High spatial resolution aerosol retrieval with MAIAC: Application to mountain regions

Emili, Emanuele; Lyapustin, A.; Wang, Y.; Popp, Christoph; Korkin, Sergey; Zebisch, Marc; Wunderle, Stefan; Petitta, Marcello

doi:10.1029/2011jd016297

Cited by 53 publications

(40 citation statements)

References 28 publications

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“…Several studies published in the last 3 years have shown that high spatial resolution is essential to detect spatial variability in PM levels [18] and in aerosol loadings at regional and at a sub-10 km scale (e.g. intra-urban domain) [19,20]. Our study using MAIAC data and mixed effect approach showed high accuracy in the New England domain thereby indicating that our model based on MAIAC data can be used to investigate the intra-urban exposure contrasts in PM 2 5 levels.…”

Section: Discussionmentioning

confidence: 99%

High resolution aerosol data from MODIS satellite for urban air quality studies

Chudnovsky¹,

Lyapustin

Wang

et al. 2014

Open Geosciences

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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.

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Section: Discussionmentioning

confidence: 99%

High resolution aerosol data from MODIS satellite for urban air quality studies

Chudnovsky¹,

Lyapustin

Wang

et al. 2014

Open Geosciences

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“…On the other hand, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS, which provides aerosol information at 1 km resolution (Lyapustin et al, 2011a, b). Emili et al (2011) evaluated MAIAC AOD in the European Alpine region and demonstrated its enhanced capabilities compared to the standard MODIS AOD product. Chudnovsky et al (2013) assessed the potential of the MAIAC AOD for examining the spatial patterns of PM 2.5 in the Boston metropolitan area (intra-urban scale, < 10 km) and parts of New England (regional scale).…”

Section: A Chudnovsky Et Al: a Critical Assessment Of High-resolutimentioning

confidence: 99%

A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Chudnovsky

Tang²,

Lyapustin

et al. 2013

Atmos. Chem. Phys.

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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.

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“…This effect becomes particularly noticeable in rather pristine Alpine conditions with low average mid-visible aerosol optical thickness AOT ∼ 0.05-0.2. The problem of bias was successfully overcome by Emili et al (2011) with AOT data filtering where the main filter was based on the 3 × 3 pixel spatial variance test (σ ≤ 0.05). In more detail, this filter successively removed the highest AOT value from the 3 × 3 km 2 area if the standard deviation exceeded 0.05, and then averaged the remaining values effectively leading to 3 km resolution of the aerosol product.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent work, Emili et al (2011) evaluated MAIAC cloud/snow mask and aerosol products in the region of European Alps characterized by a heterogeneous aerosol distribution with strong impact of topography and aerosol sources localized in the narrow valleys with width of several km. While this study clearly demonstrated benefits of the high resolution data as compared to the standard 10 km MODIS product (Levy et al, 2007), including improved spatial coverage and 50 % increase in the number of observations, it has also revealed residual cloud and snow contamination.…”

Section: Introductionmentioning

confidence: 99%

Improved cloud and snow screening in MAIAC aerosol retrievals using spectral and spatial analysis

et al. 2012

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Abstract. An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.

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High spatial resolution aerosol retrieval with MAIAC: Application to mountain regions

Cited by 53 publications

References 28 publications

High resolution aerosol data from MODIS satellite for urban air quality studies

High resolution aerosol data from MODIS satellite for urban air quality studies

A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Improved cloud and snow screening in MAIAC aerosol retrievals using spectral and spatial analysis

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