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Huan Jing Ke Xue ; 42(6): 2796-2809, 2021 Jun 08.
Artigo em Chinês | MEDLINE | ID: mdl-34032079

RESUMO

Non-point source pollution has become an important factor affecting the aquatic ecological environment and human health, and the analysis of spatial-temporal variations in non-point source pollution risks is an important prerequisite for pollution control. Based on land-use and land-cover data from 1980 to 2020, the potential non-point source pollution index (PNPI) model was applied in the upper Beiyun River Basin using different weighting methods. The results showed that:① The potential risk of non-point source pollution is high in the southeast and low in the northwest of the basin. Between 1980 and 2020, the total area of extremely high-risk and high-risk non-point source pollution regions showed a decreasing trend, and the main types of land use for extremely high-risk and high-risk regions gradually evolved from paddy fields, drylands, and orchards to urban and rural residential land; ② The weighting of the land use index determined by the mean-square deviation decision, entropy, coefficient of variation, and expert scoring methods was largest among the three PNPI indices, with average weightings of 0.46, 0.53, 0.45, and 0.48, respectively. However, the weightings for runoff and distance indices determined by different weighting methods were notably different, and the proportions of regions with different levels of non-point source pollution risk also varied; ③ The exponential function method, which describes the relationship between source factors and transport factors by constructing the exponential functions of land use, runoff, and distance indices, provided results that are more consistent with the spatial distribution characteristics of non-point source pollution risk in the basin. The proportions of extremely low-risk and extremely high-risk regions are 54.22% and 6.23%, respectively. These results provide scientific reference for risk analysis and the control of non-point source pollution in this basin.

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