The main idea of this project is to incorporate human activity, observational data, and chemical transport model (CTM) outcomes to calibrate the human-activity-based bottom-up emissions inventory during the pandemic period. The measurement data includes human activity, Air Quality System (AQS), and satellite datasets (OMPS and CrIS); the CTM tool will be Community Multiscale Air Quality Modeling (CMAQ) developed by U.S. Environmental Protection Agency (U.S. EPA). CMAQ is the state-of-science model that can simulate complex atmospheric chemistry and physics between air pollutants under various meteorological conditions and understand risk assessments to local and regional air quality. This proposal will first establish a high-resolution bottom-up emissions inventory based on the latest U.S. EPA. National Emissions Inventory (NEI) during the COVID-19 pandemic that reflects the changes in human activity and emission patterns. Once the enhanced bottom-up emissions inventory, the COVID-19 NEI, is developed, the project team will apply the CMAQ modeling system to simulate the air quality during COVID- 19 and then evaluate them with the AQS and the satellite observations. Furthermore, those observational data will be applied to compare the COVID-19 NEI using a top-down approach based on inverse emission modeling. As a result, this process can further reduce the uncertainties of the COVID-19 NEI and allow for the development of accurately calibrated bottom-up emissions data for the air quality modeling community and policymakers.