Determinants of personal exposure to fine particulate matter (PM2.5) in adult subjects in Hong Kong.

Personal monitoring for fine particulate matter (PM2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM2.5 was the dominant predictor of (R2=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM2.5 mass and most components. For all subjects, a one-unit (2.72μg/m3) increase in ambient PM2.5 was associated with a 0.75μg/m3 (95% CI: 0.59-0.94μg/m3) increase in personal PM2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM2.5 components in personal exposure (R2β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.