Quantifying the Health Burden Misclassification from the Use of Different PM2.5 Exposure Tier Models

A Case Study of London

Bibliographic Data

ID17801210
AuthorsVasilis Kazakos (University of Reading), Zhiwen Luo (0000-0002-0524-9951, University of Reading, corresponding author), Ian Ewart (0000-0002-9502-2338, University of Reading)
Year2020
Volume17
Issue3
Pages1099-1099
Publication date2020-02-09
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17031099
PMID32050474
OpenAlexW3004856948
LanguageEN
References cited53

Exposure to PM 2.5 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 quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM 2.5 ) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated 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, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM 2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration (i.e., approximately 37%) and as a result the largest change in mortality (i.e., health burden/mortality misclassification) was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174-1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments

Environmental health · Exposure assessment · Geography · Multiple exposure · Population · Statistics · Air Quality and Health Impacts · Climate Change and Health Impacts · Computer Science · Engineering · Environmental Science · Mathematics · Medicine · Urban Transport and Accessibility