The Application of Local Moran’s I and Getis–Ord Gi* to Identify Spatial Patterns and Critical Source Areas of Agricultural Nonpoint Source Pollution

Abstract

Agricultural nonpoint source pollution (ANPS) can cause systemic pollution of the ecological environment, directly threatening sustainable agricultural development and human health and safety. Accurate estimation of ANPS loads, clarification of the sources and spatial distribution patterns of ANPS, and effective control and management of ANPS in the watershed require identifying critical source areas. This case study focuses on the Danjiang River basin, a critical water source for China’s South-to-North Water Division and demonstrates the efficiency of ArcGIS’s spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS and extracting the causes associated with the spatial pattern, as well as the accuracy of identifying critical source areas of ANPS. In this study, two spatial statistical analysis methods, Anselin local Moran’s I and hot-spot analysis (Getis–Ord Gi*), are applied to propose a method for accurately identifying critical source areas of ANPS based on the spatial distribution of ANPS loads. The results of the study are as follows: (1) based on the MIKE LOAD model, the annual ANPS loads of chemical oxygen demand (COD), ammonia nitrogen (NH4), total nitrogen (TN), and total phosphorus (TP) in the Danhan River basin are calculated as 182,530.15, 16,137.39, 58,285.92, and 2,962.84 t/yr, respectively. (2) Clusters are mainly distributed in the southwestern part of the watershed, and the spatial pattern is directly related to land use and rural population; the spatial pattern of outliers is related to agricultural modes and geographical characteristics. (3) Hot spot clusters are concentrated in the hinterland of Hanzhong Plain; the regional specialty of agriculture is the dominant factor in determining the spatial pattern of cold-spot and hot-spot clusters. (4) Based on these findings, seven critical subbasins and one critical source area of ANPS that need to be prioritized for control in the study area are identified. Agricultural nonpoint source pollution (ANPS) is a significant cause of ecosystem pollution, which directly threatens the sustainable development of agriculture, human health, and safety. The Dan–Han River Basin is a crucial water source in China for the South-to-North Water Diversion Project and provides water to 140 million people. It is essential to ensure that the water quality of the Dan-Han River remains unpolluted. The ANPS as a major source of water pollution in the watershed is difficult to identify and control. Accurately identifying the sources and spatial distribution patterns of ANPS pollutants is a prerequisite for maintaining water quality and controlling ANPS. Our study offers a new perspective on methods to reveal the hidden spatial pattern and identify critical source areas of ANPS in watersheds. This research demonstrates the effectiveness of ArcGIS’s spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS, extracting the causes associated with the spatial pattern, and accurately identifying critical source areas of ANPS. These methods can also be applied to study nonpoint source pollution in other watersheds.