Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence MCIP2AERMOD: A Prototype Tool for Preparing Meteorological Inputs for AERMOD Neil Davis and Sarav Arunachalam Institute for the Environment University of North Carolina at Chapel Hill Roger Brode U.S. Environmental Protection Agency Presented at the 7 th Annual Models-3 CMAS Users Conference October 6-8, 2008

2 Motivation Meteorological fields are key inputs for air quality modeling NWS data typically used in AERMOD modeling have some limitations –Observed sites may be far from source location, espl. RAOB sites –Wind measurements at ASOS locations have large number of calm measurements –Gridded meteorological models potentially helpful For Hybrid (combining regional and local-scale) modeling, a consistent set of meteorology for CMAQ and AERMOD simulations is desirable –Avoids inconsistent meteorological fields confounding differences in AQM outputs EPA is exploring utilizing gridded met data and created MM5 – AERMOD tool, which was helpful in developing this tool Using MCIP outputs helps using either MM5 or WRF to drive AERMOD –No transition needed down the road

3 FAA Modeling Approach

4 Approach Created Fortran-based utility with EDSS/Models-3 I/O API library Process 2002 MM5 simulations at 12-km through MCIP 2.3 Use grid cell containing AERMOD source region for both surface and upper air fields –No interpolation is performed Make use of METCRO2D, METCRO3D, METDOT3D, and GRIDDESC files from MCIP output Use all available fields directly from MCIP output as these are the values CMAQ will be using Only calculate variables which are not in MCIP output Adjust for AERMOD time requirements (LST, some parameters require noon LST values)

5 Met Fields Directly from MCIP Sensible Heat Flux Surface Roughness Length Surface Friction Velocity Wind Speed / Direction Temperature Surface Pressure Cloud Fraction Monin Obukhov Length Convective Velocity Scale Convective Mixing Height Mechanical Mixing Height Notes: Some massaging of these variables performed –Maximum / minimum thresholds –Units conversion Mechanical Mixing height is both used directly and calculated –Calculated only for convective conditions

6 Fields Calculated in MCIP2AERMOD Mechanical Mixing Height Relative Humidity Potential Temperature gradient above convective mixing height (VPTG), or lapse rate above mixing height Bowen Ratio Albedo

7 AERMOD Study Location Red – Airport Location Blue – NWS Surface Site Green – RAOB Site T.F. Green Airport in Providence, Rhode Island

8 Evaluation Simulations Developed AERMOD simulations for several pollutants using both AERMET and MCIP2AERMOD meteorological outputs –Benzene, Formaldehyde, Primary EC, PM 2.5 –Will focus on PEC in this presentation Emissions inputs created using the FAA EDMS model to provide hourly emissions estimates of aircraft activity at airport 2002 NWS values were processed through AERMET with constant surface characteristics in time and space –Midday Albedo 0.5 –Daytime Bowen ratio 1.0 –Surface Roughness 0.1 Receptors were placed at the center of every census tract within a 50-km radius as well as at routine AQ monitor locations Our evaluation will look at comparing both meteorology fields as well as the AERMOD concentrations from both simulations Diurnal plots are calculated using averages of annual data

9 Evaluation of met. fields (1 of 4) Very high correlation between NWS and MCIP data Diurnal and monthly patterns match very well MCIP is slightly cooler overall Surface Temperature

10 Evaluation of met. fields (2 of 4) MCIP produces lower mixing heights at night than NWS, but higher mixing heights in general MCIP also produces higher mixing heights during summer months High correlation, but MCIP results seem to fall into discrete bins Mechanical Mixing Height

11 Evaluation of met. fields (3 of 4) Shows stronger winds with NWS data, both diurnal and seasonal NWS data is grouped into threshold values High correlation overall Wind Speed

12 Evaluation of met. fields (4 of 4) NWS has more calms MCIP has fewer high wind values Directionally MCIP shows more south westerly flow Wind Rose

13 Meteorology comparison Good agreement across most variables Comparison of vertical data unavailable, due to lack of data in the NWS simulation –AERMET only calculates the lowest level values when onsite data is not included Common meteorological parameters (i.e., Temp, pressure, winds) show more agreement than other parameters MCIP precipitation and clouds show large discrepancies compared to NWS values (not presented here)

14 Evaluation of AERMOD outputs for PEC (1 of 5) Only slight differences can be seen here Most perceivable changes occur away from the airport, deceptive due to log scale

15 Evaluation of AERMOD outputs for PEC (2 of 5) Significant changes in airport vicinity MCIP-based AERMOD shows higher concentrations Zoomed-in domain

16 Evaluation of AERMOD outputs for PEC (3 of 5) Maximum change of 0.1 ug/m 3 Largest changes to the North of the airport MCIP shown to have larger concentrations Annual Average Absolute Difference

17 Evaluation of AERMOD outputs for PEC (4 of 5) MCIP data always higher Largest differences in the winter months Comparison of Monthly Means

18 Evaluation of AERMOD outputs for PEC (5 of 5) NWS has more lower concentration values MCIP has higher maximum concentrations Median, 25 th and 75 th percentiles are similar Good correlation overall Additional comparisons

19 Discussion New prototype tool developed to use gridded meteorology (from either MM5 or WRF) for AERMOD Evaluation of tool performed for AERMOD study of T.F. Green (Providence) airport emissions of several pollutants Comparison of meteorological fields showed reasonable agreement for most variables –Only limited comparison of upper air data was possible MCIP2AERMOD meteorology lead to higher concentrations throughout the domain for PEC Despite magnitude differences, correlations were high between model simulations Evaluation of outputs for other pollutants showed similar patterns Evaluation of AERMOD outputs with RIDEM field study at T.F. Green is ongoing

20 Future Work Complete evaluation of AERMOD inputs and outputs using RIDEM field study data for 2005 Explore sensitivity of AERMOD to different physics options in MM5 (or WRF) Additional tests in MCIP2AERMOD –Include only noon time Bowen ratio –Investigate interpolation –Allow user to override surface parameters Set AERMET surface fields to be closer in agreement to the MCIP values and reevaluate Develop hybrid calculations of CMAQ and AERMOD using consistent meteorology

21 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the FAA, NASA or Transport Canada. This work was funded by the FAA, under Grant No.03-C-NE-MIT, Amendment No. 027 (w/ UNC-CH Subaward No ) 06-C-NE-MIT, Amendment No. 002 (w/ UNC-CH Subaward No ) 07-C-NE-UNC, Amendment No. 001 The Local Air Quality project is managed by Mohan Gupta. Acknowledgments