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Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project

Journal of Exposure Science & Environmental Epidemiology volume 30, pages 981–989 (2020)Cite this article

Abstract

Background

Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.

Methods

Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants’ residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.

Results

Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.

Conclusions

Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

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Fig. 1: Flow chart of time-activity model.
Fig. 2: Boxplots of ambient and personal air pollution levels in urban and peri-urban Beijing during the winter and summer campaigns.
Fig. 3: Correlation between the concentration measurements of two different pollution species (NO2 and PM2.5).
Fig. 4: Dose estimations per time unit of various pollutants during the winter deployment.
Fig. 5: Weekly air pollution dose by participant, split by contribution from different microenvironments (winter deployment).
Fig. 6: Mean pollutant dose per unit time during different activities.

References

  1. 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.

    Article  Google Scholar 

  2. World Health Organisation. Air pollution levels rising in many of the world’s poorest cities. http://www.who.int/mediacentre/news/releases/2016/air-pollution-rising/; 2016.

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

    Article  Google Scholar 

  4. Committee on the Medical Effects of Air Pollutants. Associations of long-term average concentrations of nitrogen dioxide with mortality. UK: Public Health England; 2018.

  5. Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery D, et al. Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect. 2000;108:419–26.

    Article  CAS  Google Scholar 

  6. Sarnat SE, Sarnat JA, Mulholland J, Isakov V, Özkaynak H, Chang HH, et al. Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta. J Exposure Sci Environ Epidemiol. 2013;23:593–605.

    Article  Google Scholar 

  7. 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.

    Article  CAS  Google Scholar 

  8. Karanasiou A, Viana M, Querol X, Moreno T, de Leeuw F. Assessment of personal exposure to particulate air pollution during commuting in European cities—Recommendations and policy implications. Sci Total Environ. 2014;8:785–97.

    Article  Google Scholar 

  9. Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, et al. The national human activity pattern survey (NHAPS): a resource for assessing expo- sure to environmental pollutants. J Exposure Anal Environ Epidemiol. 2001;11:231–52.

    Article  CAS  Google Scholar 

  10. Sundell J, Levin H, Nazaroff WW, Cain WS, Fisk WJ, Grimsrud DT, et al. Ventilation rates and health: multidisciplinary review of the scientific literature. Indoor Air. 2011;21:191–204.

    Article  CAS  Google Scholar 

  11. Schweizer C, Edwards RD, Bayer-Oglesby L, Gauderman WJ, Ilacqua V, Juhani Jan- tunen M, et al. Indoor time-microenvironment-activity patterns in seven regions of Europe. J Exposure Sci Environ Epidemiol. 2007;17:170–81.

    Article  CAS  Google Scholar 

  12. US Environmental Protection Agency (EPA). Exposure Factors Handbook: 2011 Edition. National Center for Environmental Assessment, Washington, DC. 2011. p. 1–1466.

  13. Han Y, Chen W, Chatzidiakou L, Yan L, Zhang H, Chan Q, et al. Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing: protocol for the AIRLESS study. Atmos Chem Phys Discuss. 2020. https://acp.copernicus.org/preprints/acp-2020-208/ (In review).

  14. Shi Z, Vu T, Kotthaus S, Harrison RM, Grimmond S, Yue S, et al. Introduction to the special issue “in-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing)”. Atmos Chem Phys. 2019;19:7519–46.

    Article  CAS  Google Scholar 

  15. Chatzidiakou L, Krause A, Popoola OAM, Di Antonio A, Kellaway M, Han Y, et al. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments. Atmos Meas Tech. 2019;5:4643–57.

    Article  Google Scholar 

  16. OpenStreetMap contributors. OpenStreetMap. Planet dump. https://planet.osm.org 2017.

  17. Lyons AJ, Turner WC, Getz WM. Home range plus: aspace-time characterization of movement over real landscapes. Mov Ecol. 2013;1:2.

    Article  Google Scholar 

  18. Spalt EW, Curl CL, Allen RW, Cohen M, Williams K, Hirsch JA, et al. Factors influencing time-location patterns and their impact on estimates of exposure: the multi-ethnic study of atherosclerosis and air pollution (MESA Air). J Exposure Sci Environ Epidemiol. 2016;26:341–8.

    Article  Google Scholar 

  19. Ott WR. Concepts of human exposure to air pollution. Environ Int. 1982;7:179–96.

    Article  Google Scholar 

  20. Monn C. Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone. Atmos Environ 2001;35:1–32.

    Article  CAS  Google Scholar 

  21. Brauer M. How much, how long, what, and where: Air pollution exposure assessment for epidemiologic studies of respiratory disease. Proc Am Thorac Soc. 2010;7:111–5.

    Article  Google Scholar 

  22. De Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J, Nieuwenhuijsen MJ, et al. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environ Pollut. 2013;5:92–99.

    Article  Google Scholar 

  23. Calenge C. Analysis of animal movements in R: the adehabitatLT package. Vienna: R Foundation for Statistical Computing; 2011.

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Acknowledgements

We would like to thank all members of the AIRLESS team who helped during fieldwork in urban Beijing and peri-urban sites. We would also like to thank the AIRPOLL and AIRPRO study team for the collected ambient air pollution data for reference calibration and further health analysis.

AIRLESS team

Ben Barratt2,4,9, Yutong Cai2, Queenie Chan2, Lia Chatzidiakou1, Shiyi Chen3, Wu Chen3, Xi Chen3, Paul Elliott2, Majid Ezzati2, Yunfei Fan3, Xueyu Han7, Yiqun Han2,3,4, Min Hu3,8, Aoming Jin6, Roderic L. Jones1, Frank J. Kelly2,4,9, Anika Krause1, Yingruo Li3, Pengfei Liang3, Jing Liu7, Yan Luo6, Xinghua Qiu3, Qi Wang3, Teng Wang3, Yanwen Wang3, Yangfeng Wu6, Gaoqiang Xie6, Wuxiang Xie6, Tao Xue3, Li Yan2,4, Hanbin Zhang2, Junfeng Zhang10, Meiping Zhao11, Tong Zhu3,8, Yidan Zhu6

Funding

This project is funded under the Newton Fund Programme awarded by Natural Environmental Research Council (NERC Grant NE/N007018/1) with support from Medical Research Council (MRC) and by the National Natural Science Foundation of China (NSFC Grant 81571130100). The NSFC funding is mainly used to support the field work in China, and NERC funding is mainly used for coordination and the further analysis.

Author information

Author notes

  1. These authors contributed equally: Lia Chatzidiakou, Anika Krause

Authors and Affiliations

  1. Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK

    Lia Chatzidiakou, Anika Krause, Olalekan A. M. Popoola, Lia Chatzidiakou, Roderic L. Jones, Anika Krause & Roderic L. Jones

  2. MRC Centre for Environment & Health, Imperial College London and King’s College London, London, UK

    Yiqun Han, Li Yan, Ben Barratt, Yutong Cai, Queenie Chan, Paul Elliott, Majid Ezzati, Frank J. Kelly, Li Yan, Hanbin Zhang, Ben Barratt & Frank J. Kelly

  3. College of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China

    Yiqun Han, Wu Chen, Min Hu, Shiyi Chen, Wu Chen, Xi Chen, Yunfei Fan, Min Hu, Yingruo Li, Pengfei Liang, Xinghua Qiu, Qi Wang, Teng Wang, Yanwen Wang, Tao Xue, Tong Zhu & Tong Zhu

  4. Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK

    Yiqun Han, Li Yan, Ben Barratt, Frank J. Kelly, Li Yan, Ben Barratt & Frank J. Kelly

  5. Atmospheric Sensors Ltd, Bedfordshire, SG19 3SH, UK

    Mike Kellaway

  6. Peking University Clinical Research Institute, 100191, Beijing, China

    Yangfeng Wu, Aoming Jin, Yan Luo, Yangfeng Wu, Gaoqiang Xie, Wuxiang Xie & Yidan Zhu

  7. Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China

    Jing Liu, Xueyu Han & Jing Liu

  8. The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, 100871, Beijing, China

    Min Hu, Min Hu, Tong Zhu & Tong Zhu

  9. NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK

    Ben Barratt, Frank J. Kelly, Ben Barratt & Frank J. Kelly

  10. Duke Kunshan University, Nanjing, 215316, China

    Junfeng Zhang

  11. College of Chemistry, Peking University, 100871, Beijing, China

    Meiping Zhao

Authors
  1. Lia Chatzidiakou
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  2. Anika Krause
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  4. Wu Chen
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  5. Li Yan
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  6. Olalekan A. M. Popoola
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  7. Mike Kellaway
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  8. Yangfeng Wu
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  9. Jing Liu
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  10. Min Hu
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  11. Ben Barratt
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  12. Frank J. Kelly
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  13. Tong Zhu
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  14. Roderic L. Jones
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Consortia

AIRLESS team

  • Ben Barratt
  • , Yutong Cai
  • , Queenie Chan
  • , Lia Chatzidiakou
  • , Shiyi Chen
  • , Wu Chen
  • , Xi Chen
  • , Paul Elliott
  • , Majid Ezzati
  • , Yunfei Fan
  • , Xueyu Han
  • , Min Hu
  • , Aoming Jin
  • , Roderic L. Jones
  • , Frank J. Kelly
  • , Anika Krause
  • , Yingruo Li
  • , Pengfei Liang
  • , Jing Liu
  • , Yan Luo
  • , Xinghua Qiu
  • , Qi Wang
  • , Teng Wang
  • , Yanwen Wang
  • , Yangfeng Wu
  • , Gaoqiang Xie
  • , Wuxiang Xie
  • , Tao Xue
  • , Li Yan
  • , Hanbin Zhang
  • , Junfeng Zhang
  • , Meiping Zhao
  • , Tong Zhu
  •  & Yidan Zhu

Contributions

LC and AK have contributed equally to this paper. It was conceptualised by LC, AK and RLJ. The sensor platform was developed by MK and LC. The field deployment was performed by LC, AK, YH, LY, WC and the AIRLESS team. The PIs of the two cohorts were YW and JL. MH was in charge of the Peking University monitoring station. The data curation was performed by LC and AK. LC, AK, OAMP and RLJ contributed to the formal data analysis. Data were visualised by AK and LC. Resources were provided by BB, FJK, TZ and RLJ. The original draft was written by LC and AK and reviewed and edited by YH, BB and RLJ.

Corresponding author

Correspondence to Lia Chatzidiakou.

Ethics declarations

Conflict of interest

The study protocol was approved by the Institutional Review Board of the Peking University Health Science Centre, China (IRB00001052-16028), and College Research Ethics Committee of King’s College London, UK (HR-16/17-3901). Informed consent was obtained from all subjects.The authors declare no conflict of interest.

Ethics approval

The study protocol was approved by the Institutional Review Board of the Peking University Health Science Centre, China (IRB00001052-16028), and College Research Ethics Committee of King’s College London, UK (HR-16/17-3901). Informed consent was obtained from all subjects.

Additional information

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Members of the AIRLESS team are listed below Acknowledgements.

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Cite this article

Chatzidiakou, L., Krause, A., Han, Y. et al. Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project. J Expo Sci Environ Epidemiol 30, 981–989 (2020). https://doi.org/10.1038/s41370-020-0259-6

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  • DOI: https://doi.org/10.1038/s41370-020-0259-6

Keywords

  • Novel sensor technologies
  • Gaseous pollutants
  • Particulate matter
  • Time-activity-location patterns
  • Exposure misclassification
  • Dose estimation
  • Health outcomes

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