Combining atmospheric chemistry model outputs, satellite retrievals, and ground monitoring data to improve city-scale air quality estimation and near-term forecasting

Abstract:

Poor air quality is a major challenge for human health globally, especially in urban centers where emissions and exposure are both concentrated. Air quality monitoring has traditionally relied on ground-based measurements from a relatively small number of highly accurate but expensive regulatory-grade instruments, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including remote satellite observations of pollutants in the atmospheric column, regional- and global-scale atmospheric chemistry model simulations, and less expensive in-situ monitoring systems allowing for denser spatial data collection at the expense of relatively lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources individually have their own benefits and drawbacks, and therefore there is an opportunity to combine these disparate information sources while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. This presentation investigates a potential approach to such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Primary data sources include the GEOS Composition Forecasting (GEOS-CF) system, TROPOMI tropospheric NO₂ data products, ground measurements from the EPA regulatory monitoring network, and basic information on land usage and population density. Demonstrations of the system performance are presented for several urban areas in the United States, including New York City, New Orleans, San Francisco, Las Vegas, and Salt Lake City. Performance assessments are conducted across multiple seasons, and compared with simple persistence and climatological forecasts made using in-situ data only. Investigations of the relative benefits of different methods of combining the data sources are also conducted.