Exposure measurement error in air-pollution epidemiology and its determinants: results from the MELONS study
File(s)
Published version
Author(s)
Type
Journal Article
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 the annual personal exposure from outdoor sources by using personal measurements, compared it with concentrations from surrogate metrics, and quantified the 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-type errors. Individual- and area-level characteristics, such as age, sex, socio-economic 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 of up to 10.1, 40.0, 61.7, and 2.6 μg/m3, respectively. The 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 errors dominated the ME for PM2.5 and BC (43%–81% and 26%–98%, respectively), whilst classical errors characterized gases (>94% for both NO2 and O3). Time spent outdoors, house type, and deprivation were associated with exposure error.
Conclusion
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 their determinants can be used for correction and the identification of the true exposure–response functions.
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 the annual personal exposure from outdoor sources by using personal measurements, compared it with concentrations from surrogate metrics, and quantified the 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-type errors. Individual- and area-level characteristics, such as age, sex, socio-economic 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 of up to 10.1, 40.0, 61.7, and 2.6 μg/m3, respectively. The 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 errors dominated the ME for PM2.5 and BC (43%–81% and 26%–98%, respectively), whilst classical errors characterized gases (>94% for both NO2 and O3). Time spent outdoors, house type, and deprivation were associated with exposure error.
Conclusion
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 their determinants can be used for correction and the identification of the true exposure–response functions.
Date Issued
2026-02-01
Date Acceptance
2025-10-30
Citation
International Journal of Epidemiology, 2026, 55 (1)
ISSN
0300-5771
Publisher
Oxford University Press (OUP)
Journal / Book Title
International Journal of Epidemiology
Volume
55
Issue
1
Copyright Statement
© The Author(s) 2026. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41481245
PII: 8408426
Subjects
air pollution
error determinants
measurement error
mixture error
Humans
Air Pollution
Particulate Matter
Environmental Exposure
Male
London
Female
Ozone
Air Pollutants
Nitrogen Dioxide
Adult
Middle Aged
Environmental Monitoring
Soot
Aged
Bias
Adolescent
Young Adult
Child
Publication Status
Published
Coverage Spatial
England
Article Number
dyaf214
Date Publish Online
2026-01-02