Groundwater geochemistry, quality, and pollution of the largest lake basin in the Middle East: Comparison of PMF and PCA-MLR receptor models and application of the source-oriented HHRA approach

Two-dimensional Monte Carlo simulation coupled with multilinear regression modeling of source-specific health risks from groundwater

Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO3-, and F-, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types. Three pollution sources were identified: agricultural practices, traffic, and natural processes. Moreover, the results revealed NO3- from an agricultural source as the primary control contaminant. Additionally, both adults and children in the study area face significant non-carcinogenic health risks. To mitigate these risks, this study recommends maximum consumption levels of 1.44 L/day for adults and 0.35 L/day for children. Furthermore, adults weighing > 68.1 kg and children weighing > 15.9 kg are likely to be at reduced risk of experiencing adverse health effects. Compared to deterministic health risk assessment and one-dimensional Monte Carlo simulation of health risks, two-dimensional Monte Carlo simulation showed improved performance, providing better accuracy and higher precision in health risk assessment results. Thus, this research is expected to enhance the understanding of health risk assessment related to groundwater and to provide valuable guidance for managing groundwater pollution.

Advancing wetland groundwater pollution zoning: A novel integration of Monte Carlo health risk modeling and machine learning

Wetlands serve as crucial water reservoirs, providing essential water resources for the surrounding regions. However, elevated ion concentrations in wetland groundwater may pose health risks to local populations. This study focused on Judian Lake and its adjacent areas, proposing an innovative multimodel coupled uncertainty propagation framework to establish an integrated "process characterization-risk quantification-source management" methodology. The Entropy-Weighted Water Quality Index (EWQI), deterministic and Monte Carlo-based probabilistic health risk assessments, Principal Component Analysis-Absolute Principal Component Score-Multiple Linear Regression (PCA-APCS-MLR), and Self-Organizing Map-K-means (SOM-K-means) clustering were used. Results indicated that over 50 % of the water resources in the study area were suitable for drinking and irrigation purposes. F-, Mn, NO2-, and NO3- posed non-carcinogenic risks to both adults and children, with NO3- being the most severe. Monte Carlo indicated that for high-concentration pollutants (Mn, NO2-, and NO3-), source control measures should prioritize concentration reduction, whereas for low-concentration pollutants (F-), minimizing exposure pathways was necessary. The PCA-APCS-MLR model suggested that NO3- primarily originated from agricultural activities, while F- mainly came from the weathering and dissolution of fluorite. SOM-K-means divided the study into four clusters, of which cluster III was the most polluted.

Groundwater quality drivers in the drought-prone Thakurgaon District, Northwestern Bangladesh: An integrated fuzzy logic and statistical modeling approach

Groundwater quality in the drought-prone Thakurgaon District, Northwestern Bangladesh, is deteriorating due to a combination of natural and anthropogenic factors. This study evaluates the key drivers of groundwater quality degradation by employing ecotoxicological risk indices, such as the Heavy Metal Pollution Index (HPI), Heavy Metal Evaluation Index (HEI), and Nemerow's Pollution Index (NPI). An innovative fuzzy logic approach is used to integrate these indices and reduce uncertainty, while Automatic Linear Modeling (ALM) predicts the primary impacts on the Fuzzy Groundwater Quality Index (FGWQI). Additionally, Monte Carlo simulations assess probabilistic health risks and sensitivity. Groundwater samples from 40 wells were analyzed for physicochemical parameters and heavy metal concentrations. The results show that 25 % of the samples are unsuitable for drinking, and 17.5 % are unfit for household use, based on HPI and HEI values. Fuzzy analysis reveals that 22.5 %, 47.5 %, and 30 % of the samples exhibit excellent, good, and poor quality, respectively. The overlay of FGWQI with Land Use/Land Cover (LULC) maps identifies areas with excellent groundwater quality in the southern parts of the region, while the northern areas suffer from poor quality due to overexploitation. One-way ANOVA indicates that rainfall, water discharge, and LULC significantly affect FGWQI. The ALM results highlight HEI (0.62) and HPI (0.38) as the main factors influencing FGWQI. Health risk analysis reveals elevated non-carcinogenic risks due to arsenic and lead ingestion, particularly for children. These findings emphasize the need for targeted policies and interventions to mitigate health risks and ensure the well-being of the community.

Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions. The analysis reveals a growing trend in publications over the 24-year period, with a sharp expansion since 2020. China, the USA, India, and Iran are identified as the leading contributors to publications and citations, with prominent institutions such as Jilin University, the United States Geological Survey, and the University of Tabriz. Moreover, keyword frequency analysis indicates that principal component analysis (PCA) is the most commonly used method, followed by artificial neural network (ANN) and hierarchical clustering analysis (HCA). The most studied groundwater pollutants include nitrate, arsenic, heavy metals, and fluoride. As machine learning has rapidly advanced, research focuses have evolved from fundamental tasks like hydrochemical evolution analysis, water quality index evaluation, and groundwater vulnerability assessments to more complex issues, such as pollutant concentration prediction, pollution risk assessment, and pollution source identification. Despite these advances, challenges related to data quality, data scarcity, model generalization, and interpretability remain. Future research should prioritize data sharing, improving model interpretability, broadening research horizons and advancing theory-guided machine learning. These will enhance our understanding of groundwater pollution mechanisms, and ultimately facilitate more effective pollution control and remediation strategies. In summary, this review provides valuable insights and suggestions for researchers and policymakers working in this critical field.

Assessment of trace elements contamination and human health risk based on Monte Carlo simulation in a karst groundwater system affected by industrial activities

Groundwater in karst regions is a vital drinking water source, but it is highly susceptible to contamination from industrial activities, which exacerbate pollution and pose health risks. This study investigated the concentration, spatial distribution, quality, health risks and sources of trace elements (TEs) in groundwater within a newly established industrial park in Guiyang, a representative city with a karst landscape. The results indicated that the trace element concentrations followed the order: Ti > Fe > Al > Ni > Cr > Mn > V > Cu > As > Co. Correlation Analysis (CA) and Hierarchical Cluster Analysis (HCA) suggested that the sources of TEs are multifaceted, with industrial activities identified as the primary influencing factor. Monte Carlo simulations revealed that the non-carcinogenic risk (NCR) associated with each element was negligible. However, due to industrial activities, Cr, Ni and As exhibited significant carcinogenic risks. As one of the characteristic pollutants of the electroplating industry, Cr presented the highest potential risk. The total carcinogenic risks (TCR) for adults and children were 3.24E-05 and 3.78E-05, respectively, both exceeding the acceptable risk threshold of 1E-06. These results make a meaningful contribution to the management of vulnerable aquifers in karst industrial areas, with an emphasis on protection against TEs contamination, which is critical for ensuring groundwater safeguard and protecting public health.

Identification of hydrogeochemical processes in shallow groundwater using multivariate statistical analysis and inverse geochemical modeling

Identifying key factors that control the chemical evolution of groundwater along groundwater flow direction is essential in ensuring the safety of groundwater resources in upper watersheds and lower plains. In this study, the ion ratio, multivariate statistics, and inverse geochemical modeling were used to investigate and explore the chemical characteristics of groundwater and factors driving the formation of groundwater components in the plain area of Deyang City, China. The chemical type of groundwater in the area was dominated by the HCO3-Ca type, and the variation in groundwater chemical composition was mainly affected by water-rock interaction and human interference. The water-rock interaction process includes carbonate dissolution, oxidation-reduction reactions, and cation exchange. Furthermore, inverse geochemical modeling revealed that the dissolution of evaporite minerals, dissolution/precipitation of carbonate minerals, and weathering of silicate minerals play a key role in the hydrogeochemical evolution of groundwater. Owing to dissimilar geological and hydrological conditions, hydrogeochemical evolution processes differ along different and similar paths. Overall, this study not only provides a conceptual framework for hydrochemical evolution in plains but also has important implications for the sustainable management of groundwater resources in other basins within plains.

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence

Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health impacts, and targeted strategies in such cities. The study analyzed 115 groundwater samples, with the main groundwater chemical type being HCO₃-Na·Ca. Significant exceedances were found in Mg²⁺, HCO₃-, F-, total hardness (TH), and Mn, with HCO₃- and Mg²⁺ surpassing standards in nearly all samples. The average Comprehensive Environmental Water Quality Index (CEWQI) was 100.68, indicating that overall groundwater quality in the study area is good. High-quality water is mainly found near reservoirs and rivers, while urban and eastern regions have relatively poorer water quality. The proportion of groundwater unsuitable for drinking is low. Monte Carlo risk assessments revealed that F- and NO₃- pose non-carcinogenic risks to both adults and children, with NO₃- presenting a higher potential health risk. The Positive Matrix Factorization (PMF) model identified that groundwater pollution primarily results from natural geological processes and human activities, with agriculture being the major anthropogenic factor. AI-based zoning strategies highlighted industrial areas and high-fluoride zones as critical areas requiring enhanced prevention and control measures.

Spatiotemporal variability of groundwater chemistry, source identification and health risks in the southern Chinese Loess Plateau

Groundwater pollution of the loess plateau regions has become a global concern due to its vulnerability to natural and anthropogenic influences. In this study, 146 water samples were investigated to identify the spatiotemporal variability in groundwater chemistry, pollution sources and nitrate health risks in two interconnected river basins of a typical loess region. The results showed that except for bicarbonate, spatiotemporal variability of hydrochemical components in Malian River Basin (ML) was generally greater than that in Upper Jinghe River basin (JH-U) due to the hydrogeological conditions, and the hydrochemical facies in two river basins transformed from SO4·Cl and Cl·SO4 types to HCO3 and HCO3·SO4 types. The results of integrated-weight quality index (IWQI) showed that 77.8 % (1970s), 33.3 % (2004), 34.3 % (2015) of samples in ML exceeded the standard limits of Class IV groundwater quality, displaying a high pollution level with an improvement trend, while groundwater quality in JH-U indicated a very low pollution level with a deterioration trend. The geogenic source was identified as a main factor affecting groundwater quality, with contributions of 59.2 % and 48.7 % in JH-U and ML (2015), respectively. The anthropogenic sources including agricultural activities (20.7 % and 21.8 % in JH-U and ML) and coal mining activities (20.1 % and 29.5 % in JH-U and ML) also played a role in affecting groundwater quality. The nitrate health risk assessment demonstrated that 39.1 % and 20.3 % of groundwater samples (2015) significantly exceeded the standard threshold (Hazard Index = 1), implying a higher health risk to children than adults, and the nitrate health risk in ML was obviously greater than that in JH-U. This study provides novel insight into the spatiotemporal variability in groundwater chemistry, quality and health risk in loess regions under the influence of geogenic and anthropogenic factors.

Nitrate sources and transformation in surface water and groundwater in Huazhou District, Shaanxi, China: Integrated research using hydrochemistry, isotopes and MixSIAR model

Global water resources affected by excessive nitrate (NO3-) have caused a series of human health and ecological problems. Therefore, identification of NO3- sources and transformations is of pivotal significance in the strategic governance of widespread NO3- contaminant. In this investigation, a combination of statistical analysis, chemical indicators, isotopes, and MixSIAR model approaches was adopted to reveal the hydrochemical factors affecting NO3- concentrations and quantify the contribution of each source to NO3- concentrations in surface water and groundwater. The findings revealed that high groundwater NO3- concentration is concentrated in the southwestern region, peaking at 271 mg/L. NO3- concentration in the Wei River and Yuxian River exhibited an increase from upstream to downstream, but in the Shidi River and Luowen River, its concentration was highest in the upstream. Groundwater NO3- has noticeable correlation with Na+, Ca2+, Mg2+, Cl-, HCO3-, TDS, EC, and ORP. In surface water, NO3- level is significantly correlated with NH4+ and ORP. Major sources of NO3- in surface and groundwater comprise manure & sewage and soil nitrogen. Source contribution for surface water was calculated by MixSIAR model to obtain soil nitrogen (57.7%), manure & sewage (23.8%), chemical fertilizer (12%), and atmospheric deposition (6.4%). In groundwater, soil nitrogen and manure & sewage accounted for 19% and 63.8% of nitrate sources, respectively. Both surface water and groundwater exhibited strong oxidation, with nitrification the primary process. It is expected that this study will provide insights into the dynamics of NO3- and contribute to the development of effective strategies for mitigating NO3- contaminant, leading to sustainable management of water resources.

Deciphering pollution sources and mechanisms controlling groundwater chemistry in a typical dense agricultural plain on Yungui Plateau

Groundwater is a critical resource for economic growth and livelihoods in the dense agricultural plains of plateaus. However, contaminations from various sources pose significant threats to groundwater quality. Understanding the sources of groundwater contamination and the mechanisms of hydrochemical control is essential for the sustainable development of agriculturally intensive plains. This research utilizes 23 datasets of groundwater chemical measurements to apply hierarchical clustering analysis, positive matrix factorization, and hydrochemical analysis techniques. Through these methods, the study identifies the sources of groundwater contamination and deciphers the hydrochemical control mechanisms within a representative intensive agricultural plain region of Yungui Plateau. The finds indicate that groundwater in the plain primarily derives from the rainfall occurred in the surrounding mountains. During the long underground flow process, groundwater undergoes water-rock interactions and ion exchanges with various lithological strata, resulting in the formation of distinct hydrochemical types. As it traverses regions influenced by human activities, groundwater encounters varying levels and types of contamination. Consequently, there is a notable variation in groundwater quality across different areas of the plain. Groundwater is dominated by the hydrochemical faces of HCO3-Ca type in the southern part of the plain. Groundwater in the piedmont region of this part exhibits the highest quality, acting as the baseline for the overall groundwater quality of the plain. Groundwater in agricultural areas of this part is severely polluted by nitrate-rich agricultural wastewater. In the central urban area, under the control of municipal wastewater discharge and denitrification, groundwater is to some extent polluted by NH4 +. In the northern sector of the plain, groundwater chemistry exhibits greater diversity due to variations in geological strata and exposure to a range of pollution sources. The majority of the regions are contaminated with SO4 2- and Cl- and present a predominance of Cl-Na type for groundwater hydrochemical facies. Groundwater at the northernmost end is polluted by NO2 -, NH4 +, and P. In addition, there is also a small amount of groundwater near the lake that is heavily polluted by fertilizers. This study provides valuable insights for the development of sound groundwater management strategies, applicable not only to the current agricultural plain but also to analogous regions worldwide. PRACTITIONER POINTS: This study probed the impact of agricultural pollution on the groundwater hydrochemistry in a cultivated plain. The research pinpointed the origins and contributions of groundwater chemicals in the cultivated agricultural plain. A conceptual model was established to illustrate groundwater chemistry formation in an intensive agricultural irrigation plain on Yungui Plateau.

Identifying groundwater ammonium hotspots in riverside aquifer of Central Yangtze River Basin

Elevated ammonium (NH4-N) contents in groundwater are a global concern, yet the mobilization and enrichment mechanisms controlling NH4-N within riverside aquifers (RAS) remain poorly understood. RAS are important zones for nitrogen cycling and play a vital role in regulating groundwater NH4-N contents. This study conducted an integrated assessment of a hydrochemistry dataset using a combination of hydrochemical analyses and multivariate geostatistical methods to identify hydrochemical compositions and NH4-N distribution in the riverside aquifer within Central Yangtze River Basin, ultimately elucidating potential NH4-N sources and factors controlling NH4-N enrichment in groundwater ammonium hotspots. Compared to rivers, these hotspots exhibited extremely high levels of NH4-N (5.26 mg/L on average), which were mainly geogenic in origin. The results indicated that N-containing organic matter (OM) mineralization, strong reducing condition in groundwater and release of exchangeable NH4-N in sediment are main factors controlling these high concentrations of NH4-N. The Eh representing redox state was the dominant variable affecting NH4-N contents (50.17 % feature importance), with Fe2+ and dissolved organic carbon (DOC) representing OM mineralization as secondary but important variables (26 % and 5.11 % feature importance, respectively). This study proposes a possible causative mechanism for the formation of these groundwater ammonium hotspots in RAS. Larger NH4-N sources through OM mineralization and greater NH4-N storage under strong reducing condition collectively drive NH4-N enrichment in the riverside aquifer. The evolution of depositional environment driven by palaeoclimate and the unique local environment within the RAS likely play vital roles in this process.

Enhancing source apportionment of carbon, nitrogen, and phosphorus through integrating PMF and observed source profiles in a subtropical river

Apportioning pollution sources under compound pollution conditions is challenging in river pollution source analysis. The positive matrix factorization (PMF) model is widely used to analyze river pollution sources. However, the identification of pollutants in this model relies primarily on the subjective experience of the researchers, leading to ineffective identification of different contaminants from similar sources. In this study, we propose a comprehensive deviation index (CDI) to quantitatively identify pollution source types based on the PMF and observed source profiles. Taking the subtropical Xizhijiang River Basin as a case study, we quantitatively identified the pollution sources and their contributions to dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) using observed water quality and pollution sources data. The results showed that the eight major pollutants in the study region exhibited significant positive correlations, indicating the similarity of pollutant sources in the watershed. The PMF model identified three primary pollution sources with coefficients of determination for observed versus predicted concentrations ranging from 0.60 to 0.98. The CDI unveiled that the watershed's three pollution sources were farmland, rural, and wastewater treatment plants (WTPs). Farmland emerges as the predominant contributor to DOC (68.04 %), TC (63.29 %), and TDP (44.51 %). Rural notably contributes to NH3-N, PO4 3-, TDP, and TN, with percentages of 86.37 %, 57.65 %, 41.40 %, and 30.45 %, respectively. WTPs significantly contribute to NO2 -, NO3 -, and TN, accounting for 71.81 %, 57.39 %, and 37.26 %, respectively. Incorporating source fingerprints into the PMF model, the CDI can accurately identify pollution sources, improve the interpretability of source identification, and mitigate uncertainty in the multiple-source unknown receptor model. These findings have immediate and practical implications for river ecosystem management and pollution control, providing a more effective method for identifying and addressing pollution sources.

Natural background levels, source apportionment and health risks of potentially toxic elements in groundwater of highly urbanized area

Identifying the natural background levels (NBLs), threshold values (TVs), sources and health risks of potentially toxic elements in groundwater is crucial for ensuring the water security of residents in highly urbanized areas. In this study, 96 groundwater samples were collected in urban area of Sichuan Basin, SW China. The concentrations of potentially toxic elements (Li, Fe, Cu, Zn, Al, Pb, B, Ba and Ni) were analyzed for investigating the NBLs, TVs, sources and health risks. The potentially toxic elements followed the concentration order of Fe > Ba > B > Al > Zn > Li > Cu > Ni > Pb. The NBLs and TVs indicated the contamination of potentially toxic elements mainly occurred in the northern and central parts of the study area. The Positive Matrix Factorization (PMF) model identified elevated concentrations of Fe, Al, Li, and B were found to determine groundwater quality. The primary sources of Fe, Al, Pb, and Ni were attributed to the dissolution of oxidation products, with Fe additionally affected by anthropogenic reduction environments. Li and B were determined to be originated from the weathering of tourmaline. High levels of Ni and Cu concentrations were derived from electronic waste leakage, while excessive Ba and Zn were linked to factory emissions and tire wear. The reasonable maximum exposure (RME) of hazard index (HI) was higher than safety standard and reveal the potential health risks in the southwestern study area. Sensitivity analysis demonstrated the Li concentrations possessed the highest weight contributing to health risk. This study provides a valuable information for source-specific risk assessments of potentially toxic elements in groundwater associated with urban areas.

Evidence of deviations between experimental and empirical mixing lengths: Multi-discharge field tests in an arid river system

Despite advances in wastewater treatment plant (WWTP) efficiencies, multiple contaminants of concern, such as microplastics, pharmaceuticals, and per- and poly-fluoroalkyl substances (PFAS) remain largely untreated near discharge points and can be highly concentrated before they are fully mixed within the receiving river. Environmental agencies enforce mixing zone permits for the temporary exceedance of water quality parameters beyond targeted control levels under the assumption that contaminants are well-mixed and diluted downstream of mixing lengths, which are typically quantified using empirical equations derived from one-dimensional transport models. Most of these equations were developed in the 1970s and have been assumed to be standard practice since then. However, their development and validation lacked the technological advances required to test them in the field and under changing flow conditions. While new monitoring techniques such as remote sensing and infrared imaging have been employed to visualize mixing lengths and test the validity of empirical equations, those methods cannot be easily repeated due to high costs or flight restrictions. We investigated the application of Lagrangian and Eulerian monitoring approaches to experimentally quantify mixing lengths downstream of a WWTP discharging into the Rio Grande near Albuquerque, New Mexico (USA). Our data spans river to WWTP discharges ranging between 2-22x, thus providing a unique dataset to test long-standing empirical equations in the field. Our results consistently show empirical equations could not describe our experimental mixing lengths. Specifically, while our experimental data revealed "bell-shaped" mixing lengths as a function of increasing river discharges, all empirical equations predicted monotonically increasing mixing lengths. Those mismatches between experimental and empirical mixing lengths are likely due to the existence of threshold processes defining mixing at different flow regimes, i.e., jet diffusion at low flows, the Coanda effect at intermediate flows, and turbulent mixing at higher flows, which are unaccounted for by the one-dimensional empirical formulas. Our results call for a review of the use of empirical mixing lengths in streams and rivers to avoid widespread exposures to emerging contaminants.

Groundwater pollution risk, health effects and sustainable management of halocarbons in typical industrial parks

As important chemical raw materials and organic solvents, halogenated hydrocarbons not only play an important role in economic development, but are also the main source of environmental pollution. This study proposed an improved groundwater risk assessment model system, aimed at identifying and treating contaminants at leak sites. Groundwater ubiquity score (GUS) was used to evaluate the leachability of organic pollutants. The entropy-weighted water quality index (EWQI) method was used to assess the comprehensive quality of groundwater at the site. An improved groundwater health risk assessment model was constructed to analyze the health risks of groundwater. The sources of organic pollutants were identified based on the positive matrix factorization (PMF) model. Self-organizing mapping (SOM) and the K-means algorithm were integrated to classify and manage pollution source areas. The results showed that groundwater in the study area was strongly affected by human activities. The pollution source was located in a factory near S05. Different organic pollutants were highly leachable and had high potential to contaminate surrounding groundwater. 1,2-dichloropropane and 1,2,3-trichloropropane caused the largest range of contamination. The groundwater pollution index in the study area was high, and 72% of the monitoring points were non-drinkable. Both the carcinogenic and non-carcinogenic indexes of groundwater far exceeded the international standard limits and had a great impact on human health. 1,2,3-trichloropropane and 1,2-dichloropropane were major non-carcinogenic risk factors. The leakage of pollutants and pesticide solvents were the main causes of groundwater pollution. Cluster areas III and II were areas with significant pollution impacts and needed to be monitored intensively. Most areas were cluster I, with relatively low risk. This study can provide technical support for groundwater pollution risk assessment and management in similar industrial parks.

Ecological and human health risk from exposure to metal contaminated sediments in a subtropical river affected by anthropogenic activities: A case study from river Yamuna

Heavy metal enrichment in river sediments poses a significant risk to human and aquatic health. The Yamuna River faces severe challenges due to untreated industrial and domestic wastewater discharge. The study evaluates sediment metal content, ecological and human health risks, and potential sources. Results showed Cd and Pb exhibited moderate to severe contamination and displayed ecological risk based on contamination factor, enrichment factor, and potential ecological risk. According to synergistic indices (pollution load index, PINemerow, toxic risk index, contamination security index, mean probable effects level quotients, and probability of toxicity), the sediment in the Yamuna River doesn't seem to have a risk or enrichment from combined metals. Cd and Pb mainly originate from anthropogenic sources. Hazard index (< 1) and carcinogenic risk (2.2 × 10-7 to 4.7 × 10-5) assessments suggest metal didn't pose any risk to humans exposed to sediment. The present study aids in developing pollution control strategies for the Yamuna River.

Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

The main objective of this study was to map the quality of groundwater for domestic use in the Nabogo Basin, a sub-catchment of the White Volta Basin in Ghana, by applying machine learning techniques. The study was conducted by applying the Random Forest (RF) machine learning algorithm to predict groundwater quality, by utilizing factors that influence groundwater occurrence and quality such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation Index (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile Curvature (PRC), Lineament density, Distance to faults, and Drainage density. The groundwater quality of the area was predicted by building a Random Forest model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) of existing boreholes, to serve as an indicator of the groundwater quality. The predicted WQI of groundwater in the study area shows that it ranges from 9.51 to 69.99%. This implied that 21.97 %, 74.40 %, and 3.63 % of the study area had respectively the likelihood of excellent. The models were found to perform much better with an RMSE of 23.03 and an R2 value of 0.82. The study conducted highlighted an essential understanding of the groundwater quality in the study area, paving the way for further studies and policy development for groundwater management.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Elevated levels of environmental radioactivity in fluvial sediment: origin and health risk assessment

To study the geogenic processes of naturally occurring radioactive materials' (NORMs') distribution, a transboundary Himalayan river (Punarbhaba) is chosen due to its trivial anthropogenic impacts. In explaining the genesis of radionuclides, transition elements (Sc, Ti, V, and Fe), rare-earth-elements (REEs: La, Eu, Ce, Yb, Sm, and Lu), Ta, Hf, Th, and U were analysed in 30 riverbed sediments collected from the Bangladeshi portion of the river. Elemental abundances and NORMs' activity were measured by neutron activation analysis and HPGe-gamma-spectrometry, respectively. Averagen=30 radioactivity concentrations of 226Ra (68.4 Bq kg-1), 232Th (85.7 Bq kg-1), and 40K (918 Bq kg-1) were 2.0-2.3-fold higher, which show elevated results compared to the corresponding world mean values. Additionally, mean-REE abundances were 1.02-1.38-times higher than those of crustal origin. Elevated (relative to earth-crust) ratios of Th/U (=3.95 ± 1.84) and 232Th/40K and statistical demonstrations invoke Th-dominant heavy minerals, indicating the role of kaolinite clay mineral abundance/granitic presence. However, Th/Yb, La/V, Hf/Sc, and Th/Sc ratios reveal the presence of felsic abundances, hydrodynamic sorting, and recycling of sedimentary minerals. Geo-environmental indices demonstrated the enrichment of chemical elements in heavy minerals, whereas radiological indices presented ionizing radiation concerns, e.g., the average absorbed-gamma-dose rate (123.1 nGy h-1) was 2.24-fold higher compared to the threshold value which might cause chronic health impacts depending on the degree of exposure. The mean excess lifetime cancer risk value for carcinogen exposure was 5.29 × 10-4 S v-1, which is ∼2-times greater than the suggested threshold. Therefore, plausible extraction of heavy minerals and using residues as building materials can alleviate the two-reconciling problems: (1) radiological risk management and (2) fluvial navigability.

Spatial and temporal distribution characteristics and risk assessment of heavy metals in groundwater of Pingshuo mining area

Groundwater pollution in the Pingshuo mining area is strongly associated with mining activities, with heavy metals (HMs) representing predominant pollutants. To obtain accurate information about the pollution status and health risks of groundwater, 189 groups of samples were collected from four types of groundwater, during three periods of the year, and analyzed for HMs. The results showed that the concentration of HMs in groundwater was higher near the open pit, waste slag pile, riverfront area, and human settlements. Except for Ordovician groundwater, excessive HMs were found in all investigated groundwater of the mining area, as compared with the standard thresholds. Fe exceeded the threshold in 13-75% of the groundwater samples. Three sources of HMs were identified and quantified by Pearson's correlation analysis and the PMF model, including coal mining activities (68.22%), industrial, agricultural, and residential chemicals residue and leakage (16.91%), and natural sources (14.87%). The Nemerow pollution index revealed that 7.58% and 100% of Quaternary groundwater and mine water samples were polluted. The health risk index for HMs in groundwater showed that the non-carcinogenic health risk ranged from 0.18 to 0.42 for adults, indicating an acceptable level. Additionally, high carcinogenic risks were identified in Quaternary groundwater (95.45%), coal series groundwater (91.67%), and Ordovician groundwater (26.67%). Both carcinogenic and non-carcinogenic risks were greater for children than adults, highlighting their increased vulnerability to HMs in groundwater. This study provides a scientific foundation for managing groundwater quality and ensuring drinking water safety in mining areas.

Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting

The creation of predictive models with a high degree of generalizability in chemical analysis and process optimization is of paramount importance. Nonetheless, formulating a prediction model based on collected data from chemical measurements that maximize quantitative generalizability remains a challenging task for chemometrics experts. To tackle this challenge, a range of forecasting models with varying characteristics, structures, and capabilities has been developed, utilizing either accuracy-based or reliability-based modeling strategies. While the majority of models follow the accuracy-based approach, a recently proposed reliability-based approach, known as the Etemadi approach, has shown impressive performance across various scientific fields. The Etemadi models were constructed through a reliability-based parameter estimation process in such a manner that maximizes the models' reliability. However, the foundation of modeling procedures for chemometrics purposes is built upon the assumption that high generalizability in inaccessible/test data is achieved through the accuracy-based training procedure in which errors in available historical/training data are minimized. After conducting a thorough review of the current literature, we have found that none of the forecasting models for chemometrics purposes incorporate reliability into their modeling procedures. Given the dynamic and highly sensitive nature of chemistry experiments and processes, implementing a reliable model that controls performance criteria variation is a promising strategy for achieving stable and robust forecasts. To address this research gap, this paper introduces several key innovations, which can be highlighted as follows: (1) Proposing a general design structure based on a new optimal reliability-based parameter estimation process. (2) Introducing a novel risk-based modeling strategy that minimizes the performance variation of models implemented under different conditions in chemical laboratory experiments, to generate a more generalizable model for diverse applications in chemometrics. (3) Specifying the degree of influence that each reliability and accuracy factor has in enhancing the generalizability and uncertainty modeling of chemometric models. Empirical evidence confirms the effectiveness and superior performance of reliability-based models compared to accuracy-based models in 78.95% of the cases across various fields, including Pharmacology, Biochemistry, Agrochemical, Geochemical, Biological, Pollutants, Physicochemical Properties, and Gases Experiment. Furthermore, the study's findings demonstrate that the reliability-based modeling approach outperforms the accuracy-based strategy in terms of MAE, MSE, ARV, and RMSE by an average of 4.697%, 5.646%, 5.646%, and 4.342%, respectively. It is also statistically proven that reliability has a more significant impact on improving the generalizability of chemometric models than accuracy. This emphasizes the importance of including reliability as a crucial factor in chemometrics modeling, a consideration that has been overlooked in traditional modeling processes. Consequently, reliability-based modeling approaches can be regarded as a viable alternative to conventional accuracy-based modeling methods for chemical modeling purposes.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.

Spatial Variability of Rare Earth Elements in Groundwater in the Vicinity of a Coal-Fired Power Plant and Associated Health Risk

This study investigated the occurrence and distribution of rare earth elements (REEs), including 14 lanthanoids, scandium (Sc), and yttrium (Y), in groundwater around a large coal-fired thermal power plant (TPP). The ICP-MS technique was used to analyze 16 REEs in groundwater samples collected from monitoring wells. REE concentrations ranged from 59.9 to 758 ng/L, with an average of 290 ng/L. The most abundant was Sc, followed by La, accounting for 54.2% and 21.4% of the total REE concentration, respectively. Geospatial analysis revealed the REE enrichment at several hotspots near the TPP. The highest REE concentrations were observed near the TPP and ash landfill, decreasing with the distance from the plant and the landfill. REE fractionation ratios and anomalies suggested the Light REE dominance, comprising over 78% of the total REEs. Correlation and principal component analyses indicated similar behavior and sources for most REEs. Health risk assessment found hazard indices (HI) of 1.36 × 10-3 and 1.98 × 10-3 for adults and children, respectively, which are far below the permissible limit (HI = 1). Likewise, incremental lifetime cancer risks (ILCR) were all below 1 × 10-6. Nevertheless, ongoing ash disposal and potential accumulation in the environment could elevate the REE exposure over time.

Microbial and potentially toxic elements risk assessment in high Andean river water based on Monte Carlo simulation, Peru

The study evaluated microbial and Potentially Toxic Elements-PTEs risks in high Andean river water in Peru using Monte Carlo simulation. A total of 144 water samples were collected from four rivers and evaluated for physicochemical parameters, PTEs and bacterial pathogens. The microbial risk analysis for exposure to pathogens present in the water was based on the probability of occurrence of diseases associated with Escherichia coli, Pseudomonas aeruginosa and enterococci. PTEs risk analysis was performed using a Monte Carlo simulation approach. The results showed that the highest microbial risk due to exposure to water contaminated by E. coli, P. aeruginosa and enterococci was recorded in the Miraflores and Chia rivers. Meanwhile, the analysis of carcinogenic and non-carcinogenic risk by PTEs in adults and children revealed that the Chia river presents a high risk of contamination by PTEs, especially the carcinogenic risk for children. The Monte Carlo simulation indicated a 56.16% and 94.85% probability of exceeding the limit value of 0.0001 for carcinogenic risk in adults and children, respectively. It can be concluded that children consuming the waters of the Chia river are potentially at risk of As toxicity.

Geospatial assessment of groundwater quality using entropy-based irrigation water quality index and heavy metal pollution indices

Groundwater contamination has become a serious environmental threat throughout the world in the era of Anthropocene. Thus, the present study examined the groundwater quality for irrigation purposes based on the entropy method and heavy metal pollution indices. To compute the entropy-based groundwater irrigation quality index (EIWQI), physicochemical parameters such as pH, chloride (Cl-) and nitrate (NO3-), irrigation indices including electrical conductivity (EC), sodium absorption ratio (SAR), sodium percentage (%Na), soluble sodium percentage (SSP), residual sodium carbonate (RSC), magnesium hazard (MH), Kelley's ration (KR), permeability index (PI) and heavy metals such as manganese (Mn), iron (Fe), zinc (Zn) and arsenic (As) have been employed for the 37 sample wells of the Damodar fan delta (DFD), India, which is a semi-critical agriculture-dominated region. Shannon's entropy method has been used to assign the weights of the different parameters for constructing the EIWQI. The results portray a spatial variation of the irrigation water quality in the DFD. The EIWQI revealed that 27.03%, 59.46%, 8.11%, 2.7% and 2.7% of the sample wells, respectively, contain excellent, good, moderate, poor and very poor quality of irrigation water. On the other hand, heavy metal pollution indices (modified degree of contamination, pollution load index, Nemerow index and modified heavy metal pollution index) show that 15-20% of sample wells of the DFD are contaminated by heavy metal pollution. The pockets of pollution are concentrated in the southwestern, northeastern and central parts of the DFD. The study found that the spatial variation in groundwater quality is controlled by the higher sodium concentration, carbonate weathering and expansion of agricultural and urban-industrial areas.

Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model

Fluoride (F-), and arsenic (As) in the groundwater cause health problems in developing countries, including Pakistan. We evaluated the occurrence, distribution, sources apportionment, and health hazards of F-, and As in the groundwater of Mardan, Pakistan. Therefore, groundwater samples (n = 130) were collected and then analyzed for F-, and As by ion-chromatography (IC) and Inductively-coupled plasma mass-spectrometry (ICP-MS). The F-, and As concentrations in groundwater were 0.7-14.4 mg/L and 0.5-11.2 µg/L. Relatively elevated F-, and As coexists with higher pH, Na+, HCO3-, SO4-2, and depleted Ca+2 due to fluoride, sulfide-bearing minerals, and anthropogenic inputs. Both F-, and/or As are transported in subsurface water through adsorption and desorption processes. Groundwater samples 45%, and 14.2% exceeded the WHO guidelines of 1.5 mg/L and 10 µg/L. Water quality indexing (WQI-model) declared that 35.7% samples are unfit for household purposes. Saturation and undersaturation of minerals showed precipitation and mineral dissolution. Groundwater contamination by PCA-MLR and PMF-model interpreted five factors. The fitting results and R2 values of PMF (0.52-0.99)>PCA-MLR (0.50-0.95) showed high accuracy of PMF-model. Human health risk assessment (HHRA-model) revealed high non-carcinogenic and carcinogenic risk for children than adults. The percentile recovery of F- and As was recorded 98%, and 95% with reproducibility ± 5% error.

Source identification and health risk assessment of heavy metals with mineralogy: the case of soils from a Chinese industrial and mining city

Understanding the precise sources of heavy metals (HMs) in soil and the contribution of these sources to health risks has positive effects in terms of risk management. This study focused on the HMs in the soil of five land uses in an industrial and mining city. The sources of HMs in soils were identified, and the soil mineralogical characteristics and health risks of HMs were discussed. The results showed that the HMs (Cu, Zn, Ni, Cd, Pb) found in the soil of the five land uses were affected by human activities. For example, the Cu in grassland, gobi beach, woodland, green belt, and farmland is 22.3, 3.5, 22.5, 16.7, and 21.3 times higher than the soil background values in Gansu Province, respectively. The Positive Matrix Factorization model (PMF) results revealed that traffic emissions and industrial and agricultural activities were the primary sources of HMs in the soil, with industrial sources accounting for the largest share at 55.79%. Furthermore, various characteristics proved that the studied HMs were closely related to smelting products. Concentration-oriented health risk assessments showed that HMs in the different soil types held non-carcinogenic and carcinogenic risks for children and adults. Contamination source-oriented health risk assessments of children and adults found that industrial activities controlled non-carcinogenic and carcinogenic risks. This study highlighted the critical effects of smelting on urban soil and the contribution of pollution sources to health risks. Furthermore, this work is significant in respect of the risk control of HMs in urban soils.

MLR index-based principal component analysis to investigate and monitor probable sources of groundwater pollution and quality in coastal areas: a case study in East India

Identifying groundwater contamination sources and supervising groundwater quality conditions are urgently needed to protect the groundwater resources of coastal areas like Contai of India, as communities here are heavily relying on groundwater which deteriorates progressively. So current research aims to address in detail about origins and influencing factors of groundwater contamination, status, and monitoring water quality by employing extremely useful leading technologies like principal component and factor analyses (PCA/FA), groundwater quality index (GWQI), and multiple linear regression (MLR) that helps to simplify complicated works instead of the conventional methods. Eight groundwater quality parameters were evaluated here, such as pH, TH (total hardness), Tur (turbidity), EC (electrical conductivity), TDS (total dissolved solids), Mn (manganese), Fe (iron), and Cl (chloride) for 38 sites. Three principal components with ~ 81% of the total variance were extracted from the PCA/FA analysis. The origin of maximum loadings of each factor is identified as a result of saline water, disintegration and leaching process, organic or else biogenic activities, and lithogenic or otherwise non-lithogenic links through percolating water. GWQI results show that ~ 87% of the samples fall into the good category and ~ 13% of the samples fall into the poor to very poor category. A model consisting of Tur, Fe, EC, Mn, TH, and Cl as independent parameters is more feasible and is proposed to predict GWQI obtained from MLR analysis. This MLR model also suggests that turbidity with the highest beta coefficient (0.820) is a key contributor relative to the entire groundwater class in this affected area. The findings relating to this research may support the designer and officials in monitoring and protecting coastal groundwater resources like selected areas.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

An integrated approach using multi-source data for effective pollution risk monitoring of urban rivers: a case study of Hangzhou

With the rapid urbanization of cities, water pollution has emerged as a major challenge to their sustainable development. To tackle this problem, we developed a river pollution risk management system that integrates multi-source data and includes risk identification, early warning, and control. We applied this system to a case study of urban rivers in Hangzhou, China. The results indicated that the measured and effect values of urban river water quality in Hangzhou were 1.01 and 1.14, respectively, indicating mild pollution levels. NH₃-N is the main risk factor, with poor supervision and land use being the main risk sources. River pollution risk in different drainage zones demonstrated stratification. Since 2012, the measured risk of water quality in Hangzhou has been decreasing, mainly concentrated in the built-up area; however, the effect risk has been increasing, especially in the new center and sub-center of the city. Based on these findings, three strategies for urban river pollution control are suggested: water ecology source rehabilitation, water environment process supervision, and water pollution end management. The results of this study extend the understanding of urban water environment risk and provide implications for sustainable urban planning.

Evaluation of non-carcinogenic causing health risks (NCHR) associated with exposure of fluoride and nitrate contaminated groundwater from a semi-arid region of south India

Groundwater is the foremost resource for drinking water supply in arid and semi-arid regions of the world, and also intake of contaminated drinking water is the major source for creating a several health risk for humans. To estimate the groundwater suitability for drinking and also to measure the non-carcinogenic health risk for infants, children, and adults, a total of 35 groundwater samples were collected from the semi-arid region of India and analyzed major ions including fluoride and nitrate. The results revealed that the concentration of fluoride ranges from 0.6 to 3.6 mg/L and is about 2.4 times higher than the maximum allowable limit of 1.5 mg/L for drinking water purposes. And nitrate contents varied from 17 to 120 mg/L in which 54.29% of the groundwater samples exceeded the recommended limit of 50 mg/L. The estimated individual non-carcinogenic health risk (INCHR) frequency is evidently displayed that intake of higher concentration of nitrate creates the greater detrimental health effects than fluoride. The contribution of individual non-carcinogenic health risk (INCHR) of nitrate is greater detrimental health effects than the fluoride. The results of total non-carcinogenic health risk (TNCHR) reflect the infants and also children were found to be more susceptible towards fluoride and nitrate-associated health risks in the investigated region. Fluoride-bearing minerals and different anthropogenic sources such as septic tank leakages, nitrogen fertilizers, domestic, agricultural, and animal wastes played a vital role in groundwater pollution and thereby non-carcinogenic human health risks. Therefore, a proper sustainable future plan is most important to mitigate the fluoride and nitrate contamination in the groundwater of the study region.

Method for establishing soil contaminant discharge inventory: An arsenic-contaminated site case study

The existing method to survey site pollution is generally based on soil-groundwater sampling and instrumental analysis, which enables us to access the detailed soil pollution status while lacking quantitative association with industrial activities. It is urgent to understand contaminant discharge modes and establish a discharge inventory for achieving process-targeted pollution control. This study took a 40-year phosphate fertilizer-sulfuric acid site as an example and constructed a contaminant tracing method based on on-site investigations and detailed industrial data. These investigations and data were combined to determine the characteristic pollutant of this site, arsenic. And the calculation process of four-pathway pollution modes (atmospheric deposition, wastewater, solid waste leaching, and storage dripping) is derived from the existing acceptance criteria and risk assessment guidelines. They are set to calculate the arsenic's factory-to-soil discharge flux. The absent process contaminant release information and parameters, such as discharge coefficient, were obtained from soil-groundwater pollution control standards and discharge handbooks. It was found that the high concentration of arsenic (around 1930 mg g^-1) was preponderantly caused by sulfur-iron slag and tailing leaching (96.19%), while the other pathways accounted for only 0.13% (atmospheric deposition), 3.59% (wastewater) and 0.09% (storage tank). Results were verified by the measured arsenic concentration, and the difference was +16.29%, which was acceptable. Finally, a contaminant discharge inventory was established with high-resolution spatial distribution and time-scale (historical discharge) evolution. The innovation of this study lies in the preliminary construction of a method for formulating soil discharge inventory. This study would contribute to the refined management of site pollution and reduction of source contaminants discharge. In addition, it will help infer the pollution condition of sites that are difficult to sample so as to help the government achieve precise source control.

Fuzzy logic-based health risk assessment of fluoride in groundwater used as drinking source in Sira region, Tumkur, India

Fluoride contamination in drinking water is a serious public health concern across the world, and more than 90 million people in India are affected by health risks associated with fluoride. Though the fatality due to fluoride chronic toxicity is uncommon, the exposure to fluoride at different concentration levels shows various adverse health effects such as dental and skeletal fluorosis, multiorgan failure, cognitive and behavioural effects. Hence, the objectives of the present study are to understand the hydrogeochemistry and drinking water suitability of groundwater of the Sira region, Karnataka, India, and to understand the occurrence of fluoride and its health risks using the United State Environmental Protection Agency (US EPA) method and fuzzy logic concepts. Forty-six samples were collected from each pre and post-monsoon season. The hydrogeochemistry studied through Chadha's diagram and Gibb's diagram indicated that the groundwater in this region is of Na-Cl type and the hydrogeochemistry is majorly controlled by rock-water interaction and followed by evaporative dominance. Water quality parameters were compared with drinking water standards guidelines, and the results showed that around 50% of the samples were contaminated with fluoride. The occurrence of high levels of fluoride in the study region is associated to the presence of granitic rocks and it is influenced by high pH and low calcium dissolution in the groundwater. Based on US EPA method, the order of population group under the risk of dental and skeletal fluorosis, is children > adolescents > adults. A fuzzy inference system model is developed to assess the health risk due to fluoride and its output gives severity levels of each type of health risk, i.e. dental caries, dental fluorosis and skeletal fluorosis. The results of the application of the fuzzy inference system model in the Sira region showed that the children (< 8 Yr.) are more susceptible to the moderate risk of dental caries, dental fluorosis and skeletal fluorosis. Whereas adolescents (8-18 Yr.) and adults (> 18 Yr.) are less susceptible to low-very low risk. Hence, health risks associated with fluoride can be better addressed with the help of a fuzzy inference system model which can be used for more reliable and grounded results to improve the quality of decision-making.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Assessment of the water quality of Bartın Kışla (Kozcağız) Dam by using geographical information system (GIS) and water quality indices (WQI)

This study was conducted to evaluate the water quality of the Kışla (Kozcagiz) Dam located in the province of Bartın in the Western Black Sea Region of Turkey. Water samples were collected monthly from 5 stations for a year and analyses were conducted using 27 water quality parameters. The quality of the dam and the water quality parameters were evaluated using different indices in comparison to the limits determined according to the standards set by the World Health Organization (WHO) and Turkey Surface Water Quality Regulation (SWQR). Water quality index (WQI), organic pollution index (OPI), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), permeability index (PI), and metal pollution index (MPI) were calculated and spatial assessment of pollution was made seasonally by making use of the geographic information system (GIS). A piper diagram was used in determining the facies of the water. The types of Ca²⁺-Mg²⁺-HCO₃^- predominated in the dam water. Moreover, statistical analyses were used in order to determine if there was a significant difference between the parameters. WQI results generally indicate that the water quality was good in all seasons; however, only in the autumn, sampling points S1 (101.58), S2 (100.59), S4 (102.31), and S5 (102.12) showed poor water characteristics. According to the OPI results, while winter and spring yielded good water quality, summer samples were lightly polluted and autumn samples were moderately polluted. Given SAR results, it can be stated that the water of Kışla Dam could be used as irrigation water. Considering the standards specified by WHO and SWQR, the parameters generally exceeded the threshold values, but the water hardness value was much higher than 100 mg L^-1 specified in SWQR as very hard water. The principal component analysis (PCA) results showed that the pollution sources were anthropogenic. Thus, for the dam water to not be affected by the increasing pollutant factors, it should be continuously monitored, and attention should be paid to the irrigation methods used in agricultural activities.

Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin

The current research is focused on detecting a river basin suitable for agriculture and priority for management using a new clustering tool of groundwater quality with fuzzy logic technique in R and Geographical Information System. A new fuzzy clustering-soft computing technique has been executed to determine the different hydrochemical zones considering 13 essential parameters such as electrical conductivity, hardness, chloride, sodium adsorption ratio, residual sodium carbonate, soluble sodium percent, magnesium hazard, permeability index, potential salinity, residual sodium bicarbonate, Kelly's ratio, synthetic harmful coefficient, and exchangeable sodium percentage. The derived fuzzy C-mean clustering (FCM) outperformed other available hard computing techniques like hierarchical clustering, K-means clustering, and agglomerative clustering. It divided the sampling sites into 2 clustering groups (FCM I and FCM II) which has been validated using fuzzy silhouette index (0.85), the partition coefficient (0.76), the partial entropy (0.68), and the modified partition coefficient (0.52). The hydrogeochemical analysis confirmed that the rock-water interaction, chemical weathering, and ion exchange process are predominant in the aquifer system of the study area. According to the correlation plots, the studied groundwater samples largely evolved from [Formula: see text], mixed [Formula: see text] types, and [Formula: see text] types. The spatial distribution map and the hydrochemical analysis also gives a clear depiction of the fluoride (> 1.0 mg/l) and high iron (> 0.3 mg/l) contamination in groundwater quality, making it unsuitable for both drinking and irrigation. A fuzzy EDAS priority map has been prepared based on all the irrigation suitability parameters which concludes that the groundwater at the upstream and downstream section of the basin requires the most attention. Based on the highest priority for management, five zones have been delineated: very high (5.98%), high (22.31%), medium (16.39%), low (32.30%), and very low (23.02). The findings of this study will be beneficial to planners and policymakers as they can develop schemes to solve similar problems across the country.

Emerging contaminants in the northwest area of the Tai Lake Basin, China: Spatial autocorrelation analysis for source apportionment and wastewater-based epidemiological analysis

In the present study, 60 emerging contaminants (ECs) were detected from 88 target compounds in the district of Wujin, which is the northwest area of Tai Lake Basin, China. Among them, CF (caffeine), a type of PhAC (pharmaceutically active compound), was detected as the pollutant with the highest concentration. It was observed that the removal efficiencies of PFASs (per-/polyfluoroalkyl substances) in wastewater treatment plants were lower than those of pesticides; further, those of pesticides were lower than those of PhACs. Based on the spatial agglomeration estimated by the spatial autocorrelation model, the probable sources of 28 contaminants were identified. This model provided a new perspective that would help to clarify the location of sources with high accuracy. The point sources of 6 PFASs and 14 PhACs were successfully found; in contrast, the main source of pesticides was identified as an agricultural nonpoint source. Finally, the potential risks of the ECs were also assessed in this study, including their aquatic ecological risks and human exposure risks. It was concluded that pesticides generally had the highest ecological risk, followed by PFASs and PhACs. To evaluate the population risk of pesticides, the wastewater-based epidemiological model was extended to back-calculate the per capita pesticide consumption, which was 0.22 g d^-1 (10³capita)^-1.

Buffer zone-based trace elements indicating the impact of human activities on karst urban groundwater

The tanglesome allocation of landscape types at various spatial dimensions is an important component influencing the quality of groundwater environment in karst cities. Trace elements can be used as indicators of the extent of impact on groundwater which is an effective means of tracing groundwater contamination. In this study, we studied the influence of landscape patterns on trace elements in groundwater of typical karst cities in Southwest China (Guiyang City) on a multi-spatial scale by using multivariate statistical analysis. According to the sampling points, buffer zone scales with different radii (500 m, 1000 m, 1500 m, and 4000 m) were established to quantify the land use model. There are suburban and urban differences in trace element content. The city center has higher levels of trace elements compared to suburban areas, especially Li, Ni, Tl, Cu, Sr, Co, As, and Mn. In addition, the outcomes of the multiple linear regression had shown that the size effect of the association from landscape pattern to trace elements varies with different indicators and parameters. The results of redundancy analysis showed an overall change in trace elements was better interpreted by the landscape pattern of the 1500 m-scale buffer. At the same time, at the 1500 m scale, Ni, Tl, Cu, Co, As, Cr, Sr, Li, and Mn were positively correlated with the urban landscape index (4LPI, 4LSI), influenced by urban anthropogenic activities, while Cd, Zn, and Pb were positively correlated with the cropland landscape index (1AI, 1LPI), influenced by agricultural activities. This study indicates that trace elements are a reliable indicator for tracing groundwater contamination. The buffer zone can reflect the extent of urban impacts on groundwater and provide a new and effective analytical tool for groundwater management.

Emerging pollutants characterization, mitigation and toxicity assessment of sewage wastewater treatment plant- India: A case study

India faces major challenges related to fresh water supply and the reuse of treated wastewater is an important strategy to combat water scarcity. Wastewater in Gorakhpur, India, is treated by a decentralised wastewater treatment system (DEWATS) and the treated wastewater is reused in the rural area. This research provides important scientific data that ascertain the safety of wastewater reuse in this region. The physicochemical characteristics, including pigment, ionic strength, BOD, COD, TDS, TSS, salinity, total N, ammonium N, phenolics, heavy metals, and sulphate, of the inlet and outlet sewage water samples (SWWs) from a wastewater treatment facility was conducted. These parameters were found to be significantly over the national limit. The inlet and outlet samples were further characterised by using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR) and gas chromatography-mass spectrometry (GC-MS). SEM showed microstructure and the presence of various metals, polymers, and other co-pollutants in the samples and FT-IR confirmed the presence of aldehyde, hard liquor, and nitrogen molecules in the SWW's discharge. Many endocrine disruptors and potentially mutagenic chemical substances (e.g., Dodecane, Hexadecane, Octadecane etc.) were identified in the outlet SWW by the GC-MS analysis. Toxicity of the SWW was assessed via phytotoxicity assessment using Phaseolus mungo L. and histological and biochemical analyses of Heteropneustes fossilis in a 24-h exposure study. Results confirmed the wastewater was harmful and inhibited germination of P. mungo L. by >80% compared to the control, destroyed gill laminae and significantly increased oxidative stress (above 5% increase in catalase production) in H. fossilis. This work clearly demonstrated that the quality of the treated wastewater in Gorakhpur was poor and immediate action is needed before it can be discharged or reused.

Source apportionment and ecological health risks assessment from major ions, metalloids and trace elements in multi-aquifer groundwater near the Sunan mine area, Eastern China

Evaluating the ecological health risks created by major ions, metalloids and trace elements concentrations in groundwater and pollution sources were essential to effectively protect groundwater resources. For this study, A total of 93 samples were collected from multiple aquifers in the Sunan mining area, eastern China. The Positive matrix factorization (PMF) model results revealed the following sources, in percentages. The Quaternary loose aquifer (QLA) water includes CaMg mineral dissolution (30.3 %), salinity (28.2 %), metal industrial wastewater (26.3 %), iron and manganese minerals (8.0 %) and coal gangue (7.2 %). The Permian fractured sandstone aquifer (PFA) water includes CaMg mineral dissolution sources (29.8 %), mine wastewater (28.6 %), aluminosilicate (21.6 %) and pyrite source (20.0 %). The Carbonifer fractured limestone aquifer (CFA) water includes and mine wastewater (34.2 %), CaMg mineral dissolution (25.4 %), pyrite (22.6 %) and aluminosilicate (17.7 %). The Ordovician fractured limestone aquifer (OFA) water includes manganese and aluminum metal minerals (27.9 %), halite dissolution materials (24.9 %), industrial and agricultural waste water (24.0 %) and calcium‑magnesium minerals (23.2 %). A PMF-based assessment of ecological health risk indicates that the concentrations of elements As and Co are the dominant elements impacting non-carcinogenic and carcinogenic risks; and As, Cr, and Cu are the dominant elements impacting potential ecological risks. These mainly originate from geological sources, coal gangue sources, mine drainage sources and agricultural sewage discharge sources. The study showed the sources of groundwater pollution in multiple aquifers and their priority treatment areas, providing a basis for groundwater management and protection.

Human health risk assessment and pollution index of groundwater in Jammu plains of India: A geospatial approach

To examine the drinking water suitability as well as to study the influence of local lithology in controlling groundwater chemistry a study has been carried out in the Jammu plains of India by using 50 groundwater samples during the post-monsoon (POM) and pre-monsoon (PRM) seasons. The groundwater samples are found to be dominated by Mg-Ca-HCO₃, and Ca-Mg-HCO_3, types. Besides this, the Pollution Index of Groundwater (PIG) was also calculated to assess the overall groundwater quality of the study area. As per the PIG, the groundwater quality of the study is suitable for domestic utilization except for a few samples (2%) which need conventional treatment in order to make the groundwater resources potable. Based on rock water interaction, there is a considerable variation in the POM and PRM seasons, which indicates the role of weathering and dissolution of rock minerals. The multivariate statistical analysis reveals that the lithogenic factors, such as rock-water interactions and weathering of carbonate-bearing rocks, are predominantly controlling groundwater chemistry. Further, trace elements such as As, Cu, Cd, Fe, Mn, and Zn were also analyzed to determine the Human Health Risk Assessment (HHRA) in order to know about the carcinogenic risk in adults and children in the study area.

Potential health risk assessment, spatio-temporal hydrochemistry and groundwater quality of Yamuna river basin, Northern India

Groundwater which is an essential source of freshwater for various domestic, agricultural, industrial applications is facing a severe deterioration in quality due to demographic pressure and intense industrial activities. Present study appraises the influence of human induced activities on groundwater quality of Agra-Firozabad industrial belts of Western Uttar Pradesh, Yamuna basin, India. The maximum concentrations of metals and anions found during pre and post monsoon are as follows: Lead 0.302; 0.086, calcium 672; 1260, magnesium 215; 16.8, cadmium 0.0; 0.066, chromium 0.016; 0.005, manganese 0.340; 0.076, nickel 0.044; 0.028, sulfate 514; 286, nitrate 66.7; 3.56 and fluoride 1.17; 2.02 mg/L respectively. Based on results of Water Quality Index, groundwater samples were classified under 'Poor water' category in 34.2% and 52.63% during pre and post-monsoon period, respectively. Accordingly, higher concentrations of bicarbonate and sulfate might have attributed to excess hardness, instrumental in making it unsuitable for industrial usage. However, values of Percent Sodium, Sodium Adsorption Ratio, Magnesium Hazard and Permeability Index signified that groundwater from majority of locations was fit for agricultural use. Health risk assessment studies revealed that children consuming polluted water were affected more as compared to adults. Timely action and strict compliance of regulation is recommended towards groundwater management for defined usage to avert severe health effects and to meet sustainable development goals.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Potential Risk, Spatial Distribution, and Soil Identification of Potentially Toxic Elements in Lycium barbarum L. (Wolfberry) Fruits and Soil System in Ningxia, China

Eight potentially toxic elements (PTEs, including nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb), chromium (Cr), and mercury (Hg)) in Lycium barbarum L. (wolfberries) and the associated root soil from a genuine producing area were analyzed. The potential ecological risk of PTEs in the soil and the health risk of PTEs through wolfberry consumption were determined. Geostatistical methods were used to predict the PTE concentrations in the wolfberries and soil. Positive matrix factorization (PMF) was applied to identify the source of PTEs in the soil. The PTE concentrations in the soils were within the standard limits, and Cd in the wolfberries exceeded the standard limit at only one site. The bioconcentration factors (BCF) order for the different PTEs was Cd > Cu > 1 > Zn > Cr > As > Ni > Pb, indicating that Cd and Cu were highly accumulated in wolfberries. The multiple regression models for Ni, Cu, Zn, As, Pb, and Cr concentrations in the wolfberries exhibited good correlations (p < 0.1). The ecological risk for Hg in the soil was high, whereas the risks for the remaining PTEs were mostly medium or low. Health risks for inhabitants through wolfberry consumption were not obvious. The spatial distributions of the PTEs in the soil differed from the PTE concentrations in the wolfberries. Source identification results were in the order of natural source (48.2%) > industrial activity source (27.8%) > agricultural activity source (14.5%) > transportation source (9.5%). The present study can guide the site selection of wolfberry cultivation and ensure the safety of wolfberry products when considering PTE contamination.

The plight of Najafgarh drain in NCT of Delhi, India: assessment of the sources, statistical water quality evaluation, and fate of water pollutants

The Najafgarh drain is the first major drain that joins the Yamuna River at Wazirabad in Delhi, India, and is known to contribute to the maximum pollution load to this river. The drain is originally an extension of the Sahibi River and was intentionally constructed as a canal to carry stormwater, but presently, it is carrying more of sewage, agricultural, and industrial effluents received through various small and large secondary drains. The present study has analyzed the water quality status of this interconnected system, i.e., the Najafgarh drain, its associated secondary drains, and the Yamuna River for physicochemical parameters (n = 16), microbiological parameter (n = 1), and heavy metal concentrations (n = 8). The analysis of the surface water samples collected during pre- and post-monsoon seasons showed that secondary drain discharges significantly impacted the water quality of the Najafgarh drain, which in turn affected the Yamuna River. Out of the eight selected secondary drains for this study, the Goyla dairy outlet came out as the most polluted site in terms of organic pollutants while the Basaidarapur drain was loaded with heavy metal contaminants. Statistical tools comprising hierarchical cluster analysis (HCA), Pearson's correlation, and principal component analysis (PCA) were further implemented on the water quality dataset for a better understanding of the possible sources of contamination for organic and inorganic pollutants in the selected sampling sites. The present study, thus, might help in providing key highlights to the policymakers for effective regulation and management of the point source discharges in Najafgarh drain, which will ultimately restrict its pollution loadings in Yamuna River, Delhi, and also help in the restoration of this important water body.

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.