Abstract
The river is a vital component of the water ecosystem in both urban and rural regions. However, its rapidly increasing pollutants are posing a severe threat for water ecosystem security. Using Multivariate statistical technique and Integrated water quality index model (IWQI) to evaluate surface water quality and its spatial distribution based on Geographic information system (GIS). This combinatorial model have been proved to be a feasible tool for evaluating surface water quality at large-scale basin. This study analyzed the spatio-temporal variations of surface water quality, which were determined monthly from samples collected in the Maozhou River Basin Guangdong Province, China from 2018 to 2020. The results demonstrated that the surface water quality status of in the Maozhou River Basin has been steadily improved during the study period. The surface water quality of 82.17% of monitoring site reached the water quality target of function zones (surface water quality of the class V standards), with the IWQI values ranging from 12.118 to 3.650. By the end of 2019, black-odorous water in Maozhou River basin has disappeared from our sight. By 2020, the water quality status of the Maozhou River Basin has been steadily maintained at “Medium and good” level, and the main background pollutants for the water quality target of function zones is NH3-N. However, the some area in which the surface water quality still need to further improve is estuary and southwest tributary in the basin. This finding calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. It is concluded that this combined surface water quality evaluation model is more efficient and reasonable for surface water quality evaluation at a larger scale. It can provide scientific foundation for the water ecosystem management and planning in efficiently managing and evaluating surface water quality at river or basin scales.
Correction to: The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil
