Interpreting Highly Variable Indoor PM2.5 in Rural North China Using Machine Learning.
Interpreting Highly Variable Indoor PM2.5 in Rural North China Using Machine Learning.
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DOI：
10.1021/acs.est.3c02014
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发表时间：
2023-05
影响因子：
11.4
通讯作者：
Yatai Men;Yaojie Li;Zhihan Luo;Ke Jiang;Fan Yi;Xinlei Liu;Ran Xing;Hefa Cheng;G. Shen-G.-She
中科院分区：
文献类型：
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作者：
Yatai Men;Yaojie Li;Zhihan Luo;Ke Jiang;Fan Yi;Xinlei Liu;Ran Xing;Hefa Cheng;G. Shen-G.-She
与固体燃料使用相关的家庭空气污染是公众长期关注的问题。主要使用固体燃料做饭的全球人口仍然很大。除了烹饪之外，许多地区在寒冷季节还燃烧大量煤炭和生物质燃料用于空间供暖。本研究在中国北方开展了冬季多区域实地调查，以评估室内 PM2.5 变化。利用约 1600 个家庭的每小时解析数据，通过机器学习方法识别了室内 PM2.5 的关键影响因素，并进一步开发了随机森林回归（RFR）模型来定量评估家庭能源转型对室内 PM2.5 的影响。室内 PM2.5 浓度平均为 120 μg/m3，但范围为 16 至 ∼400 μg/m3。与燃烧传统煤炭或生物质燃料的家庭相比，使用清洁供暖方法的家庭室内 PM2.5 降低约 60%。 RFR模型具有良好的性能（R2 = 0.85），并且解释与现场观察一致。过渡到清洁煤炭或生物质颗粒可以将室内 PM2.5 减少 20%，而进一步改用清洁现代能源则可将室内 PM2.5 进一步减少 30%，这表明促进家庭供暖活动的清洁转型有许多显着的好处。
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.