Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis

Jiayao Chen; Heiko J Jahn; Haitong Zhe Sun; Zhi Ning; Weisheng Lu; Kin Fai Ho; Tony J Ward

doi:10.1016/j.envpol.2022.120030

Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis

Environ Pollut. 2022 Nov 1:312:120030. doi: 10.1016/j.envpol.2022.120030. Epub 2022 Aug 26.

Authors

Jiayao Chen¹, Heiko J Jahn², Haitong Zhe Sun³, Zhi Ning⁴, Weisheng Lu⁵, Kin Fai Ho⁶, Tony J Ward⁷

Affiliations

¹ Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China. Electronic address: chenxcui@hku.hk.
² Faculty of Human Sciences, University of Kassel, Kassel, Germany.
³ Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK.
⁴ Division of Environment and Sustainability, Hong Kong University of Science and Technology, Hong Kong, China.
⁵ Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong, China.
⁶ The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
⁷ School of Public and Community Health Sciences, University of Montana, Missoula, MT, USA.

PMID: 36037851
DOI: 10.1016/j.envpol.2022.120030

Abstract

Exposure measurement error is an important source of bias in epidemiological studies. We assessed the validity of employing ambient (outdoor) measurements as proxies of personal exposures at individual levels focusing on fine particles (PM_2.5) and black carbon (BC)/elemental carbon (EC) on a global scale. We conducted a systematic review and meta-analysis and searched databases (ISI Web of Science, Scopus, PubMed, Ovid MEDLINE®, Ovid Embase, and Ovid BIOSIS) to retrieve observational studies in English language published from 1 January 2006 until 5 May 2021. Correlation coefficients (r) between paired ambient (outdoor) concentration and personal exposure for PM_2.5 or BC/EC were standardized as effect size. We used random-effects meta-analyses to pool the correlation coefficients and investigated the causes of heterogeneity and publication bias. Furthermore, we employed subgroup and meta-regression analyses to evaluate the modification of pooled estimates by potential mediators. This systematic review identified thirty-two observational studies involving 1744 subjects from ten countries, with 28 studies for PM_2.5 and 11 studies for BC/EC. Personal PM_2.5 exposure is more strongly correlated with ambient (outdoor) concentrations (0.63, 95% confidence interval [CI]: 0.57-0.68) than personal BC/EC exposure (0.49, 95% CI: 0.38-0.59), with significant differences in ṝ (0.14, 95% CI: 0.03-0.25; p < 0.05). The results demonstrated that the health status of participants was a significant modifier of pooled correlations. In addition, the personal to ambient (P/A) ratio for PM_2.5 and average ambient BC/EC levels were potential effect moderators of the pooled ṝ. The funnel plots and Egger's regression test indicated inevident publication bias. The pooled estimates were robust through sensitivity analyses. The results support the growing consensus that the validity coefficient of proxy measures should be addressed when interpreting results from epidemiological studies to better understand how strong health outcomes are affected by different levels of PM_2.5 and their components.

Keywords: Black carbon; Correlation coefficient; Fine particles; Fixed-site monitoring; Meta-analysis; Personal exposure.

Publication types

Meta-Analysis
Review
Systematic Review

MeSH terms

Air Pollutants* / analysis
Carbon / analysis
Databases, Factual
Environmental Exposure / analysis
Humans
Particulate Matter / analysis
Regression Analysis
Soot / analysis

Substances

Air Pollutants
Particulate Matter
Soot
Carbon