The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Abstract

The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2,013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals’ routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data (`a multi-sourced exposure model’). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data (`a single-sourced exposure model’). This study showed that there was a significant association between individuals’ mobility and the long-term exposure measurement error. However, the effect could be modified by individuals’ routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Comparison of static and dynamic exposures to air pollution, noise, and greenness among seniors living in compact-city environments

“…Taking most trips in the vicinities of home would likely attenuate the expected accuracy benefits from a dynamic exposure approach. This would confirm the previous hypothesis that states that daily exposures estimates obtained from the two approaches only differ substantially if an individual’s time spent away from home is large [ 21 , 93 – 95 ]. These findings, supported by the study by Yu et al [ 40 , 41 ] in China suggest that dynamic exposure assessment may only be warranted when studying those population groups that tend to spend more time in out-of-home activities in nonresidential neighborhoods and are thus exposed to considerable different conditions over the day.…”

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

“…A census-based study in the United Kingdom found a lower difference between residential and a time-weighed work-home estimate (0.1 µg/m 3 ) despite having a similar range of exposures at the national level (2.3-21.8 µg/m 3 ), 28 as did a smaller regional study from the United States (0.03 µg/m 3 ; range = 1.25-16.58). 29 A study in Israel found that there was no difference in NO 2 exposures at work and home for a majority of the study sample 30 and a study by Yu et al 31 found that homebased exposure estimates in China were not substantially different from cell-phone tracked exposures at the population level. As stated by Dhondt, "detailed exposure models are of little use in public health if they cannot be used for assessing health impacts."…”

“… 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.…”

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

“…Many research groups are currently developing and improving prediction models for exposure assessment in epidemiologic studies. However, most epidemiologic studies to date use air pollution predictions from a single model to assign exposures, although in recent years there have been additional efforts to develop statistical and computational exposure models with exhaustive datasets [ 21 , 22 ]. This is of critical importance because the results from these epidemiologic studies are often used to inform regulations, but the exposure–response functions that are generated from studies using different models for exposure assessment are not necessarily comparable, both spatially and temporally.…”