Inequality in air pollution–attributable mortality by income level between and within countries

C Chenliang Tao (Department of Environmental Science and Engineering, Fudan University) Y Yuqiang Zhang (Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University) D Drew Shindell (Earth and Climate Sciences Division, Nicholas School of the Environment, Duke University) H Hongliang Zhang (Department of Environmental Science and Engineering, Fudan University)

Abstract

Air pollution is a major global health threat, with exposure exhibiting substantial heterogeneity across and within regions. While disparities in air pollution exposure by income groups are well documented, how these inequalities translate into differential burdens of pollution-attributable premature mortality remains understudied. We utilize high-resolution estimates of secondary air pollutants, combined with income data, to investigate the relationship between global air pollution–attributable mortality and poverty across urban–rural contexts and air pollutants. We show that high-income countries face higher air pollution–attributable mortality owing to population aging, in contrast to the established exposure inequality pattern. While affluent populations within most countries also face higher mortality burdens, in several low-income countries, this pattern is reversed due to elevated exposures among impoverished rural populations. South Asia and Africa exhibit the highest levels of vulnerability to coincident mortality and poverty, where populations living in periurban transition zones bear disproportionate dual burdens. In many low-income countries, those living near wealthier urban centers also face elevated health burdens from air pollution: a “cost of opportunity” when air pollutant regulations are weak. Our findings emphasize the imperative for tailored policy interventions to mitigate amplified health risks in vulnerable communities.

Article Details

Volume / Issue Vol. 122, Issue 41
Published October 14, 2025
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (4)

C

Chenliang Tao

Department of Environmental Science and Engineering, Fudan University

Y

Yuqiang Zhang

Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University

D

Drew Shindell

Earth and Climate Sciences Division, Nicholas School of the Environment, Duke University

H

Hongliang Zhang

Department of Environmental Science and Engineering, Fudan University