High spatial resolution aerosol retrievals used for daily particulate matter monitoring over Po valley, northern Italy
- 1Dipartimento di Ingegneria Enzo Ferrari, Università di Modena e Reggio Emilia, via P. Vivarelli 10, 41125 Modena, Italy
- 2NOAA/NESDIS Advanced Satellite Products Branch, 1225 W. Dayton Street, Madison, WI 53706, USA
- 3NASA Goddard Space Flight Center, code 613, Greenbelt, Maryland 20771, USA
- 4University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA
Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data retrieved at 0.55 μm with spatial resolution of 10 km (MYD04) and the new 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm from MODIS is investigated in this work. We focus on evaluating the ability of these two products to characterize the spatial distribution of aerosols within urban areas. This is done through the comparison with PM10 measurements from 126 of the Italian Regional Agency for Environmental Protection (ARPA) ground monitoring stations during 2012. The Po Valley area (northern Italy) was chosen as the study domain since urban air pollution is one of the most important concerns in this region. Population and industrial activities are located within a large number of urban areas within the valley. We find that the annual correlations between PM10 and AOD are R2 = 0.90 and R2 = 0.62 for MYD04 and for MAIAC respectively. When the depth of the planetary boundary layer (PBL) is used to normalize the AOD, we find a significant improvement in the PM–AOD correlation. The introduction of the PBL information is needed for AOD to capture the seasonal cycle of the observed PM10 over the Po valley and significantly improves the PM vs. AOD relationship, leading to a correlation of R2 = 0.98 for both retrievals when they are normalized by the PBL depth. The results show that the normalized MAIAC retrieval provides a higher resolution depiction of the AOD within the Po Valley and performs as well in a statistical sense as the normalized standard MODIS retrieval for the same days and locations.
B. Arvani et al.
B. Arvani et al.
B. Arvani et al.
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