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Jason Ching NOAA/ARL/ASMD RTP, NC Ching.jason@epa

Fine scale air quality modeling using dispersion and CMAQ modeling approaches: An example application in Wilmington, DE. Jason Ching NOAA/ARL/ASMD RTP, NC Ching.jason@epa.gov. Collaborators. Mohammed A Majeed Delaware Department of Natural Resources and Environmental Conservation

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Jason Ching NOAA/ARL/ASMD RTP, NC Ching.jason@epa

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  1. Fine scale air quality modeling using dispersion and CMAQ modeling approaches: An example application in Wilmington, DE Jason Ching NOAA/ARL/ASMD RTP, NC Ching.jason@epa.gov

  2. Collaborators • Mohammed A Majeed Delaware Department of Natural Resources and Environmental Conservation New Castle, DE • Vlad Isakov Atmospheric Sciences Modeling Division, NOAA RTP, NC • Andrey Khlystov Duke University, Durham, NC

  3. Outline of Presentation • Background and Rationale of investigation • Conceptual Hybrid modeling approach • Description and role of EDATAS database to fine scale air toxic modeling • Future Plans

  4. For many air quality model applications, while desireable, current models are typically unable to resolve both regional and local scale features. The problem: For every grid resolution, there are unresolved sub-grid features: Sub-grid concentration distribution CMAQ 4x4 km grid Local details within a grid cell Detailed fine-scale measurements are needed for model evaluation

  5. Example of regional and local model results Hybrid approach (CMAQ with local scale modeling) 4 km CMAQ only CMAQ and Hybrid results for Philadelphia study reflecting enhanced details to CMAQ from incorporating local scale modeling with the hybrid approach

  6. A Hybrid Approach to Provide Local Details from Dispersion Model to CMAQ*: CMAQ (Hybrid) = CMAQ + Local (Fine Scale Details) * Paper “Using CMAQ for Exposure Modeling and Characterizing the Sub-Grid Variability for Exposure Estimates “ Isakov, Irwin and Ching; Accepted for Publication in JAMC CMAQ 4x4 km grid Fine scale details from a dispersion model

  7. Hybrid approach provides a means for introducing fine scale concentration detail to regional scale model simulations • This approach needs to be evaluated • A collaboration of opportunity! • Field study monitoring program, EDATAS, Enhanced Delaware Air Toxics Assessment Study focus on Wilmington, DE • Concurrent investigations to characterize fine scale fields of air toxics and PM • Continue investigations toward advancing the development of advanced air quality modeling tools for air toxics and other applications.

  8. A rich database of air toxics measurements to provide: • Better understanding of the micro-level air quality impacts of emission sources. • (2) Enhanced spatial resolution of the ambient toxics dataset throughout city and variability at spatial scales of order 100m by mobile measurements • (3) Means to evaluate our hybrid approach Measurements of Ozone, HCHO, Cr(VI), fine particles

  9. Grids for concentration distribution at neighborhood scales

  10. Neighborhood scale variability of formaldehyde(summer 2005) The X-axis shows the concentration of HCHO in mol/m3 The Y-axis shows the number of observations.

  11. COMMENTS on EDATAS • The EDATAS database of toxics (including HCHO and Cr(VI) measurements using mobile van transects provide a unique, albeit limited bases for characterizing air toxics at neighborhood scales. • Future efforts will focus on utilizing such data for evaluating and assessing advanced air toxics modeling such as hybrid modeling approaches.

  12. Fine scale model investigations for Wilmington, DE • Modeling is the only tool that provides a link between sources and ambient concentrations • Modeling at fine scales need evaluation. EDATAS will be used for this purpose. • Model ready detailed emission inventory has been prepared, including road-linked emissions data. • Air toxics version of CMAQ to be run in nested mode to 1 km grid size • Local scale modeling (using AERMOD) applied to local emission sources, thus providing sub grid information to CMAQ at 4 km or greater • Preliminary results follow: Example simulation is for a July, 2001 average.

  13. Example: Regional to fine scale using Nested grid CMAQ (benzene) Aurban Burban Crural A B C (A) 12 km (B) 4 km (C) 1 km

  14. Example of Time Series for HCHO CMAQ (12km) & 95th percentile from the 12x12 distribution (based on 1 km grid CMAQ results Grid cell “A” Urban(see previous slide) 0 2 4 6 8 10 HCHO Concentration (ppb) Jul 1 4 8 12 16 20 24 28 Aug 1

  15. Example of local contributions of HCHO using AERMOD. Information to be used with CMAQ as needed for the hybrid approach

  16. Next steps • Implement the hybrid approach by incorporating local scale with CMAQ modeling • Evaluate and assess both such outputs with EDATAS database (of mobile measurements) • Perform additional CMAQ modeling at 1 km grid size to investigate fine scale resolution of combined regional and local scale contributions to investigate the characteristics of SGV and corresponding distribution functions as a complement to CMAQ at grids sizes of 4 and 12 km. • Explore and examine the merits of applications that utilize (a) hybrid modeling approach as well as (b) an enhanced CMAQ system that is complemented with SGV distributions. e.g., • Performing exposure analyses, health risk assessments • Performing grid model evaluation • Weight of Evidence –RRF/DV(C&F) analyses for SIPS

  17. The EndThank you for your attention Disclaimer:The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.

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