Air Toxics Modeling with CMAQ: Community-Scale Approach

1. 2003 CMAS WorkshopCommunity Scale Air Toxics Modeling with CMAQby Jason ChingARL,NOAA & USEPA, RTP, NC, USAOctober 27-29, 2003

2. Contributors • A. Lacser (Visiting Scientist from IIBR) • T. Otte (ARL,NOAA) • S. Dupont (UCAR Postdoc) • J. Herwehe (ATDD/ARL, NOAA) • R. Tang (CSC)

3. PROJECT CONTEXT:Air Quality, Exposure Modeling • NAAQS: Traditional threshold goals • Toxics: Risk Based Strategy Community assessments RISK ASSESSMENT PARADIGM Source-Concentration-Exposure-Dose-Effects

4. General Steps in Performing a Risk Assessment Emissions Obtain concentrations of chemical in the medium at distance of interest Determine exposure of the population of interest Calculate the risk of injury associated with that exposure

5. National/State County Level Community Level Neighborhood Level Exposure Assessment Can Be Done At Different Scales Organ Level Personal Level

6. Study Objectives, Approaches • Objective: Develop State-of-Science emissions-based, air quality grid modeling capability as tools to support (CAA-A90) NATA, air toxics community modeling exposure and risk assessments, and NAAQS implementations . • Approach I: Resolve air quality concentration distribution in urban areas at a horizontal scale resolution needed for (population) exposure models and for hot spots assessments (Census tract scale or finer). • Approach II: Model subgrid scale pollutant concentration distributions to complement the resolved scale fields as inputs to population and other exposure assessments.

7. Neighborhood Scale (N-S) Prototype Paradigm • Air Quality modeling at N-S provides value when significant variability is present at that scale. • Both Resolved and Subgrid concentration distributions are provided and needed for human exposure assessments • CMAQ provides grid resolved concentrations • Subgrid from local sources and photochemistry in turbulent flows to be modeled as PDFs of the concentration distribution histograms • Urban focus in N-S for population exposure needs

8. Urban canopy parameterization (UCP) modeling methodology • Introduce UCPs into MM5 (and increase number of vertical layers inside the urban canopy) • Lacser and Otte UCP methodology in MM5 • Modified DA-SM2U (with gridded UCP) in MM5 Prepare gridded UCP fields for MM5, CMAQ based on high resolution raster and vector database of building and vegetation and urban features

9. Philadelphia Case Study • 14 July 1995 (sunny day). • MM5 has been run in a one-way nested configuration: 108, 36, 12, 4 and 1.33 km horizontal grid spacing. • UCPs used only for the 1.33 km domain. • Turbulent scheme model: Gayno -Seaman PBL with the turbulent length scale of Bougeault and Lacarrere (1989).

10. Results and findings • I: MM5 & CMAQ sensitivity to UCP (See Dupont et al., details in session elsewhere in CMAS Workshop) • II. Concentration fields at different grid resolutions • III. Sub-grid spatial variability in coarse grid simulation using N-S results

11. II. Modeled Concentration Sensitivity to grid resolution • Top left: 36 km (no UCP) • Top Right: 12 km (no UCP) • Bottom left: 4 km (no UCP) • Bottom right: 1.3km (UCP applied) • July 14, 1995 @1800 EDT

12. CO Jul 14, 95, 6pm (local)

13. NOx (Jul 14, 95) 6 pm local

14. Ozone(Jul 14,95) 6 pm local

15. Aldehydes (can) 6pm localHCHO CH3CHO

16. III. Results of multi-scale analyses Statistics on sub-grid variability at 12 and 4 km grid cell resolution derived using outputs from 1.3 km grid (N-S) simulations Provides initial guidance on goal to develop generalized formulations for the gridded PDFs to represent subgrid variabilities, SGVs.

17. Ozone @ 4 PM EDT (12 Km)Top Left (Mean from 1.3): Bottom Left (Parent @ 12 km): RHS: Mean -Parent

18. NOx @7 EDT,(4 km grid)Top Left: Mean from 1.3km, Bottom Left: Parent @ 4kmRHS: Difference (Mean from Parent)

19. CO @ 07 EDT Top: 12 Km Grid Bottom: 4 Km GridGrid means Std Dev/ Mean

20. Formaldehyde@ 15 EDTTop (12 km grid), Bottom 4 km gridGrid means (from 1.33) Range-to-means

21. Formaldehyde@ 07 EDTTop (12 km grid), Bottom (TS for Central Philadelphia)Grid means (from 1.33) Range-to-means

22. Skewness (Formaldehyde) @ 07 EDT 12 km grid)Left: 1 grid west of CP; Center: Central Philadelphia (CP); Right: 1 grid east of CP

23. Skewness (12 km)Top LHS CO RHS OzoneBottom LHS Acetaldehyde RHS NOx07 EDT 16 EDT

24. Kurtosis (12 km)Top LHS CO RHS OzoneBottom LHS Acetaldehyde RHS NOx07 EDT 16 EDT

25. Acetaldehyde (07 EDT)(4km grid from 1.3 km simulations)Top: LHS Mean RHS Std DevBottom: LHS Skewness RHS Kurtosis

26. Concentration DistributionCO Ozone NOx AcetaldehydeFormaldehyde

27. Generating gridded PDFs

28. FINDINGS: Fine scale concentration distributions • Characteristics of spatial concentration distribution patterns is dependent on grid resolution; details of spatial features differs for different pollutant species. • Compositing N-S simulations to coarser scales yield different results when compared to coarse grid native simulations • N-S modeling (1.3 km) provides initial insights on sub-grid spatial concentration distributions at coarser grid sizes. (Preparatory to full method development for PDFs ) • Fine scale grid simulations provide indications of variability in coarser grid solutions: Variability dependent on the scale of the coarse grid mesh. • Initial survey of results: Distribution functions appear to be highly variable in space and time, pollutant and grid resolution . .

29. Studies in progress • Texas 2000 (Houston) air toxics neighborhood scale CMAQ study using DA SM2-U in MM5 • Develop methodology for generating concentration distribution functions • Develop and apply modeling approaches to determine the contribution of sub-grid variability in CMAQ at neighborhood scale grid resolution. • Linkage of AQ models with exposure models

30. The End (Figure: curtesy of Alan Huber)