Integrating vision-based AI and large language models for real-time water pollution surveillance.

Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man-made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface-level water pollution in local water bodies, an informative real-time vision-based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi-model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run-offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi-model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention. PRACTITIONER POINTS: Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information. Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste. Supports various modules and environments, including drones and mobile apps for broad monitoring. Educates on environmental healthand alerts authorities about water pollution.

Application of a microbial and pathogen source tracking toolbox to identify infrastructure problems in stormwater drainage networks: a case study.

Water scarcity and increasing urbanization are forcing municipalities to consider alternative water sources, such as stormwater, to fill in water supply gaps or address hydromodification of receiving urban streams. Mounting evidence suggests that stormwater is often contaminated with human feces, even in stormwater drainage systems separate from sanitary sewers. Pinpointing sources of human contamination in drainage networks is challenging given the diverse sources of fecal pollution that can impact these systems and the non-specificity of traditional fecal indicator bacteria (FIB) for identifying these host sources. As such, we used a toolbox approach that encompassed microbial source tracking (MST), FIB monitoring, and bacterial pathogen monitoring to investigate microbial contamination of stormwater in an urban municipality. We demonstrate that human sewage frequently contaminated stormwater (in >50% of routine samples), based on the presence of the human fecal marker HF183, and often exceeded microbial water quality criteria. Arcobacter butzleri, a pathogen of emerging concern, was also detected in >50% of routine samples, with 75% of these pathogen-positive samples also being positive for the human fecal marker HF183, suggesting human municipal sewage as the likely source for this pathogen. MST and FIB were used to track human fecal pollution in the drainage network to the most likely point source of contamination, for which a sewage cross-connection was identified and confirmed using tracer dyes. These results point to the ubiquitous presence of human sewage in stormwater and also provide municipalities with the tools to identify sources of anthropogenic contamination in storm drainage networks.IMPORTANCEWater scarcity, increased urbanization, and population growth are driving municipalities worldwide to consider stormwater as an alternative water source in urban environments. However, many studies suggest that stormwater is relatively poor in terms of microbial water quality, is frequently contaminated with human sewage, and therefore could represent a potential health risk depending on the type of exposure (e.g., irrigation of community gardens). Traditional monitoring of water quality based on fecal bacteria does not provide any information about the sources of fecal pollution contaminating stormwater (i.e., animals/human feces). Herein, we present a case study that uses fecal bacterial monitoring, microbial source tracking, and bacterial pathogen analysis to identify a cross-connection that contributed to human fecal intrusion into an urban stormwater network. This microbial toolbox approach can be useful for municipalities in identifying infrastructure problems in stormwater drainage networks to reduce risks associated with water reuse.

Water, environment, and health nexus: understanding the risk factors for waterborne diseases in communities along the Tano River Basin, Ghana.

Freshwater pollution is a major concern in Ghana, directly impacting human health. However, the underlying drivers of exposure and risks are not comprehensively understood, emphasizing the severity and impact of these diseases. This study assessed the interaction between water and human health, specifically focusing on the risk factors for waterborne diseases and the drivers of water pollution among residents near the Tano River Basin, Ghana. A sample size of 400 households was selected from five communities within the basin based on their proximity to the Tano River. In addition, the study combined both spatial and non-spatial data sources to map potential flood zones for the basin. The study found that inadequate sanitation, poor hygiene practices, and contamination from illegal mining were the primary causative factors of waterborne diseases. Additionally, floods and improper waste management significantly contributed to disease outbreaks. The flood susceptibility analysis indicated that areas highly susceptible to flooding cover 21.2% of the basin, predominantly in the southern part. The results highlight the urgent need for comprehensive interventions to address the drivers of waterborne diseases. This study will contribute to the local authorities in developing plans to prevent waterborne diseases and mitigate their economic and public health impacts.

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities' contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77-0.91 for COD and 0.47-0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic discharges, and parameter uncertainty contributed 15, 66, and 19% to COD, and 58, 35, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6, 69, and 25% for COD and 41, 42, and 17% for TP. According to the attention weights of the coupled model, the primary drivers of lateral anthropogenic pollution sources, in descending order of importance, were temperature, date, and precipitation, reflecting seasonal pollution discharge, industrial effluent, and first flush effect and combined sewer overflows, respectively. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.

Frontal processes as drivers of floating marine debris in coastal areas.

The influence of floating marine debris (FMD) on coastal and marine communities and ecosystems is undeniable, and attention is increasingly focused on ecologically and biologically important coastal areas. To protect marine life and valuable resources from FMD pollution, identifying FMD accumulation zones is recognized as a priority. One of the coastal ocean processes found governing the distribution of FMD is water convergence (frontal zones). These fronts are driven by various oceanographical factors. To date, the transport and accumulation of FMD in relation to fronts in coastal areas is poorly understood. To address this knowledge gap, we reviewed various types of ocean fronts as well as FMD accumulation along frontal zones in coastal areas defined as the region between the coastline and the shelf break. Frontogenesis (physical processes related to frontal formation) were reviewed alongside studies on FMD accumulation in frontal zones to identify physical factors that drive the pathways and accumulation in these areas. This review will contribute to our understanding of accumulation hotspots of FMD within ocean fronts and identify gaps for further research on developing a proxy for FMD hotspot identification in ecologically important coastal areas.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin.

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52-86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.

Assessing the impact of inland navigation on the faecal pollution status of large rivers: A novel integrated field approach.

The contribution of ships to the microbial faecal pollution status of water bodies is largely unknown but frequently of human health concern. No methodology for a comprehensive and target-orientated system analysis was available so far. We developed a novel approach for integrated and multistage impact evaluation. The approach includes, i) theoretical faecal pollution source profiling (PSP, i.e., size and pollution capacity estimation from municipal vs. ship sewage disposal) for impact scenario estimation and hypothesis generation, ii) high-resolution field assessment of faecal pollution levels and chemo-physical water quality at the selected river reaches, using standardized faecal indicators (cultivation-based) and genetic microbial source tracking markers (qPCR-based), and iii) integrated statistical analyses of the observed faecal pollution and the number of ships assessed by satellite-based automated ship tracking (i.e., automated identification system, AIS) at local and regional scales. The new approach was realised at a 230 km long Danube River reach in Austria, enabling detailed understanding of the complex pollution characteristics (i.e., longitudinal/cross-sectional river and upstream/downstream docking area analysis). Faecal impact of navigation was demonstrated to be remarkably low at regional and local scale (despite a high local contamination capacity), indicating predominantly correct disposal practices during the investigated period. Nonetheless, faecal emissions were sensitively traceable, attributable to the ship category (discriminated types: cruise, passenger and freight ships) and individual vessels (docking time analysis) at one docking area by the link with AIS data. The new innovative and sensitive approach is transferrable to any water body worldwide with available ship-tracking data, supporting target-orientated monitoring and evidence-based management practices.

Balancing river water pollution and agricultural development: A tradeoff threshold approach.

Balancing environmental protection and social-economic development in agricultural land use management is a dilemma for decision-makers. Based on the modelling of the impacts of land use changes on river water pollution by SWAT model, the tradeoff between tea plantation expansion and river water quality was detected. SWAT model performs well in simulating the non-point source (NPS) pollution in agricultural watershed. The results showed that the tea plantation area expanded dramatically from 44 km2 in 2000 to 169 km2 in 2020 at the high cost of forest land. Consequently, the mean contents of NO3--N and TN have significantly increased by 100% and 91% respectively in the past 20 years. And the NO3--N in river water accounted for over 80% of TN in the tea plantation area. The NO3--N and TN concentrations were positively related with the proportions of tea plantation area (Tea%) at different periods. The high pollution levels of NO3--N and TN are priority control targets for river water quality management. The results indicated that the proportion of tea plantation thresholds lead to abrupt changes in river water quality. When the Tea% exceeded 3.0% in 2000, the probability of N pollution increased sharply. Whereas in 2020, it is suggested that the Tea% should not exceeds 18% to avoid sudden deterioration of water quality. The critical interval value of the Tea% for sudden change in N pollution showed an obvious increase tendency. The accelerating of nutrient pollution in rivers reduced the sensitivity of water quality to tea plantation expansion. Our results can provide new insights and empirical evidence for balancing the tradeoff between agricultural development and river water quality protection by demonstrating the carrying capacity threshold of river water environment for the expansion tea plantation.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Development of a "big data" groundwater microbial contamination index and spatial comparisons with enteric infection rates in southern Ontario.

Big data have become increasingly important for policymakers and scientists but have yet to be employed for the development of spatially specific groundwater contamination indices or protecting human and environmental health. The current study sought to develop a series of indices via analyses of three variables: Non-E. coli coliform (NEC) concentration, E. coli concentration, and the calculated NEC:E. coli concentration ratio. A large microbial water quality dataset comprising 1,104,094 samples collected from 292,638 Ontarian wells between 2010 and 2021 was used. Getis-Ord Gi* (Gi*), Local Moran's I (LMI), and space-time scanning were employed for index development based on identified cluster recurrence. Gi* and LMI identify hot and cold spots, i.e., spatially proximal subregions with similarly high or low contamination magnitudes. Indices were statistically compared with mapped well density and age-adjusted enteric infection rates (i.e., campylobacteriosis, cryptosporidiosis, giardiasis, verotoxigenic E. coli (VTEC) enteritis) at a subregional (N = 298) resolution for evaluation and final index selection. Findings suggest that index development via Gi* represented the most efficacious approach. Developed Gi* indices exhibited no correlation with well density, implying that indices are not biased by rural population density. Gi* indices exhibited positive correlations with mapped infection rates, and were particularly associated with higher bacterial (Campylobacter, VTEC) infection rates among younger sub-populations (p < 0.05). Conversely, no association was found between developed indices and giardiasis rates, an infection not typically associated with private groundwater contamination. Findings suggest that a notable proportion of bacterial infections are associated with groundwater and that the developed Gi* index represents an appropriate spatiotemporal reflection of long-term groundwater quality. Bacterial infection correlations with the NEC:E. coli ratio index (p < 0.001) were markedly different compared to correlations with the E. coli index, implying that the ratio may supplement E. coli monitoring as a groundwater assessment metric capable of elucidating contamination mechanisms. This study may serve as a methodological blueprint for the development of big data-based groundwater contamination indices across the globe.

Quantitative source apportionment of faecal indicator bacteria from anthropogenic and zoogenic sources of faecal contamination.

Recreational bathing waters are complex systems with diverse inputs from multiple anthropogenic and zoogenic sources of faecal contamination. Faecal contamination is a substantial threat to water quality and public health. Here we present a comprehensive strategy to estimate the contribution of faecal indicator bacteria (FIB) from different biological sources on two at-risk beaches in Dublin, Ireland. The daily FIB loading rate was determined for three sources of contamination: a sewage-impacted urban stream, dog and wild bird fouling. This comparative analysis determined that the stream contributed the highest daily levels of FIB, followed by dog fouling. Dog fouling may be a significant source of FIB, contributing approximately 20 % of E. coli under certain conditions, whereas wild bird fouling contributed a negligible proportion of FIB (<3 %). This study demonstrates that source-specific quantitative microbial source apportionment (QMSA) strategies are vital to identify primary public health risks and target interventions to mitigate faecal contamination.

Assessment of drinking water quality using Water Quality Index and synthetic pollution index in urban areas of mega city Lahore: a GIS-based approach.

The aim of the present study was to assess the drinking water quality in the selected urban areas of Lahore and to comprehend the public health status by addressing the basic drinking water quality parameters. Total 50 tap water samples were collected from groundwater in the two selected areas of district Lahore i.e., Gulshan-e-Ravi (site 1) and Samanabad (site 2). Water samples were analyzed in the laboratory to elucidate physico-chemical parameters including pH, turbidity, temperature, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), total hardness, magnesium hardness, and calcium hardness. These physico-chemical parameters were used to examine the Water Quality Index (WQI) and Synthetic Pollution Index (SPI) in order to characterize the water quality. Results of th selected physico-chemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. A GIS-based approach was used for mapping water quality, WQI, and SPI. Results of the present study revealed that the average value of temperature, pH, and DO of both study sites were within the WHO guidelines of 23.5 °C, 7.7, and 6.9 mg/L, respectively. The TDS level of site 1 was 192.56 mg/L (within WHO guidelines) and whereas, in site 2 it was found 612.84 mg/L (higher than WHO guidelines), respectively. Calcium hardness of site 1 and site 2 was observed within the range from 25.04 to 65.732 mg/L but, magnesium hardness values were higher than WHO guidelines. The major reason for poor water quality is old, worn-out water supply pipelines and improper waste disposal in the selected areas. The average WQI was found as 59.66 for site 1 and 77.30 for site 2. Results showed that the quality of the water was classified as "poor" for site 1 and "very poor " for site 2. There is a need to address the problem of poor water quality and also raise the public awareness about the quality of drinking water and its associated health impacts.

A multi-tiered approach to assess fecal pollution in an urban watershed: Bacterial and viral indicators and sediment microbial communities.

Development of effective pollution mitigation strategies require an understanding of the pollution sources and factors influencing fecal pollution loading. Fecal contamination of Turkey Creek in Gulfport, Mississippi, one of the nation's most endangered creeks, was studied through a multi-tiered approach. Over a period of approximately two years, four stations across the watershed were analyzed for nutrients, enumeration of E. coli, male-specific coliphages and bioinformatic analysis of sediment microbial communities. The results demonstrated that two stations, one adjacent to a lift station and one just upstream from the wastewater-treatment plant, were the most impacted. The station adjacent to land containing a few livestock was the least impaired. While genotyping of male-specific coliphage viruses generally revealed a mixed viral signature (human and other animals), fecal contamination at the station near the wastewater treatment plant exhibited predominant impact by municipal sewage. Fecal indicator loadings were positively associated with antecedent rainfall for three of four stations. No associations were noted between fecal indicator loadings and any of the nutrients. Taxonomic signatures of creek sediment were unique to each sample station, but the sediment microbial community did overlap somewhat following major rain events. No presence of Escherichia coli (E. coli) or enterococci were found in the sediment. At some of the stations it was evident that rainfall was not always the primary driver of fecal transport. Repeated monitoring and analysis of a variety of parameters presented in this study determined that point and non-point sources of fecal pollution varied spatially in association with treated and/or untreated sewage.

Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation.

In many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and control the water pollution situation, this study estimated the Biochemical Oxygen Demand (BOD) concentration of Hong Kong's marine waters using remote sensing and an improved machine learning (ML) method. The scheme was derived from four ML algorithms (RBF, SVR, RF, XGB) and calibrated using a large amount (N > 1000) of in-situ BOD5 data. Based on labeled datasets with different preprocessing, i.e., the original BOD5, the log10(BOD5), and label distribution smoothing (LDS), three types of models were trained and evaluated. The results highlight the superior potential of the LDS-based model to improve BOD5 estimate by dealing with imbalanced training dataset. Additionally, XGB and RF outperformed RBF and SVR when the model was developed using log10(BOD5) or LDS(BOD5). Over two decades, the BOD5 concentration of Hong Kong marine waters in the autumn (Sep. to Nov.) shows a downward trend, with significant decreases in Deep Bay, Western Buffer, Victoria Harbour, Eastern Buffer, Junk Bay, Port Shelter, and the Tolo Harbour and Channel. Principal component analysis revealed that nutrient levels emerged as the predominant factor in Victoria Harbour and the interior of Deep Bay, while chlorophyll-related and physical parameters were dominant in Southern, Mirs Bay, Northwestern, and the outlet of Deep Bay. LDS provides a new perspective to improve ML-based water quality estimation by alleviating the imbalance in the labeled dataset. Overall, the remotely sensed BOD5 can offer insight into the spatial-temporal distribution of organic matter in Hong Kong coastal waters and valuable guidance for the pollution control.

Confined within a sugarcane monoculture: A participatory assessment of water pollution and potential health risks in the community of El Tiple, Colombia.

Communities neighboring monoculture plantations are vulnerable to different forms of pollution associated with agro-industrial operations. Herein, we examine the case of El Tiple, a rural Afro descendant community embedded within one of the largest sugarcane plantations in the Americas. We implemented a participatory approach to assess water pollution, exposure via water ingestion, and non-carcinogenic health risks associated with the use of local water sources available to the community. We conducted household surveys to unveil demographic characteristics and family dynamics linked to water consumption. Additionally, we measured water quality parameters and assessed the concentration glyphosate, its major metabolite (aminomethylphosphonic acid) and metals and metalloids. Drinking water El Tiple households is sourced from three primary sources: the local aqueduct system, water delivery trucks, and private deep wells. Tests on water samples from both the local aqueduct and delivery trucks showed no traces of pesticides, metals, or metalloids surpassing regulatory limits set by Colombian or EPA standards. However, we found concentration of contaminants of primary concern, including mercury (up to 0.0052 ppm) and lead (up to 0.0375 ppm) that exceed the permissible regulatory thresholds in water from groundwater wells. Residents of the peripheric subdivisions of El Tiple are four times more reliant on well water extraction than residents of the central area of the town due to lack of access to public drinking water and sanitation infrastructure. Finally, adult women and school-age children have a higher health risk associated with exposure to local pollutants than adult men due to their constant presence in the town. We conclude that expanding the coverage of clean water and sanitation infrastructure to include all households of the community would be the most recommended measure to minimize exposure and risk via ingestion of water pollutants.

Microplastics increase the microbial functional potential of greenhouse gas emissions and water pollution in a freshwater lake: A metagenomic study.

Aquatic ecosystems are being increasingly polluted by microplastics (MPs), which calls for an understanding of how MPs affect microbially driven biogenic element cycling in water environments. A 28-day incubation experiment was conducted using freshwater lake water added with three polymer types of MPs (i.e., polyethylene, polypropylene, polystyrene) separately or in combination at a concentration of 1 items/L. The effects of various MPs on microbial communities and functional genes related to carbon, nitrogen, phosphorus, and sulfur cycling were analyzed using metagenomics. Results showed that Sphingomonas and Novosphingobium, which were indicator taxa (genus level) in the polyethylene treatment group, made the largest functional contribution to biogenic element cycling. Following the addition of MPs, the relative abundances of genes related to methane oxidation (e.g., hdrD, frhB, accAB) and denitrification (napABC, nirK, norB) increased. These changes were accompanied by increased relative abundances of genes involved in organic phosphorus mineralization (e.g., phoAD) and sulfate reduction (cysHIJ), as well as decreased relative abundances of genes involved in phosphate transport (phnCDE) and the SOX system. Findings of this study underscore that MPs, especially polyethylene, increase the potential of greenhouse gas emissions (CO2, N2O) and water pollution (PO43-, H2S) in freshwater lakes at the functional gene level.

Assessment of trace and macroelement accumulation in cyprinid juveniles as bioindicators of aquatic pollution: effects of diets and habitat preferences.

Juveniles of three cyprinids with various diets and habitat preferences were collected from the Szamos River (Hungary) during a period of pollution in November 2013: the herbivorous, benthic nase (Chondrostoma nasus), the benthivorous, benthic barbel (Barbus barbus), and the omnivorous, pelagic chub (Squalius cephalus). Our study aimed to assess the accumulation of these elements across species with varying diets and habitat preferences, as well as their potential role in biomonitoring efforts. The Ca, K, Mg, Na, Cd, Cr, Cu, Fe, Mn, Pb, Sr, and Zn concentration was analyzed in muscle, gills, and liver using MP-AES. The muscle and gill concentrations of Cr, Cu, Fe, and Zn increased with trophic level. At the same time, several differences were found among the trace element patterns related to habitat preferences. The trace elements, including Cd, Pb, and Zn, which exceeded threshold concentrations in the water, exhibited higher accumulations mainly in the muscle and gills of the pelagic chub. Furthermore, the elevated concentrations of trace elements in sediments (Cr, Cu, Mn) demonstrated higher accumulation in the benthic nase and barbel. Our findings show habitat preference as a key factor in juvenile bioindicator capability, advocating for the simultaneous use of pelagic and benthic juveniles to assess water and sediment pollution status.

Health impact assessment of the surface water pollution in China.

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

Validation and application of high-throughput quantitative PCR for the simultaneous detection of microbial source tracking markers in environmental water.

No single microbial source tracking (MST) marker can be applied to determine the sources of fecal pollution in all water types. This study aimed to validate a high-throughput quantitative polymerase chain reaction (HT-qPCR) method for the simultaneous detection of multiple MST markers. A total of 26 fecal-source samples that had been previously collected from human sewage (n = 6) and ruminant (n = 3), dog (n = 6), pig (n = 6), chicken (n = 3), and duck (n = 2) feces in the Kathmandu Valley, Nepal, were used to validate 10 host-specific MST markers, i.e., Bacteroidales (BacHum, gyrB, BacR, and Pig2Bac), mitochondrial DNA (mtDNA) (swine, bovine, and Dog-mtDNA), and viral (human adenovirus, porcine adenovirus, and chicken/turkey parvovirus) markers, via HT-qPCR. Only Dog-mtDNA showed 100 % accuracy. All the tested bacterial markers showed a sensitivity of 100 %. Nine of the 10 markers were further used to identify fecal contamination in groundwater sources (n = 54), tanker filling stations (n = 14), drinking water treatment plants (n = 5), and river water samples (n = 6). The human-specific Bacteroidales marker BacHum and ruminant-specific Bacteroidales marker BacR was detected at a high ratio in river water samples (83 % and 100 %, respectively). The results of HT-qPCR were in agreement with the standard qPCR. The comparable performances of HT-qPCR and standard qPCR as well as the successful detection of MST markers in the fecal-source and water samples demonstrated the potential applicability of these markers for detecting fecal contamination sources via HT-qPCR.

Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution.

Agricultural water resource utilization efficiency in China is facing significant challenges due to the dual constraints of carbon emissions and water pollution. The inefficiency in water usage in agriculture not only impacts the sustainability of water resources but also contributes to environmental degradation through increased carbon emissions and water pollution. Agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution has been a critical issue in China from 2005 to 2022. This study employs the Quantile Autoregressive Distributed Lag (QARDL) method to comprehensively assess and analyze the complex relationship that exists between agricultural water usage, carbon emissions, and water pollution. By analyzing distinct quantiles of the data distribution, the research investigates how different levels of water resource utilization efficiency relate to carbon emissions and water pollution under various conditions. The findings reveal nuanced insights into the dynamic interactions among these components within the agricultural sector. This research project focuses on the efficiency of water resource utilization in agriculture while considering the constraints of carbon emission and water pollution. Given the dynamic and time-dependent character of these components, the QARDL methodology makes it possible to get a detailed knowledge of how they interact within the framework of agriculture. The study aims to give significant insights and policy suggestions to improve agricultural practices while minimizing environmental concerns linked to carbon emissions and water pollution.