Abstract/Summary

Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here we quantify the misclassification that occurs when using different exposure approaches to predict the mortality burden of a population using London as a case study. We develop a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors)in the Greater London Area (GLA), in 2017. We demonstrate that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, is used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimate the change in mortality on a fine resolution. Our results showed that using the outdoor concentration as a surrogate for the total population exposure but ignoring the different exposure concentration that occurs indoors and the time spent in transit, would lead to a misclassification of 1,174 predicted mortalities in GLA. Indoor exposure to PM2.5 is the largest contributor to total population exposure, accounting for 80% of total mortality, followed by the London Underground which contributes 15%, albeit the average percentage of time spent there by Londoners is only 0.4%. We generally confirmed that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification in health burden assessments.