Risk Assessment of Nitrate Pollution in the Shallow Groundwater of the Mihe Alluvial–Diluvial Fan Based on a DEA Model

• Main Authors: Chen, H. (Author), Li, F. (Author), Sun, T. (Author), Zhang, X. (Author), Zhao, Q. (Author), Zhu, A. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Agriculture; Analysis method; Aquifers; Data envelopment analysis; Data envelopment analysis models; DEA; Decision making; Fertilizer applications; Fertilizers; Groundwater pollution; Groundwater resources; Landforms; Livestock breeding; Mihe alluvial–diluvial fan; nitrate pollution; Nitrate pollution; Nitrates; pollution risk index; Pollution risk index; Risk assessment; Risk indices; Risks assessments; Shallow groundwater
• Online Access: View Fulltext in Publisher

 Affected by excessive fertilizer application and livestock breeding, the problem of nitrate pollution in the groundwater in the Mihe alluvial–diluvial fan area is becoming increasingly promi-nent, which poses a great threat to human production and life. Given this, the risk of nitrate pollution in the shallow groundwater of the Mihe alluvial–diluvial fan is evaluated by introducing a data envelopment analysis (DEA) method. Using this model, 28 groundwater sampling points are selected as the decision-making unit (DMU); the nitrogen and pesticide application rate, livestock and poultry stock, groundwater burial depth, aquifer water abundance, and vegetable planting area are taken as the model input; and the nitrate content is taken as the model output to quantitatively calculate the pollution risk index to form a spatial distribution map of pollution risk. The calculation using the model shows that the average pollution risk index of the study area is 0.382, the spatial variation is 1.12, the pollution risk index gradually decreases from south to north, and agricultural planting and livestock and poultry breeding are the main pollution sources. The calculation of nitrate pollution risk using this model not only enriches the nitrate pollution evaluation model but also provides a basis for further implementing the action of reducing fertilizer use by increasing its efficiency and strengthening the prevention of agricultural diffused pollution. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.