4.7 Article

Coupling with high-resolution remote sensing data to evaluate urban non-point source pollution in Tongzhou, China

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 831, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2022.154632

Keywords

High-resolution; Remote sensing; Urban non-point source pollution; Total nitrogen; Total phosphorus; China

Funding

  1. National Natural Science Foundation of China [4187011226]

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A spatial continuous evaluation method coupled with high-resolution remote sensing data has been established and applied to estimate urban NPS nitrogen and phosphorus loads in Tongzhou, China. The method allows for quick spatial visualization assessment of urban NPS pollution and is applicable for entire cities or urban agglomerations.
Urban non-point source (NPS) pollution has gradually become one of the important factors affecting the urban water environment. The quantitative evaluation of urban NPS pollution is the priority to identify key control area of urban NPS pollution. Current model applied in China is mainly focused on small-scale area, large-scale spatial continuous simulation is lacking. In this study A spatial continuous evaluation method coupled with high-resolution remote sensing data has been established and the method was applied to Tongzhou, China. With the spatial distribution of land-use type and built-up area which were been obtained by remote sensing technology, the accumulative and wash-off load of urban NPS nitrogen and phosphorus were estimated for the prominent problems of nitrogen and phosphorus nutrient pollution in the rivers in the study area. The main sources of urban NPS Nitrogen and phosphorus pollution are roof and road rainfall runoff respectively. Compared to other urban NPS pollution models, the method developed in this study can quickly realize spatial visualization assessment of urban NPS pollution and provide a means to estimate urban NPS loads in entire city or urban agglomeration, it is applicable for common urban NPS pollutants and also has advantages in areas without data.

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