Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China.
Sci Rep. 2018 Jun 21;8(1):9461. doi: 10.1038/s41598-018-27771-w.
Rapid urbanization is causing serious PM (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM concentration based on more than 1 million PM recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM concentration, and obtained the 10 primary influencing factors. Data of PM concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM concentration, while nuclear power generation is the most positive factor in decreasing PM concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).
快速的城市化进程导致中国 PM(细颗粒物≤2.5μm)污染严重。然而,人类活动(包括工业生产、能源生产、农业和交通)对 PM 浓度的影响尚未得到深入研究。在这项研究中,我们根据 2013 年 1 月至 2017 年 5 月中国 31 个省超过 100 万次的 PM 记录值和气象、工业生产、能源生产、农业和交通数据,获得了一个 PM 浓度回归公式。我们使用逐步回归处理了影响 PM 浓度的 49 个因素,并得到了 10 个主要影响因素。我们使用 2017 年 6 月至 12 月的 PM 浓度和 10 个因素的数据来验证模型的稳健性。在不考虑气象因素的情况下,天然气生产、工业锅炉和矿石生产与 PM 浓度的关联度最高,而核能发电是降低 PM 浓度的最积极因素。天津、北京和河北省最容易受到工业生产、能源生产、农业和交通(IEAT)造成的高 PM 浓度的影响。