Linhua Sun

Accurate source apportionment of soil heavy metals (HMs) is critical for targeted pollution mitigation and ecological remediation. This review systematically synthesizes and compares five mainstream source apportionment approaches—receptor models (positive matrix factorization, PMF; absolute principal component score-multiple linear regression, APCS-MLR; UNMIX model), stable isotope tracing, and random forest (RF)-based machine learning—to provide researchers with a comprehensive methodological framework. The methodology includes a systematic literature review, comparative analysis of methodological principles, and synthesis of representative case studies from diverse geographical contexts. The core principles, evolutionary paths, typical use cases (e.g., industrial zones, agricultural fields, regional surveys), and inherent limitations are synthesized for each method. A practical decision framework linking research contexts (study objectives, spatial scales, data availability) to optimal method selection, along with guidelines for multi-method integration, is proposed. This review provides actionable guidance for researchers and practitioners in selecting appropriate methods for specific pollution scenarios, ultimately supporting more effective environmental management and policy development.

Getting information on evaporation is important for water resource protection and managing regional enclosed waterbodies. Some lake water samples were collected in May and June from a representative enclosed waterbody – the Pearl Lake, Suzhou, China – and the hydrogen and oxygen isotopes determined. Most samples taken in June have higher δ¹⁸O and δD values than those in May, indicating that there are differences between the intensity of evaporation of May and June. This was further confirmed by the water samples’ d-excess values, which were below the meteoric line. However, the variations of d-excess value have been shown to be influenced by source moistures or evaporation processes, which were controlled by the local environment, e.g., whether the water was shaded or not. The residual water quantities calculated were 53 to 63% and 49 to 56% for May and June, respectively, relative to the initial water, indicating that about 5% of the water evaporated between 5 May and 5 June. Keyword : evaporation, enclosed waterbody, hydrogen and oxygen stable isotopes, Pearl Lake, residual water quantities

Hydrochemistry of groundwater is important in coal mines because it can be used for understanding water–rock interaction and inrush water source identification. In this study, major ion concentrations of groundwater samples from the loose layer aquifer (LA), coal-bearing aquifer (CA) and Taiyuan Formation limestone aquifer (TA) in the Qingdong coal mine, northern Anhui province, China, have been analyzed by a series of statistical methods for identifying the source of chemical constituents in groundwater and the source of inrush water. The results indicate that the mean concentration of the major ions in the LA were ordered as follows: HCO₃⁻ > SO₄²⁻ > Na⁺ + K⁺ > Cl⁻ > Ca²⁺ > Mg²⁺ > CO₃²⁻, whereas average values of the CA in decreasing order are SO₄²⁻, Na⁺ + K⁺, HCO₃⁻, Cl⁻, Ca²⁺, Mg²⁺ and CO₃²⁻, and the major ion concentrations of the TA have the following order: SO₄²⁻ > Na⁺ + K⁺ > Ca²⁺ > HCO₃⁻ > Cl⁻ > Mg²⁺ > CO₃²⁻, and most of the samples are Na-SO₄ and Ca-SO₄ types. TDS content in water increases with aquifer depth, whereas the pH values ranged from 7.1 to 8.9, indicating a weak alkaline environment. Two sources (weathering of silicate minerals and dissolution of evaporate minerals) have been identified by principal component analysis responsible for the chemical variations of the groundwater, and their contribution ratios have been quantified by Unmix model. Moreover, based on the Q-mode cluster and discriminant analyses, the samples with known sources have been identified correctly to be 95.7% and 97.6%, respectively, and the samples with unknown sources have been determined with high probability (78–100%).

The δ2H and δ18O values in water bodies are essential to the management of water resources because of the ability to insight into hydrological processes. In this study, we have measured and analyzed the major ions (Na+, K+, Ca2+, Mg2+, Cl–, SO24– and HCO–3 ) and stable H-O isotopes (δ2H and δ18O) for fifteen surface water samples collected from the Xinbian River in Suzhou, northern Anhui Province, China. The results show that all of the water samples are classified to be Na-HCO3 type, and the mean values of δ2H and δ18O are –42.93‰ and –5.36‰, respectively. Gibbs diagram and the relationship between δ2H and δ18O indicate that both water chemistry and stable isotopes in river water are mainly controlled by evaporation. Correlation analysis reveals that a significant correlation between major ions and δ18O. Predictors (K+, SO24– and HCO–3 ) have been selected by optimal subset regression analysis were used to model the δ18O values in the river water. Moreover, the residuals of the model were normally distributed and values between –0.2‰ to 0.2‰ for most water samples, suggesting a strong relationship between the observed and predicted δ18O values.

Understanding hydrological process of surface water and groundwater is significant for the management of urban water resources. In this study, a total of thirty-seven water samples have been collected from the river (RW, 15 samples), shallow aquifer (SG, 12 samples), and deep aquifer (DG, 10 samples) in Suzhou, Northern Anhui Province, China, and their major ion concentrations and stable H–O isotopes have been measured. The results revealed that Na⁺and HCO₃⁻were the dominant cation and anion, respectively, and most of the water samples are classified to be Na-HCO₃type, to a lesser extent, Mg-HCO₃type. K-mean and Q-type clustering analyses ruled out the hydrological relationship between river and groundwater, but there was a significant connectivity between shallow and deep groundwater, which was further confirmed by the hydrogen and oxygen isotopes. The relationship between δ²H and δ¹⁸O has shown that precipitation was the main source of the groundwater in the study area. Furthermore, the values of deuterium excess (d-excess) in different water bodies suggested that the groundwater has not been affected by evaporation, which was the main process controlling the isotopic composition of river water.

Coal mining in northern Anhui Province of China has led to a series of environmental problems. In this study, a total of 68 surface soil samples around a representative coalmine (the Haizi coalmine) in the area have been collected and then analyzed for seven kinds of heavy metal concentrations (Cu, Fe, Zn, Co, Ni, Mn and Pb) for getting information about their pollution degrees and sources. The results indicate that the metal concentrations are Fe>Mn>Zn>Pb>Cu>Ni>Co, and all of them have coefficients of variation ranging between 0.13 and 0.75, and low p-values (<0.01) of normal distribution test except for Fe, Co and Ni, which suggests that their concentrations have been affected by multiple factors. The single pollution index and geo-accumulation index imply that zinc and lead are light pollution, and the Nemerow composite index and the potential ecological risk index suggest that the soils in this study are slightly polluted and with low potential ecological risk. The spatial distributions of the metal concentrations, along with the statistical analyses (including correlation, cluster and factor analyses) indicate that all of the metals can be classified to be two groups, the Fe-Co-Mn and Cu-Zn-Pb-Ni, which mean geogenic and anthropogenic sources, respectively, and their mean contributions for the heavy metal concentrations in the study area are 57.1% and 42.9%, respectively, as calculated by the Unmix model. Suzhou University, 49# Bianhe Road, Suzhou City, Anhui Province, China, 234000 Suzhou, China

Local environmental background is important for environmental management. In this study, lead concentrations of shallow groundwater samples from the urban area of Suzhou in Anhui Province, China were measured and analyzed by statistical and spatial analyses for calculating the environmental background value. The results show that the lead concentrations in the groundwater range from 4.16-11.5 μg/L, and all of the samples were classified to be Class III or better according to the groundwater quality standard of China. The samples have medium coefficient of variation and low p-values of normal distribution test, suggesting that it may have been influenced by anthropogenic activities, which was further demonstrated by the consistency of the distribution of the samples with high lead concentrations and the areas with high density of transportation, as well as the high-low cluster of the spatial autocorrelation analysis. The environmental background values have been calculated to be 3.74-8.62 and 3.48-10.3 μg/L with box plot and spatial autocorrelation analyses, respectively. The study demonstrated that for calculating the environmental background value, the statistical and spatial methods should be chosen according to the current state – especially pre-consideration about the distribution of the elements or pollutants.

Water source identification is important for water hazard controlling in coal mines. In this study, major ion concentrations of the groundwater collected from four representative aquifer systems in the Baishan coal mine, northern Anhui Province, China, have been analysed by a series of statistical methods. The results indicate that the major ion concentrations of the groundwater from different aquifer system are different with each other, and provided the possibility of water source identification based on hydrochemistry. Factor analysis indicates that these differences are controlled by different types of water rock interactions. The analysis based on US Environmental Protection Agency (EPA) Unmix model identified three sources (weathering of silicate minerals, dissolution of carbonate and evaporate minerals) responsible for the hydrochemical variations of the groundwater. Also, it shows that their contributions for the groundwater in different aquifer systems vary considerably. Based on these variations and on step by step analysis, the source aquifer system for the groundwater samples with unknown source has been determined and, similar to the result obtained by the cluster and discriminant analysis. Therefore, EPA Unmix model can be applied for water source identification in coal mine, as it can provide information about water rock interaction and water source identification simultaneously.

Water in the subsidence area is a good choice for solving the water shortage in the coal mining area of China. In this study, positive matrix factorization model has been applied for the concentrations of seven kinds of trace metals (Pb, V, Cr, Mn, Co, Ni and Cu) in water from the subsidence area in the Luling coal mine, northern Anhui Province, China for identifying and quantifying their potential sources. Statistical analyses (including coefficients of variation and P-value of Anderson–Darling test) of metal concentrations indicate that multi factors (geological weathering/dissolution, filling of coal gauge and anthropogenic discharge) are responsible for the metal concentrations in the water. Based on the variations of Q values, three sources have been determined by US EPA (US Environmental Protection Agency) positive matrix factorization model: coal gauge filling, geological weathering or dissolution and waste discharge. The contribution degrees of these sources for all of the pools are different, and therefore, different strategies (e.g. clean the waste and the coal gauge around and in the pools) should be applied with different pools.