Quantifying the Health Burden Misclassification from the Use of Different PM2.5 Exposure Tier Models: A Case Study of London

Abstract

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.

PM2.5 chemical composition and health risks by inhalation near a chemical complex

“…One of the most common exposure assessment methods that has been used in epidemiology considers that ambient levels are representative of the total population exposure, given the lack of time-activity patterns for distinct microenvironments ( Kazakos et al, 2020 ). Thus, outdoor levels are generally taken as a surrogate of daily 24 hr exposure.…”

Integrated Human Exposure to Air Pollution

“…A study focused on this issue on the Greater London Area (UK) [ 12 ], including different micro-environments where people spend time at (based on time-activity and housing stock data), identified a misclassification of 1174–1541 mean predicted mortalities attributable to PM 2.5 exposure in the studied area when only outdoor concentration was considered. Indoor exposure to PM 2.5 was found to be the largest contributor to total population exposure concentrations accounting for 83%, followed by the exposure in the London Underground with 15%, despite the time spent on it being only 0.4%.…”

The influence of outdoor PM2.5 concentration at workplace on nonaccidental mortality estimates in a Canadian census-based cohort

“… 4 , 42 Studies with time-activity surveys can inform models or datasets of varying complexity for predicting outcomes. 8 , 9 , 27 , 29 , 32 , 35 , 45 , 46 Review papers by Dias and Tchepel 4 and Steinle et al 34 summarize the different methods available and the data gaps in the field. Large, population-based cohorts like ours could be enhanced through modeling of ambient air pollution encountered throughout the day as increased complexity has been shown to reduce exposure misclassification.…”

“…Large, population-based cohorts like ours could be enhanced through modeling of ambient air pollution encountered throughout the day as increased complexity has been shown to reduce exposure misclassification. 10 , 43 , 46 , 47 47 With the location of work and home, one could use traffic and travel models to predict commute path and outdoor concentrations of air pollutants during the commute. 7 , 45 This approach would be best if time of commute were available and if hourly air pollutant levels could be used.…”