Mingo and Caney CreekContribution Assessment: Mingo and Caney Creek Contribution Assessment Draft May 29, 2007


Slide2: . . . . . . Hercules Glade, MO Mountain Class I Areas Area of Influence Groupings 1. GRSM (5) 2. MACA 3. SHEN (4) 4. SIPS 5. SWAN 6. BRIG 7. MING (4)


Slide3: Upper Buffalo Caney Creek


Slide4: Upper Buffalo Hercules Glade


Slide5: Mingo


Objectives: Objectives Pollutant Contributions: 2000-2004 20% Best and Worst Days New IMPROVE equation Natural Background Calculations Glidepath and Progress in 2018 Emissions Sensitivities Areas of Influence Back Trajectory, Residence Time Source Sector Emissions List of Contributing Sources (states to supply)


Objectives: Objectives For VISTAS contribution assessment Mingo, MO, east of Hercules Glade, MO, is considered representative of VISTAS states’ impact Caney Creek, AR, east of Upper Buffalo, is considered representative of VISTAS states’ impact


Slide8: Extinction (Mm -1 )


Slide9: Average Extinction for 20% Best Days New IMPROVE Algorithm (nia) 2000-2004 0 10 20 30 40 50 60 SWAN1 ROMA1 OKEF1 EVER1 CHAS1 SAMA1 DOSO1 SHEN1 JARI1 LIGO1 SHRO1 GRSM1 COHU1 SIPS1 MACA1 BRIG1 BRET1 MING1 HEGL1 UPBU1 CACR1 VISTAS coastal VISTAS inland non-VISTAS Extinction (Mm -1 ) Sea Salt CM Soil EC POM Amm NO3 Amm SO4 Rayleigh


Slide10: Mingo, MO 2000-2004 Reconstructed Extinction New IMPROVE Algorithm 20% Worst Days


Slide11: Mingo, MO 2000-2004 Reconstructed Extinction New IMPROVE Algorithm 20% Best Days


Slide12: Caney Creek, AR 2000-2004 Reconstructed Extinction New IMPROVE Algorithm 20% Worst Days


Slide13: Caney Creek, AR 2000-2004 Reconstructed Extinction New IMPROVE Algorithm 20% Best Days


Conclusions: Contributions: Conclusions: Contributions On 20% Worst Days SO4 dominates light extinction most days Organic carbon smaller contribution; fire indicated on few days NO3 contribution on some winter days SO4 also dominates 20% Best Days Conclude: Focus on reducing SO2 emissions


New IMPROVE Equation: New IMPROVE Equation Endorsed by IMPROVE Steering Committee as accounting for latest science Defines two terms each for SO4, NO3, and OC with higher extinction efficiencies (bext) associated with high mass and lower bext associated with low mass Increases mass multiplier for organic carbon from 1.4 to 1.8 Adds term for fine mass sea salt Adds term for absorption due to NO2 (only if NO2 measurements available) Calculates site specific Rayleigh scattering


New IMPROVE Equation: New IMPROVE Equation Light scattering measured by nephelometer and calculated using new IMPROVE equation show good correlation Original equation under estimated scattering on highest days and over estimated scattering on lowest days New equation generally indicates higher extinction on 20% worst days and lower extinction on 20% best days


VISTAS 2018 Base G2 Visibility Projections (Delivered Mar 2007): VISTAS 2018 Base G2 Visibility Projections (Delivered Mar 2007) CMAQ Air Quality Model 2018 Run Accounts for Clean Air Interstate Rule (utility controls) Does not include controls for BART (Best Available Retrofit Technology) VISTAS states inventories as of Feb 2007 Inventories for neighboring states effective Aug 2006


Slide18: Model Performance 20% Haziest Days in 2002 Observations (left) vs Modeled Base G2a (right) Mingo, MO


Slide19: Modeled Responses to 2018 Base G2a Emissions on 20% Haziest Days Mingo, MO


Slide20: Mingo, MO - 20% Worst Days New IMPROVE equation, 12 and 36 km Uniform Rate of Progress Glide Path Uniform rate of progress = 3.6 dv by 2018 28.05 27.01 24.43 21.84 19.26 16.67 14.08 12.53 24.02 0 5 10 15 20 25 30 35 2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 Year Haziness Index (Deciviews) Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction


Slide21: Model Performance 20% Haziest Days in 2002 Observations (left) vs Modeled Base G2a (right) Caney Creek, AR


Slide22: Modeled Responses to 2018 Base G2a Emissions on 20% Haziest Days Caney Creek, AR


Slide23: Caney Creek, AR - 20% Worst Days New IMPROVE equation, 12 and 36 km Uniform Rate of Progress Glide Path Uniform rate of progress = 3.4 dv by 2018 26.36 25.38 22.93 20.48 18.03 15.58 13.13 11.66 22.34 0 5 10 15 20 25 30 35 2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 Year Haziness Index (Deciviews) Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction


VISTAS Source Sector Emissions Sensitivities (Delivered Jan 2006): VISTAS Source Sector Emissions Sensitivities (Delivered Jan 2006) Evaluated responses to emissions reductions for specific pollutants and source sectors Greatest visibility improvement from further reducing SO2 emissions from utilities and industries


Slide25: Caney Creek, AR (based on five 20% Worst days in 2002) -10.00 -9.00 -8.00 -7.00 -6.00 -5.00 -4.00 -3.00 -2.00 -1.00 0.00 1.00 SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires D B ext (Mm -1 ) Bio. Antro. BCs MRPO M-VU CEN VISTAS WV VA TN SC NC MS KY GA FL AL Responses to 30% reductions in 2009 emissions


Slide26: Mingo, MO (based on five 20% Worst Days in 2002) -12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00 2.00 SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires D B ext (Mm -1 ) Bio. Antro. BCs MRPO M-VU CEN VISTAS WV VA TN SC NC MS KY GA FL AL Responses to 30% reductions in 2009 emissions


Conclusion: Source Sector Emissions Sensitivities: Conclusion: Source Sector Emissions Sensitivities Reductions in SO2 emissions from EGU and non-EGU show largest improvements in visibility Several VISTAS states contribute Contributions from CENRAP, MRPO and Boundary Conditions (outside VISTAS 12 km domain) Small benefits from reducing NOx, anthropogenic VOC or primary carbon For 20% worst days that occur in winter, reducing NH3 would be more effective than reducing NOx to reduce NH4NH3


VISTAS Geographic Areas of Influence: VISTAS Geographic Areas of Influence Hysplit model used to generate back trajectories for Class I areas (Air Resource Specialists) Back trajectories for individual 20% worst days in 2002 Helpful for evaluating model performance in 2002 Residence time plots for 20% worst days in 2000-2004 indicate probable contribution Helpful to understand geographic area most likely to influence Class I areas SO2 Area of Influence defined from residence weighted by SO4 extinction and considering SO2 emissions


Slide29: Back Trajectories for 20% Worst Visibility Days in 2002 – Caney Creek, AR


Slide30: Residence Time for 20% Worst Days in 2000-2004 Caney Creek, AR


Slide31: 2018 SO2 Emissions in Area of Influence for 20% Worst Days Caney Creek, AR Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time andgt; 10%. Orange line perimeter indicate Area of Influence with Residence Time andgt; 5%.


Slide32: Back Trajectories for 20% Worst Visibility Days in 2002 – Mingo, MO


Slide33: Residence Time for 20% Worst Days in 2000-2004 Mingo, MO


Slide34: 2018 SO2 Emissions weighted by Residence Time Mingo, MO Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time andgt; 10%. Orange line perimeter indicate Area of Influence with Residence Time andgt; 5%.


Slide35: Annual 2018 BaseG2 Emissions (%) Within Area of Influence Mammoth Cave, KY


Slide36: Annual 2018 BaseG2 Emissions (%) Within Area of Influence Mammoth Cave, KY


Slide37: Annual 2018 BaseG2 Emissions (%) Within Area of Influence Mammoth Cave, KY


Slide38: Annual 2018 BaseG2 Emissions (%) Within Area of Influence Mammoth Cave, KY


Reasonable Progress Analysis: Reasonable Progress Analysis States consider 4 Statutory Factors to determine what controls are reasonable Costs of Compliance Time to Comply Remaining Useful Life Energy and Other Environmental and Impacts


4 Statutory Factors: 4 Statutory Factors For Utilities and Industrial Boilers Switch to fuel with lower sulfur content Coal or Oil Post-combustion controls Flue Gas Desulfurization Modification trigger PSD review?


4 Statutory Factors (continued) : 4 Statutory Factors (continued) Costs of Compliance Fuel switch for coal or oil May have to blend low S fuel to maintain boiler performance Price difference for lower S fuel Cost of boiler modifications for lower S fuel andlt;$1000/ton


4 Statutory Factors (continued): 4 Statutory Factors (continued) Costs of Compliance Flue Gas Desulfurization Construction costs: absorber tower, sorbent, waste handling facility Operational and maintenance costs Costs per ton vary with boiler size, type, facility Utility costs range $1,000 - $5,000/ton Industrial costs range $3,000 - $20,000+/ton


4 Statutory Factors (continued): 4 Statutory Factors (continued) Time for Compliance 2+ years for fuel switching 3+ years for post-combustion control (dependent on market and availability of labor and materials) Remaining Useful Life Facility specific


4 Statutory Factors (continued): 4 Statutory Factors (continued) Energy and Non-Air Environmental Impacts Lower sulfur fuel may affect boiler operations FGD slightly reduces energy production Burn more coal per unit energy produced Increase disposal of sludge, wastewater Increase carbon emissions CO2 is released as byproduct from CaSO4 formation


Mingo, Caney Creek, Hercules Glade, Upper Buffalo : Mingo, Caney Creek, Hercules Glade, Upper Buffalo CENRAP PSAT 2018 results similar to VISTAS CMAQ emissions sensitivity results Results based on 2002 modeling year CENRAP SO2 Area of Influence more northern and eastern influence than VISTAS AoI Different days, methods than VISTAS AoI MO and AR are asking neighboring states (TN and KY) to provide list of controls in 2018 (VISTAS Base G inventory) Unclear if MO and AR will ask TN and KY todo more than required for GRSM and MACA


Slide46: (delivered May 2006)


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