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Using time-resolved monitor wearing data to study the effect of clean cooking interventions on personal air pollution exposures

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 386–395 (2023)Cite this article

Abstract

Background

Personal monitoring can estimate individuals’ exposures to environmental pollutants; however, accuracy depends on consistent monitor wearing, which is under evaluated.

Objective

To study the association between device wearing and personal air pollution exposure.

Methods

Using personal device accelerometry data collected in the context of a randomized cooking intervention in Ghana with three study arms (control, improved biomass, and liquified petroleum gas (LPG) arms; N = 1414), we account for device wearing to infer parameters of PM2.5 and CO exposure.

Results

Device wearing was positively associated with exposure in the control and improved biomass arms, but weakly in the LPG arm. Inferred community-level air pollution was similar across study arms (~45 μg/m3). The estimated direct contribution of individuals’ cooking to PM2.5 exposure was 64 μg/m3 for the control arm, 74 μg/m3 for improved biomass, and 6 μg/m3 for LPG. Arm-specific average PM2.5 exposure at near-maximum wearing was significantly lower in the LPG arm as compared to the improved biomass and control arms. Analysis of personal CO exposure mirrored PM2.5 results.

Conclusions

Personal monitor wearing was positively associated with average air pollution exposure, emphasizing the importance of high device wearing during monitoring periods and directly assessing device wearing for each deployment.

Significance

We demonstrate that personal monitor wearing data can be used to refine exposure estimates and infer unobserved parameters related to the timing and source of environmental exposures.

Impact statements

In a cookstove trial among pregnant women, time-resolved personal air pollution device wearing data were used to refine exposure estimates and infer unobserved exposure parameters, including community-level air pollution, the direct contribution of cooking to personal exposure, and the effect of clean cooking interventions on personal exposure. For example, in the control arm, while average 48 h personal PM2.5 exposure was 77 μg/m3, average predicted exposure at near-maximum daytime device wearing was 108 μg/m3 and 48 μg/m3 at zero daytime device wearing. Wearing-corrected average 48 h personal PM2.5 exposures were 50% lower in the LPG arm than the control and improved biomass and inferred direct cooking contributions to personal PM2.5 from LPG were 90% lower than the other arms. Our recommendation is that studies assessing personal exposures should examine the direct association between device wearing and estimated mean personal exposure.

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Fig. 1: Example individual time series showing personal PM2.5 exposure and estimated wearing compliance during a 2-day monitoring period for a participant in the control arm in July 2015.
Fig. 2: Summarizing detected device wearing and the distribution of personal PM2.5 exposures across hours of the day.
Fig. 3: Detected personal air pollution exposure device wearing declined throughout the study period.
Fig. 4: Model-based prediction of the association between daytime device wearing and mean personal PM2.5 exposure.

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Data availability

Anonymized data that underlie this study are available upon request. Proposals should be directed to kwakupoku.asante@kintampo.khrc.org and to darby.jack@columbia.edu. To gain access, requestors will need to sign a data access agreement.

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Acknowledgements

The Ghana Randomized Air Pollution and Health Study was supported by the National Institute of Environmental Health Sciences (NIEHS) Grants R01 ES019547, R01 ES026991 and P30 ES009089, NIH Shared instrument grant S10OD016219, the Thrasher Research Fund, and the Global Alliance for Clean Cookstoves. CFG received support from NIEHS grants T32 ES 023770 and F31 ES031813. AGL received support from the National Heart, Lung and Blood Institute grants K23 HL135349 and R01 MD013310. DC received support from the National Institute of Child Health and Human Development T32 HD049311. The authors acknowledge Charles Rodes for helpful discussions. The authors further acknowledge the study advisory committee who provided useful feedback and guidance in annual meetings. The authors are grateful to study participants and community opinion leaders, without whom this study would not have been possible.

Author information

Author notes

  1. These authors contributed equally: Steven N. Chillrud, Kwaku Poku Asante.

Authors and Affiliations

  1. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA

    Carlos F. Gould & Darby W. Jack

  2. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Carlos F. Gould

  3. Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana

    Mohammed Nuhu Mujtaba, Ellen Boamah-Kaali, Grace Manu, Kenneth Ayuurebobi Ae-Ngibise, Seyram Kaali & Kwaku Poku Asante

  4. Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    Qiang Yang & Steven N. Chillrud

  5. Now at Elsevier Global STM Journals, New York, USA

    Qiang Yang

  6. Berkeley Air Monitoring Group, Fort Collins, CO, USA

    Ashlinn K. Quinn

  7. Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alison G. Lee

  8. Department of Environmental Health Sciences, Yale University School of Public Health, New Haven, CT, USA

    Daniel Carrión

  9. Boston University School of Public Health, Boston, MA, USA

    Patrick L. Kinney

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Contributions

CFG: conceptualization, methodology, data curation, formal analysis, writing—original draft, writing—reviewing and editing, visualization. MNM: investigation, writing—reviewing and editing. QY: methodology, data curation, formal analysis, writing—reviewing and editing. EB-K: investigation, writing—reviewing and editing. AKQ: investigation, writing—reviewing and editing. GM: investigation, writing—reviewing and editing. AGL: methodology, writing—reviewing and editing. KAA-N: investigation, writing—reviewing and editing. DC: methodology, writing—reviewing and editing. SK: investigation, writing—reviewing and editing. PLK: conceptualization, investigation, writing—reviewing and editing, supervision, project administration, funding acquisition. DWJ: conceptualization, investigation, writing—reviewing and editing, supervision, project administration, funding acquisition. SNC: conceptualization, methodology, investigation, writing—reviewing and editing, supervision, project administration, funding acquisition. KPA: conceptualization, investigation, writing—reviewing and editing, supervision, project administration, funding acquisition.

Corresponding author

Correspondence to Steven N. Chillrud.

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Gould, C.F., Mujtaba, M.N., Yang, Q. et al. Using time-resolved monitor wearing data to study the effect of clean cooking interventions on personal air pollution exposures. J Expo Sci Environ Epidemiol 33, 386–395 (2023). https://doi.org/10.1038/s41370-022-00483-0

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