Daily and Peak 1 h Indoor Air Pollution and Driving Factors in a Rural Chinese Village

We investigate wintertime indoor air quality and personal exposures to carbon monoxide (CO) in a rural village in Jilin province, where relatively homogeneous climatic and sociocultural factors facilitate investigation of household structural, fuel-related, and behavioral determinants of air pollution as well as relationships between different measures of air quality. Our time-resolved wintertime measurements of carbon monoxide and respirable particles (RSP) enable exploration of peak pollution periods in a village in Jilin Province, China, characterized by household use of both coal and biomass, as well as several "improved" (gas or electric) fuels. Our data indicate a 6-fold increase in peak 1 h PM (1.9 mg/m3) concentrations relative to 24 h mean PM (0.31 mg/m3). Peak 1 h CO concentrations (20.5 ppm) routinely approached and often (27%) exceeded the World Health Organization's 1 h guideline of 26 ppm, although the vast majority (95%) of kitchens were within China's residential indoor air quality guideline for CO on a 24 h basis. Choice of heating fuel and household smoking status were significant predictors of indoor air quality. Whether solid or "improved" (gas or electric) fuel was used for cooking had an even stronger effect, but in the opposite direction from expected, on both peak and daily average measures of air pollution. Peak pollution period concentrations of CO and PM were strongly correlated to daily concentrations of CO and RSP, respectively. Our results suggest that due to the primary role of heating as a determinant of wintertime indoor air quality in northern Chinese villages, health-oriented interventions limited to provision of improved cooking fuel are insufficient. Our results illustrate that peak pollution periods may routinely exceed exposure regulations and evacuation limits, although this and previous studies document typical 24 h CO concentrations in rural Chinese kitchens to be within guidelines. Within a given village and for a given pollutant, daily pollutant concentrations may be strong predictors of peak pollution period concentrations.