LGHAP v2: a global gap-free aerosol optical depth and PM 2.5 concentration dataset since 2000 derived via big Earth data analytics

• Published in: Earth System Science Data
• Main Authors: K. Bai, K. Li, L. Shao, X. Li, C. Liu, Z. Li, M. Ma, D. Han, Y. Sun, Z. Zheng, R. Li, N.-B. Chang, J. Guo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/essd-16-2425-2024; https://doaj.org/article/2687da339d49481b89af6480e39c70c5

The Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset generated in our previous study has provided spatially contiguous daily aerosol optical depth (AOD) and fine particulate matter (PM 2.5 ) concentrations at a 1 km grid resolution in China since 2000. This advancement empowered unprecedented assessments of regional aerosol variations and their influence on the environment, health, and climate over the past 20 years. However, there is a need to enhance such a high-quality AOD and PM 2.5 concentration dataset with new robust features and extended spatial coverage. In this study, we present version 2 of a global-scale LGHAP dataset (LGHAP v2), which was generated using improved big Earth data analytics via a seamless integration of versatile data science, pattern recognition, and machine learning methods. Specifically, multimodal AODs and air quality measurements acquired from relevant satellites, ground monitoring stations, and numerical models were harmonized by harnessing the capability of random-forest-based data-driven models. Subsequently, an improved tensor-flow-based AOD reconstruction algorithm was developed to weave the harmonized multisource AOD products together for filling data gaps in Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals from Terra. The results of the ablation experiments demonstrated better performance of the improved tensor-flow-based gap-filling method in terms of both convergence speed and data accuracy. Ground-based validation results indicated good data accuracy of this global gap-free AOD dataset, with a correlation coefficient ( R ) of 0.85 and a root mean square error (RMSE) of 0.14 compared to the worldwide AOD observations from the AErosol RObotic NETwork (AERONET), outperforming the purely reconstructed AODs ( R = 0.83, RMSE = 0.15), but they were slightly worse than raw MAIAC AOD retrievals ( R = 0.88, RMSE = 0.11). For PM 2.5 concentration mapping, a novel deep-learning approach, termed the SCene-Aware ensemble ...