Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China
Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China
复制标题
利用中国上空精细模式气溶胶光学厚度进行卫星 PM2.5 估算
DOI：
10.1016/j.atmosenv.2017.09.023
复制
发表时间：
2017-12
影响因子：
5
通讯作者：
Yu Xue
中科院分区：
文献类型：
--
作者：
Yan Xing;Shi Wenzhong;Li Zhanqing;Li Zhengqiang;Luo Nana;Zhao Wenji;Wang Haofei;Yu Xue
从卫星获取的气溶胶光学厚度(AOT)准确估计地面PM2.5存在各种困难。这是因为AOT与地面PM2.5之间的联系受多种因素的影响，如细模AOT的贡献(FM-AOT)和天气条件。在本研究中，我们利用2016年5-6月在邢台、中国的地面观测数据，对地表PM2.5估算的总AOT和FM-AOT进行了比较。PM2.5与FM-AOT的相关性(r=0.74)高于PM2.5与总AOT的相关性(r=0.49)。在FM-AOT的基础上，我们发展了一种结合简化气溶胶反演算法(SARA)AOT、查找表-光谱反卷积算法(LUT-SDA)精细模式分数(FMF)和PM2.5遥感方法的地面PM2.5反演方法。针对PM2.5的颗粒物浓度具有较强的日变化特征，提出了一种基于实时能见度数据的PM2.5拟密度反演方法。应用该方法对北京地区2013年12月至2015年6月无云天气的地表PM2.5浓度进行了反演。与气溶胶机器人网络(AERONET)数据相比，LUT-SDA FMF比中分辨率成像光谱仪(MODIS)FMF更容易获得。将得到的PM2.5结果与地面监测值(30个站)进行比较，得到R2为0.64，均方根误差为μg/m~3(N=0.921)。这一验证表明，开发的方法运行良好，并产生了可靠的结果。
Accurate estimation of ground-level PM2.5from satellite-derived aerosol optical thickness (AOT) presents various difficulties. This is because the association between AOT and surface PM2.5can be affected by many factors, such as the contribution of fine mode AOT (FM-AOT) and the weather conditions. In this study, we compared the total AOT and FM-AOT for surface PM2.5estimation using ground-based measurements collected in Xingtai, China from May to June 2016. The correlation between PM2.5and FM-AOT was higher (r = 0.74) than that between PM2.5and total AOT (r = 0.49). Based on FM-AOT, we developed a ground-level PM2.5retrieval method that incorporated a Simplified Aerosol Retrieval Algorithm (SARA) AOT, look-up table–spectral deconvolution algorithm (LUT-SDA) fine mode fraction (FMF), and the PM2.5remote sensing method. Due to the strong diurnal variations displayed by the particle density of PM2.5, we proposed a pseudo-density for PM2.5retrieval based on real-time visibility data. We applied the proposed method to determine retrieval surface PM2.5concentrations over Beijing from December 2013 to June 2015 on cloud-free days. Compared with Aerosol Robotic Network (AERONET) data, the LUT-SDA FMF was more easily available than the Moderate Resolution Imaging Spectroradiometer (MODIS) FMF. The derived PM2.5results were compared with the ground-based monitoring values (30 stations), yielding an R2of 0.64 and root mean square error (RMSE) = 18.9 μg/m3(N = 921). This validation demonstrated that the developed method performed well and produced reliable results.
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影响因子：
4.4
作者：
Holben, BN;Tanré, D;Zibordi, G
通讯作者：
Zibordi, G
DOI：
--
发表时间：
2015-10
期刊：
Huan jing ke xue= Huanjing kexue
影响因子：
--
作者：
Yi-na Chen;P. Zhao;D. He;F. Dong;Xiujuan Zhao;Xiao-ling Zhang
通讯作者：
Yi-na Chen;P. Zhao;D. He;F. Dong;Xiujuan Zhao;Xiao-ling Zhang
影响因子：
10.4
作者：
van Donkelaar A;Martin RV;Brauer M;Kahn R;Levy R;Verduzco C;Villeneuve PJ
通讯作者：
Villeneuve PJ
影响因子：
6.3
作者：
B. Zheng;Qiang Zhang;Yang Zhang;K. He;Kai Wang;G. Zheng;F. Duan;Yong-liang Ma;T. Kimoto
通讯作者：
B. Zheng;Qiang Zhang;Yang Zhang;K. He;Kai Wang;G. Zheng;F. Duan;Yong-liang Ma;T. Kimoto
影响因子：
6.3
作者：
Lee, H. J.;Liu, Y.;Koutrakis, P.
通讯作者：
Koutrakis, P.