The concentration and composition of PAHs emitted from biomass cooking fuel were characterized in a rural non-smoking household in northern China. Twenty-two parent PAHs (pPAHs), 12 nitro-PAHs (nPAHs), and 4 oxy-PAHs (oPAHs) were measured in the kitchen, bedroom, and outdoors during both summer and winter. The most severe contamination occurred in the kitchen in the winter, where the daily mean concentrations of pPAHs, nPAHs, and oPAHs were 7500±4100, 38±29, and 8400±9200 ng/m3, respectively. Our results suggest that the nPAHs were largely from secondary formation in ambient air while oPAHs were either from primary emission of biomass burning or secondary formation from pPAHs in the kitchen. The daily mean benzo(a)pyrene equivalent exposure concentration was as high as 200±160 ng/m3 in the winter for the housewife who did the cooking compared to 59±37 ng/m3 for the control group that did not cook.

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SESAMe v3.0, a spatially explicit multimedia fate model with 50 × 50 km(2) resolution, has been developed for China to predict environmental concentrations of benzo[a]pyrene (BaP) using an atmospheric emission inventory for 2007. Model predictions are compared with environmental monitoring data obtained from an extensive review of the literature. The model performs well in predicting multimedia concentrations and distributions. Predicted concentrations are compared with guideline values; highest values with some exceedances occur mainly in the North China Plain, Mid Inner Mongolia, and parts of three northeast provinces, Xi'an, Shanghai, and south of Jiangsu province, East Sichuan Basin, middle of Guizhou and Guangzhou. Two potential future scenarios have been assessed using SESAMe v3.0 for 2030 as BaP emission is reduced by (1) technological improvement for coal consumption in energy production and industry sectors in Scenario 1 (Sc1) and (2) technological improvement and control of indoor biomass burning for cooking and indoor space heating and prohibition of open burning of biomass in 2030 in Scenario 2 (Sc2). Sc2 is more efficient in reducing the areas with exceedance of guideline values. Use of SESAMe v3.0 provides insights on future research needs and can inform decision making on options for source reduction.

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