Validation of a novel, low-cost, and low-power Escherichia coli detection kit

Personal hygiene and access to potable water, which is safe for human consumption, are critical to containing the COVID19 pandemic. Here monitoring results are reported for microbial quality of water samples from the municipal supply in Makana Local Municipality in the Eastern Cape Province, South Africa. Access of the human population to sufficient volumes of potable water of required (microbial) quality has been a problem in this local municipality. Samples were taken just before and during 30 days of the strictest phase of the nation-wide lockdown, related to COVID19 in South Africa. Aim of this short communication was to perform the water quality testing with limited to no access to laboratory facilities and using the principles of citizen science. The H2S test kit was used as the basis for the microbial testing, while a cell phone app was used for the temperature monitoring. Five H2S test kit were used per sampling at the author’s house and the kits was developed for the microbial water quality assessment in isolated settings such as those for the lockdown. During the study, the ambient temperature ranged from 17 to 29 °C, with decreases below 18 °C occurring on three out of 12 sampling occasions. Thus the results of the H2S test kit might have been slightly influenced by the fluctuations of the ambient temperature. On 8 sampling occasions between 1 and 4 H2S test kits were positive for faecal contamination. Three samples or 25 % were free of faecal contamination. One sample had all five H2S test kits were positive for faecal contamination. Results of statistical testing indicated that potable water in Makana Local Municipality was probably microbially contaminated at the author’s household on an intermittent basis. Ongoing monitoring of microbial drinking water quality is necessary and continuing at the sampled location.

Microbial water quality monitoring is crucial for managing water resources and protecting public health. However, institutional testing activities in sub-Saharan Africa are currently limited. Because the economics of water quality testing are poorly understood, the extent to which cost may be a barrier to monitoring in different settings is unclear. This study used cost data from 18 African monitoring institutions (piped water suppliers and health surveillance agencies in six countries) and estimates of water supply type coverage from 15 countries to assess the annual financial requirements for microbial water testing at both national and regional levels, using World Health Organization recommendations for sampling frequency. We found that a microbial water quality test costs 21.0 ± 11.3 USD, on average, including consumables, equipment, labor, and logistics, which is higher than previously calculated. Our annual cost estimates for microbial monitoring of piped supplies and improved point sources ranged between 8 000 USD for Equatorial Guinea and 1.9 million USD for Ethiopia, depending primarily on the population served but also on the distribution of piped water system sizes. A comparison with current national water and sanitation budgets showed that the cost of implementing prescribed testing levels represents a relatively modest proportion of existing budgets (<2%). At the regional level, we estimated that monitoring the microbial quality of all improved water sources in sub-Saharan Africa would cost 16.0 million USD per year, which is minimal in comparison to the projected annual capital costs of achieving Sustainable Development Goal 6.1 of safe water for all (14.8 billion USD).

What's worse—finding out the beach is closed due to high levels of pathogens when you arrive at your hotel for a long-awaited vacation, or finding out the beach is closing on your way out of town after swimming all weekend? Minimizing risk and exposure requires being aware of what is in the water. Backpackers assume that all streams and lakes are contaminated and boil, filter, or otherwise treat water from these sources for cooking and drinking. However, recreational water bodies, where people swim and fish, are assumed to be safe for those activities until tests show pathogens are present. Irrigation water is also of concern—the presence of microbes in irrigation water on leafy greens or other produce can cause people to get sick when these goods are consumed. Researchers in the field of microbial water quality address these issues and others. They work on identifying sources of contamination, developing mitigation strategies, predicting high levels of bacteria based on precipitation, tides, and temperatures to name a few. Microbial water quality is a global issue and is the basis of a new special section in the Journal of Environmental Quality (JEQ), “Microbial Water Quality—Monitoring and Modeling.” This special section includes papers from 12 countries and provides a global perspective on microbial water quality research. Yakov Pachepsky, a researcher with the USDA who investigates microbes in irrigation water, worked with a group of guest editors from Canada, Korea, France, Spain, the Netherlands, and the U.S. to put together this special section. Pachepsky, a member of the ASA and SSSA, says there are four facets of microbial water quality—diagnostics, monitoring, modeling, and management—and researchers are working to improve each one. The most common diagnostic tools to detect fecal contamination use the presence of E. coli as an indicator. While E. coli is a common indicator organism, important complementary information on microbial water quality can be obtained using other organisms, genes, or DNA sequences as indicators. Using additional indicators becomes possible with advances methodology and may eventually bring innovative changes in monitoring design and implementation. To improve monitoring, researchers focus on the time it takes to get results back from a sampling event and locations where samples have to be taken. Currently, it can take a day for samples to be processed in a lab. So even with daily sampling, there is a lag between the time pathogens arrive at a site and the time that test results show the water is not safe. This lag time can expose people who are drinking or swimming in water that contains harmful microbes. While new methods are being developed, they may not be implemented quickly due to the potential expense involved in adopting new technology. Modeling tools used to predict when and where microbes may be present in drinking, irrigation, or recreational water are being improved. As data sets increase and modeling tools are improved, researchers are testing new ways to predict outbreaks. Improved models are used to compare and select the mitigation measures, management decisions, and interventions that are geared to minimizing exposures before there is a problem. Management requires identifying sources of contamination. It could be from agricultural land, industry, or aging urban stormwater systems overwhelmed by heavy precipitation. Once the source is identified, management actions can be developed. This can include fencing livestock out of streams, improving filtration in the water system, or improving urban water flow. Pachepsky explains that the search is under way for more cost-efficient new management processes. Pachepsky says that microbial water quality, and minimizing citizen exposure to pathogens through drinking water, recreation, and fresh produce, is a topic that has widespread support. Both citizens and governments agree that there is a need for standards and even regulation. Publishing these papers together also demonstrates that the challenges in studying microbial water quality are universal. This special section will be published in its entirety in an upcoming issue of The Journal of Environmental Quality. A number of papers are currently available in the “Just Published” section of the journal: https://bit.ly/1U5yldx.

&amp;#160;Streams and ponds used for local irrigation tend to demonstrate high spatiotemporal variability of water quality. Microbial water quality monitoring becomes overly resource-demanding if the water quality metrics are treated as purely random values. Research on several irrigation ponds and streams showed relatively stable spatial patterns of microbial and other water quality metrics. Detection of those patterns was achieved by setting 20 to 30 monitoring locations, visiting each location seven to ten times during the irrigation season, measuring the water quality metrics at each location with in situ sampling in water samples, computing relative differences between the measurements in each sampling location, computing the average value of those measurements across the water source for each visit, and finally computing the mean relative differences (MRD) for each location over all the visits. Positive MRDs indicated the preponderance of elevated values of water quality variables, and negative MRDs indicated the prevalence of low values. The nearshore locations typically had the largest MRD in ponds, and the locations with more populated stream reaches.&amp;#160;Unmanned aerial vehicles were used for multispectral imaging of some ponds on each visit to several ponds before the water sampling. Both reflectance and remote sensing indices were determined at the same locations where water quality metrics were measured. The stable temporal patterns were detected for reflectance and remote sensing indices. Strong significant Spearman correlations were found between stable patterns of some water quality variables and remote sensing indices. Those correlations indicate the opportunities to use UAV-based remote sensing of irrigation water sources to inform the design of sampling water across ponds. Correlations between stable patterns of water quality variable patterns may help in developing monitoring design schemas when the more readily available water quality variable patterns are known.Establishing temporally stable spatial patterns via the mean relative differences points to locations where monitoring locations could be placed to represent the average across the pond or stream. Also, locations with low MRDs of the microbial pollution metrics appeared to be more suitable for establishing the irrigation water intake. Overall, stable water quality patterns, when detected, can provide useful guidance for establishing and monitoring water quality for those water sources.

Automation of on-site microbial water quality monitoring from source to tap: Challenges and perspectives.

Screening for fecal contamination via microbial water quality monitoring is acritical component of safe drinking water provision and public healthprotection. Achieving adequate levels of microbial water quality testing,however, is a challenge in resource-limited settings. One strategy foraddressing this challenge is to improve the efficiency of monitoring programs.In African countries, quantitative microbial testing methods are commonly usedto monitor chlorinated piped water systems. However, presence/absence (P/A)tests may provide an appropriate alternative for water supplies that generallyshow negative fecal contamination results. This study compares 1048 waterquality test results for samples collected from five African urban watersystems. The operators of the systems conducted parallel tests on the 1048samples using their standard quantitative methods (e.g., most probable number ormembrane filtration) and the Colitag™ method in P/A format. Combined datademonstrates agreement rates of 97.9% (1024/1046) for detecting total coliformsand 97.8% (1025/1048) for detecting E. coli. We conclude thatthe P/A test offers advantages as a simpler and similarly sensitive measure ofpotential fecal contamination for large, urban chlorinated water systems. P/Atests may also offer a cost-effective alternative to quantitative methods, asthey are quicker to perform and require less laboratory equipment.

Fecal contamination of water sources is an important water quality issue for agricultural irrigation ponds. Escherichia coli concentrations are commonly used to evaluate recreational and irrigation water quality. We hypothesized that there may exist temporally stable spatial patterns of E. coli concentrations across ponds, meaning that some areas mostly have higher and other areas mostly lower than average concentrations of E. coli To test this hypothesis, we sampled two irrigation ponds in Maryland at nodes of spatial grids biweekly during the summer of 2016. Environmental covariates-temperature, turbidity, conductivity, pH, dissolved oxygen, chlorophyll a, and nutrients-were measured in conjunction with E. coli concentrations. Temporal stability was assessed using mean relative differences between measurements in each location and averaged measurements across ponds. Temporally stable spatial patterns of E. coli concentrations and the majority of environmental covariates were expressed for both ponds. In the pond interior, larger relative mean differences in chlorophyll a corresponded to smaller mean relative differences in E. coli concentrations, with a Spearman's rank correlation coefficient of 0.819. Turbidity and ammonium concentrations were the two other environmental covariates with the largest positive correlations between their location ranks and the E. coli concentration location ranks. Tenfold differences were found between geometric mean E. coli concentrations in locations that were consistently high or consistently low. The existence of temporally stable patterns of E. coli concentrations can affect the results of microbial water quality assessment in ponds and should be accounted for in microbial water quality monitoring design.IMPORTANCE The microbial quality of water in irrigation water sources must be assessed to prevent the spread of microbes that can cause disease in humans because of produce consumption. The microbial quality of irrigation water is evaluated based on concentrations of Escherichia coli as the indicator organism. Given the high spatial and temporal variability of E. coli concentrations in irrigation water sources, recommendations are needed on where and when samples of water have to be taken for microbial analysis. This work demonstrates the presence of a temporally stable spatial pattern in the distributions of E. coli concentrations across irrigation ponds. The ponds studied had zones where E. coli concentrations were mostly higher than average and zones where the concentrations were mostly lower than average over the entire observation period, covering the season when water was used for irrigation. Accounting for the existence of such zones will improve the design and implementation of microbial water quality monitoring.

Microbial surface water quality is important, as it is related to health risk when the population is exposed through drinking, recreation or consumption of irrigated vegetables. The microbial surface water quality is expected to change with socio-economic development and climate change. This study explores the combined impacts of future socio-economic and climate change scenarios on microbial water quality using a coupled hydrodynamic and water quality model (MIKE21FM-ECOLab). The model was applied to simulate the baseline (2014-2015) and future (2040s and 2090s) faecal indicator bacteria (FIB: E. coli and enterococci) concentrations in the Betna river in Bangladesh. The scenarios comprise changes in socio-economic variables (e.g. population, urbanization, land use, sanitation and sewage treatment) and climate variables (temperature, precipitation and sea-level rise). Scenarios have been developed building on the most recent Shared Socio-economic Pathways: SSP1 and SSP3 and Representative Concentration Pathways: RCP4.5 and RCP8.5 in a matrix. An uncontrolled future results in a deterioration of the microbial water quality (+75% by the 2090s) due to socio-economic changes, such as higher population growth, and changes in rainfall patterns. However, microbial water quality improves under a sustainable scenario with improved sewage treatment (-98% by the 2090s). Contaminant loads were more influenced by changes in socio-economic factors than by climatic change. To our knowledge, this is the first study that combines climate change and socio-economic development scenarios to simulate the future microbial water quality of a river. This approach can also be used to assess future consequences for health risks.

Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques

Modeling the interannual variability of microbial quality metrics of irrigation water in a Pennsylvania stream

Infrastructural problems force South African households to supplement their drinking water consumption from water resources of inadequate microbial quality. Microbial water quality monitoring is currently based on the Colilert®18 system which leads to rapidly available results. Using Escherichia coli as the indicator microorganism limits the influence of environmental sources on the reported results. The current system allows for understanding of long-term trends of microbial surface water quality and the related public health risks. However, rates of false positive for the Colilert®18-derived concentrations have been reported to range from 7.4% to 36.4%. At the same time, rates of false negative results vary from 3.5% to 12.5%; and the Colilert medium has been reported to provide for cultivation of only 56.8% of relevant strains. Identification of unknown sources of faecal contamination is not currently feasible. Based on literature review, calibration of the antibiotic-resistance spectra of Escherichia coli or the bifidobacterial tracking ratio should be investigated locally for potential implementation into the existing monitoring system. The current system could be too costly to implement in certain areas of South Africa where the modified H2S strip test might be used as a surrogate for the Colilert®18.

Domestic wastewater pollution in environmental waters or water reuse supplies represents a threat to public health because of high concentrations of diverse pathogens associated with human excreta [1]. Since it is difficult to directly measure waterborne pathogens of concern, microbial water quality monitoring efforts often use surrogates or indicator organisms that are easily detected and whose presence reflects pathogen persistence [2]. Here, we describe an unconventional viral indicator of wastewater pollution, pepper mild mottle virus (PMMoV), a plant pathogen that was first proposed as a water quality indicator in 2009 [3] and promises to improve microbial water quality management worldwide [4].

Bacteria, protozoa and viruses are ubiquitous in aquatic environments and may pose threats to water quality for both human and ecosystem health. Microbial risk assessment and management in the water sector is a focus of governmental regulation and scientific inquiry; however, stark gaps remain in their application and interpretation. This paper evaluates how water managers practice microbial risk assessment and management in two Canadian provinces (BC and Ontario). We assess three types of entities engaged in water management along the source-to-tap spectrum (watershed agencies, water utilities, and public health authorities). We analyze and compare the approaches used by these agencies to assess and manage microbial risk (including scope, frequency, and tools). We evaluate key similarities and differences, and situate them with respect to international best practices derived from literatures related to microbial risk assessment and management. We find considerable variability in microbial risk assessment frameworks and management tools in that approaches 1) vary between provinces; 2) vary within provinces and between similar types of agencies; 3) have limited focus on microbial risk assessment for ecosystem health and 4) diverge considerably from the literature on best practices. We find that risk assessments that are formalized, routine and applied system-wide (i.e. from source-to-tap) are limited. We identify key limitations of current testing methodologies and looking forward consider the outcomes of this research within the context of new developments in microbial water quality monitoring such as tests derived from genomics and metagenomics based research.

Millennium Development Goal Target 7c (to halve between 1990 and 2015 the proportion of the global population without sustainable access to safe drinking water), was celebrated as achieved in 2012. However, new studies show that we may be prematurely celebrating. Access to safe drinking water may be overestimated if microbial water quality is considered. The objective of this study was to examine the relationship between microbial drinking water quality and drinking water source in the Puerto Plata region of the Dominican Republic. This study analyzed microbial drinking water quality data from 409 households in 33 communities. Results showed that 47% of improved drinking water sources were of high to very-high risk water quality, and therefore unsafe for drinking. This study provides evidence that the current estimate of safe water access may be overly optimistic, and microbial water quality data are needed to reliably assess the safety of drinking water.

Microbial water quality monitoring is an essential component of food safety. E. coli bacterium is the major indicator organism used in assessing microbial water quality but dense sampling of water to assess spatial variability is impractical. The objective of this work was to test the hypothesis that sUAS imaging can provide information about the differences in E. coli habitats across two Maryland irrigation ponds and guide water sampling. We used modified GoPro cameras and a MicaSense RedEdge camera in flights shortly before sampling. Ponds P1 (0.37ha) and P2 (0.48ha) were sampled from a boat in the same locations, biweekly, during the 2018 growing season. Average concentrations of E. coli were 0.60&plusmn;0.04 and 1.04&plusmn;0.04 (mean &plusmn; st. error, log CFU/100 mL) in P1 and P2, respectively. The random forest (RF) machine learning algorithm was applied to relate ground sampling data with co-located image sections. The sensitivity of results to parameters of the RF algorithm was assessed with multiple scenarios. The most influential parameters for both ponds were maximum tree depth and minimum leaf size. The maximum R2 values in predictions of E. coli concentrations were 0.941 (0.943) and 0.532(0.565) in training and validation datasets, respectively, for pond P1 (P2). The most influential inputs for both ponds were red, blue, and green obtained after demosaicing images in the visible range, while P1 included red and blue obtained after demosaicing infrared images. Overall, accurate estimation of E. coli concentrations from imagery data is possible and benefits from tuning algorithm control parameters.