Long-term variations and comparison of aerosol optical properties based on MODIS and ground-based data in Thailand

Pilahome, Oradee; Ninsawan, Waichaya; Jankondee, Yuttapichai; Janjai, Serm; Kumharn, Wilawan

doi:10.1016/j.atmosenv.2022.119218

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…There are two main methods to measure AOD in the atmosphere, which are groundbased observations using sunphotometer measurements on the ground and remote sensing inversion, which relies on sensor data from the satellite platform. At present, several ground-based monitoring networks have been established to provide AOD data in the world, such as AERosol RObotic NETwork (AERONET) [16], Multifilter Rotating Shadow Band Radiometer (MFRSR) [17], Chinese Sun Hazemeter Network (CSHNET) [18], Chinese Aerosol Remote Sensing NETwork (CARSNET) [19], European Brewer Network (EuBrewNet) [20,21], Sun-sky radiometer Observation Network (SONET) [22], Sky Radiometer Network (SKYNET) [23], etc. However, these networks are overly dependent on the density of station deployment, which leads to the fact that it is difficult to ensure the full integration of the regional scope and results in high construction and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

“…With the continuous optimization and updating of satellite sensors and inversion algorithms, AOD measurements taken by remote sensors have become more and more accurate, and the calculation of AOD from satellite remote sensing data has become the main technique for the large-scale monitoring of aerosol loads [24]. There are many sensors that can monitor AOD, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) [23], the Visible Infrared Imaging Radiometer (VIIRS) [25], the Advanced Very High Resolution Radiometer (AVHRR) [26], the Ozone Monitoring Instrument (OMI) [27], the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) [28], the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) [29], etc. Among them, the AVHRR, which is carried on the NOAA series of satellites, has been continuously conducting Earth observation missions since 1979, so it has an accumulation of data for more than 40 years and a great potential for applications.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Temporal Variation of Aerosol Optical Depth in Huaihai Economic Zone from 1982 to 2021

Xue

Sun

et al. 2023

Atmosphere

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Aerosol Optical Depth (AOD), quantifying the amount of aerosol in the atmosphere, is widely regarded as a crucial indicator for research on atmospheric physics and regional air quality. At present, the inversion of AOD from observation of satellite remote sensing sensors has become the main technology for large-scale monitoring of aerosol load. The Huaihai Economic Zone is the connecting belt of two key areas of atmospheric governance (the Yangtze River Delta and the Beijing-Tianjin-Hebei region, China), and it has been suffering from air pollution for many years and few studies of AOD focus on this region. Therefore, the spatial and temporal characteristics of the AOD are explored using MODIS AOD data and AVHRR AOD data in this region during the period from 1982 to 2021 in this study. The correlation coefficients between the AOD of satellite observation and actual air pollution were analyzed by combining PM2.5 pollutant concentration and air quality index (AQI) data. The results showed that the AOD is higher in the northwest than in the southeast, and it is different from season to season. The annual variation of AOD in the Huaihai Economic Zone is a W-shaped trend from 1982 to 2011, while the trend of annual AOD is decreasing after 2011. In terms of seasons, the whole differences in AOD are evident, exhibiting AOD values in summer > those in spring > those in autumn > and those in winter. Furthermore, it indicated that the quarterly and monthly variation of the AOD tends to be flat in recent years. Since 2015, the concentration of PM2.5 has continued to decline, the same as that of AQI. Meanwhile, the quarterly and monthly differences in PM2.5 are still obvious, with higher PM2.5 in winter and lower PM2.5 in summer. However, it also represented that PM2.5 is significantly higher in spring than in autumn from 2015 to 2018, which is the opposite for 2019 to 2021. Lastly, the correlation between AOD and PM2.5/AQI is also given; i.e., the correlation coefficients of AOD with PM2.5/AQI are 0.84/0.82, with the highest correlation coefficient in autumn (R = 0.86/0.91) and the lowest in winter (R = 0.46/0.48).

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