Exploring a multisource-data framework for assessing ecological environment conditions in the Yellow River Basin, China

Yuqing Tian; Zongguo Wen; Xiu Zhang; Manli Cheng; Mao Xu

doi:10.1016/j.scitotenv.2022.157730

Exploring a multisource-data framework for assessing ecological environment conditions in the Yellow River Basin, China

Sci Total Environ. 2022 Nov 20:848:157730. doi: 10.1016/j.scitotenv.2022.157730. Epub 2022 Jul 30.

Authors

Yuqing Tian¹, Zongguo Wen², Xiu Zhang³, Manli Cheng⁴, Mao Xu⁵

Affiliations

¹ School of Environment, Tsinghua University, Beijing 100084, PR China. Electronic address: tyq21@mails.tsinghua.edu.cn.
² School of Environment, Tsinghua University, Beijing 100084, PR China. Electronic address: wenzg@tsinghua.edu.cn.
³ School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China. Electronic address: m201973777@hust.edu.cn.
⁴ School of Environment, Tsinghua University, Beijing 100084, PR China. Electronic address: chengmanli@mail.tsinghua.edu.cn.
⁵ School of Environment, Tsinghua University, Beijing 100084, PR China. Electronic address: xu-m21@mails.tsinghua.edu.cn.

PMID: 35917964
DOI: 10.1016/j.scitotenv.2022.157730

Abstract

Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326 < Overall score ≤ 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km², accounting for 19.87 % of the YRB. The area of the highest LERI (0.157 < LERI≤0.246), the highest RND (4.4435 < RND ≤ 8.5574), and the highest ID (0.1403 < ID≤0.2597) finally converted to bad EEC was 7.22 million km², 0.78 million km², and 0.91 million km², respectively. The results indicated that the ecological risk factors were the primary challenges for improving EEC, followed by industrial agglomeration and road network factors. The primary factors affecting EEC varied between the provinces in the YRB, suggesting that provinces take the management strategies and measures should be adaptive. The correlation coefficients between EEC and the principal components of water quality characteristics were between 0.022 and 0.241, P < 0.05. These findings validated that our method framework could distinguish the spatial variation of EEC in detail and further provide effective support for watershed management.

Keywords: Ecological environment conditions; Industry density; Knowledge-based raster mapping; Landscape ecological risk; Road network density.

MeSH terms

China
Industry
Rivers*
Water Quality*