2. Session 5, CMAS 2004 INTRODUCTION: Fine scale modeling for Exposure and risk assessments

3. Session Coverage Background and concepts of fine scale modeling for exposure assessments Review neighborhood scale modeling paradigm Introduction to session presentations

4. Estimate 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 The Risk Assessment Paradigm: Source to concentration to exposure to dose to effects

6. Neighborhood-Scale Modeling PARADIGM Use of emission-based grid CMAQ modeling system to drive exposure and risk assessments, especially for photochemically active pollutants. Model within-grid (subgrid) concentration variability (SGV) as stochastic outputs for more robust exposure assessments Model output include gridded outputs of both CMAQ and its SGV distributions (CDFs) Model at scale of the assessments (grids comparable to census tract- hot spot analyses Urbanize the model system Hybrid approach: combination of CMAQ and local source dispersion modeling

7. Modeling air toxics with CMAQ at neighborhood scales CMAQ is run at 36-12-4 and O(1km) grids sizes and results assessed to perform urban scale transport for hot spot and other community based assessments. CMAQ provides the multi-pollutants simulation capability needed to handle numerous primary and secondary air toxics pollutants Studies of air quality simulations in urban areas at fine scale indicate strong sensitivity and thus, the need for improved model descriptions of land use and urban canopy features (UCPs) used in the mesoscale meteorology preprocessor, MM5. Initial guidance on subgrid spatial concentration distributions at coarse grid resolutions is provided using outputs from fine scale simulations.

8. Scope of Session: Key Features Urban Canopy Parameterizations in MM5 for Air Quality Simulations Linkage-to-exposure modeling studies Examples of Neighborhood Scale Air Quality Modeling Paradigm

9. Urban Canopy Parameterizations in MM5 for Air Quality Simulations - a brief summary Standard version of MM5 at fine resolution unable to accurately simulate the meteorology in urban areas Investigations with MM5 reconfigured with canopy layer formulations and advanced urban canopy parameterization underway. Preliminary results using this urbanized MM5 indicates a strong response of the the meteorology and air quality simulations in urban areas at fine scales. Details follow:

10. Drag Force approach Soil model for Sub-meso Urbanized Neighborhood Scale; Incorporating gridded urban canopy parameterizations (UCP) Dupont, Sylvain, T.L. Otte and J.K.S. Ching: ”Simulation of Meteorological Fields within and above Urban and Rural Canopies with a Mesoscale Model (MM5)”, Boundary-Layer Meteorology 113: 111-158, 2004

11. DA-SM2-U

12. 11 Roof area density Vegetatio n area density Building plan area density Vegetatio n plan area density Building frontal area density The knowledge of the vertical and horizontal distribution of the different surface types is necessary. Urban morphology

13. Example of building data from airborne LIDAR

14. Houston Case Study Texas 2000 AQ Study - August 30, 2000 Standard MM5 in one way nested configuration (108, 36, 12, 4 km grid sizes) UCP gridded at 1 km grid sizes available for modeling domain DA_SM2U-UCP in MM5 at 1 km

15. Results: MM5 Urbanized at 1 km grid resolution (With SST + ) (2000 GMT) DA_SM2-U-UCP Top row No UCP Bottom row Sensible heat fluxLeft side PBL heightRight side

17. Air Quality (CMAQ) Modeling ( driven by MM5 with SST + ) 1 km grid simulations –UCP version (DA_SM2-U-UCP) –noUCP versions 4 km grid simulations –Aggregated from 1 km –Native 4 km

18. 1 km (with UCP) and 4 km simulation (2100 GMT) Ozone: Top left: 1km Top right:4km Native NOx Bottom left: 1km Bottom Right:4km Native 1 km results are significantly more textured than 4 km The titrating effect of NOx on ozone (especially along highways and major point sources) are captured at 1km but not at 4 km Photochemistry at 4 km results in part from over- dilution of within- grid sources

19. Ozone 1 km simulation (2100 GMT) Left: UCPMiddle noUCPRight Difference (UCP-noUCP) Significant differences in the spatial patterns shownbetween UCP and noUCP runs (titration effect occurs in both sets) Meteorology (flow and thermodynamic) & turbulent fields do differ between the simulations

20. Ozone: Fine vs Coarse (2100 GMT) Aggregate:4 km mean Native 4 km Difference from 1km (with UCP) Aggregate - Native Differences between Mean 4km aggregated from 1 km vs Native 4km Accumulative and net of atmospheric processes acting on 1 km scale differs from that at 4km scale

21. Formaldehyde (ppb) (2100 GMT) Top left 1 km (with UCP)Top Right: Native 4km Bottom Right: RangeBottom Right: Range / Mean Potential to perform “hot spot” risk assessment Range: Exhibits large (SGV) sub- grid scale spatial variability (factor of 2 or more to its means for formaldehyde Magnitude of SGV is pollutant species specific

22. Key Points to MM5, CMAQ study Significant differences in MM5 and CMAQ results noted between urbanized vs non urbanized MM5 Fine scale modeling provides more realistic air quality simulations, especially with the more highly reactive photochemical species Fine scale modeling provide capability to resolve pollutant “hot spots” for accurate assessment of health risk. Differences are noted between coarse grid and results using fine-to-coarse aggregation; differences are likely due to contributions from cumulative and numerous atmospheric processes that operate differently at different scales.

23. Activities Further R&D using UCP in MM5 Pilot study: Operational aspects of linking CMAQ to Exposure models Community usage of CMAQ for toxic assessments Investigations on describing and parameterizing SGV concentration distributions.

24. Presentations in Session 5 Application of fine scale air toxics modeling with CMAQ to HAPEM5 (Ching, Pierce et al., –Linking annual CMAQ simulation at 36,12,4 km grid sizes to HAPEM5 –HAPEM5 at census tracts and inclusive of variability CMAQ modeling for air toxics at fine scales; a prototype study (Majeed et al., –CMAQ as Prototype for State’s air toxics assessments –Model study at fine scales (1 km grid size) Stochastic description of subgrid pollutant variability (Herwehe et al., –Development, description of PDFware –Examples of sub-grid concentration variability at coarse grids from fine scale model results