Safe drinking water is scarce in southwest coastal Bangladesh. Harvested rainwater and rain-fed pond water are the main sources of drinking water for people living in this area. Both government and non-government organizations are promoting aquifer storage and recovery (ASR) schemes to provide safe drinking water for this population. This study assessed the bacteriological quality of both source water and abstracted water from five ASR sites. Water sampling and laboratory analysis for indicator bacteria and physico-chemical parameters were conducted once every 2 months, over a period of 1 year. Samples were taken from source ponds, abstraction wells and household storage containers (point of use) for each of the ASR sites. Although the water from abstraction wells showed a 97% and 82% lowering of E. coli and Enterococci counts, respectively, from that of the source ponds, they did not satisfy the WHO drinking water standard. Moreover, the microbial quality of the water deteriorated at the point of use. This indicates the requirement for both in-house treatment and improved hygiene practice for consuming ASR water.

INTRODUCTION

The southwest coast of Bangladesh has an acute scarcity of safe drinking water. Freshwater aquifers are not available at suitable depths, and freshwater ponds are increasingly salinized by inundation during storm surges and brackish water aquaculture (Islam et al. 2015). As a result, both freshwater and available groundwater are not suitable for human consumption (Welker et al. 2005). Besides, natural sources of drinking water in coastal Bangladesh face higher microbial contamination (Kamruzzaman & Ahmed 2006; Karim 2010; Islam et al. 2011; Islam et al. 2015). People living in this area use harvested rainwater and rain-fed pond water as the main sources of drinking water. Studies conducted on rainwater harvesting in Bangladesh (APSU 2005; Howard et al. 2006) suggest that this water is consistently of high quality, free from arsenic, and satisfies the physical and chemical water quality standards. However, microbial contamination occurs to a great extent, causing significant health hazards to the rural population (Karim 2010; Islam et al. 2015). In recent years, there have been more initiatives by government and non-government organizations to find alternative sources of drinking water for this larger population, which has resulted in the emergence of several technologies to provide water supply in this region, including amongst others piped water supply, aquifer storage and recovery (ASR) and desalination plant.

According to Pyne (2005), ‘ASR is the storage of water in a suitable aquifer through a well during times when water is available, and the recovery of water from the same well during times when it is needed’. Figure 1 shows the design components of an ASR scheme. The source water is collected from rain-fed ponds or any other fresh water source and passed through a sand filter. Then it is injected into the shallow saline aquifer through a ring of infiltration wells, which creates a lens of fresh water. Finally the water is collected by households from the abstraction well manually by hand pump for consumption. Natural treatment can be achieved in the aquifer through removal of pathogens (Dillon & Toze 2005; Page et al. 2010a), and nutrients (Vanderzalm et al. 2010).
Figure 1

Design components of the ASR schemes: (a) flow diagram of the movement of water from the source pond to abstraction well; (b) photograph of an ASR scheme; (c) schematic diagram of sand filtration chamber to pre-treat pond water; (d) schematic diagram illustrating an ASR scheme with infiltration wells, abstraction well, and piezometers; (e) typical lithology encountered in coastal Bangladesh; (f) schematic diagram of a large diameter infiltration well (after Sultana et al. 2015).

Figure 1

Design components of the ASR schemes: (a) flow diagram of the movement of water from the source pond to abstraction well; (b) photograph of an ASR scheme; (c) schematic diagram of sand filtration chamber to pre-treat pond water; (d) schematic diagram illustrating an ASR scheme with infiltration wells, abstraction well, and piezometers; (e) typical lithology encountered in coastal Bangladesh; (f) schematic diagram of a large diameter infiltration well (after Sultana et al. 2015).

Southwest coastal Bangladesh is not only a highly water-stressed area, but also faces increasing population pressure, and is highly vulnerable to global climate change and sea level rise (Sultana et al. 2015). So, there is need for a long-term drinking water solution for the population living in this area. ASR is considered to be a safer, cheaper, and climate resilient drinking water supply option for the coastal areas, given the abundant monsoon precipitation in Bangladesh (Sultana et al. 2015). Considering this potential of ASR as a sustainable supply option of drinking water for the coastal communities of Bangladesh, an action research project has been initiated by UNICEF in collaboration with the Department of Public Health Engineering (DPHE) in 2009, which was implemented by the Department of Geology of Dhaka University and Acacia Water of the Netherlands. Due to the effectiveness of ASR in providing safe drinking water, both the government and non-government organizations are now promoting the technology in southwest coastal Bangladesh.

Although ASR schemes have only been recently initiated in the coastal area of Bangladesh, this technique has been widely used in the United States, Europe, Middle East, and Australia for many years to produce water supplies for drinking and irrigation purposes (Gerges et al. 2002; Maliva et al. 2005; Pyne 2005; Maliva & Missimer 2010). There is also a good number of studies conducted in different countries to assess the potential of ASR as a drinking water supply option (Jimenez & Chavez 2004; Page et al. 2010b; Du et al. 2013; Bloetscher et al. 2014; Page et al. 2015). The evaluation of risks from ASR schemes includes studies of salinity and recovery efficiency (Dillon 2005; Pavelic et al. 2006), and quantitative pathogenic risk assessment (Page et al. 2010a; Toze et al. 2010). Although no adverse effect on health has been observed so far, further research is warranted (Cisneros & Ordonez 1999; Dillon 2005). According to Page et al. (2010a), removal of pathogens depends on the pre-treatment or injected water quality and residence time of the recharge water in the aquifer. This study also reveals that the reduction of human health disease burden varies depending upon the number of pathogens in the recharge source water. The study of Vanderzalm et al. (2010) identified that the water–rock interaction during subsurface storage may cause deleterious effects on the recovered water quality.

Limited information is available on the bacteriological quality of ASR water schemes in relation to drinking water supplies. The study of Sultana et al. (2015) assessed E. coli counts of the source and recovered water of newly established low-cost small-scale ASR schemes of southwest Bangladesh. However, it is necessary to study the microbial quality of the water from the source to the point of use to understand the microbial status of the water supply system. Determination of health risks of water at different stages of the supply system and understanding the effect of seasonality on microbial contamination can give better insights into the management requirements to promote safe drinking water. Therefore, this study is intended to assess the bacteriological quality of ASR water in southwest coastal Bangladesh.

METHODS

Study area

In this study we considered the southwest coastal area of Bangladesh which includes three districts: Satkhira, Khulna and Bagerhat. About six million people live here (BBS 2014). This low lying coastal plain is gently sloped toward the Bay of Bengal at the south and consists of a complex river network. The average elevation is less than 10 m above mean sea level. At the southern fringe, there stands the Sundarbans, which is the largest single tract of mangrove forest in the world. Bangladesh has a humid climate with three distinct seasons: pre-monsoon (March to June), monsoon (July to October), and post-monsoon (November to February). Mean annual rainfall in this region varies between 1,500 and 2,000 mm, with about 70% of the rainfall occurring in the monsoon season (Sultana et al. 2015).

We selected five ASR sites from the existing ASR schemes of this area on the basis of three selection criteria: (1) the area having high salinity threshold value; (2) where households mainly depend on alternative sources for drinking water; and (3) the feasibility of collection of water samples. The feasibility of collection of sampled water was a matter of concern, because the ASR sites present in the coastal Bangladesh are mostly located in remote rural areas, and in most cases the sites do not have concrete access roads. Moreover, transportation becomes difficult during rainy season when the roads become muddy. Therefore, transportation of water samples to our laboratory maintaining the time bound to perform microbiological analysis was one of our major concerns, since we considered a year round water sampling for this study. Thus, we selected two ASR sites from Mongla Upazila under Bagerhat district, two from Dacope Upazila and one from Batiaghata Upazila under Khulna district (Figure 2). It shall be noted that the five ASR sites we selected for this study were the five out of the 13 pilot sites considered in the study of Sultana et al. (2015). All these sites store water in the shallow confined aquifer to create a fresh water lens. Infiltration rates of the sites averaged 3 m3/day (range: 3–6 m3/day) over 1 year of operation and the recovery rate ranged from 5% to 40% (Sultana et al. 2015). More details of the selected ASR sites are given in Table 1.
Table 1

Aquifer characteristics of the selected ASR Sites

Serial no.UpazilaUnionVillageSite codeLatitude °NLongitude °EClay thickness (m)Aquifer thickness (m)Aquifer lithologyVolume of infiltration (m3)Volume of abstraction (m3)
Khulna District 
 1 Batiaghata Gongarampur Boronpara BAB 22.634 89.514 >15 F-M Sand 1,049 191 
 2 Dacope Pankhali Baruikhali DAB 22.574 89.500 12 F Sand 1,444 353 
 3 Dacope Chalna Chalna DAC 22.597 89.515 21 >15 F-M Sand 2,125 734 
Bagerhat District 
 4 Mongla Sundarban Bastola MOB 22.410 89.661 >15 F-M Sand 1,804 742 
 5 Mongla Chila Chila MOC 22.405 89.644 11 F-M Sand 2,124 110 
Serial no.UpazilaUnionVillageSite codeLatitude °NLongitude °EClay thickness (m)Aquifer thickness (m)Aquifer lithologyVolume of infiltration (m3)Volume of abstraction (m3)
Khulna District 
 1 Batiaghata Gongarampur Boronpara BAB 22.634 89.514 >15 F-M Sand 1,049 191 
 2 Dacope Pankhali Baruikhali DAB 22.574 89.500 12 F Sand 1,444 353 
 3 Dacope Chalna Chalna DAC 22.597 89.515 21 >15 F-M Sand 2,125 734 
Bagerhat District 
 4 Mongla Sundarban Bastola MOB 22.410 89.661 >15 F-M Sand 1,804 742 
 5 Mongla Chila Chila MOC 22.405 89.644 11 F-M Sand 2,124 110 

Source: After Sultana et al. (2015).

Note: Upazila = sub-district; Union = lower administrative unit than Upazila, comprised of every 9 wards or villages; F-M sand = fine to medium sand; F sand = fine sand.

Figure 2

Locations of the selected aquifer storage and recovery (ASR) sites in southwest coastal Bangladesh (see Table 1 for aquifer characteristics).

Figure 2

Locations of the selected aquifer storage and recovery (ASR) sites in southwest coastal Bangladesh (see Table 1 for aquifer characteristics).

Water sampling

Water sampling and laboratory analysis were conducted once every 2 months for a period of 1 year (between May 2014 and March 2015) covering three distinct seasons: pre-monsoon, monsoon, and post-monsoon. Thus, the samples were collected in six sessions in the year, and in each of these sampling sessions a total of 20 samples were collected from the five study sites. Four samples were taken for each of the ASR sites, viz., 1 from a source pond, 1 from an abstraction well and 2 from household storage containers (point of use). Source pond samples were grab samples. In the case of the abstraction well, water samples were collected from the hand pump. Household storage container samples were collected from selected households only. In the case of unavailability of stored abstracted well water in any of the selected households, we considered the nearest household having that water. Sample water was poured in to a sample bottle from the household storage container. The condition of the storage container and hygiene practice varies among the households of the study area. Therefore, taking two samples from two different households of each of the study sites is expected to be more representative of the water quality at the point of use. The water samples were analysed for indicator bacteria (E. coli and Enterococci) and physico-chemical parameters (pH, salinity and turbidity).

Water quality analysis

Water samples were collected following the standard procedures (APHA 1998). For microbiological analysis, 500 ml water samples were aseptically collected in sterile Nalgene plastic bottles. All samples were placed in an insulated box filled with ice packs (Johnny Plastic Ice; Pelton Shepherd, Stockton, CA, USA) and transported to the Environmental Microbiology Laboratory of Environmental Science Discipline at Khulna University for bacteriological analysis immediately after collection. We assessed the concentration of E. coli and Enterococci using the membrane filtration technique. For pond water samples, several dilutions of samples were considered. For enumeration of E. coli and Enterococci, 100 ml of water samples were filtered through an 0.45 μm pore-size membrane filter (Millipore Corp., Bedford, MA, USA), and the filters were placed on mTEC and mEI agar plates, respectively, following standard procedures (APHA 1998). The mTEC agar plates were incubated at 35 ± 0.5 °C for 2 h followed by further incubation at 44.5 ± 0.2 °C for 22–24 h for enumeration of E. coli, and mEI plates were incubated at 35 ± 0.2°C for 48 h for enumeration of Enterococci. After incubation, characteristic dark red colour colonies were counted as Enterococci and pale yellow, yellow brown, yellow green colour colonies were counted as E. coli and expressed as colony forming units (cfu) per 100 ml. Physico-chemical analyses were performed according to APHA (1998).

Data analysis

Statistical analyses were done using Statistical Package for Social Science (SPSS) version 16.0. Mean, median, frequency and percentages were calculated for descriptive statistics. Since the data failed to meet the assumption of normal distribution, a parametric test (e.g., ANOVA) cannot be used to compare the samples. Therefore, a non-parametric test was used, which works with ranks rather than absolute numbers. The difference between E. coli and Enterococci concentration in source ponds, abstraction wells and household storage containers were determined by the Kruskal-Wallis test.

RESULTS AND DISCUSSION

Indicator bacterial contamination

Indicator bacterial (E. coli and Enterococci) counts in water samples were analysed and are presented in Table 2. Higher incidence of microbial contamination was found in water from the source pond, and sharply decreased in abstracted well water. It is also obvious that water from the source pond had the widest interquartile range of E. coli and Enterococci counts, whereas water from the abstraction well had a comparatively narrow interquartile range. The median value of E. coli counts in abstracted well water (30 cfu/100 ml) was found to reduce by approximately 97% compared to the source pond (1,500 cfu/100 ml) (Table 2). Similarly, the median Enterococci count decreased in abstracted well water (195 cfu/100 ml) by approximately 82% compared to that of the source pond (2,750 cfu/100 ml). Although, both of the indicator bacteria were reduced significantly (p < 0.01) in abstracted well water, they were still above the WHO drinking water standard (0 cfu/100 ml). Only 7 samples (about 23%) of the abstracted well water were found to meet the WHO standard. An upturn of both the indicator bacteria in the household storage containers compared to that of the abstraction well is observed. The concentrations of E. coli and enterococci differed significantly among the source pond, abstraction well and household storage containers as indicated by the Kruskal-Wallis test (p < 0.05).

Table 2

Indicator bacterial counts (cfu/100 ml) at different sampling sources and their reduction from source pond to abstraction well

Quantiles
Meeting standard
Indicator BacteriaSampling sourcen (number of samples)Min25% quantileMed75% quantileMaxCriteria%P value% of reduction from source pond to abstraction well
E. coli Source pond 30 300 700 1,500 2,300 3,500 < 1 < 0.01 96.86% 
Abstraction well 30 30 50 130 23.33 
Household storage container 60 30 40 78 320 
Enterococci Source pond 30 300 1,300 2,750 3,950 4,700 < 1 < 0.01 82.43% 
Abstraction well 30 90 195 365 900 
Household storage container 60 130 270 405 530 
Quantiles
Meeting standard
Indicator BacteriaSampling sourcen (number of samples)Min25% quantileMed75% quantileMaxCriteria%P value% of reduction from source pond to abstraction well
E. coli Source pond 30 300 700 1,500 2,300 3,500 < 1 < 0.01 96.86% 
Abstraction well 30 30 50 130 23.33 
Household storage container 60 30 40 78 320 
Enterococci Source pond 30 300 1,300 2,750 3,950 4,700 < 1 < 0.01 82.43% 
Abstraction well 30 90 195 365 900 
Household storage container 60 130 270 405 530 

Results show the increase of indicator bacterial contamination in waters of household storage containers compared to abstraction wells, which may be related to hygiene practices of the users. An increase in bacterial contamination at point of use has been identified in many studies (Pinfold 1990; Trevett et al. 2004; Islam et al. 2015). Lack of hygiene practices, such as improper cleaning of a storage pot before water collection, water collected by dipping the pot in the container with unwashed hands, and water stored in open containers are responsible for microbial contamination at point of use (Blum et al. 1990; Tuttle et al. 1995; Clasen & Bastable 2003; Oswald et al. 2007; Rufener et al. 2010). During field observations, we found that the households generally use kalshi (a burnt clay pot) for collection of abstracted water and store it in containers at home for domestic and potable use, often without considering protection from further contamination. In many cases, households used containers with a large mouth for collection and storage of the water. Previous studies (Mintz et al. 1995; Trevett et al. 2004) shows that contamination occurs in great extent when water is collected and stored in containers with a large mouth.

Risk category

E. coli contamination in different sampling sources was categorized according to the WHO (1997) risk category (Figure 3). In the case of source pond water, 37% of the samples were in the high risk and 63% of the water samples were in the very high risk category. In the case of abstracted well water, 23% of the samples met the WHO drinking water standard and 67% of the samples were in the moderate risk category. This indicates the effectiveness of the ASR technique to reduce health risk. In the household storage containers, 10% of the samples were in the low risk category and 70% of the samples were in the moderate risk category. Thus, from abstraction wells to household storage containers, the number of samples having no risk decreased from 23% to 10% and the number of samples having high risk increased from 3% to 20%. This indicates the lack of proper handling and hygiene practices of the households.
Figure 3

Comparison of E. coli contamination (WHO risk category) of water from different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container. Note: E. coli 0 cfu/100 ml = no risk; E. coli 1–10 cfu/100 ml = low risk; E. coli 11–100 cfu/100 ml = intermediate risk; E. coli 101–1,000 cfu/100 ml = high risk; E. coli ≥1,000 cfu/100 ml = very high risk.

Figure 3

Comparison of E. coli contamination (WHO risk category) of water from different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container. Note: E. coli 0 cfu/100 ml = no risk; E. coli 1–10 cfu/100 ml = low risk; E. coli 11–100 cfu/100 ml = intermediate risk; E. coli 101–1,000 cfu/100 ml = high risk; E. coli ≥1,000 cfu/100 ml = very high risk.

Seasonal variation of indicator bacteria

Figure 4 shows seasonal variation of E. coli and Enterococci counts as per different sampling sources. In the source pond, the highest concentration of E. coli was found during monsoon. In the case of water from both the abstraction well and household storage container, the concentration of E. coli gradually increases from pre-monsoon to post-monsoon. Enterococci counts also had similar variation pattern to E. coli except in the case of household storage containers. In household storage containers, E. coli was highest in monsoon.
Figure 4

Seasonal variation of counts of indicator bacteria in different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container.

Figure 4

Seasonal variation of counts of indicator bacteria in different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container.

Higher incidence of microbial contamination in the source pond during monsoon may be attributed to contamination from surface runoff coming from agricultural and settlement areas. Previous studies conducted in this area also addressed this fact, as they found pond waters to be heavily contaminated with faecal coliforms and other pathogenic bacteria having possible association with the polluted stream flowing into the pond (Islam et al. 1994; Islam et al. 1995; Islam et al. 2011). During fieldwork, we observed that the ponds were not protected by any embankment. The catchments of ponds usually contain bird or animal excreta and it is possible that these excreta and dust from surroundings get mixed with pond water. Bacterial counts were found to increase after pre-monsoon, which may be because of stagnant water around the well and the absence of well lining. This indicates poor drainage conditions around the abstraction wells. Parker et al. (2010) found that the absence of a well lining or sanitary seal provides pathways for contamination in the abstraction well. According to the WHO risk category, 20 (67%) of the abstraction well water samples were in the moderate risk category, having the potential to pose substantial health risk.

Physico-chemical analysis

A summary of physico-chemical data for the water samples is shown in Table 3. About 83% of the source water samples had a mean pH within the WHO standard value (6.5–8.5), and that further improved after treatment. Mean salinity of the source ponds was 0.89 ppt (range 0.09–2.74 ppt), which also increased after treatment. Mean turbidity of the source pond, abstraction well, and household storage container waters was 49.38, 23.95 and 12.41 NTU, respectively. Although none of the source pond samples meet the WHO standard for turbidity (<5 NTU/100 ml), it significantly improved after treatment. The number of samples satisfying the WHO standard for turbidity of abstraction wells and household storage container waters were about 23% and 42%, respectively. Higher turbidity in the source pond during monsoon may be attributed to surface runoff including agricultural and domestic runoffs. Relevant scientific literature (Kumpel & Nelson 2013) also mention that pond water contains high turbidity during the monsoon. Although, a significant reduction of turbidity was found in abstraction well waters that the average value of turbidity was above the WHO guideline value.

Table 3

Physico-chemical quality of waters from different sampling sources

Quantiles
Meeting standard
ParameterSampling sourcen (number of samples)MeanMinMaxCriteria%P value
pH Source pond 30 7.77 7.1 8.76 6.5–8.5 83.33 0.20 
Abstraction well 30 7.57 6.8 8.69 93.33 
Household storage container 60 7.53 8.7  93.3 
Salinity (ppt) Source pond 30 0.89 0.09 2.74   0.13 
Abstraction well 30 1.13 0.15 2.09   
Household storage container 60 1.07 0.25 2.3   
Turbidity (NTU) Source pond 30 49.38 11 123 < 5 <0.01 
Abstraction well 30 23.95 2.21 114 23.33 
Household storage container 60 12.41 0.96 129 41.66 
Quantiles
Meeting standard
ParameterSampling sourcen (number of samples)MeanMinMaxCriteria%P value
pH Source pond 30 7.77 7.1 8.76 6.5–8.5 83.33 0.20 
Abstraction well 30 7.57 6.8 8.69 93.33 
Household storage container 60 7.53 8.7  93.3 
Salinity (ppt) Source pond 30 0.89 0.09 2.74   0.13 
Abstraction well 30 1.13 0.15 2.09   
Household storage container 60 1.07 0.25 2.3   
Turbidity (NTU) Source pond 30 49.38 11 123 < 5 <0.01 
Abstraction well 30 23.95 2.21 114 23.33 
Household storage container 60 12.41 0.96 129 41.66 

Seasonal variation of salinity

Measurement of salinity of abstraction well water is important for ASR studies, since it stores fresh water into brackish water aquifers. Figure 5 shows the seasonal variation of salinity in different sampling sources. Salinity was lowest in the monsoon, and gradually increased in the following post-monsoon and pre-monsoon. As we found in physico-chemical data (Table 3) that salinity increased after treatment, a similar pattern is seen in Figure 5. From source pond to abstraction well, salinity increased from 0.45 to 0.99 ppt (120% increase) in monsoon, from 0.89 to 0.96 ppt (8% increase) in post-monsoon, and 0.99 to 1.23 ppt (24% increase) in pre-monsoon. The increase of salinity in the abstracted well water indicates the impact of brackish water aquifers to increase the salinity of the stored water. Among the source ponds, the lowest salinity was found during monsoon, which is probably the effect of dilution caused by the mixing of rainwater with water of the source pond. The highest increment in the salinity from source pond to abstraction well water was observed in monsoon, which may be attributed to the mixing of low salinity pond water with highly saline water in the shallow aquifer.
Figure 5

Seasonal variation of salinity in different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container.

Figure 5

Seasonal variation of salinity in different sampling sources: SP = source pond; AW = abstraction well; HSC = household storage container.

CONCLUSIONS

This study examined the prevalence of indicator bacteria in ASR water in southwest coastal Bangladesh. The source ponds experienced a higher incidence of microbial contamination, which reduced significantly in the abstracted water, although they did not meet the WHO standard. The microbial quality of abstracted water further deteriorated at household storage containers. In addition, salinity increased from source ponds to abstraction wells. Thus, the ASR system reduced bacteriological contamination significantly, although it did not ensure complete microbial safety of drinking water. Since abstracted water shows microbial contamination, there is a necessity to apply further in-house filtration or disinfection techniques to make the water safe for drinking. Proper education and training of the rural communities about the sources of water contamination, the health risks of consuming contaminated drinking water, and the benefits and barriers about ASR should be provided to enhance the operation and maintenance of ASR schemes. Good hygiene practices and water safety plans should be implemented for ASR to ensure the supply of safe drinking water in the long term.

ACKNOWLEDGEMENT

We would like to express our sincere gratitude to the authority and field officers of the Managed Aquifer Recharge (MAR) Project in Khulna for their cooperation by providing relevant information and support during the research.

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