Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets
Abstract
:1. Introduction
2. Data and Methodology
2.1. Study Region
2.2. Data
2.2.1. MERRA-2
2.2.2. MODIS
2.2.3. AERONET
2.3. Methodology
2.3.1. Spatial Distribution of AOD Datasets
2.3.2. Temporal Analysis of AOD and Heavy Load
2.3.3. Aerosol Identification
3. Long-Term Variation Assessment of Aerosol Load
3.1. Spatial Distribution of MERRA-2/MODIS AOD
3.2. Temporal Variation of Aerosol Load
3.2.1. Annual Frequency of Heavy Aerosol Load
3.2.2. Sub-Regions Temporal Variation Characteristics
4. Long-Term Variation Assessment of Aerosol Type
4.1. Aerosol Identification Using AE-Based Model
4.2. Aerosol Identification Using AROD-Based Model
4.2.1. Annual Aerosol Types Proportion
4.2.2. Annual Dominant Aerosol Types
4.2.3. Temporal Variation of Polluted Aerosols
5. Discussion
6. Conclusions
- Both MERRA-2 and MODIS data can accurately characterize the spatial distribution of AOD values at annual and seasonal scales, and there is adequate spatial agreement between them. The former has significantly higher spatiotemporal integrity than the latter and can support the study of aerosol load over areas with high surface albedo, while the latter has advantages in spatial resolution and accuracy and can reflect the aerosol load fluctuation in local areas more accurately.
- The aerosol load temporal variation shows that the NCP, Central China, YRD, PRD, Sichuan Basin, and RRD exhibit an increasing trend and then a declining one during the study period. Meanwhile, the IGP area, Deccan Plateau, and the Eastern Ghats show a continuously increasing variation; however, the growth rate of the past decade was lower than that of the first decade.
- For areas with more serious air pollution (e.g., IGP, NCP), the annual heavy aerosol load frequency given by MERRA-2 is much higher than that of MODIS. The annual HALF given by the two datasets differed numerically; however, the relative changes and trends remained consistent. Thus, the spatiotemporal integrity differences of datasets will directly affect the calculation of temporal variation patterns, but have a less impact.
- The regional aerosol type information with adequate spatial resolution obtained from MODIS and AROD identification model can be combined with the AOD values and HALF characterization results to explore the key contributors to air pollution and promote the investigation of heavy aerosol loads and the targeted prevention and control of air pollution. MERRA-2 datasets have the potential to support regional aerosol type recognition research; however, the accuracy of AE data and the applicability of AE identification models need to be improved.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Huang, C.; Li, J.; Sun, W.; Chen, Q.; Mao, Q.-J.; Yuan, Y. Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets. Remote Sens. 2021, 13, 3116. https://doi.org/10.3390/rs13163116
Huang C, Li J, Sun W, Chen Q, Mao Q-J, Yuan Y. Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets. Remote Sensing. 2021; 13(16):3116. https://doi.org/10.3390/rs13163116
Chicago/Turabian StyleHuang, Chunlin, Junzhang Li, Weiwei Sun, Qixiang Chen, Qian-Jun Mao, and Yuan Yuan. 2021. "Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets" Remote Sensing 13, no. 16: 3116. https://doi.org/10.3390/rs13163116
APA StyleHuang, C., Li, J., Sun, W., Chen, Q., Mao, Q. -J., & Yuan, Y. (2021). Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets. Remote Sensing, 13(16), 3116. https://doi.org/10.3390/rs13163116