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Volume 15, issue 5
Atmos. Chem. Phys., 15, 2405–2427, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 2405–2427, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Mar 2015

Research article | 05 Mar 2015

Estimating sources of elemental and organic carbon and their temporal emission patterns using a least squares inverse model and hourly measurements from the St. Louis–Midwest supersite

B. de Foy1, Y. Y. Cui2,3,1, J. J. Schauer4, M. Janssen5, J. R. Turner6, and C. Wiedinmyer7 B. de Foy et al.
  • 1Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO, USA
  • 2Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
  • 3Chemical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration (NOAA), Boulder, CO, USA
  • 4Civil and Environmental Engineering, University of Wisconsin, Madison, WI, USA
  • 5Lake Michigan Air Directors Consortium (LADCO), Rosemont, IL, USA
  • 6Energy, Environmental and Chemical Engineering Department, Washington University in St. Louis, St. Louis, MO, USA
  • 7National Center for Atmospheric Research, Boulder, CO, USA

Abstract. Emission inventories of elemental carbon (EC) and organic carbon (OC) contain large uncertainties both in their spatial and temporal distributions for different source types. An inverse model was used to evaluate EC and OC emissions based on 1 year of hourly measurements from the St. Louis–Midwest supersite. The input to the model consisted of continuous measurements of EC and OC obtained for 2002 using two semicontinuous analyzers. High resolution meteorological simulations were performed for the entire time period using the Weather Research and Forecasting Model (WRF). These were used to simulate hourly back trajectories at the measurement site using a Lagrangian model (FLEXPART-WRF). In combination, an Eulerian model (CAMx: The Comprehensive Air Quality Model with Extensions ) was used to simulate the impacts at the measurement site using known emissions inventories for point and area sources from the Lake Michigan Directors Consortium (LADCO) as well as for open burning from the Fire Inventory from NCAR (FINN). By considering only passive transport of pollutants, the Bayesian inversion simplifies to a single least squares inversion. The inverse model combines forward Eulerian simulations with backward Lagrangian simulations to yield estimates of emissions from sources in current inventories as well as from emissions that might be missing in the inventories. The CAMx impacts were disaggregated into separate time chunks in order to determine improved diurnal, weekday and monthly temporal patterns of emissions. Because EC is a primary species, the inverse model estimates can be interpreted directly as emissions. In contrast, OC is both a primary and a secondary species. As the inverse model does not differentiate between direct emissions and formation in the plume of those direct emissions, the estimates need to be interpreted as contributions to measured concentrations. Emissions of EC and OC in the St. Louis region from on-road, non-road, marine/aircraft/railroad (MAR), "other" and point sources were revised slightly downwards on average. In particular, both MAR and point sources had a more pronounced diurnal variation than in the inventory. The winter peak in "other" emissions was not corroborated by the inverse model. On-road emissions have a larger difference between weekday and weekends in the inverse estimates than in the inventory, and appear to be poorly simulated or characterized in the winter months. The model suggests that open burning emissions are significantly underestimated in the inventory. Finally, contributions of unknown sources seems to be from areas to the south of St. Louis and from afternoon and nighttime emissions.

Publications Copernicus
Short summary
Elemental carbon and organic carbon are components of fine particulate matter that are harmful to health. We use computer simulations of wind patterns and pollution dispersion to analyze a year-long time series of hourly measurements made at the St. Louis-Midwest supersite. The inverse method produced improved estimates of emissions of these pollutants by different types of sources such as on-road and off-road emissions and open burning.
Elemental carbon and organic carbon are components of fine particulate matter that are harmful...
Final-revised paper