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During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to -0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NOz, NOy, and HNO3 biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 Were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NOx, emissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NOx, emissions, followed by VOCs and CO. PAN as a reservoir of NOx, can contribute to a significant amount of surface ozone through thermal decomposition. Copyright 2007 by the American Geophysical Union.

The impacts of BC and PM2.5 emissions from different source sectors (e.g. transportation, power, industry, residential, and biomass burning) and source regions (e.g. Europe, North America, China, Russia, Central Asia, South Asia, and the Middle East) to Arctic BC and PM2.5 concentrations are investigated using a series of sensitivity runs with WRF-STEM modeling framework. The simulations are validated using aircraft observations over the Arctic during spring and summer 2008. Emissions from power, industrial, and biomass burning sectors are found to be the main contributors to the Arctic PM2.5 with contributions of ~ 30 %, ~ 25 %, and ~ 20 % respectively. In contrast, the residential and transportation sectors are identified as the major contributors to Arctic BC with contributions of ~ 38 % and ~ 30 %. Anthropogenic emissions are the most dominant contributors (~ 88 %) to the BC surface concentration over the Arctic; however, the contribution from biomass burning is significant over the summer (up to ~ 50 %). Among all geographical regions, Europe and China have the highest contributions to the BC surface concentrations with contributions of ~ 46 % and ~ 25 % respectively. Further sensitivity runs show that among various economic sectors of all geographic regions, European and Chinese residential sector contribute up to ~ 25 % and ~ 14 % to the Arctic average surface BC concentration. For Arctic PM2.5, the anthropogenic emissions contribute >~ 75 % at the surface annually, with contributions of ~ 25 % from Europe and ~ 20 % from China; however, the contributions of biomass burning emissions are significant in particular during spring and summer. The contributions of each geographical region to the Arctic PM2.5 and BC vary significantly with altitude. The simulations show that the BC from China is transported to the Arctic in the mid-troposphere, while, BC from European emission sources are transported near the surface under 5 km, especially during winter. [ABSTRACT FROM AUTHOR]