High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants.

Download Full-text

Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals

Atmospheric Environment ◽

10.1016/j.atmosenv.2014.02.019 ◽

2014 ◽

Vol 89 ◽

pp. 189-198 ◽

Cited By ~ 65

Author(s):

Alexandra A. Chudnovsky ◽

Petros Koutrakis ◽

Itai Kloog ◽

Steven Melly ◽

Francesco Nordio ◽

...

Keyword(s):

Particulate Matter ◽

High Resolution ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Fine Particulate Matter ◽

Fine Particulate

Download Full-text

The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-18375-2021 ◽

2021 ◽

Vol 21 (24) ◽

pp. 18375-18391

Author(s):

Qingqing He ◽

Mengya Wang ◽

Steve Hung Lam Yim

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Spatial Scales ◽

Pearson Correlation ◽

Eastern China ◽

Atmospheric Correction ◽

Mainland China ◽

Ground Level ◽

Central China ◽

The Relationship

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring the surface-based PM2.5 concentration due to their extensive spatial and temporal coverage. Satellite-derived PM2.5 estimations strongly rely on an accurate representation of the relationship between ground-level PM2.5 and satellite aerosol optical depth (AOD). Due to the limitations of satellite AOD data, most studies have examined the relationship at a coarse resolution (i.e., ≥ 10 km); thus, more effort is still needed to better understand the relationship between “in situ” PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Therefore, considerable uncertainty still exists in satellite-derived PM2.5 estimations due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD as well as its plausible impact factors, including meteorological parameters and topography, in mainland China during 2019, at various spatial and temporal scales. The coefficient of variation, the Pearson correlation coefficient and the slope of the linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China, and the linear slope was larger on average in northern inland regions than in other areas. Temporally, the PM2.5–AOD correlation peaked at noon and in the afternoon, and reached a maximum in winter. Simultaneously, considering relative humidity (RH) and the planetary boundary layer height (PBLH) in the relationship can improve the correlation, but the effect of RH and the PBLH on the correlation varied spatially and temporally with respect to both strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as the Sichuan Basin, the Shanxi–Shaanxi basins and the Junggar Basin. MAIAC 1 km AOD can better represent the ground-level fine particulate matter in most domains with exceptions, such as in very high terrain (i.e., Tibetan Plateau) and northern central China (i.e., Qinghai and Gansu). The findings of this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status of mainland China.

Download Full-text

A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-10907-2013 ◽

2013 ◽

Vol 13 (21) ◽

pp. 10907-10917 ◽

Cited By ~ 45

Author(s):

A. Chudnovsky ◽

C. Tang ◽

A. Lyapustin ◽

Y. Wang ◽

J. Schwartz ◽

...

Keyword(s):

New England ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Fine Particulate Matter ◽

Atmospheric Correction ◽

Site Location ◽

Lack Of Information ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002–2008 was investigated and also compared to the AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD–PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50–70% in the number of collocated AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM2.5 spatial pattern prediction based on satellite observations.

Download Full-text

A critical assessment of high resolution aerosol optical depth (AOD) retrievals for fine particulate matter (PM) predictions

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-14581-2013 ◽

2013 ◽

Vol 13 (6) ◽

pp. 14581-14611 ◽

Cited By ~ 5

Author(s):

A. Chudnovsky ◽

C. Tang ◽

A. Lyapustin ◽

Y. Wang ◽

J. Schwartz ◽

...

Keyword(s):

New England ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Fine Particulate Matter ◽

Atmospheric Correction ◽

Site Location ◽

Lack Of Information ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS) which provides Aerosol Optical Depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard supersite during 2002–2008 was investigated and also compared to the AOD/PM2.5 relationship using conventional MODIS 10 km AOD retrieval (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD/PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data we found a substantial increase, by 50–70% in the number of collocated AOD vs PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data is more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to significantly increase the number of days for PM2.5 spatial pattern prediction based on satellite observations.

Download Full-text