When, Where, and What? Characterizing Personal PM2.5 Exposure in Periurban India by Integrating GPS, Wearable Camera, and Ambient and Personal Monitoring Data.

Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and wearable camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.

[1]  Luc Int Panis,et al.  Impact of time–activity patterns on personal exposure to black carbon , 2011 .

[2]  L. Morawska,et al.  Personal exposure to ultrafine particles: the influence of time-activity patterns. , 2014, The Science of the total environment.

[3]  J. Marshall,et al.  Predictors of Daily Mobility of Adults in Peri-Urban South India , 2017, International journal of environmental research and public health.

[4]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[5]  Hannah Badland,et al.  Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity , 2013, International Journal of Behavioral Nutrition and Physical Activity.

[6]  K. Balakrishnan,et al.  Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: Design of the CHAI observational cohort study. , 2017, International journal of hygiene and environmental health.

[7]  Hadley Wickham,et al.  Dates and Times Made Easy with lubridate , 2011 .

[8]  P. Hystad,et al.  Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research , 2017, Current Environmental Health Reports.

[9]  Edmund Seto,et al.  Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. , 2015, Environmental science & technology.

[10]  S. Guttikunda,et al.  Exposure to particulate matter in India: A synthesis of findings and future directions. , 2016, Environmental research.

[11]  Ricardo Cisneros,et al.  A comparative analysis of temporary and permanent beta attenuation monitors: The importance of understanding data and equipment limitations when creating PM2.5 air quality health advisories , 2016 .

[12]  B. Brunekreef,et al.  Agreement of land use regression models with personal exposure measurements of particulate matter and nitrogen oxides air pollution. , 2013, Environmental science & technology.

[13]  A. Peters,et al.  Personal day-time exposure to ultrafine particles in different microenvironments. , 2015, International journal of hygiene and environmental health.

[14]  K. Balakrishnan,et al.  Daily average exposures to respirable particulate matter from combustion of biomass fuels in rural households of southern India. , 2002, Environmental health perspectives.

[15]  Matthew L. Thomas,et al.  Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015 , 2017, The Lancet.

[16]  Sumi Mehta,et al.  Exposure assessment for respirable particulates associated with household fuel use in rural districts of Andhra Pradesh, India , 2004, Journal of Exposure Analysis and Environmental Epidemiology.

[17]  Patrick Royston,et al.  Multiple imputation using chained equations: Issues and guidance for practice , 2011, Statistics in medicine.

[18]  Maëlle Salmon,et al.  rtimicropem: an R package supporting the analysis of RTI MicroPEM output files , 2017, J. Open Source Softw..

[19]  N. Freeman,et al.  Methods for collecting time/activity pattern information related to exposure to combustion products. , 2002, Chemosphere.

[20]  Shinichi Nakagawa,et al.  A general and simple method for obtaining R2 from generalized linear mixed‐effects models , 2013 .

[21]  K. Balakrishnan,et al.  Development of land-use regression models for fine particles and black carbon in peri-urban South India. , 2018, The Science of the total environment.

[22]  Ellison Carter,et al.  Seasonal variation in outdoor, indoor, and personal air pollution exposures of women using wood stoves in the Tibetan Plateau: Baseline assessment for an energy intervention study. , 2016, Environment international.

[23]  Roel Vermeulen,et al.  New frontiers for environmental epidemiology in a changing world. , 2017, Environment international.

[24]  C. Sabel,et al.  Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment. , 2013, The Science of the total environment.

[25]  Sunny Jose,et al.  Understanding Women's Work Using Time-Use Statistics: The Case of India , 2011 .

[26]  M. Ezzati,et al.  Personal particulate matter exposures and locations of students in four neighborhoods in Accra, Ghana , 2014, Journal of Exposure Science and Environmental Epidemiology.

[27]  J. Marshall,et al.  Wearable camera-derived microenvironments in relation to personal exposure to PM2.5 , 2018, Environment international.

[28]  Audrey de Nazelle,et al.  Benefits of Mobile Phone Technology for Personal Environmental Monitoring , 2016, JMIR mHealth and uHealth.

[29]  J. Marshall,et al.  PM2.5 exposure in highly polluted cities: A case study from New Delhi, India , 2017, Environmental research.

[30]  Hadley Wickham,et al.  ggmap: Spatial Visualization with ggplot2 , 2013, R J..

[31]  J. Marshall,et al.  Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions☆ , 2018, Environmental pollution.

[32]  A. Peters,et al.  Personal exposure to ultrafine particles: Two-level statistical modeling of background exposure and time-activity patterns during three seasons , 2016, Journal of Exposure Science and Environmental Epidemiology.

[33]  Birthe Uldahl Kjeldsen,et al.  Contribution of various microenvironments to the daily personal exposure to ultrafine particles: Personal monitoring coupled with GPS tracking , 2015 .

[34]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[35]  P. Prabhakaran,et al.  COHORT PROFILE Cohort Profile : Andhra Pradesh Children and Parents Study ( APCAPS ) , 2014 .

[36]  M. Brauer,et al.  Predicting personal exposure of pregnant women to traffic-related air pollutants. , 2008, The Science of the total environment.

[37]  J. G. Adair,et al.  The Hawthorne effect: A reconsideration of the methodological artifact. , 1984 .