Interpreting Highly Variable Indoor PM2.5 in Rural North China Using Machine Learning

Household air pollution associated with solid fuel use is a long-standing public concern. The global population mainly using solid fuels for cooking remains large. Besides cooking, large amounts of coal and biomass fuels are burned for space heating during cold seasons in many regions. In this study, a wintertime multiple-region field campaign was carried out in north China to evaluate indoor PM2.5 variations. With hourly resolved data from similar to 1600 households, key influencing factors of indoor PM2.5 were identified from a machine learning approach, and a random forest regression (RFR) model was further developed to quantitatively assess the impacts of household energy transition on indoor PM2.5. The indoor PM2.5 concentration averaged at 120 mu g/ m3 but ranged from 16 to similar to 400 mu g/m3. Indoor PM2.5 was similar to 60% lower in families using clean heating approaches compared to those burning traditional coal or biomass fuels. The RFR model had a good performance (R2 = 0.85), and the interpretation was consistent with the field observation. A transition to clean coals or biomass pellets can reduce indoor PM2.5 by 20%, and further switching to clean modern energies would reduce it an additional 30%, suggesting many significant benefits in promoting clean transitions in household heating activities.