British Journal of Environment and Climate Change, ISSN: 2231-4784,Vol.: 3, Issue.: 4 (October-December)-Special Issue
Assessing Surface PM2.5 Estimates Using Data Fusion of Active and Passive Remote Sensing Methods
Lina Cordero1*, Nabin Malakar1, Yonghua Wu1, Barry Gross1 and Fred Moshary1 1Optical Remote Sensing Lab, City College of New York, New York, NY, USA 10031, USA.
Lina Cordero1*, Nabin Malakar1, Yonghua Wu1, Barry Gross1 and Fred Moshary1
1Optical Remote Sensing Lab, City College of New York, New York, NY, USA 10031, USA.
In this paper, we focus on estimations of fine particulate matter by combining MODIS satellite Aerosol Optical Depth (AOD) with Weather Research Forecast (WRF) PBL information using a neural network approach and assess its performance. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as relevant factors in estimating PM2.5 in passive radiometer and active lidar data at CCNY and demonstrate that the PBL height is the most critical additional parameter for accurate PM2.5. Furthermore, active measurements from both ground and satellite based lidar are used to show that summer WRF model PBL heights are most accurate. We then expand our analysis to a regional domain where daily estimations are obtained and compared with operational GEOS-CHEM PM2.5 product. Using our approach, we also create regional daily PM2.5 maps and compare against GEOS-CHEM outputs. Finally, we also consider additional improvements, where multiple satellite observations are used as regressors to predict PM2.5. These results illustrate the significant improvement we obtain within this framework in comparison to a “one size fits all continental scale approach”.
PM2.5; AOD; PBL; LIDAR; CMAQ; WRF; GEOS-CHEM; MODIS; air quality.
Full Article - PDF Page 547-565
DOI : 10.9734/BJECC/2013/7668Review History Comments