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• W2562995425 abstract "Abstract Previous studies have proved that the statistical models between satellite-retrieved aerosol optical depth (AOD) and ground-level PM 10 provide a feasible and effective way to obtain the extensive and continuous spatial distribution of ground-level PM 10 . The two-year annual mean PM 10 was 119.9 ± 66.5 μg/m 3 from 2014 to 2015, which significantly exceeded the annual WHO IT-1 standard for PM 10 (70 μg/m 3 ), and the mean AOD was 0.56 ± 0.21 in Luoyang. Statistical models were proposed using a combination of HJ-1 (Environment Satellite 1) CCD (charge-coupled device) AOD and PM 10 acquired at monitoring sites. The fitting analysis of PM 10 and AOD shows that PM 10 agrees well with AOD, and the linear regression model is the most accurate one. By the land-use function analysis of PM 10 hotspots using Google Earth, it is apparent that the prevalence of industrial or bare soil areas is the key factor in determining anthropogenic pollutant emissions. In view of the small HJ-1 data available for analyzing and the limitation of dark target algorithm in the season with sparse vegetation cover, further investigation should be conducted for a more accurate understanding of the PM 10 monitoring. Despite the limitations of this work, the results prove the feasibility of retrieving remote sensing images for monitoring regional aerosol pollution, together with ground-level data. The combination of satellite images, ground monitoring and Google earth can help to better understand the spatial distributions and sources of PM on a regional scale." @default.
• W2562995425 created "2017-01-06" @default.
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• W2562995425 date "2017-05-01" @default.
• W2562995425 modified "2023-10-16" @default.
• W2562995425 title "Modeling spatiotemporal distribution of PM10 using HJ-1 CCD data in Luoyang, China" @default.
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• W2562995425 doi "https://doi.org/10.1016/j.apr.2016.12.012" @default.
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