Validation and comparison of high-resolution MAIAC aerosol products over Central Asia

Chen, Xiangyue; Ding, Jianli; Liu, Jie; Wang, Jingzhe; Ge, Xiangyu; Wang, Rui; Zuo, Hongchao

doi:10.1016/j.atmosenv.2021.118273

Cited by 45 publications

(32 citation statements)

References 59 publications

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“…The applicability of MCD19A2 data has been verified in numerous studies ( Chen et al, 2021 ; Li et al, 2021b ; Tao et al, 2019 ), so verification is not performed here. The spatial distribution of AOD in Xinjiang from 2015 to 2020 is shown in Fig.…”

Section: Resultsmentioning

confidence: 94%

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

Jin

Ding

et al. 2022

PeerJ

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PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015–2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m−3) > spring (64.76 µg m−3) > autumn (46.01 µg m−3) > summer (43.40 µg m−3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.

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Section: Resultsmentioning

confidence: 94%

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

Jin

Ding

et al. 2022

PeerJ

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“…In the present study, we validated MAIAC AOD with ground measurements of AOD and developed a 1-km-resolution satellite-based model for daily PM 2.5 concentrations during 2011-2016 by using a random forest model in Japan. The MAIAC AOD showed a high retrieval accuracy in Japan (R = 0.82, with the EE: 74.62%), which was comparable to those conducted in South Asia (including Pakistan, India, and Bangladesh) (R = 0.882, within the EE: 72.22% and R = 0.887, within the EE: 73.5 for Aqua and Terra MAIAC AOD, respectively) [47] and better than in Central Asia (R = 0.730, within the EE: 58.7% for spring; R = 0.709, within the EE: 66.7% for summer; R = 0.729, within the EE: 62.4% for autumn, and R = 0.744, with the EE: 67.9% for winter) [38].…”

Section: Discussionmentioning

confidence: 92%

“…The Pearson's correlation coefficient (R), RMSE, mean bias error (MBE), percentage of data within the expected error (EE; ±(0.05 + 0.15AERONET AOD)), and slope of the linear regression were selected to evaluate the consistency between MAIAC AOD and AERONET AOD [38,39].…”

Section: Methodsmentioning

confidence: 99%

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

2021

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Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

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“…The EE is commonly used in MODIS validation studies (e.g., [12,16,34,35]). It is designed to account for errors which are normally expected in a satellite retrieval of AOD.…”

Section: Spatio-temporal Co-location Of Aeronet and Modis Aod And Eva...mentioning

confidence: 99%

“…The MAIAC algorithm has improved spatial resolution, and regional studies have found it to have an enhanced ability over DB to retrieve fine scale AOD features, such as smoke plumes, as well as performing well over complex geographical landscapes [11]. Since the MAIAC algorithm became operational in 2018, there has been one global [12] and numerous regional evaluations of its performance and limitations, including North America [13], South America [14], Asia [11,[15][16][17][18][19] and Moscow City [20]. Falah et al [21] also use AERONET to assess the validity of MAIAC retrievals over North Africa (Algeria, Morocco and Tunisia), California and Germany, to elucidate the effect of different environments (aerosol types, surface properties) on the performance of the MAIAC algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

2022

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We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

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Validation and comparison of high-resolution MAIAC aerosol products over Central Asia

Cited by 45 publications

References 59 publications

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

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