非点源污染
环境科学
水文学(农业)
分水岭
水槽(地理)
水质
水土评价工具
支流
旱季
污染
农业
三峡
雨季
流域
水资源管理
地理
生态学
地质学
机器学习
生物
考古
地图学
岩土工程
水流
计算机科学
作者
Shaojun Tan,Deti Xie,Jiupai Ni,Fangxin Chen,Chengsheng Ni,Jing-an Shao,Jin-Liang Wang,Dun Yu Zhu,Sheng Wang,Ping Lei,Jingyi Wang,Jianfeng Xu
标识
DOI:10.1016/j.jclepro.2021.129694
摘要
Constructing a simple and accurate method to identify the risks and control nonpoint source pollution (NPSP) have become a principal target for the most watersheds with strong soil erosion and agricultural activities all over the world. In this study, the Qijiang River Basin in the Three Gorges Reservoir area (TGRA) was selected as the study area and the minimum cumulative resistance (MCR) surface method and location-weighted landscape index (LWLI) were used to identify the source/sink pattern of the NPSP. The results from MCR modeling showed that the source landscape mainly distributed in the northern hilly area near the Yangtze River mainstream, however, the sink landscape mainly concentrated in the southern Simian Mountain area. The LWLI values in the north were higher than those in the south. Regression analysis showed that there is a significant correlation between the LWLI and soil erosion ( R 2 = 0.635); meanwhile, there was also a certain relationship between the LWLI and water quality in the dry and wet seasons, ammonia nitrogen (NH 3 –N) and total phosphorus (TP) was the largest. The correlation coefficient between the chemical oxygen demand (COD) and NH 3 –N in the wet season was greater than that in the dry season, while total nitrogen (TN) and TP are exactly the opposite. The research results can help the areas with fragmented landscape, easy soil erosion, high agricultural activity, and lack of monitoring data to identify the risk of agricultural NPSP and formulate scientific water resources protection measures. • Resistance/power factor system was improved to identify source/sink landscape. • LWLI was adopted to identify the source/sink of NPSP. • Source mainly distributed in the north, and sink concentrated in the south. • The methods could promotes to sustainable environment development in TGRA.
科研通智能强力驱动
Strongly Powered by AbleSci AI