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Performance of microbiological control by a point-of-use filter system for drinking water purification
Journal of Environmental Sciences 21(2009) 1237–1246
Performance of microbiological control by a point-of-use filter system for drinking water purification SU Fengyi1 , LUO Mingfang2 , ZHANG Fei1 , LI Peng1 , LOU Kai3 , XING Xinhui1,∗ 1. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China. E-mail: [email protected] 2. College of Chemistry and Chemical Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3. Institute of Microbiology, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China Received 14 November 2008; revised 19 February 2009; accepted 03 March 2009
Abstract Purification capacity of a faucet mounted type water filter for home use was evaluated, particularly with regard to microbiological performance under different running conditions. Biofilms were formed inside the filter, affecting the bacterial quality of the effluent water. Low flow rate, long stagnation period and high filter temperature were found favorable for bacterial growth inside. By commercial analytical profile index (API) kits, ten different bacterial species were identified in drinking water, four of which were probably contributed to the biofilm formation since they were also present in the biofilm. Fluorescence in situ hybridization (FISH) was used to confirm the API identification results, and direct viable count (DVC) method was employed to improve the sensitivity of FISH for the isolated Acinetobacter spp. and Pseudomonas putida as models. Relationship between the filter operating condition and the bacterial community alteration was partly revealed, which could provide the basic knowledge for the filter design and its practical use. Key words: drinking water; point-of-use filter; biofilm; bacteria community; activated carbon DOI: 10.1016/S1001-0742(08)62410-9
Introduction Waterborne pathogens, including a variety of virus, bacteria and protozoa, account for much of the estimated 4 billion cases and 2.5 million deaths from endemic diarrhoeal disease each year (Kosek et al., 2003). Since the microbial risk control from the water treatment plants to the consumer’s taps is the major challenge of drinking water safety management (Lehtola et al., 2004), interventions to control and maintain the microbial quality of water at household levels are a promising and effective alternative. Properly designed water purifying filter systems have become popular with consumers as a countermeasure to remove the waterborne pathogens and the potential undesirable chemicals from the water reaching its destination (Crump et al., 2004; Matsui et al., 2004). These systems can be fitted to service an entire home at the point of entry or at a single faucet, with the latter termed pointof-use (POU) devices (Snyder et al., 1995). Activated carbon (AC), in granular (GAC) or powdered (PAC) form, is commonly incorporated into the POU devices (Snyder et al., 1995; Tobin et al., 1981). The attachment of microorganisms to AC particles was through strong Lifshitz-van der Waals forces by overcoming electrostatic repulsion between negatively charged cells and carbon surfaces (Jucker et al., 1996). * Corresponding author. E-mail: [email protected]
Since bacteria can form biofilms on solid surfaces or substrata, even in oligotrophic or disinfectant environment (Schwartz et al., 2003; Wimpenny et al., 2000), the biofilm is a major problem in water distribution systems as well as in domestic water filtration systems which may cause a hygiene problem of the effluent water (Daschner et al., 1996; Geldreich et al., 1985). Biofilms can deplete the disinfection agents (Regan et al., 2002), protect and support pathogenic microorganisms (Buswell et al., 1998), and provide conditions for bacterial regrowth (Dukan et al., 1996). Several research groups examined the diversity of biofilms by identifying the bacteria involved in these problems (Kalmbach et al., 1997a; Regan et al., 2002), which may provide new tools for improving the water quality (LeChevallier et al., 1996; Martiny et al., 2003). However, the knowledge about changes in bacterial communities during filtration with water filters is still limited. During recent years, the structure of biofilms from many different environments has been evaluated by using a broad variety of physico-chemical and molecular biological techniques (Schwartz et al., 2003; Wimpenny et al., 2000). Each method has its advantages and drawbacks, and their application is based on different requirements. As a standard and simple traditional technique for microbiological testing and safety management of drinking water, heterotrophic plate counts (HPC) has been widely adopted (Daschner et al., 1996; Snyder et al., 1995).
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However, the bacteriological quality of drinking water is dependent on the species encountered. It is becoming preferable to try to limit the opportunistic pathogens, rather than all the environmental bacteria (Hunter et al., 2003; Yates, 2007). Among the methods for specific pathogens detection, fluorescence in situ hybridization (FISH) has been successfully applied to the phylogenetic identification and quantification of individual bacteria in a number of different environments, avoiding culture-selection bias (Amann et al., 1995; Loge et al., 1999). Since FISH was first employed to identify the bacteria in drinking water in 1993 by Manz et al., it has been used to analyze the characteristics of biofilm in drinking water, formed on polyethylene, polycarbonate or glass surfaces (Kalmbach et al., 1997b; Batt´e et al., 2003). However, FISH can only provide limited information about the abundance of specific bacteria, due to time-consuming and probe-specificity constraints, and its sensitivity is closely dependent on the cellular abundance of rRNA in the target bacteria (Amann et al., 1995). To overcome those problems, combination of FISH with other methods is necessary and effective. Analytical profile index (API) kit, one of the commercial systems for bacterial identification, is convenient and has been widely used in fresh and saline waters, and in drinking water sources (Pisciotta et al., 2002; Tokajian and Hashwa, 2004). Applying API kit to identify the colonies formed during HPC experiments can provide the basic knowledge on the bacterial composition prior to the use of FISH. Direct viable count (DVC) technique, which is always performed by exposing bacterial cells to a resuscitation medium containing antibiotics to prevent cellular division and thus induce an elongation of the viable cells (Baudart et al., 2002), can enhance the rRNA content in the viable (elongated) cells and increase the FISH signal(Villarino et al., 2000). The DVC-FISH procedure can monitor the bacterial viability and has been used to assist the application of FISH in natural and drinking waters (Baudart et al., 2002). This study aimed to investigate the microbiological performance of a POU domestic water filter that consisted of a microfilter membrane layer and an activated carbon
Fig. 1
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filter, in terms of the change of bacterial community during the purification, including the characteristics of drinking water, of the bacteria present inside the filter or in the filtrated water. To better understand the performance of filter, some operating parameters were changed, i.e., flow rate, filtered water volume per day and filter temperature. The change of bacterial community was analyzed by HPC combined with API kits and the culture-independent methods.
1 Materials and methods 1.1 Experimental setup Three filters were operated at the same time in parallel to examine the effects of operating conditions (Fig. 1a). A POU water filter provided by P&G Company (USA) was mounted to a faucet at Tsinghua University (Beijing, China). It consists of a microfilter membrane (MFM) and a pressed annular granular activated carbon filter (ACF), as shown in Fig. 1b. The indoor drinking water at Tsinghua University is self-supplied underground water with chlorine added from the water supply plant. The parameters of water quality are listed in Table 1 (measured at room temperature (RT), 21–25°C). To simulate the use in a household, three runs in a batch mode were performed every day (around 8:00 am, 12:00 am and 6:00 pm), filtering 10 L of drinking water at the maximum initial rate (1.32–1.09 L/min) at RT for each run, unless Table 1
Characteristics of the tap water in Tsinghua University, China and of the filtered water
Parameter
Mean ± SD (n = 4 or 5) Tap water Filtered water
Turbidity (NTU) Total hardness (mg CaCO3 -eq/L) Dissolved oxygen (DO) (mg/L) pH Total organic carbon (TOC) (mg/L) Total nitrogen (TN) (mg/L) Free chlorine (mg/L) Number of total coliforms (MPN/L)
0.17 ± 0.23 203 ± 49.50 4.95 ± 1.04 7.80 ± 0.04 3.60 ± 0.60 3.32 ± 0.18 0.11 ± 0.01 <1
Experimental setup (a) and structural diagram of the water filter (b).
0.07 ± 0.10 107 ± 24.04 4.06 ± 0.35 7.81 ± 0.03 3.68 ± 0.71 3.26 ± 0.21 0.09 ± 0.01 <1
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otherwise stated. Operation was finished until the filter lifetime expired (around 300 L filtration capacity of drinking water). Characteristics of the filtered water sampled from the effluent near the end of each draining were analyzed every 2–3 d through the whole process of a filter until the end of performance, in terms of HPC, total coliforms, free chlorine, turbidity, total hardness, total organic carbon (TOC), total nitrogen (TN) and dissolved oxygen (DO). By discarding the stagnated water (more than 9 L) for each run, the results were considered to stably reflect the main condition inside the filter system. Same parameters for the tap water without purification were also analyzed for corresponding comparison. TN and total hardness were tested according to the national standard procedures (Wei, 1998a). Total coliform number was determined using a Colilert 18 system (IDEXX Laboratories, Inc., USA). Free chlorine was detected using a chlorine colorimeter (HI93711, Hanna Instruments, Hungary) and turbidity was measured using a microprocessor turbidity meter (HI93703, Hanna Instruments, Hungary). DO was analyzed by a DO meter (mk-250, Mettler Toledo International Inc., USA), and TOC analyzer (TOC-500, Shimadzu Corporation, Japan) was used to determine TOC. 1.2 Effects of flow rates on the effluent water quality Filtering initial rates of three parallel purification systems were set as 1.39, 1.15 and 0.92 L/min, respectively. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.3 Effects of filtered water volumes per day on the effluent water quality Filtered volume was set as 10, 5 and 2 L for each parallel system, respectively. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.4 Effects of filter temperatures on the effluent water quality The filter running temperature was set as 40°C, 30°C and RT (19–25°C) for each parallel system, respectively. Temperatures higher than RT were controlled by holding the filter temperature through a water jacket covering the whole filter. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.5 Analysis of adhesion intensity of biofilm formed in the filter To examine the adhesion intensity of biofilm formed inside, three parallel filters were operated at 30°C. At the end of the filters’ life time, the filters were washed at different flow rates of 0.20, 0.63, or 1.09 L/min, respectively. Other operation parameters were the same as described in Section 1.1. The effluent washed out of the filter at different time was collected for HPC measurement.
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1.6 Sampling of the biofilm in the filter for HPC measurement When the lifetime of filter had expired, the whole ACF was taken out and washed by sterilized water to evaluate the bacteria attached to its outer surface. After centrifugation at 880 ×g for 10 min, the supernatant was collected for HPC measurement. 1.7 Heterotrophic plate counts (HPC) HPC was determined by serially diluting the water sample into sterile deionized water and poured into the nutrient agar medium (Tryptone 10 g/L, beef extract 3 g/L, NaCl 5 g/L and agar 10 g/L). Duplicate counts were performed for each dilution and cultivated at 37°C for 48 h. 1.8 Bacterial identification by commercial API kits According to the different morphological properties, more than two thirds of the colonies grown on the nutrient agar plate of every sample during the HPC experiments were selected for Gram staining. The colonies identified as Gram-negative were analyzed by API 20NE or 20E assay kits (BioM´erieux Inc., France). According to the product manual, API 20NE strip was used to identify non-fastidious, non-enteric Gram-negative rods; and API 20E strip was employed to identify Enterobacteriaceae and other non-fastidious, Gram-negative rods. 1.9 Observation of biofilms inside the filter by SEM When the lifetime of filter had expired, different positions of MFM and ACF were cut by a sharp but sterilized knife, including the edge and interior region of the MFM, the interior region, the inner surface and the outer cylindrical surface of the ACF (part of the positions were marked in Fig. 4e). After fixed by 2.5% glutaraldehyde and 2% osmium tetroxide, those samples were examined with a Scanning Electron Microscope (SEM) (FEI QUANTA 200, USA). 1.10 Total direct counts (TDC) Aliquots of samples (tap water, 50–100 mL) were filtered onto black polycarbonate membrane filters (pore size, 0.2 μm; Nuclepore, Whatman, UK) and stained by 1 μg/mL DAPI (4’,6-diamidino-2-phenyl-indol; Sigma Chemical, USA) for 5 min (Manz et al., 1993). Counts were performed with an epifluorescence microscope (Eclipse E600, Nikon). More than ten microscopic fields were counted for one measurement, with each field containing at least 100 cells. 1.11 FISH analysis FISH was conducted according to the protocol described previously (Kalmbach et al., 1997a; Manz et al., 1993). The sequence and bacterial specificity of each probe employed are listed in Table 2 (referring to the information from ProbeBase website online). The fluorescence was detected with an epifluorescence microscope equipped with Nikon light filter sets (B-2A or G-2A) for FAM-
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Table 2 Probes involved in this study Probe
Sequence
Label
Specificity
ACA652 Burcep Ppu56a SPH120 Stemal COM1424
5’-ATCCTCTCCCATACTCTA-3’ 5’-CTGTGCGCCGGTTCTCTT-3’ 5’-GCTGGCCTAACCTTC-3’ 5’-GGGCAGATTCCCACGCGT-3’ 5’-GTCGTCCAGTATCCACTGC-3’ 5’-ACCTACTTCTGGCGAGA-3’
FAM FAM Cy3 Cy3 Cy3 TAMRA
Acinetobacter spp. Burkholderia cepacia Pseudomonas putida & P. mendocina Sphingomonas spp. Stenotrophomonas maltophilia Comamonas spp.
or Cy3-labeled probes. The hybridization efficiency was calculated as the number of FISH positive cells divided by the total direct counts (TDC). 1.12 DVC-FISH The isolated bacteria from drinking water by HPC method, were cultivated in the diluted nutrient medium broth (10 times dilution, abbreviated as 1/10×) with the addition of 20 μg/mL of nalidixic acid. After cultivation at 37°C for different lengths of time, the cells were harvested for FISH analysis.
2 Results 2.1 Physiochemical and microbiological features of the filters As shown in Table 1, the turbidity and total hardness of the effluent were about half of those in unfiltered tap water, and DO was about 20% lower. The other parameters of pH, TOC, TN, free chlorine and total coliforms number had no remarkable difference by the filtration. Free chlorine concentration in drinking water was stable during the whole lifetime of one filter system (one life cycle was 10– 15 d). Incidentally, both total coliforms of tap water and filtered water were less than 1 MPN/L, which were eligible to the national regulation (Wei, 1998b). 2.2 Effects of flow rates on the effluent water quality The bacterial number of the original tap water in terms of HPC remained less than 15 CFU/mL throughout the whole operation of the filter (Fig. 2a). While with the lower initial flow rate, the HPC of effluent increased faster since the one-third lifetime of the filter. At the end of the filter operation, the HPC of effluent run at 0.92, 1.15 and 1.39 was 68, 61 and 45 CFU/mL, respectively; while the corresponding HPC of the tap water was only 11 CFU/mL. In addition, two filters had higher HPC of the effluent than that of the tap water at the beginning of their usages. 2.3 Effects of filtered water volumes per day on the effluent water quality As shown in Fig. 2b, the system of the lower filtered water volume per day had a higher HPC at a certain accumulative volume, after filtering 50 L water. However, the HPC at the end of three systems were at the same level, around 50 CFU/mL. According to the water filtration capacity of the filter, the end operation time for each system with the filtered water volume per day of 30, 15 and 6 L was expected 10, 20 and 50 d, respectively. But
Fig. 2 Effects of flow rate (a), filtered water volume per day (b) and filter temperature (c) on HPC in the effluent of the filter (n = 3). RT: room temperature.
the actual running time was 12, 21 and 37 d, respectively. It showed that the filter run less water volume per day had the lower filtration capacity.
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Performance of microbiological control by a point-of-use filter system for drinking water purification
2.4 Effects of filter temperatures on the effluent water quality From Fig. 2c, it could be clearly seen that the HPC of the effluent through the filters controlled at 30 or 40°C was larger than that of the filter run at RT, since one third of the filter lifetime. At the end of the filter life cycle, the effluent HPC was 152, 164, and 49 CFU/mL for the filters run at 40, 30 and RT, respectively; while the HPC of the tap water was 20 CFU/mL. 2.5 Adhesion intensity of the biofilm formed in the filter Time courses of HPC in the effluent from the used filters at different washing flow rates are shown in Fig. 3. It displayed that bacteria washed out of the filters decreased with increasing washing time. The reduction of HPC was dependent on the washing flow rate; the faster the initial washing speed, the greater the reduction extent. The effluent HPC of the filter which was washed at the rate of 1.09 L/min could reach a constant value after 2 min of washing. This washing rate was most close to the typical initial running flow rate (1.32–1.09 L/min). 2.6 Observation of biofilm morphology in the filter by SEM Since the bacteriological quality of the filtered water was obviously worse than that of the original tap water, bacterial aggregation and regrowth were believed to occur inside the filter, leading to the formation of biofilm. The structure of the biofilm formed inside the filters run at RT was observed by SEM at the end of the filter lifetime. Different positions were chosen and part of the results are shown in Figs. 4a–4d. The results showed that there were fewer bacteria on the MFM than on the ACF surface, and most bacterial colonies could be observed at the bottom of the outer surface of the ACF cylinder (marked as position d in Fig. 4e). Almost no bacteria were found in the interior region of the ACF cylinder and the bacteria number observed on the inner surface of the ACF was less than that on the outer surface (data not shown).
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2.7 Bacterial identification with API kits To accumulate information on the bacterial community inside the filter during the operation, the bacteria in the original tap water, the effluent and the biofilm inside the ACF were identified by API kits for the colonies after HPC test. The tap water and filtrated water were directly poured into the nutrient agar medium with different dilution ratios, while the bacteria of the biofilm was obtained by washing the whole outer surface of the ACF and collecting the cell-contained supernatant after the removal of AC debris by centrifugation. More than half of all the selected colonies grown on nutrient agar plate for Gram staining were identified as Gram-negative bacteria and were further analyzed by API 20NE/20E kits, 37.1%–44.2% of the analyzed isolates identified in the tap water. Ten species of bacteria were identified in the tap water, six in the filter and four in the effluent (Table 3). The four species of bacteria detected both in the original tap water and in the biofilm, were Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia. Of all of the thirteen species identified in the tap water and in the filter, seven were pathogenic to human beings and four were opportunist pathogens. The percentage of each bacterial species of the total identified colonies by API kits, was also summarized in Table 3, which was obtained by two parallel systems at the end lifetime of filters operated at RT. It is showed that the variety of bacterial species in the effluent was less than that in the influent, comparable with or even less than that in the filter. API showed high standard deviation on identified bacterial quantification. 2.8 Confirmation of the API identification results by FISH The FISH method was used to confirm the identification results of API kits, for its further practical application in the analysis of pathogens in the drinking water. E. coli was used as a control strain. The hybridization results are shown in Table 4, and the hybridization efficiency for the positive FISH cases was more than 98%, which was calculated as the number of FISH positive cells divided by the TDC (here, TDC presented the total cell number of each isolated bacterium, after identified by API kits and cultivated in nutrient medium broth). Almost all the API identified species (genus) could be confirmed by FISH, except Burkholderia cepacia. 2.9 DVC-FISH for isolated bacteria
Fig. 3 Time courses of HPC in the effluent of the used-up filters during washing at different flow rates (n = 3). RT: room temprature.
Since the FISH signal is dependent upon the physiological activity of the bacteria, FISH does not form strong enough signals for the slowly growing cells in oligotrophic environments, due to their low intracellular rRNA content. To increase the hybridization efficiency, the direct viable count-FISH (DVC-FISH) technique was applied. Nalidixic acid of 20 μg/mL in the 1/10× nutrient medium broth was used to elongate the bacteria in the starvation state without proliferation. In higher concentration of the nutrient medium broth, 20 μg/mL nalidixic
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Fig. 4 SEM photos of microfilter membrane (MFM) surface and different positions of the activated carbon filter (ACF) at the end lifetime of the filter operated at room temperature. (a) the edge of the microfilter membrane; (b) the top of the ACF outer surface; (c) the middle of the ACF outer surface; (d) the bottom of the ACF outer surface; (e) illustration of different positions sampled for the SEM observation.
acid could not effectively restrain the cellular propagation (data not shown). Figure 5 shows the hybridization efficiency with both DVC-FISH and FISH for Acinetobacter spp. and Pseudomonas putida as the models, which had been isolated from the drinking water and cultivated in 1/10× nutrient medium broth. The results showed that the hybridization efficiency for the waterborne bacterium in the poor nutrition environment was obviously improved by DVC-FISH, i.e., by 5%–20% for Acinetobacter spp. and 4%–38% for P. putida (TDC presented the total cell number of each isolated bacterium sample after cultivated with or without adding nalidixic acid into 1/10× nutrient
medium broth, respectively). The cells became elongated during the cultivation, as shown in Fig. 6.
3 Discussion 3.1 Performance of the filter The filter tested in the present study showed good purification capability of removing the turbidity and total hardness from the tap water (Table 1), through the whole filtration process in one life cycle. The filter was able to hold back or absorb some suspended solids (like Ca/Mg-
Table 3 Waterborne bacteria identified by API kits Species
Tap water (%)
Filter (%)
Filtered water (%)
Pathogen
Acinetobacter lwoffii Acinetobacter junii Burkholderia cepacia Brevundimonas vesicularis Stenotrophomonas maltophilia Sphingomonas paucimobilis Chryseomonas luteola Pseudomonas putida Ochrobactrum anthropi Aeromonas Salmonicida Chryseobacterium indologenes Flavobacterium indoltheticum Vibrio metschnikovii
+ (6 ± 6) + (8 ± 8) + (3 ± 3) + (2 ± 2) + (5 ± 1) + (5+5) + (3+1) + (2+2) + (2 ± 2) + (1 ± 1) – – –
+ (8 ± 3) – + (18 ± 10) + (12 ± 12) + (15 ± 5) – – – – – + (5 ± 5) + (2 ± 2) –
+ (30 ± 9) – – + (25 ± 9) + (20 ± 6) – – – – – – – + (2 ± 2)
+ + + + Opportunist pathogen + + Opportunist pathogen Opportunist pathogen Opportunist pathogen – – +
“+”: the species existed in the testing system or that it was pathogenic; “–”: the species was not detected in the testing system. Data are expressed as mean ± SD (n = 2).
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Performance of microbiological control by a point-of-use filter system for drinking water purification Table 4 ACA652
Acinetobacter spp. Acinetobacter junii Pseudomonas putida Stenotrophomonas maltophilia Burkholderia cepacia Comamonas acidovorans E. coli
+ +
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Confirmation of the API identification results by FISH Ppu56a
Burcep
COM1424
+
Stemal
– + – –
– +
Hybridization efficiency > 98% ca. 100% > 99.9%
–
+: positive hybridization; –: negative hybridization.
Fig. 5 Comparison of hybridization efficiency of DVC-FISH with FISH for Acinetobacter spp. (a) and Pseudomonas putida (b) (n = 3). The bacteria were cultivated in 1/10× nutrient medium broth with (DVC-FISH) or without (FISH) 20 μg/mL nalidixic acid for different times.
deposit) in the drinking water. It has been reported that carbon filter aid in the removal of organic compounds from water, but it may be less effective in removing microbial contaminants (Snyder et al., 1995). In this study, the filter was also proved not ideal for the bacterial removal. Since one third lifetime of the filter, the HPC of the effluent was greater than that of the influent, and the difference would be elevated with the time elapsing (Fig. 2), which was consistent with the results reported by the other researchers (Reasoner et al., 1987;
Snyder et al., 1995). When the filter was operated at RT at the maximum flow rate, the HPC of the effluent would be 3–5 times of that of the influent at the end of the filter lifetime (Fig. 2). That elevation level was comparable with the other reports (Wallis et al., 1974; Taylor et al., 1979). It was suggested that the filter could retain the bacteria inside, and the trapped organic materials within the filter supported the growth of heterotrophic bacteria, which had led to the formation of the biofilms inside, resulting in the effluent HPC increase.
Fig. 6 Microscopic photographs of Acinetobacter spp. by FISH (a) and DVC-FISH (b). The bacteria were cultivated in 1/10× nutrient medium broth with (DVC-FISH) or without (FISH) 20 μg/mL nalidixic acid.
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From the bacterial adhesion intensity test, it is showed that the intensity of bacteria (biofilm) adhering on the surface of filter inside was weak, which allowed the bacteria washing out even at the low flow rate such as 0.20 L/min (Fig. 3). HPC of the bacteria washed out of the filter showed a trend of approaching to a steady value as the time elapsing, and high washing flow rate could accelerate this process. In this study, the typical initial running flow rate was 1.32–1.09 L/min. Thus for most the parallel systems running, the effluent HPC could reach the stable value in about 2 min. Since all samples of filtration were taken near the end of each draining, which were processed more than 5 min (more than 9 L discarded). It meant that most of the effluent in the other experiments was sampled for HPC after the bacterial number being stable. From Fig. 2a, the filter run at the lower initial flow rate showed the higher HPC of effluent since one third lifetime of the filter. As discussed above, the biofilm inside the filter had weak adhesion intensity, thus a high flow rate was not favorable for the attachment of bacteria on the filter, and probably not suitable for the development of the biofilm inside the filter due to the acute washing out of the bacteria. As shown in Fig. 2b, the system run at the lower filtered water volume per day showed a higher HPC of the effluent. It was reported that overnight, static water conditions might provide an opportunity for bacterial growth within the AC (Wallis et al., 1974; Snyder et al., 1995). Since the system operated at the lower filtered water volume per day had relative more time for bacterial growth when filtered the same volume of water during the lifetime, more bacteria would grow inside the filter and higher HPC of the effluent would be correspondingly caused. However, the same level of HPC was gained at the end of three systems, suggesting that the bacterial concentration inside the filters had reached the maximum. Over this value, the filters could not work. Practically, the system run at the lower filtered water volume per day had a smaller filtration capability, and it would reach the maximum bacterial concentration inside the filter with a less water filtration time. It is well known that temperature is one of the most important environmental factors that affect the growth of microorganisms, even on AC (Tobin et al., 1981). Figure 2c indicates that a higher temperature could speed up the bacterial growth and probably encouraged the development of biofilm inside the filter, which thus resulted in a higher HPC in the filtered water. The results indicated that 30°C was the optimal temperature for the microbial growth inside the filter. The filter had a higher HPC of effluent than that of tap water at the initial of the usage (Fig. 2). This phenomenon was assumed to be due to the bacteria already existed inside the filter during the storage prior to use, which would be washed out of the filter by the filtration. 3.2 Bacterial community alteration From the observation by SEM (Fig. 4), the heterogeneous spatial distribution of bacteria inside the filter was probably pertaining to the filter structure. The outer surface
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of the ACF cylinder would encounter bacteria at higher concentrations than the inner surface would, due to the preferential filtration of drinking water, which led to a higher bacterial concentration blocked in the former section. Furthermore, the bottom of the outer surface of ACF was a dead space and had smaller shearing force, which would benefit the bacterial accumulation and proliferation. Since it was more developed, the biofilm on the outer surface of the ACF could be mainly investigated to represent the characteristics of the biofilm inside the filter. However, these hypotheses need further proof. By API kits, thirteen species of the isolated bacteria had been identified in the whole water purification system, some of which were also found by other researchers in drinking water or the effluent of a household water filter (Daschner et al., 1996; Williams et al., 2004). Four species of bacteria detected in both original tap water and biofilm (Table 3), i.e., Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia, were probably related to the formation of biofilm inside the filter. Six of the ten species in the original tap water were pathogenic, and other four were opportunistic pathogens. These results displayed that although the number of total coliforms and free chlorine in the tap water (Table 1) were acceptable (< 3 MPN/L, > 0.05 mg/L (Wei, 1998b)), there still existed a microbial risk in the tested drinking water system. The potential health risk caused by these pathogens after filtration should be taken into consideration, even though the concentrations were very low. The API identification results (Table 3) suggested that the purification system could reduce the variety of the bacterial species in the effluent probably by intercepting some kinds of bacteria and/or disable their progenitive ability during the filtration. For instance, Acinetobacter junii and Burkholderia cepacia, which existed in the tap water, were not detected in the effluent. After purification, only three pathogens and one opportunist pathogen were found in the effluent. This suggested that the filter was favorable to simplify the bacterial community and reduce the health risk in the drinking water. However, some species found in the filtered water were not detected in the tap water, which might be due to the high non-detecting error of the API system as reported in the other literature (O’Hara et al., 2003). Since API kit is based on the heterotrophic cultivation, it inevitably has the cultivation-selection bias for oligotrophic environmental samples. In addition, not all the colonies grown on the nutrient agar plate were analyzed by API kit due to the high strength of laboring. Moreover, in the API test, human subjective judgment was required both for Gram staining and API biochemical reactions, which would also cause the systemic errors. All these contributed to the standard deviation of the bacterial identification by API kits (Table 3). In addition, the possible reason for FISH negative hybridization result compared with API might be the wrong identification of the API kits, since high wrong identification rate is another limitation of API kits (O’Hara et al., 2003).
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In this study, FISH was combined with API to confirm the identification result of bacteria isolated from HPC test, but could not be directly applied for the bacterial community analysis in drinking water, because the fluorescent signal was too weak. Although DVC method could enhance the hybridization efficiency of FISH to nearly 40% in the diluted nutrient medium broth (Fig. 5), it still needs further improvement to achieve a more exact estimation for the bacterial community in the oligotrophic environment. However, the above microbiological analysis implied that FISH at least could be combined with the well-adopted HPC test to strength the microbial risk management of drinking water.
4 Conclusions The POU filter for drinking water purification tested in present study showed a good capability in removing the turbidity and total hardness. However, it had little effect on removing bacteria. After one third of the filter lifetime, biofilms were supposed to form inside, allowing the bacteria to propagate gradually with the time elapsing, which resulted in the increase in the bacterial concentration of the effluent. The effluent HPC at the end lifetime of the filter run at RT and maximum flow rate was 2–5 times as that of the original tap water. Operating conditions including a high flow rate, a low filter temperature and short stagnant time of nonuse were indicated to be helpful for restraining the biofilms formation inside the filters, which could be employed to assist improving the filter performance for microbiological control. The analysis of bacterial community during the filtration indicated that even though there was still microbial risk in the effluent, the filters could reduce the variety of bacterial species, thus potentially reducing the microbial risk. Four of the isolated pathogens, Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia, were identified by API kits and found to be closely related to the biofilm formation inside the filter. Although the direct viable count (DVC) method was capable of effectively improving the FISH hybridization efficiency, a more precise and direct molecular approach should be established to study the bacterial community in drinking water and the development of biofilms in the water purification filter. Acknowledgments The authors thank the Proctor and Gamble Company for providing the water purifying filter and the assistance for SEM observation. This work was supported by the Proctor and Gamble Company and in part by Boshidian Fund of Ministry of Education of China (No. 200800030046).
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Baudart J, Coallier J, Laurent P, Pr´evost M, 2002. Rapid and sensitive enumeration of viable diluted cells of members of the family Enterobacteriaceae in freshwater and drinking water. Applied and Environmental Microbiology, 68: 5007– 5063. Batt´e M, Mathieu L, Laurent P, Pr´evost M, 2003. Influence of phosphate and disinfection on the composition of biofilms produced from drinking water, as measured by fluorescence in situ hybridization. Canadian Journal of Microbiology, 49: 741–753. Buswell C M, Herlihy Y M, Lawrence L M, McGuiggan J T M, Marsh P D, Keevil C W, Leach S A, 1998. Extended survival and persistence of Campylobacter spp. in water and aquatic biofilms and their detection by immunofluorescentantibody and -rRNA staining. Applied and Environmental Microbiology, 64(2): 733–741. Crump J A, Okoth G O, Slutsker L, Ogaja D O, Keswick B H, Luby S P, 2004. Effect of point-of-use disinfection, flocculation and combined flocculation-disinfection on drinking water quality in Western Kenya. Journal of Applied Microbiology, 97(1): 225–231. Daschner F D, Rden H, Simon R, Clotten J, 1996. Microbiological contamination of drinking water in a commercial household water filter system. European Journal of Clinical Microbiology & Infectious Diseases, 15(3): 233–237. Dukan S, Levi Y, Piriou P, Guyon F, Villon P, 1996. Dynamic modelling of bacterial growth in drinking water networks. Water Research, 30(9): 1991–2002. Geldreich E E, Taylor E, Blannon J C, Reasoner D, 1985. Bacterial colonization of point-of-use water treatment devices. Journal of the American Water Works Association, 77(2): 72–80. Hunter P R, Andersson Y, von Bonsdorff C H, Chalmers R M, Cifuentes E, Deere D et al., 2003. Surveillance and investigation of contamination incidents and waterborne outbreaks. In: Assessing Microbial Safety of Drinking Water: Improving Approaches and Methods. (Dufour A I, Snozzi M, Koster W, Bartram J, Ronchi E, Fewtrell L, eds.). London: IWA Publishing. 205–236. Jucker B A, Harms H, Zehnder A B, 1996. Adhesion of the positively charged bacterium Stenotrophomonas (Xanthomonas) maltophilia 70401 to glass and teflon. The Journal of Bacteriology, 178(18): 5472–5479. Kalmbach S, Manz W, Szewzyk U, 1997a. Isolation of new bacterial species from drinking water biofilms and proof of their in situ dominance with highly specific 16S rRNA probes. Applied and Environmental Microbiology, 63(11): 4164–4170. Kalmbach S, Manz W, Szewzyk U, 1997b. Dynamics of biofilm formation in drinking water: phylogenetic affiliation and metabolic potential of single cells assessed by formazan reduction and in situ hybridization. FEMS Microbiology Ecology, 22(4): 265–279. Kosek M, Bern C, Guerrant R L, 2003. The global burden of diarrhoeal disease, as estimated from studies published between 1992 and 2000. Bulletin of the World Health Organization, 81(3): 197–204. LeChevallier M W, Welch N J, Smith D B, 1996. Full-scale studies of factors related to coliform regrowth in drinking water. Applied and Environmental Microbiology, 62(7): 2201–2211. Lehtola M J, Miettinen I T, Kein¨anen M M, Kekki T K, Laine O, Hirvonen A et al., 2004. Microbiology, chemistry and biofilm development in a pilot drinking water distribution
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public drinking water distribution system. Journal of Applied Microbiology, 95(3): 591–601. Snyder J W J R, Mains C N, Anderson R E, Bissonnette G K, 1995. Effect of point-of-use, activated carbon filters on the bacteriological quality of rural groundwater supplies. Applied and Environmental Microbiology, 61(12): 4291– 4295. Taylor R H, Allen M J, Geldreich E E, 1979. Testing of home use carbon filters. Journal of the American Water Works Association, 71: 577–579. Tobin R S, Smith D K, Lindsay J A, 1981. Effects of activated carbon and bacteriostatic filters on microbiological quality of drinking water. Applied and Environmental Microbiology, 41(3): 646–651. Tokajian S, Hashwa F, 2004. Incidence of antibiotic resistance in coliforms from drinking water and their identification using the Biolog and the API identification systems. Journal of Chemotherapy, 16(1): 45–50. Villarino A, Bouvet O M M, Regnault B, Martin-Delautre S, Grimont P A D, 2000. Exploring the frontier between life and death in Escherichia coli: Evaluation of different viability markers in live and heat- or UV-killed cells. Research in Microbiology, 151(9): 755–768. Wallis C, Stagg C H, Melnick J L, 1974. The hazards of incorporating charcoal filters into domestic water systems. Water Research, 8: 111–113. Wei F S, 1998a. Monitoring and Analysis Method of Water and Wastewater (3rd ed.). Beijing: China Environmental Press. 500–502, 225–228, 278–280. Wei F S, 1998b. Monitoring and Analysis Method of Water and Wastewater (3rd ed.). Beijing: China Environmental Press. 575. Williams M M, Domingo J W S, Meckes M C, Kelty C A, Rochon H S, 2004. Phylogenetic diversity of drinking water bacteria in a distribution system simulator. Journal of Applied Microbiology, 96(5): 954–964. Wimpenny J, Manz W, Szewzyk U, 2000. Heterogeneity in biofilms. FEMS Microbiology Letters, 24(5): 661–671. Yates M V, 2007. Classical indicators in the 21st century – far and beyond the coliform. Water Environmental Research, 79(3): 279–286.
Performance of microbiological control by a point-of-use filter system for drinking water purification SU Fengyi1 , LUO Mingfang2 , ZHANG Fei1 , LI Peng1 , LOU Kai3 , XING Xinhui1,∗ 1. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China. E-mail: [email protected] 2. College of Chemistry and Chemical Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3. Institute of Microbiology, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China Received 14 November 2008; revised 19 February 2009; accepted 03 March 2009
Abstract Purification capacity of a faucet mounted type water filter for home use was evaluated, particularly with regard to microbiological performance under different running conditions. Biofilms were formed inside the filter, affecting the bacterial quality of the effluent water. Low flow rate, long stagnation period and high filter temperature were found favorable for bacterial growth inside. By commercial analytical profile index (API) kits, ten different bacterial species were identified in drinking water, four of which were probably contributed to the biofilm formation since they were also present in the biofilm. Fluorescence in situ hybridization (FISH) was used to confirm the API identification results, and direct viable count (DVC) method was employed to improve the sensitivity of FISH for the isolated Acinetobacter spp. and Pseudomonas putida as models. Relationship between the filter operating condition and the bacterial community alteration was partly revealed, which could provide the basic knowledge for the filter design and its practical use. Key words: drinking water; point-of-use filter; biofilm; bacteria community; activated carbon DOI: 10.1016/S1001-0742(08)62410-9
Introduction Waterborne pathogens, including a variety of virus, bacteria and protozoa, account for much of the estimated 4 billion cases and 2.5 million deaths from endemic diarrhoeal disease each year (Kosek et al., 2003). Since the microbial risk control from the water treatment plants to the consumer’s taps is the major challenge of drinking water safety management (Lehtola et al., 2004), interventions to control and maintain the microbial quality of water at household levels are a promising and effective alternative. Properly designed water purifying filter systems have become popular with consumers as a countermeasure to remove the waterborne pathogens and the potential undesirable chemicals from the water reaching its destination (Crump et al., 2004; Matsui et al., 2004). These systems can be fitted to service an entire home at the point of entry or at a single faucet, with the latter termed pointof-use (POU) devices (Snyder et al., 1995). Activated carbon (AC), in granular (GAC) or powdered (PAC) form, is commonly incorporated into the POU devices (Snyder et al., 1995; Tobin et al., 1981). The attachment of microorganisms to AC particles was through strong Lifshitz-van der Waals forces by overcoming electrostatic repulsion between negatively charged cells and carbon surfaces (Jucker et al., 1996). * Corresponding author. E-mail: [email protected]
Since bacteria can form biofilms on solid surfaces or substrata, even in oligotrophic or disinfectant environment (Schwartz et al., 2003; Wimpenny et al., 2000), the biofilm is a major problem in water distribution systems as well as in domestic water filtration systems which may cause a hygiene problem of the effluent water (Daschner et al., 1996; Geldreich et al., 1985). Biofilms can deplete the disinfection agents (Regan et al., 2002), protect and support pathogenic microorganisms (Buswell et al., 1998), and provide conditions for bacterial regrowth (Dukan et al., 1996). Several research groups examined the diversity of biofilms by identifying the bacteria involved in these problems (Kalmbach et al., 1997a; Regan et al., 2002), which may provide new tools for improving the water quality (LeChevallier et al., 1996; Martiny et al., 2003). However, the knowledge about changes in bacterial communities during filtration with water filters is still limited. During recent years, the structure of biofilms from many different environments has been evaluated by using a broad variety of physico-chemical and molecular biological techniques (Schwartz et al., 2003; Wimpenny et al., 2000). Each method has its advantages and drawbacks, and their application is based on different requirements. As a standard and simple traditional technique for microbiological testing and safety management of drinking water, heterotrophic plate counts (HPC) has been widely adopted (Daschner et al., 1996; Snyder et al., 1995).
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However, the bacteriological quality of drinking water is dependent on the species encountered. It is becoming preferable to try to limit the opportunistic pathogens, rather than all the environmental bacteria (Hunter et al., 2003; Yates, 2007). Among the methods for specific pathogens detection, fluorescence in situ hybridization (FISH) has been successfully applied to the phylogenetic identification and quantification of individual bacteria in a number of different environments, avoiding culture-selection bias (Amann et al., 1995; Loge et al., 1999). Since FISH was first employed to identify the bacteria in drinking water in 1993 by Manz et al., it has been used to analyze the characteristics of biofilm in drinking water, formed on polyethylene, polycarbonate or glass surfaces (Kalmbach et al., 1997b; Batt´e et al., 2003). However, FISH can only provide limited information about the abundance of specific bacteria, due to time-consuming and probe-specificity constraints, and its sensitivity is closely dependent on the cellular abundance of rRNA in the target bacteria (Amann et al., 1995). To overcome those problems, combination of FISH with other methods is necessary and effective. Analytical profile index (API) kit, one of the commercial systems for bacterial identification, is convenient and has been widely used in fresh and saline waters, and in drinking water sources (Pisciotta et al., 2002; Tokajian and Hashwa, 2004). Applying API kit to identify the colonies formed during HPC experiments can provide the basic knowledge on the bacterial composition prior to the use of FISH. Direct viable count (DVC) technique, which is always performed by exposing bacterial cells to a resuscitation medium containing antibiotics to prevent cellular division and thus induce an elongation of the viable cells (Baudart et al., 2002), can enhance the rRNA content in the viable (elongated) cells and increase the FISH signal(Villarino et al., 2000). The DVC-FISH procedure can monitor the bacterial viability and has been used to assist the application of FISH in natural and drinking waters (Baudart et al., 2002). This study aimed to investigate the microbiological performance of a POU domestic water filter that consisted of a microfilter membrane layer and an activated carbon
Fig. 1
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filter, in terms of the change of bacterial community during the purification, including the characteristics of drinking water, of the bacteria present inside the filter or in the filtrated water. To better understand the performance of filter, some operating parameters were changed, i.e., flow rate, filtered water volume per day and filter temperature. The change of bacterial community was analyzed by HPC combined with API kits and the culture-independent methods.
1 Materials and methods 1.1 Experimental setup Three filters were operated at the same time in parallel to examine the effects of operating conditions (Fig. 1a). A POU water filter provided by P&G Company (USA) was mounted to a faucet at Tsinghua University (Beijing, China). It consists of a microfilter membrane (MFM) and a pressed annular granular activated carbon filter (ACF), as shown in Fig. 1b. The indoor drinking water at Tsinghua University is self-supplied underground water with chlorine added from the water supply plant. The parameters of water quality are listed in Table 1 (measured at room temperature (RT), 21–25°C). To simulate the use in a household, three runs in a batch mode were performed every day (around 8:00 am, 12:00 am and 6:00 pm), filtering 10 L of drinking water at the maximum initial rate (1.32–1.09 L/min) at RT for each run, unless Table 1
Characteristics of the tap water in Tsinghua University, China and of the filtered water
Parameter
Mean ± SD (n = 4 or 5) Tap water Filtered water
Turbidity (NTU) Total hardness (mg CaCO3 -eq/L) Dissolved oxygen (DO) (mg/L) pH Total organic carbon (TOC) (mg/L) Total nitrogen (TN) (mg/L) Free chlorine (mg/L) Number of total coliforms (MPN/L)
0.17 ± 0.23 203 ± 49.50 4.95 ± 1.04 7.80 ± 0.04 3.60 ± 0.60 3.32 ± 0.18 0.11 ± 0.01 <1
Experimental setup (a) and structural diagram of the water filter (b).
0.07 ± 0.10 107 ± 24.04 4.06 ± 0.35 7.81 ± 0.03 3.68 ± 0.71 3.26 ± 0.21 0.09 ± 0.01 <1
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otherwise stated. Operation was finished until the filter lifetime expired (around 300 L filtration capacity of drinking water). Characteristics of the filtered water sampled from the effluent near the end of each draining were analyzed every 2–3 d through the whole process of a filter until the end of performance, in terms of HPC, total coliforms, free chlorine, turbidity, total hardness, total organic carbon (TOC), total nitrogen (TN) and dissolved oxygen (DO). By discarding the stagnated water (more than 9 L) for each run, the results were considered to stably reflect the main condition inside the filter system. Same parameters for the tap water without purification were also analyzed for corresponding comparison. TN and total hardness were tested according to the national standard procedures (Wei, 1998a). Total coliform number was determined using a Colilert 18 system (IDEXX Laboratories, Inc., USA). Free chlorine was detected using a chlorine colorimeter (HI93711, Hanna Instruments, Hungary) and turbidity was measured using a microprocessor turbidity meter (HI93703, Hanna Instruments, Hungary). DO was analyzed by a DO meter (mk-250, Mettler Toledo International Inc., USA), and TOC analyzer (TOC-500, Shimadzu Corporation, Japan) was used to determine TOC. 1.2 Effects of flow rates on the effluent water quality Filtering initial rates of three parallel purification systems were set as 1.39, 1.15 and 0.92 L/min, respectively. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.3 Effects of filtered water volumes per day on the effluent water quality Filtered volume was set as 10, 5 and 2 L for each parallel system, respectively. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.4 Effects of filter temperatures on the effluent water quality The filter running temperature was set as 40°C, 30°C and RT (19–25°C) for each parallel system, respectively. Temperatures higher than RT were controlled by holding the filter temperature through a water jacket covering the whole filter. Other operation parameters were the same as described in Section 1.1. HPC of filtered water for each run was measured every 2–3 d. 1.5 Analysis of adhesion intensity of biofilm formed in the filter To examine the adhesion intensity of biofilm formed inside, three parallel filters were operated at 30°C. At the end of the filters’ life time, the filters were washed at different flow rates of 0.20, 0.63, or 1.09 L/min, respectively. Other operation parameters were the same as described in Section 1.1. The effluent washed out of the filter at different time was collected for HPC measurement.
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1.6 Sampling of the biofilm in the filter for HPC measurement When the lifetime of filter had expired, the whole ACF was taken out and washed by sterilized water to evaluate the bacteria attached to its outer surface. After centrifugation at 880 ×g for 10 min, the supernatant was collected for HPC measurement. 1.7 Heterotrophic plate counts (HPC) HPC was determined by serially diluting the water sample into sterile deionized water and poured into the nutrient agar medium (Tryptone 10 g/L, beef extract 3 g/L, NaCl 5 g/L and agar 10 g/L). Duplicate counts were performed for each dilution and cultivated at 37°C for 48 h. 1.8 Bacterial identification by commercial API kits According to the different morphological properties, more than two thirds of the colonies grown on the nutrient agar plate of every sample during the HPC experiments were selected for Gram staining. The colonies identified as Gram-negative were analyzed by API 20NE or 20E assay kits (BioM´erieux Inc., France). According to the product manual, API 20NE strip was used to identify non-fastidious, non-enteric Gram-negative rods; and API 20E strip was employed to identify Enterobacteriaceae and other non-fastidious, Gram-negative rods. 1.9 Observation of biofilms inside the filter by SEM When the lifetime of filter had expired, different positions of MFM and ACF were cut by a sharp but sterilized knife, including the edge and interior region of the MFM, the interior region, the inner surface and the outer cylindrical surface of the ACF (part of the positions were marked in Fig. 4e). After fixed by 2.5% glutaraldehyde and 2% osmium tetroxide, those samples were examined with a Scanning Electron Microscope (SEM) (FEI QUANTA 200, USA). 1.10 Total direct counts (TDC) Aliquots of samples (tap water, 50–100 mL) were filtered onto black polycarbonate membrane filters (pore size, 0.2 μm; Nuclepore, Whatman, UK) and stained by 1 μg/mL DAPI (4’,6-diamidino-2-phenyl-indol; Sigma Chemical, USA) for 5 min (Manz et al., 1993). Counts were performed with an epifluorescence microscope (Eclipse E600, Nikon). More than ten microscopic fields were counted for one measurement, with each field containing at least 100 cells. 1.11 FISH analysis FISH was conducted according to the protocol described previously (Kalmbach et al., 1997a; Manz et al., 1993). The sequence and bacterial specificity of each probe employed are listed in Table 2 (referring to the information from ProbeBase website online). The fluorescence was detected with an epifluorescence microscope equipped with Nikon light filter sets (B-2A or G-2A) for FAM-
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Table 2 Probes involved in this study Probe
Sequence
Label
Specificity
ACA652 Burcep Ppu56a SPH120 Stemal COM1424
5’-ATCCTCTCCCATACTCTA-3’ 5’-CTGTGCGCCGGTTCTCTT-3’ 5’-GCTGGCCTAACCTTC-3’ 5’-GGGCAGATTCCCACGCGT-3’ 5’-GTCGTCCAGTATCCACTGC-3’ 5’-ACCTACTTCTGGCGAGA-3’
FAM FAM Cy3 Cy3 Cy3 TAMRA
Acinetobacter spp. Burkholderia cepacia Pseudomonas putida & P. mendocina Sphingomonas spp. Stenotrophomonas maltophilia Comamonas spp.
or Cy3-labeled probes. The hybridization efficiency was calculated as the number of FISH positive cells divided by the total direct counts (TDC). 1.12 DVC-FISH The isolated bacteria from drinking water by HPC method, were cultivated in the diluted nutrient medium broth (10 times dilution, abbreviated as 1/10×) with the addition of 20 μg/mL of nalidixic acid. After cultivation at 37°C for different lengths of time, the cells were harvested for FISH analysis.
2 Results 2.1 Physiochemical and microbiological features of the filters As shown in Table 1, the turbidity and total hardness of the effluent were about half of those in unfiltered tap water, and DO was about 20% lower. The other parameters of pH, TOC, TN, free chlorine and total coliforms number had no remarkable difference by the filtration. Free chlorine concentration in drinking water was stable during the whole lifetime of one filter system (one life cycle was 10– 15 d). Incidentally, both total coliforms of tap water and filtered water were less than 1 MPN/L, which were eligible to the national regulation (Wei, 1998b). 2.2 Effects of flow rates on the effluent water quality The bacterial number of the original tap water in terms of HPC remained less than 15 CFU/mL throughout the whole operation of the filter (Fig. 2a). While with the lower initial flow rate, the HPC of effluent increased faster since the one-third lifetime of the filter. At the end of the filter operation, the HPC of effluent run at 0.92, 1.15 and 1.39 was 68, 61 and 45 CFU/mL, respectively; while the corresponding HPC of the tap water was only 11 CFU/mL. In addition, two filters had higher HPC of the effluent than that of the tap water at the beginning of their usages. 2.3 Effects of filtered water volumes per day on the effluent water quality As shown in Fig. 2b, the system of the lower filtered water volume per day had a higher HPC at a certain accumulative volume, after filtering 50 L water. However, the HPC at the end of three systems were at the same level, around 50 CFU/mL. According to the water filtration capacity of the filter, the end operation time for each system with the filtered water volume per day of 30, 15 and 6 L was expected 10, 20 and 50 d, respectively. But
Fig. 2 Effects of flow rate (a), filtered water volume per day (b) and filter temperature (c) on HPC in the effluent of the filter (n = 3). RT: room temperature.
the actual running time was 12, 21 and 37 d, respectively. It showed that the filter run less water volume per day had the lower filtration capacity.
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2.4 Effects of filter temperatures on the effluent water quality From Fig. 2c, it could be clearly seen that the HPC of the effluent through the filters controlled at 30 or 40°C was larger than that of the filter run at RT, since one third of the filter lifetime. At the end of the filter life cycle, the effluent HPC was 152, 164, and 49 CFU/mL for the filters run at 40, 30 and RT, respectively; while the HPC of the tap water was 20 CFU/mL. 2.5 Adhesion intensity of the biofilm formed in the filter Time courses of HPC in the effluent from the used filters at different washing flow rates are shown in Fig. 3. It displayed that bacteria washed out of the filters decreased with increasing washing time. The reduction of HPC was dependent on the washing flow rate; the faster the initial washing speed, the greater the reduction extent. The effluent HPC of the filter which was washed at the rate of 1.09 L/min could reach a constant value after 2 min of washing. This washing rate was most close to the typical initial running flow rate (1.32–1.09 L/min). 2.6 Observation of biofilm morphology in the filter by SEM Since the bacteriological quality of the filtered water was obviously worse than that of the original tap water, bacterial aggregation and regrowth were believed to occur inside the filter, leading to the formation of biofilm. The structure of the biofilm formed inside the filters run at RT was observed by SEM at the end of the filter lifetime. Different positions were chosen and part of the results are shown in Figs. 4a–4d. The results showed that there were fewer bacteria on the MFM than on the ACF surface, and most bacterial colonies could be observed at the bottom of the outer surface of the ACF cylinder (marked as position d in Fig. 4e). Almost no bacteria were found in the interior region of the ACF cylinder and the bacteria number observed on the inner surface of the ACF was less than that on the outer surface (data not shown).
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2.7 Bacterial identification with API kits To accumulate information on the bacterial community inside the filter during the operation, the bacteria in the original tap water, the effluent and the biofilm inside the ACF were identified by API kits for the colonies after HPC test. The tap water and filtrated water were directly poured into the nutrient agar medium with different dilution ratios, while the bacteria of the biofilm was obtained by washing the whole outer surface of the ACF and collecting the cell-contained supernatant after the removal of AC debris by centrifugation. More than half of all the selected colonies grown on nutrient agar plate for Gram staining were identified as Gram-negative bacteria and were further analyzed by API 20NE/20E kits, 37.1%–44.2% of the analyzed isolates identified in the tap water. Ten species of bacteria were identified in the tap water, six in the filter and four in the effluent (Table 3). The four species of bacteria detected both in the original tap water and in the biofilm, were Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia. Of all of the thirteen species identified in the tap water and in the filter, seven were pathogenic to human beings and four were opportunist pathogens. The percentage of each bacterial species of the total identified colonies by API kits, was also summarized in Table 3, which was obtained by two parallel systems at the end lifetime of filters operated at RT. It is showed that the variety of bacterial species in the effluent was less than that in the influent, comparable with or even less than that in the filter. API showed high standard deviation on identified bacterial quantification. 2.8 Confirmation of the API identification results by FISH The FISH method was used to confirm the identification results of API kits, for its further practical application in the analysis of pathogens in the drinking water. E. coli was used as a control strain. The hybridization results are shown in Table 4, and the hybridization efficiency for the positive FISH cases was more than 98%, which was calculated as the number of FISH positive cells divided by the TDC (here, TDC presented the total cell number of each isolated bacterium, after identified by API kits and cultivated in nutrient medium broth). Almost all the API identified species (genus) could be confirmed by FISH, except Burkholderia cepacia. 2.9 DVC-FISH for isolated bacteria
Fig. 3 Time courses of HPC in the effluent of the used-up filters during washing at different flow rates (n = 3). RT: room temprature.
Since the FISH signal is dependent upon the physiological activity of the bacteria, FISH does not form strong enough signals for the slowly growing cells in oligotrophic environments, due to their low intracellular rRNA content. To increase the hybridization efficiency, the direct viable count-FISH (DVC-FISH) technique was applied. Nalidixic acid of 20 μg/mL in the 1/10× nutrient medium broth was used to elongate the bacteria in the starvation state without proliferation. In higher concentration of the nutrient medium broth, 20 μg/mL nalidixic
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Fig. 4 SEM photos of microfilter membrane (MFM) surface and different positions of the activated carbon filter (ACF) at the end lifetime of the filter operated at room temperature. (a) the edge of the microfilter membrane; (b) the top of the ACF outer surface; (c) the middle of the ACF outer surface; (d) the bottom of the ACF outer surface; (e) illustration of different positions sampled for the SEM observation.
acid could not effectively restrain the cellular propagation (data not shown). Figure 5 shows the hybridization efficiency with both DVC-FISH and FISH for Acinetobacter spp. and Pseudomonas putida as the models, which had been isolated from the drinking water and cultivated in 1/10× nutrient medium broth. The results showed that the hybridization efficiency for the waterborne bacterium in the poor nutrition environment was obviously improved by DVC-FISH, i.e., by 5%–20% for Acinetobacter spp. and 4%–38% for P. putida (TDC presented the total cell number of each isolated bacterium sample after cultivated with or without adding nalidixic acid into 1/10× nutrient
medium broth, respectively). The cells became elongated during the cultivation, as shown in Fig. 6.
3 Discussion 3.1 Performance of the filter The filter tested in the present study showed good purification capability of removing the turbidity and total hardness from the tap water (Table 1), through the whole filtration process in one life cycle. The filter was able to hold back or absorb some suspended solids (like Ca/Mg-
Table 3 Waterborne bacteria identified by API kits Species
Tap water (%)
Filter (%)
Filtered water (%)
Pathogen
Acinetobacter lwoffii Acinetobacter junii Burkholderia cepacia Brevundimonas vesicularis Stenotrophomonas maltophilia Sphingomonas paucimobilis Chryseomonas luteola Pseudomonas putida Ochrobactrum anthropi Aeromonas Salmonicida Chryseobacterium indologenes Flavobacterium indoltheticum Vibrio metschnikovii
+ (6 ± 6) + (8 ± 8) + (3 ± 3) + (2 ± 2) + (5 ± 1) + (5+5) + (3+1) + (2+2) + (2 ± 2) + (1 ± 1) – – –
+ (8 ± 3) – + (18 ± 10) + (12 ± 12) + (15 ± 5) – – – – – + (5 ± 5) + (2 ± 2) –
+ (30 ± 9) – – + (25 ± 9) + (20 ± 6) – – – – – – – + (2 ± 2)
+ + + + Opportunist pathogen + + Opportunist pathogen Opportunist pathogen Opportunist pathogen – – +
“+”: the species existed in the testing system or that it was pathogenic; “–”: the species was not detected in the testing system. Data are expressed as mean ± SD (n = 2).
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Performance of microbiological control by a point-of-use filter system for drinking water purification Table 4 ACA652
Acinetobacter spp. Acinetobacter junii Pseudomonas putida Stenotrophomonas maltophilia Burkholderia cepacia Comamonas acidovorans E. coli
+ +
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Confirmation of the API identification results by FISH Ppu56a
Burcep
COM1424
+
Stemal
– + – –
– +
Hybridization efficiency > 98% ca. 100% > 99.9%
–
+: positive hybridization; –: negative hybridization.
Fig. 5 Comparison of hybridization efficiency of DVC-FISH with FISH for Acinetobacter spp. (a) and Pseudomonas putida (b) (n = 3). The bacteria were cultivated in 1/10× nutrient medium broth with (DVC-FISH) or without (FISH) 20 μg/mL nalidixic acid for different times.
deposit) in the drinking water. It has been reported that carbon filter aid in the removal of organic compounds from water, but it may be less effective in removing microbial contaminants (Snyder et al., 1995). In this study, the filter was also proved not ideal for the bacterial removal. Since one third lifetime of the filter, the HPC of the effluent was greater than that of the influent, and the difference would be elevated with the time elapsing (Fig. 2), which was consistent with the results reported by the other researchers (Reasoner et al., 1987;
Snyder et al., 1995). When the filter was operated at RT at the maximum flow rate, the HPC of the effluent would be 3–5 times of that of the influent at the end of the filter lifetime (Fig. 2). That elevation level was comparable with the other reports (Wallis et al., 1974; Taylor et al., 1979). It was suggested that the filter could retain the bacteria inside, and the trapped organic materials within the filter supported the growth of heterotrophic bacteria, which had led to the formation of the biofilms inside, resulting in the effluent HPC increase.
Fig. 6 Microscopic photographs of Acinetobacter spp. by FISH (a) and DVC-FISH (b). The bacteria were cultivated in 1/10× nutrient medium broth with (DVC-FISH) or without (FISH) 20 μg/mL nalidixic acid.
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From the bacterial adhesion intensity test, it is showed that the intensity of bacteria (biofilm) adhering on the surface of filter inside was weak, which allowed the bacteria washing out even at the low flow rate such as 0.20 L/min (Fig. 3). HPC of the bacteria washed out of the filter showed a trend of approaching to a steady value as the time elapsing, and high washing flow rate could accelerate this process. In this study, the typical initial running flow rate was 1.32–1.09 L/min. Thus for most the parallel systems running, the effluent HPC could reach the stable value in about 2 min. Since all samples of filtration were taken near the end of each draining, which were processed more than 5 min (more than 9 L discarded). It meant that most of the effluent in the other experiments was sampled for HPC after the bacterial number being stable. From Fig. 2a, the filter run at the lower initial flow rate showed the higher HPC of effluent since one third lifetime of the filter. As discussed above, the biofilm inside the filter had weak adhesion intensity, thus a high flow rate was not favorable for the attachment of bacteria on the filter, and probably not suitable for the development of the biofilm inside the filter due to the acute washing out of the bacteria. As shown in Fig. 2b, the system run at the lower filtered water volume per day showed a higher HPC of the effluent. It was reported that overnight, static water conditions might provide an opportunity for bacterial growth within the AC (Wallis et al., 1974; Snyder et al., 1995). Since the system operated at the lower filtered water volume per day had relative more time for bacterial growth when filtered the same volume of water during the lifetime, more bacteria would grow inside the filter and higher HPC of the effluent would be correspondingly caused. However, the same level of HPC was gained at the end of three systems, suggesting that the bacterial concentration inside the filters had reached the maximum. Over this value, the filters could not work. Practically, the system run at the lower filtered water volume per day had a smaller filtration capability, and it would reach the maximum bacterial concentration inside the filter with a less water filtration time. It is well known that temperature is one of the most important environmental factors that affect the growth of microorganisms, even on AC (Tobin et al., 1981). Figure 2c indicates that a higher temperature could speed up the bacterial growth and probably encouraged the development of biofilm inside the filter, which thus resulted in a higher HPC in the filtered water. The results indicated that 30°C was the optimal temperature for the microbial growth inside the filter. The filter had a higher HPC of effluent than that of tap water at the initial of the usage (Fig. 2). This phenomenon was assumed to be due to the bacteria already existed inside the filter during the storage prior to use, which would be washed out of the filter by the filtration. 3.2 Bacterial community alteration From the observation by SEM (Fig. 4), the heterogeneous spatial distribution of bacteria inside the filter was probably pertaining to the filter structure. The outer surface
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of the ACF cylinder would encounter bacteria at higher concentrations than the inner surface would, due to the preferential filtration of drinking water, which led to a higher bacterial concentration blocked in the former section. Furthermore, the bottom of the outer surface of ACF was a dead space and had smaller shearing force, which would benefit the bacterial accumulation and proliferation. Since it was more developed, the biofilm on the outer surface of the ACF could be mainly investigated to represent the characteristics of the biofilm inside the filter. However, these hypotheses need further proof. By API kits, thirteen species of the isolated bacteria had been identified in the whole water purification system, some of which were also found by other researchers in drinking water or the effluent of a household water filter (Daschner et al., 1996; Williams et al., 2004). Four species of bacteria detected in both original tap water and biofilm (Table 3), i.e., Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia, were probably related to the formation of biofilm inside the filter. Six of the ten species in the original tap water were pathogenic, and other four were opportunistic pathogens. These results displayed that although the number of total coliforms and free chlorine in the tap water (Table 1) were acceptable (< 3 MPN/L, > 0.05 mg/L (Wei, 1998b)), there still existed a microbial risk in the tested drinking water system. The potential health risk caused by these pathogens after filtration should be taken into consideration, even though the concentrations were very low. The API identification results (Table 3) suggested that the purification system could reduce the variety of the bacterial species in the effluent probably by intercepting some kinds of bacteria and/or disable their progenitive ability during the filtration. For instance, Acinetobacter junii and Burkholderia cepacia, which existed in the tap water, were not detected in the effluent. After purification, only three pathogens and one opportunist pathogen were found in the effluent. This suggested that the filter was favorable to simplify the bacterial community and reduce the health risk in the drinking water. However, some species found in the filtered water were not detected in the tap water, which might be due to the high non-detecting error of the API system as reported in the other literature (O’Hara et al., 2003). Since API kit is based on the heterotrophic cultivation, it inevitably has the cultivation-selection bias for oligotrophic environmental samples. In addition, not all the colonies grown on the nutrient agar plate were analyzed by API kit due to the high strength of laboring. Moreover, in the API test, human subjective judgment was required both for Gram staining and API biochemical reactions, which would also cause the systemic errors. All these contributed to the standard deviation of the bacterial identification by API kits (Table 3). In addition, the possible reason for FISH negative hybridization result compared with API might be the wrong identification of the API kits, since high wrong identification rate is another limitation of API kits (O’Hara et al., 2003).
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Performance of microbiological control by a point-of-use filter system for drinking water purification
In this study, FISH was combined with API to confirm the identification result of bacteria isolated from HPC test, but could not be directly applied for the bacterial community analysis in drinking water, because the fluorescent signal was too weak. Although DVC method could enhance the hybridization efficiency of FISH to nearly 40% in the diluted nutrient medium broth (Fig. 5), it still needs further improvement to achieve a more exact estimation for the bacterial community in the oligotrophic environment. However, the above microbiological analysis implied that FISH at least could be combined with the well-adopted HPC test to strength the microbial risk management of drinking water.
4 Conclusions The POU filter for drinking water purification tested in present study showed a good capability in removing the turbidity and total hardness. However, it had little effect on removing bacteria. After one third of the filter lifetime, biofilms were supposed to form inside, allowing the bacteria to propagate gradually with the time elapsing, which resulted in the increase in the bacterial concentration of the effluent. The effluent HPC at the end lifetime of the filter run at RT and maximum flow rate was 2–5 times as that of the original tap water. Operating conditions including a high flow rate, a low filter temperature and short stagnant time of nonuse were indicated to be helpful for restraining the biofilms formation inside the filters, which could be employed to assist improving the filter performance for microbiological control. The analysis of bacterial community during the filtration indicated that even though there was still microbial risk in the effluent, the filters could reduce the variety of bacterial species, thus potentially reducing the microbial risk. Four of the isolated pathogens, Acinetobacter lwoffii, Burkholderia cepacia, Brevundimonas vesicularis and Stenotrophomonas maltophilia, were identified by API kits and found to be closely related to the biofilm formation inside the filter. Although the direct viable count (DVC) method was capable of effectively improving the FISH hybridization efficiency, a more precise and direct molecular approach should be established to study the bacterial community in drinking water and the development of biofilms in the water purification filter. Acknowledgments The authors thank the Proctor and Gamble Company for providing the water purifying filter and the assistance for SEM observation. This work was supported by the Proctor and Gamble Company and in part by Boshidian Fund of Ministry of Education of China (No. 200800030046).
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