Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden

Xingcheng Lu; Dehao Yuan; Yiang Chen; Jimmy C H Fung

doi:10.1016/j.envpol.2020.116003

Impacts of urbanization and long-term meteorological variations on global PM_2.5 and its associated health burden

Environ Pollut. 2021 Feb 1:270:116003. doi: 10.1016/j.envpol.2020.116003. Epub 2020 Dec 1.

Authors

Xingcheng Lu¹, Dehao Yuan², Yiang Chen¹, Jimmy C H Fung³

Affiliations

¹ Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
² Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
³ Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China; Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China. Electronic address: majfung@ust.hk.

PMID: 33340901
DOI: 10.1016/j.envpol.2020.116003

Abstract

PM_2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM_2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM_2.5 concentration globally. The PM_2.5-caused mortality density (deaths/100 km²) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM_2.5-caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM_2.5-caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800-317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures.

Keywords: Climate; Global PM(2.5); Machine learning; Meteorological variation; Mortality density; Urbanization.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China
Environmental Monitoring
Humans
Meteorology
Particulate Matter / analysis
Urbanization

Substances

Air Pollutants
Particulate Matter