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PM2.5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PM2.5的质量浓度进行反演。结果表明,相比于AOD与PM2.5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PM2.5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PM2.5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PM2.5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PM2.5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 PM2.5mass concentration data from March 2014 to February 2019 in Nanjing were obtained, and the mass concentration of PM2.5 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 PM2.5, the correlation analysis of fine aerosol optical depth AODf and PM2.5 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 PM2.5 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 PM2.5 mass concentration is affected by the combined effect of multiple factors, thus the result can provide a basis and reference for inversion of PM2.5 mass concentration by using multi-source data.
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2021, Vol. 33 
), 侯钰俏, 韩佳媚, 查勇(
