Health assessment of future PM_2.5 exposures from indoor, outdoor, and secondhand tobacco smoke concentrations under alternative policy pathways in Ulaanbaatar, Mongolia

<div><p>Introduction</p><p>Winter air pollution in Ulaanbaatar, Mongolia is among the worst in the world. The health impacts of policy decisions affecting air pollution exposures in Ulaanbaatar were modeled and evaluated under business as usual and two more-strict alternative emissions pathways through 2024. Previous studies have relied on either outdoor or indoor concentrations to assesses the health risks of air pollution, but the burden is really a function of total exposure. This study combined projections of indoor and outdoor concentrations of PM_2.5 with population time-activity estimates to develop trajectories of total age-specific PM_2.5 exposure for the Ulaanbaatar population. Indoor PM_2.5 contributions from secondhand tobacco smoke (SHS) were estimated in order to fill out total exposures, and changes in population and background disease were modeled. The health impacts were derived using integrated exposure-response curves from the Global Burden of Disease Study.</p><p>Results</p><p>Annual average population-weighted PM_2.5 exposures at baseline (2014) were estimated at 59 μg/m³. These were dominated by exposures occurring indoors, influenced considerably by infiltrated outdoor pollution. Under current control policies, exposures increased slightly to 60 μg/m³ by 2024; under moderate emissions reductions and under a switch to clean technologies, exposures were reduced from baseline levels by 45% and 80%, respectively. The moderate improvement pathway decreased per capita annual disability-adjusted life year (DALY) and death burdens by approximately 40%. A switch to clean fuels decreased per capita annual DALY and death burdens by about 85% by 2024 with the relative SHS contribution increasing substantially.</p><p>Conclusion</p><p>This study demonstrates a way to combine estimated changes in total exposure, background disease and population levels, and exposure-response functions to project the health impacts of alternative policy pathways. The resulting burden analysis highlights the need for aggressive action, including the elimination of residential coal burning and the reduction of current smoking rates.</p></div

Population weighted wintertime outdoor PM_2.5 concentrations.

<p>Whiskers represent 10^th and 90^th percentile concentrations.</p

Left, a traditional ger dwelling. right, houses typical of the peri-urban regions of UB.

<p>(Credit: L. Drew Hill).</p

Assignment of heating type of all UB households in BAU and each alternative pathway.

<p>Assignment of heating type of all UB households in BAU and each alternative pathway.</p

Exposures in 2014 and 2024 under BAU and alternative policy pathways, by environment.

<p>Indoor exposures are stratified by SHS and non-SHS environments. The difference between indoor and outdoor contribution to total exposure is primarily from the disproportionately high fraction of time spent indoors.</p

Summary of the assumptions made for emissions sources, by category ¹.

<p>Summary of the assumptions made for emissions sources, by category <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186834#t001fn001" target="_blank">¹</a>.</p

Estimated annual PM_2.5 emissions from major sources (tons/yr).

<p>Estimated annual PM_2.5 emissions from major sources (tons/yr).</p

Excess deaths and DALYs attributable to PM_2.5, BAU, Pathway 1, & Pathway 2 (per 1000 capita values rounded to two significant digits).

<p>Excess deaths and DALYs attributable to PM_2.5, BAU, Pathway 1, & Pathway 2 (per 1000 capita values rounded to two significant digits).</p

High-level flow chart of the general exposure and disease analysis approach.

<p>Annual average exposures were estimated for population sub-groups in 2014 and in 2024 under BAU and two alternative policy pathways from outdoor and indoor concentration models and time activity estimates using the approach summarized above. These exposures were applied to disease-specific exposure response curves to produce estimates of population attributable fraction (PAF) which were applied to background disease rates to quantify attributable disease burden. Detailed data descriptions and methods–including how interim year (2015–2023) disease burdens were calculated–are included in the manuscript and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186834#pone.0186834.s001" target="_blank">S1 Text</a>.</p

Estimated population-weighted average exposures by home type, year, and season (μg/m³).

<p>Estimated population-weighted average exposures by home type, year, and season (μg/m³).</p