Analyzing Subgrid Pollutant Variability in CMAQ Model using Stochastic Methods

1. Stochastic Description of Subgrid Pollutant Variability in CMAQ Jerold A. Herwehe Atmospheric Turbulence & Diffusion Division Air Resources Laboratory National Oceanic and Atmospheric Administration 456 S. Illinois Ave., P.O. Box 2456 Oak Ridge, Tennessee 37831-2456 (E-mail: Jerry.Herwehe@noaa.gov) Jason K. S. Ching and Jenise L. Swall NOAA/ARL/Atmospheric Sciences Modeling Division on assignment to USEPA/NERL Research Triangle Park, North Carolina

2. Motivation  Regional scale air quality (AQ) models are currently limited to relatively coarse (≥ 1 km) grids.  Emergency management, human exposure and risk assessment require more detailed information on hazardous pollutant, or air toxics, concentration “hot spots.” Research Objectives  Develop methodology and associated software tools to perform statistical analyses on available fine resolution gridded model results in order to quantify subgrid pollutant variability not represented in current AQ models.  Provide linkage between the Community Multiscale Air Quality (CMAQ) modeling system (Ching and Byun 1999; http://www.epa.gov/asmdnerl/models3/cmaq.html) and the Hazardous Air Pollutant Exposure Model (HAPEM; http://www.epa.gov/ttn/fera/human_hapem.html). Current Approach  Utilize objective Exploratory Data Analysis (EDA) approach (NIST/SEMATECH 2003) with companion freeware statistical analysis Dataplot package from NIST (http://www.itl.nist.gov/div898/software/dataplot/) to develop subgrid concentration analysis program dubbed CDFware (Concentration Distribution Function –ware).  Apply CDFware to sample AQ model output to produce probability density functions (pdfs).  CDFware conducts suite of statistical tests before determining best fit distribution.

3. Acetaldehyde Mean Mixing Ratio CMAQ 14 July 1995 15:00 LST (1.33 km)2 Grid Cells Acetaldehyde Mean Mixing Ratio CMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells Derived from (1.33 km)2 Grid Data of Figure (a)

4. CMAQ 14 July 1995 15:00 LST Acetaldehyde Histograms for (12 km)2 Grid Cells

6. Tukey-Lambda Shape Parameter for Acetaldehyde CMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells

8. Best-Choice Distribution for Acetaldehyde CMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells

11. CMAQ 14 July 1995 15:00 LST Acetaldehyde Histograms with Fitted Weibull PDFs

12. Weibull Maximum PPCC Value for Acetaldehyde CMAQ 14 July 1995 15:00 LST 12 km Grid Weibull Shape Parameter for Acetaldehyde CMAQ 14 July 1995 15:00 LST 12 km Grid Weibull Location Parameter for Acetaldehyde CMAQ 14 July 1995 15:00 LST 12 km Grid Weibull Scale Parameter for Acetaldehyde CMAQ 14 July 1995 15:00 LST 12 km Grid

14. Results and Conclusions  Concentration Distribution Function –ware (CDFware) tool was developed using EDA and Dataplot to statistically analyze fine resolution model output to determine best-fit distributions representing subgrid pollutant concentration variability.  Initial application of CDFware to example 1.33 km grid pollutant “data” from a CMAQ case study produced complex statistical results from numerous distribution family fits.  Restricting to a Weibull-only analysis did not produce any readily discernible spatial or temporal patterns in the PPCC, shape, location, or scale parameter fields.  Despite the current complexity of the CDFware results, these quantitative statistical products could still enhance the input stream to human risk and exposure models based on census tract scales. Extreme concentration values are represented in the distribution fits.  Development and refinement of CDFware will continue. Desirable additions include ability to detect and fit multimodal concentration distributions. CDFware will be applied to higher resolution output from neighborhood-scale coupled large-eddy simulation (LES)-photochemical model and computational fluid dynamics (CFD) simulations to possibly yield more coherent distribution parameter fields suitable for developing parameterizations of subgrid pollutant concentration variation within regional AQ model grid cells.

15. References Bury, K., 1999: Statistical Distributions in Engineering. Cambridge University Press, 362 pp. Ching, J., and D. Byun, 1999: Introduction to the Models-3 framework and the Community Multiscale Air Quality model (CMAQ). In Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, edited by D. W. Byun and J. K. S. Ching, EPA-600/R-99/030, Chapter 1, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. NIST/SEMATECH, cited 2003: NIST/SEMATECH e-Handbook of Statistical Methods. [Available online at http://www.itl.nist.gov/div898/handbook/.] Acknowledgments This research was supported by the National Oceanic and Atmospheric Administration’s Air Resources Laboratory and the U.S. Environmental Protection Agency’s National Exposure Research Laboratory. Disclaimer:This work has been reviewed in accordance with the United States Environmental Protection Agency’s peer and administrative review policies and approved for presentation and publication.