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Volatile organic compounds (VOCs) are important precursors of secondary organic compounds and ozone, which raise major environmental concerns. To investigate the VOC emission characteristics, measurements of VOCs based on proton transfer reaction-mass spectrometry during 2017 were conducted in a coastal industrial area in Ningbo, Zhejiang Province, China. Based on seasonal variation in species concentration, the positive matrix factorization (PMF) receptor model was applied to apportion the sources of VOCs in each season. The PMF results revealed that unknown acetonitrile source, paint solvent, electronics industry, biomass burning, secondary formation and biogenic emission were mainly attributed to VOC pollution. Biomass burning and secondary formation were the major sources of VOCs and contributed more than 70% of VOC emissions in spring and autumn. Industry-related sources contributed 8.65%-31.2% of the VOCs throughout the year. The unknown acetonitrile source occurred in winter and spring, and contributed 7.6%-43.73% of the VOC emissions in the two seasons. Conditional probability function (CPF) analysis illustrated that the industry sources came from local emission, while biomass burning and biogenic emission mainly came from the northwest direction. The potential source contribution function (PSCF) model showed that secondary formation-related source was mainly from Jiangsu Province, northeastern China and the surrounding ocean. The potential source areas of unknown acetonitrile source were northern Zhejiang Province, southern Jiangsu Province and the northeastern coastal marine environments.

The Yellow River Basin (YRB) plays a very important role in China's economic and social development and ecological security. In particular, the ecosystem of the YRB is sensitive to climate change. However, the change of nutrient fluxes in this region during the past years and its main driving forces remain unclear. In this study, a hydrologic model R System for Spatially Referenced Regressions on Watershed Attributes (RSPARROW) was employed to simulate the spatio-temporal variations in the fluxes of total nitrogen (TN) and total phosphorus (TP) during the period of 2006-2017. The results suggested that the TN and TP loads increased by 138% and 38% during 2006-2014, respectively, and decreased by 66% and 71% from 2015 to 2017, respectively. During the period of 2006-2017, the annual mean fluxes of TN and TP in the YRB were in the range of 3.9 to 591.6 kg/km

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function., Environmental Science & Technology, 57 (12), ISSN:0013-936X, ISSN:1520-5851

Roughly half of the human population lives near the coast, and coastal water pollution (CWP) is widespread. Coastal waters along Tijuana, Mexico, and Imperial Beach (IB), USA, are frequently polluted by millions of gallons of untreated sewage and stormwater runoff. Entering coastal waters causes over 100 million global annual illnesses, but CWP has the potential to reach many more people on land via transfer in sea spray aerosol (SSA). Using 16S rRNA gene amplicon sequencing, we found sewage-associated bacteria in the polluted Tijuana River flowing into coastal waters and returning to land in marine aerosol. Tentative chemical identification from non-targeted tandem mass spectrometry identified anthropogenic compounds as chemical indicators of aerosolized CWP, but they were ubiquitous and present at highest concentrations in continental aerosol. Bacteria were better tracers of airborne CWP, and 40 tracer bacteria comprised up to 76% of the bacteria community in IB air. These findings confirm that CWP transfers in SSA and exposes many people along the coast. Climate change may exacerbate CWP with more extreme storms, and our findings call for minimizing CWP and investigating the health effects of airborne exposure.

Chiral pesticides account for 30% of pesticides. Pesticides are inevitably leached into the groundwater by runoff. At the watershed level, the distribution characteristics of enantiomers in sediments collected from the river network of an agricultural area near the middle and lower reaches of the Yangtze River were tested, and their potential correlations with the physicochemical properties and microbial communities of the sediments were analyzed. The sediment pollution was serious at sites 8 and 9, with their pollution source possibly being agricultural or industrial sewage. Moreover, there were higher cumulative contents of pesticide residues at sites 4, 8, and 9. Specifically, Cycloxaprid was the most detected chiral pesticide in the study area, followed by Dinotefuran and Diclofop-methyl. Additionally, Ethiprole and Difenoconazole had strong enantioselectivity in the study area. Interestingly, the enantiomers of some chiral pesticides, such as Tebuconazole, had completely different distributions at different sites. Pearson correlation analysis showed that sediment catalase and microbial biomass carbon were important factors for enantioselectivity of chiral pesticides. The effect of sediment physicochemical properties on enantioselective distribution was achieved by influencing the microorganisms in the sediment. Furthermore, the enantioselective distribution of Tebuconazole was closely related to the genus Arenimonas. Overall, the enantioselective distribution of most of the chiral pesticides was positively correlated with the prokaryotic microbial community. This study provides empirical support for agricultural non-point source pollution caused by chiral pesticides, and also lays a research foundation for exploring the factors that affect the fate of chiral pesticides in the environment.

Microplastic (MP) contamination is a serious global environmental problem. Plastic contamination has attracted extensive attention during the past decades. While physiochemical weathering may influence the properties of MPs, biodegradation by microorganisms could ultimately mineralize plastics into CO

Streams and rivers are important sources of nitrous oxide (N

Toxic benthic cyanobacterial mats are increasingly reported worldwide as being responsible for animal mortalities due to their production of the potent neurotoxin anatoxin-a (ATX) and its analogues. Improved analytical methods for anatoxins are needed to address public health and watershed management challenges arising from extremely high spatial and temporal variability within impacted systems. We present the development, validation, and application of a direct analysis in real-time-high-resolution tandem mass spectrometry (DART-HRMS/MS) method for analysis of anatoxins in cyanobacterial field samples, including a simplified sample preparation approach. The method showed excellent sensitivity and selectivity for ATX, homoanatoxin-a, and dihydroanatoxin-a. Isotopically labeled ATX was used as an internal standard for all three analogues and successfully corrected for the matrix effects observed (86 ± 16% suppression). The limit of detection and recovery for ATX was estimated as 5 ng/g and 88%, respectively, using spiked samples. The total analysis time was ∼2 min, and excellent agreement was observed with results from a liquid chromatography-HRMS reference method. Finally, the DART-HRMS/MS method was applied to a set of 45

Manganese (Mn) ores contain substantial concentrations of mercury (Hg), and mining and smelting of Mn ores can bring Hg into the surrounding aquatic environment through atmospheric deposition, leaching of electrolytic Mn residue and Mn gangue dump. However, limited is known that how these processes influence the environmental behaviors of Hg in waterbody. Therefore, the seasonal distribution and existing form of Hg in water and sediment in one Mn ore area in Xiushan County, Chongqing were investigated. Our results showed that the mean Hg and methylmercury (MeHg) concentrations in water (n=35) were 5.8 ± 4.6 ng/L and 0.22 ± 0.14 ng/L, respectively. The mean Hg concentrations in retained riverbed and fluvial bank sediment (n=35) were 0.74 ± 0.26 mg/kg and 0.63 ± 0.27 mg/kg, respectively (the mean MeHg concentrations were 0.64 ± 0.40 µg/kg and 0.51 ± 0.30 µg/kg, respectively). It indicated that the mining and smelting of Mn ores were the main sources of anthropogenic Hg, and Mn may inhibit Hg methylation in rivers in Mn ore areas. Mercury in the bound to iron/ Mn (Fe/Mn) oxides of low crystallinity fraction (Hg-OX) accounted for 4.01% and 5.25% of the total Hg concentrations in the retained riverbed and fluvial bank sediment, respectively. The amount of Hg bound to Fe/Mn oxides in sediment increased significantly due to the manganese mining activities in the investigated area. Therefore, it could be hypothesized that high Hg concentrations in river sediment in Mn mining areas are closely related to high Mn concentration in sediment.

This study aimed to (1) monitor the occurrence and spatiotemporal variations of 100 pesticides in sediments collected monthly from July 2018 to June 2019 from sampling sites in El-Rahawy, Sabal, and Tala, along the Rosetta branch of the Nile River, Egypt, and (2) perform an ecological risk assessment for aquatic organisms upon exposure to the detected sediment pesticides based on the risk quotient (RQ) method. Out of the 100 pesticides monitored, 16 pesticides belonging to seven chemical families were detected, and 55% of the sediment samples were contaminated with one or more pesticide residues. The mean concentration (mg/kg dry weight (dw)) and detection frequency (%) of the four most frequently detected pesticides in the sediment samples were as follows: chlorpyrifos (0.18 mg/kg dw and 34%), p,p'-DDE (0.018 mg/kg dw and 30%), cypermethrin (0.03 mg/kg dw and 14%), and deltamethrin (0.026 mg/kg dw and 13%). The spatial distribution exhibited that El-Rahawy had the highest pesticide load (2.86 mg/kg dw) among the studied sites, whereas the temporal variations revealed that the highest total pesticide concentrations were detected in winter season (1.73 mg/kg dw). Meanwhile, 12 pesticides showed high RQs (1), posing a potential ecological risk to aquatic species that live and feed on such sediments.

Capturing precipitation-based episodes is a longstanding issue for estimating tributary loads; however, wind-driven resuspension in Lake Huron creates similar uncertainties in its estimated load to Lake Erie. Recent suggestions that the phosphorus load from Lake Huron is underestimated because sampling frequencies miss contributions from resuspension events are speculative because they did not include direct load measurements, address all resuspension regions, or assess the potential bioavailability of the load. We address these shortcomings by evaluating Lake Huron's nearshore regions, characterizing the biological availability of the load, and providing direct comparisons of load estimates with and without the resuspended load. We show that total phosphorus concentrations in Lake Huron and the St. Clair River are higher during resuspension events and that bioavailability of that material is comparable to that reported elsewhere. New load estimates, based on continuous turbidity measurements converted to phosphorus through P-turbidity relationships, were almost 90% higher than traditional load estimates, providing empirical evidence for the significantly underestimated previous load. This confirmation is important because if the Lake Huron load is not decreased, reductions from other sources would be needed to meet the overall reduction targets set by the binational Great Lakes Water Quality Agreement.

In a study of 44 diverse sampling sites across 16 Great Lakes tributaries, 110 pharmaceuticals were detected of 257 monitored. The present study evaluated the ecological relevance of detected chemicals and identified heavily impacted areas to help inform resource managers and guide future investigations. Ten pharmaceuticals (caffeine, nicotine, albuterol, sulfamethoxazole, venlafaxine, acetaminophen, carbamazepine, gemfibrozil, metoprolol, and thiabendazole) were distinguished as having the greatest potential for biological effects based on comparison to screening-level benchmarks derived using information from two biological effects databases, the ECOTOX Knowledgebase and the ToxCast database. Available evidence did not suggest substantial concern for 75% of the monitored pharmaceuticals, including 147 undetected pharmaceuticals and 49 pharmaceuticals with screening-level alternative benchmarks. However, because of a lack of biological effects information, screening values were not available for 51 detected pharmaceuticals. Samples containing the greatest pharmaceutical concentrations and having the highest detection frequencies were from Lake Erie, southern Lake Michigan, and Lake Huron tributaries. Samples collected during low-flow periods had higher pharmaceutical concentrations than those collected during increased-flow periods. The wastewater-treatment plant effluent content in streams correlated positively with pharmaceutical concentrations. However, deviation from this correlation demonstrated that secondary factors, such as multiple pharmaceutical sources, were likely present at some sites. Further research could investigate high-priority pharmaceuticals as well as those for which alternative benchmarks could not be developed. Environ Toxicol Chem 2022;41:2221-2239. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The pollution of the surface waters by pharmaceuticals and personal care products (PPCPs) has attracted worldwide attention, but data regarding their occurrence and potential risks for the aquatic biota on tropical coastal rivers of South America are still scarce. In this context, the occurrence and the preliminary ecological risk assessment of eleven pharmaceuticals of various therapeutic classes (including cocaine and its primary metabolite, benzoylecgonine) were investigated, for the first time, in five rivers of São Paulo, southeast Brazil, covering a coastline of about 140 km, namely Perequê River, Itinga River, Mongaguá River, Itanhaém River and Guaraú River. Although these five rivers are born in well-preserved areas of the Atlantic rainforest biome, on its way to sea and when they cross the urban perimeter, they receive untreated sewage discharges containing a complex mixture of contaminants. In addition, a "persistence, bioaccumulation and toxicity" (PBT) approach allowed to pre-select the priority PPCPs to be monitored in this coastline. Identification of several PPCPs in the samples was done using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Ten PPCPs were successfully quantified in all five rivers, namely caffeine (9.00-560.00 ng/L), acetaminophen (LOQ-22.24 ng/L), benzoylecgonine (0.30-14.93 ng/L), atenolol (0.12-13.22 ng/L), losartan (0.10-8.42 ng/L), diclofenac (0.76-3.93 ng/L), cocaine (0.05-3.22 ng/L), furosemide (LOQ-3.16), carbamazepine (0.04-0.50 ng/L) and orphenadrine (LOQ-0.14 ng/L). From an ecological risk perspective, caffeine, acetaminophen and losartan can be considered as priority PPCPs because they showed low to moderate risks to algae, crustacean and fishes. However, using the PBT approach, carbamazepine and orphenadrine were also classified as priority compounds, followed by furosemide, acetaminophen, cocaine and losartan (all in second position) and caffeine, atenolol, diclofenac and benzoylecgonine (all in third position). This study provides valuable information to reinforce the importance of continuous monitoring of the coastal rivers of South America (containing PPCPs and illicit drugs) whose diffuse loads flow continuously into the marine ecosystems. Furthermore, ecotoxicological studies (especially with tropical marine organisms) to assess the long-term toxicity of these bioactive compounds are urgent.

Convergence analysis of China's urban green land-use efficiency (GLUE) will help to explore whether regional differences in GLUE determined by regional economic development and imbalance of resource endowments gradually decrease over time. We construct a DEA model considering variable returns to scale to measure Chinese urban GLUE, and discuss the convergence of Chinese urban GLUE. We find that Chinese urban GLUE is generally low. GLUE of eastern cities is the best. GLUE of non-resource-based (NRB) cities is higher than that of resource-based (RB) cities. GLUE of Pearl River Delta Economic Zone is the highest, while that of Comprehensive Economic Zone of Central 6 Provinces is the lowest. Chinese urban GLUE is mainly affected by technological change. Chinese urban GLUE shows absolute and conditional β convergence, and the gap of GLUE between cities is constantly narrowing. Cities in different locations, resource dependence degrees, and economic zones have different convergence rates. The conclusions help the Chinese government to formulate policies for intensive land use, improve national GLUE, and promote urban intensive and high-quality development.

Riverine ecosystem services to human beings are dynamically evaluated by harmonic relationships; however, over growing human service demands (HSDs) are leading to deteriorate the river health resilience. In this study, an assessment index system of river health involving pressure-state-response (PSR) based on twenty indicators of riparian, channel geomorphic, hydroecological, and social attributes was developed to detect the multifunctional reliability and resilience of river system integrity for HSDs at upper (US), middle (MS), and lower segments (LS) of Kangsabati River using fuzzy logic, analytical hierarchical process (AHP), and entropy weight-based multi-criteria decision matrix (MCDM) methods. Borda integrating MCDM results revealed that overall indicator performance is high health score in US (77), medium score in MS (69), but mostly unhealthy score in LS (34); thus, entropy-MCDM models give highest rank to US, medium rank to MS, and least rank to LS, while AHP and fuzzy MCDM models assigned as high priority rank to MS, medium rank to US, and least rank to LS, respectively. According to model validation performances, entropy-MCDM models (RMSE 2.48) are rationalized to the harmonic relationship of riverine system, whereas fuzzy and AHP-MCDM models (RMSE 2.79) are signified to HSDs, and these results are closer to real problems. With the acceptability of AHP-MCDM models through the percentage change (73.89%) and intensity change (17.16) assessment, it points that over HSDs are crucial factors for river health degradation. Moreover, final outcome of the present research helps to find out the sick river health sites for ecological restoration.

Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32-0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall-runoff relationships. The multi-watershed flux model projects 0.60-12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7-20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.

The copper redhorse (Moxostoma hubbsi) is an endangered fish that spawns exclusively in the Richelieu River (Quebec, Canada). Tributaries of the Richelieu are contaminated with high levels of current-use pesticides, which may impact early-life stage (ELS) copper redhorse and other native fishes. We assessed the effects of exposure to contaminated river water on ELS copper redhorse and river redhorse (Moxostoma carinatum), a related fish that shares the copper redhorse's spawning grounds and nursery habitat. A riverside flow-through system was used to expose copper and river redhorse embryos (1000 each) to Richelieu River water or laboratory water as a control. Fish were maintained until 14 days posthatch, and water samples were taken daily for chemical analysis. Following a heavy rain event, concentrations of two neonicotinoid pesticides, clothianidin and thiamethoxam, exceeded water quality guidelines for aquatic life (20 ng/L). Using nontargeted screening, we tentatively identified an additional 24 pharmaceutical and personal care products and 23 pesticides in river water. Effects of river water on ELS fish were observed in both species, but the copper redhorse appeared to be more sensitive. Fish exposed to river water hatched 10.7 (copper redhorse) and 2.4 (river redhorse) cumulative degree days earlier than controls. Copper redhorse survival was significantly lower in river water (73 ± 16%) compared to laboratory water (93 ± 3%), whereas river redhorse survival was similar between treatments (84 ± 6% and 89 ± 4%, respectively). Sequencing of copper redhorse larvae RNA revealed 18 differentially expressed genes (DEGs) following 14 days of exposure to river water. Eight up-regulated DEGs were linked to immune function and injury response, and seven down-regulated DEGs were involved with digestion and nutrient absorption. The present study provided valuable data on the effects of ELS exposure to a real-world mixture of contaminants in two fish species of concern. Environ Toxicol Chem 2022;41:1950-1966. © 2022 SETAC.

Prawns are regarded as delicious food items and a source of income as it is the salient exportable fishery item of Bangladesh and a number of other countries. Climate change may inhibit the general physiological activity of prawns. This study explored the eco-climatic effects on reproduction, including size at first sexual maturity (L

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes (δ15N-NO– 3 and δ18O-NO– 3) and water quality parameters, this study combined the dual nitrate (NO– 3) stable isotope method and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. Further, the dual stable isotope method identified soil N, aquaculture tailwater, domestic wastewater and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine NO– 3 in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and dual stable isotope method showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. Moreover, this study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

The river Gomti, one of India's most polluted rivers, passing through Lucknow, Uttar Pradesh, India, has been selected for this study. An attempt has been made to assess its water quality status by combining the water quality index (WQI) and synthetic pollution index (SPI). Further, the data integration with the geographic information system (GIS) along with twelve water quality parameters for the seven sampling stations (S1 to S7) over 5 years (2013-2017) has been performed. The study area showed a variation of WQI from 78.993 to 249.388 and SPI from 0.868 to 2.096 in 5 years. The map interpolated through GIS revealed that the WQI falls into the category of severely polluted (76-100) and unsuitable for human consumption ( 100), while SPI lies in the category of moderately polluted (0.5-1.0) and severally polluted (1.0-3.0). The BOD and COD were found to significantly influence the WQI and SPI scores. With the constant release of waste effluents into the river, all selected parameters increased from S1 to S7. Based on the study, effective wastewater management is immediately required to improve water quality and support any sustainable river restoration plan.

Antimicrobial resistance (AR) is a serious global problem due to the overuse of antimicrobials in human, animal, and agriculture sectors. There is intense research to control the dissemination of AR, but little is known regarding the environmental drivers influencing its spread. Although AR genes (ARGs) are detected in many different environments, the risk associated with the spread of these genes to microbial pathogens is unknown. Recreational microbial exposure risks are likely to be greater in water bodies receiving discharge from human and animal waste in comparison to less disturbed aquatic environments. Given this scenario, research practitioners are encouraged to consider an ecological context to assess the effect of environmental ARGs on public health. Here, we use a stratified, probabilistic survey of nearly 2000 sites to determine national patterns of the anthropogenic indicator class I integron Integrase gene (

The occurrence, spatial distribution, and source analysis of antibiotics in global coastal waters and estuaries are not well documented or understood. Therefore, the distribution of 14 antibiotics in inflowing river and bay water of Taizhou Bay, East China Sea, was studied. Thirteen antibiotics, excluding roxithromycin (ROM), were all detected in inflowing river and bay water. The total antibiotic concentrations in bay water ranged from 3126.62 to 26,531.48 ng/L, which were significantly higher than those in the inflowing river (17.20-25,090.25 ng/L). Macrolides (MAs) and sulfonamides (SAs) were dominant in inflowing river (accounting for 24.40% and 74.9% of the total antibiotic concentrations, respectively), while SAs in bay water (93.6% of the total concentrations). Among them, clindamycin (CLI) (concentration range: ND-8414 ng/L, mean 1437.59 ng/L) and sulfadimidine (SMX) (ND-25,184.00 ng/L, mean concentrations: 9107.88 ng/L) were the highest in those surface water samples. Source analysis showed that MAs and SAs in the inflowing river mainly came from the wastewater discharge of the surrounding residents and pharmaceutical companies, while SAs in the bay water mainly came from surrounding industrial activities and mariculture. However, the contribution of the inflowing river to the bay water cannot be ignored. The risk assessment showed that SMX and ofloxacin (OFX) have potential ecological risks. These data will support the various sectors of the environment in developing management strategies and to prevent antibiotic pollution.

Spatial distribution and pollution assessment of selected heavy metals such as barium (Ba), chromium (Cr), nickel (Ni), lead (Pb), vanadium (V), arsenic (As), zinc (Zn), and copper (Cu) in sediments of the Brahmaputra River watershed in Bangladesh was investigated. The mean abundances (ppm) of heavy metals in sediment samples were in decreasing order Ba (375.60) V (67.60) Cr (54.10) Zn (48.20) Ni (22.28) Pb (20.25) Cu (7.59) As (4.21). Concentrations of Pb and As in the sediments are enriched relative to the average upper continental crust composition, while Ba, V, Cr, Zn, Ni, and Cu decrease considerably. A higher concentration of Pb and Ni indicates that Brahmaputra River watershed samples receive a significant contribution from anthropogenic sources of heavy metals. Chromium displays marked positive correlation with V (r = 0.91, p = 0.01), inferring a similar source materials input into the watershed. The geo-accumulation index (I

During their production, use, and disposal, active pharmaceutical ingredients (APIs) are released into aquatic systems. Because they are biologically active molecules, APIs have the potential to adversely affect nontarget organisms. We used the results of a global monitoring study of 61 APIs alongside available ecotoxicological and pharmacological data to assess the potential ecotoxicological effects of APIs in rivers across the world. Approximately 43.5% (461 sites) of the 1052 sampling locations monitored across 104 countries in a recent global study had concentrations of APIs of concern based on apical, nonapical, and mode of action-related endpoints. Approximately 34.1% of the 137 sampling campaigns had at least one location where concentrations were of ecotoxicological concern. Twenty-three APIs occurred at concentrations exceeding "safe" concentrations, including substances from the antidepressant, antimicrobial, antihistamine, β-blocker, anticonvulsant, antihyperglycemic, antimalarial, antifungal, calcium channel blocker, benzodiazepine, painkiller, progestin, and lifestyle compound classes. At the most polluted sites, effects are predicted on different trophic levels and on different endpoint types. Overall, the results show that API pollution is a global problem that is likely negatively affecting the health of the world's rivers. To meet the United Nations' Sustainable Development Goals, work is urgently needed to tackle the problem and bring concentrations down to an acceptable level. Environ Toxicol Chem 2022;41:2008-2020. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Surface water quality is among the significant challenges in the Sutlej River basin, passing through Pakistan's most densely populated province. Currently, the overall surface water quality is grossly polluted, mainly due to the direct discharge of wastewater from the urban areas to the Sutlej River directly or through stream networks. Escherichia coli concentrations vary under extreme weather events like floods and droughts and socioeconomic circumstances like urbanization, population growth, and treatment options. This paper assesses the future E. coli load and concentrations using the Soil and Water Assessment Tool (SWAT) along with scenarios based on Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) developed by the Intergovernmental Panel on Climate Change (IPCC). E. coli concentrations according to a more polluted scenario disclose a near and mid future increase by 108% and 173%, and far future increases up to 251% compared to the reference period (baseline) concentrations. The E. coli concentration is reduced by - 54%, - 68%, and - 81% for all the projected time steps compared to the baseline concentrations. While highly improved sewerage and manure management options are adapted, the concentration is further reduced by - 96%, - 101%, and - 105%, respectively, compared to the baseline. Our modeling and scenario matrix study shows that reducing microbiological concentrations in the surface water is possible. Still, it requires rigorous sanitation and treatment options, and socioeconomic variables play an essential role besides climate change to determine the microbiological concentration of water resources and be included in future studies whenever water quality and health risks are considered.

Since the main canal of the Middle Route of South-to-North Water Diversion Project was put into operation, the species, quantity and biomass of algae and shellfish have responded quickly to the environment, and a single dominant species has appeared in the community, demonstrating the remarkably abnormal proliferation property of algae and shellfish. In order to evaluate the safety risk of abnormal proliferation of algae and shellfish in the Middle Route and realize dynamic analysis of all kinds of influencing factors, a comprehensive risk evaluation system of algae and shellfish in the Middle Route of South-to-North Water Diversion Project based on comprehensive weighting and Four-Element Connection Number (MCEN) was constructed by integrating the Analytic Hierarchy Process (AHP) and the weight assignment theory of CRITIC. The system consists of 21 evaluation indexes selected from risk factors and carriers. Taking Henan section in the Middle Route as an example, comprehensive risk evaluation system of algae and shellfish in the Middle Route of South-to-North Water Diversion Project was applied to calculate the partial connection number of each order and obtain the risk development trend of each indicator. The results showed that algae and shellfish in the Middle Route were in a very safe state at the safety risk level of level Ⅰ. Finally, reasonable measures to reduce the risks facing algae and shellfish in the Middle Route of South-to-North Water Diversion Project are given, which effectively make up the deficiency of existing evaluation methods.

As the health impacts of climate change take on a more serious form, this study for the first time investigates the effect of meteorological factors on the risk of death from respiratory diseases (RD) in Wuhu, a representative city along the Yangtze River in subtropical humid region. Daily meteorological element data and RD deaths in Wuhu City were collected from 2014 to 2020. Time series analysis was conducted using distributed lagged nonlinear model (DLNM) combined with generalized additive model (GAM), and stratified by age and gender. In 7 years, a total of 8016 RD death cases were collected in Wuhu, China. The results demonstrated that the maximum impacts of short-term exposure to exceedingly low temperatures mean (Tmean) were at lag 9, with the maximum relative risk (RR) of 1.044 (lag 1, 95% CI: 1.001, 1.098). The risk of exceedingly high Tmean reached its maximum at lag 0 (RR = 1.070, 95% CI: 1.018, 1.125). Low relative humidity (RH) was negatively associated with the risk of RD death, with the lowest RR values occurring at lag 12 (RR = 0.987, 95% CI: 0.975, 0.999). No significant correlation was found for diurnal temperature range (DTR). Stratified analysis showed that Tmean exposure remained statistically significant for male, female and elderly, while RH and DTR only seemed to increase the mortality risk in the young. In a word, short-term exposure to extreme temperatures may increase the RD mortality risk in the population, and young people needed to be aware that exposure to exceedingly high RH and DTR also increased the risk.

Water environment conditions directly support aquatic life. It is important to maintain a suitable water environment to improve the efficient use of water resources. Supersaturation of total dissolved gas (TDG) in the water will cause fish suffer from gas bubble disease and even mortalities. Measures should be taken to mitigate the adverse effect of supersaturated TDG. Considering the adsorption effect of porous medium, activated carbon (AC) was utilized in this experiment to explore the effect of AC on supersaturated TDG removal. The effects of AC properties, AC dosage, and initial TDG saturation were investigated. The results showed that adding AC in the water could effectively accelerate the supersaturated TDG removal rate, which was positively correlated with the AC specific surface area and dosage. Meanwhile, the average dissipation rate of TDG increased and then decreased with increasing initial TDG saturation. The adsorption characteristics of AC on supersaturated TDG were also explored. The maximum equilibrium adsorption capacity and removal rate were 0.262 mg/g and 48.5% respectively. It was concluded that the adsorption process of AC on supersaturated TDG conformed to the Langmuir equation and pseudo-first-order kinetic model. Recycling test indicated that the used AC could be reused after drying. It was hoped that this research could contribute to improving water environment and ensuring the healthy development of the aquatic livings.

The water quality of Hong Kong's four water control zones (Tolo Harbour and Channel, Port Shelter, Victoria Harbour, and Junk Bay) is of vital importance and has attracted much attention. This study aims to more objectively and comprehensively assess the water quality and its health impact based on the four-year monitoring data of 21 parameters collected from four zones. First, physicochemical characteristics of the water system were investigated based on multivariate statistical approaches, including Kruskal-Wallis test, hierarchical cluster analysis, and Mann-Kendall test. Then, water quality levels over space and time and the element sources were analyzed using adaptive-weight water quality index (AWQI) method, and factor analysis, respectively. Finally, the potential harm of trace elements for humankind was identified based on the health risk assessment model. The results revealed that (1) the values of more than half of the water quality parameters exhibited significant interannual changes, and the values of all parameters distinctly varied over space; (2) The water quality status in four water control zones showed a steady and long-term improvement trend from 2016 to 2019; (3) The sources of pollution elements impacting water quality status were related to the comprehensive influence of human activities and natural processes; (4) The carcinogenic risks of all trace elements were negligible or acceptable, while Mn and As may cause noncarcinogenic harm to humankind.

Index of biotic integrity (IBI) based on fish has been applied globally. However, few have considered that fish assemblages change among different aggregate ecoregions when conducted their health assessment. Indeed, some comprehensive indices, such as functional and phylogenetic diversity indices and ABC curve, can be used to identify aspects that are not captured by traditional metrics. Consequently, we try to integrate comprehensive indices and spatial patterns of fish assemblages to develop IBI systems and then verified their effectiveness and accuracy for assessing the environmental health of the Chishui River basin. The comprehensive disturbance index (CDI), based on 11 water quality parameters and 4 human land use, was set to distinguish reference sites and impaired sites. According to the spatial patterns of fish assemblages, the 40 sites were finally divided into 2 aggregate ecoregions, include wadeable streams and nonwadeable rivers. 97 candidate metrics were selected to develop our IBI systems based on the systematic screening method. The result also showed that our IBI systems performed well in discriminating anthropogenic disturbances at both aggregate ecoregions, which suggests that our systems could provide a reliable evaluation. The mean IBI score of the Chishui River basin was 72.09 ± 16.58, and was classified as good status. However, S1 (Chishuiyuan Town), Baisha River, Tongzi River, and Xishui River were disturbed by various human activities. We conclude that the spatial patterns of fish assemblages should be combined with more comprehensive indices to assess river health. On the other hand, we do believe that the process of developing and verifying our IBI systems could be regarded as a reference for biomonitoring in more mountain river systems.

Dairy wastewaters (DWW) are rich in several pollutants, including high biochemical oxygen demand (BOD) and chemical oxygen demand (COD), and their unsafe disposal may cause damage to the environment. In this study, Chlorella vulgaris (identified as NIES:227 strain based on 28s rRNA sequencing) was isolated from the freshwater habitat of the Ganga River at Haridwar, India, and further tested for its efficacy in treating DWW. The phycoremediation experiments were conducted using three different DWW concentrations (0, 50, and 100%), operating temperatures (20, 25, and 30 °C), and light intensities (2000, 3000, and 4000 lx) using response surface methodology. Results showed that after 16 days of experiments, a significant (P 0.05) reduction in BOD (96.65%) and COD (87.50%) along with a maximum biomass production of 1.757 g/L was achieved using 57.72% of dairy industry wastewater, 24.16 °C of reactor temperature, and 3874.51 lx of light intensity. The RSM models had coefficient of determination (R

Compared with the urban sewage treatment plants, the operation of rural decentralized sewage facilities is trapped by the absence of professionals, thus having to be run dependently on the self-adaptive operation of the facilities, which makes timely monitoring particularly important. In this study, organic pollutants in rural domestic sewage and urban domestic sewage are analyzed using ultraviolet-visible (UV-vis) absorption spectroscopy, fluorescence excitation-emission matrix (EEM) and Fourier transform infrared reflectance (FTIR). Compared with the UV-vis absorption spectrum, EEM can not only make up the deficiency in the detection of some easily degradable organics in sewage, but also reveal the transformation of different components, thus indicating timely the treatment progress of rural sewage. Linear fitting of COD and spectrum shows that UV

Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on

The present study attempts to delineate wetlands in the lower Tangon river basin in the Barind flood plain region using spectral water body extraction indices. The main objectives of this present study are simulating and predicting wetland areas using the advanced artificial neural network-based cellular automata (ANN-CA) model and water depth using statistical (adaptive exponential smoothing) as well as advanced machine learning algorithms such as Bagging, Random Subspace, Random Forest, Support vector machine, etc. The result shows that RmNDWI and NDWI are the representative wetland delineating indices. NDWI map was used for water depth prediction. Regarding the prediction of wetland areas, a remarkable decline is likely to be identified in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the predicted period, where the major wetland patches nearer to the master stream with greater water depth are rather sustainable, but their depth of water is predicted to be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the random subspace model was identified as the best-suited water depth predicting method with an acceptable prediction accuracy (root mean square error0.34 in all the years) and the machine learning models explored better result than adaptive exponential smoothing. This recent study will be very helpful for the policymakers for managing wetland landscape as well as the natural environment.

This study applied multivariate statistical analysis (MSA) to the synthetic data simulated by the river water quality model to investigate how two pollution sources with different characteristics and contributions affect the results of MSA. The results showed that when assessing the number and possible locations of pollution sources based on the results of cluster analysis (CA), hydrological information about surface water should be obtained to improve the accuracy of the results; when applying principal component analysis (PCA), the results of the second principal component (PC2) and the Pearson correlation coefficients among the pollutants should both be included, which can add more information about the characteristics of pollutant sources. In addition, this study found that the solid and liquid partition coefficients (Kd) of pollutants can affect the interpretation of the PCA results, so the Kd values should be determined before tracing the pollution sources to facilitate the evaluation of the source characteristics and potential targets. This study established a working framework for surface water pollution traceability to enhance the effectiveness of pollution traceability.

Various per- and polyfluoroalkyl substances (PFASs) remain undiscovered and unexplored in the environment. The goals of this study were to discover new species of PFASs in effluent and surface waters from a fluorochemical industrial zone, and to assess their concentration, distribution, and temporal trends in the adjacent natural environment. In total, 83 emerging PFASs from 14 classes were identified, 22 of which were reported for the first time. Authentic standards were synthesized for 13 per- and polyfluoroalkyl ether carboxylic acids (PFECAs), thereby greatly expanding the scope of PFAS-targeted monitoring. The newly identified compounds accounted for 27%-95% of the total PFAS concentrations. Of note, a novel diether carboxylic acid, 2-[2-(trifluoromethoxy)hexafluoropropoxy]tetrafluoropropanoic acid (C7 HFPO-TA) was detected at an extremely high concentration in the fluorochemical zone effluent (447 000 ng/L) and at a median concentration in the fluorochemical zone surface water (670 ng/L), with detectable levels also found in the natural environment, that is, Wangyu River (23 ng/L) and Taihu Lake (5.6 ng/L). The distinct geographic distribution of C7 HFPO-TA suggests transport from the industrial point source to Taihu Lake via the Wangyu River. The concentration of C7 HFPO-TA in Taihu Lake, along with that of many other emerging PFASs, continued to grow in three sampling campaigns from 2016 to 2021. Considering the environmental persistence and toxicity of structurally similar PFECAs (e.g., HFPO-DA), studies on C7 HFPO-TA are urgently needed.

The construction of artificial reservoirs with various regulation types on river is currently an important form of comprehensive utilization of water energy and water resources in river basins. The type of regulation is important in controlling the residence time, which in turn affects the photosynthesis-respiration balance in the water. This process has a significant impact on carbon dioxide (CO

In this study, a data-driven way is proposed to evaluate and optimize the sustainable development of the logistics industry (LI). Based on a comprehensive consideration of economic, societal, and environmental factors, an evaluation index system was established for the sustainable development of the logistics industry (LISD). Logistics industry-related data were collected from the Yangtze River Delta (YRD) from 2011 to 2020. The anti-entropy method was used to determine the index weight and process the data. Furthermore, the coupling harmonization degree and barrier degree models were used to analyze the coordinated development of each subsystem and identify key obstacles. Our results indicate that there are significant temporal and spatial differences in the level of LISD in YRD, with Shanghai (score 0.4834) being the best and Anhui (score 0.4553) the worst, showing a wave-like evolution in time. The coupling and coordination states among the subsystems are significantly different, with that of environmental benefits and other subsystems being poor. Moreover, innovation ability and environmental benefits are the main obstacle factors of this system. Based on the results of this study, targeted optimization countermeasures are put forward and evaluation indicators and research methods are suggested, which will provide the government and practitioners decision support, as well as provide theoretical and methodological support for LISD.

In the Yangtze River Economic Belt (YREB), the green economy is an essential to sustainable economic development. In this study, we calculated a comprehensive index of environmental pollution based on the global entropy weight method and used the super slacks-based measure (SBM) model to estimate the green economic efficiency (GEE) of provinces and cities in the YREB from 2005 to 2018. Subsequently, we explored temporal and spatial evolution characteristics combined with the Theil and Moran indexes, and adopted the spatial Dubin model to analyze its influencing factors. We divided the YREB into three watersheds to facilitate the analysis. The results show that the GEE in the YREB initially decreased and then increased, and the difference among the three major watersheds was higher than that within the watershed. We found a positive spatial autocorrelation in the development level of the green economy in the YREB. While industrial structure had a negative effect, economic development, scientific and technological level, and environmental regulation all had a positive effect on GEE. Finally, we offered policy recommendations to improve the level of green development in the YREB.

Turions production is the dominant means of propagation in Potamogeton crispus L. To understand how parental population and their living environmental factors influence the turions production of P. crispus L., a field research was conducted in Nansi Lake in Yellow River Flood Plain and southwestern Shandong Province, China. This study showed that P. crispus biomass and asexual turions showed an almost uniform spatial distribution pattern. Water depth, attenuation coefficient of light, sediment water content, sediment organic matter content, and pH were significantly related to P. crispus biomass, turions weight, and turions number. Single turion weight was determined only by P. crispus biomass, while turions number was simultaneously determined by P. crispus biomass and sediment water content. Turions weight mainly depended on turions number rather than single turion weight, and maternal biomass and sediment water content determined turions weight by affected turions number rather than single turion weight.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011-2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10

Any contamination in potable water leads to high risk on human health. Hence, it is inevitable to characterize water quality and assess temporal change with reference to human health. In this paper, nineteen water quality parameters from 50 sources of drinking water supply operated by state agency Uttarakhand Jal Sansthan (UJS) were analysed. The water samples were collected for pre-monsoon (May) and post-monsoon (November) seasons for the year 2010, 2011, 2018 and 2019. Multivariate analysis such as Pearson's correlation coefficient (r), K-mean, hierarchical clustering and principle component analysis (PCA) were applied to examine the drinking water quality at source sites. The strength of correlation among the parameters is reduced over the time. Cluster analysis provides six major hydro-chemical clusters in the study region. Clusters were compact during 2010-2011 and converted to overlapping structure in later period. The size of clusters was change from two clusters to six clusters during 2018-2019. These hydro-chemical clusters were examined through PCA and established six major hydro-chemical components of water quality. The physico-chemical parameters, namely, pH, alkalinity, TH, TDS, Na, K, Ca, Mg, Cl and SO

The Yangtze River Economic Belt's industrial layout is characterized by industrial agglomeration. However, industrial agglomeration, while promoting economic development, has an uncertain impact on the ecological environment. This research studies the threshold impacts of pollution-intensive industrial agglomeration and green-based industrial agglomeration on the carbon productivity of the Yangtze River Economic Belt through the panel threshold regression models to find the "optimal industrial agglomeration scale." The results of the "optimal industrial agglomeration scale" show that under the existing economic conditions, only if pollution-intensive industrial agglomeration is controlled within a reasonable range can it contribute to carbon productivity. Green-based industries can only enhance carbon productivity when the scale of agglomeration reaches a certain value. In addition, this paper also points out that along the Yangtze River Economic Belt, regions with high agglomeration of green industries should consider investing more technological resources in emerging technologies that use clean energy as a production condition. In contrast, regions with high agglomeration of pollution-intensive industries should focus on improving existing technologies in which traditional energy sources are used as production conditions to increased carbon productivity.

A fully non-targeted analytical workflow for the investigation of a riverbank filtration site located at the river Danube has been developed and applied. Variations of compound intensities at different sampling locations of the riverbank filtration site and, for a single production well, over a monitoring period of one year have been investigated using liquid chromatography combined with time-of-flight-mass spectrometry followed by evaluation via non-targeted data analysis. Internal standardization and appropriate quality control strategies have been implemented into the workflow for reduction of possible methodological biases influencing data interpretation. Emphasis was placed on the assessment of different blank elimination steps and the final blank elimination strategy is reported. The spatial study of the selected riverbank filtration site revealed a homogenous composition of the filtered water sampled at 11 different locations across the 32,000 m2 site, except for one sampling location in a zone of the aquifer, which was only weakly connected to the well field in terms of hydrogeological conditions. The examination of time-dependent changes of the composition of surface and groundwater obtained at the riverbank filtration system revealed that the non-targeted workflow is fit-for-purpose regarding the assessment the stability of filtration efficiency and compound residence time in the riverbank filtration compartment. In total, 677 compounds were selected for the investigation of the time-dependent variations of the filtration process. Analysis of the signal intensities of these compounds revealed that the riverbank filtration is significantly reducing the intensity and number of compounds present in surface water over a wide polarity range. In addition, the method enabled the determination of compound residence times in the riverbank filtration system ranging from 5 to 7 days.

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH

Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset. The dataset was obtained by a Chemcatcher passive sampler (PS) monitoring campaign of three small streams and one major river over four sampling periods from spring to summer. Monitoring of small streams is important as they are impacted by non-point source introduction of organic micropollutants (OMPs). The use of a PS provides a higher representativeness of sampling, and NTS broadens the range of detectable OMPs. A comparison of ASCA results of target analysis and NTS showed for both datasets a dominant influence of different sampling locations and individual temporal pollution patterns for each river. With the limited set of target analytes, general seasonal pollution patterns were apparent, but NTS data provide a more holistic view on site-specific pollutant loads. The similarity of temporal pollution patterns of two geographically close small streams was revealed, which was not observed in undecomposed data analysis like principal component analysis (PCA). With a complementary partial least squares-discriminant analysis (PLS-DA) and Volcano-based prioritization strategy, 223 site- and 45 season-specific features were selected and tentatively identified.

Climatic and non-climatic factors affect the chemical weathering of silicate rocks, which in turn affects the CO

This study investigates possible improvement in water quality and ecosystem functions in the Ganga River as influenced by COVID-19 lockdown in India. A total of 132 samples were collected during summer-2020 low flow (coinciding COVID-19 lockdown) for water (sub-surface and sediment-water interface) and 132 samples separately for sediment (river bottom and land-water interface) considering 518-km main river stem including three-point sources (one releases urban sewage and the other two add metal-rich industrial effluents) and a pollution-impacted tributary. Parameters such as dissolved oxygen deficit and the concentrations of carbon, nutrients (N and P), and heavy metals were measured in water. Sediment P-release was measured in bottom sediment whereas extracellular enzymes (EE; alkaline phosphatase, FDAase, protease, and β-D-glucosidase) and CO