Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China

Ka Hung Chan; Xi Xia; Cong Liu; Haidong Kan; Aiden Doherty; Steve Hung Lam Yim; Neil Wright; Christiana Kartsonaki; Xiaoming Yang; Rebecca Stevens; Xiaoyu Chang; Dianjianyi Sun; Canqing Yu; Jun Lv; Liming Li; Kin-Fai Ho; Kin Bong Hubert Lam; Zhengming Chen; China Kadoorie Biobank collaborative group

doi:10.1016/j.scitotenv.2023.166647

Characterising personal, household, and community PM_2.5 exposure in one urban and two rural communities in China

Sci Total Environ. 2023 Dec 15:904:166647. doi: 10.1016/j.scitotenv.2023.166647. Epub 2023 Aug 28.

Authors

Ka Hung Chan¹, Xi Xia², Cong Liu³, Haidong Kan³, Aiden Doherty⁴, Steve Hung Lam Yim⁵, Neil Wright⁶, Christiana Kartsonaki⁷, Xiaoming Yang⁶, Rebecca Stevens⁶, Xiaoyu Chang⁸, Dianjianyi Sun⁹, Canqing Yu⁹, Jun Lv⁹, Liming Li⁹, Kin-Fai Ho¹⁰, Kin Bong Hubert Lam⁶, Zhengming Chen⁷; China Kadoorie Biobank collaborative group

Affiliations

¹ Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK; Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, UK. Electronic address: kahung.chan@ndph.ox.ac.uk.
² School of Public Health, Xi'an Jiaotong University, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong.
³ School of Public Health, Key Lab of Public Health Safety of the ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, China.
⁴ Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK; National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, UK.
⁵ Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore.
⁶ Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK.
⁷ Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, UK.
⁸ NCDs Prevention and Control Department, Sichuan CDC, China.
⁹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, China; Peking University Center for Public Health and Epidemic Preparedness and Response, China.
¹⁰ The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong. Electronic address: kfho@cuhk.edu.hk.

Abstract

Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM_2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.

Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM_2.5 in summer (May-September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326-80,980 person-hours (n_total = 441; n_summer = 384; n_winter = 364; 307 had repeated PM_2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM_2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM_2.5 levels across different microenvironments were computed.

Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM_2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM_2.5 from summer to winter, whereas community levels of PM_2.5 were 2-4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM_2.5 exposure at personal (78.2 [95 % CI 71.6-85.3] μg/m³ vs 41.6 [37.3-46.5] μg/m³), kitchen (102.4 [90.4-116.0] μg/m³ vs 52.3 [44.8-61.2] μg/m³) and living room (62.1 [57.3-67.3] μg/m³ vs 41.0 [37.1-45.3] μg/m³) microenvironments. There was a remarkable diurnal variability in PM_2.5 exposure among the participants, with 5-min moving average from 10 μg/m³ to 700-1200 μg/m³ across different microenvironments. Personal PM_2.5 was moderately correlated with living room (Spearman r: 0.64-0.66) and kitchen (0.52-0.59) levels, but only weakly correlated with community levels, especially in summer (0.15-0.34) and among solid fuel users (0.11-0.31).

Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM_2.5 exposure than clean fuel users. Household PM_2.5 appeared a better proxy of personal exposure than community PM_2.5.

Keywords: Cooking; Exposure assessment; Fine particulate matter; Heating; Solid fuels; Wearable sensor.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Air Pollution, Indoor* / analysis
China
Cooking
Environmental Monitoring
Humans
Particulate Matter / analysis
Rural Population

Substances

Particulate Matter
Air Pollutants

Abstract

MeSH terms

Substances

Grants and funding