note based on the PlanetLabs' (https://www.planet.com/terms-of-use/) and Weather Undergorund's terms of use (https://www.wunderground.com/company/legal), we are unable to publish/distribute the images data and meteorology data used in this study. But, we have provided a script for you to download the images used in our study from PlanetLabs and we have provided links to direct you to the corresponding Weather Underground webpages where you can download the meteorology data used in our study

This dataset includes the following items: 1) raw 1 h PM2.5 mass concentration measurements of the 35 regulatory air quality monitoring stations and the US Embassy station in Beijing from January 1, 2017 to July 20, 2019. 2) raw 1 h PM2.5 mass concentration measurements of the 10 regulatory air quality monitoring stations in Shanghai from January 1, 2017 to July 20, 2019. 3) "Links_to_Download_Meteorology_Data" links to direct you to the corresponding Weather Underground webpages where you can download the meteorology data used in our study 4) "Image_Downloader" python code to download the Beijing and Shanghai stations' images used in our study from PlanetLabs 5) "PM_Meteorology_Image_process_filter_match" python code to process the raw PM25 data and the raw meteorology data; to process and filter the raw images data; to match the processed PM25 data and meteorology data with the filtered and processed images data; store the matched records for model training and evaluation purposes 6) "Model_Training_and_Evaluation" python code to build, train, and evaluate the CNN-RF models used in our study 7) "670m_model" the trained CNN model trained on the Beijing training dataset at a spatial resolution of 670 * 670 m 8) "500m_model" the trained CNN model trained on the Beijing training dataset at a spatial resolution of 500 * 500 m 9) "200m_model" the trained CNN model trained on the Beijing training dataset at a spatial resolution of 200 * 200 m