Numerous genetic markers for microbial source tracking (MST) have been evaluated by testing a panel of target and nontarget faecal samples. However, the performance of MST markers may vary between faecal and water samples, thereby resulting in inaccurate water quality assessment. In this study, a 30-day sampling study was conducted in an urban river impacted by human- and sewage-associated pollution to evaluate the performance of different human-associated markers in environmental water. Additionally, marker decay was assessed via a microcosms approach. Overall, Bacteroidales 16sRNA and crAssphage markers exhibited higher prevalence in the study area, and their detection frequencies exceeded 90%. In contrast, Bacteroidales protein markers exhibited poor detection frequencies compared to other markers, with the prevalence of Hum2 and Hum163 reaching only 63% and 84%, respectively. Regarding marker abundance, there was no significant difference in the detection concentrations between Bacteroidales 16sRNA and crAssphage markers (p > 0.05); however, the concentrations of Bacteroidales protein markers were nearly 1 order of magnitude lower than those of other MST markers. The microcosm experiments indicated that the decay rate of crAssphage markers was significantly lower than that of other bacterial target markers, which may improve their detectability when the pollution source is located far from the sampling site. Due to the observed differences in performance and decay patterns among Bacteroidales 16sRNA, crAssphage, and Bacteroidales protein markers, we recommend the simultaneous use of multiple markers from different target microorganisms to obtain a more comprehensive understanding of the pollution sources. This approach would also provide an accurate assessment of pollution levels and health risks.

Nitrogen in urban stormwater has been widely studied, and effective management of nitrogen pollution is critical for improving urban stormwater and receiving water quality. This requires an in-depth understanding of the transport process and source contribution to both dissolved and particulate nitrogen in stormwater from urban catchments. In this study, 123 stormwater runoff samples were collected from an urban catchment during different rainfall events. Dissolved and particulate nitrogen concentrations in roof runoff, road runoff, and sewer flow were analyzed. The concentration of dissolved nitrogen was higher in roof runoff than in road runoff and sewer flow. However, the concentration of particulate nitrogen was lower in roof runoff than in road runoff and sewer flow. Isotopic analysis and Bayesian mixing models showed that road runoff was the largest source contributor of both nitrate and particulate organic nitrogen (PON) in sewer flow discharged from the study catchment. In addition, road runoff contributed the majority of PON associated with coarse particles (>105 μm), whereas PON associated with fine particles (<105 μm) was primarily washed-off of sewer sediments. The results provided several suggestions for the management of nitrogen pollution in urban catchments. This study could help to fully understand the transport and sources of nitrogen pollution in urban stormwater and provide recommendations to the government for implementing appropriate stormwater management strategies to minimize stormwater pollution.

Excessive nutrient discharges have resulted in pervasive water pollution and aquatic eutrophication. China has made massive efforts to improve water quality since 2000. However, how long-term policy interventions govern external and internal fluxes as well as nitrogen (N) concentrations is not well known. Here we examined the historical N concentration change and its key drivers in eutrophic Lake Dianchi (southwest China) over the period 2002–2018, based on monthly observations of water quality and external N fluxes, local surveys of mitigation measures, and process-based model simulations of internal N fluxes. Our data indicated that N concentrations peaked at 3.0 mg L⁻¹ in 2007–2010 but afterwards declined down to 1.2 mg L⁻¹ in 2018. Compared with 2010, the decline in lake N concentrations was attributed to reduced riverine N inflow decreasing by 0.20 g N m⁻³ month⁻¹ and the water-sediment exchange flux decreasing by 0.07 g N m⁻³ month⁻¹ from 2010 to 2018. Adoptions of wastewater treatment, pollution interception, and transboundary water transfer dominated the changes in external and internal fluxes of N and thereby the decline of lake N concentrations. These findings underscore the priority of reducing external discharge for historical lake water quality improvement and the need of enhancing internal N removal for future lake ecosystem restoration.

Pollution of riverine ecosystems through the multidimensional impact of human footprints around the world poses a serious challenge. Research studies that communicate potential repercussions of landscape structure metrics on snowmelt riverine water quality particularly, in climatically fragile Himalayan watersheds are very scarce. Though, worldwide, grasping the influence of land-use practices on water quality (WQ) has received renewed attention yet, the relevance of spatial scale linked to landscape pattern is still elusive due to its heterogenic nature across diverse geomorphic regions. In this work, therefore, we tried to capture the insights on landscape-aquascape interface by juxtapositioning the impacts of landscape structure pattern on snowmelt stream WQ of the whole Jhelum River Basin (JRB) under three varying spatial scales viz., watershed scale, riparian corridor (1000 m wide) and reach buffer (500 m wide). The percentage of landscape pattern composition and configuration metrics in the JRB were computed in GIS utilizing Landsat-8 OLI/TIRS satellite image having 30 m resolution. To better explicate the influence of land-use metrics on riverine WQ with space and time, we used Redundancy analysis (RDA) and multilinear regression (MLR) modeling. MLR selected land-use structure metrics revealed the varied response of WQ parameters to multi-scale factors except for total faecal coliform bacteria (TC) which showed perpetual presence. The reach-scale explained slightly better (76%) variations in WQ than riparian (75%) and watershed (70%) scales. Likewise, across seasonal scale, autumn (75%), winter (83%), and summer (77%) captured the most WQ variation at catchment, riparian, and reach scales respectively. We observed impairing WQ linkages with agriculture, built-up and barren rocky areas across watersheds, besides, pastures in riparian buffer areas, and fragmentation of landscape patches at the reach scale. Due to little appearance of spatial scale differences, a multi scale perspective landscape planning is emphasized to ensure future sustainability of Kashmir Himalayan water resources.

Management of water-quality in a river ecosystem needs to be focused on susceptible regions to eutrophication based on proper measurements. The stress–response relationships between nutrients and primary productivity of phytoplankton allow the derivation of ecologically acceptable thresholds of stressors under field conditions. However, spatio-temporal variations in heterogeneous environmental conditions have hindered the development of locally applicable criteria. To address these issues, we utilized a combination of a geographically specialized artificial neural network (Geo-SOM, geo-self-organizing map) and linear mixed-effect models (LMMs). The model was applied to a 24-month dataset of 54 stations that spanned a wide spatial gradient in the Nakdong River basin. The Geo-SOM classified 1286 observations in the basin into 13 clusters that were regionally and seasonally distinct. Inclusion of the random effects of Geo-SOM clustering improved the performance of each LMM, which suggests that there were significant spatio-temporal variations in the Chla–stressor relationships. These variations arise owing to differences in background seasonality and the effects of local pollutant variables and land-use patterns. Among the 16 environmental variables, the major stressors for Chla were total phosphate (TP) as a nutrient and biological oxygen demand (BOD) as a non-nutrient according to the results of both Geo-SOM and LMM analyses. Based on LMMs with the random effect of the Geo-SOM clusters on the intercept and the slope, we can propose recommended thresholds for TP (18.5 μg L⁻¹) and BOD (1.6 mg L⁻¹) in the Nakdong River. The combined method of LMM and Geo-SOM will be useful in guiding appropriate local water-quality-management strategies and in the global development of large-scale nutrient criteria.

Agricultural activities often lead to nutrient enrichment and habitat modification in rivers, possibly altering macroinvertebrate assemblages and hence ecosystem functioning. For the sake of environmental management and conservation, therefore, assessing the impacts of agricultural activities becomes indispensable, especially when these activities are predicted to be intensified in the future. In this study, the plain river network in the Lake Chaohu Basin was chosen to examine how agricultural activities influence the functioning of rivers by assessing land use, water quality, habitat condition and macroinvertebrate assemblages, followed by calculating the macroinvertebrate-based multimetric index (MMI) to indicate overall ecosystem health of the rivers. We found that agricultural activities lowered the diversity of macroinvertebrates (e.g. total number of taxa and Simpson index) primarily due to elevated ammonium concentrations in water and reduced microhabitat types, thereby impairing the habitat integrity and nutrient cycling of rivers. The macroinvertebrate-based MMI was positively correlated with increasing habitat quality but negatively with increasing nutrient concentrations, suggesting its high reliability for indicating the impacts of agricultural activities, which was further substantiated by classification and regression tree (CART) analysis. We recommend analyzing macroinvertebrate assemblages using both multivariate and multimetric approaches to offer a more comprehensive evaluation of the impacts of agricultural activities on ecosystem health. Some environmental (CODMₙ, NH₄⁺-N and PO₄³⁻-P) and biological parameters (total number of taxa), however, can be used as good proxies for MMI, when time and resources for gathering information to develop MMI are limited.

The occurrence of veterinary antibiotics and hydro-chemical parameters in eleven natural springs in a livestock production area is evaluated, jointly with the characterization of their DOM fingerprint by Orbitrap HRMS. Tetracycline and sulfonamide antibiotics were ubiquitous in all sites, and they were detected at low ng L⁻¹ concentrations, except for doxycycline, that was present at μg L⁻¹ in one location. DOM analysis revealed that most molecular formulas were CHO compounds (49 %–68 %), with a remarkable percentage containing nitrogen and sulphur (16 %–23 % and 11 %–24 %, respectively). Major DOM components were phenolic and highly unsaturated compounds (~90 %), typical for soil-derived organic matter, while approximately 11 % were unsaturated aliphatic, suggesting that springs may be susceptible to anthropogenic contamination sources. Comparing the DOM fingerprint among sites, the spring showing the most different profile was the one with surface water interaction and characterized by having lower CHO and higher CHOS formulas and aliphatic compounds. Correlations between antibiotics and DOM showed that tetracyclines positively correlate with unsaturated oxygen-rich substances, while sulfonamides relate with aliphatic and unsaturated oxygen-poor compounds. This indicates that the fate of different antibiotics will be controlled by the type of DOM present in groundwater.

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.

Aquatic pollution from emerging organic contaminants (EOCs) is of key environmental importance in India and globally, particularly due to concerns of antimicrobial resistance, ecotoxicity and drinking water supply vulnerability. Here, using a broad screening approach, we characterize the composition and distribution of EOCs in groundwater in the Gangetic Plain around Patna (Bihar), as an exemplar of a rapidly developing urban area in northern India. A total of 73 EOCs were detected in 51 samples, typically at ng.L⁻¹ to low μg.L⁻¹ concentrations, relating to medical and veterinary, agrochemical, industrial and lifestyle usage. Concentrations were often dominated by the lifestyle chemical and artificial sweetener sucralose. Seventeen identified EOCs are flagged as priority compounds by the European Commission, World Health Organisation and/or World Organisation for Animal Health: namely, herbicides diuron and atrazine; insecticides imidacloprid, thiamethoxam, clothianidin and acetamiprid; the surfactant perfluorooctane sulfonate (and related perfluorobutane sulfonate, perfluorohexane sulfonate, perfluorooctanoic acid and perfluoropentane sulfonate); and medical/veterinary compounds sulfamethoxazole, sulfanilamide, dapson, sulfathiazole, sulfamethazine and diclofenac. The spatial distribution of EOCs varies widely, with concentrations declining with depth, consistent with a strong dominant vertical flow control. Groundwater EOC concentrations in Patna were found to peak within ∼10 km distance from the River Ganges, indicating mainly urban inputs with some local pollution hotspots. A heterogeneous relationship between EOCs and population density likely reflects confounding factors including varying input types and controls (e.g. spatial, temporal), wastewater treatment infrastructure and groundwater abstraction. Strong seasonal agreement in EOC concentrations was observed. Co-existence of limited transformation products with associated parent compounds indicate active microbial degradation processes. This study characterizes key controls on the distribution of groundwater EOCs across the urban to rural transition near Patna, as a rapidly developing Indian city, and contributes to the wider understanding of the vulnerability of shallow groundwater to surface-derived contamination in similar environments.

This study aimed to evaluate the hydrochemistry of the water resources of the Weibei Plain, Northern China, as well as the risks posed by high groundwater nitrate concentrations to human health. Groundwater and surface water samples numbering 168 and 14, respectively, were collected during the dry and wet seasons. Water in the study area was weakly alkaline, falling into a hard-fresh or hard-brackish category. The groundwater chemical types were mainly SO₄·Cl–Ca·Mg (59.5%) and HCO₃–Ca·Mg (28.6%), whereas the dominant chemistry type of surface water was SO₄·Cl–Na (78.6%). Groundwater showed relatively high concentrations of NO₃⁻, with average dry and wet season concentrations of 212 mg·L⁻¹ and 223 mg·L⁻¹, respectively, whereas surface water had a low NO₃⁻ content. The major processes affecting water chemistry were determined to be rock weathering, such as silicate weathering and evaporative dissolution, as well as cation exchange. NO₃⁻ in groundwater was found to mainly originate from anthropogenic inputs such as agricultural production and domestic sewage. The entropy-weight water quality index (EWQI) assessment showed that although the quality of surface water was generally good, more than half of the groundwater samples failed drinking water standards, with NO₃⁻ identified as being the most problematic parameter affecting the water quality evaluation. Risk assessment of high groundwater nitrate concentrations indicated that long-term domestic use of groundwater in the study area can put the health of residents at great risk. Totals of 81% and 75% of the groundwater samples exceeded the acceptable limit for non-carcinogenic risk (HI = 1) to infants during the dry and wet seasons, respectively, whereas 75% and 71.3% of samples exceeded the acceptable limit for children, respectively. Future management of water in the Weibei Plain should prioritize the control groundwater nitrate pollution.