Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff

Scenario-based assessment of fecal pathogen sources affecting bathing water quality: novel treatment options to reduce norovirus and Campylobacter infection risks

Wastewater discharge and runoff waters are significant sources of human and animal fecal microbes in surface waters. Human-derived fecal contamination of water is generally estimated to pose a greater risk to human health than animal fecal contamination, but animals may serve as reservoirs of zoonotic pathogens. In this study, quantitative microbial risk assessment (QMRA) tools were used to evaluate the hygienic impact of sewage effluents and runoff water from municipalities and animal farms on surface and bathing waters. The human-specific microbial source tracking (MST) marker HF183 was used to evaluate the dilution of fecal pathogens originating from the sewage effluent discharge to the downstream watershed. As novel risk management options, the efficiency of UV-LED disinfection and wetland treatment as well as biochar filtration was tested on-site for the contamination sources. According to the dilution pattern of the MST marker HF183, microbes from wastewater were diluted (2.3-3.7 log10) in the receiving waters. The scenario-based QMRA revealed, that the health risks posed by exposure to human-specific norovirus GII and zoonotic Campylobacter jejuni during the bathing events were evaluated. The risk for gastroenteritis was found to be elevated during wastewater contamination events, where especially norovirus GII infection risk increased (1-15 cases per day among 50 bathers) compared with the business as usual (BAU) situation (1 case per day). The noted C. jejuni infection risk was associated with animal farm contamination (1 case per day, versus 0.2-0.6 cases during BAU). Tertiary treatment of wastewater with wetland treatment and UV-LED disinfection effectively reduced the waterborne gastroenteritis risks associated with bathing. Based on the experiences from this study, a QMRA-based approach for health risk evaluations at bathing sites can be useful and is recommended for bathing site risk assessments in the future. In case of low pathogen numbers at the exposure sites, the MST marker HF183 could be used as a pathogen dilution coefficient for the watershed under evaluation. The full-scale implementation of novel tertiary treatment options at wastewater treatment plants (WWTPs) as well as on-site runoff water treatment options should be considered for infection risk management at locations where scenario-based QMRA implies elevated infection risks.

Comparing the decay of human wastewater-associated markers and enteric viruses in laboratory microcosms simulating estuarine waters in a temperate climatic zone using qPCR/RT-qPCR assays

This study investigated the decay rates of wastewater-associated markers and enteric viruses in laboratory microcosms mimicking estuarine water environments in temperate Sydney, NSW, Australia using qPCR and RT-qPCR assays. The results demonstrated the reduction in concentrations of Bacteroides HF183, Lachnospiraceae Lachno3, cross-assembly phage (crAssphage), pepper mild mottle virus (PMMoV), human adenovirus (HAdV 40/41), and enterovirus (EV) over a span of 42 days under spring/summer temperatures, presence/absence of microbiota, and different light conditions. The study found that HF183, Lachno3, crAssphage, PMMoV, HAdV 40/41, and EV exhibited varying decay rates depending on the experimental conditions. The average T90 values ranged from a few days to several months, indicating the rapid decay or prolonged persistence of these markers and enteric viruses in the estuarine environment. Furthermore, the study examined the effects of indigenous microbiota and spring/summer temperatures on wastewater-associated markers and enteric viruses decay rates. It was found that the presence of microbiota and temperature significantly influenced the decay rates of HF183 and PMMoV. Additionally, the study compared the effects of artificial sunlight and spring/summer temperatures on marker decay rates. Bacterial markers decayed faster than viral markers, although among viral markers crAssphage decay rates were relatively faster when compared to PMMoV. The exposure to artificial sunlight significantly accelerated the decay rates of bacterial markers, viral markers, and enteric viruses. Temperature also had an impact on the decay rates of Lachno3, crAssphage, and HAdV 40/41. In conclusion, this study provides valuable insights into the decay rates of wastewater-associated markers and enteric viruses under different experimental conditions that mimicked temperate environmental conditions. The findings contribute to our understanding of the fate and persistence of these markers in the environment which is crucial for assessing and managing risks from contamination by untreated human wastewater.

Assessing the additional health burden of antibiotic resistant Enterobacteriaceae in surface waters through an integrated QMRA and DALY approach

Antibiotic resistant Enterobacteriaceae pose a significant threat to public health. However, limited studies have evaluated the health risks associated with exposure to antibiotic-resistant bacteria (ARB), especially in natural environments. While quantitative microbial risk assessment (QMRA) assesses microbial risks in terms of the probability of infection, it does not account for the severity of health outcomes. In this study, a QMRA-DALY model was developed to integrate QMRA with health burden (disability-adjusted life years (DALY)) from infections caused by ARB. The model considers uncertainties in probability of infection and health burden assessment using Monte Carlo simulations. The study collected antimicrobial resistance (AMR) surveillance data from surface waters with different land uses. Results revealed water bodies with agricultural land use to be the main AMR hotspots, with the highest additional health burden observed in infections caused by meropenem-resistant E. coli (∆DALY = 0.0105 DALY/event) compared to antibiotic-susceptible E. coli. The estimated ∆DALY for antibiotic-resistant K. pneumoniae was lower than for antibiotic-resistant E. coli (highest ∆DALY = 0.00048 DALY/event). The study highlights the need for better evaluation of AMR associated health burden, and effective measures to mitigate the risks associated with antibiotic-resistant bacteria in natural environments.

Microbial contamination in the coastal aquaculture zone of the Ba Lat river mouth, Vietnam

Contamination of aquaculture products by pathogenic organisms is a major concern in areas where this activity is of high economic importance. The abundances of total coliforms (TC), Escherichia coli (EC) and faecal streptococci (FS) (in CFU.100 mL^-1) in seawater in the Red River coastal aquaculture zone were determined. The results showed TC numbers (200 to 9100; average 1822), EC (<100 to 3400; average 469) and FS (<100 to 2100; average 384), of which TC exceeded the allowable threshold of the Vietnam regulation for coastal aquaculture water. TC and EC numbers in 4 wastewater types (domestic, livestock farming sewage, agricultural runoff, and mixed sewage canals) were investigated and revealed the importance of point sources of faecal contamination in seawater. These results underline the need to reduce the release of untreated wastewater and to put into place seawater microbial quality monitoring in areas where the development of sustainable aquaculture is an objective.

Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA

Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log₁₀ geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log₁₀ geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.

Genetic fecal source identification in urban streams impacted by municipal separate storm sewer system discharges

Municipal stormwater systems are designed to collect, transport, and discharge precipitation from a defined catchment area into local surface waters. However, these discharges may contain unsafe levels of fecal waste. Paired measurements of Escherichia coli, precipitation, three land use metrics determined by geographic information system (GIS) mapping, and host-associated genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), dog (DG3), and avian (GFD) fecal sources were assessed in 231 urban stream samples impacted by two or more municipal stormwater outfalls. Receiving water samples were collected twice per month (n = 24) and after rain events (n = 9) from seven headwaters of the Anacostia River in the District of Columbia (United States) exhibiting a gradient of impervious surface, residential, and park surface areas. Almost 50% of stream samples (n = 103) were impaired, exceeding the local E. coli single sample maximum assessment level (410 MPN/100 ml). Fecal scores (average log10 copies per 100 ml) were determined to prioritize sites by pollution source and to evaluate potential links with land use, rainfall, and E. coli levels using a recently developed censored data analysis approach. Dog, ruminant, and avian fecal scores were almost always significantly increased after rain or when E. coli levels exceeded the local benchmark. Human fecal pollution trends showed the greatest variability with detections ranging from 9.1% to 96.7% across sites. Avian fecal scores exhibited the closest connection to land use, significantly increasing in catchments with larger residential areas after rain events (p = 0.038; R2 = 0.62). Overall, results demonstrate that combining genetic fecal source identification methods with GIS mapping complements routine E. coli monitoring to improve management of urban streams impacted by stormwater outfalls.

Quantifying the effects of land use change and aggregate stormwater management practices on fecal coliform dynamics in a temperate catchment

Changes in land use and land cover (LULC) due to land development can lead to an increase in diffuse microbial pollutions and, consequently, degradation of the receiving aquatic ecosystem. However, the mechanisms underlying these phenomena are rarely considered in hydrological models. Therefore, in this study, fecal indicator bacteria (FIB) and total suspended solids (TSS) in a temperate catchment were simulated using a well-established water quality model (Personal Computer Storm Water Management Model) to systematically quantify the factors influencing their dynamics and the effects of stormwater management. Additionally, high-resolution data (e.g., water quality variables and LULC changes) were used to calibrate the model, which accurately reproduced the physical and biological features of the catchment. The results showed that increases in bare land areas and impervious cover in the catchment exceeded the Korean (as well as the USEPA-based) standard recreational water quality criteria for fecal contamination and TSS. Dissolved organic compounds (only during storm events), TSS, and total nitrogen (except during the pre-development phase) were the strongest predictors in shaping FIB dynamics. The multiple control of stormwater management reduced the FIB and TSS concentrations by approximately 65% in the catchment. The results of this study not only provide conclusions on the drivers of FIB and TSS dynamics and their quantitative contribution but also help in designing a methodology for empirical and ecological predictions of diffuse microbial and TSS pollution in a catchment with ongoing land development.

A comparison of soil Staphylococcus aureus and fecal indicator bacteria concentrations across land uses in a Hawaiian watershed

Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and fecal indicator bacteria (FIB; Enterococcus spp., Clostridium perfringens) concentrations increase in Hawaiian streams and estuaries following storms and pose a health threat to recreational water users. To reduce this risk, watershed bacteria sources need to be identified for management actions. This study's goals were to identify soil bacteria sources among different land uses and to determine if their concentrations were associated with different soil properties. Soil samples were collected three times on 24 d between October 2017 and November 2018 at urban, agriculture, and native-forest land uses in the Hilo Bay watershed, Hawai'i Island, Hawai'i. Soil bacteria concentrations were quantified using culturing techniques with selective media. Staphylococcus aureus, MRSA, and FIB were present in soil from all land uses. Bacteria concentrations were highest in urban soils and lowest in native-forest soils, with up to three orders of magnitude differences among land uses. Staphylococcus aureus, MRSA, and FIB soil concentrations were positively correlated with each other and with soil temperature and pH, but inversely correlated with soil moisture and organic matter content. Our results demonstrate that soils are a watershed bacteria source and that some soil properties affect their concentrations. Identifying these sources is critical for implementing management actions to reduce pathogen loads to estuaries and transmission to recreational water users.

Microbial source tracking of fecal contamination in stormwater runoff

Concerns over fecal contamination in stormwater canals have promoted the need for pollution control strategies, including the use of microbial source tracking, to identify fecal contamination in the Greater New Orleans Area. Surface water samples were collected over a 12-month period at five canal locations within Jefferson Parish, Louisiana. Quantitative polymerase chain reaction and the IDEXX method were used to assess the concentrations of coliforms, Escherichia coli (E. coli) and human fecal 183 bacteroides (HF183) in stormwater samples. A 100% positive detection rate of total coliforms and E. coli was observed across all tested sites. Despite the closeness of the five sites, when averaged across all sampling time points, Kruskal-Wallis tests indicated that E. coli was present at significantly different concentrations in these locations (χ²(5) = 19.8, p = 0.0005). HF183 was detected in 62% of the water samples collected during the stormwater sampling. Without further testing for HF183 markers, the conclusion from this study would have been that fecal contamination from an unknown source was always present at varying levels during the study period. Analysis of HF183 markers therefore adds another layer of conclusions to the results deductible from E. coli concentrations. A 100% E. coli detection rate, high E. coli concentrations coupled with low rates of HF183 detection particularly at the Esplanade, Poplar Street, and Bonnabel Boat Launch sites, the sites closest to the lake outlet, throughout the study period, indicate that fecal contamination at these stormwater canal sites comes primarily from non-human sources. However, the Metairie Road and Napoleon Avenue sites, which have the highest HF183 detection rates, on top of chronic pollution by other non-human sources, are also influenced by human fecal pollution, possibly because of human development and faulty infrastructure. This study highlights the advantages of the use of microbial source-tracking methods to complement traditional indicator bacteria.

Implementation of P-Reactive Layer for Improving Urban Water Quality: Kinetic Studies, Dimensioning and Economic Analysis

Urbanization and climate change affecting water quality are the most critical problems that humanity has to encounter globally. Undoubtedly, urban water bodies are heavily contaminated by phosphorus (P). This study aims to identify the mechanisms and efficiency of the P sorption process for selected reactive materials (Autoclaved Aerated Concrete (AAC), Filtralite® Nature P, lightweight expanded clay aggregate (Leca®), limestone, opoka, and zeolite) with surface water as adsorbate and dimension of P-reactive reactive layer supported with economic analysis. Four kinetic models were used to know the sorption mechanism: pseudo-first order, pseudo-second order, Elovich, and intra-particle diffusion model. Calculating the P-reactive layer was based on dimensioning rain retention spaces standards. The pseudo-second model provided the best description of the adsorption kinetics of most materials. The sorption properties obtained after 72 h showed the reduction of 83, 81, 59, 53, 37, and 36% for AAC, opoka, Filtralite® Nature P; limestone, Leca®, and zeolite, respectively. Depending on the volume, the P-reactive layer can remove 29–77 or 61–163 g of P-PO4. The unit cost of removing P-PO4 by the P-reactive layer range from 49.57 to 85.53 €/P-PO4 g. For these reasons, reactive materials seem to be an effective way of removing P from the urban water environment worldwide from both environmental and economic points of view.

Observations and Correlations from a 3-Year Study of Fecal Indicator Bacteria in the Mohawk River in Upstate NY

Fecal indicator bacteria (FIB), such as E. coli and Enterococci, are used to indicate the potential of fecal contamination in waterways. One known source of FIB in urbanized areas is the occurrence of combined sewer overflows (CSOs). To explore the impact of CSOs on local water quality and FIB presence, sampling was conducted during the summers of 2017–2019 of two cities, one with CSOs and one without, on the Mohawk River in upstate New York, USA. Sampling included in situ physiochemical parameters of pH, temperature, and dissolved oxygen and laboratory tests for E. coli, Enterococci, nitrates, and total organic carbon (TOC). Correlations between parameters were explored using the Wilcoxon rank sum test and Spearman’s Rank correlation with and without considerations of site and city location. Overall, positive correlations between FIB and rainfall were identified in one city but were less significant in the other, suggesting a buffering of FIB concentrations likely due to inflow contributions from a reservoir. Samples collected downstream from an active CSO reached the detection limit of the FIB tests, demonstrating a 2-log or greater increase in FIB concentrations from dry weather conditions. The city with CSOs demonstrated greater FIB concentrations, which are likely a combination of greater urban runoff, CSOs, and the potential resuspension of sediment during high flow events. Due to the widespread presence of FIB in the region, future research includes utilizing microbial source tracking to identify the sources of contamination in the region.

Comparing stormwater quality and watershed typologies across the United States: A machine learning approach

Watersheds continue to be urbanized across different regions of the United States, increasing the number of impaired waterbodies due to urban stormwater. Using machine learning techniques, this study examined how stormwater quality and watershed characteristics are related at a national scale and compared stormwater quality across watersheds in diverse climates. We analyzed a selection of data from the National Stormwater Quality Database (NSQD) comprising 1,881 stormwater samples taken from 182 watersheds in 26 metropolitan areas in the United States between 1992 and 2003. Using an ensemble clustering algorithm, the stormwater quality in these samples was classified into "stormwater signatures," defined as distinct combinations of 9 contaminants including metals (Pb, Zn, Cu), particulates (TSS, TDS), and nutrients (BOD, TP, TKN, NOx). Next, multinomial logistic regression was applied to the NSQD data now classified by signature and combined with climate, weather, land use, and imperviousness data obtained from multiple sources. The results yielded 5 stormwater signatures with distinct aquatic toxicity implications and relationships to climate, weather, land use, and imperviousness: Signature 1 ("Ecotoxic and Eutrophic"), defined by high median concentrations of contaminants, likely represents the first flush in moderate-to-high imperviousness watersheds; Signature 2 ("Reduced Nitrates") represents a wet season signature, particularly for dry climates; Signature 3 ("Potentially Eutrophic") represents the first flush in low imperviousness watersheds; Signature 4 ("Elevated Particulates and Metals") represents a wet season signature, particularly on warmer days; finally, Signature 5 ("Most Dilute") is primarily a regional signature associated with the warm, wet climate of the southeastern US. This study serves as a proof-of-concept demonstrating how machine learning techniques can be used to identify patterns in high-dimensional and highly variable data. Applied to stormwater quality, these techniques identify major patterns in stormwater quality across the United States using a stormwater signature approach, which examines how contaminants co-occur and under what climate, weather, land use, and impervious conditions. The findings point to dominant processes driving stormwater generation and inform watershed monitoring, green infrastructure planning, stormwater quality under climate change, and opportunities for public engagement.

Bacterial assemblages of urban microbiomes mobilized by runoff waters match land use typologies and harbor core species involved in pollutant degradation and opportunistic human infections

Cities are patchworks of urban catchments divided into functional units according to their commercial, residential and industrial activities, and socio-urbanistic patterns. The hypothesis of city surface microbiomes being structured by socio-urbanistic variables leading to an emergence of synurbic taxa was tested. According to the r/K microbial ecology theory, a gradient of well-adapted synurbic K-strategists and of opportunistic -r-strategists should occur over city surfaces. K-strategists would be core components while r-ones would be transiently detected. To resolve these patterns, sub-catchments (n = 21) of an area of high commercial and industrial activities were investigated over three time periods covering one year. The sub-catchments' land use patterns and associated human behaviors were converted into socio-urbanistic variables and groupings. Bacterial cells mobilized by runoffs per sub-catchment were recovered, and analyzed by classical approaches, microbial source tracking DNA assays and DNA meta-barcoding approaches. Relationships between these datasets, the runoff physico-chemical properties, and descriptors of the socio-urbanistic groupings were investigated. 16S rRNA meta-barcoding analyses showed evidence of the occurrence of K- and r-like strategists. Twenty-eight core genera were identified, and correlation networks revealed large bacterial modules organized around actinobacterial taxa involved in hydrocarbon degradation processes. Other bacterial networks were related to the occurrences of hygienic wastes, and involved bacteria originating from fecal contaminations. Several r-strategists like Sulfurospirillum were recorded and found associated to point source pollutions. The tpm-metabarcoding approach deciphered these r / K strategists at the species level among more than ten genera. Nine core K-like Pseudomomas species were identified. The P. aeruginosa human opportunistic pathogen and P. syringae phytopathogens were part of these K-strategists. Other tpm-harboring bacterial pathogens showed r-like opportunistic distribution patterns. Correlation network analyses indicated a strong incidence of hygienic wastes and hydrocarbon-pollutions on tpm-harboring bacteria. These analyses demonstrated the occurrence of core synurbic bacterial K-strategists over city surfaces.

Chemical characterization of urban stormwater: Traditional and emerging contaminants

Increases in urbanization have led to increased stormwater runoff and mobilization of pollutants from urban watersheds. Discharge of these pollutants often leads to contamination of receiving water bodies. Chemical characterization of urban stormwater is necessary to gain deeper insights into the ecological impacts of urban runoff and to evaluate parameters that influence possible treatment technologies. This study assessed stormwater event mean concentrations and particle size fractions from field studies reported in national/international stormwater quality databases (The National Stormwater Quality and The Best Management Practices databases) and peer-reviewed literature. This characterization of urban stormwater includes statistical evaluation of probability distribution, consideration of dissolved and particulate-bound pollutants and focuses on partitioning and speciation behavior. Solids, nutrients, metals, organic pollutants, and bacterial pathogen indicators were evaluated. A significant fraction of stormwater phosphorus, metals and organic pollutants are particle-bound. Results from the speciation of metals demonstrated that metals are predominantly present as either inner-sphere or electrostatic complexes with dissolved organic matter. This study provides a comprehensive overview of the myriad pollutants found in urban stormwater and provides a starting point for addressing ubiquitous and emerging contaminants. Finally, research needs for further detailed stormwater characterization were identified.

Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

The dynamics of fecal indicator bacteria, such as fecal coliforms (FC) in streams, are influenced by the interactions of a myriad of factors. To predict complex spatiotemporal patterns of FC in streams and assess the relative importance of numerous controlling factors, the adoption of a hierarchical Bayesian network (HBN) was proposed in this study. By introducing latent variables correlated to the observed variables into a Bayesian network, the HBN can represent causal relationships among a large set of variables with a multilevel hierarchy. The study area encompasses 215 sites across the watersheds of the four major rivers in South Korea. The monitoring data collected during the 2012-2019 period included 32 input variables pertaining to meteorology, geography, soil characteristics, land cover, urbanization index, livestock density, and point sources. As model endpoints, the exceedance probability of the FC standard concentration as well as two pollution characteristics (i.e., pollution degree and type), derived from FC load duration curves were used. The probability of exceeding an FC threshold value (200 CFU/100 mL) showed spatiotemporal variations, whereas pollution degree and type showed spatial variations that represent long-term severity and relative dominance of nonpoint and point source fecal pollution, respectively. The conceptual model was validated using structural equation modeling to develop the HBN. The results demonstrate that the HBN effectively simplified the model structure, while showing strong model performance (AUC = 0.81, accuracy = 0.74). The results of the sensitivity analysis indicate that land cover is the most important factor in predicting the probability of exceedance and pollution degree, whereas the urbanization index explains most of the variability in pollution type. Furthermore, the results of the scenario analysis suggest that the HBN provides an interpretable framework in which the interaction of controlling factors has causal relationships at different levels that can be identified and visualized.

Climate Change Impacts on Microbiota in Beach Sand and Water: Looking Ahead

Beach sand and water have both shown relevance for human health and their microbiology have been the subjects of study for decades. Recently, the World Health Organization recommended that recreational beach sands be added to the matrices monitored for enterococci and Fungi. Global climate change is affecting beach microbial contamination, via changes to conditions like water temperature, sea level, precipitation, and waves. In addition, the world is changing, and humans travel and relocate, often carrying endemic allochthonous microbiota. Coastal areas are amongst the most frequent relocation choices, especially in regions where desertification is taking place. A warmer future will likely require looking beyond the use of traditional water quality indicators to protect human health, in order to guarantee that waterways are safe to use for bathing and recreation. Finally, since sand is a complex matrix, an alternative set of microbial standards is necessary to guarantee that the health of beach users is protected from both sand and water contaminants. We need to plan for the future safer use of beaches by adapting regulations to a climate-changing world.

Evaluation of E. coli in sediment for assessing irrigation water quality using machine learning

Fresh produce irrigated with contaminated water poses a substantial risk to human health. This study evaluated the impact of incorporating sediment information on improving the performance of machine learning models to quantify E. coli level in irrigation water. Field samples were collected from irrigation canals in the Southwest U.S., for which meteorological, chemical, and physical water quality variables as well as three additional flow and sediment properties: the concentration of E. coli in sediment, sediment median size, and bed shear stress. Water quality was classified based on E. coli concentration exceeding two standard levels: 1 E. coli and 126 E. coli colony forming units (CFU) per 100 ml of irrigation water. Two series of features, including (FIS) and excluding (FES) sediment features, were selected using multi-variant filter feature selection. The correlation analysis revealed the inclusion of sediment features improves the correlation with the target standards for E. coli compared to the models excluding these features. Support vector machine, logistic regression, and ridge classifier were tested in this study. The support vector machine model performed the best for both targeted standards. Besides, incorporating sediment features improved all models' performance. Therefore, the concentration of E. coli in sediment and bed shear stress are major factors influencing E. coli concentration in irrigation water.

Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches

Water quality degradation is one of the major problems with artificial lakes in estuaries. Long-term spatiotemporal patterns of water quality in a South Korean estuarine reservoir were analyzed using seasonal datasets from 2002 to 2020, and some functional changes in relations of trophic state variables due to the construction of serial weirs in the upper river were also investigated. A total of 19 water quality parameters were used for the study, including indicators of organic matter, nutrients, suspended solids, water clarity, and fecal pollution. In addition, chlorophyll-a (CHL-a) was used to assess algal biomass. An empirical regression model, trophic state index deviation (TSID), and principal component analysis (PCA) were applied. Longitudinal fluctuations in nutrients, organic matter, sestonic CHL-a, and suspended solids were found along the axis of the riverine (Rz), transition (Tz), and lacustrine zones (Lz). The degradation of water quality was seasonally caused by resuspension of sediments, monsoon input due to rainfall inflow, and intensity of Asian monsoon, and was also related to intensive anthropic activities within the catchment. The empirical model and PCA showed that light availability was directly controlled by non-algal turbidity, which was a more important regulator of CHL-a than total nitrogen (TN) and total phosphorus (TP). The TSID supported our hypothesis on the non-algal turbidity. We also found that the construction of serial upper weirs influenced nutrient regime, TSS, CHL-a level, and trophic state in the estuarine reservoir, resulting in lower TP and TN but high CHL-a and high TN/TP ratios. The proportions of both dissolved color clay particles and blue-green algae in the TSID additionally increased. Overall, the long-term patterns of nutrients, suspended solids, and algal biomass changed due to seasonal runoff, turnover time, and reservoir zones along with anthropic impacts of the upper weir constructions, resulting in changes in trophic state variables and their mutual relations in the estuarine reservoir.

Integrating culture and molecular quantification of microbial contaminants into a predictive modeling framework in a low-lying, tidally-influenced coastal watershed

Examinations of stormwater delivery in the context of tidal inundation are lacking. Along the coastal plains of the southeastern United States, tidal inundation is increasing in frequency and severity, often with dramatic adverse impacts on timely stormwater discharge, coastal flooding hazards, and even "sunny day flooding". Therefore, a comprehensive study was conducted to examine tidally-influenced stormwater outfalls discharging to Taylor's Creek, an estuary proximal to Beaufort, NC used regularly for recreation and tourism. Over a wide range of meteorological conditions, water samples were collected and analyzed for fecal indicator bacteria (FIB, used for water quality management) and previously published quantitative microbial source tracking (qMST) markers. Nineteen sampling events were conducted from July 2017-June 2018 with samples classified according to tidal state and defined as either inundated, receding, or transition. A first-of-its-kind multiple linear regression model was developed to predict concentrations of Enterococcus sp. by tidal cycle, salinity and antecedent rainfall. We demonstrated that the majority of variability associated with the concentration of Enterococcus sp. could be predicted by E. coli concentration and tidal phase. FIB concentrations were significantly (<0.05) influenced by tide with higher concentrations observed in samples collected during receding (low) tides (EC: log 3.12 MPN/100 mL; ENT: 2.67 MPN/100 mL) compared to those collected during inundated (high) (EC: log 2.62 MPN/100 mL; ENT: 2.11 MPN/100 mL) or transition (EC: log 2.74 MPN/100 mL; ENT: 2.53 MPN/100 mL) tidal periods. Salinity, was also found to significantly (<0.05) correlate with Enterococcus sp. concentrations during inundated (high) tidal conditions (sal: 17 ppt; ENT: 2.04 MPN/100 mL). Tide, not precipitation, was shown to be a significant driver in explaining the variability in Enterococcus sp. concentrations. Precipitation has previously been shown to be a driver of Enterococcus sp. concentrations, but our project demonstrates the need for tidal parameters to be included in the future development of water quality monitoring programs.

Revealing the characteristics of dissolved organic matter in urban runoff at three typical regions via optical indices and molecular composition

Dissolved organic matter (DOM) plays a major role in ecological systems and influences the fate and transportation of many pollutants. Despite the significance of DOM, understanding of how environmental and anthropogenic factors influence its composition and characteristics is limited, especially in urban stormwater runoff. In this article, the chemical properties (pollutant loads, molecular weight, aromaticity, sources, and molecular composition) of DOM in stormwater extracted from three typical end-members (traffic, residential, and campus regions) were characterized by UV-visible (UV-vis) spectroscopy, excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC), and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). There are three findings: (1) The basic properties of DOM in stormwater runoff varied obviously from three urban fields, and the effect of initial flush was also apparent. (2) The DOM in residential areas mainly came from autochthonous sources, while allochthonous sources primarily contributed to the DOM in traffic and campus areas. However, it was mainly composed of terrestrial humic-like components with CHO and CHON element composition and HULO and aliphatic formulas. (3) The parameters characterizing DOM were primarily related to terrestrial source and aromaticity, but their correlations varied. Through the combination of optical methods and UPLC-Q-TOF spectrometry, the optical and molecular characteristics of rainwater are effectively revealed, which may provide a solid foundation for the classification management of stormwater runoff in different urban regions.

Identifying Sources of Faecal Contamination in a Small Urban Stream Catchment: A Multiparametric Approach

Small urban streams discharging in the proximity of bathing waters may significantly contribute to the deterioration of water quality, yet their impact may be overlooked. This study focuses on the Elm Park stream in the city of Dublin that is subject to faecal contamination by unidentified sources. The aim of the study was to identify a minimum number of “sentinel” sampling stations in an urban catchment that would provide the maximum amount of information regarding faecal pollution in the catchment. Thus, high-resolution sampling within the catchment was carried out over the course of 1 year at 11 stations. Faecal indicator bacteria were enumerated and microbial source tracking (MST) was employed to evaluate human pollution. In addition, ammonium, total oxidised nitrogen, and phosphorus levels were monitored to determine if these correlated with faecal indicator and the HF183 MST marker. In addition, the effect of severe weather events on water quality was assessed using automated sampling at one of the identified “sentinel” stations during baseflow and high flow conditions over a 24-h period. Our results show that this urban stream is at times highly contaminated by point source faecal pollution and that human faecal pollution is pervasive in the catchment. Correlations between ammonium concentrations and faecal indicator bacteria (FIB) as well as the human MST marker were observed during the study. Cluster analysis identified four “sentinel” stations that provide sufficient information on faecal pollution in the stream, thus reducing the geographical complexity of the catchment. Furthermore, ammonium levels strongly correlated with FIB and the human HF183 MST marker under high flow conditions at key “sentinel” stations. This work demonstrates the effectiveness of pairing MST, faecal indicators, and ammonium monitoring to identify “sentinel” stations that could be more rapidly assessed using real-time ammonium readouts to assess remediation efforts.

Land use intensification destabilizes stream microbial biodiversity and decreases metabolic efficiency

Urbanization and agricultural intensification can transform landscapes. Changes in land-use can lead to increases in storm runoff and nutrient loadings which can impair the health and function of stream ecosystems. Microorganisms are an integral component of stream ecosystems. Due to the sensitivity of microorganisms to perturbations, changes in hydrology and water chemistry may alter microbial activity and structure. These shifts in microbial community dynamics may alter stream metabolism and water quality, potentially impacting higher trophic levels. Here we examine the effects of land-use and associated changes in water chemistry on sediment microbial communities by studying the West Run Watershed (WRW) a mixed-land-use system in West Virginia, USA. Streams were sampled throughout the growing season at six sites within the WRW spanning different levels of land use intensification. The proportion of land impacted by agricultural and urban development was positively correlated with temporal variation in stream sediment microbial community composition (adj R² = 0.65), suggesting development can destabilize microbial communities. Moreover, streams in developed watersheds had an increased metabolic quotient (20-50% higher), this indicates that microorganisms have greater respiration per unit biomass and signifies reduced metabolic efficiency. Further, our results suggest that land use associated changes in water chemistry alter microbial function both directly and indirectly via changes in microbial community composition and biomass. Taken together our results suggest that highly developed watersheds with elevated conductivity, metal ion concentration, and pH impose stress on microbial communities resulting in reduced microbial efficiency and elevated respiration.

Prevalence of coliform bacterial contamination in cat drinking water in households in Thailand

Background and Aim

Bacterial contamination of drinking water is a leading cause of gastrointestinal infections. Cats may be at risk of water contamination from feces due to poor sanitation and hygiene. The objectives of the present study were to (1) evaluate the prevalence of coliform bacteria in cat drinking water and (2) identify possible risk factors leading to contamination.

Materials and Methods

Fifty-five drinking water samples were collected from water containers used by cats (median age [range]: 5 years [8 months-15 years]) at their home. Using a sterile syringe, 50 mL water was collected directly from water containers. The water samples were stored in coliform enhancement media for 24 h and then submitted for bacterial culture.

Results

The prevalence of fecal coliform contamination of cat drinking water was 67.27% (37/55; 95% confidence interval: 53.29-79.32%). There was no significant difference in the prevalence of coliform bacterial contamination of drinking water by age or gender of the cat or by water container type. However, bacterial contamination differed significantly between shorthaired cats and longhaired cats when comparing Escherichia coli (9/44 [20.45%] vs. 8/11 [72.72%], p<0.001) and Enterobacter spp. (16/44 [36.36%] vs. 9/11 [81.82%], p=0.007). For water that had been in a container longer than 12 h, there were significantly more contaminated tap water samples (16/19 [84.21%]) than contaminated processed water samples (9/17 [52.94%], p=0.047).

Conclusion

Coliform contamination in cat drinking water is common and occurs more often in households with longhaired cats. Drinking water for cats should be changed every 12 h, especially for households using tap water.

The relationship between environmental parameters and microbial water quality at two Costa Rican beaches from 2002 to 2017

Environmental conditions influence fecal indicator bacteria (FIB) levels, which are routinely used to characterize recreational water quality. This study examined 15 years of environmental and FIB data at Puntarenas and Jacó beach, Costa Rica. FIB relationships with sea level, wave height, precipitation, direct normal irradiance (DNI), wind, and turbidity were analyzed. Pearson's correlations identified lags between 24 and 96 h among environmental parameters and FIB. Multiple linear regression models composed of environmental parameters explained 24% and 27% of fecal coliforms and enterococci variability in Jacó, respectively. Puntarenas's models explained 17-26% of fecal coliforms and 12-18% enterococci variability. Precipitation, sea level anomalies, and wave height most frequently explained FIB variability. Hypothesis testing often identified significant differences in precipitation, wave height, daily sea level anomalies, and maximum sea level 24 h prior between days with and without FIB threshold exceedance. Unexpected FIB interactions with DNI, sea level, and turbidity highlight the importance of future investigations.

Epidemiological surveillance of SARS-CoV-2 by genome quantification in wastewater applied to a city in the northeast of France: Comparison of ultrafiltration- and protein precipitation-based methods

The aim of the present study was to develop a simple, sensitive, and specific approach to quantifying the SARS-CoV-2 genome in wastewater and to evaluate this approach as a means of epidemiological surveillance. Twelve wastewater samples were collected from a metropolitan area in north-eastern France during April and May 2020. In addition to the quantification of the SARS-CoV-2 genome, F-specific RNA phages of genogroup II (FRNAPH GGII), naturally present in wastewater, were used as an internal process control for the viral concentration and processing of RT-PCR inhibitors. A concentration method was required to allow the quantification of the SARS-CoV-2 genome over the longest possible period. A procedure combining ultrafiltration, phenol-chloroform-isoamyl alcohol purification, and the additional purification of the RNA extracts was chosen for the quantification of the SARS-CoV-2 genome in 100-mL wastewater samples. At the same time, the COVID-19 outbreak was evaluated through patients from the neighbouring University Hospital of Nancy, France. A regular decrease in the concentration of the SARS-CoV-2 genome from ~10⁴ gc/L to ~10² gc/L of wastewater was observed over the eight weeks of the study, during which the population was placed under lockdown. The SARS-CoV-2 genome was even undetectable during one week in the second half of May and present but non-quantifiable in the last sample (28 May). A concordant circulation in the human community was highlighted by virological diagnosis using respiratory samples, which showed a decrease in the number of COVID-19 cases from 677 to 52 per week over the same period. The environmental surveillance of COVID-19 using a reliable viral quantification procedure to test wastewater is a key approach. The real-time detection of viral genomes can allow us to predict and monitor the circulation of SARS-CoV-2 in clinical settings and survey the entire urban human population.

Correlation between antimicrobial resistance and faecal contamination in small urban streams and bathing waters

Antibiotic resistance represents the greatest challenge to healthcare systems around the world. As antibiotic resistance genes (ARGs) are shed in faeces, many studies have focused on how wastewater effluent contributes to ARG pollution in rivers. However, small urban streams and bathing waters not impacted by treated wastewater have received little attention though they may be important reservoirs of ARGs. The main objective of this study was to assess the extent to which ARG and faecal pollution impact small urban streams and bathing waters and to determine if there is a relationship between these contaminants. For one year, bi-monthly water samples were collected from two urban streams and Dublin city's three designated bathing waters. The Liffey Estuary, that receives treated wastewater, was also sampled. The sul1, tet(O), qnrS, bla_TEM, bla_SHV and bla_CTX-M ARGs were quantified. E. coli and intestinal enterococci levels were determined and the source of faecal pollution (human, dog, gull) quantified by microbial source tracking. Our results show that the Liffey Estuary, the urban streams and the bathing waters are highly impacted by ARGs and human faeces. There were clear correlations between all of the studied faecal indicators and ARGs in the Liffey Estuary. In the urban streams relationships were observed for only some of the ARGs and faecal indicators, which is likely a result of non-continuous sewage leaks and overflows to the streams. Similarly, only some ARGs correlated with faecal indicators in the urban bathing waters. The source of ARGs in the bathing waters is likely to be multifaceted as we detected sporadic dog and gull faecal markers. This study demonstrates that small urban streams and bathing waters are reservoirs of ARGs and that they may pose a previously unrecognised public health risk as they have the potential to transmit enteric pathogens and antibiotic resistance determinants.

Residential urban stormwater runoff: A comprehensive profile of microbiome and antibiotic resistance

Non-point stormwater runoff is a major contamination source of receiving waterbodies. Heightened incidence of waterborne disease outbreaks related to recreational use and source water contamination is associated with extreme rainfall events. Such extreme events are predicted to increase in some regions due to climate change. Consequently, municipal separate storm sewer systems (MS4s) conveying pathogens to receiving waters are a growing public health concern. In addition, the spread of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria in various environmental matrices, including urban runoff, is an emerging threat. The resistome and microbiota profile of MS4 discharges has yet to be fully characterized. To address this knowledge gap, we first analyzed the relationship between rainfall depth and intensity and E. coli densities (fecal indicator) in stormwater from four MS4 outflows in Columbus, Ohio, USA during the spring and summer of 2017. Microbial source tracking (MST) was conducted to examine major fecal contamination sources in the study sewersheds. A subset of samples was analyzed for microbial and resistome profiles using a metagenomic approach. The results showed a significant positive relationship between outflow E. coli density and rainfall intensity. MST results indicate prevalent fecal contamination from ruminant populations in the study sites (91% positive among the samples tested). Protobacteria and Actinobacteria were two dominant bacteria at a phylum level. A diverse array of ARGs and potentially pathogenic bacteria (e.g. Salmonella enterica Typhimurium), fungi (e.g. Scedosporium apiospermum), and protists (e.g. Acanthamoeba palestinensis) were found in urban stormwater outflows that discharge into adjacent streams. The most prevalent ARGs among samples were β-lactam resistance genes and the most predominant virulence genes within bacterial community were related with Staphylococcus aureus. A comprehensive contamination profile indicates a need for sustainable strategies to manage urban stormwater runoff amid increasingly intense rainfall events to protect public and environmental health.

Water treatment residual: A critical review of its applications on pollutant removal from stormwater runoff and future perspectives

In recent years, many studies have been conducted on using different filter media in bioretention systems for stormwater runoff treatment. This critical review paper provides a comprehensive review on the current state of water treatment residual (WTR), a recycled material that can be used as bioretention filter media for removals of key stormwater runoff pollutants (especially phosphorus) and future perspectives with innovative modification on WTR applied for pathogen removal from stormwater runoff. This review paper comprised (i) a brief summary of the reported WTR characteristics, (ii) a thorough evaluation of WTR performance on major pollutants removal from stormwater runoff (iii) a discussion on phosphorus removal mechanisms by WTR applied in the stormwater runoff treatment, and (iv) a review of the future perspectives of WTR for pathogen removal and other potential practical application in the field of stormwater treatment. As outlined in this review, WTR in stormwater runoff treatment has yet to be fully explored. The possible enhancements, especially metal surface modification on WTR are reviewed to bring about the widespread use of WTR in stormwater reuse practices.

The pollution conveyed by urban runoff: A review of sources

Urban stormwater and snowmelt pollution contributes significantly to the deterioration of surface waters quality in many locations. Consequently, the sources of such pollution have been studied for the past 50 years, with the vehicular transportation sector and the atmospheric deposition identified early as the major pollution sources. In search for mitigation of this pollution, source controls, besides other measures, were recognised as effective pollution mitigation tools, whose successful implementation requires a good knowledge of pollution sources. Even though great research efforts have been exerted to document specific sources of urban runoff pollution, or specific groups of pollutants present in urban runoff, a comprehensive overview of all known contributing sources is still missing. This review contributes to closing this gap by compiling findings of previous research and critically synthesizing the current knowledge of various stormwater pollution sources. As the emphasis is placed on the sources, the related issues of implications for urban surface water quality and possible source controls for individual sources are touched upon just briefly, where required. The review showed that the atmospheric deposition, vehicular transportation-related activities and metallic building envelopes continue to be among the major pollution sources, which have been studied in a far greater detail than other sources. Furthermore, it was noted that because of the rapid advances in clean manufacturing and pollution control technologies, a large part of the body of data on stormwater quality available in the literature should be considered as historical data, which may no longer describe well the current conditions. Progressing historical data obsolescence, combined with continuing releases of new materials and chemicals, and, in some cases of new substances of potential concern, into the environment, suggests that the identification of important stormwater runoff/snowmelt pollution sources, and the associated pollutants, has been and will remain to be a work in progress.

Tracking faecal microorganisms using the qPCR method in a typical urban catchment in China

Faecal microorganisms represent a key threat to human health. Potential origins of faecal microbial contamination in a typical urban-representative micro-scale were evaluated. The quantitative polymerase chain reaction (qPCR) method was used in this study. The Bacteroidetes is selected as the indicative microorganism in runoff samples that are collected during four representative stormwater events in north China. The principal component analysis (PCA) method indicated the distribution feature of the environmental factors. The largest contributor is dog, followed by bird and human to the faecal pollution in stormwater runoff. The output of human and dog faecal pollutants in response to the first flush effect of nonpoint source pollution while the transmit time of bird faecal pollutant is relatively longer. In addition, the number of antecedent drying days represents the key factor for dog faecal pollution, while human faecal pollution is impacted by more factors. The results of this study will provide sound evidence for the tracking and management of nonpoint source faecal pollution in urban catchment areas.

Effect of the time scale on the uncertainty of geometric mean concentrations of fecal indicators in creek under baseflow conditions

Geometric mean concentrations of fecal indicator bacteria E. coli and enterococci are commonly used to evaluate the microbial quality of irrigation, recreation, and other types of waters, as well in watershed-scale microbial water quality modeling. It is not known how the uncertainty of those geometric mean concentrations depends on the time period between sampling. We analyzed data collected under baseflow conditions from three years of weekly and several daily sampling campaigns at Conococheague Creek in Pennsylvania. Standard deviations of logarithms of geometric mean concentrations were computed over weeks, months, and seasons. The increase in standard deviations from weekly to seasonal time scale was on average about 0.1 and 0.2 for log(E. coli) and log(enterococci), respectively, and in most cases was statistically significant. This may need to be accounted for when evaluating the uncertainty of measurements for modeling purposes and in risk assessment of microbial water quality.

Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain

Karst aquifers have distinctive hydrology and supply 25% of the world's population with drinking water, making them a critical geological setting for understanding and managing microbial water pollution. Rainfall causes elevated concentrations and loading of faecal microorganisms, e.g. E. coli, in catchment surface and groundwater systems, increasing the risk of human exposure to faecally-contaminated water. However, effective management of microbial water quality in complex karst catchments is constrained by limited understanding of E. coli - discharge responses to rainfall. We analysed how rainfall events of varying magnitude (2.4-100 mm) control E. coli-discharge dynamics at increasing spatial scales in a mixed land-use karst catchment in southwest China. During the wet season, hourly water sampling was undertaken throughout five storm events to characterise in high detail E. coli emergence with resulting flow across multiple sites of varying catchment area, stream order, and land-use. E. coli concentration was found to increase by 1-3 orders of magnitude following rainfall events. Maximum E. coli concentration and speed of E. coli recession were influenced by rainfall (amount, intensity), timing of agricultural activities, and position in the hydrological system. For high intensity events ∼90% of the cumulative E. coli export occurred within 48 h. E. coli concentration increased with increasing discharge at all sites. E. coli concentration at low discharge was higher in the headwaters than at the catchment outlet, while the rate of increase in E. coli concentration with increasing discharge appears to follow the opposite trend, being higher at the catchment outlet than the headwaters. This was attributed to the decreasing flow path gradient and increasing degree of development of the fissure network, but further event monitoring at varying catchment scales is required to confirm this relationship. The results provide novel insight into how rainfall characteristics combine with land-use and catchment hydrology to control E. coli export in karst landscapes.

Climate and Land Use Influences on Bacteria Levels in Stormwater

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

Demands on global water supplies are increasing in response to the need to provide more food, water, and energy for a rapidly growing population. These water stressors are exacerbated by climate change, as well as the growth and urbanisation of industry and commerce. Consequently, urban water authorities around the globe are exploring alternative water sources to meet ever-increasing demands. These alternative sources are primarily treated sewage, stormwater, and groundwater. Stormwater including roof-harvested rainwater has been considered as an alternative water source for both potable and non-potable uses. One of the most significant issues concerning alternative water reuse is the public health risk associated with chemical and microbial contaminants. Several studies to date have quantified fecal indicators and pathogens in stormwater. Microbial source tracking (MST) approaches have also been used to determine the sources of fecal contamination in stormwater and receiving waters. This review paper summarizes occurrence and concentrations of fecal indicators, pathogens, and MST marker genes in urban stormwater. A section of the review highlights the removal of fecal indicators and pathogens through water sensitive urban design (WSUD) or Best Management Practices (BMPs). We also discuss approaches for assessing and mitigating health risks associated with stormwater, including a summary of existing quantitative microbial risk assessment (QMRA) models for potable and non-potable reuse of stormwater. Finally, the most critical research gaps are identified for formulating risk management strategies.

Antibiotic resistance in urban runoff

Aquatic ecosystems subjected to anthropogenic pressures are places of rapid evolution of microbial communities and likely hotspots for selection and emergence of antibiotic resistant bacteria. In urban settings, water quality and the risk of infection are generally assessed in sewers and in effluents of wastewater treatment plants. Physical and chemical parameters as well as the presence of antibiotics, antibiotic-resistant bacteria and genes of resistance are driven by urban activities, with adverse effects on aquatic ecosystems. In this paper we review the environmental pressures exerted on bacterial communities in urban runoff waters and discuss the impact of these settings on antibiotic resistance. Considering the worrisome epidemiology of infectious diseases and estimated mortality due to antimicrobial resistance in the coming decades, there is an urgent need to identify all environmental reservoirs of resistant bacteria and resistance genes to complete our knowledge of the epidemiological cycle and of the dynamics of urban antibiotic resistance.

Comparison of two receptor models PCA-MLR and PMF for source identification and apportionment of pollution carried by runoff from catchment and sub-watershed areas with mixed land cover in South Korea

The application and comparison of receptor modeling techniques based on ambient air quality and particulate matter increasingly being studied. However, less is known about the comparison of receptor modeling techniques using spatial runoff quality data to identify and quantify the stormwater runoff pollution. This study compared the performance of principal component analysis-multiple linear regressions (PCA-MLR) and positive matrix factorization (PMF) models on stormwater runoff data collected from a small catchment (Site 1) with urban development activity and a sub-watershed outlet (Site 2). In both sites, the PCA-MLR model identified three pollution sources, whereas PMF identified five with a detailed source mechanism including two additional sources. Furthermore, the spatial land-use land-cover (LULC) analysis results indicate that the Site 1 exhibited a rapid conversion of the native area into a built-up area over the monitoring period compared to Site 2. Based on the modeling results, domestic wastewater and soil erosion were the major source of pollution at Site 1 and Site 2, respectively. The performance evaluation statistics including Nash coefficient (0.86-0.99), % error (<-14 to 2), and coefficient of determination (R² ≤ 0.99) showed better performance for the PMF model than the PCA-MLR model. Overall, the PMF receptor modeling approach was found to be more robust for the current study sites with different land use types. The findings of this study could provide a basis for further application of these receptor models and their comparison using spatial-temporal ionic and sediment related runoff monitoring data. Also, the models from this research could be combined with other receptor models on runoff quality data (e.g. CMB or UNMIX) to explore and inter-compare the outcomes, and to determine how the model results are affected by modifications to input data and model parameters. Therefore, further research is required to precisely assess the accuracy of both receptor models.

A Semi-distributed Model for Predicting Faecal Coliform in Urban Stormwater by Integrating SWMM and MOPUS

The microbial contamination of urban stormwater has an important impact on human health and stormwater reuse. This study develops an exploratory semi-distributed model, MOPUS_S, which can simulate faecal coliform levels in separate sewer systems in urban catchments. The MOPUS_S was built by coupling the SWMM model and the microbial MOPUS model. The parameters associated with the deposition and wash-off of microorganisms were more influential than those related to microorganism survival processes. Compared to other existing bacterial models, MOPUS_S showed comparable performance in predicting faecal coliform concentrations. The performance varied largely between rainfall events, with Nash-Sutcliffe efficiency (NSE) values ranging from −5.03 to 0.39 and R² ranging from −0.02 to 0.83, respectively. The model simulation results for low and medium concentrations were better than those for the peak concentrations. Poor simulation results of peak concentrations obviously affect the overall model performance. In general, MOPUS_S could be capable of predicting the faecal coliform load in urban catchments and be a useful tool for urban stormwater management planning.

Temporal variability of suspended solids in construction runoff and evaluation of time-paced sampling strategies

The construction sites have been considered the type of land use with the highest pollution potential, especially due to the erosion of exposed soil surfaces. The runoff monitoring of the construction site was carried out since June 2011 through December 2015. Based on land use land cover (LULC) classification, the monitoring period was divided into active and post-construction phases. Total suspended solids (TSS) showed evident inter-phase variability in average annual event mean concentration (AAEMC) and wash-off pattern. We suggested that stringent runoff control measures should be adopted during active construction phase. Similarly, Personalized Computer Storm Water Management Model (PCSWMM) was applied to evaluate the performance of the time-paced discrete and composite sampling scheme in continuously changing LC scenario. It was found that even though the time-paced composite sampling scheme is more cost effective, it showed lower performance in EMC estimation when compared with the time-paced discrete sampling approach. The results also showed that the storm event monitored at a time discrete frequency of 5 min, 10 min, and 15 min, the maximum expected mean bias will be under the accepted level of 10% of the true EMC value. We concluded that construction phase-specific modifications in sampling scheme provides a view to generate near accurate estimates.

Assessment of the treatment efficiency of an urban stormwater pond and its impact on the natural downstream watercourse

During the last few decades, stormwater ponds have become an alternative management practice in order to avoid flooding and to contain rainwater and runoff in urban areas where impervious land cover has increased. A second purpose of stormwater ponds is to improve the quality of runoff water that is usually contaminated with nitrogen, phosphorus, metals and organic micropollutants. Processes used are based on natural methods such as settlement and contribute to minimize the impact of these inputs to the natural aquatic system. This study aims to better understand the behavior of a wet stormwater pond, Heron Lake (33 ha) located in the city of Villeneuve d'Ascq in northern France through various indicators [trace metals, PAHs, PCBs, caffeine (CAF), carbamazepine (CBZ), nutrients and pathogens]. For that purpose, water quality was monitored for 1 year, mainly at the entrance and at the outlet of the lake. Sampling have also been done in the downstream aquatic environment, the Marque River. Sediments were sampled in the lake to evaluate the pollution trapped during sedimentation. Our results of both water and sediment sampling highlight: (i) the wastewater input into the Heron Lake is estimated to be equivalent to that of roughly 3800 inhabitants; (ii) the removal rates observed at the outlet, relative to concentrations at the entrance channel, vary as follows for these dissolved species: 24% for NO₃^- and PO₄^3-, 28% for CBZ, 35% for Cu, 63% for Pb, 78% for CAF, 84% for Zn and up to 93% for NH₄⁺; (iii) there are high levels of sediment contamination with metals, PAHs and PCBs at the entrance channel; (iv) the eutrophication of this pond is attributed to persistent high nutrient concentrations in both water and sediment, and has contributed to the development of an invasive macrophyte, the Elodea nuttallii; and (v) there appears to be only a negligible impact of the discharge from the lake to the natural watercourse, contributing annual loads of <2 up to 6% of the total amount of Cu, Pb, Zn, CAF, CBZ and nutrients measured in the Marque River, and having a slight diluting effect on concentrations in the Marque River.

Implementation of an automated beach water quality nowcast system at ten California oceanic beaches

Fecal indicator bacteria like Escherichia coli and entercococci are monitored at beaches around the world to reduce incidence of recreational waterborne illness. Measurements are usually made weekly, but FIB concentrations can exhibit extreme variability, fluctuating at shorter periods. The result is that water quality has likely changed by the time data are provided to beachgoers. Here, we present an automated water quality prediction system (called the nowcast system) that is capable of providing daily predictions of water quality for numerous beaches. We created nowcast models for 10 California beaches using weather, oceanographic, and other environmental variables as input to tuned regression models to predict if FIB concentrations were above single sample water quality standards. Rainfall was used as a variable in nearly every model. The models were calibrated and validated using historical data. Subsequently, models were implemented during the 2017 swim season in collaboration with local beach managers. During the 2017 swim season, the median sensitivity of the nowcast models was 0.5 compared to 0 for the current method of using day-to-week old measurements to make beach posting decisions. Model specificity was also high (median of 0.87). During the implementation phase, nowcast models provided an average of 140 additional days per beach of updated water quality information to managers when water quality measurements were not made. The work presented herein emphasizes that a one-size-fits all approach to nowcast modeling, even when beaches are in close proximity, is infeasible. Flexibility in modeling approaches and adaptive responses to modeling and data challenges are required when implementing nowcast models for beach management.

Prevalent fecal contamination in drinking water resources and potential health risks in Swat, Pakistan

Fecal bacteria contaminate water resources and result in associated waterborne diseases. This study assessed drinking water quality and evaluated their potential health risks in Swat, Pakistan. Ground and surface drinking water were randomly collected from upstream to downstream in the River Swat watershed and analyzed for fecal contamination using fecal indicator bacteria (Escherichia coli) and physiochemical parameters (potential of hydrogen, turbidity, temperature, electrical conductivity, total dissolved solid, color, odor and taste). The physiochemical parameters were within their safe limits except in a few locations, whereas, the fecal contaminations in drinking water resources exceeded the drinking water quality standards of Pakistan Environmental Protection Agency (Pak-EPA), 2008 and World Health Organization (WHO), 2011. Multivariate and univariate analyses revealed that downstream urbanization trend, minimum distance between water sources and pit latrines/sewerage systems, raw sewage deep well injection and amplified urban, pastures and agricultural runoffs having human and animal excreta were the possible sources of contamination. The questionnaire survey revealed that majority of the local people using 10-20years old drinking water supply schemes at the rate of 73% well supply, 13% hand pump supply, 11% spring supply and 3% river/streams supply, which spreads high prevalence of water borne diseases including hepatitis, intestinal infections and diarrhea, dysentery, cholera, typhoid fever, jaundice and skin diseases in children followed by older and younger adults.

Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

Occurrence of Traditional and Alternative Fecal Indicators in Tropical Urban Environments under Different Land Use Patterns

This study evaluated the geospatial distribution of fecal indicator bacteria (FIB) (i.e., Escherichia coli, Enterococcus spp.) and the alternative fecal indicator pepper mild mottle virus (PMMoV) in tropical freshwater environments under different land use patterns. Results show that the occurrence and concentration of microbial fecal indicators were higher for urban than for parkland-dominated areas, consistent with land use weightage. Significant positive correlations with traditional FIB indicate that PMMoV is a suitable indicator of fecal contamination in tropical catchments waters (0.549 ≤ rho ≤ 0.612; P < 0.01). PMMoV exhibited a strong significant correlation with land use weightage (rho = 0.728; P < 0.01) compared to traditional FIB (rho = 0.583; P < 0.01). In addition, chemical tracers were also added to evaluate the potential relationships with microbial fecal indicators. The relationships between diverse variables (e.g., environmental parameters, land use coverage, and chemical tracers) and the occurrence of FIB and PMMoV were evaluated. By using stepwise multiple linear regression (MLR), the empirical experimental models substantiate the impact of land use patterns and anthropogenic activities on microbial water quality, and the output results of the empirical models may be able to predict the sources and transportation of human fecal pollution or sewage contamination. In addition, the high correlation between PMMoV data obtained from quantitative real-time PCR (qPCR) and viral metagenomics data supports the possibility of using viral metagenomics to relatively quantify specific microbial indicators for monitoring microbial water quality (0.588 ≤ rho ≤ 0.879; P < 0.05).IMPORTANCE The results of this study may support the hypothesis of using PMMoV as an alternative indicator of human fecal contamination in tropical surface waters from the perspective of land use patterns. The predictive result of the occurrence of human fecal indicators with high accuracy may reflect the source and transportation of human fecal pollution, which are directly related to the risk to human health, and thereafter, steps can be taken to mitigate these risks.

Waste-Based Pervious Concrete for Climate-Resilient Pavements

For the sake of environmental protection and circular economy, cement reduction and cement substitutes have become popular research topics, and the application of green materials has become an important issue in the development of building materials. This study developed green pervious concrete using water-quenched blast-furnace slag (BFS) and co-fired fly ash (CFFA) to replace cement. The objectives of this study were to gauge the feasibility of using a non-cement binder in pervious concrete and identify the optimal binder mix design in terms of compressive strength, permeability, and durability. For filled percentage of voids by cement paste (FPVs) of 70%, 80%, and 90%, which mixed with CFFA and BFS as the binder (40 + 60%, 50 + 50%, and 60 + 40%) to create pervious concrete with no cement. The results indicate that the complete (100%) replacement of cement with CFFA and BFS with no alkaline activator could induce hydration, setting, and hardening. After a curing period of 28 days, the compressive strength with different FPVs could reach approximately 90% that of the control cement specimens. The cementless pervious concrete specimens with BFS:CFFA = 7:3 and FPV = 90% presented better engineering properties and permeability.

Predicting nonpoint stormwater runoff quality from land use

Evaluating the impact of urban development on natural ecosystem processes has become an increasingly complex task for planners, environmental scientists, and engineers. As the built environment continues to grow, unregulated nonpoint pollutants from increased human activity and large-scale development severely stress urban streams and lakes resulting in their currently impaired or degraded state. In response, integrated water quality management programs have been adopted to address these unregulated nonpoint pollutants by utilizing best management practices (BMPs) that treat runoff as close to the source as possible. Knowing where to install effective BMPs is no trivial task, considering budget constraints and the spatially extensive nature of nonpoint stormwater runoff. Accordingly, this paper presents an initial, straightforward and cost-effective methodology to identify critical nonpoint pollutant source watersheds through correlation of water quality with land use. Through an illustrative application to metropolitan Denver, Colorado, it is shown how this method can be used to aid stormwater professionals to evaluate and specify retrofit locations in need of water quality treatment features reduce, capture and treat stormwater runoff prior to entering receiving waters.

Influence of land development on stormwater runoff from a mixed land use and land cover catchment

Mitigating for the negative impacts of stormwater runoff is becoming a concern due to increased land development. Understanding how land development influences stormwater runoff is essential for sustainably managing water resources. In recent years, aggregate low impact development-best management practices (LID-BMPs) have been implemented to reduce the negative impacts of stormwater runoff on receiving water bodies. This study used an integrated approach to determine the influence of land development and assess the ecological benefits of four aggregate LID-BMPs in stormwater runoff from a mixed land use and land cover (LULC) catchment with ongoing land development. It used data from 2011 to 2015 that monitored 41 storm events and monthly LULC, and a Personalized Computer Storm Water Management Model (PCSWMM). The four aggregate LID-BMPs are: ecological (S1), utilizing pervious covers (S2), and multi-control (S3) and (S4). These LID-BMPs were designed and distributed in the study area based on catchment characteristics, cost, and effectiveness. PCSWMM was used to simulate the monitored storm events from 2014 (calibration: R² and NSE>0.5; RMSE <11) and 2015 (validation: R² and NSE>0.5; RMSE <12). For continuous simulation and analyzing LID-BMPs scenarios, the five-year (2011 to 2015) stormwater runoff data and LULC change patterns (only 2015 for LID-BMPs) were used. Results show that the expansion of bare land and impervious cover, soil alteration, and high amount of precipitation influenced the stormwater runoff variability during different phases of land development. The four aggregate LID-BMPs reduced runoff volume (34%-61%), peak flow (6%-19%), and pollutant concentrations (53%-83%). The results of this study, in addition to supporting local LULC planning and land development activities, also could be applied to input data for empirical modeling, and designing sustainable stormwater management guidelines and monitoring strategies.

Stormwater loadings of antibiotic resistance genes in an urban stream

Antibiotic resistance presents a critical public health challenge and the transmission of antibiotic resistance via environmental pathways continues to gain attention. Factors driving the spread of antibiotic resistance genes (ARGs) in surface water and sources of ARGs in urban stormwater have not been well-characterized. In this study, five ARGs (sul1, sul2, tet(O), tet(W), and erm(F)) were quantified throughout the duration of three storm runoff events in an urban inland stream. Storm loads of all five ARGs were significantly greater than during equivalent background periods. Neither fecal indicator bacteria measured (E. coli or enterococci) was significantly correlated with sul1, sul2, or erm(F), regardless of whether ARG concentration was absolute or normalized to 16S rRNA levels. Both E. coli and enterococci were correlated with the tetracycline resistance genes, tet(O) and tet(W). Next-generation shotgun metagenomic sequencing was conducted to more thoroughly characterize the resistome (i.e., full complement of ARGs) and profile the occurrence of all ARGs described in current databases in storm runoff in order to inform future watershed monitoring and management. Between 37 and 121 different ARGs were detected in each stream sample, though the ARG profiles differed among storms. This study establishes that storm-driven transport of ARGs comprises a considerable fraction of overall downstream loadings and broadly characterizes the urban stormwater resistome to identify potential marker ARGs indicative of impact.

Identification of Factors Affecting Fecal Pollution in Beaver Lake Reservoir

Standard methods for the evaluation of recreational water quality rely on generic bacterial indicators such as . However, does not provide enough information to determine fecal source or public health risk. The stsudy objective was to determine factors influencing the presence of and host-specific markers (HSM) from upstream to downstream in Beaver Lake Reservoir (BLR). From February 2014 to September 2015, 420 base flow and rain event samples were collected from seven sites-two sites from streams (White River [WR] and War Eagle Creek) draining into BLR and five sites from within BLR. Each sample was analyzed for and by quantitative polymerase chain reaction for HSM related to human, bovine, and poultry. The data indicate that overall levels of were significantly greater in the WR and significantly lower at the most downstream sampling location in BLR. is more likely present during spring (adjusted odds ratio [aOR] = 1.86), at the WR sampling site (aOR = 3.39), or during a rain event (aOR = 2.73). Moreover, the HSM HumM2 is more likely present (aOR = 1.99) when is present. These same factors were associated with concentrations >126 most probable number 100 mL (aOR = 2.76-12.48), except the poultry marker CL was more likely associated (aOR = 3.81) than HumM2. This study revealed that both seasonal and locational factors are important variables for fecal pollution in BLR. Moreover, these same factors may apply to fecal pollution in manmade reservoirs within similar types of watersheds across the United States, as well as internationally.

Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China

Streams in urban areas are prone to degradation. While urbanization-induced poor water quality is a widely observed and well documented phenomenon, the mechanism to pinpoint local drivers of urban stream degradation, and their relative influence on water quality, is still lacking. Utilizing data from the citizen science project FreshWater Watch, we use a machine learning approach to identify key indicators, potential drivers, and potential controls to water quality across the metropolitan areas of Shanghai, Guangzhou and Hong Kong. Partial dependencies were examined to establish the direction of relationships between predictors and water quality. A random forest classification model indicated that predictors of stream water colour (drivers related to artificial land coverage and agricultural land use coverage) and potential controls related to the presence of bankside vegetation were found to be important in identifying basins with degraded water quality conditions, based on individual measurements of turbidity and nutrient (N-NO₃ and P-PO₄) concentrations.