Validation and Accuracy Analysis of the Collection 6.1 <scp>MODIS</scp> Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET

Huang, Guan; Chen, Yonghang; Li, Zhengqiang; Liu, Qiong; Wang, Yanyu; He, Qianshan; Liu, Tongqiang; Liu, Xin; Zhang, Yang; Gao, Jiacheng; Yao, Yifeng

doi:10.1029/2019ea001041

Cited by 26 publications

(6 citation statements)

References 73 publications

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“…This inconsistency may be attributed to the accuracy of AOD retrieval in winter. The quality of AOD retrieval can be degraded due to a high solar zenith angle [41,42] or bright surface albedo caused by snow cover [43,44], which are representative of winter conditions on the Korean peninsula. Some retrieval algorithms can be largely affected by the winter characteristics of surface albedo.…”

Section: Resultsmentioning

confidence: 99%

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

et al. 2021

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Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

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Section: Resultsmentioning

confidence: 99%

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

et al. 2021

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“…Compared to Aqua MODIS, Terra MODIS shows better agreement with the ground observation which is indicated by the higher R 2 value. The evaluation of these products over other areas in the Asian region show results with lower R 2 values (Bilal et al, 2018;Huang et al, 2020;Sharma et al, 2021;Tian and Gao, 2019). This may be attributed to the lower number of data samples in the present study, and the difference in the spatial and temporal window size taken for collocation.…”

Section: Accepted Manuscriptmentioning

confidence: 61%

Aerosols over the Foothills of the Eastern Himalayan Region during Post-monsoon and Winter Seasons

Dahal

Rupakheti

Sharma

et al. 2022

Aerosol Air Qual. Res.

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In this study a Microtops II sunphotometer is used for the first time over Birtamode, a rapidly growing city located in the eastern Himalayan foothills of Nepal, to measure aerosol optical depth (AOD). The average AOD for the observation period (October 2018 -February 2019) was 0.68 ± 0.39 with the post-monsoon season having a higher value (0.74 ± 0.43) compared to the winter season (0.60 ± 0.32). The Angstrom exponent (α) for post-monsoon and winter are found to be 1.08 ± 0.10 and 1.11 ± 0.16 respectively. During the monitoring period, the majority of AOD values (47%) are above 0.60, indicating moderately polluted conditions in the region. Anthropogenic, biomass burning, and mixed aerosols are identified as the prevalent aerosol types in the study region. The observed aerosol classification is also explained in terms of CAMS near-real-time model datasets. The AOD values retrieved by MODIS, VIIRS, Himawari-8, and CAMS show a good correlation with the observed Microtops AOD with R 2 values ranging from 0.60 to 0.94. Moreover, different MODIS aerosol products (DB, DT, and combined DB-DT) are evaluated based on a comparison of Collection 6.1 AOD with the ground truth obtained from Microtops. The spatial distribution of AOD as observed by various satellites are compared and the vertical distribution of aerosol is also explained with the extinction coefficient provided by the CALIOP lidar onboard CALIPSO and aerosol types provided by CAMS.

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“…For example, the inconsistent changes in springtime PM 2.5 and AOD in southern China (Guangzhou, Guilin, Nanning) are likely related to the cross-boundary transport of biomass burning aerosols at high levels (Deng et al, 2008). In addition, the large enhancements of wintertime PM 2.5 in Kashi (Figure 2) are likely associated with the increased dust emissions (Feng et al, 2002;Huang et al, 2020). These coarse particles show limited impacts on AOD but contribute to the decoupling between PM 2.5 and AOD.…”

Section: Discussionmentioning

confidence: 99%

“…More than 20 aerosol parameters including AOD and single scattering albedo (SSA) are estimated. The SONET data have been fully evaluated and widely used in the analyses of particle size distribution and aerosol radiative effects (Xie et al, 2015a;Zhang et al, 2019a;Huang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Decoupling between PM2.5 concentrations and aerosol optical depth at ground stations in China

Yue

et al. 2022

Front. Environ. Sci.

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Surface PM2.5 concentrations and aerosol optical depth (AOD) are two air pollution metrics tightly connected. Many studies have used AOD to derive PM2.5 concentrations without investigating their inconsistencies. Here, we explored the associations between surface PM2.5 and AOD using ground-level data from 19 stations in China during 2017–2019. Unexpectedly, we found low correlation coefficients of 0.03–0.60 between daily PM2.5 and AOD for most sites. Such decoupling between PM2.5 and AOD is further compared to simultaneous meteorological factors such as air temperature, specific humidity, sea level pressure, and wind speed. We found that specific humidity dominates the correlations with normalized PM2.5-AOD differences at 14 out of 19 sites. On average, specific humidity increases from 2.83 g kg−1 for the cases with low AOD but high PM2.5–11.89 g kg−1 for those with high AOD but low PM2.5, indicating that hygroscopic growth of aerosols may play an important role in decoupling the associations between PM2.5 and AOD. Random forest (RF) models using AOD as the only input yield a low R of 0.49 between the predicted and observed PM2.5 concentrations. The inclusion of specific humidity in the RF model increases the R to 0.74, close to the R of 0.81 with three additional meteorological factors. Our study revealed a strong decoupling between PM2.5 and AOD and suggested including specific humidity as a key parameter in the retrieval of long-term PM2.5 using AOD data in China.

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Validation and Accuracy Analysis of the Collection 6.1 MODIS Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET

Cited by 26 publications

References 73 publications

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Aerosols over the Foothills of the Eastern Himalayan Region during Post-monsoon and Winter Seasons

Decoupling between PM2.5 concentrations and aerosol optical depth at ground stations in China

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