Accumulation characteristics and fate modeling of phthalic acid esters in surface water from the Three Gorges Reservoir area, China.

Phthalic acid esters (PAEs) are a group of compounds widespread in the environment. To investigate the occurrence and accumulation characteristics of PAEs, surface water samples were collected from the Three Gorges Reservoir area, China. The total concentrations of 11 analyzed PAEs (∑11PAEs) in the collected water samples ranging from 197.7 to 1,409.3 ng/L (mean ± IQR: 583.1 ± 308.4 ng/L). While DEHP was the most frequently detected PAE, DnBP and DnNP were the most predominant PAEs in the analyzed water samples with a mean contribution of 63.3% of the ∑11PAEs. The concentrations of the ∑11PAEs in the water samples from the upper reaches of the Yangtze River were significantly higher than those from the middle reaches. To better understand the transport and fate of the PAEs, seven detected PAEs were modeled by Quantitative Water Air Sediment Interaction (QWASI). The simulated and measured values were close for most PAEs, and differences are within one order of magnitude even for the worst one. For all simulated PAEs, water and particle inflow were main sources in the reservoir, whereas water outflow and degradation in water were important removal pathways. The contribution ratios of different sources/losses varied from PAEs, depending on their properties. The calculated risk quotients of DnNP in the Three Gorges Reservoir area whether based on monitoring or simulating results were all far exceeded the safety threshold value, implying the occurrence of this PAE compound may cause potential adverse effects for the aquatic ecology of the Three Gorges Reservoir area.

You can bring plankton to fecal indicator organisms, but you cannot make the plankton graze: particle contribution to E. coli and MS2 inactivation in surface waters.

Organisms that are associated with feces ("fecal indicator organisms") are monitored to assess the potential for fecal contamination of surface water bodies in the United States. However, the effect of the complex mixtures of chemicals and the natural microbial community within surface water ("particles") on fecal indicator organism persistence is not well characterized. We aimed to better understand how particles, including biological (e.g., potential grazers) and inert (e.g., minerals) types, affect the fecal indicator organisms Escherichia coli K-12 ("E. coli") and bacteriophage MS2 in surface waters. A gradient of particles captured by a 0.2-µm-pore-size filter ("large particles") was generated, and the additional particles and dissolved constituents that passed through the filter were deemed "small particles." We measured the ratio of MS2 and E. coli that survived over a 24-h incubation period for each condition (0%-1,000% large-particle concentration in raw water) and completed a linear regression that included large- and small-particle coefficients. Particles were characterized by quantifying plankton, total bacterial cells, and total solids. E. coli and MS2 persistence was not significantly affected by large particles, but small particles had an effect in most waters. Small particles in higher-salinity waters had the largest, negative effect on E. coli and MS2 survival ratios: Significant small-particle coefficients ranged from -1.7 to -5.5 day-1 in the marine waters and -0.89 to -3.2 day-1 in the fresh and estuarine waters. This work will inform remediation efforts for impaired surface water bodies.IMPORTANCEMany surface water bodies in the United States have organisms associated with fecal contamination that exceed regulatory standards and prevent safe recreation. The process to remediate impaired water bodies is complicated because these fecal indicator organisms are affected by the local environmental conditions. For example, the effect of particles in surface water on fecal indicator concentrations are difficult to quantify in a way that is comparable between studies and water bodies. We applied a method that overcomes this limitation to assess the effects of large particles, including natural plankton that could consume the seeded fecal indicator organisms. Even in environmental water samples with diverse communities of plankton present, no effect of large particles on fecal indicator concentrations was observed. These findings have implications for the interpretation and design of future studies, including that particle characterization of surface water may be necessary to assess the fate of fecal indicators.

Land use, land cover changes and expansion of artificial reservoirs in Eastern Thailand: implications for agriculture and vegetation drought reduction.

Eastern Thailand and Rayong province face perennial drought and water scarcity due to natural characteristics of climate and geology. Therefore, increasing water surface by man-made reservoirs is one of the priorities in the regional development plan to provide water adequately for industrial purposes, domestic consumption, and agriculture. The large reservoir constructions may induce land use, land cover changes (LULCC), yet it also is expected to alleviate the drought harshness in the region. By delineating Landsat satellite images and spatial analysis, this study revealed the LULCC in Rayong from 1990 to 2020. The most prominent LULCC was surface water expansion, about 10.9% per year, yet the increase was the most substantial in the first decade rather than the last two decades. Vegetation expansion was observed, contributing to an increase in forests/plantations and intensified agriculture by 39.19% and 25.54%, respectively. The LULCC corresponded to a 3.64% increase in ecosystem service values (ESV), implying positive benefits from the LULCC. Vegetation drought conditions monitored by the vegetation health index (VHI) exhibited an improvement trend, especially in the eastern basins. The development of artificial reservoirs was proven to stimulate the expansion of intensive agriculture and vegetation drought mitigation with spatial heterogeneity, spreading mainly across areas of the basins rather than remote areas. The research findings inform the efficiency of the reservoirs and irrigation systems regarding the beneficial effects on drought mitigation and water scarcity for agricultural cultivation. They also provide spatial information on areas still hindered by water problems that should be addressed in future strategies.

Occurrence and spatial distribution of microplastics in water and sediments of Hatiya Island, Bangladesh and their risk assessment.

This research has evaluated the MPs distribution, characteristics, and potential threats of MPs in surface water and sediments from Hatiya Island. The results showed that the abundance of MPs was 139 ± 44 items/m3 in surface water and 493 ± 80 items/kg dw in sediments, indicating higher levels of MPs contamination in sediment samples. Fibers were the predominant kind of microplastics, and microscopic sizes (0.3-1.5 mm) MPs were generally more frequent and largely present in both the surface water and sediments. Fourier-transform infrared spectroscopy (FTIR) confirmed that polyethylene terephthalate was the major polymer component of microplastics in surface water, whereas polyethylene was the most abundant polymer in sediments. MPs contamination risk was examined based on multiple risk assessment models. Nemerow pollution index (NPI) and pollutant load index (PLI) show minimal pollution levels of MPs. But potential hazard index (PHI), potential ecological risk factor (Er), and potential ecological risk index (RI), indicate severe MPs contamination due to the presence of polyurethane, polycarbonate, polyvinyl chloride, epoxy that were hazardous MPs and exhibited a critical concern for MPs risk. These statistics will help to understand the environmental difficulties generated by MPs and which hazard is waiting for mankind in the future.

Rain Gardens and Rain Gauges: Students Get Hands on With Data While Evaluating Green Infrastructure at Their School.

Two modules were designed for high school science students to investigate the performance of a rain garden installed on school property. The rain garden, a green infrastructure system which allows soil infiltration, was installed to reduce impacts to urban streams and can increase the community's resilience to flooding. By involving students in the analysis of this mitigation strategy, students learned new technical skills, gained varied experiences in collecting and analyzing data, were exposed to new STEM careers, and learned about local issues that impact their community while collaborating with local professionals. In each module, students used their authentic data to address research questions. In the first module, students conducted a land survey and calculated the volumetric capacity of the rain garden. In the second module, students collected rainfall data using rain gauges and analyzed various aspects of rainfall collection. Although these modules were focused on a rain garden already installed on school property, they can be implemented at schools without this mitigation strategy present. The surveying module can easily be applied to measure any land surface feature, and the rain gauge module can be implemented anywhere as it is focused on rainfall collection.

Evaluation of potential human health risks associated with Li and their relationship with Na, K, Mg, and Ca in Romania's nationwide drinking water.

Background: Increasing lithium (Li) demand worldwide due to its properties and role in renewable energy will raise water reservoir pollution and side effects on human health. Divergent results regarding Li concentration in water and affective disorders are found in the literature, which is why regional reports are expected. Objective: The present study evaluated the occurrence and human health risks resulting from oral exposure, respectively, and the relationship between alkali metals (Li, Na, and K) and minerals (Mg, Ca) in balanced purified water (bottled) and spring water. Methods: The ICP-MS technique was used to measure a national database with 53 bottled and 42 spring water samples randomly selected. One-way ANOVA, Pearson correlation, and HCA analysis were applied to assess the possible relationship between metals in water. The possible side effects of Li poisoning of water resources on human health have been evaluated using the Estimated Daily Intake Index (EDI) and Total Hazard Quotient (THQ). Results: The toxic metals (As, Hg, and Pb) were measured, and the results indicate values above the detection limit of 22.3% of samples in the case of lead but not exceeding the safety limits. Depending on the water sources, such as bottled and spring water, the Li concentration varied between 0.06-1,557 and 0.09-984% µg/L. We found a strong positive correlation between Li and Na and Mg, varying between bottled and spring waters (p% <%0.001). Li exceeded the limit set by the Health-Based Screening Level (HBSL) in 41.37 and 19% of bottled and spring water samples. The oral reference doses (p-RfDs) for the noncancer assessment of daily oral exposure effects for a human lifetime exceeded threshold values. The THQ index shows potential adverse health effects, requiring further investigations and remedial actions in 27.58% of approved bottled waters and 2.38% of spring waters. Conclusion: We can conclude that water is safe based on the Li concentration found in drinking water and supported by a gap in strict regulations regarding human Li ingestion. The present study can serve decision-makers and represent a starting database with metals of interest for further clinical studies. Decision-makers can also use it to find solutions for sustainable management of clean and safe drinking water.

An integrative analysis of SARS-CoV 2 during the first and second waves of COVID-19 in Salta, Argentina.

Wastewater surveillance has been extensively applied to provide information about SARS-CoV-2 circulation in the community. However, its applicability is limited in regions lacking adequate sewerage infrastructure, without wastewater treatment plants (WWTP) or with insufficient coverage. During the COVID-19 pandemic, from July 2020 to September 2021, comprehensive epidemiological data encompassing positive, recovered, and deceased cases were collected alongside precipitation records. Additionally, wastewater samples from 13 main sewersheds and river water from two points (up- and downstream the main WWTP), in the city of Salta, were gathered. A total of 452 water samples were analyzed for quantitative detection of SARS-CoV-2 using reverse transcription real-time PCR. Across the 62-week study period, two distinct waves of COVID-19 were identified. The dynamics of deceased cases showed peaks 10 and 28 days after the peaks of positive cases in the first and second waves, respectively. Downstream river water exhibited higher fecal contamination than the upstream samples, evincing the impact of the WWTP discharges. Viral concentration in river waters mirrored those from wastewater, reflecting the progression of cases. Despite the lower reported number of cases during the first wave in comparison to the second (5420 vs. 8516 cases at the respective peaks), higher viral concentrations were detected in water samples (1.97 × 107 vs. 2.36 × 106 gc/L, respectively), suggesting underreporting during the first wave, and highlighting the positive effect of vaccination during the second. To the best of our knowledge, this is the first study that simultaneously and systematically analyzed surface water and wastewater over a prolonged period, the effect of precipitations were considered for the variations in the concentrations, and the findings compared with epidemiological information. Environmental surveillance was demonstrated to be a great tool to obtain valuable information about the circulation patterns of SARS-CoV-2, especially under resource constraints to massively test the population, thus, underreporting cases. Furthermore, the methodology employed herein can be easily expanded to the community-level surveillance of other pathogens excreted in urine and feces, encompassing viruses, bacteria, and protozoa.

Typical Tire Additives in River Water: Leaching, Transformation, and Environmental Risk Assessment.

Tire wear particles (TWPs) released during vehicle driving can enter water bodies, leading to leaching of tire additives (TAs) in aquatic environments. However, the transformation behavior and related ecological impacts of TAs and their transformation products (TPs) remain unclear. In this study, laboratory-based simulation experiments and field investigations were conducted to explore the transformation mechanisms and ecological risks of TAs. After being placed in river water for 24 h, about 7-95% of 12 investigated TAs in TWPs were leached. Forty-eight TPs from eight TAs were tentatively identified along with different transformation pathways via suspect screening by high-resolution mass spectrometry. Semiquantitative results indicated that TPs derived from N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylene-diamine (6PPD) were predominant in leachates, while aryl hydrolysis and quinone pathways were the main transformation pathways. Field investigations on urban surface water samples from 16 sites in Hong Kong revealed the occurrence of 17 TAs and 1 TP, with concentrations ranging from 13.9 to 2230 ng/L (median ± standard deviation: 226 ± 534 ng/L). Sixteen TPs from six TAs were additionally identified via suspect screening. It is estimated that 6PPD-quinone and seven TAs could pose medium to high ecological risk, while N-(1,3-dimethylbutyl)-N'-phenyl-p-quinonediimine, a frequently detected TP, was identified as a persistent-bioaccumulative-toxic substance.

Campylobacter species, Salmonella serotypes and ribosomal RNA-based fecal source tracking in the Kokemäki River watershed.

Fecal contamination of surface water compromises the usability of surface water for drinking water production due to an increase in human health risks. In this study, we collected surface water samples for two years from the Kokemäki River (Finland). The downstream river stretch is used for feeding production of artificial ground water for a major drinking water treatment plant. The prevalence of Campylobacter species and Salmonella serotypes together with fecal source identifiers targeting general, human, gull, swine, and ruminant were evaluated at 16 sampling sites throughout the studied watershed. We detected Campylobacter spp. from all 16 sampling sites with Campylobacter jejuni and Campylobacter lari as the most detected species. Salmonella spp. was detected in 10 out of 16 sampling sites, with Salmonella Typhimurium being the most common serovar. Regarding spatial variation in the hygienic quality of surface water, the upstream area (urban proximity) and downstream area (agricultural proximity) had higher microbial loads than the middle section of the study area. Samples taken in fall and spring had higher microbial loads than summer and winter samples. The lower ratio of rRNA to rRNA-gene (rDNA) of studied microbes in the winter than in other seasons may indicate low metabolic activity of bacterial targets during winter. The number of gulls, swine, and cattle in the catchment area concorded with the number of fecal source identifiers in the surface water. Further, the prevalence of gull-specific source identifier agreed with the detection of C. coli, C. lari, and S. Typhimurim, whereas the prevalence of swine- and ruminant-specific source identifiers agreed with the detection of C. jejuni and C. coli. Thus, fecal source identifiers are shown to be important tools for monitoring zoonotic pathogens affecting microbial quality of surface water. Further, variation in fecal loads indicates such variation in health risks related to surface water use.

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Origin, distribution, fate, and risks of potentially toxic elements in the aquatic environment of Bengaluru metropolis, India.

This study probes the water quality, including pH, total dissolved solids (TDS), and potentially toxic elements (PTEs) concentrations, and the associated environmental and human health implications, in forty one surface water bodies in Bengaluru metropolis, southern India. The pH in 54 %, TDS in 63 %, Ni in 12 %, and U, Mo, Pb, Cr, Co, and Cu in 5 % of the forty one water bodies exceed the WHO's permissible limits for drinking purpose. Total dissolved solids display a significant positive correlation with Pb, Cr, Co, Cu, and Ni, suggesting the association of these elements with particulate matter. Sources of metal pollution include industries in the city's west (high levels of Pb, Cr, Co, Ni, and Cu), traffic in the city Centre (Mo), and geogenic (U) sources in the city's north, east, and south. The degree of contamination is high in 25 %, moderate in 10 %, and low in 65 % of the forty one water bodies, with the highest degree of contamination in Narasappanehalli Lake in the industrial zone, Deepanjali Nagara Lake, and Govindraj Nagar drainage systems. Uranium, Pb, and Mo display a low to medium degree of contamination, whereas Cr, Co, Ni, and Cu display a medium to high degree of contamination. The non-carcinogenic risk through ingestion of contaminated water is medium to high for adults and children and the carcinogenic risk is high in all water bodies. Channels transport contaminated water from Bengaluru water bodies to the Pinakani and Cauvery Rivers and then to the northern Indian Ocean. The states of Karnataka and Tamil Nadu rely on water from these rivers for domestic and agricultural use exposing large populations to contaminated waters. Additionally, contaminated waters can negatively impact flora and fauna of Peninsular India as well as the marine biota of the northern Indian Ocean.

Characterization of anthropogenic impacts in Mediterranean intermittent rivers with chemical, ecological and hydrological indicators.

Water scarcity in the Mediterranean area has increased the number of intermittent rivers, whose flow ceases either occasionally or totally. Key elements to characterize their dynamics are water quality, hydrological, and ecological status, when wastewater effluents dominate flow. Regarding water quality, pharmaceuticals are major pollutants, and serve as indicators of wastewater presence. Intermittent rivers are biodiversity hotspots where their hydrological regime may suffer alterations associated with wastewater effluents, making them harder to characterize than perennial streams. This study aimed to integratively characterize intermittent rivers through chemical, ecological and hydrological status calculating respective indices in twenty Mediterranean intermittent wastewater-impacted rivers located in Spain, France, Italy, Algeria and Tunisia. Pharmaceuticals were used as indicators assessing their frequency of PNEC exceedance and detection; while two ecological indicators and one hydrological indicator were used to evaluate wastewater stress on catchments. All indicators displayed a noticeable decline from upper to lower parts of the rivers, proving the effect of anthropogenic stressors on the aquatic environment. The Tunisian catchment displayed the most compromised conditions across all indicators, and the Algerian site even though low concentrations were detected, five compounds exceeded PNEC thresholds. This highlights the need for increased dedication and the adoption of water pollution solutions.

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

Convergence of mcr-1 and broad-spectrum ß-lactamase genes in Escherichia coli strains from the environmental sector.

The mcr-type gene encodes the main plasmid-mediated mechanism of colistin resistance and has been reported in several bacterial species obtained from different sources. Anthropogenic activities in the environment favor the evolution of antimicrobial resistance. Indeed, mcr-1-positive Escherichia coli strains were susceptible to non-polymyxins antimicrobials, but now emerging as multidrug-resistant (MDR) lineages. In this regard, hundreds of surface water and agricultural soil samples were screened for the presence of E. coli carrying the mcr-type genes and mcr-1-positive strains were subjected to in-depth genomic analysis. Almost all colistin-resistant strains were classified as MDR, highlighting those obtained from soils that showed resistance to extended-spectrum cephalosporins and carbapenems. International and high-risk clones of E. coli were identified, with ST10 and ST1720 shared between water and soil samples. Resistome analysis showed a broad resistome (AMR, metal tolerance, and biocide resistance). The mcr-1.1 and mcr-1.26 allelic variants were detected on IncX4 and IncI2 plasmids. Curiously, mcr-1-positive E. coli strains from agricultural soils harbored plasmid-mediated blaCTX-M-1, blaCTX-M-8, or blaKPC-2 genes. Virulome analysis demonstrated traits of a high putative virulence potential, with the presence of extraintestinal pathogenic E. coli. Comparative analysis revealed the persistence and dissemination of plasmid-mediated antimicrobial resistance genes in genetically diversity E. coli strains at the human-animal-environmental interface. These findings demonstrate a possible emerging AMR trend with the convergence of resistance to colistin and broad-spectrum ß-lactams in environmental-derived E. coli strains.

Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine.

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Increasing phosphorus ratios between overlying and surface water inhibits intracellular antibiotic resistance gene transformation in a large shallow lake.

The rising prevalence of antibiotic resistance genes (ARGs) in the surface waters of lakes poses a significant threat to human health. The overlying water in these lakes serves as a critical hotspot for the accumulation of ARGs in surface water. However, the mobility of extracellular (adsorbed ARGs (a-eARGs) and free ARGs (f-eARGs)), and intracellular ARGs (i-ARGs) from overlying water to surface water remains unknown. This study examined the mobility of ARGs between water layers, as well as the underlying mechanisms involved. Significant variations in ARG abundance were observed between overlying and surface water according to PCoA analysis (p < 0.05), with significant reductions in i-ARGs (p < 0.05) in surface water and no significant difference in a-eARGs and f-eARGs. Aminoglycoside and tetracycline i-ARGs had the highest and lowest mobility from overlying water to surface water, respectively. Additionally, the transformation of i-ARGs from overlying water to surface water was significantly correlated with total phosphorus ratio. According to the direct analysis of partial least squares-path modeling, the key drivers of a- and f-eARGs movement from overlying water to surface water were free-living and particle-attached bacteria, respectively, whereas heavy metals gradually became the driving force for i-ARGs by regulating mobile genetic elements. This study illustrated the transmission mechanisms of ARGs from overlying water to surface water in lakes, which will be useful for ARG treatment strategies, especially in eutrophic water.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions.

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

Water quality constrained adjustment planning for regional breeding management with nonlinear programming model under uncertainty in Wenchang City, China.

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

Multi-evidences investigation into spatiotemporal variety, sources tracing, and health risk assessment of surface water nitrogen contamination in China.

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

Chemical profiling of organic contaminants in rural surface waters combining target and non-target LC-HRMS/MS analysis.

The pollution of natural waters by contaminants of emerging concern (CECs) is one of the pressing problems due to their global distribution and potential negative effects on the environment and human health. In rural areas with lower population density and limited industrial development, less contamination is expected. However, the lack of wastewater treatment plants (WWTPs) or their poor removal efficiency can lead to significant input of pollutants. In this context, 11 streams of rural areas in the Guadalquivir River basin, southeast of Spain, were studied over two years to obtain an overview of the origin and distribution of contaminants. A target method using solid-phase extraction and liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) was developed for the analysis of 316 compounds in surface waters. A total of 78 target analytes were detected, comprising pesticides, pharmaceuticals, personal care products (PCPs), transformation products (TPs), and industrial chemicals. The flame retardant tributyl phosphate (16-3572 ng L-1) was detected in all samples, followed by caffeine (30-8090 ng L-1) and the analgesic tramadol (3-1493 ng L-1). The target approach was combined with a non-target analysis (NTA) strategy to obtain an overall perspective of the chemical profile of unexpected or unknown compounds in the samples. Up to 79 contaminants were tentatively identified, and 12 of them were finally confirmed with standards. Most of the contaminants determined by NTA were pharmaceuticals and their TPs. The results indicated that most of CECs have an urban origin despite traditional agriculture is the main economic activity in this region. Moreover, the absence of WWTPs in small towns is significant, as contamination levels at these sites were comparable to or higher than those in larger populations with sewage treatments.