EGU24-11978, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of a Land-Use Regression of Hourly Surface NO2 in preparation for GeoXO Atmospheric Composition Data

Omar Nawaz, Susan Anenberg, Daniel Goldberg, Gaige Kerr, and Shobha Kondragunta
Omar Nawaz et al.
  • George Washington University, Department of Environmental and Occupational Health, United Kingdom of Great Britain – England, Scotland, Wales (

The Geostationary Extended Observations (GeoXO) satellite system is the intended successor to the GOES-R Series from NOAA, and it is planned to begin operating in the early 2030s. This next generation system will be outfitted with an Atmospheric Composition Instrument (ACX) that will provide hourly observations of tropospheric trace gases and aerosols including pollutants associated with poor health; the highest priority factors for air quality monitoring in this new system include some of the pollutants most hazardous to human health such as ozone (O3), particulate matter (PM), and nitrogen dioxide (NO2). GeoXO will be a geostationary satellite with multiple overpasses of the United States per day in contrast to the TROPOspheric Monitoring Instrument (TROPOMI) on board the sun-synchronous polar-orbiting Sentinel-5P satellite. This satellite – launched by the European Space Agency – overpasses each place on Earth 1-2 times per day around 1:30 PM.

In this project, we evaluate the influence that GeoXO remote sensing capabilities could have for assessing air pollution-related health impacts in the United States. We do this by comparing the health effects associated with predicted NO2 exposure at TROPOMI overpass times to predicted NO2 exposure during daylight hours. To conduct this comparison, we develop a land-use regression (LUR) model that predicts hourly surface-level NO2 data per month in the United States using monthly oversampled TROPOMI NO2 columns, static land-use data including roads, population density, built environment and elevation differential, and hourly meteorological reanalysis data of temperature, boundary layer height, precipitation, and total liquid water column amount. We fuse these variables using different regression techniques including both a lasso and multi-layer perceptron regression to predict monthly surface-level NO2 during daylight hours.

We compare the predicted hourly surface-level NO2 to NO2 derived just at TROPOMI overpass time – approximately 1:30 PM – to quantify how geostationary observations could better reveal how populations are exposed to pollutants like NO2 than exposures that are reliant on data from a single overpass time. We additionally investigate the health disparity introduced from this assumption by estimating the new pediatric asthma cases and premature mortality associated with hourly daylight NO2 exposure versus NO2 exposure from just a single overpass time. Additionally, we discuss how data from the new TEMPO instrument – that was launched by NASA in 2023 – will influence and improve this TROPOMI-derived LUR.

How to cite: Nawaz, O., Anenberg, S., Goldberg, D., Kerr, G., and Kondragunta, S.: Development of a Land-Use Regression of Hourly Surface NO2 in preparation for GeoXO Atmospheric Composition Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11978,, 2024.