Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide

Abstract

Urban air pollution is high on global health and sustainability agendas, but information is limited on associated city-level disease burdens. We estimated fine particulate matter (PM_2.5) mortality in the 250 most populous cities worldwide using PM_2.5 concentrations, population, disease rates, and concentration-response relationships from the Global Burden of Disease 2016 Study. Only 8% of these cities had population-weighted mean concentrations below the World Health Organization guideline for annual average PM_2.5. City-level PM_2.5-attributable mortality rates ranged from 13-125 deaths per 100,000 people. PM_2.5 mortality rates and carbon dioxide (CO₂) emission rates were weakly positively correlated, with regional influences apparent from clustering of cities within each region. Across 82 cities globally, PM_2.5 concentrations and mortality rates were negatively associated with city gross domestic product (GDP) per capita, but we found no relationship between GDP per capita and CO₂ emissions rates. While results provide only a cross-sectional snapshot of cities worldwide, they point to opportunities for cities to realize climate, air quality, and health co-benefits through low-carbon development. Future work should examine drivers of the relationships (e.g. development stage, fuel mix for electricity generation and transportation, sector-specific PM_2.5 and CO₂ emissions) uncovered here and explore uncertainties to test the robustness of our conclusions.

Figure 1

PM_2.5-attributable premature deaths in 2016 in 250 cities worldwide. (a) Number of PM_2.5-attributable deaths on a world map; (b) Box plots of population-weighted annual average PM_2.5 concentration (PM2.5 pop-wt) and PM_2.5 attributable deaths per 100,000 people (PM2.5 death rate) across all cities in each region. Boxes indicate the middle 50% of the data; whiskers show data within 1.5 times the interquartile range. HI = High-Income.

Figure 2

City-specific estimates of PM_2.5-attributable premature deaths per capita in 2016 versus other city indicators. (a) Population-weighted annual average PM_2.5 concentration (µg/m³) vs. annual CO₂ emissions rate (t C per 100,000 people); (b) PM_2.5 death rate (deaths per 100,000 people) vs. annual CO₂ emissions rate; (c) comparison of population-weighted PM_2.5, PM_2.5 death rate, CO₂ emissions rate, and 2013 carbon footprint rate (kt CO₂ per 100,000 people) vs. GDP per capita ($) in 2015 in 82 cities. Colors indicate world regions (see Fig. 1 legend). Linear regression lines are shown where correlations are significant, r is the correlation coefficient, and p is the correlation significance level. (Note: Riyadh was removed from panels a and b to show more detail in the rest of the dataset. Its CO₂ emission rate is likely unrealistically high due to very low population estimate in the GPWv4 dataset: CO₂ emission rate = 290,000 kt CO₂ per 100,000 people, PM_2.5 pop-wt = 280 µg/m³, and PM_2.5 death rate = 40.) Similar graphs for each region (using “super-regions” from the Global Burden of Disease 2016 Study) and the 50 most populous cities globally are in the Supplemental Information (Figs S5–S12).