Improved export coefficient model for identification of watershed environmental risk areas

Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed

Nutrient pollution caused by excessive total nitrogen (TN) and total phosphorus (TP) is a significant environmental challenge globally, threatening water quality and ecosystem health. This study investigates the interplay between rainfall, topography, and human disturbances in shaping TN and TP dynamics, aiming to identify critical factors driving nutrient pollution. Using integrated machine learning and statistical approaches, including Self-Organizing Maps (SOMs), Generalized Additive Models (GAMs), and logistic regression, the study reveals that rainfall acts as a dominant dilution factor, reducing nutrient concentrations and explaining 72.8% of TN variance and 55.6% of TP variance. Conversely, steep slopes and human-disturbed land use are positively correlated with nutrient levels but are moderated by rainfall's mitigating effect. Logistic regression further confirms rainfall's predictive significance, achieving high classification accuracy for TN (AUC = 0.82) and TP (AUC = 0.76). The identification of Critical Source Areas (CSAs) highlights priority regions for intervention, demonstrating the utility of the proposed framework for targeting water quality improvement measures. By integrating spatial and statistical modeling, this study provides actionable solutions and contributes to advancing water quality management strategies through enhanced understanding of natural and anthropogenic influences on nutrient pollution.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Temporal patterns and driving factors of sediment carbon, nitrogen, and phosphorus stoichiometry in a eutrophication plateau lake

Stoichiometry determines the key characteristics of organisms and ecosystems on a global scale and provides strong instructions on the fate of sediment carbon, nitrogen, and phosphorus (C-N-P) during the sedimentation process, contributing to the Earth's C-N-P balance. However, the mechanisms underlying C-N-P stoichiometry in response to intensive human activity and organic matter sources remain underexplored, especially in freshwater ecosystems. This study identifies the temporal patterns of C-N-P stoichiometry, reveals the inner driving factors, and clarifies its impact path, especially in eutrophication (the late 1970s). The results revealed that sediment RCP and RNP increased significantly and were controlled by TCAR and TNAR, respectively, indicating the direct impact of burial rate on C-N-P stoichiometry. Based on redundancy analysis and the STM model, autochthonous origin, GDP, and population had positive effects on sediment TCAR, TNAR, and TPAR, which, in turn, affected RCN, RCP, and RNP. Organic matter sources and human activities have a significant influence on RCN, RCP, and RNP, possibly regulated by the variation of TCAR and TNAR. Autochthonous origin had an indirect positive impact on RCN and RCP through the mediating effect of TCAR. Similarly, through the mediating effect of TNAR, it had an indirect negative impact on RCN and an indirect positive impact on RNP. This study showed that TCAR, TNAR, TPAR, GDP, autochthonous, allochthonous and population better explained the changes in RCN, RCP, and RNP over a-hundred-year deposition, highlighting an in-depth understanding of the dynamic change mechanism of sediment C-N-P stoichiometry during the lake deposition process.