研究报告

  • 许建明,高伟,瞿元昊.上海地区降雨清除PM2.5的观测研究[J].环境科学学报,2017,37(9):3271-3279

  • 上海地区降雨清除PM2.5的观测研究
  • Observation of the wet scavenge effect of rainfall on PM2.5 in Shanghai
  • 基金项目:国家重点研发计划(No.2016YFC0203400);环保部行业专项(No.201509001)
  • 作者
  • 单位
  • 许建明
  • 1. 上海市气象局, 上海 200030;2. 长三角环境气象预报预警中心, 上海 200030;3. 上海市气象与健康重点实验室, 上海 200030
  • 高伟
  • 1. 上海市气象局, 上海 200030;2. 长三角环境气象预报预警中心, 上海 200030;3. 上海市气象与健康重点实验室, 上海 200030
  • 瞿元昊
  • 1. 上海市气象局, 上海 200030;2. 长三角环境气象预报预警中心, 上海 200030;3. 上海市气象与健康重点实验室, 上海 200030
  • 摘要:分析2012-2016年上海徐家汇站的雨量和颗粒物(PM1、PM2.5、PM10)观测数据发现,降雨对PM2.5的湿清除作用明显,降雨日的PM2.5质量浓度较非降雨日平均降低约30%,在污染季节降低更加显著约50%.降雨时PM1在PM2.5中的占比明显下降,PM1质量浓度下降幅度占PM2.5下降幅度的84%,表明降雨对PM1的有效清除是PM2.5质量浓度下降的主要原因.降雨过程结束后PM2.5质量浓度是否下降和降雨前PM2.5的初始质量浓度关系密切,当初始浓度在冬季大于70 μg·m-3、在其他季节大于45 μg·m-3时,80%以上的降雨过程结束后PM2.5质量浓度较降雨前下降,因此可作为研判降雨过程对PM2.5湿清除影响的预报因子.
  • Abstract:The effect of wet scavenge of PM2.5 by rainfall in Shanghai, China is analyzed. The analysis includes the in-situ measurements of precipitation and particulate matter (PM1, PM2.5 and PM10) from 2012-2016 at Xujiahui in downtown Shanghai. The results show that precipitation plays an important role in reducing PM2.5 mass concentration. For example, the mean PM2.5 mass concentration on rainy days decreases about 30% compared with non-rainy days, with a maximum reduction of 50% in winter. The ratio of mass concentration between PM1 and PM2.5 also significantly decreases on rainy days. The reduced PM1 mass concentration contributes 84% to the PM2.5 reduction, suggesting that wet scavenge is more effective for smaller particles than large particles. Therefore, the decrease in PM2.5 concentrations on rainy days is mostly attributed to the effective wet scavenge of PM1. The PM2.5 tendency related to rainfall is closely associated with the initial PM2.5 level prior to precipitation. There are about 80% of the rainfall processes leading to PM2.5 decreasing under the condition that the initial PM2.5 mass concentration is above 70 μg·m-3 in winter and 45 μg·m-3 in other seasons, which could be considered as a predictor for forecasting the PM2.5 tendency due to precipitations.

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