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AIR QUALITY MODELING

AIR QUALITY MODELING. Kenneth Schere, Prakash Bhave, Roger Brode CMAS Conference October 13, 2010 Chapel Hill, NC. Major Types of Air Quality Models. grid cell. Statistical Models. Depend upon local air quality observations e.g., land-use regression models

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AIR QUALITY MODELING

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  1. AIR QUALITY MODELING Kenneth Schere, Prakash Bhave, Roger Brode CMAS Conference October 13, 2010 Chapel Hill, NC

  2. Major Types of Air Quality Models

  3. grid cell

  4. Statistical Models • Depend upon local air quality observations • e.g., land-use regression models • Rely on density of observations and land-use data to define air quality gradients • Specific to a given area and time • Cannot be generalized or extrapolated

  5. Emissions-Based Models • Independent of local measurements • Can be generalized for applications in space and time • Can be extrapolated to future conditions • Subject to difficult-to-quantify errors and biases • Emissions, meteorology, computational/process algorithms

  6. Dispersion Models • e.g., AERMOD and others • Local-scale modeling • Single-source or source complex; near-field (< 50 km range) • Neighborhoods • Explicit parameterizations of local turbulence and dispersion • Requires on-site meteorological data or meteorological modeling • Requires local source emissions • Generally used for passive gases and aerosols • Uses a specified receptor grid for concentration estimates

  7. Model-to-Monitor Comparison – Atlanta (JST)

  8. Grid-Based Air Quality Modeling Systems • e.g., CMAQ, CAMx, WRF-Chem, among others • System of linked models • meteorology  emissions  air quality • Scalable • global  continental  regional  urban • Variability of concentration estimates increase with increasing model resolution • Applied to passive and reactive trace gases and aerosols • Used fixed 3-D grid system for concentration estimates

  9. Atlanta NOx Emissions 4-km grids 1-km grids

  10. Grid-Based Air Quality Modeling Systems - Limitations • Volume average estimates; not points • Complex systems of models • Subject to greater parametric uncertainty • Applications are resource-intensive • Model trouble-shooting/ diagnostics can be difficult

  11. Hybrid Modeling Systems • Combines strengths of different types of models • Examples: • Plume-in-grid techniques • Regional grid models + subgrid plume enhancement for major point sources (e.g., CAMx + P-in-G; CMAQ+APT) • Linked models • CMAQ for regional characterization + AERMOD for local hotspots (e.g., benzene; NO2)

  12. Specialized Models – e.g. near-road 600 K 300 K 300 K Ambient Background ~ km ~ 100 m from roadway Curbside Tailpipe Tailpipe-to-Road Road-to-Ambient Ambient Processing Plume Processing Road-level Emission: The emission profiles on or near the roadway curb Grid-level Emission: The emission profiles near the end of plume processing (particle dynamics slows down significantly at this point) Tailpipe-level Emission: The emission profiles near the exit of the tailpipe Zhang, K. M., A. S. Wexler, et al. (2005). "Evolution of particle number distribution near roadways. Part III: Traffic analysis and on-road size resolved particulate emission factors." Atmospheric Environment 39(22): 4155-4166.

  13. Modeling the Particle Size Distribution Near a Roadway (K.M. Zhang et al., Atmos. Env. 2004) Modeled with Dilution Only Full Particle Dynamics Model Particle dynamics (e.g., condensation & evaporation) are important. When those processes are neglected, size distribution is simulated poorly.

  14. Model Evaluation • A necessary step in building confidence in a model application • Requires observed data at appropriate spatial/temporal scale as the model • Observed data need to be characterized in terms of uncertainties and representativeness • Confidence in model results increase as the rigor of the evaluation increases • Operational  diagnostic  dynamic

  15. Closing Comments • Air quality models are most adept for assessing relative changes • i.e., how do ambient concentrations change as a result of changes in meteorology, climate, land-use, emissions, etc. • The larger the spatial and temporal scales of model integration, the more confidence in the model results • Absolute predictions at a particular place and time are the most uncertain model estimates

  16. Closing Comments • Models can predict the local concentration gradients based on the given emissions distribution • Probabilistic use of model results • Model estimates as concentration distributions • Multi-model ensembles • Combined use of models and observations for assessments is optimal

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