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

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: http://www.epa.gov/castnet

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

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.

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

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

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

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

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

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

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

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

ACKNOWLEDGMENTS Funding provided by EPA Contract 68-W-03-033 Work assignments 2-15, 3-15 Project Manager: Bryan J. Bloomer