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Hybrid Approach to Estimating Total Deposition Using CMAQ and Monitoring Data

Hybrid Approach to Estimating Total Deposition Using CMAQ and Monitoring Data. Gary Lear Donna Schwede NADP Spring 2013 Madison, WI. Project Goals/Outputs:. Total, dry and wet deposition maps for all components for 2000-present Maps of inorganic N and S

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Hybrid Approach to Estimating Total Deposition Using CMAQ and Monitoring Data

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  1. Hybrid Approach to Estimating Total Deposition Using CMAQ and Monitoring Data Gary Lear Donna Schwede NADP Spring 2013 Madison, WI

  2. Project Goals/Outputs: • Total, dry and wet deposition maps for all components for 2000-present • Maps of inorganic N and S • Web accessible – will be available on CASTNET site Spring 2013 • Provided as ESRI grid products

  3. Review comments from Fall 2012 NADP meeting • Immediate Changes to Methodology: • Investigation of the ‘range of influence’ for HNO3 and NH3 • Inclusion of more measurements, if compatible, from SEARCH • Adjustment of concentration surface by the known bias • Maintain 4-km gridding for wet deposition

  4. Review comments from Fall 2012 NADP meeting • Immediate Changes to Methodology: • Investigation of the ‘range of influence’ for HNO3 and NH3 • Inclusion of more measurements, if compatible, from SEARCH • Adjustment of concentration surface by the known bias • Maintain 4-km gridding for wet deposition

  5. Why do we need Total Deposition maps? • Critical loads work is big driver • Biogeochemical models need a long time series for spin-up • CLAD creating national critical loads maps/database (FOCUS) • Other environmental analyses • Previous analyses have used simplified assumptions – e.g. wet/dry ratios which are incorrect

  6. Why hybrid and why CMAQ? • Need an operational method for estimating dry and total deposition without the limitations of constructing emission and meteorology profiles required for Eulerian models • 3-5 year time lag • Historical • Actual measurements are probably more accurate than models, at least at point of measurement, but we have spatial and chemical gaps in the measurements • CMAQ is what we have available • 57 years

  7. Monitoring Data • NADP/PRISM wet deposition • NH4, NO3 • CASTNET air concentrations • HNO3, SO2, pNO3, pNH4, pSO4 • AMoN air concentration • NH3 • SEARCH air concentrations • HNO3, SO2, NH3 • (Particulate data is mostly 1-in-3)

  8. CMAQ CDC PHASE Data (Public Health Air Surveillance Evaluation) runs • 2002-2006 (Appel et al, 2011) • CMAQ v4.7 – 36 km CONUS, 12k Eastern, 24 layers • MM5 meteorology, USGS 24 category land use • Emissions: 2002 NEI platform w/ year specific major pt sources, mobile sources (MOBILE6), fires • 2007-2008 (EPA/600/R-12/538, EPA/600/R-12/048) • CMAQ v4.7.1 – 12 km CONUS, 24 layers • WRF meteorology, NLCD 2001 land use • Emissions: 2005 NEI platform w/ year specific major pt sources, mobile sources (MOVES), fires

  9. Methodology • All calculations were done as rasters (NetCDF or ArcGRID) • Calculate hourly mean, min, max, and standard deviation for deposition velocity (Vd) and flux (Dq) for N and S species • 57 years of CMAQ runs (2002-2006, 2007-2008) • N=57 for each of 24-hours, 365 days • 3612-km grids • Aggregate CMAQ runs to same timeframe as monitoring data

  10. 3. Estimate weekly ambient air concentrations using monitoring networks • Ambient Networks • CASTNET:HNO3, SO2, pNO3, pNH4, pSO4 • SEARCH: HNO3, SO2 • Ammonia (SEARCH, AMoN) >2008 • Inverse-Distance Weighting (IDW) interpolation • 12km grids

  11. 4. Estimate weekly dry deposition • Multiply concentration grids and CMAQ Vd to create deposition product grids for measured species • CMAQ Vd merged from 5 years 36-km grid and 2 years 12-km grids

  12. 5. Create weekly deposition product weighting grids (Wd) • Linear inverse distance weighting (IDW) • Maximum distance • 288km (824 grid cells) for CASTNET species • 144km (412) for NH3 • Values range from 0 to 1.0 • Measurement values have greater weight closer to monitors • CMAQ weight • WCMAQ = 1-Wd

  13. 6. Merge the weekly deposition products and CMAQ deposition grids using IDW grids • Measurement values have greater weight closer to monitors • CMAQ is used to fill in interstitial spaces (WCMAQ =1, Wd=0)

  14. 6 ½. Adjust CMAQ dry deposition grids to account for systematic bias • Systematic bias is the consistent difference between observations (measurements) and prediction from model • Bias for each species has seasonal and spatial components

  15. 6 ½. Adjust CMAQ dry deposition grids to account for systematic bias • Seasonal differences in bias is more significant than interannual difference or trends

  16. 6 ½. Adjust CMAQ dry deposition grids to account for systematic bias Measured=2.0 • Compare the measured concentration with average CMAQ concentration to get the bias • Average of grid cells around the measurement point • 9 cells for the 36 km grids • 27 cells for the 12 km grids • Average bias for years2002-2010, by season Grid mean=1.4 Bias = 1.4/2 = 0.7

  17. 6 ½. Adjust CMAQ dry deposition grids to account for systematic bias Decreasing weight of bias • Create a surface of the average seasonal bias • Fitted a 5th order regression surface to smooth and minimize the difference between points • Assume grid cells > 500km from measurement point are unbiased (Bias=1) • Inverse distance weight bias from measurement point to 500km • Multiply the CMAQ deposition by the bias factor to get new CMAQ deposition values 500 km

  18. Average Seasonal Bias for HNO3 Fall Winter Summer Spring

  19. Total N 2010 Unadjusted Total N 2010 Bias Adjusted

  20. Total N 2010 Unadjusted Total N 2010 Bias Adjusted

  21. Winter Average Seasonal Bias for SO2 Fall Summer Spring

  22. Total S 2010 Unadjusted Total S 2010 Bias Adjusted

  23. 7. Sum the weekly dry deposition into annual aggregations for all measured species 8. Add the annual dry deposition and the unmeasured species • HONO, N2O5, NO, NO2, PAN, PANX, NTR 9. Sum dry deposition and PRISM-adjusted wet deposition from NADP/NTN for total deposition • 12 4-km grids

  24. Total N 2002

  25. Total N 2010

  26. Total S 2002

  27. Total S 2010

  28. Total N 2011

  29. What differences have these changes made? • Better resolution • The bias adjustment presented here made little difference in total deposition of either N or S • Other approaches may differ • Addition of SEARCH measurements has made modest increases in N and S in Southeast

  30. Caveats to be published with the Data • There is likely an incomplete characterization of the wet and dry organic N component resulting in an underestimate of total nitrogen deposition. • The bidirectional flux of NH3 is not accounted for in the CMAQ model runs. This results in underestimates of N deposition natural areas not close to croplands and overestimates in others. • Recent updates to the diurnal profile of emissions from confined animal feeding operations (CAFOs) is not included in the CMAQ runs. This likely causes reduced N deposition around CAFOs and increased downwind deposition. • NOx due to lightning was not included in the CMAQ runs. Since this would primarily affect the modeled wet deposition and observed wet deposition is being used, the overall impact is considered minimal. • AMoN data is only included in the methodology starting in 2008. As of the end of 2008, there were 21 sites in the network whereas there were 67 sites as of 2012. Users will notice an artifact in the maps where there appear to be drastic changes in reduced nitrogen deposition between 2007 and 2008 which result from the inclusion of this new data source. Additional artifacts may occur annually as new sites were added to the network. • The deposition velocities used in this method are averages and the deposition of unmeasured species (e.g. NO, NO2, HONO, PAN) is held constant in the method. This may underestimate the interannual variability of the nitrogen deposition. • Since the measurement sites used in the method are located in primarily rural areas, deposition in urban areas may not be well represented. • Interpolation techniques inherently minimize extreme values, so more variability would be expected if more spatially resolved observations were available for use.

  31. Next steps • Finalize maps • Publish to CASTNET website by June 1, 2013 • Journal paper to document (in prep) • TDEP to promote work on longer term research projects listed previously • Account for cross-correlation of concentration and Vd for weekly samples (not for June 1)

  32. Longer term research suggestions from Fall 2012 NADP meeting • Testing the range of influence with CMAQ and passives (Robin and John) • Use 12 km CMAQ vd’s for all years (Donna, John) • Move to finer scale of deposition (Rich, Donna) • Investigate NO3 Vd (John, Donna, Mike Barna) • Better characterization of organic N • Intercomparison of CMAQ/CAMx/AURAMS for 2009 (ROMANS II, more AMoN sites) (Donna, Mike, Bret, Eladio, Krish) • Research on other forms of Organic N. Review of Clegg and Wexler AE paper that was supported by EPRI (Mike, John) • Inclusion of NH3 bidirectional flux • One to Five year variation study (George, Gary, Donna)

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