Identification Of Pollution Sources Research Articles

Identification and apportionment of groundwater pollution sources in the Guanzhong region based on PMF model

Identifying pollution sources and measuring their impact is essential for protecting groundwater. Groundwater pollution sources in the Guanzhong region were identified using the positive matrix decomposition (PMF) model, and their contributions were quantified. The spatial distribution of socio-economic factors (population, GDP, fertilizer use, farmland, and livestock) was analyzed. Redundancy and correlation analyses were used to assess the impact of land use on groundwater quality. The results showed that the primary groundwater physicochemical constituents exceeding the standard limits were, in descending order, NO3 −, Na+, TDS, F−, SO4 2−, and Cl−, and the most polluted areas were found in the eastern and southern parts of the study area. Land use and socio-economic factors can have diverse impacts on groundwater quality. Five main sources responsible for groundwater quality deterioration in the Guanzhong were comprehensively identified, i.e., agricultural nonpoint sources, domestic pollution from urban and rural development, natural release of fluorine from stratigraphic minerals as well as coal mine storage and combustion, industrial discharges, and water-rock reactions. Natural release from minerals and industrial discharges are the major causes of groundwater degradation in the study area, which is followed by water-rock reactions, agriculture, and domestic pollution.

Water Quality Assessment, Possible Pollution Source Identification from Anthropogenically Stressed River Yamuna, India using Hydrochemical, Water Quality Indices and Multivariate Statistics Analysis

Water Quality Assessment, Possible Pollution Source Identification from Anthropogenically Stressed River Yamuna, India using Hydrochemical, Water Quality Indices and Multivariate Statistics Analysis

Groundwater quality assessment and pollution sources identification using statistical analyses at Missan Governorate, Southeast Iraq

The shortage in surface water quantities led to a shift in dependence on the groundwater as an alternative water source in southern parts of Iraq. The groundwater is decreasing in quantity and water quality is degrading due to different factors. Therefore, it is important to assess the groundwater quality of the Missan Governorate of the country by analyzing the physicochemical parameters and distinguishing the probable sources of contaminants in the area. The present study used water quality diagrams and statistical methods such as factor analysis and agglomerative cluster analysis to determine the sources of chemical ions in the forty-four groundwater samples collected from wells in the study area. In addition, the Water Quality Index (WQI) was calculated to evaluate the groundwater quality by combining the parameters into a single index. The WQI results varied from − 66.27 to 1064.13 indicating that the groundwater is unsuitable for drinking at various places and good in quality at most of the wells of the area. The main factors that contribute to the differences in the groundwater quality include the variation in geological materials, influence of climate such as the high rate of evaporation, and anthropogenic factors such as industrial activities in central areas such as oil production in the Bazergan oil field. The most affected wells by the petroleum industry that were clustering in cluster R1which show high levels in most water chemistries. The results have implications for the management of the groundwater resources, which are expected to be under more risk of contamination in near future.

Shallow Groundwater Quality Assessment and Pollution Source Apportionment: Case Study in Wujiang District, Suzhou City

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I (28%), NH4-N (18%), and Mn (14%); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry (10.63%) > urban domestic sewage discharge (8.69%) > agricultural non-point source pollution (6.26%).

Pollution assessment, ecological risk and source identification of heavy metals in paddy soils and rice grains from Salem, South India

The present study investigated concentrations of metals (Cu, Zn, Fe, Mn, Ni, Pb, and Cd) in paddy soils and rice tissues of nine different widely used indica rice varieties in Salem district, India. The order of DTPA soil available metal contents (mg kg-1) were Fe (33.50) > Mn (8.86) > Cu (2.58) > Pb (1.99) > Zn (1.67) > Ni (1.48) > Cd (0.13). Pollution load index (PLI) levels for Cd (1.28) were ‘moderately polluted’, contamination factor (CF) and enrichment factor (EF) values for all metals showed ‘medium contamination’ and ‘less enrichment’ except for Cu and Pb. Ecological risk factor (ERF) and potential ecological risk index (PRI) values of Cd (31.94 and 287.50) showed ‘moderate risk’ and ‘strong ecological risk’. Bio-accumulation factor (BAF) levels of Cd (48.12) and Pb (27.89) are classified as ‘high concentrators’. Translocation factor (TF) values of Pb (2.9), Cd (2.5) and Ni (1.7) were greater than one for roots to plants. Children had higher target hazard quotient (THQ) and hazard index (HI) values for Cd, Pb and Ni than adults. Principal component analysis (PCA) revealed with all parameters that they fell within the two primary factors 1 and 2, with an eigenvalue of 4.69 (33.52% variation) and 3.73 (26.66% variation), respectively. However, cluster analysis (CA) revealed three distinct groups between the metals studied. Although the soil metal contents were within the permissible levels, the grain Cd (6.07 mg kg-1), Pb (56.20 mg kg-1), and Fe (116.11 mg kg-1) contents in all the rice varieties exceeded the prescribed limits of 0.4, 0.2, and 15 mg kg-1, respectively. Our findings will provide important data for metal enrichment in soils to implement more effective measures to reduce the contamination of metals in paddy fields.

Advanced Kernel DM+V/W+ method for precise mapping of pollutant dispersion in indoor environments

This study introduces an advanced method for reconstructing pollutant concentration fields in indoor ventilated environments, aimed at enhancing air quality and ensuring occupant health. Our newly developed Kernel DM + V/W+ method significantly refines existing models by accurately capturing variations in pollutant dispersion in downwind areas while minimizing overestimations in upwind zones. This innovative approach overcomes the limitations of the traditional Kernel DM + V method, which neglects the impact of airflow, and the Kernel DM + V/W method, which inadequately differentiates between upwind and downwind dispersion. The effectiveness of this method is validated through a dataset derived from previously confirmed computational fluid dynamics (CFD) simulations across two standard indoor ventilation scenarios. A subset of this dataset was randomly selected for evaluation using the three methods, enabling a detailed performance analysis. The results demonstrate that the Kernel DM + V/W+ method markedly enhances accuracy in estimating concentrations at unsampled locations and significantly improves the identification of pollution sources, providing substantial benefits for environmental engineering applications.

Pollution Characteristics, Risk Assessment and Source Identification of Heavy Metals in Surface Sediments from the Cage Fish Farm of Poyang Lake, China

ABSTRACT With the development of cage culture, increasing heavy metals concentrations are being taken into sediments. The accumulation of heavy metals in sediments may cause long term environmental issues and pose threat to human health. Fourteen surface sediment samples were collected to assess the contamination status and potential ecological risk of heavy metals, and identify their potential sources from the cage fish farm of Poyang Lake. The concentrations of Cu, Pb, Zn, Cd, Cr, As and Ni in the sediments were measured by ICP-MS. The geo-accumulation index, enrichment factor and potential ecological risk index were implied to evaluate their pollution levels and ecological risk. The principal component analysis was used to identify their potential sources. The results showed that all of the examined heavy metals content exceeded their background values. These heavy metals (except Cd, Cr and As) presented moderately polluted level. Cu showed significant enrichment, Zn, Pb and Ni exhibited moderate enrichment, As and Cd presented minimal enrichment. Cu and Cd posed a moderate ecological risk, and the other heavy metals posed a low ecological risk. The comprehensive potential ecological risk was associated with considerable ecological risk. Cage culture, smelting, mining and agriculture activities were their primary pollution sources.

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements. It was found that regional sources of pollution were mostly responsible for the PM2.5 and PM1 concentrations. In contrast, PM10 was mostly associated with local sources. The total particle number and the lung deposited surface area of PM were almost solely associated with traffic, while black carbon was associated with both the sources from the urban background and local traffic. Our analysis showed that while the effect of the hyperlocal sources, such as emissions from construction works or traffic, do not exceed the distance of a couple of hundred meters, they can influence the health of thousands of people in densely populated areas. Thus, using this novel approach we illustrate the limitations of the present measurement network paradigm and offer an alternative and versatile approach to understanding the hyperlocal factors that affect urban air quality. Mobile monitoring by citizen scientists is shown to have huge potential to enhance spatiotemporal resolution of air quality data without the need of extensive and expensive campaigns.

Nitrogen Transport Pathways and Source Contributions in a Typical Agricultural Watershed Using Stable Isotopes and Hydrochemistry

The increasing global nitrogen input poses a significant threat to aquatic environments, particularly in agricultural watersheds, where intensive human activities and insufficient water protection infrastructure exacerbate the risk of nitrogen pollution. Accurate identification of nitrogen pollution sources and the associated transformation processes is essential for protecting watershed ecosystems. In this study, a combination of hydrochemical analysis, correlation and principal component analysis, and stable nitrate isotopes (δ15N-NO3− and δ18O-NO3−) were employed to trace nitrogen transport pathways and source contributions in both surface water and groundwater within a typical agricultural watershed. The results revealed the presence of nitrogen pollution, including total nitrogen (TN), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3−-N), with significant spatial and seasonal variations in both surface water and groundwater. The spatiotemporal evolution of hydrochemical indicators and nitrate isotope compositions highlighted multiple potential sources of nitrogen, including soil input, agricultural input, and manure and sewage input. The results from stable isotope analysis in an R (SIAR) model indicated that ammonium fertilizers (7.1~78.4%) and manure and sewage (2.6~69.7%) were the primary sources of nitrates in surface water, while manure and sewage were the main sources in groundwater (67.9~73.7%). This research demonstrated that nitrification, seasonal variations, and human activities significantly impact nitrogen migration and transformation in agricultural watersheds. However, the issue of groundwater severely polluted by manure and sewage has received insufficient attention. To effectively control nitrogen pollution in agricultural watersheds, it is necessary to improve septic tanks and sewage networks, as well as implement scientific fertilization practices.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Mechanisms of evolution and pollution source identification in groundwater quality of the Fen River Basin driven by precipitation

Groundwater pollution has attracted widespread attention as a threat to human health and aquatic ecosystems. However, the mechanisms of pollutant enrichment and migration are unclear, and the spatiotemporal distributions of human health risks are poorly understood, indicating insufficient groundwater management and monitoring. This study assessed groundwater quality, human health risks, and pollutant sources in the Fen River Basin(FRB). Groundwater quality in the FRB is good, with approximately 87 % of groundwater samples rated as “excellent” or “good” in both the dry and rainy seasons. Significant precipitation elevates groundwater levels, making it more susceptible to human activities during the rainy season, slightly deteriorating water quality. Some sampling points in the southern of Taiyuan Basin are severely contaminated by mine drainage, with water quality index values up to 533.80, over twice the limit. Human health risks are mainly from As, F, NO3−, and Cr. Drinking water is the primary pathway of risk. From 2019 to 2020, the average non-carcinogenic risk of As, F, and NO3− increased by approximately 28 %, 170 % and 8.5 %, respectively. The average carcinogenic risk of As and Cr increased by 28 % and 786 %, the overall trend of human health risks is increasing. Source tracing indicates As and F mainly originate from geological factors, while NO3− and Cr are significantly influenced by human activities. Various natural factors, such as hydrogeochemical conditions and aquifer environments, and processes like evaporation, cation exchange, and nitrification/denitrification, affect pollutant concentrations. A multi-tracer approach, integrating hydrochemical and isotopic tracers, was employed to identify the groundwater pollution in the FRB, and the response of groundwater environment to pollutant enrichment. This study provides a scientific basis for the effective control of groundwater pollution at the watershed scale, which is very important in the Loess Plateau.

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Crucial time of emergency monitoring for reliable numerical pollution source identification

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Seasonal variations and the prevalence of phenolic profiles in ambient fine particulate matter and their impact on oxidative potential

Exposure to fine particulate matter (PM2.5) poses numerous health risks, with oxidative potential (OP) serving as a critical marker of its toxicity. Synthetic phenolic antioxidants (SPAs) and bisphenols (BPs) influence reactive oxygen species (ROS) levels in PM2.5, and exposure to these compounds induces oxidative stress in organisms, thereby potentially affecting the OP of PM2.5. We detected 26 phenols (including 12 SPAs, 5 transformation products (TPs), and 9 BPs) in PM2.5 sample collected from October 2018 to September 2021 in Wuhan, China. Among them, 19 substances were detected at a detection frequency greater than 50 % in PM2.5 sample. AO 2246 and BHT were the main components of SPAs, and BHT-Q and BPA had the highest concentrations in TPs and BPs, respectively. PM2.5 mass concentrations and phenolic levels were higher in winter and autumn. Substances within groups were strongly correlated, suggesting the same or similar source of exposure. This finding aid in more precise pollution source identification and is crucial for comprehensively evaluating their combined health effects. Furthermore, we determined the OP of PM2.5 and found that BPs were related to increased OP and ROS. This suggests that the toxicity of PM2.5 is influenced not only by its concentration but also by its chemical composition, with BPs potentially enhancing its toxic effects. These factors should be fully considered when assessing the health impacts of PM2.5.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (<3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

A multi-analytical approach for the identification of pollutant sources on black crust samples: Stable isotope ratio of carbon, sulphur, and oxygen

This study is focused on the identification of pollutant sources on black crust (BC) samples from the Monumental Cemetery of Milan (Italy), through a multi-analytical approach based on the determination of stable isotope ratios of carbon, sulphur, and oxygen. Six black crust samples, mainly developed on marble sculptures over a time span of 100–150 years, were analysed.For the first time, δ13C was measured for BC samples: δ13C values of the pulverized samples (from −1.2 to +1.3 ‰) are very close to the values obtained from the carbonate matrix, whereas after the removal of the matrix through acidification, δ13C values of BC samples from Milan range from −27.2 to −22.1 ‰, with no significant variation between samples with different ratios of organic carbon to elemental carbon. In sum, the δ13C values obtained for all BC samples fall within the range of anthropogenic emissions such as vehicle traffic, coal combustion and industrial emissions.δ34S and δ18O values of sulphate from BC samples range from −6.3 to +7.0 ‰ and from +7.6 to +10.5 ‰, respectively. Coupling the analysis of the oxygen isotope ratio with that of sulphur enables a more precise identification of the origin of sulphates: the observed isotopic composition falls in the range typical for anthropogenic emission of sulphur dioxide.Overall, in this study, C, S and O isotopes were combined for the first time to assess pollutant sources on black crust samples: this multi-stable isotope approach allowed to show that the BC formation on monuments from the Monumental Cemetery of Milan mostly results from anthropogenic emissions from fossil fuels combustion by road vehicles and factories, as well as domestic heating.

Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Deepor Beel Area, Guwahati, Assam

Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Deepor Beel Area, Guwahati, Assam

Diagnostic ratios of polyaromatic hydrocarbons for the identification of pollution sources in clams from Okwagbe, Delta State Nigeria

Diagnostic ratios of polyaromatic hydrocarbons for the identification of pollution sources in clams from Okwagbe, Delta State Nigeria

Tracing phosphorus sources in the river-lake system using the oxygen isotope of phosphate

The biogeochemical cycling of phosphorus (P) in river-lake systems presents significant challenges in tracing P sources, highlighting the importance of effective traceability approaches for formulating targeted management measures to mitigate lake eutrophication. In this study, we used the oxygen isotope of phosphate (δ18Op) as a tracer in the river-lake systems, establishing a tracing pathway from potential end-members, through inflow rivers, and eventually to the lake. Taking Dianshan Lake and its main inflow rivers as the study area, we measured δ18Op values of potential end-members, including domestic sewage treatment plant effluents, industrial effluents from phosphorus-related enterprises (printing and dyeing, electroplating, plastics, etc.), and farmland soils. Notably, the industrial effluent signatures ranged from 13.1 ‰ to 21.0 ‰ with an average of 16.8 ‰ ± 3.2 ‰, enriching the δ18Op threshold database. Using the MixSIAR model, it was found that phosphorus in the Jishuigang River primarily originated from agricultural non-point sources and domestic sewage in the dry season, while the Qiandengpu River, with a higher proportion of urban area, had a greater influence from domestic sewage and industrial effluents. Moreover, significant differences were observed between δ18Op values at the lake entrances of the inflow rivers (13.7 ‰ ± 1.0 ‰) and in acid-soluble phosphate of the lake sediments (9.9 ‰ ± 1.0 ‰). Isotopic tracing revealed that phosphorus in the lake originated from both external inputs (80.6 %) and internal release (19.4 %) in the dry season. Alongside pollutant flux calculations based on the hydrological conditions and water quality of the inflow rivers, our findings indicated that phosphorus in Dianshan Lake was mainly attributed to agricultural non-point sources, domestic sewage and sediment release in the dry season. This study provided novel insights into the identification of pollution sources in the river-lake systems, with broad implications for pollution control and environmental protection.