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

Chudnovsky, Alexandra; Tang, C.; Lyapustin, A.; Wang, Y.; Schwartz, Joel; Koutrakis, Petros

doi:10.5194/acp-13-10907-2013

Cited by 77 publications

(40 citation statements)

References 50 publications

(42 reference statements)

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“…In São Paulo site, the smoke plumes increased AOD 550 by about 1.6 times from the first half of the year (0.167 ± 0.099) to the austral spring (0.268 ± 0.185). Air quality control for these big cities demands routine observation of fine particulate matter emissions that are potentially predicted by satellite AOD products [ Chudnovsky et al ., ].…”

Section: Resultsmentioning

confidence: 99%

Validation of high‐resolution MAIAC aerosol product over South America

et al. 2017

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Multiangle Implementation of Atmospheric Correction (MAIAC) is a new Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm that combines time series approach and image processing to derive surface reflectance and atmosphere products, such as aerosol optical depth (AOD) and columnar water vapor (CWV). The quality assessment of MAIAC AOD at 1 km resolution is still lacking across South America. In the present study, critical assessment of MAIAC AOD550 was performed using ground‐truth data from 19 Aerosol Robotic Network (AERONET) sites over South America. Additionally, we validated the MAIAC CWV retrievals using the same AERONET sites. In general, MAIAC AOD Terra/Aqua retrievals show high agreement with ground‐based measurements, with a correlation coefficient (R) close to unity (RTerra:0.956 and RAqua: 0.949). MAIAC accuracy depends on the surface properties and comparisons revealed high confidence retrievals over cropland, forest, savanna, and grassland covers, where more than 2/3 (~66%) of retrievals are within the expected error (EE = ±(0.05 + 0.05 × AOD)) and R exceeding 0.86. However, AOD retrievals over bright surfaces show lower correlation than those over vegetated areas. Both MAIAC Terra and Aqua retrievals are similarly comparable to AERONET AOD over the MODIS lifetime (small bias offset ~0.006). Additionally, MAIAC CWV presents quantitative information with R ~ 0.97 and more than 70% of retrievals within error (±15%). Nonetheless, the time series validation shows an upward bias trend in CWV Terra retrievals and systematic negative bias for CWV Aqua. These results contribute to a comprehensive evaluation of MAIAC AOD retrievals as a new atmospheric product for future aerosol studies over South America.

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Section: Resultsmentioning

confidence: 99%

Validation of high‐resolution MAIAC aerosol product over South America

et al. 2017

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“…This is probably due to the abundance of lakes and coastlines in the region, which leads the retrieval to flag pixels as open water bodies, and thus, unsuitable for aerosol retrieval. Other studies have used 1 km )1 km MODIS AOT data derived with the MAIAC algorithm for air-quality studies (Chudnovsky et al, 2013). Future work in the Stockholm region will consider these data as well as high-resolution AOT derived from calibrated level 1 data of MODIS and MERIS using the BAER algorithm (Glantz et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…We used the MODIS Collection 6 (3 km)3 km) AOT product ) to obtain better pixel coverage at the coast and for partly cloudy conditions (Chudnovsky et al, 2013;Munchak et al, 2013). This higher resolution product, therefore, has the potential to increase the number of collated AOT/PM observations as well as to allow for an improved resolution of small-scale features in the retrieved scenes of surface PM2.5.…”

Section: Application and Discussionmentioning

confidence: 99%

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Tesche¹,

Glantz²,

Johansson

2016

Tellus B: Chemical and Physical Meteorology

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A B S T R A C TThis article investigates the feasibility of using spaceborne observations of aerosol optical thickness (AOT) derived with the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring of fine particulate matter (PM2.5) in an environment of low aerosol loading. Previous studies of the AOT-to-PM2.5 relationship benefit from the large range of observed values. The Stockholm region features a comprehensive network of ground-based monitoring stations that generally show PM2.5 values B20 mg m (3 . MODIS AOT at 555 nm is usually B0.20 and in good agreement with ground-based sun photometer observations in this region. We use MODIS Collection 5 AOT data with a horizontal resolution of 10 km )10 km and ground-based in-situ PM2.5 observations to derive an AOT-to-PM2.5 relationship that can be used to estimate fields of PM2.5. This has been carried out with respect to the months from April to September of the period 2000Á2013. Relative average absolute deviations of 33Á55 % (mean of 45 %) are obtained between MODIS-retrieved and ground-based PM2.5. The root mean square error is 0.2159 mg m (3 between retrieved and measured PM2.5. From spaceborne lidar observations, it is found that elevated aerosol layers are generally sparse in the Stockholm region. This favours remote sensing of PM2.5 from space. The deviations found between measured and retrieved PM2.5 are mainly attributed to infrequent situations of inhomogeneous aerosol layering for which column-integrated observations cannot be connected to surface conditions. Using MODIS Collection 6 data with a resolution of 3 km)3 km in a case study actually gives far fewer results than the coarser Collection 5 product. This is explained by the complex geography of the Stockholm region with a coastline and an abundance of lakes, which seems to induce biases in the retrieval of AOT at higher spatial resolution.

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“…However, the 10 km resolution of the conventional Collection 5.1 (C5.1) MODIS AOD is coarse for predicting PM 2.5 at the urban scale (Chudnovsky et al, 2013). The spatial resolution of the recent C6 MODIS AOD has increased to 3 km for resolving fine-scale AOD gradients and point sources .…”

Section: Introductionmentioning

confidence: 99%

Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China

Gong

Chen

et al. 2015

Aerosol Air Qual. Res.

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Estimating ground-level PM 2.5 in urban areas from satellite-retrieved AOD data is limited because of the coarse resolution of the data. The spatial resolution of recent MODIS Collection 6 aerosol data has increased from 10 km to 3 km. Taking advantage of this new AOD dataset, we used a mixed effects model to calibrate the day-to-day relationship between satellite AOD and ground-level PM 2.5 concentrations. Regional daily PM 2.5 concentrations were estimated by the AOD from March 1, 2013, to February 28, 2014, in the megacity of Beijing. Compared with the simple linear regression model, the accuracy of the PM 2.5 prediction improved significantly, with an R 2 of 0.796 and a root mean squared error of 16.04 µg/m 3 . The results showed high PM 2.5 concentrations in the intra-urban region of Beijing because of local emissions. The PM 2.5 concentrations were relatively low in the northern rural area but high in the southern rural area, which was close to the industrial sector in Hebei Province. We found that the 3 km AOD produces detailed spatial variability in the Beijing area but introduces somewhat large biases due to missing AOD pixels.

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

Cited by 77 publications

References 50 publications

Validation of high‐resolution MAIAC aerosol product over South America

Validation of high‐resolution MAIAC aerosol product over South America

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China

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