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

Source-oriented health risk assessment of groundwater based on hydrochemistry and two-dimensional Monte Carlo simulation

Accurately assessing the health risks posed by major contaminants is essential for protecting groundwater. However, the complexity of pollution sources and the uncertainty of parameters pose challenges for quantitative health risk assessment. In this study, a source-oriented groundwater risk evaluation process was improved by screening key pollutants, employing a combined hydrochemical and positive matrix factorization (PMF) approach for source apportionment, and incorporating two-dimensional Monte Carlo simulation for risk characterization. The application of this process to groundwater assessment in Central Jiangxi Province identified NO3-, F-, Se and Mn as the key pollutants. The pollution sources were anthropogenic activities, rock dissolution, regional geological processes, and ion exchange. Anthropogenic sources contributed 36.8 % and 28.8 % of the pollution during the wet season and dry season, respectively, and accounted for more than half of the health risks. NO3- from anthropogenic sources was the primary controlling pollutant. Additionally, the risk assessment indicated that children were at the highest health risk during the dry season, with ingestion rate suggested to be controlled below 1.062 L·day-1 to make the health risk within an acceptable range. The improved assessment methodology could provide more accurate results and recommended intakes.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Hydrochemical and microbial community characteristics and the sources of inorganic nitrogen in groundwater from different aquifers in Zhanjiang, Guangdong Province, China

Groundwater from different aquifers in the Zhanjiang area suffers from different degrees of nitrogen pollution, which poses a serious threat to the health of urban and rural residents as well as the surrounding aquatic ecological environment. However, neither the water chemistry and microbial community characteristics in different aquifer media nor the sources of inorganic nitrogen pollution have been extensively studied. This study integrated water quality parameters, dual isotopes (δ15N-NO3- and δ18O-NO3-), and 16S rRNA data to clarify the hydrochemical and microbial characteristics of loose rock pore water (LRPW), layered bedrock fissure water (LBFW), and volcanic rock pore fissure water (VRPFW) in the Zhanjiang area and to determine inorganic nitrogen pollution and sources. The results show that the hydrochemistry of groundwater in different aquifers is complex and diverse, which is mainly affected by rock weathering and atmospheric precipitation, and the cation exchange is strong. High NO3- concentration reduces the richness of the microbial community (VRPFW). There are a large number of bacteria related to nitrogen (N) cycle in groundwater and nitrification dominated the N transformation. A quarter of the samples exceeded the relevant inorganic nitrogen index limits specified in the drinking water standard for China. The NO3- content is highest in VRPFW and the NH4+ content is highest in shallow loose rock pore water (SLRPW). In general, NO3-/Cl-, dual isotope (δ15N-NO3- and δ18O-NO3-) data and MixSIAR quantitative results indicate manure and sewage (M&S) and soil organic nitrogen (SON) are the main sources of NO3-. In LRPW, as the depth increases, the contribution rate of M&S gradually decreases, and the contribution rate of SON gradually increases. The results of uncertainty analysis show that the UI90 values of SON and M&S are higher. This study provides a scientific basis for local relevant departments to address inorganic nitrogen pollution in groundwater.

Entropy-weighted water quality index, hydrogeochemistry, and Monte Carlo simulation of source-specific health risks of groundwater in the Morava River plain (Serbia)

Population growth, urbanization, industry, floods, and agriculture globally degrade groundwater in river plains, necessitating action for its quality assessment and management. Hence, a comprehensive methodology, including hydrogeochemical facies (Piper, Gibbs), irrigation indices (SAR, Wilcox), entropy-weighted water quality index (EWQI), positive matrix factorization (PMF), and Monte Carlo simulation of source-specific health risks was used in this study to analyze groundwater in the Morava river plain (Serbia). The results revealed a prevalent Ca-Mg-HCO3 groundwater type, influenced by water-rock interactions. Although groundwater was found suitable for irrigation, only 66.7 % of the samples were considered drinkable. Agricultural activities, natural processes, and municipal wastewater were identified as primary pollution sources. The incremental lifetime cancer risk (ILCR) and hazard index (HI) threshold exceedance for adults and children ranged from 8.5 % to 39 % of the samples, with arsenic identified as the most risk-contributing contaminant. These findings provide valuable insights for researchers studying groundwater vulnerability in river plains.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K++Na+, Ca2+, Mg2+, Cl-, SO42-, HCO3-, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K++Na+ and anion Cl-. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.

Hydrogeochemical characterization, quality assessment, and potential nitrate health risk of shallow groundwater in Dongwen River Basin, North China

Assessing groundwater geochemical formation processes and pollution circumstances is significant for sustainable watershed management. In the present study, 58 shallow groundwater samples were taken from the Dongwen River Basin (DRB) to comprehensively assess the hydrochemical sources, groundwater quality status, and potential risks of NO3- to human health. Based on the Box and Whisker plot, the cation's concentration followed the order of Ca2+  > Mg2+  > Na+  > K+, while anions' mean levels were HCO3-  > SO42-  > NO3-  > Cl-. The NO3- level in groundwater samples fluctuated between 4.2 and 301.3 mg/L, with 67.2% of samples beyond the World Health Organization (WHO) criteria (50 mg/L) for drinking. The Piper diagram indicated the hydrochemical type of groundwater and surface water were characterized as Ca·Mg-HCO3 type. Combining ionic ratio analysis with principal component analysis (PCA) results, agricultural activities contributed a significant effect on groundwater NO3-, with soil nitrogen input and manure/sewage inputs also potential sources. However, geogenic processes (e.g., carbonates and evaporite dissolution/precipitation) controlled other ion compositions in the study area. The groundwater samples with higher NO3- values were mainly found in river valley regions with intense anthropogenic activities. The entropy weight water quality index (EWQI) model identified that the groundwater quality rank ranged from excellent (70.7%) and good (25.9%) to medium (3.4%). However, the hazard quotient (HQ) used in the human health risk assessment (HHRA) model showed that above 91.38% of groundwater samples have a NO3- non-carcinogenic health risk for infants, 84.48% for children, 82.76% for females, and 72.41% for males. The findings of this study could provide a scientific basis for the rational development and usage of groundwater resources as well as for the preservation of the inhabitants' health in DRB.

Risk assessment of nitrate groundwater contamination using GIS-based machine learning methods: A case study in the northern Anhui plain, China

Long-term agricultural activities have affected the sustainable development of groundwater in the Northern Anhui Plain, East China. It is, therefore, important to identify areas at high groundwater pollution risk in the Northern Anhui Plain to ensure effective protection of regional water resources. In this study, 60 groundwater samples were collected from the shallow aquifer of the plain and analyzed for nitrate (NO3-) concentrations. In addition, 10 environmental and geological factors including the elevations, distances-to-rivers, slope angles, orientations of slopes, land cover types, topographic wetness index (TWI), geomorphology, lithology, soil types, and precipitation amounts in the study area were selected as input layers. The light gradient boosting machine (LightGBM) and random forest (RF) algorithms, combined with the geographic information system (GIS), were performed to generate the groundwater pollution occurrence probability maps. The descriptive statistics showed that the NO3- concentrations in the shallow groundwater ranged from 4.3 to 73.6 mg/L. Most sampling wells exhibited NO3- concentrations above the threshold of 18.3 mg/L. The prediction results of the LightGBM and RF algorithms indicated a high groundwater NO3- pollution risk in the southern part of the plain. However, the LightGBM algorithm had a better prediction performance than RF, with a higher Kappa value of 0.84. Moreover, the frequency ratio method revealed that the precipitation amounts contributed to the groundwater NO3- pollution risk in the study area by 38.14%, followed by the elevations, slope angles, TWI, land cover types, and slope aspects, with contributions of 21.4, 13.02, 8.37, 7.44, and 6.51%, respectively. In the future, sampling of additional wells and further anthropogenic factors shall be considered for the development of more effective groundwater nitrate pollution prevention strategies provided to decision makers.

Hydrochemistry, quality, and integrated health risk assessments of groundwater in the Huaibei Plain, China

Groundwater is an essential freshwater resource utilized in industry, agriculture, and daily life. In the Huaibei Plain (HBP), where groundwater significantly influences socio-economic development, information about its quality, hydrochemistry, and related health risks remains limited. We conducted a comprehensive groundwater sampling in the HBP and examined its rock characteristics, water quality index (WQI), and potential health risks. The results revealed that the primary factors shaping groundwater hydrochemistry were rock dissolution and weathering, cation exchange, and anthropogenic activities. WQI assessment indicated that only 73% of the groundwaters is potable, as Fe2+, Mn2+, NO3-, and F- contents in the water could pose non-carcinogenic hazards to humans. Children were more susceptible to these health risks through oral ingestion than adults. Uncertainty analysis indicated that the probabilities of non-carcinogenic risk were approximately 57% and 31% for children and adults, respectively. Sensitivity analysis further identified fluoride as the primary factor influencing non-carcinogenic risks, indicating that reducing fluoride contamination should be prioritized in future groundwater management in the HBP.

Groundwater pollution source identification and health risk assessment in the North Anhui Plain, eastern China: Insights from positive matrix factorization and Monte Carlo simulation

Groundwater contaminants from natural and anthropogenic sources pose a serious threat to the ecological environment and public health. In this study, 30 groundwater samples were collected from shallow wells at a large central water source in the North Anhui Plain, eastern China. Hydrogeochemical methods, positive matrix factorization (PMF) model, and Monte Carlo simulation were used to determine the characteristics, sources, and human health risks of inorganic and organic analytes in groundwater. The groundwater was weakly alkaline with high total hardness and was dominated by HCO3-Mg·Ca, HCO3-Ca·Mg, and HCO3-Ca·Mg·Na hydrochemical facies. The concentration of naphthalene was at a safe level, while the concentrations of F-, NO3- and Mn in 16.7 %, 26.7 % and 40 % of the samples, respectively, exceeded threshold risk-based values based on Chinese groundwater quality standards. Hydrogeochemical methods revealed that water-rock interactions (including weathering of silicate minerals, dissolution of carbonates, and cation exchange), acidity, and runoff conditions control the migration and enrichment of these analytes in groundwater. The PMF model indicated that local geogenic processes, hydrogeochemical evolution, agricultural activities, and petroleum-related industrial sources were the main factors affecting groundwater quality, with contributions of 38.2 %, 33.7 %, 17.8 %, and 10.3 %, respectively. A health risk evaluation model based on Monte Carlo simulation indicated that 77.9 % of children were exposed to a total noncarcinogenic risk above safe thresholds, about 3.4 times higher than the risk to adults. The main contributor to human health risk was F- originating from geogenic processes; thus, F- was identified as a priority for control. This study demonstrates the feasibility and reliability of combining source apportionment techniques and health risk assessment to evaluate groundwater quality.

Evaluating natural background levels of heavy metals in shallow groundwater of the Pearl River Delta via removal of contaminated groundwaters: Comparison of three preselection related methods

Assessing natural background levels (NBLs) in groundwater is a global concern. Knowledge on groundwater NBLs in urbanized areas is challenging due to the impact of complex human activities. Preselection related methods are common ones for assessing groundwater NBLs. The present study used three preselection related methods to assess groundwater heavy metals (lead, zinc, barium) NBLs in four groundwater units of the Pearl River Delta (PRD) where urbanization continues, and to identify the best one for assessing groundwater NBLs in urbanized areas. Here, methods include a preselection method (method-P), a preselection dominated method (method-PD), and a statistic dominated method (method-SD). Results showed that the method-PD was better than other two methods for assessing groundwater NBLs of heavy metals in the PRD. This is supported by the evidence that differences among heavy metals concentrations in various land-use types in residual datasets formed by the method-PD were insignificant. NBLs of lead in groundwater units I to IV assessed by the method-PD were 2.8 μg/L, 5.9 μg/L, 5.8 μg/L, and 2.6 μg/L, respectively. NBLs of zinc in groundwater units I to IV assessed by the method-PD were 30 μg/L, 180 μg/L, 160 μg/L, and 100 μg/L, respectively. NBLs of barium in groundwater units I to IV assessed by the method-PD were 120 μg/L, 120 μg/L, 90 μg/L, and 50 μg/L, respectively. Compared to the method-PD, the method-SD often underestimates groundwater NBLs of heavy metals because of using the experiential evaluation for residual datasets. The method-P also has an inaccurate evaluation of groundwater NBLs of heavy metals in comparison with the method-PD, owing to both of using the experiential evaluation and the absence of a function for outliers test. The method-P combining with an outliers test would be better than itself for assessing groundwater NBLs. Therefore, the method-PD is the first choice to be recommended for assessing groundwater NBLs in urbanized areas such the PRD. However, this method should not be taken into account for assessing groundwater NBLs in areas where groundwater Cl/Br mass ratios are invalid. Instead, the method-SD and the method-P combining with one outliers test may be choices, because no constraint for these two methods.

Interaction between the hydrochemical environment, dissolved organic matter, and microbial communities in groundwater: A case study of a vegetable cultivation area in Huaibei Plain, China

Intensive vegetable planting has a profound impact on the surrounding aquatic environment. The self-purification ability of groundwater is poor, and it is difficult to return groundwater to its original state once polluted. Therefore, it is necessary to clarify the impact of intensive vegetable planting on groundwater. This study selected the groundwater of a typical intensive vegetable planting base in the Huaibei Plain of China as the research object. This work analyzed the content of major ions, the dissolved organic matter (DOM) composition, and the bacterial community structure in groundwater. Redundancy analysis was used to explore the interactions between the major ions, the DOM composition, and the microbial community. The results showed that under the influence of intensive vegetable planting, the F- and NO3--N contents in groundwater were significantly increased; the excitation-emission matrix combined with parallel factor analysis identified four fluorescent components (C1 and C2 were humus-like components, while C3 and C4 were protein-like components), which mainly consisted of protein-like components. Proteobacteria was the dominant phylum (mean = 69.27 %), followed by Actinobacteriota (mean = 7.25 %) and Firmicutes (mean = 4.02 %), which together explained over 80 % of the total abundance; and TDS, pH, K+, and C3 were the main influencing factors affecting the microbial community structure. This study provides a better understanding of the impact of intensive vegetable cultivation on groundwater.

A preliminary approach based on numerical simulation modelling and evaluation of permeable reactive barrier for aquifer remediation susceptible to selenium contaminant

In this study, numerical groundwater modelling software (GMS) was applied for a 2D transient state predictive (flow and contaminant fate and transport) conceptual model for heavy metal (Selenium in this research) contaminated groundwater, Imamzadeh-Jafar Aquifer, Kohgiluyeh and Boyer-Ahmad Province, Iran. The performances of permeable reactive barrier (PRB) in pollutant removal in the contaminated aquifers were studied by helping the MODFLOW-MT3DMS model. The spatiotemporal distribution of Selenium (Se) contaminant over the aquifer was illustrated using the calibrated flow and contaminant model. According to the findings, the downward movement of Se has resulted in an unsafe and undesirable water quality status in the Imamzadeh-Jafar aquifer, which is supported by field data. The sensitivity analysis of PRB layouts, geometric features, and reactant material characteristics was conducted in groundwater remediation. The numerical model results illustrated that the PRB thickness, ranging from 10 to 500 m, manifested the drop in Se concentration approximately from 40 to 46%. The results shed light on the hydraulic conductivity variations of reactant materials have effects less than 0.5% in Se removals. Furthermore, the decay rate variations in the ranges from 0.0001 to 0.01 d^-1 could result in Se removal from 5 to 100%. According to studies, if the contaminant sources are prevented, in a) installation of PRB and b) not installation of PRB scenarios, the Imamzadeh-Jafar aquifer remediation will take 6 months and 84 months, respectively.

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.

Risk Control Values and Remediation Goals for Benzo[a]pyrene in Contaminated Sites: Sectoral Characteristics, Temporal Trends, and Empirical Implications

Benzo[a]pyrene (BaP) is a highly carcinogenic pollutant of global concern. There is a need for a comprehensive assessment of regulation decisions for BaP-contaminated site management. Herein, we present a quantitative evaluation of remediation decisions from 206 contaminated sites throughout China between 2011 and 2021 using the cumulative distribution function (CDF) and related statistical methodologies. Generally, remediation decisions seek to establish remediation goals (RGs) based on the risk control values (RCVs). Cumulative frequency distributions, followed non-normal S-curve, emerged multiple nonrandom clusters. These clusters are consistent with regulatory guidance values (RGVs), of national and local soil levels in China. Additionally, priority interventions for contaminated sites were determined by prioritizing RCVs and identifying differences across industrial sectors. Notably, we found that RCVs and RGs became more relaxed over time, effectively reducing conservation and unsustainable social and economic impacts. The joint probability curve was applied to model decision values, which afforded a generic empirically important RG of 0.57 mg/kg. Overall, these findings will help decision-makers and governments develop appropriate remediation strategies for BaP as a ubiquitous priority pollutant.

Health risks of shallow groundwater in the five basins of Shanxi, China: Geographical, geological and human activity roles

Rapid economic development often leads to groundwater degradation, posing health risks to those who rely on it. The groundwater discharge conditions in basins are poor. The health risk of shallow groundwater in basins needs more attentions. The health risk of shallow groundwater in the five basins of Shanxi Province, China was discussed based on the hydrochemical evolution of shallow groundwater and the water quality assessment. The results showed that arsenic (As) and chromium (Cr) in the shallow groundwater of the basins caused prominent health risks followed by fluoride (F) and nitrate (NO₃^-). The non-carcinogenic risks of As, F and NO₃- to children were higher than that to adults, and the carcinogenic risks of As and Cr were higher for adults than children. Various hydrogeochemical reactions, geological conditions, climatic factors, and human activities are closely related to groundwater health risks, and basin topography is considered as one of key factors. Water-rock interaction, dedolomitization and cation exchange are the natural processes in the evolution of groundwater hydrochemistry, while agricultural and mining activities are the anthropogenic factors causing groundwater degradation. The leaching/dilution effects of infiltration precipitation in the basin-mountain systems cause distinct temporal changes in the chemical composition and health risks of the groundwater in the basins. Differences in climate and farming practices among the basins further complicate the spatio-temporal changes. The basin-mountain system is conducive to the convergence and enrichment of water flow and solutes in the basins, which aggravates the degradation of groundwater quality. This study highlights that the combined influences of geographical and geological factors and anthropogenic activities amplify the human health risks of groundwater in the basins.

Quantifying nitrate pollution sources of shallow groundwater and related health risks based on deterministic and Monte Carlo models: A study in Huaibei mining area, Huaibei coalfield, China

Nitrate pollution in groundwater is a global environmental concern. As a result, accurate identification of potential sources for such pollution is of critical significance to the effective control of groundwater quality. In this study, forty-nine shallow groundwater samples were collected from the Huaibei mining area. Hydro-chemical characterization, geospatial analysis technique, dual nitrate isotopes (δ15N-NO3- and δ18O-NO3-), Bayesian model and health risk assessment model were adopted for exploring the conditions, sources, proportion, and potential health risks of nitrate pollution for the first time in the study area. The results showed that the nitrate concentration ranged from 0.00 to 293.21 mg/L, and that 18.37% groundwater samples exceeded the standard of drinking water in China (GB 5749-2006). Based on the dual isotopic values of nitrate, it could be concluded that nitrification was dominated migration and transformation process of nitrogen. The results of Bayesian model showed that the proportional contributions of the potential nitrate pollution sources in shallow groundwater were manure and sewage (M&S) (39.54 %), NH4+ in fertilizer and precipitation (NHF&P) (34.93 %), soil nitrogen (SN) (14.89 %), and NO3- in atmospheric deposition (NAD) (10.64 %). The health risk assessment indicated that non-carcinogenic risks posed by NO3--N was higher for children than adults. The primary exposure pathway was oral ingestion. Monte Carlo simulation were applied to evaluate model uncertainty. The probabilities of non-carcinogenic risks were up to 12.54 % for children and 5.22 % for adults. In order to protect water quality and drinking water safety, it was suggested that effective nitrate reduction strategies and better management practices can be implemented.

Statistical analyses of hydrochemistry in multi-aquifers of the Pansan coalmine, Huainan coalfield, China: implications for water-rock interaction and hydraulic connection

Understanding the groundwater hydrogeochemical processes and aquifer hydraulic connections are essential for effective prevention of water inrush in concealed coal mines. In this study, 40 groundwater samples were collected from the loose layer aquifer (LA), coal measure aquifer (CA), and limestone aquifer (LA) in the Pansan coal mine, Huanan coalfield, China, and the major ion concentrations were analyzed by bivariate diagrams (Na⁺ + K⁺ - Cl⁻ versus Ca²⁺ + Mg²⁺ - SO₄²⁻ - HCO₃⁻ and CAI-I versus CAI-II), multivariate statistical methods, and receptor model in order to identify the water-rock interactions and aquifer hydraulic connections. Piper diagram showed that groundwater in LA and TA was dominated by the Na–Cl type, while groundwater in CA was mainly of the Na–HCO₃ type. Based on the results of bivariate diagrams and PCA/FA, weathering of silicate minerals and cation exchange (source 1), sulfate dissolution (source 2) and chloride dissolution (source 3) were the main processes controlling the groundwater chemistry. Unmix model revealed that the mean contribution of source 1 to CA samples was 74%, while LA and TA samples have higher contributions from evaporite dissolution (source 2 and source 3) relative to CA samples. Moreover, both clustering analysis methods (Q-type hierarchical and K-means cluster) confirmed the existence of a hydraulic connection between LA and TA in the northeastern part of the study area. It is concluded that the application of multivariate statistical analysis to interpret groundwater chemistry can provide useful guidance to prevent water inrush in coal mines.

Seasonal changes of heavy metals and health risk assessment based on Monte Carlo simulation in alternate water sources of the Xinbian River in Suzhou City, Huaibei Plain, China

The urban alternate water source (AWS) is of great significance to the sustainable development of the city, the pollution degree, and source of heavy metals (HMs) in AWS, and whether it will adversely affect human health has received widespread attention. In this study, the urban AWS of Xinbian River in Suzhou City, Huaibei Plain, China, was used as the research object to study the seasonal changes of HMs (As, Cr, Cu, Cd, Pb, and Zn), quantitative identification of pollution sources, and human health risks (HHR). Research results show that the contents of those HMs, except As, are less than the drinking standards limit set by the World Health Organization (WHO), and the contents of As, Cr, and Zn are the largest in summer. The multivariate statistical analysis combined with positive matrix factorization (PMF) model analysis revealed that industrial sources accounted for 44.83%, and agricultural sources accounted for 55.17%. HHR assessment based on Monte Carlo simulation shows that the noncarcinogenic risks of adults and children are in the acceptable range (hazardous ingestion (HI) < 1), and the probability of carcinogenic risk values of children and adults are 95.03% and 38.96%, respectively, which are exceed the acceptable range (1 × 10^-4) recommended by the United States Environment Protection Agency (USEPA). Approximately 30.75% of the carcinogenic risk value of agricultural source HMs to children exceeds the acceptable range (1 × 10^-4). The above research results indicate that the effect of agricultural non-point source pollution on AWS should be prevented.

Combined Modeling of Multivariate Analysis and Geostatistics in Assessing Groundwater Irrigation Sustenance in the Middle Cheliff Plain (North Africa)

The assessment of groundwater irrigation using robust tools is essential for the sustenance of the agro-environment in arid and semi-arid regions. This study presents a reliable method consisting of a combination of multivariate analysis and geostatistical modeling to assess groundwater irrigation resources in the Western Middle Cheliff (Algeria). For this goal, mean data from 87 wells collected during April to July 2017 were used. The hierarchical cluster analysis (HCA) using the Q-mode approach revealed three distinct water types, with mineralization increasing from cluster 1 to cluster 3. The Principal Component Analysis (PCA) utilizing the Varimax method approach allowed the extraction of three main components: the first and second (PC1, PC2), revealing that the geogenic process, have influenced the hydrogeochemical composition of groundwater. The pollution induced by agriculture activities has been related to PC3. Based on the combination of multivariate analysis and geostatistical modeling, the distribution maps were created by interpolating the factor distribution values acquired in the study region using the ordinary kriging (OK) interpolation method. The findings revealed that both natural processes and man-made activities have a substantial impact on the quality of groundwater irrigation. Cluster mapping, another often used combining approach, has shown its effectiveness in assisting groundwater resource management.