1. 2003 CMAS Workshop Community Scale Air Toxics Modeling with CMAQ by Jason Ching ARL,NOAA & USEPA, RTP, NC, USA October 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. Exposure Assessment Can Be Done At Different Scales
National/State
County
Level
Neighborhood
Community
Exposure assessments can be done on many different scales. For example, the NATA analysis was performed to provide a national scale picture of air toxics across the country. Those results, however, are not very useful for describing the exposure to any one person.
If you want to better understand what is going on in a given place, you may need to do a refined assessment at the community, neighborhood, or even personal level.
To really understand what is actually happening in a person, you may even resort to monitoring or modeling what is actually going on in the body itself.
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, 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 local HCHO 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
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
EDT,(4 km grid) Top Left: Mean from 1.3km, Bottom Left: 4km RHS: Difference (Mean from Parent)

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

20. Formaldehyde@ 15 EDT Top (12 km grid), Bottom 4 km grid Grid means (from 1.33) Range-to-means

21. 07 EDT Top (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 Ozone Bottom LHS Acetaldehyde RHS NOx 07 EDT 16 EDT

24. Kurtosis (12 km) Top LHS CO RHS Ozone Bottom LHS Acetaldehyde RHS NOx 07 EDT 16 EDT

25. Acetaldehyde (07 EDT) (4km grid from 1
Acetaldehyde (07 EDT) (4km grid from 1.3 km simulations) Top: LHS Mean RHS Std Dev Bottom: LHS Skewness RHS Kurtosis

26. Concentration Distribution CO Ozone NOx Acetaldehyde Formaldehyde

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)