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Ecosystem health of the Beiyun River basin (Beijing, China) as evaluated by the method of combination of AHP and PCA
Ist Teil von
Environmental science and pollution research international, 2022-06, Vol.29 (26), p.39116-39130
Ort / Verlag
Berlin/Heidelberg: Springer Berlin Heidelberg
Erscheinungsjahr
2022
Quelle
SpringerLink
Beschreibungen/Notizen
Ecosystem services provided by river ecosystems rely on healthy ecosystem structure and ecological processes. The Beijing-Tianjin-Hebei urban is a typical water-deficient area. As an important part of the urban–rural integration construction, evaluating the health status of the Beiyun River Basin and discovering the weak links in the water environment are the basis for improving the health of the basin. In this study, analytic hierarchy process (AHP) was used to establish an evaluation index system for the Beiyun River Basin from 5 aspects including water quality, biology, ecology, hydrology, and social functions, and the principal component analysis (PCA) was then used to assign weights to the index layer. The evaluation results showed that the health evaluation results of the Beiyun River Basin in 2019 are “sub-healthy,” and the overall health status is getting worse from northwest to southeast. In the middle reaches of the region, the evaluation result is “healthy,” followed by the upstream, and the downstream is the worst. The results showed that areas with less human interference or orderly intervention are in better health. High eutrophication level, low bio-diversity, and low vegetation coverage are the main indicators that leads to poor ecosystem health in the Beiyun River Basin. For the comprehensive management of the Beiyun River, the improvement of water quality and habitat ecological restoration are key actions to the health of the upstream ecosystem. The improvement of the health status of the downstream should focus on equal emphasis on water quality and quantity, restoration of biodiversity, and improvement of the quality of the riparian ecological environment.
Graphical abstract