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

We present an approach to infer ground-level nitrogen dioxide (NO2) concentrations by applying local scaling factors from a global three-dimensional model (GEOS-Chem) to tropospheric NO2 columns retrieved from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. Seasonal mean OMI surface NO2 derived from the standard tropospheric NO2 data product (Version 1.0.5, Collection 3) varies by more than two orders of magnitude (< 0.1-> 10 ppbv) over North America. Two ground-based data sets are used to validate the surface NO2 estimate and indirectly validate the OMI tropospheric NO2 retrieval: photochemical steady-state (PSS) calculations of NO2 based on in situ NO and O-3 measurements, and measurements from a commercial chemiluminescent NO2 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 NO2 mixing ratios are compared with an in situ surface NO2 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 NO2 with the corrected in situ measurements and PSS-NO2 is -11-36%. A larger difference in winter/spring than in summer/fall implies a seasonal bias in the OMI NO2 retrieval. The correlation between the OMI-derived surface NO2 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 NO2 concentrations could be valuable for assessing exposures of humans and vegetation to NO2, supplementing the capabilities of the ground-based networks, and evaluating air quality models and the effectiveness of air quality control strategies.