Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument

We present an approach to infer ground‐level nitrogen dioxide (NO 2 ) concentrations by applying local scaling factors from a global three‐dimensional model (GEOS‐Chem) to tropospheric NO 2 columns retrieved from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. Seasonal mean OMI surface NO 2 derived from the standard tropospheric NO 2 data product (Version 1.0.5, Collection 3) varies by more than two orders of magnitude (10 ppbv) over North America. Two ground‐based data sets are used to validate the surface NO 2 estimate and indirectly validate the OMI tropospheric NO 2 retrieval: photochemical steady‐state (PSS) calculations of NO 2 based on in situ NO and O 3 measurements, and measurements from a commercial chemiluminescent NO 2 analyzer equipped with a molybdenum converter. An interference correction algorithm for the latter is developed using laboratory and field measurements and applied using modeled concentrations of the interfering species. The OMI‐derived surface NO 2 mixing ratios are compared with an in situ surface NO 2 data obtained from the U.S. Environmental Protection Agency's Air Quality System (AQS) and Environment Canada's National Air Pollution Surveillance (NAPS) network for 2005 after correcting for the interference in the in situ data. The overall agreement of the OMI‐derived surface NO 2 with the corrected in situ measurements and PSS‐NO 2 is −11–36%. A larger difference in winter/spring than in summer/fall implies a seasonal bias in the OMI NO 2 retrieval. The correlation between the OMI‐derived surface NO 2 and the ground‐based measurements is significant (correlation coefficient up to 0.86) with a tendency for higher correlations in polluted areas. The satellite‐derived data base of ground level NO 2 concentrations could be valuable for assessing exposures of humans and vegetation to NO 2 , supplementing the capabilities of the ground‐based networks, and evaluating air quality models and the effectiveness of air quality control strategies.