Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Franklin, Meredith; Chau, Khang; Калашникова, О. В.; Garay, M. J.; Enebish, Temuulen; Sorek–Hamer, Meytar

doi:10.3390/rs10081317

Cited by 20 publications

(16 citation statements)

References 26 publications

(42 reference statements)

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“…In terms of exposure estimation, MISR AOD products resulted in better and more robust estimates than did AOD mixtures, except for dust. Non-linear models (GBM, RF, and SVM) performed better than linear models (Ridge and LASSO), which was consistent with previous studies where linear models were inadequate in explaining the relationship between AOD and ground-monitored PM [22,35,46]. Although MISR aerosol data have coarser temporal and spatial resolution compared to MAIAC (every 3-5 days vs. daily and 4.4 km vs. 1 km, respectively), our model achieved high prediction performance (Table 1) using MISR-specific data products on size, shape, and absorption, which proved vital in the prediction models.…”

Section: Discussionsupporting

confidence: 90%

“…We expand upon our previous work [22,35] to include MISR aerosol properties of absorption (absorbing or non-absorbing), shape (spherical or nonspherical), and type provided by 74 weighted aerosol optical depths (mixtures) [21] to predict PM 2.5 and PM 2.5 SO 2− 4 , NO − 3 , EC, and dust. We matched daily PM 2.5 and PM 2.5 speciation measurements from ground monitors to the nearest available MISR pixel within 4.4 km ( Figure A2) and then further matched them to the nearest gridMET pixel within 4 km.…”

Section: Exposure Estimation Methodsmentioning

confidence: 99%

“…The MISR particle properties provide robust aerosol-type classification for distinguishing aerosol mass types including polluted, smoky, maritime, and dusty conditions [21,34]. Details of processing MISR data, particularly AOD mixtures, are documented in our previous paper [35].…”

Section: Misr Aerosol Optical Depth Datamentioning

confidence: 99%

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Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

2020

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Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: − 232 , − 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: − 273 , − 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 − ), and by 177 mL (95% CI: − 306 , − 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: − 310 , − 29 ) per 1.2-µg/m 3 increase in SO 4 2 − and by 212 mL (95% CI: − 391 , − 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 − ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates.

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Section: Discussionsupporting

confidence: 90%

Section: Exposure Estimation Methodsmentioning

confidence: 99%

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Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

2020

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“…Although the accuracy of fine-scale AOD features cannot be independently evaluated in this case, the aerosol spatial variability is consistent with field observations , and the V23 product provides the type of high-resolution, large-scale content that is needed for air quality applications. Garay et al (2017) demonstrated that a prototype version of the V23 4.4 km retrievals agree substantially better with ground-based observations than the previous V22 17.6 km retrievals, in that study MISR retrievals were compared against observations carried out during several AERONET-DRAGON deployments around the globe. This would be expected, especially in places where aerosol amount varies on kilometer spatial scales.…”

Section: Scene Comparisonsmentioning

confidence: 85%

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

et al. 2020

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The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The level 2 (swath) product, which is reported on a 4.4 km spatial grid, is designated as version 23 (V23) and contains retrieved aerosol optical depth (AOD) and aerosol particle property information derived from MISR's multiangle observations over both land and water. The changes from the previous version of the algorithm (V22) have significant impacts on the data product and its interpretation. The V23 data set is created from two separate retrieval algorithms that are applied over dark water and land surfaces, respectively. Besides increasing the horizontal resolution to 4.4 km compared with the coarser 17.6 m resolution in V22 and streamlining the format and content, the V23 product has added geolocation information, pixel-level uncertainty estimates, and improved cloud screening. MISR data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://eosweb.larc.nasa. gov/project/misr/misr_table (last access: 11 October 2019). The version number for the V23 level 2 aerosol product is F13_0023. The level 3 (gridded) aerosol product is still reported at 0.5 • ×0.5 • spatial resolution with results aggregated from the higher-resolution level 2 data. The format and content at level 3 have also been updated to reflect the changes made at level 2. The level 3 product associated with the V23 level 2 product version is designated as F15_0032. Both the level 2 and level 3 products are now provided in NetCDF format.

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“…To determine the origin of prevailing air masses, Sateesh et al used backward trajectories computed with the HYSPLIT model in India [13]. Additionally, Franklin et al used the Multi-Angle Imaging SpectroRadiometer (MISR) instrument onboard the NASA Terra satellite to reliably estimate ground-level concentrations of PM 2.5 and SO 2 in Ulaanbaatar, Mongolia [14]. Furthermore, Zheng et al obtained measurement data from Aura, a sun-synchronous orbit satellite, to describe the long-term spatiotemporal distribution of NO 2 , SO 2 , and trace gases, such as HCHO, BrO, and OCIO, in Inner Mongolia [4].…”

Section: Introductionmentioning

confidence: 99%

Origin and Background Estimation of Sulfur Dioxide in Ulaanbaatar, 2017

Prikaz

Fang

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et al. 2018

Environments

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Particulate matter studies have been conducted regularly in the capital city of Mongolia. In contrast, studies related to the source and general estimation of levels of sulfur dioxide (SO 2 ) over whole years are lacking. To explore the yearly trend in SO 2 , whole-year data of air pollutants were obtained from the Air Pollution Reducing Department. The results showed that the annual average concentration of SO 2 was 32.43 µg/m 3 at the Amgalan official monitoring station in 2017, which changed from 53 µg/m 3 in 2016, representing a reduction of around 40%. The back-trajectory model and the National Oceanic and Atmospheric Administration (NOAA)'s hybrid single particle Lagrangian integrated trajectory model (HYPSLIT) were used to determine the source of SO 2 . A total of 8760 backward trajectories were divided into eight groups. The results showed that 78.8% of the total trajectories in Ulaanbaatar came from an area inside Mongolia. The results showed that pollutants enter Ulaanbaatar mainly from the northwest and north during the winter season. There are industrial cities, such as Darkhan and Sukhbaatar, in North Mongolia. Air pollutants created in the industrial area traveled into Ulaanbaatar during the winter season.

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Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Cited by 20 publications

References 26 publications

Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

Origin and Background Estimation of Sulfur Dioxide in Ulaanbaatar, 2017

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