New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a significant challenge. This study employed machine learning (ML) techniques to model heavy metal pollution in soils within this critical ecosystem. A total of 91 standardized soil samples were analyzed to predict the accumulation of eight heavy metals using four distinct ML algorithms. Among them, random forest model outer performed in predicting As (0.79), Cd (0.89), Cr (0.63), Ni (0.56), Cu (0.60), and Zn (0.52), achieving notable R squared values. The feature attribute analysis identified As-K, Pb-K, Cd-S, Zn-Fe2O3, Cr- Fe2O3, Ni-Al2O3, Cu-P, and Mn- Fe2O3 relationships resembled with correlation coefficients among them. The developed models revealed that the contamination factor for metals in soils indicated extremely high levels of Pb contamination (CF ≥ 6). In conclusion, this research offers a robust framework for predicting heavy metal pollution in coal mining soils, highlighting critical areas that require immediate conservation efforts. These findings emphasize the necessity for targeted environmental management and mitigation to reduce heavy metal pollution in mining sites.

Research on the traceability and treatment of nitrate pollution in groundwater: a comprehensive review

The preservation of groundwater quality is essential for maintaining the integrity of the water ecological cycle. The preservation of groundwater quality is crucial for sustaining the integrity of the water ecological cycle. Nitrate (NO3-) has emerged as a pervasive contaminant in groundwater, attracting significant research attention due to its extensive distribution and the potential environmental consequences it poses. The primary sources of NO3- pollution include soil organic nitrogen, atmospheric nitrogen deposition, domestic sewage, industrial wastewater, landfill leachate, as well as organic and inorganic nitrogen fertilizers and manure. A comprehensive understanding of these sources is imperative for devising effective strategies to mitigate NO3- contamination. Technologies for tracing NO3--polluted groundwater include hydrochemical analysis, nitrogen and oxygen isotope techniques, microbial tracers, and numerical simulations. Quantitative isotope analysis frequently necessitates the application of mathematical models such as IsoSource, IsoError, IsoConc, MixSIR, SIAR, and MixSIAR to deduce the origins of pollution. This study provides a summary of the application scenarios, as well as the strengths and limitations of these models. In terms of remediation, pump and treat and permeable reactive barrier are predominant technologies currently employed. These approaches are designed to remove or reduce NO3- concentrations in groundwater, thereby restoring its quality. The study offers a systematic examination of NO3- pollution, encompassing its origins, detection methodologies, and remediation approaches, highlighting the role of numerical simulations and integrating multidisciplinary knowledge. Additionally, this review delves into technological advancements and future trends concerning the detection and treatment of NO3- pollution in groundwater. It proposes methods to control the spread of pollution and acts as a guide for identifying and preventing pollution sources.

New insights into pollution source analysis using receptor models: Effects of interaction between heavy metals and DOM on source identification and apportionment in rivers across industrial city

To effectively control pollution and protect the ecosystem, it is essential to accurately analyze the potential pollution sources of heavy metals (HMs) in rivers. However, the traditional source apportionment methods based on HMs disregard the interaction between HMs and dissolved organic matter (DOM). In this study, data of HMs and DOM was combined for tracing sources and assessing the effect of interaction between HMs and DOM on source apportionment in urbanized rivers that cross urban (URR), industrial (INR), and rural (RUR) regions. Four types of fluorescent substances were extracted from DOM: tryptophan-like (TRLF), microbial byproduct (MB), fulvic-like (FLF), and humic-like (HLF) fluorescence substances. Anthropogenic activities (42.3 %), microbial products (21.5 %), and geogenic origin (23.7 %) were respectively recognized as the dominant source in URR, INR, and RUR. Additionally, significant correlations were obtained between HMs and high molecular mass DOM. There was no direct effect pathway obtained between HMs and sources and distributional characteristics of HMs are influenced by both economic and social factors. HMs have been found to indirectly affect source apportionment through interaction with DOM. This work provided a comprehensive understanding of the effects mechanism of the interaction of HMs and DOM on source identification and offered an effective method for tracing pollution sources.

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.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%-3.54%) characterized by NO3-N, phytoplankton source (38.43%-50.05%) indicated by chl and PC, agricultural cultivation (16.1%-17.63%) marked by TP and CODMn, industrial wastewater (17.64%-25.1%) primarily associated with TN, and natural source (10.32%-13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

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.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.