Estimating the contribution of environmental variables to water quality in the postrestoration littoral zones of Taihu Lake using the APCS-MLR model

Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir

Danjiangkou Reservoir has been widely concerned as the water source of the world's longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC) were significantly higher in wet season than in dry season, while no significant differences were found for dissolved inorganic carbon (DIC) and dissolved silica (DSi). Spatially, the concentrations of dissolved carbon, DIC, TN, and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO2, seasonal factor, and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95%, and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge, and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin.

Identification of the primary pollution sources and dominant influencing factors of soil heavy metals using a random forest model optimized by genetic algorithm coupled with geodetector

Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation. Geodetector and a genetic algorithm optimized random forest model (GA-RF) were employed to quantify the dominant factors and precisely identify pollution sources. A Monte Carlo model was further applied to assess source-oriented health risk probabilities across age groups in the study area. The results revealed three principal components representing pollution sources, with contribution rates of 47.2 %, 33.3 %, and 19.5 %, respectively. For pollution source 1, industrial activities were dominant, with factory density (27.7 %) and distance from the factory (36.3 %) identified as the main factors. Cr, Cu, Mn, and Ni had high loads in this source. Pollution source 2, a combination of natural and transportation influences, was primarily affected by the normalized difference vegetation index (NDVI, 37.8 %), road network density (16.8 %), and proximity to roads (15.3 %). Pollution source 3 was linked to agricultural activities, with cultivated land density (CLD) contributing 39.1 %. As exhibited a high load (91.1 %) in this source, with an exceedance rate of 93 % in cultivated soil, a moderate enrichment factor of 2.33, and a strong ecological risk index of 615.72, making it the most polluted metal in the area. The source-oriented Health Risk Assessment (HRA) showed that agricultural activities contributed 88.7 % to the carcinogenic risk from As in cultivated land. Overall, 99.3 % of the population faced an acceptable cancer risk level. Unlike traditional source apportionment methods, the GA-RF model effectively quantified the contributions of specific influencing factors (e.g., factory density) to pollution sources, rather than merely estimating the percentage contributions of the sources themselves. This approach provides a novel perspective for HM source apportionment under complex environmental conditions.

Long-term responses of internal environment dynamics in a freshwater lake to variations in external nutrient inputs: A model simulation approach

Lake restoration usually focuses on reducing external nutrient sources. However, when sediments contain nutrients accumulated over multiple years, internal nutrient release can delay restoration progress. In lake restoration and management, it is important to understand the dynamic relationship between nutrient concentrations in a lake and internal and external nutrient sources. In this study, we quantified external nutrient inputs through measurements and compared them with internal sediment release from simulation using the PCLake+ model. Additionally, we evaluated alterations in the internal nutrient release, lake nutrient concentrations, and algae biomass (chlorophyll-a) within the lake following varying degrees of reduction in external nutrient loads. The results demonstrate that the PCLake+ effectively simulated the lake's nutrient concentration and algae biomass. Based on the PCLake+ estimates, internal nutrient loads accounted for 51 % of the total nitrogen (N) and 80 % of the total phosphorus (P) loadings in Lake Erhai in 2019. In 2020, the total contributions were 43 % for TN and 72 % for TP. We simulated four scenarios where external nutrient inputs were reduced to 25 %, 50 %, 75 %, and 99.99 % of their original levels. The 40-year simulation showed that the lake's ecological system initially exhibited a fast internal response but reached equilibrium after eight years. P concentrations took longer to reach equilibrium compared to N concentrations, probably due to the stronger binding characteristics of P. To meet the water quality target in the future, it is necessary to reduce external N and P inputs into Lake Erhai by at least 23 % and 15 %, respectively, under current conditions. Although reducing external nutrient loads can indirectly lower internal nutrient loads, water management should address both external and internal loads simultaneously, as internal release cannot be effectively reduced by external reductions alone. Additionally, the lake's internal release may continue for several years, even with reductions in external inputs.

Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management

To strengthen the control of pollution sources and promote soil pollution management of agricultural land, this study constructed a comprehensive source apportionment framework, which significantly improved the reliability of potential source analysis compared with the traditional single model. The spatial distribution pattern of agricultural soil heavy metals (SHMs) content in Lintong, a typical river valley city in China, was determined and the degree of contamination was evaluated. A scientific source apportionment methodological framework was constructed through correlation analysis methods together with multiple source apportionment receptor models. Finally, the Monte Carlo simulation method was used to derive the results of the human health risk assessment (HHRA). The results revealed the following: (1) Agricultural soils were moderately and mildly polluted, accounting for 28.8 % and 71.2 % of the total number of sampling points, respectively. (2) The overall correlation of heavy metals (HMs) was strong according to the coupling analysis of the SHMs, in which a strong correlation (0.8-1) was reached among Cu, Ni, Pb, Cr and Zn, indicating that these HMs were most likely homologous or composite. (3) Multimodel analysis of the SHMs sources revealed that the first and second principal components were agricultural (41.36 %) and industrial (19.69 %) sources, respectively, and the remaining principal components were road traffic, natural factors, and atmospheric deposition or surface runoff, respectively. (4) The average comprehensive noncarcinogenic health risk indices for adults and children were 4.2259E-02 and 1.4194E-01, respectively, which were within the slight risk range, indicating that the risk caused by SHMs to the human body can be almost negligible. This study adopted a mixed method to reveal the risk of SHMs pollution and its sources, which provides some reference and technical support for traceability analysis, zoning control, and health risk studies of regional pollutants and is helpful for formulating scientific management measures and targeting control policies.

Pollution characteristics and quantitative source apportionment of heavy metals within a zinc smelting site by GIS-based PMF and APCS-MLR models

The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater. In this study, 63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics, ecological risks, and source apportionment of heavy metal(loid)s (HMs). The results revealed that the average contents of Zn, Cd, Pb, As, and Hg were 0.4, 12.2, 3.3, 5.3, and 12.7 times higher than the risk screening values of the construction sites, respectively. Notably, the smelter was accumulated heavily with Cd and Hg, and the contribution of Cd (0.38) and Hg (0.53) to ecological risk was 91.58%. ZZ3 and ZZ7 were the most polluted workshops, accounting for 25.7% and 35.0% of the pollution load and ecological risk, respectively. The influence of soil parent materials on pollution was minor compared to various workshops within the smelter. Combined with PMF, APCS-MLR and GIS analysis, four sources of HMs were identified: P1(25.5%) and A3(18.4%) were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter; P2(32.7%) and A2(20.9%) were surface runoff of As-Pb foul acid; P3(14.5%) and A4(49.8%) were atmospheric deposition from the leach slag drying workshop; P4(27.3%) and A1(10.8%) were the smelting process of zinc products. This paper described the distribution characteristics and specific sources of HMs in different process workshops, providing a new perspective for the precise remediation of the smelter by determining the priority control factors.

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Discovery of novel benzotriazole ultraviolet stabilizers in surface water

The comprehensive understanding of the occurrence of benzotriazole UV stabilizers (BZT-UVs) in environmental surface water is imperative due to their widespread application and potential aquatic toxicity. We conducted an analysis of 13 traditional BZT-UVs in surface water samples collected from Taihu Lake (TL, n = 23) and Qiantang River (QR, n = 22) in China. The results revealed that 5‑chloro-2-(3,5-di-tertbutyl-2-hydroxyphenyl)-benzotriazole (UV-327) was consistently the predominant BZT-UV in water samples from TL (mean 16 ng/L; detection frequency 96 %) and QR (14 ng/L; 91 %). Furthermore, we developed a characteristic fragment ion-based strategy to screen and identify unknown BZT-UVs in collected surface water, utilizing a high-resolution mass spectrometer. A total of seven novel BZT-UVs were discovered in water samples, and their chemical structures were proposed. Four of these novel BZT-UVs were further confirmed with standards provided by industrial manufacturers. Semi-quantitative analysis revealed that among discovered novel BZT-UVs, 2-(2‑hydroxy-3‑tert‑butyl‑5-methylphenyl)-benzotriazole was consistently the predominant novel BZT-UV in TL (mean 4.1 ng/L, detection frequency 70 %) and QR (2.8 ng/L, 77 %) water. In TL water, the second predominant novel BZT-UV was 2-(3-allyl-2‑hydroxy-5-methylphenyl)-2H-benzotriazole (mean 3.9 ng/L, Collapse

Key Words

• Benzotriazole UV stabilizers
• Non-target analysis
• Novel BZT-UVs
• Surface water
• UV-327

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MESH Headings

• Triazoles/chemistry
• Water Pollutants, Chemical/chemistry
• China
• Lakes/chemistry
• Ultraviolet Rays
• Rivers/chemistry
• Environmental Monitoring

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Grants Collapse

Affiliation(s)

Yuanchen Chen Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Ruyue Guo Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Kaizhen Liao Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Wenfei Yu Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Pengfei Wu Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
Hangbiao Jin Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China.

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Collaborators Collapse

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.

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.

Risk assessment and source apportionment of available atmospheric heavy metal in a typical sandy area reservoir in Inner Mongolia, China

This study evaluated dry and wet deposition of atmospheric heavy metals (HMs) in a sandy area of Inner Mongolia, China, with the Dahekou Reservoir, Xilin Gol League, adopted as the study area. Monthly monitoring of atmospheric HM dry and wet deposition was conducted over one year (2021 to 2022) at 12 monitoring points, producing 144 dry and wet deposition samples, respectively. The sample contents of eight HMs (Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd) were determined to estimate the fluxes of available forms of heavy metal (AHM) in dry and wet deposition. The potential ecological index (Eri), risk assessment coding (RAC), and ratio of secondary phase to primary phase (RSP) were used to evaluate the impact of atmospheric HM dry deposition on ecological security. Correlation analysis, principal component analysis, and the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model were used to quantitatively analyze the sources of AHMs in atmospheric dry and wet deposition. The results showed that the study area experienced annual dry and wet deposition fluxes of AHMs of 1712.59 kg and 534.97 kg, respectively. Atmospheric heavy metal dry deposition over the entire year presented a strong ecological risk, with Cd contributing most to this risk. Risk assessment of HM speciation showed that the greatest risks of migration and transformation were for Cd and Pb. The APCS-MLR receptor model identified five and three sources of dry and wet deposition, respectively, in order of proportion of total contribution of: natural wind and sand > road traffic and coal combustion > mineral mining > other human activities > industrial soot.

Ecological and health risk assessments of polycyclic aromatic hydrocarbons (PAHs) in soils around a petroleum refining plant in China: A quantitative method based on the improved hybrid model

Polycyclic aromatic hydrocarbons (PAHs) are extensively released into the environment by petroleum refining activities, predominantly affecting soil as a major reservoir. This study focuses on an active petroleum refinery in central China and employs a multi-faceted approach, combining geo-statistics, the absolute principal component-multiple linear regression model, and the Monte Carlo simulation, to comprehensively unravel the sources and risks associated with 12 PAHs. The analysis reveals a wide range of PAH concentrations, spanning from 60.23 to 1678.00 μg·kg-1, with an average of 278.91 μg·kg-1. Strikingly elevated PAH levels are primarily concentrated in construction and transportation lands, whereas woodland and grasslands exhibit lower PAH concentrations. In terms of ecological impact, the risk arising from oil-coal combustion significantly surpasses that linked to biomass combustion. meticulous assessments indicate negligible carcinogenic risks for both children and adults within the study area. An innovative hybrid model, which seamlessly integrates risk assessments with source identification, emerges as a pivotal advancement. This hybrid model not only quantifies PAH emission levels from refining activities but also effectively quantifies potential risks from distinct sources. Consequently, this study furnishes a robust theoretical foundation for strategizing PAH pollution risk mitigation. In essence, our research not only contributes a comprehensive understanding of PAH distribution around an active petroleum refinery but also introduces an advanced hybrid model, culminating in valuable insights for devising measures to curtail PAH-related environmental risks.

Framework of wind joint analysis for different lake regions and its effects on the water quality

Water quality of Lake Taihu (LT) is paid wide attention, and the wind field makes wind-induced flow, significantly impacting the water quality. As a wide lake, the wind field suffers spatial and temporal changes. The correlated relationship among the wind fields for different lake regions has not been studied, and the joint effects of wind field on the water quality is still unknown. Hence, this paper proposed a framework of wind joint analysis for different lake regions and modelling its effects on the water quality, including joint analysis of wind field for different lake regions using Copula function, random time series generation of wind field using Monte-Carlo simulation, and hydrodynamic and water quality numerical simulation. Taking the Lake Taihu (LT) as study case, the joint relationships of wind field for four lake regions were analyzed, and the CODMN, TP and TN distributions under different wind fields were simulated. This paper showed the following: (1) The relationship between the wind speed and direction was weak, and the correlated relationships among the wind fields for four lake regions were significant (ρ > 0.5). (2) The water quality of LT was more influenced by the wind direction rather than the wind speed, with an impacting ratio of ∼4.5. (3) The discharged weighted angle of runoff outflow (DWARO) was a practical index to improve the water quality. When regulating the input/output runoff discharge, water diversion project and water intakes in practice, the administrations should make the DWARO less than or equal to the average wind direction, to decrease the average pollutant concentration and improve the water quality of lake and intake. The proposed framework and results of LT could be extended to other wide shallow lakes to support the environmental management and basin runoff regulation.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

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.