Lia Chatzidiakou1, Anika Krause2, Yiqun Han3,4,5, Wu Chen4, Li Yan3,5, Olalekan A M Popoola2, Mike Kellaway6, Yangfeng Wu7, Jing Liu8, Min Hu4,9, Ben Barratt3,5,10, Frank J Kelly3,5,10, Tong Zhu4,9, Roderic L Jones2. 1. Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. ec571@cam.ac.uk. 2. Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. 3. MRC Centre for Environment & Health, Imperial College London and King's College London, London, UK. 4. College of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China. 5. Department of Analytical, Environmental and Forensic Sciences, King's College London, London, SE1 9NH, UK. 6. Atmospheric Sensors Ltd, Bedfordshire, SG19 3SH, UK. 7. Peking University Clinical Research Institute, 100191, Beijing, China. 8. Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China. 9. The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, 100871, Beijing, China. 10. NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King's College London, London, SE1 9NH, UK.
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.
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.
Authors: Charlotte Scheerens; Lina Nurhussien; Amro Aglan; Andrew J Synn; Brent A Coull; Petros Koutrakis; Mary B Rice Journal: ERJ Open Res Date: 2022-03-14