Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Quantifying the health burden misclassification from the use of different PM2.5 exposure tier models: a case study of London

Kazakos, Vasilis, Luo, Zhiwen ORCID: and Ewart, Ian 2020. Quantifying the health burden misclassification from the use of different PM2.5 exposure tier models: a case study of London. International Journal of Environmental Research and Public Health 17 (3) , 1099. 10.3390/ijerph17031099

[thumbnail of ijerph-17-01099.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (3MB)


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 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 (PM2.5) 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 PM2.5 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Architecture
Additional Information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Publisher: MDPI
ISSN: 1661-7827
Date of First Compliant Deposit: 24 August 2022
Date of Acceptance: 4 February 2020
Last Modified: 10 Nov 2022 11:43

Citation Data

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics