1. Using CMAQ to Interpolate Among CASTNET Measurements
Betty Pun and Christian Seigneur
Atmospheric and Environmental Research, Inc.
San Ramon, CA
CMAS Conference
Chapel Hill, NC
18 October 2006

2. MOTIVATION Number of monitors limited for any network (e.g., CASTNET)
Traditional interpolation approaches (e.g., inverse distance weighting method) have limitations
In areas without monitor coverage
impossible to provide estimates
In areas with limited coverage
undue influence of a single monitor
missing or crude gradient information
source:

3. GOAL
Chemical transport models (e.g., CMAQ) provide concentration fields on a uniform grid, accounting for
topography
emission density
land use
meteorology
current science regarding transformations
An interpolation scheme that uses CTM to provide gradient information between point measurements

4. MATCHING SPECIES CASTNET quantities CMAQ variables Notes PM sulfate
SO4
ASO4I+ASO4J 1
sulfur dioxide
totalSO2
SO2
total nitrate
totalNO3
ANO3I+ANO3J+HNO3
PM ammonium
NH4
ANH4I+ANH4J
1,2
PM sulfate dry deposition
SO4_FLUX
ASO4Idd+ASO4Jdd
1,3
sulfur dioxide dry deposition
SO2_FLUX
SO2dd 3
total nitrate dry deposition
NO3_FLUX+HNO3_FLUX
ANO3Idd+ANO3Jdd+HNO3dd
PM ammonium dry deposition
NH4_FLUX
ANH4Idd+ANH4Jdd
1,2,3
1. particle size range may not correspond exactly because open-face filters do not have size cut off but model simulates Aitken and accumulation modes.
2. may be subject to volatilization.
3. deposition velocity estimates may differ between CASTNET and CMAQ.

5. INTERPOLATION METHOD Guiding Principles
At locations with measurements
interpolated concentrations equal measured concentrations
At locations with no nearby concentrations
interpolated concentrations equal modeled concentrations
At locations near one or more monitors
interpolated concentrations governed by magnitudes of concentration(s) at nearby monitor(s) AND gradients in the modeled concentration field

6. INTERPOLATION METHOD Formulation
Error at each site (ksite)
Interpolated concentration at center of each grid cell (icell, jcell)
Error at center of each grid cell
Weight of each site error term
if ricell,jcell,ksite < rinfluence
if ricell,jcell,ksite > rinfluence

7. INTERPOLATION METHOD Parameter: Radius of Influence
Ensure all locations within the contiguous U.S. is influenced by at least one CASTNET site
rinfluence = 720 km

8. INTERPOLATION METHOD Parameter: Number of Virtual Sites
Eksite = 0
ricell,jcell,ksite = rinfluence
temper the influence of single monitors where sites are sparse
avoid abrupt changes in interpolated concentrations when transitioning from monitor to model values
nvirtual= 0
nvirtual= 4

9. RESULTS Annual Concentrations (mg/m3)
Sulfate
model added gradients away from OH River Valley
Total Nitrate
model added gradients from lower Midwest
urban areas highlighted

10. RESULTS Dry Deposition Fluxes (kg/ha)
Sulfate
dominated by measurements
predictions biased low
Total Nitrate
model adds strong gradients and spatial variability

11. EVALUATION PM Sulfate 2001 Number of IMPROVE sites = 93
Interpolated CASTNET field versus IMPROVE measurements
Number of IMPROVE sites = 93
Mean observation = 1.63 mg/m3
Mean prediction = 1.67 mg/m3
Mean bias = 0.03 mg/m3
Mean normalized bias = 7%
Mean error = 0.18 mg/m3
Mean normalized error = 14%
r2 = 0.96
Comparable performance for eastern vs. western U.S.
Comparable performance for different seasons

12. Models can be used to interpolate measurements effectively
CONCLUSIONS
Models can be used to interpolate measurements effectively
realistic gradients
add information where there are sharp gradients not captured by monitors
For sulfate, evaluation shows interpolation to be reliable for annual and seasonal concentrations for non-urban areas
Interpolation subject to potential weaknesses in models

13. ACKNOWLEDGMENTS
Funding provided by EPA
Contract 68-W
Work assignments 2-15, 3-15
Project Manager: Bryan J. Bloomer