Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory Kenneth Schere, Prakash Bhave, Roger Brode CMAS Conference October 13, 2010 Chapel Hill, NC AIR QUALITY MODELING

Major Types of Air Quality Models StatisticalDispersionGrid-BasedHybrid Attributes Dependent on observations Steady-state plume or puff models Gaussian distributions Dynamic Volume averages Complex chemistry Combined dispersion/grid models Space Scales Variable, depending upon obs network Generally, urban or metropolitan Mostly source-to urban-scale up to 50 km from source Urban to global Grid resolutions from 1 to 50 km Source to global scale Time Scales Hourly or longer averaging times Daily to multi- annual integrations Hourly or longer averaging times Daily to multi-annual integrations Examples Land-use or other regression Neural networks AERMOD ISC CMAQ CAMx CMAQ- AERMOD

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory grid cell

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Model-to-Monitor Comparison – Atlanta (JST)

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Atlanta NOx Emissions 4-km grids1-km grids

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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; NO 2 )

Curbside ~ 100 m from roadway Tailpipe 600 K300 K Tailpipe-to-RoadRoad-to-Ambient Ambient Processing Plume Processing Tailpipe-level Emission: The emission profiles near the exit of the tailpipe 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) Ambient Background ~ km 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): Specialized Models – e.g. near-road

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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development National Exposure Research Laboratory 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