APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Revealing nitrate sources seasonal difference between groundwater and surface water in China's largest fresh water lake (Poyang Lake): Insights from sources proportion, dynamic evolution and driving forces

Tracing the source of nitrate is the key path to solve the problem of nitrogen pollution. However, the seasonal difference of nitrate sources in groundwater and surface water and its dynamic evolution process and mechanism in large fresh water lake area are still not clear. In this study, 126 water samples were collected from groundwater and surface water in China's largest fresh water lake (Poyang Lake) region from 2022 to 2023. Bayesian stable isotope mixing model, absolute principal component score-multiple linear regression, ion ratio coefficients and uncertainty index (UI90) were used to investigate the nitrate sources variation in groundwater and surface water as well as its uncertainty in Poyang Lake area. Results showed that anthropogenic influence had significant influence on nitrate sources, which was mainly affected by chemical fertilizer (CF), soil nitrogen (SN) and manure and sewage input (M&S). Specifically, from 2022 to 2023, CF contributed 16.6 % to 32.4 %, SN contributed 26.0 % to 38.1 %, M&S contributed 26.5 % to 48.2 % to groundwater. CF contributed 38.8 % to 43.9 %, SN contributed 37.6 % to 40.6 %, M&S contributed 12.3 % to 18.6 % to surface water. The sources and proportion of nitrate in groundwater and surface water exhibited obvious difference. Temporal heterogeneity, land use type, population density and vegetation cover type had influence on nitrate sources. UI90 results showed that there was uncertainty in nitrate sources tracing process, with SN (mean 0.78), CF (mean 0.64), M&S (mean 0.35) and AD (mean 0.09), respectively. These results will provide vital references for understanding nitrate sources variation, controlling and removing nitrate surplus in groundwater-surface water system in the similar large fresh water lake areas.

Mapping of heavy metal pollution density and source distribution of campus soil using geographical information system

In this study, the pollution intensity, spatial distribution, and index-based risk distribution in campuses, which are a small prototype of cities, were mapped and the sources of heavy metals in the soil were investigated. Soil samples were taken from 9 different points from the Aksaray University Central campus, which was determined as the study area. It has been determined that the pH value in the collected soil samples varies between 8.7 and 11.0. This situation created an effect on reducing the accumulation and mobility of heavy metals in the soil. When the study area was evaluated based on the geo-accumulation index, Pb heavy metal was much denser in the places indicated as circulation areas and where students were actively present. Based on the pollution load index, it was concluded that 75% of the study area was moderately/highly polluted, and the rest consisted of unpolluted soils. Pearson correlation analysis and APCS-MLR analyses conducted to determine the source distribution showed that the contributions of natural sources, mixed sources of industrial and traffic activities, agricultural activity-based sources, and other sources were 57.49%, 21.44%, 12.67%, and 8.40%, respectively. Pb is mainly related to the mixed sources of industrial and traffic activities. Therefore, to clear up its long-term impact on the accumulation of heavy metals in the soil, it is important to conduct continuous heavy metal monitoring in the soil throughout the campus.

New insights into the controlling factors of nitrate spatiotemporal characteristics in groundwater of Dagu aquifer in Qingdao, China

Identifying spatiotemporal variation of groundwater NO3-N and its primary controlling factors are vital for groundwater protection. This study, under the data scarce conditions and based on time series monitoring data in Dagu aquifer, applied methods including hydrochemical ion ratio, multiple linear regression, support vector regression and grey relational analysis and dedicated to revealing primary controlling factors of temporal variation patterns of groundwater NO3-N. The results showed that agricultural and manure fertilizer are the main sources of NO3-N in north and central area (vegetable farming area), and that domestic sewage discharge and manure fertilizer are the main sources of NO3-N in south area (residential and grain planting area). In addition, results identified the dominant influencing factors of variation of NO3-N in different regions, with human wastewater discharge, nitrogen load amount and water-table depth being the dominant factors of variations of NO3-N in north area, human wastewater discharge being the main factor of variations of NO3-N in central area, and irrigation water and human wastewater being the leading factors of variations of NO3-N in south area. Moreover, types of controlling factors can influence the seasonal variations of NO3-N. NO3-N in vegetable farming area that dominantly affected by fertilization generally shows higher concentration and larger variation range of concentration during summer and autumn than that during spring. NO3-N which mainly affected by human wastewater discharge and manure inputs shows minimal seasonal variation of mean concentration. NO3-N in grain area influenced by irrigation could show more significant variations during spring and autumn than that during summer. The conclusions can enhance understandings of major influencing factors on NO3-N variation in local aquifer. Importantly, the dominant roles of water-table depth and irrigation in NO3-N variation of N2 site (vegetable planting area) and S5 site (grain planting area), respectively, were highlighted.

Evaluation of groundwater quality with multi-source pollution based on source identification and health risks

Groundwater is a crucial water supply source in Chengdu City, western China, a region experiencing significant water scarcity. The sources of inorganic pollutants in groundwater and their potential health risks are of great concern. In this study, based on 156 groundwater samples collected in 2021 in the study area were analyzed for hydrochemical characterization and controlling factors. The Positive Matrix Factorization (PMF) model was used for contaminant source analysis, and Monte Carlo Simulation (MCS) combined with the Health Risk Evaluation Model (HREM) was used to quantify the health risks. The results indicate that the groundwater in the study area is predominantly of the Ca·Na-SO4·HCO3, Ca·Na-HCO3·SO4 and Ca-HCO3·SO4 types, mainly influenced by the combination of evaporation-concentration-crystallization and rock leaching-weathering. K+, Na+, and Cl- mainly originate from the weathering and dissolution of potassium feldspar and rock salt, while Ca2+, Mg2+, HCO3-, and SO42- primarily come from the weathering and dissolution of sulfate minerals. The main sources of groundwater pollution and their contributions are as follows: domestic pollution (25.6 %), dissolution-filtration-evaporation-concentration action (22.8 %), hydrogeochemical evolution (15.8 %), water-rock interactions (12.8 %), primary geologic context (12.1 %), and agricultural non-point source pollution (11.0 %). Cl- and As are the primary contributors to non-carcinogenic and carcinogenic risks, respectively. Non-carcinogenic risks are below USEPA standards, while the average carcinogenic risk for arsenic exceeded the maximum acceptable risk level thresholds by 23 and 109 times for adults and children, respectively. Non-carcinogenic and carcinogenic health risks were mainly influenced by pollutant concentrations. The primary geological background and domestic pollution contributed the most to the non-carcinogenic risk for adults (50.3 %) and children (77.1 %), and 38.2 % and 10.3 %, respectively. This study highlights the necessity of establishing a comprehensive groundwater pollution monitoring system, enhancing industrial waste management practices, and raising public awareness to mitigate contamination and ensure the sustainable use of groundwater resources in Chengdu City.

Assessment of health risks based on different populations and sources of heavy metals on agricultural lane in Tengzhou City by APCS-MLR models

To identify the sources of heavy metals in local soils and their risks to human health. This study quantified the concentrations of eight heavy metals in 504 soil samples collected in Tengzhou, China. The ecological risks of a single heavy metal (EI), a comprehensive ecological risk index (RI), and a health risk assessment model were used to evaluate the level of contamination in the city. The results of the research study indicate that there are different levels of heavy metal pollution in rural and urban agricultural areas in Tengzhou. Moreover, the spatial variability of mercury (Hg) is considerable, reaching 0.96, indicating a significant impact of anthropogenic activities. For the ecological risk, the heavy metal element with the highest EI value was mercury with a mean value of 67.22 and a peak value of 776.00. The heavy metal with the lowest mean EI value was Zn with only 1.03. Meanwhile, the average RI is only 128.59, but some areas have an RI as high as 842.2. The sources of heavy metals were identified using principal component analysis, correlation analysis, and an absolute principal component score multiple linear regression model (APCS-MLR). The non-carcinogenic risk for children, the carcinogenic risk for children, and the carcinogenic risk for adults were 1.23, 2.42×10-4 and 1.00×10-4, respectively, and these values exceeded their respective recommended values, and As and Cr had some carcinogenic hazards. Heavy metals in the soil come from natural, industrial, traffic and agricultural sources and represent 39.59%, 29.48%, 25.17% and 5.77%, respectively. The main source of heavy metals in local agricultural soils is the geological background, and the government needs to strengthen the monitoring of As and Cr in drinking water resources, as well as reduce traffic pollution and factory waste emissions to reduce Hg in soils.

Chronological evaluation of polycyclic aromatic hydrocarbons in sediments of tangxun lake in central China and impacts of human activities

This study sheds light on the contamination of polycyclic aromatic hydrocarbons (PAHs) in Tangxun Lake sediments, an urban lake reflecting environmental changes in Central China. By analyzing sediment cores from both the inner and outer areas of the lake, we determined the historical trends and sources of PAHs over the past century. The results reveal a significant increase in PAHs concentrations, particularly since the 1980s, coinciding with China's rapid urbanization and industrialization. Using diagnostic ratios and Absolute principal component score-multivariate linear regression (APCS-MLR) methods, we identified petroleum combustion, coal combustion, and biomass combustion as the primary sources of PAHs in the lake sediments. The spatial analysis indicates higher PAHs levels in the inner lake, likely due to its closer proximity to industrial activities. Moreover, by comparing PAH trends in Tangxun Lake with those in other urban, suburban, and remote lakes across China, based on data from 49 sedimentary cores, we highlight the impact of regional socio-economic dynamics on PAH deposition. These insights are crucial for developing effective pollution mitigation strategies and promoting sustainable development in rapidly urbanizing regions.

How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5 years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.

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

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

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

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.

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

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

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

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

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

Spatial distribution characteristics and source analysis of shallow groundwater pollution in typical areas of Yangtze River Delta

With the accelerated industrialisation and urbanisation in the Yangtze River Delta region, shallow groundwater has also suffered some pollution. In order to clarify the current state of pollution and its causes, principal component analysis (PCA) was used to classify the main factors. An absolute principal component-multiple linear regression model (APCS-MLR) was also used to determine the contribution of the main pollution sources to the shallow groundwater quality. The analysis results showed that the exceedance of water quality indicators such as iron, manganese, aluminium and COD in shallow groundwater in the study area was more serious. With the exception of Mg2+, the other indicators are highly variable and are more likely to be influenced by human factors. The main pollution factor affecting the quality of shallow groundwater is the dissolution filtration - migration enrichment factor, followed by agricultural and urban sewage factors, industrial pollution factors, and geological environmental background factors. And the main sources of pollution in shallow groundwater are mainly located in the western, northwestern and northern parts of the study area. The use of fertilizers and pesticides in urban life and urban runoff, industrial production and agricultural production are the main causes of pollution. The effects of leaching and transport enrichment, agricultural and urban runoff, and industrial pollution on groundwater quality are significant. The predicted concentrations calculated by the model are generally consistent with the measured concentrations, indicating the accuracy of the calculation of the contribution of each pollution source. This study is applicable to the analysis of shallow groundwater pollution sources in the study area, and also provides a scientific basis for the evaluation of regional groundwater resources and pollution prevention.

A critical review of the recent trends in source tracing of microplastics in the environment

Microplastics are found across the globe because of their size and ability to transport across environments. The effects of microplastics on the micro- and macro-organisms have brought out concern over the potential risk to human health and the need to regulate their distribution at the source. Control of microplastic pollution requires region-specific management and mitigation strategies which can be developed with the information on sources and their contributions. This review provides an overview of the sources, fate, and distribution of microplastics along with techniques to source-trace microplastics. Source-tracing approaches provide both qualitative and quantitive information. Since better outcomes have been produced by the integration of techniques like backward trajectory analysis with cluster analysis, the significance of integrated and multi-dimensional approaches has been emphasized. The scope of the plastisphere, heavy metal, and biofilm microbial community in tracing the sources of microplastics are also highlighted. The present review allows the researchers and policymakers to understand the recent trends in the source-tracing of microplastics which will help them to develop techniques and comprehensive action plans to limit the microplastic discharge at sources.

Combining multi-isotope technology, hydrochemical information, and MixSIAR model to identify and quantify nitrate sources of groundwater and surface water in a multi-land use region

Accurate identification of nitrate (NO3-) sources is the premise of non-point source pollution control in watersheds. The multiple isotope techniques (δ15N-NO3-, δ18O-NO3-, δ2H-H2O, δ18O-H2O), combined with hydrochemistry characteristics, land use information, and Bayesian stable isotope mixing model (MixSIAR), were used to identify the sources and contributions of NO3- in the agricultural watershed of the upper Zihe River, China. A total of 43 groundwater (GW) and 7 surface water (SFW) samples were collected. The results showed that NO3- concentrations of 30.23% GW samples exceeded the WHO maximum permissible limit level, whereas SFW samples did not exceed the standard. The NO3- content of GW varied significantly among different land uses. The averaged GW NO3- content in livestock farms (LF) was the highest, followed by vegetable plots (VP), kiwifruit orchards (KF), croplands (CL), and woodlands (WL). Nitrification was the main transformation process of nitrogen, while denitrification was not significant. Hydrochemical analysis results combined with NO isotopes biplot showed that manure and sewage (M&S), NH4+ fertilizers (NHF), and soil organic nitrogen (SON) were the mixed sources of NO3-. The MixSIAR model summarized that M&S was the main NO3- contributor for the entire watershed, SFW, and GW. For contribution rates of sources in GW of different land use patterns, the main contributor in KF was M&S (contributing 59.00% on average), while M&S (46.70%) and SON (33.50%) contributed significantly to NO3- in CL. Combined with the traceability results and the situation that land use patterns are changing from CL to KF in this area, improving fertilization patterns and increasing manure use efficiency are necessary to reduce NO3- input. These research results will serve as a theoretical foundation for controlling NO3- pollution in the watershed and adjusting agricultural planting structures.

Residues, potential source and ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in surface water of the East Liao River, Jilin Province, China

The environmental risks posed by polycyclic aromatic hydrocarbons (PAHs) and the diversity of their anthropogenic origins make them a global issue. Therefore, it is of utmost significance for protecting the aquatic environment and the growth of neighboring populations to identify their possible origins and ecological risk. Here, we detail the contamination profiles of 15 PAHs found in the East Liao River's surface waters in Jilin Province and use the receptor model Absolute Principal Component Analysis - Multiple Linear Regression (APCS-MLR) and diagnostic ratios method to identify the primary potential sources of pollution. Based on the natural hazard risk formation theory (NHRFT), an ecological risk assessment (ERA) model for PAHs in the East Liao River was developed. The method assesses the ecological risk status of PAHs by integrating the risk quotient (RQ) approach and the DPSIRM (driving force, pressure, state, impact, response, management) conceptual framework. Total concentrations in the surface water body were between 396.42 and 624.06 ng/L, with an average of 436.99 ng/L. The source research revealed that coal, biomass, and traffic emission sources are the most likely PAH contributors to the East Liao River. The ERA found that the majority of the sites' locations of the study were at low risk for PAHs in surface water bodies (30.7 % and 32.2 %, respectively), while only a tiny percentage of sites were at high or very high risk (1.8 % and 13.6 %). The study results provide theoretical support for the East Liao River's ecological, environmental protection, and policy formulation.

Dynamic influence of land reclamation on the nitrate contamination and saltwater redistribution

Previous research concerning the effect of land reclamation on seawater intrusion mostly focused on the modification of the saltwater wedge and the dynamics of freshwater-saltwater interface after land reclamation, utilizing both analytical and numerical models. So far, the impact of land reclamation on the recharging and accumulation of land-based pollutants such as nitrate has been disregarded. In this work, we are the first to examine the impact of land reclamation on the discharge of nitrate together with the movement of saltwater. The influence of reclamation area and filled soil permeability on nitrate pollution and saltwater redistribution is revealed using a series of field-scale simulations based on numerical models including density flow combined with reactive transport. It was discovered that land reclamation might, on the one hand, result in a substantial redistribution based on the initial saltwater-freshwater interface and, on the other hand, significantly modify the nitrate discharge. This in total would drastically alter the distribution of nitrate in the subsurface. The reclamation area and the permeability of the reclamation material are the two elements that determine the amount of variance. For the cases with hydraulic conductivities increasing from 5 to 50 m/d, the salt mass reduction rate showed a trend of first increased (84.78 %-95.58 %) and then slowly decreased (95.58 %-74.01 %). Meanwhile, the nitrate reduction rate decreased from 80.08 % to 12.93 %, when hydraulic conductivities increased from 5 to 50 m/d. It was also found that coastal nitrate accumulation was always intensified with the enlargement of the reclamation area. Finally, we are able to assist engineers in optimizing their land reclamation strategies by taking into account both the degree of saltwater intrusion and nitrate enrichment.

A novel pollution index to assess the metal bioavailability and ecological risks in sediments

The chemical forms of metals in sediments of ports around Taiwan were investigated using sequential extraction. Based on the availability of different chemical forms, novel indices such as bioavailability, mobility, availability, and availability risk of metals in sediments were developed. The results showed that Co, Zn, Pb, Mn, and Cu were mainly present in available forms (49-84 %), and the proportion of oxidative or reductive fractionation was the highest. This suggests that the redox potential is a major factor for metal mobility in the sediments. The results from the proposed indexes showed that metals in sediments have low bioavailability but high to very high mobility and availability. Primarily, the proposed index is more appropriate, as the current index for assessing total metal content may overestimate the level of risk. The indexes established can comprehensively evaluate the bioavailability, mobility, availability, and ecological risk of metals in sediments.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Comparing the applicability of ecological risk indices of metals based on PCA-APCS-MLR receptor models for ports surface sediments

This study collected surface sediments from seven ports in Taiwan and analyzed their characteristics along with 10 metals. Enrichment factor (EF), relative EF (REF), potential ecological risk index (PERI), and mean effect range median quotient (m-ERM-q) were used to evaluate the levels of metal contamination and ecological risks in sediments. Principal component analysis (PCA) and the absolute principal component score-multiple linear regression (APCS-MLR) model were applied to quantify the main factors affecting the variations in sediment metals. The different normalization techniques that vary between indexes significantly affect the estimates of risk levels for sediment metals. APCS-MLR model confirmed the significant difference among the sediment quality indices in the degree of anthropogenic pollution, ranging in the order of REF (normalized with reference site and Fe, 97.0 %), PERI (normalized with reference site, 85.5 %), EF (normalized with crust and Fe, 79.4 %), and m-ERM-q (not normalized, 56.6 %).

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.