[1] An aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented. MAIAC is a generic algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS), which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing. The MAIAC look-up tables explicitly include surface bidirectional reflectance. The aerosol algorithm derives the spectral regression coefficient (SRC) relating surface bidirectional reflectance in the blue (0.47 mm) and shortwave infrared (2.1 mm) bands; this quantity is prescribed in the MODIS operational Dark Target algorithm based on a parameterized formula. The MAIAC aerosol products include aerosol optical thickness and a fine-mode fraction at resolution of 1 km. This high resolution, required in many applications such as air quality, brings new information about aerosol sources and, potentially, their strength. AERONET validation shows that the MAIAC and MOD04 algorithms have similar accuracy over dark and vegetated surfaces and that MAIAC generally improves accuracy over brighter surfaces due to the SRC retrieval and explicit bidirectional reflectance factor characterization, as demonstrated for several U.S. West Coast AERONET sites. Due to its generic nature and developed angular correction, MAIAC performs aerosol retrievals over bright deserts, as demonstrated for the Solar Village Aerosol Robotic Network (AERONET) site in Saudi Arabia.

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μgm−3, and RMSPE from 3.12 to 5.00 μgm−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

Abstract. During the July 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field experiment in Maryland, significant enhancements in Aerosol Robotic Network (AERONET) sun-sky radiometer measured aerosol optical depth (AOD) were observed in the immediate vicinity of non-precipitating cumulus clouds on some days. Both measured Ångström exponents and aerosol size distribution retrievals made before, during and after cumulus development often suggest little change in fine mode particle size; therefore, implying possible new particle formation in addition to cloud processing and humidification of existing particles. In addition to sun-sky radiometer measurements of large enhancements of fine mode AOD, lidar measurements made from both ground-based and aircraftbased instruments during the experiment also measured large increases in aerosol signal at altitudes associated with the presence of fair weather cumulus clouds. These data show modifications of the aerosol vertical profile as a result of the aerosol enhancements at and below cloud altitudes. The airborne lidar data were utilized to estimate the spatial extent of these aerosol enhancements, finding increased AOD, backscatter and extinction out to 2.5 km distance from the cloud edge. Furthermore, in situ measurements made from aircraft vertical profiles over an AERONET site during the experiment also showed large increases in aerosol scattering and aerosol volume after cloud formation as compared to before. The 15-year AERONET database of AOD measurements at the Goddard Space Flight Center (GSFC), Maryland site, was investigated in order to obtain a climatological perspective of this phenomenon of AOD enhancement. Analysis of the diurnal cycle of AOD in summer showed significant increases in AOD from morning to late afternoon, corresponding to the diurnal cycle of cumulus development.

Abstract. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the "smoke test" to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 µm as compared to 0.47-0.67 µm due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements.

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Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM 2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002-2008 was investigated and also compared to the AOD-PM 2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MA-IAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM 2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD-PM 2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50-70 % in the number of collocated AOD-PM 2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM 2.5 . Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM 2.5 spatial pattern prediction based on satellite observations.

[1] Aerosol spatial distribution in populated mountain areas is very heterogeneous and often characterized by scales of variability of several kilometers. Satellites provide an effective tool to map aerosols on an operational basis, but most of the aerosol products intended for continental/global applications have a coarse spatial resolution (10-18 km). The Multiangle Implementation of Atmospheric Correction (MAIAC) is a recently developed algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS), which provides Aerosol Optical Depth (AOD) at a high resolution of 1 km. We analyze the quality and potential of MAIAC AOD in the Alpine region and we derive high resolution AOD maps for the years 2008 and 2009. Cloudiness and snow in mountain regions occasionally lead to an overestimation of AOD due to unresolved cloud and snow pixel contamination. Therefore, we developed a filter that almost preserves the spatial resolution of the product to ensure the good accuracy of MAIAC AOD for air-quality and climatological applications. The AOD is validated with AERONET measurements in the region and compared to the standard MODIS AOD product (MOD04). Similar accuracies are found for both products (RMSE = 0.05) but with MAIAC providing about 50% more observations at the examined locations, because of its higher spatial resolution and less restrictive filtering. Comparison with ground measurements of aerosol mass (PM 10 ) shows that MAIAC AOD can be used to detect the fine scales of aerosol variability (2-3 km) in the mountains. Finally, AOD maps for the Alpine region demonstrate that topography is correlated with the average aerosol spatial distribution.

Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2 5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R 2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM 2 5 ground concentrations. Finally, we studied the relationship between PM 2 5 and AOD at the intra-urban scale ( 10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM 2 5 relationship does not depend on relative humidity and air temperatures below~7°C. The correlation improves for temperatures above 7 -16°C. We found no dependence on the boundary layer height except when the former was in the range 250-500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM 2 5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM 2 5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM 2 5 mass concentrations are highly correlated with the actual observations (out-of-sample R 2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM 2 5 relationship provides a means for obtaining spatially-resolved PM 2 5 concentrations.