Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.
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.
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.
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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Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
Environmental Health Open Access 09 December 2022
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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.
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
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.
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.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Members of the AIRLESS team are listed below Acknowledgements.
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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
- Novel sensor technologies
- Gaseous pollutants
- Particulate matter
- Time-activity-location patterns
- Exposure misclassification
- Dose estimation
- Health outcomes