College of Applied Chemical Engineering, Zhangzhou Institute of Technology, Zhangzhou, 363000, China.
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA.
Environ Monit Assess. 2022 Mar 16;194(4):284. doi: 10.1007/s10661-022-09934-5.
Understanding the drivers of PM is critical for the establishment of PM prediction models and the prevention and control of regional air pollution. In this study, the Yangtze River Delta is taken as the research object. Spatial cluster and outlier method was used to analyze the temporal and spatial distribution and variation of surface PM in the Yangtze River Delta from 2015 to 2020, and Random Forest was utilized to analyze the drivers of PM in this area. The results indicated that (1) based on the spatial cluster distribution of PM, the northwest and north of Yangtze River Delta region were mostly highly concentrated and surrounded by high concentrations of PM, while lowly concentrated and surrounded by low concentrations areas were distributed in the southern; (2) the relationship between PM concentrations and drivers in the Yangtze River Delta was modeled well and the explanatory rate of drivers to PM were more than 0.9; (3) temperature, precipitation, and wind speed were the main driving forces of PM emission in the Yangtze River Delta. It should be noted that the repercussion of wildfire on PM was gradually prominent. When formulating air pollution control measures, the local government normally considers the impact of weather and traffic conditions. In order to reduce PM pollution caused by biomass combustion, the influence of wildfire should also be taken into account, especially in the fire season. Meanwhile, high leaf area was conducive to improving air quality, and the increasing green area will help reduce air pollutants.
理解 PM 的驱动因素对于建立 PM 预测模型以及预防和控制区域空气污染至关重要。本研究以长江三角洲地区为研究对象，采用时空聚类和异常值方法分析了 2015-2020 年长江三角洲地区地表 PM 的时空分布和变化，并利用随机森林分析了该地区 PM 的驱动因素。结果表明：（1）基于 PM 的空间聚类分布，长江三角洲西北部和北部地区大多高度集中且周围 PM 浓度较高，而低度集中且周围 PM 浓度较低的地区分布在南部；（2）对长江三角洲 PM 浓度与驱动因素之间的关系进行了很好的建模，驱动因素对 PM 的解释率超过 0.9；（3）温度、降水和风速是长江三角洲地区 PM 排放的主要驱动力。值得注意的是，野火对 PM 的影响逐渐显著。在制定空气污染控制措施时，地方政府通常会考虑天气和交通状况的影响。为了减少生物质燃烧引起的 PM 污染，还应考虑野火的影响，特别是在火灾季节。同时，高叶面积有利于改善空气质量，增加绿地面积将有助于减少空气污染物。
