HR: 1340h
AN: A33C-1410    [Abstracts]
TI: Validation of OMI NO2 Data to Enhance EPA Ground Network Data: An RPC Experiment
AU: Kleb, M M
EM: mary.m.kleb@nasa.gov
AF: NASA LaRC, Mail Stop 401B, Hampton, VA 23681-2199, United States
AU: * Pippin, M R
EM: m.pippin@nasa.gov
AF: NASA LaRC, Mail Stop 401B, Hampton, VA 23681-2199, United States
AU: Parker, P A
EM: peter.a.parker@nasa.gov
AF: NASA LaRC, Mail Stop 238, Hampton, VA 23681-2199, United States
AU: Rhew, R D
EM: ray.d.rhew@nasa.gov
AF: NASA LaRC, Mail Stop 489, Hampton, VA 23681-2199, United States
AU: Szykman, J J
EM: james.j.szykman@nasa.gov
AF: US EPA, Mail Stop 401A, Hampton, VA 23681-2199, United States
AU: Neil, D O
EM: d.neil@nasa.gov
AF: NASA LaRC, Mail Stop 401B, Hampton, VA 23681-2199, United States
AB: We present an RPC validation study to determine the potential use of OMI tropospheric NO2 column data to enhance spatial surface predictions of NO2 as an augmentation to the continuous NO2 ground network data collected by the State and Local Air Monitoring Stations (SLAMS) and National Air Monitoring Stations (NAMS) for the continental United States. Using one year of OMI and SLAMS/NAMS ground based data from the EPA's Air Quality System (AQS), NO2 values are compared using a variety of statistical techniques including a time series analysis at each EPA ground station in the continental United States, a site-by- site correlation analysis, site-by-site comparison of mean and standard deviation values, and regional (defined by the ten EPA regions) spatial statistics. In addition, a multivariate statistical prediction model with significance testing is developed to determine within a 95% confidence level the impact of concentration, latitude, region, season, environment (urban vs. rural), and pixel size on the correlation of OMI to EPA NO2 data. The robustness of the statistical model is evaluated using statistical methods. Results of this experiment quantify the ability to use OMI-derived NO2 observations to provide predicted surface concentrations to augment the coverage of the existing NO2 ground networks in regions of sparse or non-existent ground monitors. This predictive capability could facilitate a more capable and integrated observing network for NO2 and lead to more informed air quality management decisions at the local, state, and national level.
DE: 0345 Pollution: urban and regional (0305, 0478, 4251)
DE: 0365 Troposphere: composition and chemistry
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting