Assessing the ecological risk induced by PM2.5 pollution in a fast developing urban agglomeration of southeastern China

Lin Wang; Qianyu Li; Quanyi Qiu; Lipeng Hou; Jingyi Ouyang; Ruihan Zeng; Sha Huang; Jing Li; Lina Tang; Yang Liu

doi:10.1016/j.jenvman.2022.116284

Assessing the ecological risk induced by PM_2.5 pollution in a fast developing urban agglomeration of southeastern China

J Environ Manage. 2022 Dec 15:324:116284. doi: 10.1016/j.jenvman.2022.116284. Epub 2022 Sep 23.

Authors

Lin Wang¹, Qianyu Li², Quanyi Qiu³, Lipeng Hou⁴, Jingyi Ouyang⁵, Ruihan Zeng⁶, Sha Huang⁷, Jing Li⁸, Lina Tang⁹, Yang Liu¹⁰

Affiliations

¹ Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, United States. Electronic address: wanglin@iue.ac.cn.
² Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; Fujian Agriculture and Forestry University, Fujian, 350002, China. Electronic address: qyli@iue.ac.cn.
³ Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China. Electronic address: qyqiu@iue.ac.cn.
⁴ Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: lphou@iue.ac.cn.
⁵ Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: jyouyang@iue.ac.cn.
⁶ Charles H. Dyson School of Applied Economics & Management, Cornell University, Ithaca, NY, 14853, United States. Electronic address: raymondzeng624@foxmail.com.
⁷ Songjiang Yunjian High School Affiliated to Shanghai Foreign Language School, Shanghai, 201600, China. Electronic address: sandy_huang2021@163.com.
⁸ Ministry of Ecology and Environment of the People's Republic of China, 100035, China. Electronic address: jingli@iue.ac.cn.
⁹ Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China. Electronic address: lntang@iue.ac.cn.
¹⁰ Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, United States. Electronic address: yang.liu@emory.edu.

PMID: 36162318
DOI: 10.1016/j.jenvman.2022.116284

Abstract

High PM_2.5 concentration threats ecosystem functions but limited quantitative studies have recognized PM_2.5 pollution as an individual stressor in evaluating ecological risk. In this study, we applied a machine-learning-based simulation model incorporating full-coverage satellite-driven PM_2.5 dataset to estimate high-resolution ground PM_2.5 concentration for the Golden Triangle of Southern Fujian Province, China (GTSF) in 2030 under two Representative Concentration Pathways (RCPs). Based on the simulation output, the ecological risk's spatiotemporal change and the risk for different land cover types, which were caused by PM_2.5 pollution, were assessed. We found that the PM_2.5 levels and ecological risk in the GTSF under RCP 4.5 would be reduced while those under RCP 8.5 would continue to increase. The regions with the highest ecological risk under RCP 4.5 are the most urbanized and industrialized districts, while those with the highest ecological risk under RCP 8.5 are of the highest rate in urbanization and the greatest decrease in planetary potential layer height. For both base years and 2030 under two RCPs, the ecological risk on developed land is the highest, while that on the forest is the lowest. Our study can provide useful information for environmental policy risk assessment.

Keywords: China; Ecological risk assessment; Future PM(2.5) simulations; Golden triangle of southern fujian province; The representative concentration pathways (RCP).

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China
Ecosystem
Environmental Monitoring
Particulate Matter / analysis

Substances

Particulate Matter
Air Pollutants