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https://hdl.handle.net/10356/160599
Title: | Understanding haze: modeling size-resolved mineral aerosol from satellite remote sensing | Authors: | Sanwlani, Nivedita Das, Reshmi |
Keywords: | Science::Geology | Issue Date: | 2022 | Source: | Sanwlani, N. & Das, R. (2022). Understanding haze: modeling size-resolved mineral aerosol from satellite remote sensing. Remote Sensing, 14(3), 761-. https://dx.doi.org/10.3390/rs14030761 | Project: | MOE-NTU_RG125/16-(S) | Journal: | Remote Sensing | Abstract: | Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in situ measurements of dust samples. Here we derive the size-resolved mineralogical composition of airborne dust aerosols from MODIS (Terra and Aqua) satellite-acquired optical measurements and compare it with chemically analyzed elemental (Al, Fe, Ca, Mg) concentrations of aerosols for PM2.5 and PM10 from Chonburi, Chiang Rai, and Bangkok in Thailand, and from Singapore. MODIS-derived mineral retrievals exhibited high correlations with elemental concentrations with R2 ≥ 0.84 for PM2.5 and ≥0.96 for PM10 . High mineral dust activity was detected in the vicinity of biomass-burning areas with gypsum and calcite exhibiting tracer characteristics of combustion. The spatiotemporal pattern of the MODIS-derived minerals matched with Ozone Monitoring Instrument (OMI)-derived dust, sulfates, and carbonaceous aerosols, indicating the model’s consistency. Variation in aerosol loading by ±90% led to deviation in the mineral concentration by <10%. An uncertainty of 6.4% between AERONET-measured and MODIS-derived AOD corresponds to a < ± 2% uncertainty in MODIS-derived mineral concentration, demonstrating the robustness of the model. | URI: | https://hdl.handle.net/10356/160599 | ISSN: | 2072-4292 | DOI: | 10.3390/rs14030761 | Research Centres: | Earth Observatory of Singapore Satellite Remote Sensing Centre |
Rights: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EOS Journal Articles |
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remotesensing-14-00761-v2.pdf | 7.75 MB | Adobe PDF | ![]() View/Open |
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