Regional and seasonal variations in household and personal exposures to air pollution in one urban and two rural Chinese communities: A pilot study to collect time-resolved data using static and wearable devices

• Published in: Environment international Vol. 146; p. 106217
• Main Authors: Chan, Ka Hung, Xia, Xi, Ho, Kin-fai, Guo, Yu, Kurmi, Om P, Du, Huaidong, Bennett, Derrick A, Bian, Zheng, Kan, Haidong, McDonnell, John, Schmidt, Dan, Kerosi, Rene, Li, Liming, Lam, Kin Bong Hubert, Chen, Zhengming
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01-01-2021; Elsevier Science; Elsevier
• Subjects: Air Pollutants - analysis; Air Pollution - analysis; Air Pollution, Indoor - analysis; Ambient air pollution; China; Cooking; Environmental Monitoring; Exposure assessment; Female; Household air pollution; Humans; Male; Middle Aged; Particulate Matter - analysis; Pilot Projects; Rural Population; Seasons; Solid fuels; Time-activity; Wearable Electronic Devices; China; Household air pollution; Exposure assessment; Solid fuels; Time-activity; Ambient air pollution
• ISSN: 0160-4120; 1873-6750
• DOI: 10.1016/j.envint.2020.106217

Averaged 24-hour variation of fine particulate matter (PM2.5) concentrations (µg/m3) recorded in the personal, kitchen, living room, and ambient monitors across the three study sites in the warm and cool season.
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•We collected detailed fuel use, time-activity, and air pollution data from 477 Chinese adults.•Mix of solid and clean fuels was common for cooking or heating in rural areas.•Real-time PM2.5 data at personal, household, and ambient environments are presented.•PM2.5 levels in rural areas with solid fuel use were 2–3 times higher than in urban areas.•Personal, household and ambient PM2.5 levels were 2–3 times higher in the cool season.
Previous studies of the health impact of ambient and household air pollution (AAP/HAP) have chiefly relied on self-reported and/or address-based exposure modelling data. We assessed the feasibility of collecting and integrating detailed personal exposure data in different settings and seasons.
We recruited 477 participants (mean age 58 years, 72% women) from three (two rural [Gansu/Henan] and one urban [Suzhou]) study areas in the China Kadoorie Biobank, based on their previously reported fuel use patterns. A time-resolved monitor (PATS+CO) was used to measure continuously for 120-hour the concentration of fine particulate matter (PM2.5) at personal and household (kitchen and living room) levels in warm (May-September 2017) and cool (November 2017–January 2018) seasons, along with questionnaires on participants’ characteristics (e.g. socio-demographic, and fuel use) and time-activity (48-hour). Parallel local ambient monitoring of particulate matter (PM1, PM2.5 and PM10) and gaseous pollutants (CO, ozone, nitrogen oxides) was conducted using regularly-calibrated devices. The air pollution exposure data were compared by study sites and seasons.
Overall 76% reported cooking at least weekly (regular-cooks), and 48% (urban 1%, rural 65%) used solid fuels (wood/coal) for cooking. Winter heating was more common in rural sites than in urban site (74–91% vs 17% daily), and mainly involved solid fuels. Mixed use of clean and solid fuels was common for cooking in rural areas (38%) but not for heating (0%). Overall, the measured mean PM2.5 levels were 2–3 fold higher in the cool than warm season, and in rural (e.g. kitchen: Gansuwarm_season = 142.3 µg/m3; Gansucool_season = 508.1 µg/m3; Henanwarm_season = 77.5 µg/m3; Henancool_season = 222.3 µg/m3) than urban sites (Suzhouwarm_season = 41.6 µg/m3; Suzhoucool_season = 81.6 µg/m3). The levels recorded tended to be the highest in kitchens, followed by personal, living room and outdoor. Time-resolved data show prominent peaks consistently recorded in the kitchen at typical cooking times, and sustained elevated PM2.5 levels (> 100 µg/m3) were observed in rural areas where use of solid fuels for heating was common.
Personal air pollution exposure can be readily assessed using a low-cost time-resolved monitor in different settings, which, in combination with other personal and health outcome data, will enable reliable assessment of the long-term health effects of HAP/AAP exposures in general populations.