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Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, Sobol’s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient ${\mathsf{\epsilon }}_0$, natural factor correction coefficient ${\mathrm{N}}_{\mathrm{i}}$, the newly produced TN pollutants per area ${\mathrm{Q}}_{\mathrm{i}\mathrm{N}}$, and runoff coefficient $\mathsf{\epsilon }$. For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, ${\mathrm{N}\mathrm{H}}_4^{+}$-N, and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources. Full article

Potential risk identification of agricultural nonpoint source pollution (ANPSP) is essential for pollution control and sustainable agriculture. Herein, we propose a novel method for potential risk identification of ANPSP via a comprehensive analysis of risk sources and sink factors. A potential risk assessment index system (PRAIS) was established. The proposed method was used to systematically evaluate the potential risk level of ANPSP of Yichang City, Hubei Province. The potential risk of ANPSP in Yichang City was 18.86%. High-risk areas account for 4.95% and have characteristics such as high nitrogen and phosphorus application rates, large soil erosion factors, and low vegetation coverage. Compared with the identification results of the Diffuse Pollution estimation with the Remote Sensing (DPeRS) model, the area difference of the same risk level calculated by the PRAIS was reduced by 33.9% on average. This indicates that PRAIS has the same level of accuracy as the DPeRS model in identifying potential risks of ANPSP. Thus, a rapid and efficient identification system of potential risks of regional ANPSP was achieved. Full article