Estimation of personal PM 2.5 and BC exposure by a modeling approach - Results of a panel study in Shanghai, China.

BACKGROUND: Epidemiologic studies of PM2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and black carbon (BC) typically use ambient measurements as exposure proxies given that individual measurement is infeasible among large populations. Failure to account for variation in exposure will bias epidemiologic study results. The ability of ambient measurement as a proxy of exposure in regions with heavy pollution is untested.

OBJECTIVE: We aimed to investigate effects of potential determinants and to estimate PM2.5 and BC exposure by a modeling approach.

METHODS: We collected 417 24 h personal PM2.5 and 130 72 h personal BC measurements from a panel of 36 nonsmoking college students in Shanghai, China. Each participant underwent 4 rounds of three consecutive 24-h sampling sessions through December 2014 to July 2015. We applied backwards regression to construct mixed effect models incorporating all accessible variables of ambient pollution, climate and time-location information for exposure prediction. All models were evaluated by marginal R2 and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV) and a 10-fold cross-validation (10-fold CV).

RESULTS: Personal PM2.5 was 47.6% lower than ambient level, with mean (±Standard Deviation, SD) level of 39.9 (±32.1) μg/m3 ; whereas personal BC (6.1 (±2.8) μg/m3 ) was about one-fold higher than the corresponding ambient concentrations. Ambient levels were the most significant determinants of PM2.5 and BC exposure. Meteorological and season indicators were also important predictors. Our final models predicted 75% of the variance in 24 h personal PM2.5 and 72 h personal BC. LOOCV analysis showed an R2 (RMSE) of 0.73 (0.40) for PM2.5 and 0.66 (0.27) for BC. Ten-fold CV analysis showed a R2 (RMSE) of 0.73 (0.41) for PM2.5 and 0.68 (0.26) for BC.

CONCLUSION: We used readily accessible data and established intuitive models that can predict PM2.5 and BC exposure. This modeling approach can be a feasible solution for PM exposure estimation in epidemiological studies.