opinion is consistent, and results are reliable (Azahari et al. 2021).

The AHP method involves assembling experts from diverse fields, providing them with criteria and allowing them to use their experience, understanding, and backgrounds to make decisions. This approach enables experts to prioritize quantitatively and enhance analytical thinking (Jan et al. 2020). The choice of each option relies on individual human traits, so assembling a diverse group of experts could both strengthen and challenge different ideas. In the present study, a questionnaire was sent to around 100 experts who have sufficient experience in wastewater and greywater treatment/management. The experts included research scholars, faculty members, policymakers, and industry personnel. Responses from 30 experts were received. Once comparison matrixes were formulated, relative weights of the elements from each level with respect to an element in the adjacent upper level were computed (Nabavi et al. 2021) and a normalized eigen vector related to the largest eigen value (λmax) was determined. Geometric mean was preferred over arithmetic means for taking the final and aggregated decision (Sadhya et al. 2022).

Raw greywater characteristics

The characteristics of RGW used in the present study and different reuse standards are presented in Table 1. The quality characteristics of RGW varied significantly during the study period. RGW turbidity varied between 15 and 82 NTU with a mean turbidity of 40.6 NTU. The turbidity values observed in this study fell within the range reported in the literature (Patil & Munavalli 2016; Vinitha et al. 2018). Body fat, toothpaste residue, shaving waste, hair, skin particles, laundry detergent, and shoe debris contribute to the turbidity of greywater, along with non-biodegradable cloth fibres (Ghaitidak & Yadav 2016; Oteng-Peprah et al. 2018). Discharging greywater with elevated turbidity levels into water bodies not only impacts aquatic life by accelerating water heating and heat retention but also impedes the photosynthesis of aquatic plants due to diminished clarity and reduced light penetration (Ghaly et al. 2021). The turbidity of RGW was significantly (p < 0.05) higher than both CPCB (2015) standards for discharge into inland surface water and discharge into land for irrigation and USEPA (2012) standards for urban reuse.

Table 1

Characteristics of raw greywater and reuse standards

Raw greywater
CPCB (2015) aUSEPA (2012) b
ParameterUnitnMin–maxMean ± SDMean ± SDMean ± SD
Turbidity NTU 52 15.0–82.0 40.6 ± 11.8  <2.0 
TDS mg/L 52 180–450 330 ± 96   
pH  52 7.18–7.81 7.54 ± 0.18 5.50–9.00 6.00–9.00 
EC μS/cm 52 340–680 540 ± 132   
BOD5 mg/L 16 37–123 76 ± 25 <30 <30 
COD mg/L 52 98–283 154 ± 35 <250  
NH4-N mg/L 16 4.64–12.18 7.73 ± 2.43   
PO4-P mg/L 52 0.51–1.01 0.69 ± 0.13   
FC CFU/100 mL 1.3 × 103–1.0 × 105 5.5 × 104 ± 2.7 × 104  UDL 
Raw greywater
CPCB (2015) aUSEPA (2012) b
ParameterUnitnMin–maxMean ± SDMean ± SDMean ± SD
Turbidity NTU 52 15.0–82.0 40.6 ± 11.8  <2.0 
TDS mg/L 52 180–450 330 ± 96   
pH  52 7.18–7.81 7.54 ± 0.18 5.50–9.00 6.00–9.00 
EC μS/cm 52 340–680 540 ± 132   
BOD5 mg/L 16 37–123 76 ± 25 <30 <30 
COD mg/L 52 98–283 154 ± 35 <250  
NH4-N mg/L 16 4.64–12.18 7.73 ± 2.43   
PO4-P mg/L 52 0.51–1.01 0.69 ± 0.13   
FC CFU/100 mL 1.3 × 103–1.0 × 105 5.5 × 104 ± 2.7 × 104  UDL 

Note: n, number of samples; SD, standard deviation; UDL, under detectable limit.

aCPCB (2015) Standards for discharge into inland surface water and discharge into land for irrigation.

bUSEPA (2012) Standards for urban reuse.

pH was in the range of neutral to slightly alkaline and was within the range reported in the literature (Patil & Munavalli 2016; Vinitha et al. 2018). Greywater from laundry and washing machines is mostly in the alkaline range due to the use of detergents (Katukiza et al. 2014). The use of sodium hydroxide-based detergents in laundry leads to a slightly alkaline pH in RGW (Oteng-Peprah et al. 2018). Powdered laundry detergents often contain elevated salt concentrations, leading to higher electrical conductivity (EC) values (De Gisi et al. 2016). In the present study, the average EC value of the untreated greywater was 540 ± 132 μS/cm, with a higher standard deviation, suggesting increased variability. The pH of greywater is influenced by the use of fabric softeners, disinfectants, and bleaching agents (Eriksson et al. 2002). The rise in EC is linked to greater dissolved solids stemming from the utilization of detergents containing phosphate, potassium, and sodium (De Gisi et al. 2016).

Organic content in greywater is attributed to chemicals and xenobiotic compounds in bathroom soaps, laundry detergents, and surfactants (Abdel-Shafy et al. 2014a; Oteng-Peprah et al. 2018). The use of biodegradable detergents in handbasin and bathroom contributes to organic content in MGW (Nabavi et al. 2021). Elevated COD concentrations result from the usage of detergents, soaps, and shampoos during bathing, which is consistent with the findings by Mohamed et al. (2018) who reported increased COD levels due to detergent compounds. Cloth impurities may serve as a source of organic impurities in greywater (Noutsopoulos et al. 2018), while surfactants, detergents, and xenobiotic organic compounds also contribute to the organic content within greywater (Delhiraja & Philip 2020). However, the elevated COD/BOD5 ratio in the present study suggests that biological systems of greywater treatment may not be suitable.

The mean NH4-N concentration in this study, at 7.73 mg/L, exceeded the concentrations reported by Ghaitidak & Yadav (2016) and Patil & Munavalli (2016) for households in India. This disparity may be attributed to lower water consumption in the present study, resulting in a higher NH4-N concentration compared to the literature. Washing products and protein-containing shampoo used in the bathroom contribute to NH4-N along with habits of individuals of urinating during bathing (Jong et al. 2010). The NH4-N in greywater originates primarily from salt-based cationic surfactants, which are commonly found as the primary active ingredient in most cleaning products (Noutsopoulos et al. 2018). The use of products having ammonia and ammonia cleansing are other sources of NH4-N in greywater (Radin Mohamed et al. 2013). The NH4-N concentration in RGW was significantly less compared to municipal wastewater due to the exclusion of urine.

Use of PO4-P containing detergents and soaps in laundry and bathroom is responsible for PO4-P in greywater. The use of powdered laundry detergents, which are alkaline as used in the present study, adds to the PO4-P in greywater. Urine from the bathroom is also responsible for an increase in PO4-P concentration in greywater (De Gisi et al. 2016). Soaps, detergents containing phosphorus (PO4-P), and other cleaning products contribute to the presence of PO4-P in greywater (Bakare et al. 2017). Greywater from the bathroom was reported to be the major source of PO4-P in greywater (Sall & Takahashi 2006). The concentration of PO4-P found in the present study was much lower compared to that reported by Friedler (2004) but was higher than that reported by Antonopoulou et al. (2013).

The faecal coliform (FC) concentration in the RGW varied significantly with an average of 5.5 × 104 CFU/100 mL. Sweating, dead skin, traces of urine, contact with potentially contaminated objects, and use of hand basin after toilet use are the primary sources of FC in the greywater. The average FC concentration observed in the present study was one log less than the value reported by Friedler et al. (2005) and Ghaitidak & Yadav (2016) for greywater from the girls' hostels in Israel and India, respectively. The FC concentration observed in the present study was well within the range reported in the literature (Vinitha et al. 2018; Kabiri et al. 2021).

In summary, the concentration of different physicochemical parameters exceeded the different reuse guidelines (USEPA 2012; CPCB 2015), and hence, RGW without treatment is not safe for any reuse purpose. The main aim of this study is to identify the optimal pre-treatment method for greywater before it undergoes granular media filtration. The chosen pre-treatment, in conjunction with granular media filtration, should adhere to the standards outlined by CPCB (2015) and USEPA (2012) for various applications. The treated greywater is intended for non-potable uses such as irrigation for agriculture, home gardens, and urban purposes like toilet flushing and car washing.

Settling

To remove large settleable solids and associated organic contents and nutrients from RGW, settling up to 24 h was used as a pre-treatment alternative. RGW was settled for 1–24 h, and the effect of settling is shown in Figure 3. Mean turbidity removal after 1 h of settling was 25%, which was increased to 31, 35, 38, and 41% after 4, 6, 12, and 24 h, respectively. Tukey's significance test at a significance level of 0.05 indicates that no significant difference in turbidity removal. Shaikh & Ahammed (2021b) reported 15% turbidity removal after 1 h settling of MGW. Dixon et al. (2000) conducted a study in the United Kingdom, reporting turbidity removal of 13, 14, and 15% after 24, 48, and 72 h of settling in MGW. The author attributed the lower turbidity reduction to the presence of suspended cloth fibres in washing machine greywater.
Figure 3

Effect of settling (a) turbidity, (b) COD, (c) NH4-N, (d) PO4-P, (e) FC. RGW, raw greywater; S1–S24, settling effluent for the duration of 1–24 h, respectively.

Figure 3

Effect of settling (a) turbidity, (b) COD, (c) NH4-N, (d) PO4-P, (e) FC. RGW, raw greywater; S1–S24, settling effluent for the duration of 1–24 h, respectively.

Close modal

Storing greywater for more than 24 h is impractical given the average generation of 70–100 L/person/day in low-income countries (Shaikh & Ahammed 2023a). Aeration or settling beyond 24 h would require larger tanks and additional infrastructure. Abdel-Shafy et al. (2014b) reported no significant difference between the quality of greywater either aerated or settled from 24 to 36 h compared to the power and time spent. Therefore, settling and aeration were limited to a 24 h period. Dixon et al. (2000) found that storing untreated greywater for less than 24 h partially removes solids and organics, but beyond 24 h, storage has a negative impact, leading to unpleasant odours due to degradation initiation.

Settling for 1 h resulted in COD removal of 19%, which was increased to 30, 36, and 40% after 4, 12, and 24 h of settling, respectively. This indicates that organic contents are in the suspended form and associated with suspended solids. Abdel-Shafy et al. (2014a) reported 14, 17, and 40% COD removal after 1.5, 3, and 4.5 h settling of MGW from Egypt. After settling DGW for 1 h, Katukiza et al. (2014) reported 48% COD removal in the treatment of MGW from Uganda. The COD removal of 17% was reported by Karabelnik et al. (2012) while treating MGW from a household in Norway. The difference in the performance of settling is attributed to the variations in the characteristics of greywater. Removal of COD is mainly due to the settling of solids and oxidation of organic content during the settling duration (Abdel-Shafy et al. 2014b; Daniel et al. 2023).

Settling of greywater for 1–24 h made no significant difference (p < 0.05) in the removal of NH4-N with mean removal in the range of 8–27%. Shaikh & Ahammed (2021b) reported a reduction in NH4-N of 10% after 1 h settling of MGW from India, while Karabelnik et al. (2012) reported a 24% removal of NH4-N in the settling of high-strength greywater. These results indicate that NH4-N is less associated with suspended solids. According to Tukey's significance test, mean PO4-P removal in the present was significantly (p < 0.05) reduced by 39% only after 24 h of settling, while settling for 1–6 h resulted in 10–25% removal of PO4-P. Katukiza et al. (2014) reported a 4% removal of PO4-P after settling MGW for 1 h, while 33% removal was reported by Karabelnik et al. (2012) after 1 h settling of MGW. The removal observed in the present study was less than the removal reported by Katukiza et al. (2014), while it was more than that reported by Karabelnik et al. (2012).

The settling of greywater for 1 h resulted in 16% removal of FC, which was increased to 34 and 39% after 12 and 24 h, respectively. The settling of greywater even up to 24 h did not contributed significantly to the removal of FC with less than 0.2 log removal. Shaikh & Ahammed (2021b) also reported only 20% removal of FC after 1 h settling. The differences in the performance reported in different studies might be due to the use of solid and liquid detergents and surfactants in the laundry (Shaikh & Ahammed 2023a). Mohamed et al. (2018) reported a difference in quality characteristics of greywater when solid and liquid detergents were used in the laundry.

Chemical coagulation

Table 2 presents the effect of greywater coagulation. The real RGW was coagulated using alum and the optimum coagulant dose was determined. The optimum coagulant dose varied between 170 and 420 mg/L with a mean coagulant dose of 280 ± 46 mg/L. Determination of the optimum coagulant dose is important since the rate of destabilization of colloids depends on the alum dose (Thompson et al. 2020; Shaikh & Ahammed 2023b). Wastage of chemicals can be restricted by choosing an optimum coagulant dose as the addition of coagulants beyond the optimum coagulant dose does not increase the removal of pollutants. For example, Ucevli & Kaya (2021) reported an increase in the turbidity removal efficiency of MGW from Turkey using alum up to 100 mg/L, and a further increase in alum dose did not increase the removal of turbidity. The average optimal coagulant dose of 280 ± 46 mg/L employed in this study is lower than the doses reported in the literature (Pidou et al. 2008; Ghaitidak & Yadav 2015).

Table 2

Effect of coagulation

ParameterUnitnRaw greywater
Coagulated greywater
Average removal (%)
Min–maxMean ± SDMin–maxMean ± SD
Turbidity NTU 52 15.0–82.0 40.6 ± 11.8 0.7–6.2 2.8 ± 1.3 93.1 
TDS mg/L 52 180–450 330 ± 96 190–556 323 ± 79 
pH  52 7.18–7.81 7.54 ± 0.18 5.87–7.16 6.71 ± 0.16 
EC μS/cm 52 340–680 540 ± 132 342–961 621 ± 124 
BOD5 mg/L 16 37–123 76 ± 25 13–33 27 ± 7 64.5 
COD mg/L 52 98–283 154 ± 35 32–87 53 ± 14 65.6 
NH4-N mg/L 16 4.64–12.18 7.73 ± 2.43 1.98–7.28 4.02 ± 0.98 48.0 
PO4-P mg/L 52 0.51–1.01 0.69 ± 0.13 BDL BDL >99.0 
FC CFU/100 mL 1.3 × 103–1.0 × 105 5.5 × 104 ± 2.7 × 104 1.3 × 103–4.2 × 103 1.8 × 103 ± 1.1 × 103 96.67 
Coagulant dose mg/L 52   170–420 280 ± 46  
ParameterUnitnRaw greywater
Coagulated greywater
Average removal (%)
Min–maxMean ± SDMin–maxMean ± SD
Turbidity NTU 52 15.0–82.0 40.6 ± 11.8 0.7–6.2 2.8 ± 1.3 93.1 
TDS mg/L 52 180–450 330 ± 96 190–556 323 ± 79 
pH  52 7.18–7.81 7.54 ± 0.18 5.87–7.16 6.71 ± 0.16 
EC μS/cm 52 340–680 540 ± 132 342–961 621 ± 124 
BOD5 mg/L 16 37–123 76 ± 25 13–33 27 ± 7 64.5 
COD mg/L 52 98–283 154 ± 35 32–87 53 ± 14 65.6 
NH4-N mg/L 16 4.64–12.18 7.73 ± 2.43 1.98–7.28 4.02 ± 0.98 48.0 
PO4-P mg/L 52 0.51–1.01 0.69 ± 0.13 BDL BDL >99.0 
FC CFU/100 mL 1.3 × 103–1.0 × 105 5.5 × 104 ± 2.7 × 104 1.3 × 103–4.2 × 103 1.8 × 103 ± 1.1 × 103 96.67 
Coagulant dose mg/L 52   170–420 280 ± 46  

Note: n, number of samples; BDL, below detectable limit; SD, standard deviation.

Coagulation resulted in a mean turbidity removal of 93% in the present study at a mean optimum coagulant dose of 280 mg/L. Chitra & Muruganandam (2019) also reported 93% removal of turbidity in alum coagulation of MGW with an optimum alum dose of 1,000 mg/L. Pidou et al. (2008) achieved a 91% turbidity removal with an optimal alum dose of 24 mg Al/L. Ghaitidak & Yadav (2015) reported an 85% turbidity removal with a 20 mg Al/L alum dose for MGW treatment. In the present study, a lower coagulant dose of 13.3 mg Al/L yielded higher turbidity removal. The effectiveness of alum in turbidity removal during coagulation is primarily attributed to charge neutralization and sweep flocculation mechanisms within the pH range of 6.5–8.0 (Ghaitidak & Yadav 2015). The difference in the treatment potential of coagulation reported in different studies is due to the difference in particle size and solid content since particle size and total solid content in greywater govern the efficiency of the coagulation process (Chitra & Muruganandam 2019; Thompson et al. 2020).

The significant difference in the optimum coagulant dose can also be attributed to the variations in influent turbidity and pH of RGW since alum coagulation works best in the pH range of 5–9. The quantity of alum required decreased with a decrease in the pH of RGW. Alum is not suitable for coagulation of greywater having a pH value higher than 9 since alum loses its stability above 9 pH (Ucevli & Kaya 2021). The formation of positive polymeric species results in a decrease in the pH of coagulated greywater from 7.54 to 6.71, while the dissolution of alum contributed to the increase in EC (Antonopoulou et al. 2013). The rise in EC is more pronounced when using alum as a coagulant, in contrast to other chemical coagulants (Thompson et al. 2020). However, alum stands out for its cost-effectiveness and ready availability in local markets. The hydrolysis reaction releasing H+ ions leads to a decrease in the pH of greywater. The primary mechanisms driving the removal of various pollutants include sweep coagulation, adsorption, precipitation, and charge neutralization (Patil & Munavalli 2016). Several studies reported the decrease in pH of greywater after coagulation using alum (Pidou et al. 2008; Antonopoulou et al. 2013; Ghaitidak & Yadav 2016).

Coagulation is moderate in the removal of organic content with mean BOD5 and COD removals of 65 and 66%, respectively. Both BOD5 and COD removals of 65% were reported by Singh et al. (2021) while treating MGW from India at a pH of 7.92. In another study, Ghaitidak & Yadav (2015) reported 77% removal of both BOD5 and COD using alum at a pH of 7.5. The results indicate that organic content removal using alum coagulation favours slightly acidic to neutral pH, and the same was reported by Ghaitidak & Yadav (2015). The difference in results is due to the sensitivity of the coagulants to the source of greywater (Antonopoulou et al. 2013). The BOD5 and COD concentrations in coagulated greywater were below the standards set by CPCB (2015) for discharge into inland surface water and discharge onto land for irrigation, as well as the standards established by USEPA (2012) for urban reuse purposes like toilet flushing and garden irrigation.

NH4-N concentration was reduced by 48% after alum coagulation in the present study. Shaikh & Ahammed (2021b) reported an average NH4-N removal of 51% in alum coagulation of MGW from girls' hostel. Ghaitidak & Yadav (2015) reported 47% removal of NH4-N using ferric chloride (FeCl3) for coagulation of MGW in India. Alum reacts with ammonium ions, producing insoluble precipitates that can be eliminated during sedimentation. Coagulants can create complexes with ammonia, and these complexes may settle more efficiently than ammonia itself, assisting in its removal (BinAhmed et al. 2015; Walle et al. 2023). Adsorption of ammonium ions onto the surface of coagulant-induced flocs further facilitates their removal during the subsequent sedimentation process (Antonopoulou et al. 2013).

PO4-P concentration was reduced to below detectable limits after coagulation in the present study. Shaikh & Ahammed (2021b) also reported the complete removal of PO4-P during alum coagulation of MGW from a student hostel in India. Alum reacts with phosphate ions, generating insoluble precipitates that are later eliminated during sedimentation. The adsorption of PO4-P ions onto the surface of flocs enhances their removal during this sedimentation process (Ucevli & Kaya 2021).

Coagulation resulted in 1.5 log removal of FC. Similar results for removal of FC with coagulation were reported in the literature (Pidou et al. 2008; Vinitha et al. 2018). The average concentration of coagulated greywater was much higher than the USEPA (2012) standards for FC (FC < 200 CFU/100 mL) for greywater reuse in restricted access area irrigation, indicating the need of further treatment. FCs have the potential to be captured within flocs induced by coagulants in the chemical coagulation process (Dong et al. 2015).

Coarse filtration

Table 3 presents the summary of the performance of the coarse filter treating greywater, while Figure 4 presents its performance in the removal of turbidity and COD. Coarse filtration was moderate at the removal of turbidity with a mean removal of 61%. However, the efficiency increased with days of filter operation due to the formation and maturation of the biolayer. Ghaitidak & Yadav (2016) while treating MGW in a coarse filter reported a turbidity removal of 51%. A turbidity removal of 69% was reported in a coarse filter operated in a series of upflow and downflow filters by Patil & Munavalli (2016). Higher removal observed in the present study compared to Ghaitidak & Yadav (2016) could be due to the lower flow rate adopted in the present study. Higher removal reported by Patil & Munavalli (2016) might be due to significantly higher influent concentration in their study. Shaikh & Ahammed (2022) reported that percentage removal is directly proportional to the influent values. Turbidity removal is mainly due to straining and sedimentation within the filter media (Katukiza et al. 2014).
Table 3

Performance of a coarse media filter treating greywater

ParameterUnitnRaw greywaterCoarse filter effluentAverage removal (%)
Mean ± SDMean ± SD
Turbidity NTU 52 40.6 ± 11.8 15.9 ± 5.8 60.7 
TDS mg/L 52 330 ± 96 331 ± 96 – 
pH  52 7.54 ± 0.18 7.55 ± 0.18 – 
EC μS/cm 52 540 ± 132 541 ± 132 – 
BOD5 mg/L 16 83 ± 16 49 ± 13 43.7 
COD mg/L 52 154 ± 35 91 ± 22 40.9 
NH4-N mg/L 16 7.73 ± 2.43 5.77 ± 1.78 25.3 
PO4-P mg/L 52 0.69 ± 0.18 0.44 ± 0.14 36.2 
FC CFU/100 mL 5.5 × 104 ± 2.7 × 104 3.6 × 104 ± 2.1 × 104 34.55 
ParameterUnitnRaw greywaterCoarse filter effluentAverage removal (%)
Mean ± SDMean ± SD
Turbidity NTU 52 40.6 ± 11.8 15.9 ± 5.8 60.7 
TDS mg/L 52 330 ± 96 331 ± 96 – 
pH  52 7.54 ± 0.18 7.55 ± 0.18 – 
EC μS/cm 52 540 ± 132 541 ± 132 – 
BOD5 mg/L 16 83 ± 16 49 ± 13 43.7 
COD mg/L 52 154 ± 35 91 ± 22 40.9 
NH4-N mg/L 16 7.73 ± 2.43 5.77 ± 1.78 25.3 
PO4-P mg/L 52 0.69 ± 0.18 0.44 ± 0.14 36.2 
FC CFU/100 mL 5.5 × 104 ± 2.7 × 104 3.6 × 104 ± 2.1 × 104 34.55 

Note: n, number of samples; SD, standard deviation.

Figure 4

Performance of the coarse filter in the removal of (a) turbidity and (b) COD. RGW, raw greywater; CF, coarse filtration.

Figure 4

Performance of the coarse filter in the removal of (a) turbidity and (b) COD. RGW, raw greywater; CF, coarse filtration.

Close modal

The pH, EC, and total dissolved solids (TDS) are unaffected during the coarse filtration, and similar results were reported in the literature (Ghaitidak & Yadav 2016; Patil & Munavalli 2016). The coarse filter resulted in BOD5 and COD removals of 44 and 41%, respectively. A BOD5 and COD removals of 28 and 27%, respectively, were reported by Patil & Munavalli (2016) in the coarse filtration of bathroom greywater. Kabiri et al. (2021) reported 16 and 25% removal of BOD5 while treating hand basin and bathroom greywater from dormitories in Iran. The higher removal of organic content observed in the present study is due to the use of finer size media and greater media depth used. Organic content removal in the filter is mainly because of biological degradation, sedimentation, straining, and adsorption (Assayed et al. 2015). Development and maturation of biolayer formed on the top of media also contributed to the removal of organic content from greywater (Spychala et al. 2019). However, high solubility of COD and interference of specific chemical characteristics of greywater may interfere with BOD5 and COD removal processes (Patil & Munavalli 2016; Bahrami et al. 2020; Subramanian et al. 2020; Patel et al. 2023).

Figure 5 presents the performance of a coarse filter in the removal of NH4-N and PO4-P. The coarse filter reduced the NH4-N and PO4-P concentrations by 25 and 36%, respectively. Ghaitidak & Yadav (2016) reported a 16% removal of NH4-N, while Patil & Munavalli (2016) reported a PO4-P reduction of 26%. Less removal of nutrients reported in the literature can be attributed to the limited adsorption capacity of the filter media used since nutrient removal in the filter is mainly due to adsorption. The difference in pH of greywater also affects the PO4-P removal in the filters (Dalahmeh et al. 2012). Microorganisms attached to the granular media play a role in converting ammonium ions to nitrate through nitrification (Bahrami et al. 2020). The less electrostatic force of attraction between PO4-P and media surface has been reported by Kaewsarn & Yu (2001). Precipitation, biodegradation, and adsorption are mechanisms for the removal of PO4-P in the filters (Abdel-Shafy et al. 2014a; Katukiza et al. 2014). The presence of inhibitors and high solubility of NH4-N and PO4-P restricted their removals in the coarse filtration system (Bahrami et al. 2020; Patel et al. 2023).
Figure 5

Performance of coarse filter in the removal of (a) NH4-N and (b) PO4-P. RGW, raw greywater; CF, coarse filtration.

Figure 5

Performance of coarse filter in the removal of (a) NH4-N and (b) PO4-P. RGW, raw greywater; CF, coarse filtration.

Close modal

Coarse filtration did not contribute significantly to the removal of FC with an average removal of 0.2 log. The FC removal observed in the present study was similar to the removal reported by Ghaitidak & Yadav (2016) but less than the removal reported by Subramanian et al. (2020) and Mandal et al. (2011). Higher influent concentration might have contributed to the higher FC removal in their studies. The surfaces of granular media can adsorb FCs through various interactions, such as electrostatic forces and chemical bonding (Friedler et al. 2005; Subramanian et al. 2020). In addition, microbial communities established on the granular media actively consume and biodegrade organic matter, including FCs (Bahrami et al. 2020). Insufficient availability of oxygen for microbial activity could be responsible for less FC removal in coarse filtration.

Aeration

Figure 6 presents the performance of the aeration system treating greywater. Turbidity removal increased significantly (p < 0.05) with an increase in the duration of aeration. Turbidity removal increased from 59 to 68% with an increase in aeration duration from 1 to 4 h, while it further improved to 76 and 81% after 6 and 12 h of aeration. No studies have been reported on the potential of aeration to remove turbidity from greywater. Abdel-Shafy et al. (2019) reported a reduction in TSS by 24 and 69% after 1 and 2 h of aeration, respectively. Abdel-Shafy et al. (2014b) in the aeration treatment of MGW from a household in Egypt reported an increased TSS removal from 24 to 69% when the aeration duration was increased from 1 to 2 h.
Figure 6

Performance of aeration system treating greywater: (a) turbidity, (b) COD, (c) PO4-P, (d) NH4-N, and (e) FC. A1–A24, aeration effluent for the duration of 1–24 h, respectively.

Figure 6

Performance of aeration system treating greywater: (a) turbidity, (b) COD, (c) PO4-P, (d) NH4-N, and (e) FC. A1–A24, aeration effluent for the duration of 1–24 h, respectively.

Close modal

An increase in the duration of aeration was also beneficial in terms of organic content removal since COD removal of 15% after 1 h aeration was increased to 27, 37 and 58% after 2, 3, and 12 h, respectively. Younus (2014) in the aeration of MGW from Iran reported a COD removal of 73% after 23 h of aeration. An increase in COD removal from 33 to 44% when the duration of aeration was increased from an hour to a couple of hours was reported by Abdel-Shafy et al. (2014b) while treating MGW by aeration.

Supply of oxygen in aeration favoured the growth of microorganisms resulting in the reduction of organic matter by oxidation (Shi et al. 2023). In an oxygen-rich environment, aerobic microorganisms develop and metabolize organic pollutants in wastewater, converting complex compounds into simpler and less harmful substances (Walle et al. 2023). Aeration stimulates aerobic microbial activity, utilizing dissolved oxygen for the breakdown of organic matter. This process leads to a reduction in BOD5 and COD as the organic pollutants are transformed into microbial biomass, carbon dioxide, and water (Younus 2014; Abdel-Shafy et al. 2019).

NH4-N removal was significantly improved from 8 to 23% when aeration duration was increased from 1 to 4 h and with further increase in aeration duration to 6 and 12 h, NH4-N removal increased to 36 and 42%, respectively. No studies have been reported on the performance of aeration systems treating greywater in the removal of NH4-N. The presence of anionic surfactants in the greywater resulted in less reduction of NH4-N (Priyanka et al. 2020). Aeration creates an oxygen-rich environment that fosters the growth of nitrifying bacteria, facilitating the removal of NH4-N through nitrification (Sedory & Stenstorm 1995). Aerobic microorganisms in wastewater utilize ammonium ions as a nitrogen source during their metabolic processes, contributing to the reduction of ammoniacal nitrogen levels in the water (Younus 2014; Kabiri et al. 2021). In addition, aeration enhances the volatilization of ammonia by converting it to its volatile form.

An increase in the duration of aeration from 1 to 2 and 4 h resulted in PO4-P removal from 12 to 18 and 28%, while a further increase in the duration of aeration to 6 and 12 h resulted in 37 and 49% removal of PO4-P, respectively. Younus (2014) reported 85% removal of PO4-P concentration after aerating MGW including kitchen greywater for 23 h. Higher influent concentration of PO4-P and higher duration of aeration adopted by Younus (2014) might be the reason for higher PO4-P removal in their study. The removal of PO4-P in aeration is due to the absorption of PO4-P by phosphorus-accumulating organisms (Shi et al. 2023). However, inadequate conditions for phosphorus-accumulating microorganisms can result in reduced biological uptake of phosphate (PO4-P) during the aeration process. Insufficient levels of phosphorus in the influent greywater limited the effectiveness of biological phosphorus removal in the aeration system. Aeration of RGW from 1 h resulted in 16% removal of FC, which was improved to 38 and 59% after 4 and 12 h of aeration, respectively. No literature reported the potential of aeration in the removal of FC in greywater. The FC concentration in all aerated greywater samples exceeded the under detectable limit, the standard set by USEPA (2012) for urban reuse.

Comparison of the systems

Figure 7 presents the summary of the performance of different pre-treatment alternatives. Results indicated that chemical coagulation is significantly better in the removal of different parameters from greywater. Coagulation resulted in 93, 65, 48, and 97% removal of turbidity, COD, NH4-N, and FC, while PO4-P was completely removed. An increase in the duration of aeration results in a significant increase in the removal of different parameters. The 24 h aeration resulted in 86, 64, 49, 56, and 66% removal of turbidity, COD, NH4-N, PO4-P, and FC, respectively. The quality of treated greywater in aeration even after 24 h of aeration was poorer compared to the greywater treated by coagulation. Coarse filtration performed better than settling in the removal of different pollutants from greywater. Settling for the duration of up to 24 h could not improve the turbidity, COD, NH4-N, and PO4-P removal beyond 41, 40, 27, and 39%, respectively. Settling and coarse filtration resulted in less than 0.2 log removal of FC.
Figure 7

Comparison of the performance of different pre-treatment alternatives. Results for aeration and settling are for 24 h duration.

Figure 7

Comparison of the performance of different pre-treatment alternatives. Results for aeration and settling are for 24 h duration.

Close modal

Chemical coagulation and aeration promote agglomeration of finer particles into larger flocs by neutralizing the negative charges on colloidal particles, reducing the electrostatic repulsion and enhancing the removal of turbidity compared to settling and coarse filtration (Pidou et al. 2008; Younus 2014; Patel et al. 2023). Chemical coagulation can effectively target a broad range of particles, including those that may be resistant to settling or removal through coarse filtration methods (Pidou et al. 2008; Oteng-Peprah et al. 2018). Flocs formed in the coagulation encapsulate organic compounds, contributing to the higher removal of BOD5 and COD (BinAhmed et al. 2015; Walle et al. 2023).

Coagulation can facilitate the precipitate of PO4-P, which helps in more effective removal of PO4-P in chemical coagulation than settling, aeration, and coarse filtration (Dong et al. 2015). The flocs formed during chemical coagulation can encapsulate and trap FCs, preventing their release back into the water. This is a mechanism not as pronounced in settling or coarse filtration alone. Coagulants can selectively adsorb and remove specific contaminants, including NH4-N and PO4-P compounds, contributing to a more targeted approach compared to the general mechanisms of settling, aeration, and coarse filtration (BinAhmed et al. 2015). Chemical coagulants may interact directly with microbial cells, promoting their aggregation and settling. This interaction can be more effective in reducing FCs compared to physical processes like aeration and coarse filtration (Kabiri et al. 2021). Less availability of oxygen in settling and coarse filtration could have affected the removal of different parameters. None of the systems achieved the different reuse standards mentioned in Table 1.

Figure 8 indicates the analysis of variance (ANOVA) results according to Turkey's significance test for different parameters. Coarse filtration resulted in the higher removal of different parameters compared to settling though the difference was not significant (p < 0.05). Also, no significant (p < 0.05) difference was observed in COD and NH4-N removals in chemical coagulation and aeration. There is a significant (p < 0.05) difference in the treatment potential of coarse filtration-aeration, settling-aeration, chemical coagulation-coarse filtration, and settling-chemical coagulation for different physicochemical and microbiological parameters.
Figure 8

ANOVA for (a) turbidity, (b) COD, (c) NH4-N, (d) PO4-P, and (e) FC. S, settling; C, chemical coagulation; CF, coarse filter; A, aeration.

Figure 8

ANOVA for (a) turbidity, (b) COD, (c) NH4-N, (d) PO4-P, and (e) FC. S, settling; C, chemical coagulation; CF, coarse filter; A, aeration.

Close modal

Ranking by analytical hierarchy process

The decision-making process involves four steps. First, the decision-maker established a hierarchy of criteria and elements for evaluation, organizing them as input data. Second, the alternatives were weighted, and pairwise comparisons were conducted based on each individual criterion involved in the process. Ratio scales, integrating values ranging from 1 to 9 and their reciprocals, were employed to represent experts' judgements (Table 4). These comparisons were recorded in a positive reciprocal matrix (aij = 1/aji), evaluating their impact on the elements at the next higher level (Jan et al. 2020).

Table 4

Pairwise comparison of the criteria considered in the present study

EconomicalEnvironmentalTechnicalPerformanceGeometric mean (GM)aPercentageb
Economical 3.00 0.33 0.20 1.00 0.67a 16b 
Environmental 0.33 0.14 1.00 5.00 0.70 19 
Technical 1.00 0.20 3.00 0.33 0.67 13 
Performance 5.00 1.00 7.00 3.00 3.20 52 
Total 9.33 1.67 11.20 9.33 5.24 100 
EconomicalEnvironmentalTechnicalPerformanceGeometric mean (GM)aPercentageb
Economical 3.00 0.33 0.20 1.00 0.67a 16b 
Environmental 0.33 0.14 1.00 5.00 0.70 19 
Technical 1.00 0.20 3.00 0.33 0.67 13 
Performance 5.00 1.00 7.00 3.00 3.20 52 
Total 9.33 1.67 11.20 9.33 5.24 100 

aGeometric mean: GM = (3.00 × 0.33 × 0.20 × 1.00)1/4 = 0.67.

bPercentage = .

The initial phase in employing AHP methodologies involved constructing a model by dissecting the problem into its constituent parts. This entails recognizing concerns associated with diverse pre-treatment systems to evaluate all the factors implicated, across different levels of analysis: the objective, the criteria, and the alternatives (Bottero et al. 2011). The objective serves as a declaration of the overarching goal; in the examined case study, it embodies the primary aim of the project, namely, identifying the most sustainable pre-treatment method to the granular media filters treating greywater (Nabavi et al. 2021).

In the second phase, a pairwise table was created to assess the relationships between variables across different options (Table 4). This table reflects the inclinations, beliefs, and experiences of each professional participating in the process. Table 4 presents the weightage of the criteria for the selection of the pre-treatment alternative. During the analysis, elements at each hierarchical level have been systematically compared in pairs relative to the upper-level element (Srdjevic et al. 2012). The ultimate priorities of the alternatives heavily rely on the weights assigned to the overarching criteria. Even minor adjustments in the relative weights can consequently lead to significant alterations in the final ranking (Arroyo & Molinos-Senante 2018).

The higher cruciality of the criteria is indicated by higher weightage. Experts were more concerned about the performance of the treatment system as indicated by the higher weightage being given to the performance criterion among the different criteria (Table 4). This might be because the performance of a pre-treatment alternative determines the days of filter operation and the efficiency of the combined system. Environment criteria were the second most important criterion, while technical criterion was the least important.

In the third phase, a table was established with values corresponding to the parameters utilized for comparing the alternatives. Each of the four sections of the table was created from four distinct viewpoints. Consequently, every section of the table was compiled with pairwise data, assessing the input based on the four parameters: economy, environment, technical aspects, and system performance (Jan et al. 2020). Table 5 presents comparison of different alternatives under different criterion.

Table 5

Comparison of alternatives under different criterion

SettlingChemical coagulationCoarse filtrationAerationGeometric mean (GM)aPercentageb
Comparison of alternatives under the economic criterion 
Settling 1.00 5.00 3.00 5.00 2.94* 56$ 
Chemical coagulation 0.20 1.00 0.33 0.50 0.43 
Coarse filtration 0.33 3.00 1.00 3.00 1.32 25 
Aeration 0.20 2.00 0.33 1.00 0.60 11 
Total 1.73 11.00 3.76 9.50 5.29 100 
Comparison of alternatives under the environmental criterion 
Settling 1.00 7.00 3.00 5.00 3.20 56 
Chemical coagulation 0.14 1.00 0.20 0.33 0.31 
Coarse filtration 0.33 5.00 1.00 3.00 1.50 26 
Aeration 0.20 3.00 0.33 1.00 0.67 12 
Total 1.67 16.00 4.53 9.33 5.68 100 
Comparison of alternatives under the technical criterion 
Settling 1.00 3.00 0.33 3.00 1.32 30 
Chemical coagulation 0.33 1.00 0.25 0.33 0.41 
Coarse filtration 3.00 4.00 1.00 0.25 1.32 31 
Aeration 0.33 3.00 4.00 1.00 1.41 32 
Total 4.66 11.00 5.58 4.58 4.45 100 
Comparison of alternatives under the performance criterion 
Settling 1.00 0.14 0.33 0.25 0.33 
Chemical coagulation 7.00 1.00 3.00 4.00 3.03 53 
Coarse filtration 3.00 0.33 1.00 0.20 0.67 13 
Aeration 4.00 0.25 5.00 1.00 1.50 28 
Total 15 1.73 9.33 5.45 5.52 100 
SettlingChemical coagulationCoarse filtrationAerationGeometric mean (GM)aPercentageb
Comparison of alternatives under the economic criterion 
Settling 1.00 5.00 3.00 5.00 2.94* 56$ 
Chemical coagulation 0.20 1.00 0.33 0.50 0.43 
Coarse filtration 0.33 3.00 1.00 3.00 1.32 25 
Aeration 0.20 2.00 0.33 1.00 0.60 11 
Total 1.73 11.00 3.76 9.50 5.29 100 
Comparison of alternatives under the environmental criterion 
Settling 1.00 7.00 3.00 5.00 3.20 56 
Chemical coagulation 0.14 1.00 0.20 0.33 0.31 
Coarse filtration 0.33 5.00 1.00 3.00 1.50 26 
Aeration 0.20 3.00 0.33 1.00 0.67 12 
Total 1.67 16.00 4.53 9.33 5.68 100 
Comparison of alternatives under the technical criterion 
Settling 1.00 3.00 0.33 3.00 1.32 30 
Chemical coagulation 0.33 1.00 0.25 0.33 0.41 
Coarse filtration 3.00 4.00 1.00 0.25 1.32 31 
Aeration 0.33 3.00 4.00 1.00 1.41 32 
Total 4.66 11.00 5.58 4.58 4.45 100 
Comparison of alternatives under the performance criterion 
Settling 1.00 0.14 0.33 0.25 0.33 
Chemical coagulation 7.00 1.00 3.00 4.00 3.03 53 
Coarse filtration 3.00 0.33 1.00 0.20 0.67 13 
Aeration 4.00 0.25 5.00 1.00 1.50 28 
Total 15 1.73 9.33 5.45 5.52 100 

aGeometric mean: GM = (1.00 × 5.00 × 3.00 × 5.00)1/4 = 2.94.

bPercentage = .

Chemical coagulation and aeration were the most scored alternatives by experts about the performance of the system. This can be attributed to the fact that significantly better performance of chemical coagulation and aeration in the removal of different physicochemical parameters have been reported in the literature (Abdel-Shafy et al. 2014b; Vinitha et al. 2018). It is interesting to note that chemical coagulation scored least in all other criteria except technical. The cost associated with coagulants makes chemical coagulation a costlier alternative, while the generation and disposal of harmful sludge lead it to score least in the environmental criteria. Determination of optimum coagulant dose, difficulty in scalability, adaptability, and flexibility technically hinders the choice of chemical coagulation as a pre-treatment alternative.

Aeration scored the highest in the technical criteria, while it scored second highest in the performance criteria. Arroyo & Molinos-Senante (2018) ranked the aerobic system highest in performance criteria, particularly emphasizing aeration, when choosing the best wastewater treatment methods. The potential of the aeration system for the removal of solids, organic content, and nutrients is well reported and observed in the present study. Aeration was low scored in economic criteria, and this can be because the costs of the aerator and cost terms of electricity make the aeration system a costlier alternative compared to settling and coarse filtration. Noise pollution might be the other reason for the low scores given by experts in aeration in environmental criteria.

Coarse filtration scored second in economic, environmental, and technical criteria. Less capital cost, easy operation, and maintenance of coarse filtration help it score second highest in economic criteria. Gichamo et al. (2021) identified the soil filter as the top-ranking alternative in economic criteria when evaluating wastewater treatment methods, including waste stabilization pond, constructed wetland, soil filter, and the use of aquatic plants. Coarse filtration is energy efficient. Ease in flexibility, adaptability, scalability, and no requirement of skilled supervision make coarse filtration a technically sound criterion.

Using AHP Srdjevic et al. (2012) reported that biological treatment of wastewater was the best wastewater treatment alternative compared to chemical treatment, evaporation, and separation. Greenhouse gas emission and landfill capacity were ranked higher than the cost associated with the operation of the plant and health damages associated with the treatment plant between impact categories presented by Contreras et al. (2008).

Figure 9 presents a radar plot for different alternatives for each of the criteria. Settling scored the highest in both economic and environmental criteria while settling scored third in the technical criteria. No additional material requirement for settling makes it the least expensive alternative among the different alternatives, while no energy requirements in settling make it the most preferred alternative in the environmental criteria. Ease in adaptability, scalability, and no requirement of skilled supervision make settling a technically sound pre-treatment alternative. On the other hand, settling scored least in performance criterion compared to other alternatives (Table 5).
Figure 9

Radar plot for different alternatives for each criterion by AHP.

Figure 9

Radar plot for different alternatives for each criterion by AHP.

Close modal

In the fourth phase, Tables 4 and 5 are integrated using the weights outlined in Table 4, resulting in the creation of the AHP final summary table (Table 6). This process involves taking the final column from each section of Table 5 and combining them with their corresponding weights (Table 4), ultimately determining the final priority of each alternative. Table 6 presents the score and ranking of the alternatives by the AHP method. The CR value for all the criteria was less than 0.1, indicating all the results are consistent. Coarse filtration was the most preferred pre-treatment alternative, while chemical coagulation was the least preferred. Moderate performance, low cost, easy operation, and maintenance of coarse filtration make it the most suitable pre-treatment alternative to the granular media filter.

Table 6

Score and ranking of the alternatives by AHP

AlternativeScoreRank
Settling 0.2662a 
Chemical coagulation 0.1973 
Coarse filtration 0.3089 
Aeration 0.2276 
AlternativeScoreRank
Settling 0.2662a 
Chemical coagulation 0.1973 
Coarse filtration 0.3089 
Aeration 0.2276 

aScore = (0.16 × 0.56) + (0.19 × 0.56) + (0.13 × 0.30) + (0.52 × 0.06) = 0.2662.

Results indicated that settling had limited potential to remove different physicochemical parameters, and no significant difference was observed with an increase in settling up to 6 h, while overnight settling results in moderate removal of different physicochemical parameters. A significant increase in the removal of different pollutants with an increase in the duration of aeration was observed. Aeration for 24 h resulted in 86, 64, 49, 56, and 66% average removal of turbidity, COD, NH4-N, PO4-P, and FC, respectively. Aeration was also efficient in the removal of different parameters, while treating greywater but requires electricity and also results in the generation of sludge. The lack of continuous electricity supply in undeveloped countries specifically in rural areas makes the choice of aeration for onsite treatment of greywater difficult. Chemical coagulation performed the best among the four alternatives with average turbidity, COD, NH4-N, and FCs removal of 93, 66, 48, and 97%, respectively. Onsite determination of optimum coagulant dose is difficult along with the difficulty in adaptability and flexibility. Chemical coagulation resulted in harmful sludge generation and associated sludge disposal costs. The availability and cost of coagulants are other limitations in the use of coagulation for the treatment of greywater. AHP was used to select the best pre-treatment alternative considering economic, environmental, technical, and performance criteria. Coarse filtration was the most preferred pre-treatment alternative by the experts though coarse filtration resulted in 61, 41, 36, and 35% removal of turbidity, COD, PO4-P, and FCs, respectively. Coarse filtration is low-cost, compact, energy-efficient, easy operation and maintenance, and easy to adopt onsite since suitable under variations in quantity and quality characteristics of greywater. Cost, suitability of onsite implementation, ease of operation, and maintenance and environmental effects of a system define the choice of a treatment system along with the treatment performance.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Supplementary data