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 ∼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 µg/m3 but ranged from 16 to ∼400 µg/m3. Indoor PM2.5 was ∼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.

Identification of Factors Determining Household PM2.5 Variations at Regional Scale and Their Implications for Pollution Mitigation.

Indoor PM2.5, particulate matter no more than 2.5 µm in aerodynamic equivalent diameter, has very high spatiotemporal variabilities; and exploring the key factors influencing the variabilities is critical for purifying air and protecting human health. Here, we conducted a longer-term field monitoring campaign using low-cost sensors and evaluated inter- and intra-household PM2.5 variations in rural areas where energy or stove stacking is common. Household PM2.5 varied largely across different homes but also within households. Using generalized linear models and dominance analysis, we estimated that outdoor PM2.5 explained 19% of the intrahousehold variation in indoor daily PM2.5, whereas factors like the outdoor temperature and indoor-outdoor temperature difference that was associated with energy use directly or indirectly, explained 26% of the temporal variation. Inter-household variation was lower than intrahousehold variation. The inter-household variation was strongly associated with distinct internal sources, with energy-use-associated factors explaining 35% of the variation. The statistical source apportionment model estimated that solid fuel burning for heating contributed an average of 31%-55% of PM2.5 annually, whereas the contribution of sources originating from the outdoors was ≤10%. By replacing raw biomass or coal with biomass pellets in gasifier burners for heating, indoor PM2.5 could be significantly reduced and indoor temperature substantially increased, providing thermal comforts in addition to improved air quality.

Mass Absorption Efficiency of Black Carbon from Residential Solid Fuel Combustion and Its Association with Carbonaceous Fractions.

Black carbon (BC) emissions, derived primarily from incomplete fuel combustion, significantly affect the global and regional climate. Mass absorption efficiency (MAE) is one important parameter in evaluating the climate impacts of BC. Here, values and variabilities in the MAE of BC (MAEBC) from real-world residential emissions were investigated from a field campaign covering 163 burning events for different fuel-stove combinations. MAEBC (average: 12 ± 5 m2/g) was normally distributed and varied greatly by 2 orders of magnitude. Statistically significant differences in MAEBC were found for various fuels, while no significant differences were observed among different stoves. The fuel difference explained 72 ± 7% of the MAEBC variation. MAEBC did not correlate with the modified combustion efficiency but positively correlated with the ratio of organic carbon (OC) to elemental carbon (EC) and negatively correlated with char-EC. The OC/EC ratio was not always lower in coal emissions in comparison to biomass burning emissions. Coal- and biomass-burning emissions had different profiles of carbon fractions. Char-EC, OC, OC/EC, and char-EC/soot-EC can explain 68.7% of the MAEBC variation, providing the potential for predicting MAEBC from the carbon fractions, since they are more commonly measured and available.

Spatiotemporal variation of atmospheric nitrated polycyclic aromatic hydrocarbons in semi-arid and petrochemical industrialized Lanzhou City, Northwest China.

Polyurethane foam-based passive air sampler (PUF-PAS) and passive dry deposition sampler (PAS-DD) were adopted, for the first time ever in China, to investigate the atmospheric levels and spatial-temporal distributions of air burdens and dry deposition fluxes of 12 nitrated polycyclic aromatic hydrocarbons (NPAHs) during winter and summer seasons in a multiple site field campaign in a petrochemical industrialized capital city in Northwest China. The results showed that the highest air concentration and dry deposition fluxes of ∑12NPAHs occurred at a heavy traffic site among 18 sampling sites in both winter and summer season. The lowest air concentration and dry deposition fluxes were observed at the background site. The mean concentrations of ∑12NPAHs in the ambient air were 8.6 ± 8.1 ng m-3 in winter and 15 ± 11 ng m-3 in summer. The mean dry deposition fluxes of ∑12NPAHs were 1.8 × 103 ± 1.9 × 103 ng (m2 day)-1 in winter and 1.4 × 103 ± 1.3 × 103 ng (m2 day)-1 in summer, respectively. The total concentration of 12 NPAHs was mainly dominated by 1-nitro-naphthalene (1N-NAP) and 2-nitro-naphthalene(2N-NAP) in air, accounting for 32% in winter and 45% in summer of ∑12NPAHs. 7-Nitro-benzo [a] anthracene (7N-BaA) made the largest contribution to dry deposition fluxes of ∑12NPAHs, accounting for 28% in winter and 24% in summer. The ratios of ∑12NPAHs/∑15pPAHs (parent polycyclic aromatic hydrocarbons) were calculated to identify potential sources of NPAHs across the city. The results revealed that the main atmospheric air concentration and dry deposition fluxes of 12 NPAHs could be attributed to the primary emissions in winter and the secondary reaction formation in summer. The sources of primary emissions could be traced back to petrochemical, steel mills, as well as aluminum industries in winter and vehicle exhaust in summer. Higher ∑12NPAH/∑15pPAH concentration ratios in summer indicated that the oxidation of pPAHs contributed to the secondary formation of NPAHs via atmospheric chemical reactions in this petrochemical industrialized mountain-valley city.