Data de publicació electrònica: 01-07-2019

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM2.5 personal exposure were 55.1(82.8) µg/m3 for men and 58.5(58.8) µg/m3 for women; corresponding figures for black carbon were 4.6(7.0) µg/m3 and 6.1(9.6) µg/m3. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (Rspearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (Rspearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

The research leading to these results received funding from the European Research Council under ERC Grant Agreement number 336167 for the CHAI Project. The third wave of data collection and village socio-demographic surveys for the APCAPS study were funded by the Wellcome Trust (Grant 084674/Z). C.T. was funded through a Ramón y Cajal fellowship (RYC-2015–17402) awarded by the Spanish Ministry of Economy and Competitiveness. We thank all participants and study teams of the APCAPS and CHAI studies.