Abstract
BackgroundPrevious 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. Methods/designWe 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. FindingsOverall 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. DiscussionPersonal 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.
Highlights
Previous studies of the health impact of ambient and household air pollution (AAP/HAP) have relied on self-reported and/or address-based exposure modelling data
The recent rapid urbanisation and industrialisation in many low- and middle-income countries (LMICs) has resulted in a notable rise in ambient air pollution (AAP) and a considerable decline in the proportion of exposure to household air pollution (HAP) from solid fuel use, yet the number exposed to HAP remained substantial at over 3.6 billion in 2018. (Health Effect Institute, 2019) many LMICs including China face a “double burden” of HAP and AAP. (Health Effect Institute, 2019; Zhao et al, 2018) the epidemiological evidence on the health impact of air pollution remains to be improved, it has been estimated that AAP and HAP together account for over 7 million premature deaths annually worldwide. (Landrigan et al, 2017)
Considering the decline in solid fuel use in China in the past decade, in each of the sites, one or two of the largest villages or street communities with the highest prevalence of solid fuel use at baseline were selected in order to recruit sufficiently large numbers of solid fuel users
Summary
Previous studies of the health impact of ambient and household air pollution (AAP/HAP) have relied on self-reported and/or address-based exposure modelling data. 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. (Duan et al, 2015; Klepeis et al, 2001; Brauer et al, 2016) Previous epidemiological studies on HAP mostly focused on respiratory diseases They were constrained by small sample sizes, use of cross-sectional study design, relying on self-reported primary cooking fuel or stove types for exposure classification, (Clark et al, 2013; Kurmi et al, 2010) or assessment of intermediate traits (e.g. blood pressure) rather than incident diseases. We conducted a feasibility study to collect and integrate detailed, multi-dimensional AAP and HAP data to enhance personal air pollution exposure characterisation in a large contemporary cohort in China, the China Kadoorie Biobank (CKB). (Chen et al, 2011, 2005) This report describes the design, major procedures and early findings on fuel use, time-activity and air pollution exposure patterns in the study
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