Nitrogen and Phosphorus Retention Risk Assessment in a Drinking Water Source Area under Anthropogenic Activities

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 9; p. 2070
• Main Authors: Zheng, Yuexin, Wang, Qianyang, Zhang, Xuan, Yu, Jingshan, Li, Chong, Chen, Liwen, Liu, Yuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Anthropogenic factors; Classification; Drinking water; drinking water source; Embedding; Environmental management; Environmental risk; Eutrophication; Exports; Human influences; Livestock; Machine learning; Nitrogen; Nonpoint source pollution; Nutrient balance; nutrient prediction; Nutrient retention; Nutrient sources; Nutrients; Phosphorus; Point source pollution; Pollutants; Pollution; Pollution sources; Population growth; Prediction models; random forest; Remote sensing; Retention; retention risk; Risk assessment; Rivers; Root-mean-square errors; Satellite imagery; Satellites; Soil pollution; Spatial distribution; Statistical analysis; support vector machine; Support vector machines; Uncertainty; Water quality; Water supply; Watershed management; Watersheds; China; Taihu Lake
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14092070

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R2) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.