Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data
Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MOD...
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Published in: | Environmental monitoring and assessment Vol. 192; no. 6; p. 369 |
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Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Cham
Springer International Publishing
01-06-2020
Springer Nature B.V |
Subjects: | |
Online Access: | Get full text |
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Summary: | Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM
10
) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014–2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March–April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM
10
concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM
10
than Terra AODs-PM
10
with correlation coefficients (
r
) of 0.72–0.83 and 0.57–0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (
r
= 0.65–0.73) than with CO total columns (
r
= 0.56–0.72). The
r
values of satellite and ground datasets were greatest when the analysis was restricted to November–March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM
10
and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited. |
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ISSN: | 0167-6369 1573-2959 |
DOI: | 10.1007/s10661-020-08346-7 |