학술논문
Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence
Document Type
Academic Journal
Author
Source
Journal of Exposure Science and Environmental Epidemiology. July 2020, Vol. 30 Issue 4, p596, 10 p.
Subject
Language
English
ISSN
1559-0631
Abstract
Author(s): Margaux Sanchez [sup.1] [sup.2] [sup.3], Carles Milà [sup.1] [sup.2] [sup.3], V. Sreekanth [sup.4], Kalpana Balakrishnan [sup.5], Sankar Sambandam [sup.5], Mark Nieuwenhuijsen [sup.1] [sup.2] [sup.3], Sanjay Kinra [sup.6], Julian D. [...]
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 PM.sub.2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM.sub.2.5 personal exposure were 55.1(82.8) [micro]g/m.sup.3 for men and 58.5(58.8) [micro]g/m.sup.3 for women; corresponding figures for black carbon were 4.6(7.0) [micro]g/m.sup.3 and 6.1(9.6) [micro]g/m.sup.3. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R.sub.spearman < 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 [less than or equal to] 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 (R.sub.spearman: 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.
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 PM.sub.2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM.sub.2.5 personal exposure were 55.1(82.8) [micro]g/m.sup.3 for men and 58.5(58.8) [micro]g/m.sup.3 for women; corresponding figures for black carbon were 4.6(7.0) [micro]g/m.sup.3 and 6.1(9.6) [micro]g/m.sup.3. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R.sub.spearman < 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 [less than or equal to] 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 (R.sub.spearman: 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.