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Big Earth Data Analytics with Multiscale and Multi-source Data Fusion for Long-term Gap-free High-resolution Monitoring of Air Pollutants from Space


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The Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset recently developed can provide spatially contiguous daily aerosol optical depth (AOD) and fine particulate matter (PM2:5) concentrations at a 1 km grid resolution 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. Such a big Earth data analytics was generated 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. Ground-based validation results indicated good data accuracy of this global gap-free AOD dataset. Based on the global AOD datasets of LGHAP, a novel deep-learning approach, termed the SCene-Aware ensemble learning Graph ATtention network (SCAGAT), was hereby applied to conduct PM 2.5 concentration mapping. While accounting for the scene representativeness of data-driven models across regions, the SCAGAT algorithm performed better during spatial extrapolation, largely reducing modeling biases over regions with limited and/or even absent in situ PM 2.5 concentration measurements. Overall, while leveraging state-of-the-art methods in data science and artificial intelligence, a quality enhanced LGHAP dataset was generated through big Earth data analytics by cohesively weaving together multimodal AOD sources and air quality measurements from diverse sources. The gap-free, high-resolution, and global coverage merits render the final LGHAP v2 dataset that is an invaluable database for advancing aerosol- and haze-related studies as well as triggering multidisciplinary applications for environmental management, health-risk assessment, and climate change attribution.