长三角地区PM_(2.5)时空分布及环境驱动因素分析

An Analysis of Temporal and Spatial Distribution of PM_(2.5)and Environ⁃mental Drivers in the Yangtze River Delta

本研究分析了2015—2020年长三角地区地面PM_(2.5)浓度的时空变化特征,并利用随机森林回归法分析环境驱动因素。结果发现:2015—2020年长三角地区PM_(2.5)浓度整体呈下降趋势,2020年初受疫情影响PM_(2.5)浓度降至趋势线最低点,之后浓度呈回升趋势。长三角地区的西部和北部多为高高聚集区,是污染防治的重点和难点;南部和东部多为低低聚集区,空气质量较好。随机森林分析发现7个环境变量对PM_(2.5)季节平均浓度的解释率均超过98%。其中,降水和温度是PM_(2.5)重要的驱动因素,秋季和冬季野火密度是冬季PM_(2.5)浓度最重要的驱动因素。火灾信息的纳入有助于提高PM_(2.5)浓度预测的准确性,并为政府制定空气污染防控措施提供可靠依据。

The findings revealed a decreasing trend in PM_(2.5)concentration during the specified period,reaching its lowest point in early 2020 due to the impact of the COVID-19 pandemic,followed by a slight increase thereafter.The analysis identified high-high clusters of PM_(2.5)concentration in the west and north regions,signifying areas of focus and challenge for pollution prevention and control efforts,while low-low clusters were observed in the south and east with better air quality conditions.Random forest analysis indicated that seven environmental variables accounted for over 98%of the variance,with precipitation and temperature emerging as significant drivers.Notably,wildfire density in winter and autumn emerged as the primary driver of PM_(2.5)concentration during winter months.The integration of fire-related data improved the accuracy of PM_(2.5)concentration predictions,offering valuable insights for policymakers to develop effective air pollution control measures.