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In-time source tracking of watershed loads of Taihu Lake Basin, China based on spatial relationship modeling
- Wang, Ce, Bi, Jun, Zhang, Xu-Xiang, Fang, Qiang, Qi, Yi
- Environmental science and pollution research international 2018 v.25 no.22 pp. 22085-22094
- algal blooms, arsenic, environmental policy, geographic information systems, lakes, landscapes, livestock, livestock breeding, models, monitoring, phosphorus, pollution load, risk, rivers, sewage, water quality, water quality standards, watersheds, China
- Influent river carrying cumulative watershed load plays a significant role in promoting nuisance algal bloom in river-fed lake. It is most relevant to discern in-stream water quality exceedance and evaluate the spatial relationship between risk location and potential pollution sources. However, no comprehensive studies of source tracking in watershed based on management grid have been conducted for refined water quality management, particularly for plain terrain with complex river network. In this study, field investigations were implemented during 2014 in Taige Canal watershed of Taihu Lake Basin. A Geographical Information System (GIS)-based spatial relationship model was established to characterize the spatial relationships of “point (point-source location and monitoring site)-line (river segment)-plane (catchment).” As a practical exemplification, in-time source tracking was triggered on April 15, 2015 at Huangnianqiao station, where TN and TP concentration violated the water quality standard (TN 4.0 mg/L, TP 0.15 mg/L). Of the target grid cells, 53 and 46 were identified as crucial areas having high pollution intensity for TN and TP pollution, respectively. The estimated non-point source load in each grid cell could be apportioned into different source types based on spatial pollution-related entity objects. We found that the non-point source load derived from rural sewage and livestock and poultry breeding accounted for more than 80% of total TN or TP load than another source type of crop farming. The approach in this study would be of great benefit to local authorities for identifying the serious polluted regions and efficiently making environmental policies to reduce watershed load.