Examining drinking water quality: analysis of physico-chemical properties and bacterial contamination with health implications for Shangla district, Khyber Pakhtunkhwa, Pakistan.

A comprehensive understanding of water quality is essential for assessing the complex relationship between surface water and sources of pollution. Primarily, surface water pollution is linked to human and animal waste discharges. This study aimed to investigate the physico-chemical characteristics of drinking water under both dry and wet conditions, assess the extent of bacterial contamination in samples collected from various locations in District Shangla, and evaluate potential health risks associated with consuming contaminated water within local communities. For this purpose, 120 groundwater and surface water samples were randomly collected from various sources such as storage tanks, user sites, streams, ponds and rivers in the study area. The results revealed that in Bisham, lakes had the highest fecal coliform levels among seven tested sources, followed by protected wells, reservoirs, downstream sources, springs, rivers, and ditches; while in Alpuri, nearly 80% of samples from five sources contained fecal coliform bacteria. Similarly, it was observed that the turbidity level, total dissolved solids, electrical conductivity, biological oxygen demand, and dissolved oxygen in the surface drinking water sources of Bisham were significantly higher than those in the surface drinking water sources of Alpuri. Furthermore, the results showed that in the Alpuri region, 14% of the population suffers from dysentery, 27% from diarrhea, 22% from cholera, 13% from hepatitis A, and 16% and 8% from typhoid and kidney problems, respectively, while in the Bisham area, 24% of residents are affected by diarrhea, 17% by cholera and typhoid, 15% by hepatitis A, 14% by dysentery, and 13% by kidney problems. These findings underscore the urgent need for improved water quality management practices and public health interventions to mitigate the risks associated with contaminated drinking water. It is recommended to implement regular water quality monitoring programs, enhance sanitation infrastructure, and raise awareness among local communities about the importance of safe drinking water practices to safeguard public health.

Occurrence and removal of fecal bacteria and microbial source tracking markers in a stormwater detention basin overlying the Edwards Aquifer recharge zone in Texas.

The Edwards Aquifer is the primary water resource for over 2 million people in Texas and faces challenges including fecal contamination of water recharging the aquifer, while effectiveness of best management practices (BMPs) such as detention basins in mitigating fecal pollution remains poorly understood. For this study, the inlet and outlet of a detention basin overlying the aquifer's recharge zone were sampled following storm events using automated samplers. Microbial source tracking and culture-based methods were used to determine the occurrence and removal of fecal genetic markers and fecal coliform bacteria in collected water samples. Markers included E. coli (EC23S857), Enterococcus (Entero1), human (HF183), canine (BacCan), and bird (GFD). Fecal coliforms, EC23S857, and Entero1 were detected following each storm event. GFD was the most frequent host-associated marker detected (91% of samples), followed by BacCan (46%), and HF183 (17%). Wilcoxon signed rank tests indicated significantly lower outlet concentrations for fecal coliforms, EC23S857, and Entero1, but not for HF183, GFD, and BacCan. Higher GFD and BacCan outlet concentrations may be due to factors independent of basin design, such as the non-point source nature of bird fecal contamination and domestic dog care practices in neighborhoods contributing to the basin. Mann-Whitney tests showed marker concentrations were not significantly higher during instances of fecal coliform water quality criterion exceedance, except for E. coli, and that fecal coliform concentrations were not significantly different based on marker detection. Overall, results suggest that the detention basin is effective in attenuating fecal contamination associated with fecal coliforms and the general markers, but not for host-associated markers. Consequently, management efforts should focus on mitigating dog and bird-associated fecal pollution in the study region.

Uncertainty assessment for three-dimensional hydrodynamic and fecal coliform modeling in the Danshuei River estuarine system: The influence of first-order parametric decay reaction.

Modeling fecal contamination in water bodies is of importance for microbiological risk assessment and management. This study investigated the transport of fecal coliform (e.g., up to 2.1 × 106 CFU/100 ml at the Zhongshan Bridge due to the main point source from the Xinhai Bridge) in the Danshuei River estuarine system, Taiwan with the main focus on assessing model uncertainty due to three relevant parameters for the microbial decay process. First, a 3D hydrodynamic-fecal coliform model (i.e., SCHISM-FC) was developed and rigorously validated against the available data of water level, velocity, salinity, suspended sediment and fecal coliform measured in 2019. Subsequently, the variation ranges of decay reaction parameters were considered from several previous studies and properly determined using the Monte Carlo simulations. Our analysis showed that the constant ratio of solar radiation (α) as well as the settling velocity (vs) had the normally-distributed variations while the attachment fraction of fecal coliform bacteria (Fp) was best fitted by the Weibull distribution. The modeled fecal coliform concentrations near the upstream (or downstream) stations were less sensitive to those parameter variations (see the smallest width of confidence interval about 1660 CFU/100 ml at the Zhongzheng Bridge station) due to the dominant effects of inflow discharge (or tides). On the other hand, for the middle parts of Danshuei River where complicated hydrodynamic circulation and decay reaction occurred, the variations of parameters led to much larger uncertainty in modeled fecal coliform concentration (see a wider confidence interval about 117,000 CFU/100 ml at the Bailing Bridge station). Overall, more detailed information revealed in this study would be helpful while the environmental authority needs to develop a proper strategy for water quality assessment and management. Owing to the uncertain decay parameters, for instance, the modeled fecal coliform impacts at Bailing Bridge over the study period showed a 25 % difference between the lowest and highest concentrations at several moments. For the detection of pollution occurrence, the highest to lowest probabilities for a required fecal coliform concentration (e.g., 260,000 CFU/100 ml over the environmental regulation) at Bailing Bridge was possibly greater than three.

Drinking water contamination potential and associated factors among households with under-five children in rural areas of Dessie Zuria District, Northeast Ethiopia.

Objective: More than half of the 700 million people worldwide who lack access to a safe water supply live in sub-Saharan Africa, including Ethiopia. Globally, approximately 2 billion people use drinking water sources that are contaminated with fecal matter. However, little is known about the relationship between fecal coliforms and determinants in drinking water. Therefore, the objective of this study was to investigate the potential for contamination of drinking water and its associated factors in households with children under 5 years of age in Dessie Zuria district in northeastern Ethiopia. Methods: The water laboratory was conducted based on the American Public Health Association guidelines for water and wastewater assessment using a membrane filtration technique. A structured and pre-tested questionnaire was used to identify factors associated with the potential for contamination of drinking water in 412 selected households. A binary logistic regression analysis was performed to determine the factors associated with the presence or absence of fecal coliforms in drinking water, with a 95% confidence interval (CI) and a value of p ≤ 0.05. The overall goodness of the model was tested using the Hosmer-Lemeshow test, and the model was fit. Results: A total of 241 (58.5%) households relied on unimproved water supply sources. In addition, approximately two-thirds 272 (66.0%) of the household water samples were positive for fecal coliform bacteria. Water storage duration ≥3 days (AOR = 4.632; 95% CI: 1.529-14.034), dipping method of water withdrawal from a water storage tank (AOR = 4.377; 95% CI: 1.382-7.171), uncovered water storage tank at control (AOR = 5.700; 95% CI: 2.017-31.189), lack of home-based water treatment (AOR = 4.822; 95% CI: 1.730-13.442), and unsafe household liquid waste disposal methods (AOR = 3.066; 95% CI: 1.706-8.735) were factors significantly associated with the presence of fecal contamination in drinking water. Conclusion: Fecal contamination of water was high. The duration of water storage, the method of water withdrawal from the storage container, covering of the water storage container, the presence of home-based water treatment, and the method of liquid waste disposal were factors for fecal contamination in drinking water. Therefore, health professionals should continuously educate the public on proper water use and water quality assessment.

Household drinking water quality and its predictors in flood-prone settings of Northwest Ethiopia: A cross-sectional community-based study.

Globally, the deterioration of drinking water quality is a major public health problem that contributes to the spread of disease and causes death. Therefore, it is important to have regular quality control monitoring. This study aimed to assess the level of physicochemical and bacteriological quality of household drinking water and its contributing factors in flood-prone settlements of South Gondar Zone, Ethiopia. A community-based cross-sectional study was conducted in flood-prone settings of Northwest Ethiopia from January 17 to March 30, 2021. Structured questionnaires were used to gather the sociodemographic, environmental, and behavioral data. A total of 675 drinking water samples were collected from water storage containers of selected households. Logistic regression models were used for both univariate and multivariable studies. The survey included a total of 675 households. The mean values of pH (5.9 ± 1.03), turbidity (6.7 ± 2.21 NTU), and free residual chlorine (0.02 ± 0.01 mg/l) did not meet the WHO recommended limits for drinking water. The prevalence of fecal contamination of drinking water in the study area was 62.2% with [95% CI (53-60%)]. Family size [AOR = 2.205, 95% CI (1.375-3.536), absence of latrine [AOR = 3.449, 95% CI (1.349-8.823)], and lack of a separate container to draw water from its storage [AOR = 0.454, 95% CI (0.249-0.827)] were significant predictors for fecal contamination of household drinking water. In conclusion, the water quality in terms of pH, turbidity, residual chlorine, and bacteriological parameters was poor and not suitable for consumption. High prevalence of fecal contamination of water was found, and it was significantly associated with family size, the absence of a latrine, and the lack of a separate cap to take water from the storage. Therefore, continuous chlorination and monitoring its concentration, educating the community on how to use stored water, educating the advantage of having a latrine, and promoting point-of-use treatments such as filtration and boiling are needed.

The impact of combined sewer outflows on urban water quality: Spatio-temporal patterns of fecal coliform in indianapolis.

Many urban waterways with older stormwater drainage systems receive a significant amount of untreated or poorly treated waste from Combined Sewer Outflow (CSO) systems during precipitation events. The input of effluent waste from CSO to urban water streams during storm events often leads to elevated fecal coliform, specifically Escherichia Coli (E. Coli) in these waterways. The aim of the study is to examine fecal coliform concentration, water chemistry, and water quality parameters to better understand spatio-temporal patterns of fecal coliform associated with CSO events in three waterways from Indianapolis, Indiana (USA). The waterways are Pleasant Run Creek (PRW), Fall Creek (FC) and White River (WR). The sampling occurred biweekly over one year for PRW, nine months for FC, and an intense (∼every three days) sub-analysis of the presumed peak period of fecal coliform growth (July) for WR. All PRW and FC sampling sites significantly exceeded the EPA contact standard limit of 200 CFU/100 mL for fecal coliform concentrations during the sampling period. We found no relationship between fecal coliform levels and the number or density of CSO outfalls above a given site. The most significant predictors of increased fecal coliform concentrations were precipitation on the sampling day and cumulative degree days. The most significant predictors of decreased fecal coliform were maximum precipitation during the ten-day window prior to sampling and median discharge during a three-day window prior to sampling. These findings suggest a push-pull balance within the system where CSO activation and seasonal gradients replenish and promote fecal coliform growth. At the same time, large hydrologic events act to flush and dilute fecal coliform concentrations. The results from this study help us to better understand how different drivers influence fecal coliform growth and how this information can be potentially used to predict and remediate the conditions of urban water streams.

Nowcasting of fecal coliform presence using an artificial neural network.

At least 2 billion people worldwide use drinking water sources that are contaminated with feces, causing waterborne diseases; poor sanitation, poor hygiene, and unsafe drinking water result in a daily death rate of more than 800 children under 5 years of age from diarrheal diseases. This study shows the feasibility of a novel method to nowcast fecal coliforms' (FC) presence in drinking water sources by applying a multilayer perceptron artificial neuron network (MLP-ANN) model. The model gives a binary answer for FC presence or absence in drinking water sources using a minimum of water quality and geographical parameters, which can be monitored in real-time as predictors with low-cost and in-situ equipment. Using 51,400 samples to train, validate and test the model with temperature, pH, electrical conductivity, turbidity, dissolved oxygen, and total dissolved solids (TDS) as water-quality inputs and the water source type and location (as districts in India) as geographical inputs. The model achieved a total accuracy of 92.8% and a sensitivity of 98.2%, meaning that most FC-contaminated samples were classified correctly. In addition, precision reached 93.1%, meaning that most FC-contamination classifications were actually contaminated. The MLP-ANN performed better than the Linear Regression and K-Nearest Neighbors models, with lower accuracies of 90.2% and 91.0%, respectively. The MLP-ANN model could characterize the water quality geospatially, learn from the parameters whether the water is contaminated by FC, and predict with high accuracy on new testing data. This method can be used as a part of a sensor for FC monitoring and management in water, reducing the time gaps between routine lab testing and thus improving drinking water quality and addressing the SDG 6 targets.

Dynamics of fecal coliform bacteria along Canada's coast.

The vast coastline provides Canada with a flourishing seafood industry including bivalve shellfish production. To sustain a healthy bivalve molluscan shellfish production, the Canadian Shellfish Sanitation Program was established to monitor the health of shellfish harvesting habitats, and fecal coliform bacteria data have been collected at nearly 15,000 marine sample sites across six coastal provinces in Canada since 1979. We applied Functional Principal Component Analysis and subsequent correlation analyses to find annual variation patterns of bacteria levels at sites in each province. The overall magnitude and the seasonality of fecal contamination were modelled by functional principal component one and two, respectively. The amplitude was related to human and warm-blooded animal activities; the seasonality was strongly correlated with river discharge driven by precipitation and snow melt in British Columbia, but such correlation in provinces along the Atlantic coast could not be properly evaluated due to lack of data during winter.

Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades.

BACKGROUND: Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention. RESULTS: Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality. CONCLUSIONS: This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

Water quality and antibiotic resistance in the recreational waters.

The overuse and improper disposal of antibiotics results in antibiotic resistance. This raises concern over the presence of antibiotic resistant bacteria (ARB) in waterways and pose health risks of antibiotic resistant infections to water recreationists. The purpose of this study was to monitor water quality, microbial ecology, and antibiotic resistance in water and biofilm on submerged plastics at two public boat launches in southeastern Louisiana. Water and biofilm samples were collected once a month, in triplicate, from two public boat launches in Louisiana, USA for a year. Water quality metrics included nitrate, ammonia, sulfate, phosphate, and organic carbon. Water samples were tested for total and fecal coliform abundance and the presence of ARB. Out of 131 bacterial isolates studied from these two sites, 86% of them tested positive for antibiotic resistance with multi-drug resistance. Antibiotic resistance genes (ARGs) for sulfonamide (sul2), bacitracin (bacA) and ampicillin (ampA) were identified in bacterial isolates from water and biofilm samples at both sites. Molecular genetic diversity analysis identified distinct taxonomic diversity differences in biofilm bacteria compared to the planktonic bacteria in the surrounding water. Biofilm samples showed increased diversity at the phylum, genus, and species levels.

Aquifer vulnerability assessment for fecal coliform bacteria using multi-threshold logistic regression.

Assessing aquifer vulnerability is crucial for preventing groundwater pollution. In this study, aquifer vulnerability to fecal coliform (FC) pollution was assessed using auxiliary environmental data in the Pingtung Plain, Taiwan. Moreover, key environmental factors inducing different fecal pollution levels were determined. First, 23 explanatory variables on land uses, population density, livestock and poultry densities, sanitary condition, antecedent precipitation, groundwater quality, aquifer characteristics, and subsurface hydrology were obtained using geographic information systems in 2014. As dependent variables, groundwater FCs were also simultaneously obtained. Then, multi-threshold logistic regression (LR) was adopted to model aquifer vulnerability assessment after cross validation. The thresholds of aquifer vulnerability causing risks of incidental ingestion were analyzed by risk assessment. Risks to human health were acceptable for a low-level threshold and exceeded the acceptable level for medium- and high-level thresholds when residents incidentally ingested FC-polluted groundwater. Finally, key environmental factors inducing low, medium, and high levels of groundwater FC pollution were characterized. The key environmental factors for the LR with low- and medium-level thresholds were sand and gravel soil textures of unsaturated aquifers and antecedent 3-day cumulative precipitation, and those for the LR with high-level thresholds were chicken farming, urban land use, and ratio of tap water use. Thus, the multi-threshold LR indicated that environmental factors must be ranked for assessing aquifer vulnerability.

Assessment of supplied water quality during mass gatherings in arid environments.

Mass gathering events commonly encounter environmental challenges that necessitate assurance of water quality and food security. The current outbreak of the coronavirus disease 2019 (COVID-19) call for maintaining safe drinking water supply and providing assessment tools of drinking water quality to avoid contamination in water sources or distribution networks. Arid environmental conditions also add more stress on supplied water to mass gathering events. Herein, we assess the quality of the water supply (desalinated 95% and groundwater 5%) in Makkah city, Saudi Arabia during a mass gathering event in 2019 (9.6 million people) for religious purposes. Fifty five samples were randomly collected from nine different districts of Makkah city, analyzed for TDS, turbidity, pH, EC, free Cl2, Al, Cd, Pb, Cr, F, major ions, coliform and E.coli bacteria and were finally used to estimate the water quality index (WQI). Major ions, trace elements and heavy metals analyses show values below permissible limits in most of the samples, while a few samples show slightly higher values. No bacterial count found in any sample. WQI values of all fifty-five samples were below 50 and were identified as "excellent water". The WQI variations could be attributed to the distribution network conditions rather than a direct impact of adding groundwater with uncontrolled chemical composition. The use of WQI to report the quality of water during mass gathering events to governmental authorities has been proved to be beneficial and should be applied for further mass gathering events worldwide.

Evaluating seasonal variations of fecal coliform colonization in Magallana hongkongensis and Crassostrea rhizophorae: A preliminary study of oyster quality grown in Hong Kong aquacultural farms.

The objective of this research is to investigate the spatial and temporal patterns of bacteriological quality in raw oysters sampled from different aquacultural farms located in Aberdeen Typhoon Shelter, Carp Gates, Lau Fau Shan, Ma Wan, and Mui Wo in Hong Kong. Magallana hongkongensis and Crassostrea rhizophorae were collected and analyzed for fecal coliforms. Throughout the 13-month monitoring period, all samples had generally high bacterial loads, ranging from 1.4 × 107 cfu/g to 8.9 × 107 cfu/g and exceeded the guideline suggested by the HKSAR government (i.e. 700 MPN/100 g). Besides, a linear regression analysis showed that the amount of fecal coliforms in raw oysters had strong correlations (p < 0.05) to the monthly rainfall records throughout the monitoring period. Such findings illustrate the high loading of pathogenic microorganisms in the tissue of oysters which represent a potential threat of people contracting foodborne diseases.

Bacterial prediction using internet of things (IoT) and machine learning.

Water is a basic and primary resource which is required for sustenance of life on the Earth. The importance of water quality is increasing with the ascending water pollution owing to industrialization and depletion of fresh water sources. The countries having low control on reducing water pollution are likely to retain poor public health. Additionally, the methods being used in most developing countries are not effective and are based more on human intervention than on technological and automated solutions. Typically, most of the water samples and related data are monitored and tested in laboratories, which eventually consumes time and effort at the expense of producing fewer reliable results. In view of the above, there is an imperative need to devise a proper and systematic system to regularly monitor and manage the quality of water resources to arrest the related issues. Towards such ends, Internet of Things (IoT) is a great alternative to such traditional approaches which are complex and ineffective and it allows taking remote measurements in real-time with minimal human involvement. The proposed system consists of various water quality measuring nodes encompassing various sensors including dissolved oxygen, turbidity, pH level, water temperature, and total dissolved solids. These sensors nodes deployed at various sites of the study area transmit data to the server for processing and analysis using GSM modules. The data collected over months is used for water quality classification using water quality indices and for bacterial prediction by employing machine learning algorithms. For data visualization, a Web portal is developed which consists of a dashboard of Web services to display the heat maps and other related info-graphics. The real-time water quality data is collected using IoT nodes and the historic data is acquired from the Rawal Lake Filtration Plant. Several machine learning algorithms including neural networks (NN), convolutional neural networks (CNN), ridge regression (RR), support vector machines (SVM), decision tree regression (DTR), Bayesian regression (BR), and an ensemble of all models are trained for fecal coliform bacterial prediction, where SVM and Bayesian regression models have shown the optimal performance with mean squared error (MSE) of 0.35575 and 0.39566 respectively. The proposed system provides an alternative and more convenient solution for bacterial prediction, which otherwise is done manually in labs and is an expensive and time-consuming approach. In addition to this, it offers several other advantages including remote monitoring, ease of scalability, real-time status of water quality, and a portable hardware.

Exploration of the driving factors and distribution of fecal coliform in rivers under a traditional agro-pastoral economy in Kyrgyzstan, Central Asia.

Fecal coliform (FC) in river water is one of the threats to human health. To explore the pollution status of FC in rivers of Kyrgyzstan, a mountainous country with traditional agro-pastoral economy, 184 water samples from the rivers of Kyrgyzstan in low and high river flow period were analyzed. Spatial autocorrelation and classical statistical methods were used to analyze the spatiotemporal distribution and driving factors of FC. The results showed that the surface water quality of Kyrgyz rivers was good, and the concentration range of FC was 0-23 MPN/100 mL. Temporally, the maximum FC concentration was 4 MPN/100 mL in low river flow period, while in the period of high river flow, the highest value reached to 23 MPN/100 mL. Spatially, the concentration of FC in high altitude areas was low, while that in the lowland areas was relatively high, which indicated that animal husbandry in high altitude areas contributed little to FC in rivers, and urban domestic sewage and agricultural activities in lowlands were the main pollution sources of FC in rivers. There was no correlation between FC and hardness, electrical conductivity (EC), pH and total organic carbon (TOC) in river water of Kyrgyzstan, and the distribution of FC in high river flow period was mainly driven by population and human modification of terrestrial systems. The results can provide a basis for the prevention and control of surface water FC pollution and related diseases in Kyrgyzstan.

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed.

Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land-use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. PRACTITIONER POINTS: The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R2 value, RMSE and MAE indicate the effectiveness of DTs in prediction.

Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin.

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January-March 2020), syn-lockdown (April-May), and post-lockdown periods (June-July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.

A changing estuary: Understanding historical patterns in salinity and fecal coliform levels in the May River, SC.

The May River, South Carolina watershed has undergone rapid increases in population and development from 1999 to 2017. This study aimed to understand the factors that influence salinity and fecal coliform levels in this estuary and how these levels changed from 1999 to 2017. This analysis revealed that salinity levels decreased in the headwaters, while variability increased. Additionally, fecal coliform increased from 1999 to 2017 throughout the hydrological network, with drastic changes occurring in the headwaters. Salinity and fecal coliform were influenced by spatial (distance from the mouth of the river), temporal (year, season, and tidal cycles), environmental (El Niño Southern Oscillation and rainfall), and anthropogenic parameters (population). This analysis suggests that the synergistic nature of climate change, resulting in more intense and frequent El Niño events, and watershed development may lead to further decreases in salinity and increases in fecal coliform levels in the May River estuary.

An inverse approach to estimate bacterial loading into an estuary by using field observations and residence time.

Pathogen, whose abundance is often measured by the concentration of fecal indicator bacteria, is listed as the top cause of waterbody impairments in the US. An accurate estimation of the bacterial loading from watershed is thus fundamentally important for water quality management. Despite advances in watershed modeling, accurate estimation of bacterial load is still very challenging due to large uncertainties associated with bacterial sources, accumulation, and removal in the watershed. We introduce an inverse method using field-measured bacterial concentrations and numerical model-calculated residence time to estimate the bacterial loading from the drainage basin. In this method, an estuary is divided into multiple segments. Water and bacterial fluxes between neighboring segments are computed from a set of linear equations derived based on mass balance equation and the relationship between residence time and water fluxes. Loading to each segment can then be estimated by combining the computed water fluxes and observed bacterial concentrations. The approach accounts for seasonal and interannual variations in hydrodynamics due to tide, river discharge, and estuarine circulations. The method was applied to Nassawadox Creek, a sub-estuary of Chesapeake Bay, where Fecal Coliform concentrations at 46 stations were continuously monitored. The method is verified by the high consistency between estimated loadings and presumably known input loadings in numerical experiments with either constant or time-varying input loadings. With sparse observational data, the inversely estimated loadings agree well with the loadings from a previously calibrated watershed model, demonstrating the reliability of the method. The inverse approach can be used to cross-check the result of watershed models and assess changes in watershed condition. The method is also readily applicable to other types of materials, such as inorganic nutrients.

Impacts of rapid urbanization on characteristics, sources and variation of fecal coliform at watershed scale.

Microbial pollution is an environmental problem of growing concern for threatening human health. However, the impacts of rapid urbanization on characteristics, sources and variation of fecal coliform (FC) at watershed scale have not been fully explored. In this study, FC characteristics were monitored monthly for 2 years at 21 river sections in an urbanizing watershed, while the sources and continuously annual variation were quantified by integrating two commonly-used models. The results showed that FC varied from 103 to 106 MPN/L, indicating a great spatiotemporal variation at watershed scale. Peak FC occurred in summer and autumn among upstream and downstream areas, respectively. Besides, 65% impermeable surface was identified as the threshold of urban level, beyond which the key FC source would shift from agriculture to urban. It was also found that the changes of urban landscape patterns had poor correlation with annual variation of FC. In comparison, urbanization speed was identified as the major driver with the threshold of 30% for deteriorating FC pollution. The Low Impact Development could result in a 5.13%-97.59% reduction of FC at watershed scale.