Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%-3.54%) characterized by NO3-N, phytoplankton source (38.43%-50.05%) indicated by chl and PC, agricultural cultivation (16.1%-17.63%) marked by TP and CODMn, industrial wastewater (17.64%-25.1%) primarily associated with TN, and natural source (10.32%-13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

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.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36 mg/L and 44.72 mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Arsenic-contaminated groundwater of the Western Banat (Pannonian basin): Hydrogeochemical appraisal, pollution source apportionment, and Monte Carlo simulation of source-specific health risks

Due to rapid urbanization and industrial growth, groundwater globally is continuously deteriorating, posing significant health risks to humans. This study employed a comprehensive methodology to analyze groundwater in the Western Banat Plain (Serbia). Using Piper and Gibbs plots, hydrogeochemistry was assessed, while the entropy-weighted water quality index (EWQI) was used to evaluate groundwater quality. Pollution sources were identified using positive matrix factorization (PMF) accompanied by Pearson correlation and hierarchical cluster analysis, while Monte Carlo simulation assessed health risks associated with groundwater consumption. Results showed that groundwater, mainly Ca-Mg-HCO3 type, is mostly suitable for drinking. Geogenic pollution, agricultural activities, and sewage were major pollution sources. Consumption of contaminated groundwater poses serious non-carcinogenic and carcinogenic health risks. Additionally, arsenic from geogenic source was found to be the main health risks contributor, considering its worryingly elevated concentration, ranging up to 364 μg/L. These findings will be valuable for decision-makers and researchers in managing groundwater vulnerability. PRACTITIONER POINTS: Groundwater is severely contaminated with As in the northern part of the study area. The predominant hydrochemical type of groundwater in the area is Ca-Mg-HCO3. The PMF method apportioned three groundwater pollution sources. Monte Carlo identified rock dissolution as the primary health risk contributor. Health risks and mortality in the study area are positively correlated.

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

Pollution and Health Risk Assessment of Potentially Toxic Elements in Groundwater in the Kǒnqi River Basin (NW China)

Potentially toxic elements (PTEs) pose a significant threat to the groundwater system and human health. Pollution and the potential risks of PTEs in groundwater in the Kǒnqi River Basin (KRB) of the northwest arid zones of China are still unknown. A total of 53 groundwater samples containing eight PTEs (Al, As, Cd, Cu, Mn, Pb, Se, and Zn) were collected from the KRB, and the pollution levels and probabilistic health risks caused by PTEs were assessed based on the Nemerow Index (NI) method and the health risk assessment model. The results revealed that the mean contents of Al, As, and Mn in the groundwater surpassed the Class III threshold of the Standard for Groundwater Quality of China. The overall pollution levels of the investigated PTEs in the groundwater fall into the moderate pollution level. The spatial distributions of contents and pollution levels of different PTEs in the groundwater were different. Health risk assessment indicated that all the investigated PTEs in groundwater in the KRB may pose a probabilistic non-carcinogenic health risk for both adults and children. Moreover, As may pose a non-carcinogenic health risk, whereas the non-carcinogenic health risk posed by the other seven PTEs in groundwater will not have the non-carcinogenic risks. Furthermore, As falls into the low carcinogenic risk level, whereas Cd falls into the very low carcinogenic risk level. Overall, As was confirmed as the dominant pollution factor and health risk factor of groundwater in the KRB. Results of this study provide the scientific basis needed for the prevention and control of PTE pollution in groundwater.

Hydrogeochemical characteristics, stable isotopes, positive matrix factorization, source apportionment, and health risk of high fluoride groundwater in semiarid region

Chronic exposure to high fluoride (F-) levels in groundwater causes community fluorosis and non-carcinogenic health concerns in local people. This study described occurrence, dental fluorosis, and origin of high F-groundwater using δ2H and δ18O isotopes at semiarid Gilgit, Pakistan. Therefore, groundwater (n = 85) was collected and analyzed for F- concentrations using ion-chromatography. The lowest F- concentration was 0.4 mg/L and the highest 6.8 mg/L. F- enrichment is linked with higher pH, NaHCO3, NaCl, δ18O, Na+, HCO3-, and depleted Ca+2 aquifers. The depleted δ2H and δ18O values indicated precipitation and higher values represented the evaporation effect. Thermodynamic considerations of fluorite minerals showed undersaturation, revealing that other F-bearing minerals viz. biotite and muscovite were essential in F- enrichment in groundwater. Positive matrix factorization (PMF) and principal component analysis multilinear regression (PCAMLR) models were used to determine four-factor solutions for groundwater contamination. The PMF model results were accurate and reliable compared with those of the PCAMLR model, which compiled the overlapping results. Therefore, 28.3% exceeded the WHO permissible limit of 1.5 mg/L F-. Photomicrographs of granite rocks showed enriched F-bearing minerals that trigger F- in groundwater. The community fluorosis index values were recorded at > 0.6, revealing community fluorosis and unsuitability of groundwater for drinking.

Anthropogenic processes drive spatiotemporal variability of sulfate in groundwater from a multi-aquifer system: Dilution caused by mine drainage

The water quality evolution of surface and groundwater caused by mining activities and mine drainage is a grave public concern worldwide. To explore the effect of mine drainage on sulfate evolution, a multi-aquifer system in a typical coal mine in Northwest China was investigated using multi-isotopes (δ34SSO4, δ18OSO4, δD, and δ18Owater) and Positive Matrix Factorization (PMF) model. Before mining, the Jurassic aquifer was dominated by gypsum dissolution, accompanied by cation exchange and bacterial sulfate reduction, and the phreatic aquifers and surface water were dominated by carbonate dissolution. Significant increase in sulfate in phreatic aquifers due to mine drainage during the early stages of coal mining. However, in contrast to common mining activities that result in sulfate contamination from pyrite oxidation, mine drainage in this mining area resulted in accelerated groundwater flow and enhanced hydraulic connections between the phreatic and confined aquifers. Dilution caused by the altered groundwater flow system controlled the evolution of sulphate, leading to different degrees of sulfate decrease in all aquifers and surface water. As the hydrogeochemical characteristic of Jurassic aquifer evolved toward phreatic aquifer, this factor should be considered to avoid misjudgment in determining the source of mine water intrusion. The study reveals the hydrogeochemical evolution induced by mine drainage, which could benefit to the management of groundwater resources in mining areas.

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.

Deciphering groundwater pollution in the Lower Anayari Catchment: insights from using δ2H, δ18O, PMF, and APCS-MLR receptor model

This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ2H and δ18O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na+ and K+, complexometric titration for Ca2+, ion chromatography for Cl-, F-, NO3-, SO42-, and PO43-, and atomic absorption spectrometry for Mg2+, Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na+ has the highest average concentration of 63.0 mg/L, while Mg2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl- has the highest mean concentration of 18.7 mg/L, and Fl- has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ18O and δ2H were - 3.64‰ and - 20.7‰, respectively; the average values for rainwater isotopic composition were - 3.41‰ for δ18O and - 17.4‰ for δ2H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO3-, and PO43-. This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management.

Agricultural nonpoint source pollutant loads into water bodies in a typical basin in the middle reach of the Yangtze River

Phosphorus and nitrogen pollution from agricultural nonpoint sources heavily burden the water environment, and a scientific calculating system is needed to calculate the pollutant loads under the water pollution treatment. This study established a system to calculate the coefficients of agricultural nonpoint source pollutants into water bodies in the subregion in Poyang Lake basin in the middle reach of the Yangtze River combining with multiple driving factors. Validation results showed that the errors of the typical unit were 30.58% for total phosphorus (TP), 13.43% for total nitrogen (TN) and 33.93% for ammonia nitrogen (NH3-N), respectively. The errors of the subregion were 26.92% for TP, 31.83% for TN and 29.15% for NH3-N, respectively. Besides, there were higher TP and TN loads in the east area of subregion in both units and county scales, which indicated the heavy phosphorus and nitrogen burden on water environment. In contrast, higher NH3-N loads occurred in the north area of subregion. The establishment of coefficient system for agricultural pollutants into water bodies and the pollutant loads calculation would provide enlightenment for water pollution treatment and agricultural nonpoint source pollution controlling.

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.

Distribution, source investigation, and risk assessment of topsoil heavy metals in areas with intensive anthropogenic activities using the positive matrix factorization (PMF) model coupled with self-organizing map (SOM)

Over the past decade, heavy metal (HMs) contamination in soil environments has become severe worldwide. However, their resulting ecological and health risks remained elusive across a variety of soil ecosystems due to the complicated distributions and sources. This study investigated the HMs (Cr, As, Cu, Pb, Zn, Ni, Cd, and Hg) in areas with multi-mineral resources and intensive agricultural activities to study their distribution and source apportionment using a positive matrix factorization (PMF) model coupled with self-organizing map (SOM). The potential ecological and health risks were assessed in terms of distinct sources of HMs. The results disclosed that the spatial distribution of HM contaminations in the topsoil was region-dependent, primarily located in areas with high population intensity. The geo‑accumulation index (Igeo) and enrichment factor (EF) values collectively displayed that the topsoils were severely contaminated by Hg, Cu, and Pb, particularly in residential farmland areas. The comprehensive analysis combined with PMF and SOM identified both geogenic and anthropogenic sources of HMs including natural, agricultural, mining, and mixed sources (caused by multi-anthropogenic factors), accounting for 24.9%, 22.6%, 45.9%, and 6.6% contribution rates, respectively. The potential ecological risk was predominantly due to the enrichment of Hg, followed by Cd. The non-carcinogenic risks were mostly below the acceptable risk level, while the potential carcinogenic health risks caused by As and Cr should be paid prime attention to, particularly for children. In addition to the 40% geogenic sources, agricultural activities contributed to 30% of the non-carcinogenic risk, whereas mining activities contributed to nearly half of the carcinogenic health risks.

Spatial distribution and driving factors of groundwater chemistry and pollution in an oil production region in the Northwest China

Concerns have been raised on the deterioration of groundwater quality associated with anthropogenic impacts such as oil extraction and overuse of fertilizers. However, it is still difficult to identify groundwater chemistry/pollution and driving forces in regional scale since both natural and anthropogenic factors are spatially complex. This study, combining self-organizing map (SOM, combined with K-means algorithm) and principal component analysis (PCA), attempted to characterize the spatial variability and driving factors of shallow groundwater hydrochemistry in Yan'an area of Northwest China where diverse land use types (e.g., various oil production sites and agriculture lands) coexist. Based on the major and trace elements (e.g., Ba, Sr, Br, Li) and total petroleum hydrocarbons (TPH), groundwater samples were classified into four clusters with obvious geographical and hydrochemical characteristics by using SOM - K-means clustering: heavily oil-contaminated groundwater (Cluster 1), slightly oil-contaminated groundwater (Cluster 2), least-polluted groundwater (Cluster 3) and NO₃^- contaminated groundwater (Cluster 4). Noteworthily, Cluster 1, located in a river valley with long-term oil exploitation, had the highest levels of TPH and potentially toxic elements (Ba, Sr). Multivariate analysis combined with ion ratios analysis were used to determine the causes of these clusters. The results revealed that the hydrochemical compositions in Cluster 1 were mainly caused by the oil-related produced water intrusion into the upper aquifer. The elevated NO₃^- concentrations in Cluster 4 were induced by agricultural activities. Water-rock interactions (e.g., carbonate as well as silicate dissolution and precipitation) also shaped the chemical constituents of groundwater in clusters 2, 3, and 4. In addition, SO₄^2--related processes (redox, precipitation of sulfate minerals) also affected groundwater chemical compositions in Cluster 1. This work provides the insight into the driving factors of groundwater chemistry and pollution which could contribute to groundwater sustainable management and protection in this area and other oil extraction areas.

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.

Spatial-temporal distribution, source identification, risk assessment and water quality assessment of trace elements in the surface water of typical tributary in Yangtze River delta, China

As China's first cross-province ecological compensation mechanism pilot area in the hinterland of the Yangtze River Delta, Xin'an River has been hotspot in the study of rational utilization of ecological resources, and the functional value of its ecosystem services has been widely concerned. As an important tributary of the upper reaches of Xin'an River, Fengle River may affect the whole basin. The spatial-temporal distributions, occurrence, water quality and risk assessment of trace elements were studied in Fengle River in three seasons. High element concentrations were found in the downstream. Traceability models results showed that the major sources of trace elements were related to different human activities. The water quality was worse downstream in the wet season, and was more suitable for irrigation in the dry season. Risk assessment results showed that Zn, Cu, Mn, Co, and As were able to pose the risk to the ecological environment and human.

Spatial variability of source contributions to nitrate in regional groundwater based on the positive matrix factorization and Bayesian model

Groundwater nitrate (NO₃^-) pollution has attracted widespread attention; however, accurately evaluating the sources of NO₃^- and their contribution patterns in regional groundwater is difficult in areas with multiple sources and complex hydrogeological conditions. In this study, 161 groundwater samples were collected from the Poyang Lake Basin for hydrochemical and dual NO₃^- isotope analyses to explore the sources of NO₃^- and their spatial contribution using the Positive Matrix Factorization (PMF) and Bayesian stable isotope mixing (MixSIAR) models. The results revealed that the enrichment of NO₃^- in groundwater was primarily attributed to sewage/manure (SM), which accounted for more than 50 %. The contributions of nitrogen fertilizer and soil organic nitrogen should also be considered. Groundwater NO₃^- sources showed obvious spatial differences in contributions. Regions with large contributions of SM (>90 %) were located in the southeastern part of the study area and downstream of Nanchang, which are areas with relatively high population density. Nitrogen fertilizer and soil organic nitrogen showed concentrated contributions in paddy soil in the lower reaches of the Gan and Rao Rivers, and these accumulations were mainly driven by the soil type, land use type, and topography. This study provides insight into groundwater NO₃^- contamination on a regional scale.

Identification of pollutant sources and evaluation of water quality improvement alternatives of a large river

While pollutants are the most important factors for the deterioration of surface water quality, the identification of major pollutant sources for rivers is challenging, especially in areas with diverse land covers and multiple pollutant inputs. This study aims to identify the significant pollutant sources from the tributaries that are affecting the water quality and identify the limiting nutrient for algal growth in the Geum river to provide a management alternative for an improvement of the water quality. The positive matrix factorization (PMF) was applied for pollutant source identification and apportionment of the two major tributaries, Gab-cheon and Miho-cheon. Positive matrix factorization identifies three and two major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plants, urban, and agricultural pollution are identified as major pollutant sources. Furthermore, for Miho-cheon, agricultural and urban pollution were identified as major pollutant sources. Total phosphorus (TP) is also identified as a limiting nutrient for algal growth in the Geum river. Water quality control scenarios were formulated and improvement of water quality in the river locations was simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC). Scenario results were evaluated using a water quality index. The reduction of total phosphorus (TP) from the tributaries has greatly improved the water quality, especially algal bloom in the downstream stations.

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.

Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geo-accumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run.

Water Quality Evaluation and Pollution Source Apportionment of Surface Water in a Major City in Southeast China Using Multi-Statistical Analyses and Machine Learning Models

The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011-2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQI_RF model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQI_RF and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies.

Statistical assessment of seasonal variations in water quality for different regions in Lake Van (Türkiye)

On earth, surface water bodies interact and change with the natural ecosystems. These surface waters and water quality may be adversely affected due to different factors. To analyze the effects, parameters indicating water pollution and quality and the possible causes of these parameters should be examined. In addition, environmental pollution issues should be controlled by taking measures. The most important surface water body in the province of Van, located in the east of Türkiye, is the biggest soda Lake Van. The population density around the lake, human polluting factors, unconscious beach use, inadequate wastewater treatment, agriculture and livestock activities, small-scale industrial areas, and chemicals used create a pollution effect. In the study, data were obtained during year of 2018 from six important sampling points around Lake Van and from the middle of the lake. Twenty-seven water quality parameters were analyzed separately and together. These variables' yearly values were evaluated with Turkish Surface Water Quality Regulation (TSWQR, 2015). As a result, these points were determined to have class I in terms of water parameters according to the seasonal data. The basic descriptive statistics were compared with the regulation, and max, mean, and min values were examined. Data analyzed were done with probability-normality, trend analysis, correlation, and regression methods. The results of this study are that general parameters were normal and the quality of the six points continued to be similar. Na⁺, Cl^-, salinity, and TDS were highly correlated, while DO and F were high matrix value parameters. EC, TDS, and SS regression equations provided high correlation parameters.

Characteristics, Possible Origins, and Health Risk Assessment of Trace Elements in Surface Waters of the Han River Watershed, South Korea

To safeguard aquatic environments in and around the Han River watershed in South Korea, a multivariate statistical evaluation of trace elements, a trace element concentration analysis and source determination, and a human health risk assessment were conducted on 10 trace elements at 25 sites. The results demonstrated that the Han River watershed was mainly affected by anthropogenic activities (traffic/industrial activity). The range of concentrations was arranged in descending order: Fe (217.13 ± 301.03 µg/L) > Mn (102.36 ± 153.04 µg/L) > Zn (23.33 ± 79.63 µg/L) > Ba (29.05 ± 12.37 µg/L) > Ni (5.14 ± 11.57 µg/L) > Cu (3.80 ± 3.56 µg/L) > Pb (0.46 ± 0.52 µg/L) > Se (0.06 ± 0.04 µg/L) > Cd (0.01 ± 0.01 µg/L) > Ag (0.004 ± 0.013 µg/L). The hazard index values of trace elements in surface water for combined pathways (ingestion and dermal contact) were < 1.0 for both adults and children, indicating no possible human health hazards. The estimated total cancer risk did not exceed the acceptable limit (1 × 10^-4) for adults and children. The findings of this study provide data-driven guidelines for water environment policy decisions in the study area.

Characteristics analysis of water pollutants in Cihu Lake, China, based on a multivariate statistical analysis method

In order to understand the sources of pollutants and the temporal and spatial distribution characteristics of the water quality in Cihu Lake, China, the monitoring data of seven water quality indicators from 12 sampling sites from 2015 to 2019 were selected, and the temporal and spatial variation laws of the water quality and pollution sources were analyzed by the use of the multivariate statistical analysis method. The results show that nitrogen and phosphorus pollution in the lake is dominant. The average concentrations of total nitrogen (TN) and total phosphorus (TP) exceed the surface water quality Class III standards by 1.6 and 2.2 times, respectively. Spatially, the results of the cluster analysis showed that the water quality in Cihu Lake can be categorized into three regions: the northern half of the lake, the southern half of the lake, and the canal entering the lake. Temporally, the water quality in these three regions can be classified into three categories: March to May (the northern half of Cihu Lake), September to November (the southern half of Cihu Lake), and September (the canal entering Cihu Lake). The discriminant analysis results showed that NH₃-N, TN, COD_Cr, and BOD₅ are the main factors that affect the uneven spatial distribution of the water quality of Cihu Lake, while TN, DO, and COD_Mn are the main factors that affect the temporal difference in the northern half of Cihu Lake, and NH₃-N, TP, COD_Cr, DO, COD_Mn, TN, and TP are the main factors affecting the temporal difference in the southern half of Cihu Lake and the canal entering Cihu Lake. It was found that the water pollution in the study area can be mainly attributed to the incoming water and urban domestic pollution. The main pollution sources for the canal entering Cihu Lake and the southern half of Cihu Lake are the water from the sewage treatment plant and the domestic sewage that has not been intercepted, while the northern half of Cihu Lake is mainly affected by surface runoff, mixed rainwater and sewage, and internal pollution.

Health Risk Assessment Based on Source Identification of Heavy Metal(loid)s: A Case Study of Surface Water in the Lijiang River, China

In this study, 24 surface water samples were collected from the main trunk/tributary of the Lijiang River during the wet season (April) and the dry season (December) in 2021. The total concentration of 11 heavy metal(loid)s (Al, Cu, Pb, Zn, Cr, Ni, Co, Cd, Mn, As, and Hg) was determined to investigate their physicochemical properties and spatial-temporal distribution characteristics. The heavy metal evaluation index (HEI) and the positive matrix factorization (PMF) model were employed to evaluate water quality and to reveal quantitatively identified pollution sources for further investigation to obtain a health risk assessment using the hazard index (HI) and carcinogenic risk (CR) of various pollution sources. The mean concentrations of heavy metal(loid)s in surface water in the wet and dry seasons were ranked as: Al > Mn > Zn > Ni > Cd > Cr > Cu > As >Hg = Pb > Co, with the mean concentration of Hg being higher than the national Class II surface water environmental quality standard (GB3838-2002). In terms of time scale, the concentration of most heavy metal(loid)s was higher in the wet season; most heavy metal(loid)s were distributed mainly in the midstream area. HEI index indicated that the main water quality status was “slightly affected” in the study area. Five potential sources of pollution were obtained from the PMF model, including industrial activities, traffic sources, agricultural activities, domestic waste emissions, and natural resources. The source-oriented risk assessment indicated that the largest contributions of HI and CR were agricultural sources in the Lijiang River. This study provides a “target” for the precise control of pollution sources, which has a broad impact on improving the fine management of the water environment in the basin.

Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China

Heavy metal(loid)s accumulation in groundwater has posed serious ecological and health concerns worldwide. Source-specific risk apportionment is crucial to prevent and control potential heavy metal(loid)s pollution in groundwater. However, there is very limited comprehensive information on the health risk apportionment for groundwater heavy metal(loid)s in arid regions. Thus, the Zhangye Basin, a typical arid oasis region in Northwest China, was selected to investigate the contamination characteristics, possible pollution sources, and source-specific health risks of groundwater heavy metal(loid)s. The heavy metal pollution index (HPI), the Nemerow index (NI), and the contamination degree (CD) were adopted to assess the pollution level of heavy metal(loid)s; then source-specific health risk was apportioned integrating the absolute principal component scores-multiple linear regression (APCS-MLR) with health risk assessment. Noticeable accumulation of Mn, Fe, and As was observed in this region with especially Fe/As in 12.68%/2.11% of the samples revealing significant enrichment. Approximately 3.5% of the groundwater samples caused moderate or higher pollution level based on the HPI. The APCS-MLR model was more physically applicable for the current research than the positive matrix factorization (PMF) model. Industrial-agricultural activity factor (12.56%) was the major source of non-cancer (infants: 59.15%, children: 64.87%, teens: 64.06%, adults: 64.02%) and cancer risks (infants: 77.36%, children: 77.35%, teens: 77.40%, adults: 77.41%). Industrial-agricultural activities should be given priority to control health risks of heavy metal(loid)s in groundwater. These findings provide fundamental and significant information for mitigating health risks caused by heavy metal(loid)s in groundwater of typical arid oasis regions by controlling priority sources.

Site prioritization and performance assessment of groundwater monitoring network by using information-based methodology

The arbitrary distribution of groundwater monitoring sites and the redundancy of observation data restrict the ability of monitoring network to provide reliable and effective data information. The purpose of this study is aimed at finding a quantitative method to screen ideal monitoring locations and evaluate the efficiency of the monitoring network. In terms of site selection, we use hydrogeological information, monitoring density and monitoring location to select the suitable site to monitor groundwater quality, understand the temporal trends and identify the abnormal signals of pollution sources. To evaluate the efficiency of monitoring network we used the groundwater quality data for consecutive years to evaluate the groundwater monitoring network based on information entropy and principal component analysis (PCA). The results show that the optimized groundwater monitoring network is comprised of 10 monitoring wells. The efficiency evaluation results of information entropy and PCA are basically consistent. The maximum mutual information (T) and comprehensive index of monitoring site (Laiguangying) were 1.29 and 3.25 respectively, while the minimum T and comprehensive index of monitoring site (Jinzhan) were 1.05 and -0.36 respectively, and the data efficiency was low. This study provides a good example for optimizing a groundwater pollution monitoring network.

Source Apportionment, Hydrodynamic Influence, and Environmental Stress of Pharmaceuticals in a Microtidal Estuary with Multiple Outlets in South China

Pharmaceutical residues in the environment are of great concern as ubiquitous emerging contaminants. This study investigated the presence of 40 pharmaceuticals in water and sediment of the Pearl River Estuary (PRE) in the wet season of 2020. Among psychiatric drugs, only diazepam was found in water samples while six of them were detected in the sediment. The Σantibiotics levels ranged from 6.18 to 35.9 ng/L and 2.63 to 140 ng/g dry weight in water and sediment samples, respectively. Fluoroquinolones and tetracyclines were found well settling in the outlet sediment, while sulfonamides could be released from disturbed sediment under stronger tidal wash-out conditions. After entering the marine waters, pharmaceuticals tended to deposit at the PRE mouth by the influence of the plume bulge and onshore invasion of deep shelf waters. Low ecological risks to the aquatic organisms and of causing antimicrobial resistance were identified. Likewise, hydrological modeling results revealed insignificant risks: erythromycin-H₂O and sulfamethoxazole discharged through the outlets constituted 30.8% and 6.74% of their environmental capacity, respectively. Source apportionment revealed that pharmaceutical discharges through the Humen and Yamen outlets were predominantly of animal origin. Overall, our findings provide strategic insights on environmental regulations to further minimize the environmental stress of pharmaceuticals in the PRE.

Evaluation of water quality index and geochemical characteristics of surfacewater from Tawang India

In this study,the water samples were collected from 31 sites of Tawang, Arunachal Pradesh, India (North-Eastern Himalaya), during the winter season to check the suitability of water for drinking and irrigation purposes.The study scientifically demonstrates the estimation of Water quality index (WQI) andhydrogeochemical characteristics of surface water samples by utilizing multivariate statistical methods. The main water quality parameters considered for this study were TDS, conductivity, salinity, pH, hardness, cations and anions. WQI was calculated in order to find out the deviation in the water quality parameters particularly with respect to BIS permissible limits.The major influencing factors responsible for the variation in these parameters were derived by using Principal component analysis (PCA) and Correlation matrix.To check the suitability of water for drinking purpose, hydrogeochemical facies and rock water interaction was derived by using well established methods such as Piper Plot (determine water type), WQI (Quality monitoring), and saturation index (for mineral dissolution). The results revealed that the silicate weathering was the main ionic source in comparison to carbonate weathering which is due to the higher dissolution capacity of silicate minerals.The results of the scattered plot between (Ca²⁺ + Mg²⁺)-(HCO₃- + SO₄²-) versus (Na⁺ + K⁺)-Cl- (meq/L) highlighted thation exchange occurs between Mg²⁺ and Ca²⁺ofsurface water with Na⁺ and K⁺of rock /soil. This means that calcium ion was getting adsorbed, and sodium ion was getting released. The Ca²⁺-Mg²⁺-HCO₃-, Na⁺-HCO₃-and Na⁺-Cl- type of surface water suggested permanent and temporary hardness respectively in the studied region. The dominant cations of this study were Na⁺ and Ca²⁺ while the dominant anions were HCO₃- and SO₄²-. In order to check the suitability of water sources for irrigation, parameters like, Magnesium hazard (MH), Total hardness (TH), Permeability Index (PI), Kelly Index (KI), Sodium adsorption rate (SAR), Sodium percentage (Na%), and Residual sodium carbonate (RSC) were determined. The results showed that 93% of the samples had PI score < 75, which indicates the suitability of the water for irrigation. Also the WQI calculation showed an average WQI value of 82.49, amongst which 61% samples were in the range of 0-50 being considered as good for drinking, while 39% were catageorised as unsuitable for drinking showing a value of > 50. Hence the above findings reveal that geogenic activities play a major role in influencing the water quality of Tawang region. Hence suitable water treatment technologies or methods might be used to eliminate thenon desirable elements and minerals present in surface water.

The quantification of the influencing factors for spatial and temporal variations in surface water quality in recent ten years of the Huaihe River Basin, China

Temporal and spatial variations in river water quality and the factors influencing such variations are important basis and prerequisites for identifying pollution sources and improving river water environment. Monthly data for 22 indicators at 485 surface water quality (SWQ) monitoring sites (46,560 groups) in the Huaihe River Basin (HRB) from 2011 to 2018 were analyzed. This paper assessed temporal and spatial changes in SWQ in the HRB and identified the main factors influencing the changes and each factor's contribution to the changes. The five-day biological oxygen demand, permanganate index, fluoride, ammonium nitrogen, and total phosphorus were the main pollutants. Spatial cluster analysis indicated that the HRB could be divided by SWQ into areas I-IV from light to heavy pollution. Areas I and IV were nitrogen and phosphorus nutrients pollution, and areas II and III were heavy metals and organic pollution. Area IV (poor SWQ) locates mainly north of the Huaihe mainstream with annual average rainfall ≤ 640 mm. SWQ in the HRB has been improving for two decades, with an inflection point in 2015 between 2011 and 2018, and rainfall change is an important factor for the inflection point. The urbanization rate, industrial water consumption, and rainfall were the key factors influencing SWQ changes in the watershed with significant hydrological zonation, with urbanization rate and rainfall increased, industrial water consumption decreased, the SWQ was gradually improved. The key factors contributing to SWQ changes in the future will be the sewage treatment rate and rainfall changes caused by natural variations.

Influence of Source Apportionment of PAHs Occurrence in Aquatic Suspended Particulate Matter at a Typical Post-Industrial City: A Case Study of Freiberger Mulde River

Polycyclic aromatic hydrocarbons (PAHs) have received extensive attention because of their widespread presence in various environmental media and their high environmental toxicity. Thus, figuring out the long-term variances of their occurrence and driving force in the environment is helpful for environmental pollution control. This study investigates the concentration levels, spatial variance, and source apportionment of PAHs in suspended particulate matter of Freiberger Mulde river, Germany. Results show that the concentrations of the 16 priority PAHs suggested by USEPA (Σ16PAHs) were in the range of 707.0–17,243.0 μg kg−1 with a mean value of 5258.0 ± 2569.2 μg kg−1 from 2002 to 2016. The relatively high average concentrations of Σ16PAHs were found in the midstream and upstream stations of the given river (7297.5 and 6096.9 μg kg−1 in Halsbrucke and Hilbersdorf, respectively). In addition, the annual average concentration of Σ16PAHs showed an obvious decreasing pattern with time. Positive Matrix Factorization (PMF) receptor model identified three potential sources: coke ovens (7.6–23.0%), vehicle emissions (35.9–47.7%), and coal and wood combustion (34.5–47.3%). The source intensity variation and wavelet coherence analysis indicated that the use of clean energy played a key role in reducing PAHs pollution levels in suspended sediments. The risk assessment of ecosystem and human health suggested that the Σ16PAHs in the given area posed a non-negligible threat to aquatic organisms and humans. The data provided herein could assist the subsequent management of PAHs in the aquatic environment.

Revealing the Chemical Profiles of Airborne Particulate Matter Sources in Lake Baikal Area: A Combination of Three Techniques

Positive matrix factorization (PMF) is a widely used multivariate source apportionment technique. However, PMF-derived source profiles are never compared to real ones because of the absence of data on the chemical composition of source emissions. The aim of this study was to verify the validity of PMF-derived source profiles using the diagnostic ratios (DR) method and end-member mixing analysis (EMMA). The composition of polycyclic aromatic hydrocarbons (PAHs) in particulate matter (PM) sampled in the air above Lake Baikal in summer and the composition of inorganic elements (IE) in PM accumulated in Lake Baikal snowpack were used as study objects. Five PAH sources and five IE sources were identified using PMF. Eight PAHs and six IEs selected from PMF-derived source profiles were recognized as eligible for calculating the DRs (species 1/(species 1 + species 2)) suitable for testing PMF results using EMMA. EMMA was based on determining whether most samples in mixing diagrams that use DR values as coordinates of source points could be bound by a geometrical shape whose vertices are pollution sources. It was found that the four PAH sources and four IE sources obtained using PMF were also identified using EMMA. Thus, the validity of the most of PMF-derived source profiles was proved.

Development of modified integrated water quality index to assess the surface water quality: a case study of Tuo River, China

Water quality evaluation is an important step in water environment control and management. The water quality index (WQI) is considered to be an effective method for water quality evaluation. However, when constructing the WQI, the contribution of the lower threshold limits of water quality parameters to water quality has received little attention. The principle of the modified integrated water quality index (IWQI) is that the concentration of any water quality parameter below the lower threshold limits as well as above the upper threshold limits will lead to an increase in the overall index value. Based on the concentration of water quality parameters, the modified IWQI classified water quality into five categories, i.e., bad (> 8), poor (5-8), medium (2-5), good (1-2), and excellent (< 1). Tuo River plays a crucial role in potable and irrigation water sources of Sichuan Province, and the assessment result of modified IWQI reveals that 67.8% of samples were classified as "medium," 29% "poor," and 3.2% "bad." The high concentrations of N and P from agricultural activities and industrial wastewater are the main contributors to the deterioration of water quality in the Tuo River. Additionally, the Tuo River presents the characteristics of worse water quality in the midstream. The evaluation results of the modified IWQI are consistent with that of the conventional WQI, which proves the accuracy of the modified IWQI as a surface water quality evaluation method.

A four-way model (FEST) for source apportionment: Development, verification, and application

In studying the spatial, temporal, and particle size variations heavy metal sources, a source apportionment model for a four-way array of data is required. In this study, referencing two-way and three-way models, a four-way (particle fractions, elements, sites, and time) source apportionment model (FEST) was developed. Errors in the three-way models solving four-way problems verified the necessity of developing the FEST model. The results showed that the FEST model had a higher accuracy than the existing models, which was probably because of more constraints and input data in the FEST model. Based on the sampled data in Beijing, sources were apportioned for the four-way array of data using the FEST model, and the spatial, temporal, and particle size variations of sources were evaluated. The main sources of heavy metals were similar to those in our prior studies, whereas the contributions of sources to specific heavy metals differed. Traffic exhaust and fuel combustion contributed more to fine particles than coarse particles, indicating that the two should be controlled preferentially among all sources. The management of traffic exhaust should be focused on the central and northern areas in each season, and the control of fuel combustion should be strengthened in the southern area in winter.

Hydrochemical characteristics and groundwater quality in the thick loess deposits of China

Water quality and quantity should be paid more attention in regions with arid climate and thick vadose zones since the limited groundwater cannot be replenished rapidly once polluted. This study focused on the Loess Plateau of China to investigate the geochemical mechanism affecting groundwater chemistry and to calculate contribution rates of multiple sources to groundwater solutes. We employed multiple methods (diagrams, bivariate analyses, hierarchical cluster analysis (HCA), sodium adsorption ratio (SAR), water quality index (WQI), correlation analysis, and forward model) for the above purposes. We collected 64 groundwater samples in the thick loess deposits in June 2018 (flood season) and April 2019 (dry season). The average concentrations of cation were in the order of Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ in the flood season, and Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ in the dry season. The order of anions contents in the flood season and the dry season were HCO₃^- > SO₄^2- > Cl^- > NO₃^-. The major hydrochemical facies were Ca-HCO₃ and Ca·Mg-HCO₃ in the flood season and Na·Ca-HCO₃·SO₄ and Na-HCO₃ in the dry season, respectively. Most of the groundwater (95% in the flood season and 96% in the dry season) was suitable for drinking, and the overall water quality was acceptable for irrigation. Mineral dissolution and cation exchange were important natural processes affecting groundwater chemistry. The forward model showed that the contribution of atmospheric input, anthropogenic input, evaporite dissolution, silicate weathering, and carbonate weathering to solutes in groundwater was 2.3±1.5%, 5.0±7.1%, 19.3±21.4%, 42.8±27.3%, and 30.6±27.1% in the flood season, and 9.1±6.4%, 3.4±5.2%, 20.3±15.9, 56.6±23.2%, and 10.7±15.4% in the dry season, respectively. Obviously, silicate and carbonate weathering contribute the most to groundwater chemistry in the flood season, while silicate weathering and evaporite dissolution contribute the most in the dry season. Although the overall contribution of anthropogenic inputs was insignificant, it was the dominant source of solutes for local groundwater. This study provides fundamental information for water management in arid areas.

Source identification of surface water pollution using multivariate statistics combined with physicochemical and socioeconomic parameters

Accurate identification of potential contamination sources of river water is a basis for effective pollution control and sustainable water management. Pollution source identification based on physicochemical-parameters-only method may lead to uncertainty and subjectivity. In this study along with hydrochemistry parameters (HPs), socioeconomic parameters (SPs) were considered as an auxiliary in multivariate statistics to achieve a comprehensive assessment on pollution sources with accurate estimates of source identification and apportionment. Fifteen physicochemical parameters were combined with twelve socioeconomic parameters in multivariate statistics to quantitatively assess potential pollution sources and their contributions to river water pollution. Multivariate statistics in the study included regression analysis, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR). Regression analysis between hydro-chemical parameters and socioeconomic parameters indicated that industrial and population growths were the main factors related to ammonium nitrogen (NH₄⁺-N), total nitrogen (TN) contamination, while total phosphorus (TP) was more correlated with domestic discharge and poultry breeding. Based on the results of PCA, four latent factors were extracted for hydrochemistry parameters (HPs) and socioeconomics parameters (SPs), accounting for 68.59% and 82.40% of the total variance, respectively. With integrating the PCA results of the two parameter groups, pollution sources were ranked as industrial effluents > rural wastewater > municipal sewage > phytoplankton growth and agricultural cultivation. Source apportionment in APCS-MLR revealed that industrial wastewater and rural wastewater averagely contributed 35.68% and 25.08% of pollution, respectively, followed by municipal sewage (18.73%) and phytoplankton pollution (15.13%) with relatively small percentage of unrecognized source. It is concluded that socioeconomic parameters assisting hydrochemistry parameters in multivariate statistics can improve the accuracy and certainty of pollution source identification, supporting decision makers to formulate strategies on protection of river water quality.

Source apportionment and natural background levels of major ions in shallow groundwater using multivariate statistical method: A case study in Huaibei Plain, China

Understanding the sources, natural background levels (NBLs), and threshold values (TVs) of the major ions in groundwater is essential for the effective protection of water resources. In this study, a total of 70 shallow groundwater samples were collected in Suzhou, Huaibei Plain, China. A variety of statistical methods and cumulative probability distribution techniques were performed to identify the sources, NBLs, and TVs of the major ions. The major ion concentrations found in decreasing order as follows: HCO₃^- > SO₄^2- > NO₃^- > Cl^- and Na⁺ > Ca²⁺ > Mg²⁺. Piper diagram for hydrochemical types shows that groundwater types were Mg-HCO₃ (36%), Ca-HCO₃ (34%), and Na-HCO₃ (30%). According to the factor and the Unmix model analysis, anthropogenic (agriculture-related) and geogenic source (water-rock interactions-related) were identified to be responsible for the chemical composition of the groundwater in the study area, and their mean contributions for the major ion concentrations are 47.9% and 52.1%, respectively. The NBLs for Na⁺, Ca²⁺, Mg²⁺, Cl^-, SO₄^2-, and NO₃^- were determined to be 29.5-44.2, 26.2-38.9, 18.9-39.5, 1.0-9.9, 12.9-19.4, and 2.1-16.5 mg/L, respectively, and the TVs were calculated as 122.1, 169.5, 39.5, 129.6, 134.7, and 18.3 mg/L, respectively. Moreover, this study shows the feasibility and reliability of using these multivariate statistical methods and natural background levels to evaluate the status of groundwater quality.

Groundwater Pollution Source Identification and Apportionment Using PMF and PCA-APCS-MLR Receptor Models in Tongchuan City, China

Potential sources of groundwater pollution in Tongchuan City, China, were qualitatively identified based on 14 key water quality indicators of 59 groundwater samples, and the contribution of each source to groundwater quality was quantitatively evaluated. Groundwater pollution sources were analyzed using PMF and PCA-APCS-MLR models, and their applicability in groundwater pollution assessment in Tongchuan City was tested. Results indicate that both models identified four sources of groundwater contamination. Natural evolution was the main cause of groundwater pollution in the study area, followed by the coal industry, agriculture, and urbanization. Although the spatial distribution of pollution sources in the two models differed, the urbanized area to the east of the study area was more severely affected by sewage discharge, the west was more obviously affected by the coal industry, and the north was mainly polluted by agriculture. Both of the fitting results of the two models are good, but R² values obtained by the PMF model (0.4440-0.9991) were generally higher than those obtained by the PCA-APCS-MLR model (0.5180-0.9530), indicating that PMF model results were more accurate than the PCA-APCS-MLR model.

Water resources pollution associated with risks of heavy metals from Vatukoula Goldmine region, Fiji

Although mining is essential for human economic development, is amongst the most polluting anthropogenic sources that influence seriously in water resources. Thus, understanding the presence and concentration of heavy metals in water and sediment in the vicinity of mines is important for the sustainability of the ecosystem. In this work, a multidisciplinary approach was developed to characterize the contamination level, source apportionment, co-existence, and degree of ecological and human health risks of HMs on water resources in the Vatukoula Goldmine region (VGR), Fiji. The outcomes suggested significant contamination by Cd (range: 0.01-0.95 g/L), Pb (range: 0.03-0.53 g/L), and Mn (range: 0.01-3.66 g/L) in water samples surpassed the level set by Fiji and international laws, whereas higher concentration of Cd (range: 2.60-23.16 mg/kg), Pb (range: 28.50-200.90 mg/kg) and Zn (range: 36.50-196.66 mg/kg) were detected in sediment samples. Lead demonstrated a strong significant co-existence network with other metals (e.g., Mn, Ni). Source apportionment recognized four source patterns (Cd, Pb, Ni, and Mn) for water and (Cr, Cd-Pb, Mn, and Zn) for sediment which was further confirmed by principal component analysis. The mine inputs source mainly contributed to Cd (66.07%) for water, while mineral processing mostly contributed to Zn (76.10%) for sediment. High non-carcinogenic (>1) and carcinogenic (>10^-4) health risks, particularly in children, are related to the elevated Cd, Pb and Cr contents from the VGR. Uncertainty analysis demonstrates that the 90th quantile of Cd led to higher carcinogenic risk. Pollution indices disclosed a moderate to extremely contamination status mainly along the Toko dam which poses high ecological risks identified by index calculation. However, sediment quality indicators based on probable effect levels showed that there was a 75% of likelihood that the concentrations of Cd and Pb adjacent to the VGR have a severe toxic impact on aquatic lives.

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

Spatial-temporal characteristics, source-specific variation and uncertainty analysis of health risks associated with heavy metals in road dust in Beijing, China

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

Effects on microbiomes and resistomes and the source-specific ecological risks of heavy metals in the sediments of an urban river

This study aims to better understand the effects of heavy metal enrichment on microbiomes and resistomes and the source-specific ecological risks of metals in the sediments of an urban river. Geo-accumulation index and enrichment factor suggested the river sediments were contaminated by Cd, Cu, Pb, and Zn in varying degrees. High-throughput sequencing-based metagenomics analysis identified 430 types of antibiotic resistance genes (ARGs), dominated by the multidrug, MLS, bacitracin, quinolone, and aminoglycoside ARGs, and 52 metal resistance genes (MRGs) mainly conferring resistance to zinc, copper, cadmium, lead, mercury and multiple metals. Spearman correlation analysis and Mantel test showed the heavy metal enrichment exerted significant effects on the microbial community, ARGs and MRGs. Source apportionment using positive matrix factorization revealed that natural source (42.8%) was the largest contributor of metals in the river sediments, followed by urban activities (35.4%) and a mixed source (21.7%). However, when incorporating the apportionment results into a modified risk model to evaluate the source-specific ecological risks, results showed human activities dominated the risks of metals. Comparatively, the urban activities majorly caused moderate- and considerable- ecological risks, while the mixed source with respect to agricultural and industrial activities contributed higher percentages on high- and extremely high- ecological risks.

Use of Multivariate Statistical Techniques to Study Spatial Variability and Sources Apportionment of Pollution in Rivers Flowing into the Laizhou Bay in Dongying District

Spatial variability and source apportionment of river pollution flowing into the Bohai Sea are of great significance to the pollution liability and development of control strategies to reduce the terrestrial discharge of pollution in the ocean. In this study, ten water quality variables from 14 monitoring sites in rivers flowing into Laizhou Bay were obtained to investigate the spatial variation and pollution sources in Dongying District from 2018–2019. The survey area was divided into a low pollution (LP) zone and a high pollution (HP) zone by cluster analysis based on ten indicators. Principle component analysis/factor analysis with a geographic information system was performed to identify the four main pollution sources in the survey area. Compared with the positive matrix factorization model, the absolute principal component score-multiple linear regression (APCS-MLR) model was more appropriate for the source apportionment of pollution in the surface water of Dongying District. The point source pollution of domestic sewage (23.6%) was the most crucial pollution source of water in the LP zone, followed by non-point pollution from agricultural activity (16.4%). The contribution rate in the HP zone analyzed by the APCS-MLR model followed a decreasing order: point source pollution from domestic sewage (28.5%) > non-point pollution source of overland runoff (14.8%) > point source pollution of hybrid wastewater (12.4%) > point source pollution from industries sewage (10.6%). Therefore, the spatial distribution and sources of pollution in the investigated area should be considered while developing control measures to reduce the discharge of pollution to Laizhou Bay.

Overlapping redox zones control arsenic pollution in Pleistocene multi-layer aquifers, the Po Plain (Italy)

Understanding the factors that control As concentrations in groundwater is vital for supplying safe groundwater in regions with As-polluted aquifers. Despite much research, mainly addressing Holocene aquifers hosting young (<100 yrs) groundwater, the source, transport, and fate of As in Pleistocene aquifers with fossil (>12,000 yrs) groundwaters are not yet fully understood and so are assessed here through an evaluation of the redox properties of the system in a type locality, the Po Plain (Italy). Analyses of redox-sensitive species and major ions on 22 groundwater samples from the Pleistocene arsenic-affected aquifer in the Po Plain shows that groundwater concentrations of As are controlled by the simultaneous operation of several terminal electron accepters. Organic matter, present as peat, is abundant in the aquifer, allowing groundwater to reach a quasi-steady-state of highly reducing conditions close to thermodynamic equilibrium. In this system, simultaneous reduction of Fe-oxide and sulfate results in low concentrations of As (median 7 μg/L) whereas As reaches higher concentrations (median of 82 μg/L) during simultaneous methanogenesis and Fe-reduction. The position of well-screens is an additional controlling factor on groundwater As: short screens that overlap confining aquitards generate higher As concentrations than long screens placed away from them. A conceptual model for groundwater As, applicable worldwide in other Pleistocene aquifers with reducible Fe-oxides and abundant organic matter is proposed: As may have two concentration peaks, the first after prolonged Fe-oxide reduction and until sulfate reduction takes place, the second during simultaneous Fe-reduction and methanogenesis.

Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring

This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. The Shiyang River Basin in Minqin County was selected as the research object for analyzing the natural components distribution and its preliminary forecast in partial areas. With the priority control of groundwater pollutants, the concentration changes of four indicators (including the permanganate index) in different spatial distributions were analyzed based on the GIS technology, so as to provide a basis for the groundwater quality prediction. Taking the permanganate as a benchmark, this study evaluated the prediction effects of the conventional back propagation (BP) neural network (BPNN) model and the optimized BPNN based on the golden section (GBPNN) and wavelet transform (WBPNN). The algorithm proposed in this study is compared with several classic prediction algorithms for analysis. Groundwater quality level and distribution rules in the research area are evaluated with the proposed algorithm and GIS technology. The results reveal that GIS technology can characterize the spatial concentration distribution of natural indicators and analyze the chemical distribution of groundwater quality based on it. In contrast, the WBPNN has the best prediction result. Its average error of the whole process is 3.66%, and the errors corresponding to the six predicated values are all below 10%, which is dramatically better than the values of the other two models. The maximal prediction accuracy of the proposed algorithm is 97.68%, with an average accuracy of 96.12%. The prediction results on the water quality level are consistent with the actual condition, and the spatial distribution rules of the groundwater water quality can be shown clearly with the GIS technology combined with the proposed algorithm. Therefore, it is of great significance to explore the distribution and changes of regional groundwater quality, and this studywill play a critical role in determining the groundwater quality.

Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China

Anthropogenic activities pose challenges on security of water quality. Identifying potential sources of pollution and quantifying their corresponding contributions are essential for water management and pollution control. In our study, 2-year (2017-2018) water quality dataset of 15 parameters from eight sampling sites in tributaries and mainstream of the Min River was analyzed with multivariate statistical analysis methods and absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique to reveal potential sources of pollution and apportion their contributions. Temporal and spatial cluster analysis (CA) classified 12 months into three periods exactly consistent with dry, wet, and normal seasons, and eight monitoring sites into two regions, lightly polluted (LP) and highly polluted (HP) regions, based on different levels of pollution caused by physicochemical properties and anthropogenic activities. The principal component analysis (PCA) identified five latent factors accounting for 75.84% and 73.46% of the total variance in the LP and HP regions, respectively. The main pollution sources in the two regions included agricultural activities, domestic sewage, and industrial wastewater discharge. APCS-MLR results showed that in the LP region, contribution of five potential pollution sources was ranked as agricultural non-point source pollution (22.13%) > seasonal effect and phytoplankton growth (19.86%) > leakage of septic tanks (15.73%) > physicochemical effect (12.86%) > industrial effluents and domestic sewage (11.59%), while in the HP region ranked as point source pollution from domestic and industrial discharges (20.81%) > municipal sewage (16.66%) > agricultural non-point source pollution (15.23%) > phytoplankton growth (14.82%) > natural and seasonal effects (12.67%). Based on the quantitative assessment of main pollution sources, the study can help policymakers to formulate strategies to improve water quality in different regions.

Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China

The quality of groundwater in a region is regarded as a function of natural and anthropogenic factors. Receptor models have advantages in source identification and source apportionment by testing the physicochemical properties of receptor samples and emission sources. In our study, receptor models PMF and PCA-APCS-MLR were developed to qualitatively identify the latent sources of groundwater pollution in the study area and quantitatively evaluate the contribution of each source to groundwater quality. The performances of PMF and APCS-MLR models were compared to test their applicability on the assessment of groundwater pollution sources. Results showed that both of the models identified five sources of groundwater contamination with similar main load species of each potential source. The comparable source apportionment of species NO₂^- and NO₃^- with two models indicated the reliable source estimation for these species, whereas the contributions of sources to species Fe, Mn, Cl^-, SO₄^2- and NH₄⁺ were significantly different due to the large variability of data, difference of uncertainty analysis and algorithm of unexplained variability in the two models. R-squared value between observation and model prediction was 0.603-0.931 in PMF and 0.497-0.859 in PCA-APCS-MLR. The significant disagreement of average source contribution was detected in agricultural source and unexplained variability using PMF and PCA-APCS-MLR models. Average contributions of other sources to groundwater quality parameters had similar estimates between the two models. Higher R² and smaller proportion of unexplained variability in the PMF model suggested that PMF approach could provide more physically plausible source apportionment in the study area and a more realistic representation of groundwater pollution than solutions from PCA-APCS-MLR model. The study showed the advantages of application of multiple receptor models on achieving reliable source identification and apportionment, particularly, providing a better understanding of applicability of PMF and PCA-APCS-MLR models on the assessment of groundwater pollution sources.

A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses

Groundwater resources are the main supply of freshwater for human activities. However, in the last fifty years aquifers have become more susceptible to chemical pollution due to human activities. The concept of groundwater vulnerability constitutes a worldwide accepted tool for water protection and planning. However, the existing methods and modified versions do not account for all the hydrogeochemical processes that drive anthropogenic pollution. The hydrogeochemical processes occurring within an aquifer can be determined using multivariate statistical analysis. In this study a specific vulnerability method named SVAP (Specific Vulnerability to Anthropogenic Pollution) is proposed. The index is based on seven quantitative parameters: depth to groundwater, recharge, nitrate losses, hydraulic resistance of the vadose zone, aquifer thickness, hydraulic conductivity of the aquifer, and slope. Weights of anthropogenic factors were determined by factor analysis and used to validate the SVAP methodology. The parameters' classification was selected according to the highest Pearson's correlation coefficient with factor weights and then grouped via a linear combination. The new index was applied in two watersheds: the Florina basin (Greece) and the Garigliano River basin (Italy), both of which possess complex hydrogeochemical regimes. The main hydrogeochemical processes acting in the study areas were identified via factor analysis, which revealed that the anthropogenic pollution in both sites was due mainly to chemical fertilizers and manure. Verification of the SVAP method produced correlation coefficients with nitrate concentrations of 0.75 and 0.62 in Florina and Garigliano, respectively. The proposed SVAP method is more reliable and flexible than standard vulnerability assessment methods and can be easily adapted for complex aquifers.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl^-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.