Abstract

Introduction In air pollution epidemiology, measured or modelled surrogate exposure estimates, prone to measurement error (ME), are used to investigate the health effects of exposure to pollution of outdoor origin, potentially leading to biased effect estimates. We predicted annual personal exposure from outdoor sources using personal measurements, compared it with concentrations from surrogate metrics and quantified ME magnitude, type and determinants. Methods We used measurements from four panel studies in London, UK, and predicted personal exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3) and black carbon (BC). We compared those with surrogate exposures, including measurements from fixed-site monitors, modelled ambient concentrations or hybrid methods accounting for people’s mobility. We estimated the exposure ME magnitude, correlations and variance ratios between surrogate measures and personal exposure and the percentages of classical/Berkson in the errors. Individual- and area-level characteristics, such as age, sex, socioeconomic status and time spent outdoors were assessed as potential error determinants. Results Predicted annual personal exposures to PM2.5, NO2, O3 and BC from outdoor sources were overestimated by surrogate metrics, with mean differences up to 10.1 μg/m3, 40.0 μg/m3, 61.7 μg/m3 and 2.6 μg/m3, respectively. Variance ratios and Pearson correlation coefficients between surrogate and predicted personal exposures ranged from 0.03 to 165.02 and -0.24 to 0.25. Time-activity adjustment reduced errors substantially. Berkson type dominated ME for PM2.5 and BC (43-81% and 26-98%, respectively), whilst classical error characterised gases (>94% for both NO2 and O3). Time spent outdoors, house type and deprivation were associated with exposure error. Conclusions The use of surrogate exposures to investigate the health effects of long-term exposure to air pollution from outdoor sources may bias the epidemiological estimates due to ME. Information about the error structures and its determinants can be used for correction and the identification of the true exposure-response functions.