干旱区生态环境知识资源中心(Arid): A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

Arid

DOI	10.3390/rs12183099
	A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products
	Leon, Jean-Francois; Martiny, Nadege; Merlet, Sebastien
通讯作者	Leon, JF
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:18
英文摘要	Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 mu g/m(3) are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.
英文关键词	mineral dust Africa lidar PM10
类型	Article
语种	英语
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000581704100001
WOS关键词	MINERAL DUST ; SAHARAN DUST ; VERTICAL-DISTRIBUTION ; MENINGITIS INCIDENCE ; AFRICAN DUST ; DESERT DUST ; GLOBAL DUST ; LIDAR ; CALIPSO ; CLOUD
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327069
作者单位	[Leon, Jean-Francois] Univ Paul Sabatier, Lab Aerol, CNRS, F-31400 Toulouse, France; [Martiny, Nadege; Merlet, Sebastien] Univ Bourgogne, Biogeosci, Ctr Rech Climatol, CNRS, F-2100 Dijon, France
推荐引用方式 GB/T 7714	Leon, Jean-Francois,Martiny, Nadege,Merlet, Sebastien. A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products[J],2020,12(18).
APA	Leon, Jean-Francois,Martiny, Nadege,&Merlet, Sebastien.(2020).A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products.REMOTE SENSING,12(18).
MLA	Leon, Jean-Francois,et al."A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products".REMOTE SENSING 12.18(2020).