HEI launches two new satellite studies

HEI has launched two new studies under RFA 25-1, Advancing Satellite-Derived Air Quality Data and Approaches for Use in Health Studies. These projects will develop resources for health research that link satellite-derived air quality products to quantified uncertainties and strengthen the understanding of the implications of such uncertainties for exposure, epidemiological, and health assessment research.

• Daily NO2 Estimates with Rigorous Spatiotemporal Uncertainty Quantification for Use in Health Studies led by Marianthi-Anna Kioumourtzoglou, Brown University, will develop a novel Bayesian ensemble that integrates data from TROPOMI and TEMPO satellite instruments along with other data that can be used to (a) predict daily NO2 concentrations and (b) rigorously quantify spatiotemporal uncertainty for use in health studies.
• Bayesian Ensemble Estimation of Uncertainty in High-Resolution Satellite-Based Air Pollution Predictions: Patterns and Driving Factors, led by Longxiang Li, Emory University, will create a harmonized database that integrates satellite retrievals from TROPOMI and VIIRS and generate daily NO2, O3, and PM2.5 surfaces with Bayesian pixel-level posterior uncertainty estimates.