2003 CMAS Workshop Community Scale Air Toxics Modeling with CMAQ by Jason Ching ARL,NOAA & USEPA, RTP, NC, USA October 27-29, 2003

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

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

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

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

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.

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

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

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).

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

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

CO Jul 14, 95, 6pm (local)

NOx (Jul 14, 95) 6 pm local

Ozone (Jul 14,95) 6 pm local

Aldehydes (can) 6pm local HCHO CH3CHO

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.

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

NOx @7 EDT,(4 km grid) Top Left: Mean from 1 NOx @7 EDT,(4 km grid) Top Left: Mean from 1.3km, Bottom Left: Parent @ 4km RHS: Difference (Mean from Parent)

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

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

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

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

Skewness (12 km) Top LHS CO RHS Ozone Bottom LHS Acetaldehyde RHS NOx 07 EDT 16 EDT

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

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

Concentration Distribution CO Ozone NOx Acetaldehyde Formaldehyde

Generating gridded PDFs

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 . .

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

The End (Figure: curtesy of Alan Huber)