Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1

Similar content being viewed by others

References

  1. World Health Organization. Review of evidence on health aspects of air pollution - REVIHAAP Project. 2013. http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/2013/review-of-evidence-on-health-aspects-of-air-pollution-revihaap-project-final-technical-report.

  2. U.S. EPA. Integrated Science Assessment for Particulate Matter. 2009 EPA/600/R-08/139F.

  3. Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, 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. Lancet. 2017;389:1907–18.

    PubMed  PubMed Central  Google Scholar 

  4. Tonne C. A call for epidemiology where the air pollution is. Lancet Planet Heal. 2017;1:e355–e356.

    Google Scholar 

  5. Özkaynak H, Baxter LK, Dionisio KL, Burke J. Air pollution exposure prediction approaches used in air pollution epidemiology studies. J Expo Sci Environ Epidemiol. 2013;23:566–72.

    PubMed  Google Scholar 

  6. Marshall JD, Nethery E, Brauer M. Within-urban variability in ambient air pollution: comparison of estimation methods. Atmos Environ. 2008;42:1359–69.

    CAS  Google Scholar 

  7. Lane KJ, Levy JI, Scammell MK, Patton AP, Durant JL, Mwamburi M, et al. Effect of time-activity adjustment on exposure assessment for traffic-related ultrafine particles. J Expo Sci Environ Epidemiol. 2015;25:506–16.

    PubMed  PubMed Central  Google Scholar 

  8. Deffner V, Küchenhoff H, Maier V, Pitz M, Cyrys J, Breitner S, et al. Personal exposure to ultrafine particles: two-level statistical modeling of background exposure and time-activity patterns during three seasons. J Expo Sci Environ Epidemiol. 2016;26:17–25.

    PubMed  Google Scholar 

  9. Glasgow ML, Rudra CB, Yoo E-H, Demirbas M, Merriman J, Nayak P, et al. Using smartphones to collect time–activity data for long-term personal-level air pollution exposure assessment. J Expo Sci Environ Epidemiol. 2016;26:356–64.

    PubMed  Google Scholar 

  10. Steinle S, Reis S, Sabel CE. Quantifying human exposure to air pollution-Moving from static monitoring to spatio-temporally resolved personal exposure assessment. Sci Total Environ. 2013;443:184–93.

    CAS  PubMed  Google Scholar 

  11. Isaacs K, McCurdy T, Glen G, Nysewander M, Errickson A, Forbes S, et al. Statistical properties of longitudinal time-activity data for use in human exposure modeling. J Expo Sci Environ Epidemiol. 2013;23:328–36.

    PubMed  Google Scholar 

  12. McCracken JP, Schwartz J, Bruce N, Mittleman M, Ryan LM, Smith KR. Combining individual- and group-level exposure information. Epidemiology. 2009;20:127–36.

    PubMed  Google Scholar 

  13. Tonne C, Salmon M, Sanchez M, Sreekanth V, Bhogadi S, Sambandam S, et al. Integrated assessment of exposure to PM 2.5 in South India and its relation with cardiovascular risk: design of the CHAI observational cohort study. Int J Hyg Environ Health. 2017;220:1081–8.

    CAS  PubMed  Google Scholar 

  14. Kinra S, Radha Krishna K, Kuper H, Rameshwar Sarma K, Prabhakaran P, Gupta V, et al. Cohort Profile: Andhra Pradesh Children and Parents Study (APCAPS). Int J Epidemiol. 2014;43:1417–24.

    PubMed  Google Scholar 

  15. Balakrishnan K, Sambandam S, Ramaswamy P, Ghosh S, Venkatesan V, Thangavel G, et al. Establishing integrated rural-urban cohorts to assess air pollution-related health effects in pregnant women, children and adults in Southern India: an overview of objectives, design and methods in the Tamil Nadu Air Pollution and Health Effects (TAPHE) s. BMJ Open. 2015;5:e008090–e008090.

    PubMed  PubMed Central  Google Scholar 

  16. Zanatta M, Gysel M, Bukowiecki N, Müller T, Weingartner E, Areskoug H, et al. A European aerosol phenomenology-5: Climatology of black carbon optical properties at 9 regional background sites across Europe. Atmos Environ. 2016;145:346–64.

    CAS  Google Scholar 

  17. Kumar MK, Sreekanth V, Salmon M, Tonne C, Marshall JD. Use of spatiotemporal characteristics of ambient PM 2.5 in rural South India to infer local versus regional contributions. Environ Pollut. 2018;239:803–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Sanchez M, Ambros A, Milà C, Salmon M, Balakrishnan K, Sambandam S, et al. Development of land-use regression models for fine particles and black carbon in peri-urban South India. Sci Total Environ. 2018;634:77–86.

    CAS  PubMed  Google Scholar 

  19. Balakrishnan K, Sambandam S, Ramaswamy P, Mehta S, Smith KR. Exposure assessment for respirable particulates associated with household fuel use in rural districts of Andhra Pradesh, India. J Expo Sci Environ Epidemiol. 2004;14:S14–S25.

    CAS  Google Scholar 

  20. Sanchez M, Ambros A, Salmon M, Bhogadi S, Wilson R, Kinra S, et al. Predictors of daily mobility of adults in Peri-Urban South India. Int J Environ Res Public Health. 2017;14:783.

    PubMed Central  Google Scholar 

  21. Salmon M, Milà C, Bhogadi S, Addanki S, Madhira P, Muddepaka N, et al. Wearable camera-derived microenvironments in relation to personal exposure to PM 2.5. Environ Int. 2018;117:300–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Snijders TAB, Berkhof J Diagnostic Checks for Multilevel Models. In: Handbook of Multilevel Analysis. New York: Springer New York, pp 141–75.

  23. Wang M, Brunekreef B, Gehring U, Szpiro A, Hoek G, Beelen R. A New Technique for Evaluating Land-use Regression Models and Their Impact on Health Effect Estimates. Epidemiology. 2016;27:51–6.

    PubMed  PubMed Central  Google Scholar 

  24. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. https://www.r-project.org/.

  25. Nieuwenhuis R, Te Grotenhuis M, Pelzer B. Influence.ME: tools for detecting influential data in mixed effects models. R J. 2012;4:38–47.

    Google Scholar 

  26. Wickham H. tidyverse: Easily Install and Load ‘Tidyverse’ Packages. R package version 1.0.0. 2016. https://cran.r-project.org/package=tidyverse.

  27. Wickham H. ggplot2: Elegant Graphics for Data Analysis. R package version 2.2.0. 2009. http://ggplot2.org.

  28. Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.

    Google Scholar 

  29. McCracken JP, Schwartz J, Diaz A, Bruce N, Smith KR. Longitudinal relationship between personal CO and personal PM2.5 among women cooking with woodfired cookstoves in Guatemala. PLoS One. 2013;8:e55670.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Dionisio KL, Howie SRC, Dominici F, Fornace KM, Spengler JD, Donkor S, et al. The exposure of infants and children to carbon monoxide from biomass fuels in The Gambia: a measurement and modeling study. J Expo Sci Environ Epidemiol. 2012;22:173–81.

    CAS  PubMed  Google Scholar 

  31. Lee K, Bartell SM, Paek D. Interpersonal and daily variability of personal exposures to nitrogen dioxide and sulfur dioxide. J Expo Sci Environ Epidemiol. 2004;14:137–43.

    CAS  Google Scholar 

  32. Nethery E, Teschke K, Brauer M. Predicting personal exposure of pregnant women to traffic-related air pollutants. Sci Total Environ. 2008;395:11–22.

    CAS  PubMed  Google Scholar 

  33. Nethery E, Leckie SE, Teschke K, Brauer M. From measures to models: an evaluation of air pollution exposure assessment for epidemiological studies of pregnant women. Occup Environ Med. 2008;65:579–86.

    CAS  PubMed  Google Scholar 

  34. MacNeill M, Wallace L, Kearney J, Allen RW, Van Ryswyk K, Judek S, et al. Factors influencing variability in the infiltration of PM2.5 mass and its components. Atmos Environ. 2012;61:518–32.

    CAS  Google Scholar 

  35. Johannesson S, Gustafson P, Molnár P, Barregard L, Sällsten G. Exposure to fine particles (PM2.5 and PM1) and black smoke in the general population: personal, indoor and outdoor levels. J Expo Sci Environ Epidemiol. 2007;17:613–24.

    CAS  PubMed  Google Scholar 

  36. Milà C, Salmon M, Sanchez M, Ambrós A, Bhogadi S, Sreekanth V, et al. When, where, and what? characterizing personal PM2.5 exposure in Periurban India by integrating GPS, wearable camera, and ambient and personal monitoring data. Environ Sci Technol. 2018;52:13481–90.

    PubMed  Google Scholar 

  37. Carter E, Archer-Nicholls S, Ni K, Lai AM, Niu H, Secrest MH, et al. Seasonal and diurnal air pollution from residential cooking and space heating in the Eastern Tibetan Plateau. Environ Sci Technol. 2016;50:8353–61.

    CAS  PubMed  Google Scholar 

  38. Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P. Ambient gas concentrations and personal particulate matter exposures. Epidemiology. 2005;16:385–95.

    PubMed  Google Scholar 

  39. Michikawa T, Nakai S, Nitta H, Tamura K. Validity of using annual mean particulate matter concentrations as measured at fixed site in assessing personal exposure: an exposure assessment study in Japan. Sci Total Environ. 2014;466–7:673–80.

    Google Scholar 

  40. Miller KA, Spalt EW, Gassett AJ, Curl CL, Larson TV, Avol E, et al. Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis. J Expo Sci Environ Epidemiol. 2019;29:227–37.

    CAS  PubMed  Google Scholar 

  41. Nieuwenhuijsen MJ, Donaire-Gonzalez D, Rivas I, de Castro M, Cirach M, Hoek G, et al. Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. Environ Sci Technol. 2015;49:2977–82.

    CAS  PubMed  Google Scholar 

  42. Montagne D, Hoek G, Nieuwenhuijsen M, Lanki T, Pennanen A, Portella M, et al. Agreement of land use regression models with personal exposure measurements of particulate matter and nitrogen oxides air pollution. Environ Sci Technol. 2013;47:8523–31.

    CAS  PubMed  Google Scholar 

  43. Wilson WE, Brauer M. Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study. J Expo Sci Environ Epidemiol. 2006;16:264–74.

    CAS  PubMed  Google Scholar 

  44. Balakrishnan K, Parikh J, Sankar S, Padmavathi R, Srividya K, Venugopal V, et al. Daily average exposures to respirable particulate matter from combustion of biomass fuels in rural households of Southern India. Environ Health Perspect. 2002;110:1069–75.

    PubMed  PubMed Central  Google Scholar 

  45. Pant P, Habib G, Marshall JD, Peltier RE. PM 2.5 exposure in highly polluted cities: a case study from New Delhi, India. Environ Res. 2017;156:167–74.

    CAS  PubMed  Google Scholar 

  46. Van Vliet EDS, Asante K, Jack DW, Kinney PL, Whyatt RM, Chillrud SN, et al. Personal exposures to fine particulate matter and black carbon in households cooking with biomass fuels in rural Ghana. Environ Res. 2013;127:40–8.

    PubMed  PubMed Central  Google Scholar 

  47. Nayek S, Padhy PK. Daily personal exposure of women cooks to respirable particulate matters during cooking with solid bio-fuels in a rural community of West Bengal, India. Aerosol Air Qual Res. 2017;17:245–52.

    CAS  Google Scholar 

  48. McCreddin A, Alam MS, McNabola A. Modelling personal exposure to particulate air pollution: an assessment of time-integrated activity modelling, Monte Carlo simulation and artificial neural network approaches. Int J Hyg Environ Health. 2015;218:107–16.

    CAS  PubMed  Google Scholar 

  49. Meng QY, Spector D, Colome S, Turpin B. Determinants of indoor and personal exposure to PM2.5 of indoor and outdoor origin during the RIOPA study. Atmos Environ. 2009;43:5750–8.

    CAS  PubMed Central  Google Scholar 

  50. Rivas I, Donaire-Gonzalez D, Bouso L, Esnaola M, Pandolfi M, de Castro M, et al. Spatiotemporally resolved black carbon concentration, schoolchildren’s exposure and dose in Barcelona. Indoor Air. 2016;26:391–402.

    CAS  PubMed  Google Scholar 

  51. McCracken JP, Wellenius GA, Bloomfield GS, Brook RD, Tolunay HE, Dockery DW. et al. Household air pollution from solid fuel use. Glob. Heart. 2012;7:223–34.

    PubMed  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cathryn Tonne.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanchez, M., Milà, C., Sreekanth, V. et al. Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence. J Expo Sci Environ Epidemiol 30, 596–605 (2020). https://doi.org/10.1038/s41370-019-0150-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41370-019-0150-5

Keywords

This article is cited by

Search

Quick links