基于精细模式气溶胶与WRF模式估算PM_(2.5)质量浓度

The estimation of PM_(2.5) mass concentration based on fine-mode aerosol and WRF model

将气象要素加入到基于气溶胶光学厚度(aerosol optical depth,AOD)的近地面大气颗粒物浓度估算是目前热门的技术手段之一。获取了江苏省南京市2014年3月—2019年2月期间的AOD,精细模式分数(fine-mode fraction,FMF)和PM_(2.5)质量浓度数据,并结合天气研究和预报(weather research and forecast,WRF)模式得到的气象模拟数据,对南京市PM_(2.5)的质量浓度进行反演。结果表明,相比于AOD与PM_(2.5)进行相关性分析,通过FMF校正得到的精细气溶胶光学厚度AODf与PM_(2.5)的相关性分析能够取得更高的拟合系数,R2最高达到0.40。利用随机森林模型,引入含不同高度的气象因子对PM_(2.5)质量浓度建立反演模型,得到的拟合系数与各误差指标均优于仅含近地面气象因子的模型,表明PM_(2.5)质量浓度受到多因子共同作用的影响,能较好地为利用多源数据反演PM_(2.5)质量浓度提供依据和参考。

The addition of meteorological factors to the estimation of near-ground atmospheric particulate concentration based on AOD is one of the most popular techniques nowadays.In this paper,AOD(Aerosol Optical Depth),FMF(Fine-Mode Fraction)and PM_(2.5) mass concentration data from March 2014 to February 2019 in Nanjing were obtained,and the mass concentration of PM_(2.5) in Nanjing was retrieved in combination with the meteorological simulation data from WRF(Weather Research and Forecast)model.The results show that,compared with correlation between AOD and PM_(2.5),the correlation analysis of fine aerosol optical depth AODf and PM_(2.5) obtained by FMF correction can obtain a higher fitting coefficient,and the maximum R2 reaches 0.40.By adding meteorological factors on different heights into random forest model to establish an inversion model for PM_(2.5) mass concentration,the obtained fitting coefficients and various error indicators are better than those from models with only near-surface meteorological factors,which indicates that the PM_(2.5) mass concentration is affected by the combined effect of multiple factors,thus the result can provide a basis and reference for inversion of PM_(2.5) mass concentration by using multi-source data.