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Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performan...
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... the AHI hourly AOT used the optimal spatiotemporal interpolation in the hourly AOT algorithm ( Kikuchi et al., 2018), during the moderate and heavy pollution, the percentage of successful retrieval is still low, and AHI AOT values are significantly overestimated or underestimated. Table 1 shows that the percentage of successful retrieval for AHI and AERONET AOT is less than 20% for the moderate pollution, and less than 2% during heavy pollution. For the AERONET AOT observations, this is most likely due to the cloud contamination, while for the AHI hourly AOT retrievals, the reason may result from the strict cloud screening of AHI L2 AOT used to interpolate the AHI merged AOT in the hourly merged AOT algorithm. ...Similar publications
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... AHI AOD is much smaller than the MODIS AOD (MAIAC, DT, DB) during the fall and winter seasons. However, there is no significant difference between AHI AOD and MODIS AOD (MAIAC, DT, DB) in Figure 7b, implying the AHI AOD differences in the morning and afternoon, as discovered by Xu et al. (2020) [57]. With respect to the same sensor (Terra MODIS), the AOD of the DB algorithm is lower than the AOD of the MAIAC and DT algorithms in the GZP, HBEY, and LX regions. ...
... AHI AOD is much smaller than the MODIS AOD (MAIAC, DT, DB) during the fall and winter seasons. However, there is no significant difference between AHI AOD and MODIS AOD (MAIAC, DT, DB) in Figure 7b, implying the AHI AOD differences in the morning and afternoon, as discovered by Xu et al. (2020) [57]. With respect to the same sensor (Terra MODIS), the AOD of the DB algorithm is lower than the AOD of the MAIAC and DT algorithms in the GZP, HBEY, and LX regions. ...
... AHI AOD is much smaller than the MODIS AOD (MAIAC, DT, DB) during the fall and winter seasons. However, there is no significant difference between AHI AOD and MODIS AOD (MAIAC, DT, DB) in Figure 7b, implying the AHI AOD differences in the morning and afternoon, as discovered by Xu et al. (2020) [57]. With respect to the same sensor (Terra MODIS), the AOD of the DB algorithm is lower than the AOD of the MAIAC and DT We investigated the spatio-temporal relationship between various satellite AODs and the near-surface PM 2.5 concentration over China, but there were a few limitations. ...
Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (η > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.
... The satellite AOD product used in this study is AHI level-3 hourly AOD data from version 3.0, which is released at 5 km spatial resolution by Japan Meteorology Agency (JMA), with a low level of uncertainty (Jiang et al., 2019;Li et al., 2019;Zhang et al., 2019a;Xu et al., 2020). The AHI L3 hourly operational AOD product is carried out strict quality control of cloud contamination and used an optimal spatiotemporal interpolation to fill missing values Yoshida et al., 2018). ...
Himawari-8 aerosol products have been widely used to estimate the near-surface hourly PM2.5 concentrations due to the high temporal resolution. However, most studies focus on the evaluation model. As the foundation of the estimation, the relationship between near-surface PM2.5 and columnar aerosol optical depth (AOD) has not been comprehensively investigated. In this study, we investigate the relationship between PM2.5 and advanced Himawari imager (AHI) AOD for 2016-2018 across mainland China on different spatial and temporal scales and the factors affecting the association. We calculated the Pearson correlation coefficients and the PM2.5/AOD ratio as the analysis indicators in 345 cities and 14 urban agglomerations based on the collocations of PM2.5 and AHI AOD. From 9:00 to 17:00 local time, the PM2.5-AOD correlation become significantly stronger while The PM2.5/AOD ratio markedly decrease in Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengyu regions. The strongest correlation is between 12:00 and 14:00 LT (at noon) and between 13:00 and 17:00 LT (afternoon), respectively. The ratio in a day shows an obvious unimodal mode, and the peak occurred at around 10:00 or 11:00 LT, especially in autumn and winter. There is a pronounced variation of the PM2.5-AOD relationship in a week during the winter. Moreover, there are the strongest correlation and the largest ratio for most urban agglomerations during the winter. We also find that PM2.5 and AOD are not always correlated under different meteorological conditions and precursor concentrations. Furthermore, for the scattering-dominated fine-mode aerosol, there is a high correlation and a low ratio between PM2.5 and AOD. The correlation between PM2.5 and AHI AOD significantly increases with increasing the number of AOD retrievals on a day. The findings will provide meaningful information and important implications for satellite retrieval of hourly PM2.5 concentration and its exposure estimation in China, especially in some urban agglomerations.
The advanced Himawari imager (AHI) onboard Himawari-8 can provide full-disk observations with high temporal resolution (10 min), which has outstanding advantages for dynamic real-time aerosol monitoring in East Asia. In this study, a new aerosol retrieval algorithm for AHI by integrating regional PM
2.5
concentrations (IRPAR) was proposed. The IRPAR algorithm constructed the surface reflectance library by integrating regional PM
2.5
levels as a quantitative indicator of atmospheric aerosol loadings. The IRPAR algorithm was used to obtain the aerosol optical depth (AOD) retrievals over Beijing–Tianjin–Hebei (BTH) region from March 2019 to February 2020, and its performance was preliminarily evaluated by aerosol robotic network (AERONET) measurements. The results showed that the IRPAR algorithm was able to obtain more highly accurate AOD retrievals compared to the JAXA L2 algorithm during the autumn in BTH region, with a large
R
of 0.87 (0.71 for JAXA L2 AOD) and a global climate observing system fraction (GCOSF) percentage of 28% (21% for JAXA L2 AOD). During different daytime hours, the IRPAR AOD showed a stable retrieval performance, while the JAXA L2 AOD exhibited a worst performance from 12:00 to 14:00 Beijing standard time (BST). These results demonstrated that the IRPAR algorithm was relatively less affected by the viewing angle. Future work will require a comprehensive evaluation of the IRPAR algorithm on a larger spatial scale.
