AI Summary & Key Takeaways
AI InsightThe 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.
📌 3 Key Takeaways
Özet
Previous studies have proved that the statistical models between satellite-retrieved aerosol optical depth (AOD) and ground-level PM10 provide a feasible and effective way to obtain the extensive and continuous spatial distribution of ground-level PM10. The two-year annual mean PM10 was 119.9 ± 66.5 mg/m3 from 2014 to 2015, which significantly exceeded the annual WHO IT-1 standard for PM10 (70 mg/m3), 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 PM10 acquired at monitoring sites. The fitting analysis of PM10 and AOD shows that PM10 agrees well with AOD, and the linear regression model is the most accurate one. By the land-use function analysis of PM10 hotspots using Google Earth, it is apparent that the prevalence of industrial or bare soil areas is the key factor in determining anthro-pogenic 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 con-ducted for a more accurate understanding of the PM10 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.