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Applied and Environmental Microbiology, January 2005, p. 65-71, Vol. 71, No. 1
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.1.65-71.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Discrimination Efficacy of Fecal Pollution Detection in Different Aquatic Habitats of a High-Altitude Tropical Country, Using Presumptive Coliforms, Escherichia coli, and Clostridium perfringens Spores

Denis Byamukama,¹ Robert L. Mach,² Frank Kansiime,¹ Mohamad Manafi,³ and Andreas H. Farnleitner^2*

Makerere University Institute of Environment and Natural Resources, Kampala, Uganda,¹ Institute of Chemical Engineering, Vienna University of Technology,² Institute of Hygiene, University of Medicine of Vienna, Vienna, Austria³

Received 26 March 2004/ Accepted 25 August 2004

Top Abstract Introduction Materials and Methods Results Discussion References

 
The performance of rapid and practicable techniques that presumptively identify total coliforms (TC), fecal coliforms (FC), Escherichia coli, and Clostridium perfringens spores (CP) by testing them on a pollution gradient in differing aquatic habitats in a high-altitude tropical country was evaluated during a 12-month period. Site selection was based on high and low anthropogenic influence criteria of paired sites including six spring, six stream, and four lakeshore sites spread over central and eastern parts of Uganda. Unlike the chemophysical water quality, which was water source type dependent (i.e., spring, lake, or stream), fecal indicators were associated with the anthropogenic influence status of the respective sites. A total of 79% of the total variability, including all the determined four bacteriological and five chemophysical parameters, could be assigned to either a pollution, a habitat, or a metabolic activity component by principal-component analysis. Bacteriological indicators revealed significant correlations to the pollution component, reflecting that anthropogenic contamination gradients were followed. Discrimination sensitivity analysis revealed high ability of E. coli to differentiate between high and low levels of anthropogenic influence. CP also showed a reasonable level of discrimination, although FC and TC were found to have worse discrimination efficacy. Nonpoint influence by soil erosion could not be detected during the study period by correlation analysis, although a theoretical contamination potential existed, as investigated soils in the immediate surroundings often contained relevant concentrations of fecal indicators. The outcome of this study indicates that rapid techniques for presumptive E. coli and CP determination may be reliable for fecal pollution monitoring in high-altitude tropical developing countries such as those of Eastern Africa.

Top Abstract Introduction Materials and Methods Results Discussion References

 
The use of indicator bacteria such as fecal coliforms (FC) and Escherichia coli for the assessment of fecal pollution and possible water quality deterioration in various freshwater sources is a widely used and accepted concept in temperate regions (1, 23). Fecal indicators such as E. coli tend to die out in temperate waters due to various factors such as temperature conditions, nutrient availability, and grazing by protozoa. In the tropics, however, where these indicators are also applied as microbiological water quality monitoring tools, various studies have yielded results that doubt their reliability (2, 5, 6, 9, 15, 19-22, 24, 26). The conditions of temperature, solar radiation, higher nutrient levels, and a more diverse microbial community present a different environment in the tropics compared to that of the temperate regions (11). Classical fecal indicators are suspected to proliferate in tropical waters and thus be detectable at levels which do not reflect the original extent of fecal contamination (5, 6, 11, 15, 19-22, 24, 26), or even worse, they become an autochthonous part of the aquatic microbial community in the tropical waters considered (2, 11, 20, 21). In a review of tropical source water, Hazen and Toranzos (11) quoted literature about the isolation of high numbers of E. coli in the complete absence of any known fecal source. This apparent unreliability of traditional fecal pollution indicators in tropical conditions has led to alternative suggestions for pollution indicators, such as using Clostridium perfringens spores (3, 10) and some Bifidobacterium spp. (5).

However, it is important that most of these studies that doubted the performance of classical indicators have focused mainly on a few developed countries in tropical regions. Studies for developing countries in tropical regions are yet to be done, especially taking into account the complexity of such environments in relation to the expected indicator performance. Consequently, these areas have continued to accept and rely on guidelines established for temperate regions. Most tropical countries also happen to be the world's developing nations, where water supply and sanitary facilities are still insufficient. The majority of the population relies on untreated point-of-use surface water and groundwater sources that are highly prone to fecal contamination, especially in congested urban and periurban areas. Waterborne disease outbreaks such as cholera have been reported in such settlements (13, 17). Additionally, research infrastructure is lacking, and advanced water quality monitoring is unaffordable. Biological, physical-chemical (e.g., increased temperature), and socioeconomic factors, therefore, combine to make accurate determination of biological water pollution in developing tropical countries much more difficult than in temperate ones (11). Thus, there is a need to evaluate existing novel methods and parameters to come up with affordable, easy-to-perform, and reliable techniques for fecal contamination monitoring in these regions.

This study evaluates the performance of rapid and practicable techniques which presumptively identify members of the coliform group (i.e., total coliforms [TC], FC, and E. coli) and C. perfringens spores (CP) by testing them on a pollution gradient with different water source types in a high-altitude tropical country, Uganda. Sites with clear sources of pollution such as nearby congested human settlements, pit latrines, wastewater pools, and cattle grazing areas were considered to be under higher risk of being influenced, and those that did not have these observable water source polluters were considered to be under lower risk of possible influence. We hypothesized that sampling sites with a high level of anthropogenic influence should be more polluted than the closely located but less influenced ones, and the appropriate indicator of fecal contamination should be able to detect these differences. In order to get information on the possible relationships between the immediate surroundings and the water, soil samples from within a 20-m radius of the water sampling points were also investigated. It is important that the sites with a low level of anthropogenic influence were not pristine and that the comparison was based on relative pollution level discriminatory efficacy.

Top Abstract Introduction Materials and Methods Results Discussion References

 
Study sites and sampling.
A total of 16 sampling sites from various aquatic habitats (i.e., water and corresponding soil samples) were covered by the study in two regions of central and eastern Uganda (Fig. 1) from October 2001 to September 2002. These included six spring, six stream, and four lakeshore sites. Over the whole study period, six samples were collected from each sampling site, making a total of 96 water and soil samples. Site selection was based on influence criteria of high and low levels of anthropogenic influence (high and low influence) of paired sites located close to each other.

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FIG. 1. Map of the study area showing the sampling sites. Legends are shown on the map.

All the springs studied were protected in the sense of having the basic construction infrastructure such as concrete retention walls, drainage channels, and outlet pipes but not protected catchments. Presumptive high-influence springs Spi 1, Spi 2, and Spi 3 (Fig. 1) were located in shanty urban settlements and had observable possible pollution sources such as pit latrines, stagnant wastewater pools, and garbage in the immediate (within 20 m) surroundings. Low-influence springs Spu 1, Spu 2, and Spu 3 (Fig. 1) were located away from human settlements with no observable pollution sources within a distance of about 30 to 50 m from the water source. The presumptive low-influence (Stu 1, Stu 2, and Stu 3) and high-influence (Sti 1, Sti 2, and Sti 3) stream sampling sites (Fig. 1) were located at various streams before and after possible fecal pollution sources. Low-influence stream site Stu 1 was located at a forest stream just before it joins another stream from an aquaculture facility where cattle fecal waste is used as feed in the ponds. Presumptive high-influence stream site Sti 1 was located 50 m downstream after the two streams had joined each other. Stu 2 and Sti 2 were respectively located at a stream before and after a cattle grazing and watering area, respectively. There were no observable fecal pollution sources upstream of Stu 2; the stream is sandwiched between a road on one side and an expanse of swamp on the other. Stu 3 was located at a stream in a protected forest reserve with minimal human activity; Sti 3 was located downstream in the same stream, after it has crossed an urban area (Fig. 1) of about 5,000 residents. All the lakeshore sampling sites were located on the Ugandan side of Lake Victoria (Fig. 1). Presumptive high-influence sites Lsi 1 and Lsi 2 were on the shores adjacent to fishing villages characterized by high human populations, very poor sanitary conditions, and a high level of activity such as swimming, bathing, and washing directly in the lake. On some of the sampling days, cattle were observed grazing in the immediate catchments (the shorelines) of Lsi 1 and Lsi 2 sampling sites. Low-influence sites Lsu 1 and Lsu 2 were located at uninhabited sheltered shores; with Lsu 1 being about 1,500 m from Lsi 1 and Lsu 2 being about 2,000 m from Lsi 2. The catchments of the low-influence lakeshore sites were forested, and the sites were only accessible either by foot trails or from the open water side of the lake by boat.

To take climatic conditions into account, the study was conducted over a 1-year cycle which was characterized by a mixture of rainy and dry patterns. The rainfall and air temperature data for the various study regions were obtained from daily records of weather stations of the Uganda Meteorological Department located in the sampling areas. The coordinates of the sampling locations were determined by a Garmin global positioning system (Sailtron B.V., Houten, The Netherlands) and plotted on a map of Uganda with Arcview GIS version 3.2 software (ESRI, Redlands, Calif.).

Sampling and chemophysical water quality determination.
Dissolved oxygen (DO), electrical conductivity (EC), pH, and temperature were measured in situ with field meters (Wissenshaftlich Technische Werkstätten GmbH). Total suspended solids (TSS) were determined in the laboratory by the gravimetric method (1). Water samples from the various water source types were collected according to standard methods (1). The sample bottles were immediately put into dark, ice-cooled boxes and transported to the laboratory, where they were analyzed within 6 to 8 h of collection of the first sample.

Soil sampling.
A soil auger was used to obtain the first 15-cm layer of the integrated soil sample core from 10 randomly selected spots within a 20-m radius of the catchment of the water sampling site. To avoid cross contamination of the soil samples from different sites, a fresh sterile spoon was used for each site to scrape an approximately 10-cm³ aliquot from the outer part of the soil core which was not in contact with the auger. Samples were put into sterile plastic bags (Packaging Industries Limited, Kampala, Uganda) and transported to the laboratory for analysis as described above for the water samples. In the laboratory, the aliquots from each of the 10 spots were then homogenized in a self-made blender for 5 min using sterile stirring units at speed settings of approximately 250 rpm to form composite soil samples for the respective sampling sites.

Microbiological analysis.
Medium preparation and enumeration of colonies after the incubation was done according to the manufacturers' instructions. Presumptive TC, presumptive E. coli, presumptive FC, and presumptive CP were determined by the membrane filtration technique using Chromocult coliform agar (CCA) for TC and E. coli, Fluorocult-tryptose sulfite cycloserine (F-TSC) for CP (Merck, Darmstadt, Germany), and m-FC agar (Difco, Detroit, Mich.) for FC. CCA, F-TSC, and m-FC media were amended to select against possible background bacteria by the addition of cefsulodin (5 mg/liter; Sigma, Vienna, Austria), C. perfringens supplement (0.4 g/liter; Merck), and rosolic acid (0.1 g/liter; Difco). Appropriate volumes of each sample were filtered through 0.45-µm-pore-size and 47-mm-diameter Whatman cellulose nitrate membrane filters (Sartorius, Vienna, Austria). For simultaneous enumeration of TC and E. coli, filters were placed onto CCA plates and incubated at 37°C for 18 h in a Paqualab 25 incubation kit (ELE, Bedfordshire, United Kingdom), while those for FC were placed on m-FC agar plates, enclosed in stainless steel cans, and incubated at 44°C for 18 h in a water bath. To select for spores of CP, samples were first preheated at 75°C for 15 min in a water bath before filtration. After filtration, the membrane filters were placed on F-TSC agar plates, put in an anaerobic jar containing Anaerocult A anaerobic system (Merck), and incubated at 44°C for 24 h in a dry incubator. The total coliforms and E. coli isolates on CCA were enumerated according to the method of Byamukama et al. (4). Blue colonies on m-FC plates were classified as FC, while black colonies growing on F-TSC plates and fluorescing upon UV (366 nm) illumination as a result of the acid phosphatase cleavage of 4-methylumbelliferyl-phosphate to 4-methylumbelliferone were classified as CP.

To enumerate the respective indicator bacteria in the soil, 10 g of the homogenized composite soil sample was added to 100 ml of distilled sterile water, hand shaken, and sonicated for 1 min in a Bransonic PC-650 bath sonicator (Branson Ultrasonics Corp., Danbury, Conn.) and was allowed to stand for 1 h to allow the particles to settle. Sterile distilled water rather than a buffer solution was used in order to mimic bacterial extraction conditions by possible rainwater influence, and a sonication procedure was used to achieve a more conservative estimate. Different volumes (10^–4 to 10 ml) of the supernatant water were filtered and treated as described above for the water samples and the respective indicator bacteria.

For both water and soil, triplicate analyses were made per volume filtered and for each medium. Well-isolated presumptive E. coli and fecal coliform colonies were randomly selected from CCA and m-FC agar plates, respectively, and were characterized by the API 20E biochemical technique (Biomerieux Sa, Marcy-I'Etoile, France) according to the manufacturer's instructions. A total of 89% of 53 colonies from CCA and 73% of the 66 colonies from m-FC were confirmed as E. coli (identification likelihood of 0.95).

Statistical analysis.
All the statistical analyses were done with the Statistical Package for the Social Sciences, version 11.0 (SPSS Inc., Chicago, Ill.). Nonparametric tests (Kruskal-Wallis, Mann-Whitney U, or Spearman's rank) were used to analyze differences or correlations in the given data sets. A generally significant level (P 0.05) was set for all the tests. For multiple testing, a Bonferroni correction was applied. In order to calculate a non-parametric-based but standardized indication of variability (as in analogy to the parametric coefficient of variation), we defined the indication of non-parametric-based variation (NBV) as NBV = (p75 – p25)/p50, where p75, p25, p50 are the 75th and 25th percentiles and the median, respectively. Cluster analysis based on z-score standardization and squared Euclidean distance and within group average linkages (7) are given in order to evaluate for group associations in the data. Other distance and linkage algorithms (data not shown) were also applied and yielded comparable results for the given data analyses. Principal-component analysis (PCA) was performed to characterize related factor complexes in the investigated system. For PCA analysis, data were log + 1 transformed (i.e., 1 was added to each value and the sum was log₁₀ transformed) and the Varimax rotation was selected for the extracted components. Only components showing an Eigen value of >1 were selected (7). For discrimination sensitivity analysis, the investigated microbiological indicator techniques were tested for the ability to differentiate between presumptive high- and low-influence sites by means of the nonparametric Monte Carlo test, using 10,000 resampling procedures. In order to further analyze the detected differences in discrimination ability of the indicators, the bacteriological concentration ratios of medians (BCRM) of the various indicators for high- and low-influence sites were calculated. This calculation was done by using the medians from the corresponding high- and low-influence sites.

Top Abstract Introduction Materials and Methods Results Discussion References

 
Climatic and chemophysical characterization of the sampling sites.
No remarkable differences in either air temperature or rainfall between the two selected regions could be detected. The air temperature was in a close range over the study period, showing a mean of 23.1°C and varying from 21.6 to 26.4°C. Rainfall, with a mean of 4.0 mm and a range of 0.0 to 14.0 mm per day, did not exhibit a consistent pattern in the two regions. No significant correlation between monthly air temperature and the determined water temperature could be detected. The different sampling sites, however, showed various chemophysical water quality characteristics (Table 1). Using median values of the chemophysical data (i.e., dissolved oxygen, total suspended solids, temperature, pH, and electrical conductivity) and cluster analysis, sites could be classified into the water source types of springs, streams, and lakes (Fig. 2). There were, however, three outlying sites; one stream site, Sti 1, was clustered with the lake shore sites, while two springs, Spu 1 and Spu 2, formed a subcluster that was more closely associated with the streams than with the other springs.

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TABLE 1. Chemophysical characteristics of water habitats studied

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FIG. 2. Classification of sampling sites by cluster analysis and chemophysical parameters. Shown is a dendrogram using average linkage clustering and squared Euclidean distance. Lsi, high-influence lakeshore sites; Lsu, low-influence lakeshore sites; Spi, high-influence springs; Spu, low-influence springs; Sti, high-influence stream sites; Stu, low-influence stream sites.

Spring sites showed a mean temperature that was 2.6°C lower and 2.9°C higher than lakeshores and streams, respectively (Table 1). There was a significant difference in pH between high- and low-influence lakeshore sites (P < 0.05, n = 24) and DO in stream sites (P < 0.01, n = 36). High-influence springs showed remarkably increased EC (median, 366.5 µS/cm) compared to the low-influence ones (median, 71.25 µS/cm; P < 0.01, n = 36), although no detectable differences in EC between high- and low-influence stream and lakeshore sites could be observed. On the other hand, TSS showed significant differences between high-influence (median, 32.5 mg/liter) and low-influence (median 11.0 mg/liter) stream sites (P = 0.05, n = 36) and high-influence (median, 21.3 mg/liter) and low-influence (median, 6.9 mg/liter) lakeshore sites (P < 0.05, n = 24), but no such difference could be detected between the springs.

Occurrence of fecal indicators.
The different sampling sites displayed various microbial indicator level characteristics for both water and soil that generally reflected the presumed anthropogenic influence status of the sampled sites. Unlike the chemophysical parameters, especially temperature and EC, which were mainly water source type dependent as proven by cluster analysis (Fig. 2), the fecal indicators were associated with the anthropogenic influence status of the sites. This could be demonstrated by PCA, which indicated that the log + 1 transformed data could be explained mainly by three significant principal components which account for 79.1% of the total explainable variation in the data (Table 2). The first component could be assigned to pollution (43.7% explained variance), as only significant correlations to the coliform group indicators, the C. perfringens spores, as well as to TSS and EC were observed (Table 2). Inclusion of TSS and EC in the pollution component is logical, as they also showed significant increased values for high-influence stream and lakes sites and for spring sites, respectively. The second component significantly correlated particularly with pH and DO but also with temperature and TSS. Therefore, the component was attributable to a metabolic activity component (20.2% explained variance), as it is a well-understood fact that DO and pH are influenced by photosynthesis (e.g., photosynthesis produces oxygen and also increases the pH) and respiration (i.e., contrary to photosynthesis) in most natural aquatic systems under the prevailing conditions (25). In addition, in general, temperature determines metabolism rate, and TSS itself is also dependent on the suspended biomass of the organisms. The third component (15.2% explained variance) could be assigned to a habitat structure component because it was mainly associated with temperature and EC (Table 2). This third component is in agreement with the fact that each habitat type showed a characteristic water temperature and EC was remarkably increased for springs (cf. Table 1 and the cluster analysis described above). In contrast to the coliform group, CP also showed correlation with the third component (Table 2), indicating a habitat-specific background concentration level.

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TABLE 2. PCA of determined water quality parametersa

TC were present in water at all sites throughout the investigated period, while E. coli contamination could not be detected frequently (25%) at the low-influence sites. Occasionally, FC and CP (8.3%) were also not detected in water at the low-influence sites. E. coli and the FC were isolated in water all the time at the high-influence sites, while at two sampling dates (2.1%), CP could not be detected in the presumptive high-influence spring Spi 3. In soil at the low-influence sites, E. coli, FC, and CP could not be detected for 21, 8.3, and 4.1% of the sampling dates, respectively. In contrast, at the high-influence sites, E. coli could not be detected on only two (2.1%) of the sampling dates, and FC couldn't be detected on one (1.0%) of the sampling dates. TC and CP were detectable in the soil at high-influence sites all the time.

The highest recorded levels of all the indicators in water were the TC with a pooled median of log 3.7 CFU/100 ml (n = 96), while the other indicators were in the order FC (log 2.6 CFU/100 ml), E. coli (log 2.3 CFU/100 ml), and CP (log 1.7 CFU/100 ml). For the median values of aquatic habitats under different levels of anthropogenic influence, all the indicators showed that presumptive high-influence sites were generally more polluted than the presumptive low-influence sites (Table 3). For soil, indicators showed median values (n = 96) of TC (log 5.1 CFU/g), FC (log 3.6 CFU/g), E. coli (log 2.9 CFU/g), and CP (log 2.4 CFU/g). There was correlation between the members of the coliform group (r = 0.46 to 0.70; P < 0.01), but in contrast to the water column, there was no significant correlation between CP and the coliforms in soil (Table 4). Correlation analysis for water and corresponding soil habitats showed markedly decreased coefficients between indicators (r = 0.04 to 0.29), although E. coli levels in the water still revealed significant correlations with E. coli, TC, and FC found in the soil (Table 4).

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TABLE 3. Water bacteriological characteristics

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TABLE 4. Spearman's correlation matrix of pooled data for springs, streams, and lake sites

Discrimination efficacy of fecal indicators.
The level of discrimination was evaluated based on eight corresponding low- to high-influence sites and pooled data pair comparisons (Table 5). According to these comparisons, the various microbial fecal indicators showed varying levels of sensitivity and discrimination ability. E. coli, CP, FC, and TC could significantly discriminate between six (75%), five (63%), four (50%), and three (37.5%) site-by-site comparisons, respectively (Table 5). Using pooled data, based on water source type and anthropogenic influence status category, E. coli could be discriminated among all three pairs, while CP, FC, and TC could be discriminated between only two of the pairs (Table 5). Calculated BCRM showed pronounced variations of up to 3 orders of magnitude for the different anthropogenic status categories in the waters investigated (Table 5). The highest BCRM were observed for E. coli (2.4 to 1,450) and FC (1.7 to 1,100), which were significantly higher than those for TC (0.3 to 185) and CP (0.9 to 36.8) (Mann-Whitney U test), respectively. The BCRM ranges of E. coli and FC were at least 1 order of magnitude higher than TC and CP. In general, when water source types were examined, the highest BCRM for all the indicators were in lakeshore sites, while the stream sites had the lowest BCRM. The analysis of variance using the NBV coefficient indicated that CP data were less variable than those for TC and FC (P = 0.06 and 0.07, respectively; Mann-Whitney U test) but not for E. coli (Table 3).

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TABLE 5. Pairwise habitat comparison based on bacteriological parameters of high- and low-influence sites

Top Abstract Introduction Materials and Methods Results Discussion References

 
Based on the samples from the different water source types (i.e., spring, rivers, and lakes), the result of the PCA points to the presence of a well-defined pollution factor (Table 2). As more than 79% of the total variability was explainable by the first three components (i.e., one pollution component and two other ecological-based nonpollution components), it is thus evident that at least a dominating fraction of the fecal indicator variability at the different aquatic systems was determined by pollution activities. This finding was further supported by the fact that almost all sampling locations could be correctly classified by cluster analysis into one of the three water source types according to the median chemophysical water quality, whereas this classification failed when bacterial indicators were used (cf. results). This finding provides evidence that the occurrence of bacterial indicators was not dependent on specific habitat characteristics but, in contrast, was principally governed by the selected presumptive pollution gradients irrespective of environmental conditions. Inclusion of the TSS in the pollution component can be explained either by co-occurrence of particle-associated fecal indicators or by the concurrent resuspension of deposited fecal indicators and particles in the water body itself, such as by the physical disturbance of sediments by bathing activities or boats as observed for the lake environments. It is also a well-known fact that TSS can enhance survival of microbes due to the protective mechanism of the particle itself (8). In addition, EC was also correlated with the pollution component, which is explainable by the observed high sensitivity of EC changes with respect to spring pollution (cf. results).

According to the presumptively selected pairs of higher and lower levels of anthropogenic fecal pollution, all investigated bacterial indicators could be used to detect differences in anthropogenic fecal pollution of water to a certain degree (i.e., 45.5 to 81.8%). However, it should be mentioned that a 100% discrimination efficacy was not expected since the sampling sites were not controlled, and one could not rule out cases where a fecal contamination influence was detected which had not been presumed. E. coli determination (81.8%) was the most efficient indicator for the Ugandan environment studied. The finding of improved discrimination abilities of fecal pollution detection by E. coli compared to other commonly used fecal pollution indicators agrees with data from previous studies in Uganda (4) and Sierra Leone, another tropical African country (26). The fact that E. coli contamination could not frequently be detected in water (21%) and soil (23%) at the less-influenced sites suggested that E. coli was not a ubiquitous autochthonous member in the studied water and soil habitats. In case of significant regrowth of the fecal indicators, a correlation to metabolic activity (e.g., increased substrate supply by increased photosynthesis and algae exudate production) and temperature would have been expected. In contrast to that, no relationship between water temperature and metabolic activity could be detected with the fecal indicators, as these parameters were localized in principal components other than the pollution component (Table 2). However, we did not undertake detailed investigations on possible regrowth of E. coli; thus, we cannot exclude this mechanism for certain habitats under certain conditions. Possible regrowth phenomena in the water body may be included in the variance that is not explained by the first three PCA components. Further investigations have to clarify whether E. coli regrowth in the water body may limit its applicability for certain Ugandan habitats; this is especially important for situations where a decreased BCRM is detectable, as was evident for some of the stream and spring sites throughout this study (cf. to Table 5). In addition, the role of possible surface runoff from nonpoint soil sources has to be further evaluated. Comparison of the median occurrence of E. coli in the water column (Tables 3) and surrounding soils (data not shown) indicates a theoretical contamination potential. During this study, as a result of the study design, the low but still significant correlation (r = 0.29) between E. coli contamination in water and soil was likely a result of simultaneous anthropogenic influence on the respective soil and water systems (Table 4) rather than a significant direct soil runoff contamination. This was strongly supported by the fact that no surface runoff effect was detectable for presumptive CP contamination (r = –0.11), although a significant correlation could be detected between E. coli and CP in the water column (Table 4). Since CP has been shown not to regrow even in tropical environments (6, 26), this correlation also suggests that possible regrowth of E. coli was not the dominating factor for the outcome on this study.

Apart from E. coli, CP, which was able to discriminate 63.6% of the paired presumptive high- and low-influence categories (Table 5), also seems to be applicable and promising. The observed discrimination level displayed by the determination of CP reinforces proposition of its use as an alternative indicator when some of the traditional indicators might not be adequate (3, 10), as in the case of lower discrimination by FC and TC found in this study. Fujioka and Shizumura (10) reported that C. perfringens might exist in tropical soils and streams at low concentrations but tends to increase to significant levels when there is sewage (fecal) contamination. Our findings are in principal in agreement with this scenario, although we used a different investigation design based on pairwise and relative pollution comparisons. CP revealed lower differences between presumptive less polluted and highly polluted sampling locations (i.e., lower BCRM ratios) compared to TC and FC but resulted in better discrimination ability. This apparent contradiction can be explained by the lower observed variations in CP data compared to TC and FC which contributes to an improved statistically based ability to detect differences even at slight changes in CP contamination. The longevity characteristic and its existence as a background bacterium remains a source of questions such as when to use or when not to use CP as an indicator of fecal contamination (3). The resistant nature and the accumulation potential was also supported in our study, as CP was the only bacteriological indicator that was also included in the habitat characteristics component, which is explainable by a habitat-dependent CP background level. However, the physical turnover rate of the spores in the considered habitat may govern whether it correlates with recent fecal tracers or reflects past and remote pollution. Note that when E. coli and CP were considered together, all compared pairs of selected sites with low and high levels of anthropogenic pollution could be distinguished (Table 5). Thus, utilization of both indicators might prove to be a useful combination in future research. The higher concentration levels of E. coli compared to C. perfringens (at least 2 orders of magnitude higher in feces [3] and at least 1 order of magnitude higher in this study) should be taken into account in their application as indicators. In water with low levels of pollution, a higher sample volume (>250 ml) might be needed to detect C. perfringens compared to that for E. coli.

The air (range, 21.6 to 26.4°C) and water (range, 16 to 28.8°C) temperatures recorded during this study were far below the ranges reported for air (14) and water (12, 16, 18) temperatures in some other tropical countries. These differences in temperature regimes could have profound effects on bacteriological communities and indeed the whole application of the indicator concept even within the tropics. Thus, the difference between the indicator value found for E. coli in this study and those of other studies performed in tropical but relatively lower-altitude countries (2, 5, 6, 9, 15, 19-22, 24, 26) might at least be partly attributable to climatic factors.

In terms of handling and sample processing, the simultaneous enumeration of E. coli and other coliforms on CCA was highly practicable. The ability to obtain test results within 18 h with a potable field kit shows that this kind of assay can be done even when the facilities of a fully equipped laboratory are lacking. Requirements for CP isolation still make its determination less practicable for routine monitoring. However, so far, the ability of C. perfringens spores to resist environmental stress makes them the best available parameter for potential remote pollution indication (3, 26). In environments of low physical turnover (e.g., springs and lakes), simultaneous use of E. coli and C. perfringens spores could prove to be a useful combination to detect both recent and remote fecal pollution events. The performance of these indicators in the East African environment should, however, be further reevaluated and authenticated by more tests (for example, by tests of extremely pristine sources in various tropical environments) and by microcosm experiments.

 
We thank the Merck Pharmaceutical Company, Darmstadt, Germany, for providing all the media used in this study.

This study was supported by EZA 894/00, a scholarship by the Austrian government under the North to South Dialogue program awarded to D.B., and partially funded by grants awarded from the Austrian Academy of Sciences to A.H.F. (APART 10794) and from the FWF (P15662-B07) to R.L.M.

In addition, the anonymous reviewers, who significantly improved the manuscript, are greatly acknowledged.

 
* Corresponding author. Mailing address: Institute of Chemical Engineering, Vienna University of Technology, Getreidemarkt 9, A-1060 Vienna, Austria. Phone: 43 (01) 5880117251. Fax: 43 (01) 5816266. E-mail: A.FARNLEITNER{at}aon.at.

Top Abstract Introduction Materials and Methods Results Discussion References

 

1
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Applied and Environmental Microbiology, January 2005, p. 65-71, Vol. 71, No. 1
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.1.65-71.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

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