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Hickory and Triad PM2.5 SIP Development Stakeholder Meeting Presented By: NC Division Of Air Quality Attainment Planning Branch Hosted At: Piedmont Authority.

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Presentation on theme: "Hickory and Triad PM2.5 SIP Development Stakeholder Meeting Presented By: NC Division Of Air Quality Attainment Planning Branch Hosted At: Piedmont Authority."— Presentation transcript:

1 Hickory and Triad PM2.5 SIP Development Stakeholder Meeting Presented By: NC Division Of Air Quality Attainment Planning Branch Hosted At: Piedmont Authority for Regional Transportation Offices November 14, 2007

2 Meeting Outline Fine Particulate Matter Background Fine Particulate Matter Background Air Quality Modeling Overview Air Quality Modeling Overview Emissions Inventory Development Emissions Inventory Development Model Performance Model Performance Attainment Test Attainment Test General Insignificance of PM2.5 Species General Insignificance of PM2.5 Species Clean Air Act Requirements Clean Air Act Requirements Motor Vehicle Emissions Budgets Motor Vehicle Emissions Budgets Summarize / Next Steps Summarize / Next Steps

3 Fine Particulate Matter Background Air Quality Modeling Overview Emissions Inventory Development George Bridgers, NCDAQ Meteorologist II Acting Chief of Attainment Planning

4 Human Hair (70 µm diameter) Hair cross section (70 m) PM 2.5 (2.5 µm) PM 10 ( 10µm ) M. Lipsett, California Office of Environmental Health Hazard Assessment A complex mixture of extremely small particles and liquid droplets Particulate Matter: What is It?

5 Public Health Risks Are Significant Particles are linked to: Premature death from heart and lung disease Premature death from heart and lung disease Aggravation of heart and lung diseases Aggravation of heart and lung diseases Hospital admissions Hospital admissions Doctor and ER visits Doctor and ER visits Medication use Medication use School and work absences School and work absences And possibly to And possibly to Lung cancer deaths Lung cancer deaths Infant mortality Infant mortality Developmental problems in children, such as low birth weight Developmental problems in children, such as low birth weight

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8 Typical PM Size Distribution

9 2002

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12 National Ambient Air Quality Standard (NAAQS) Annual PM2.5 NAAQS Annual PM2.5 NAAQS A monitor is violating the annual standard, if the annual design value is > 15.0 µg/m 3 A monitor is violating the annual standard, if the annual design value is > 15.0 µg/m 3 The annual design value is defined as: The annual design value is defined as: Annual mean concentration averaged over 3 yearsAnnual mean concentration averaged over 3 years Daily PM2.5 NAAQS Daily PM2.5 NAAQS A monitor is violating the daily standard, if the daily design value is > 35 µg/m 3 A monitor is violating the daily standard, if the daily design value is > 35 µg/m 3 The daily design value is defined as: The daily design value is defined as: Annual 98 th percentile concentrations averaged over 3 yearsAnnual 98 th percentile concentrations averaged over 3 years

13 North Carolina Areas Designated Nonattainment for PM – 2003 Design Value Catawba – 15.5 µg/m3 Davidson – 15.8 µg/m3 Guilford – 14.0 µg/m3

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15 PM2.5 Nonattainment Timeline Effective date = SIP submittal date = Attainment date = Data used to determine attainment = (Modeling) Attainment year = Maintenance years = April 5, 2005 April 5, 2008 April 5, 2010* TBD * Or as early as possible

16 VISTAS / ASIP Visibility Improvement State and Tribal Association of the Southeast Visibility Improvement State and Tribal Association of the Southeast Association of Southeastern Integrated Planning Association of Southeastern Integrated Planning Collaborative effort of States and Tribes to support management of regional haze, and attainment demonstrations for fine particulate matter and ozone nonattainment areas in the Southeastern US Collaborative effort of States and Tribes to support management of regional haze, and attainment demonstrations for fine particulate matter and ozone nonattainment areas in the Southeastern US No independent regulatory authority and no authority to direct or establish State or Tribal law or policy. No independent regulatory authority and no authority to direct or establish State or Tribal law or policy.

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18 NC / SC SIP Coordination Working together in VISTAS / ASIP Working together in VISTAS / ASIP Making use of VISTAS 2002 meteorological, emissions and air quality modeling Making use of VISTAS 2002 meteorological, emissions and air quality modeling Future year (2009) work completed through ASIP Future year (2009) work completed through ASIP Control strategies for the Metrolina area developed through a consultation process involving NCDAQ, SCDHEC and appropriate stakeholders Control strategies for the Metrolina area developed through a consultation process involving NCDAQ, SCDHEC and appropriate stakeholders

19 Air Quality Modeling System Meteorological Model Emissions Processor Air Quality Model MM5 SMOKE CMAQ Sparse Matrix Operator Kernel Emissions Community Multiscale Air Quality System Temporally and Spatially Gridded Air Quality Output predictions

20 Model Selection Meteorological Model Meteorological Model Mesoscale Meteorological Model (MM5) Mesoscale Meteorological Model (MM5) Emissions Model Emissions Model Sparse Matrix Operator Kernel Emissions (SMOKE) Sparse Matrix Operator Kernel Emissions (SMOKE) Air Quality Model Air Quality Model Community Multiscale Air Quality (CMAQ) model Community Multiscale Air Quality (CMAQ) model

21 Modeling Season / Episode Full Year of 2002 selected for VISTAS / ASIP modeling Full Year of 2002 selected for VISTAS / ASIP modeling Regional Haze / Fine Particulate: Full Year Regional Haze / Fine Particulate: Full Year The higher portion of the 2002 ozone season selected for the Attainment Demonstration modeling The higher portion of the 2002 ozone season selected for the Attainment Demonstration modeling No exceedances in April or October No exceedances in April or October Used modeling for May through September Used modeling for May through September

22 Emission Processing GriddingSpeciationTemporalEmission Inventory SMOKE Emission Model

23 Emission Source Categories Point sources: utilities, refineries, industrial sources, etc. Point sources: utilities, refineries, industrial sources, etc. Area sources: gas stations, dry cleaners, farming practices, fires, etc. Area sources: gas stations, dry cleaners, farming practices, fires, etc. On-road mobile sources: cars, trucks, buses, etc. On-road mobile sources: cars, trucks, buses, etc. Nonroad mobile sources: agricultural equipment, recreational marine, lawn mowers, construction equipment, etc. Nonroad mobile sources: agricultural equipment, recreational marine, lawn mowers, construction equipment, etc. Biogenic: trees, vegetation, crops Biogenic: trees, vegetation, crops

24 Emissions Inventory Definitions Actual = the emissions inventory developed to simulate what happened in 2002 Actual = the emissions inventory developed to simulate what happened in 2002 Used for model performance evaluation only. Used for model performance evaluation only. Typical = the emissions inventory developed to characterize the current emissions… It does not include specific events, but rather averages or typical conditions Typical = the emissions inventory developed to characterize the current emissions… It does not include specific events, but rather averages or typical conditions Only effects emissions from electric generating units and forest management/wild fires Only effects emissions from electric generating units and forest management/wild fires Future = the emissions inventory developed to simulate the attainment year 2009 Future = the emissions inventory developed to simulate the attainment year 2009

25 VISTAS / ASIP Actual 2002 Inventory Utilized Consolidated Emissions Reporting Rule (CERR) submittals for calendar year 2002 Utilized Consolidated Emissions Reporting Rule (CERR) submittals for calendar year 2002 Point, Area and select Nonroad mobile sources Point, Area and select Nonroad mobile sources Augment State data where pollutants missing Augment State data where pollutants missing Generate large forest management/wild fires as specific daily events Generate large forest management/wild fires as specific daily events Utility Emissions refined using actual Continuous Emissions Monitor (CEM) distributions Utility Emissions refined using actual Continuous Emissions Monitor (CEM) distributions On-road mobile processed through MOBILE6 module of SMOKE emissions system On-road mobile processed through MOBILE6 module of SMOKE emissions system Majority of Nonroad mobile emissions estimated using NONROAD2005c model Majority of Nonroad mobile emissions estimated using NONROAD2005c model Biogenic emissions estimated with BEIS3 model Biogenic emissions estimated with BEIS3 model

26 VISTAS / ASIP Typical 2002 Inventory Nonroad Mobile, On-road Mobile & Biogenic Sources Nonroad Mobile, On-road Mobile & Biogenic Sources Same as Actual 2002 Inventory Same as Actual 2002 Inventory Area Sources Area Sources Only forest management/wild fires changed Only forest management/wild fires changed Worked with Forest Service to develop typical fire inventory Worked with Forest Service to develop typical fire inventory Point Sources Point Sources Only utility emissions changed Only utility emissions changed Used 2000 – 2004 average heat input from CEM data to adjust 2002 emissions Used 2000 – 2004 average heat input from CEM data to adjust 2002 emissions

27 VISTAS / ASIP Typical 2009 Inventory Nonroad Mobile Sources Nonroad Mobile Sources Re-ran NONROAD2005c model for 2009 Re-ran NONROAD2005c model for 2009 Grew aircraft, locomotive and commercial marine engine emissions Grew aircraft, locomotive and commercial marine engine emissions On-road Mobile Sources On-road Mobile Sources Re-ran MOBILE module in SMOKE for 2009 Re-ran MOBILE module in SMOKE for 2009 Used transportation partners speed, vehicle miles traveled, etc Used transportation partners speed, vehicle miles traveled, etc Area Sources Area Sources Grew all sources except forest management/wild fire emissions Grew all sources except forest management/wild fire emissions Forest management/wild fire typical emissions kept constant Forest management/wild fire typical emissions kept constant Point Sources Point Sources Grew all sources except utility emissions Grew all sources except utility emissions Ran Integrated Planning Model (IPM) for projected utility emissions Ran Integrated Planning Model (IPM) for projected utility emissions Biogenic – same as 2002 emissions Biogenic – same as 2002 emissions

28 Controls Applied NO x SIP Call NO x SIP Call Seasonal NO x emission caps large industrial boilers Seasonal NO x emission caps large industrial boilers Clean Smokestacks Act Clean Smokestacks Act Effects North Carolina Duke Energy & Progress Energy sources Effects North Carolina Duke Energy & Progress Energy sources Year-round caps of NO x (2007 & 2009) and SO 2 (2009 & 2013) Year-round caps of NO x (2007 & 2009) and SO 2 (2009 & 2013) No trading allowed to meet caps No trading allowed to meet caps Required to submit compliance plan annually Required to submit compliance plan annually Clean Air Interstate Rule (CAIR) Clean Air Interstate Rule (CAIR) Year-round NO x (2009 & 2015) and SO 2 (2010 & 2015) caps for utilities Year-round NO x (2009 & 2015) and SO 2 (2010 & 2015) caps for utilities Allows for trading credits Allows for trading credits

29 Controls Applied (continued) Vehicle emissions testing Vehicle emissions testing Expanded from 9 to 48 Counties; Expanded from 9 to 48 Counties; All of the North Carolina Metrolina counties have I/M program All of the North Carolina Metrolina counties have I/M program Ultra-Low sulfur fuels Ultra-Low sulfur fuels Both diesel and gasoline Both diesel and gasoline Cleaner engines Cleaner engines Tier 2 vehicle standards Tier 2 vehicle standards Heavy duty gasoline & diesel highway vehicle standards Heavy duty gasoline & diesel highway vehicle standards Large nonroad diesel engine standards Large nonroad diesel engine standards Nonroad spark engine & recreational engine standards Nonroad spark engine & recreational engine standards

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34 Model Performance Evaluation Nick Witcraft, NCDAQ Meteorologist I

35 Meteorological Modeling Penn State / NCAR MM5 meso-scale meteorological model Penn State / NCAR MM5 meso-scale meteorological model Version Version Widely used in the research and regulatory communities Widely used in the research and regulatory communities VISTAS Contracted With Barons Advanced Meteorological Systems (BAMS) VISTAS Contracted With Barons Advanced Meteorological Systems (BAMS) Run at both 36km (Nationwide) and 12km (Southeastern US) resolutions for 2002 Run at both 36km (Nationwide) and 12km (Southeastern US) resolutions for 2002

36 Modeling Domains 36 km 12 km

37 Grid Structure Horizontal: 36 km & 12 km Vertical: MM5 = 34 layers SMOKE & CMAQ = 19 layers Layer 1 = 36 m deep Ground ~48,000 ft

38 Met Model Performance Model Performance For Key Variables: Model Performance For Key Variables: Temperature Temperature Moisture (Mixing Ratio & Relative Humidity) Moisture (Mixing Ratio & Relative Humidity) Winds Winds Precipitation Precipitation Summary Of Met Model Performance Summary Of Met Model Performance

39 Overall diurnal pattern captured very well Overall diurnal pattern captured very well Slight cool bias in the daytime Slight cool bias in the daytime Slight warm bias overnight Slight warm bias overnight Temperature

40 Little bias in summer, low bias in winter Little bias in summer, low bias in winter Lower error in summer, greater error in winter Lower error in summer, greater error in winter Temperature

41 Moisture (Mixing Ratio) Tracks observed trends fairly well Tracks observed trends fairly well Low bias in the morning through the early afternoon Low bias in the morning through the early afternoon High bias in the late afternoon and at night High bias in the late afternoon and at night

42 Moisture (Mixing Ratio) Negligible bias most of year; lowest in Sep/Oct Negligible bias most of year; lowest in Sep/Oct Higher error in summer Higher error in summer

43 High bias in the daytime High bias in the daytime Low bias at night Low bias at night RH is linked to temperature and moisture biases Moisture (Relative Humidity)

44 Slight high bias most of year Slight high bias most of year Low bias Sep-Nov Low bias Sep-Nov RH is linked to temperature and moisture biases Moisture (Relative Humidity)

45 ~1 mph high bias day, ~2 mph high bias at night ~1 mph high bias day, ~2 mph high bias at night Partly due to relative inability of winds in the model to go calm (There is always some wind) Partly due to relative inability of winds in the model to go calm (There is always some wind) Also due to starting thresholds of observation network… network cant measure winds < 3 mph, so winds < 3 mph are reported as calm Also due to starting thresholds of observation network… network cant measure winds < 3 mph, so winds < 3 mph are reported as calm Wind Speed

46 Improved performance when factoring out calm winds Improved performance when factoring out calm winds Bias and error fairly steady throughout the year Bias and error fairly steady throughout the year Wind Speed

47 ObservedPrecipJanuary ObservedPrecipApril ModeledPrecipJanuary ModeledPrecipApril

48 ObservedPrecipJULY ObservedPrecipOctober ModeledPrecipJULY ModeledPrecipOctober

49 Model Performance Statistics Meteorology In North Carolina Meteorological Quarterly Meteorological Statistics

50 Met Model Performance Model Performance For Key Variables: Model Performance For Key Variables: Temperature Temperature Moisture (Mixing Ratio & Relative Humidity) Moisture (Mixing Ratio & Relative Humidity) Winds Winds Precipitation Precipitation Summary Of Met Model Performance Summary Of Met Model Performance

51 Take Away Messages The 2002 meteorological model performance: The 2002 meteorological model performance: Compares favorably to the performance in similar modeling projects / studies, including that of EPA Compares favorably to the performance in similar modeling projects / studies, including that of EPA Can be considered State Of The Science Can be considered State Of The Science The precipitation biases would tend to inversely affect PM2.5 peaks in the AQ model: The precipitation biases would tend to inversely affect PM2.5 peaks in the AQ model: Under-predicted precip -> over-predicted PM2.5 (Fall) Under-predicted precip -> over-predicted PM2.5 (Fall) Over-predicted precip -> under-predicted PM2.5 (Apr-Sep) Over-predicted precip -> under-predicted PM2.5 (Apr-Sep) Slightly higher wind speeds -> dispersion of pollutants, under- prediction Slightly higher wind speeds -> dispersion of pollutants, under- prediction Low temp bias in winter -> more Nitrate formation??? Low temp bias in winter -> more Nitrate formation??? Moisture biases may impact secondary aerosol formation Moisture biases may impact secondary aerosol formation

52 Met Model Performance Brief questions before we proceed? Brief questions before we proceed? Please reference Appendix I of the PM2.5 Attainment Demonstration documentation for more exhaustive model performance metrics. Please reference Appendix I of the PM2.5 Attainment Demonstration documentation for more exhaustive model performance metrics.

53 Air Quality Modeling Community Multiscale Air Quality Model (CMAQ) Community Multiscale Air Quality Model (CMAQ) Version 4.5 (With SOA Modifications) Version 4.5 (With SOA Modifications) Widely used in the research & regulatory communities Widely used in the research & regulatory communities VISTAS Contracted With UC-Riverside, Alpine Geophysics LLC, and ENVIRON International Corp VISTAS Contracted With UC-Riverside, Alpine Geophysics LLC, and ENVIRON International Corp Run at both 36km (Nationwide) and 12km (Southeastern US) resolutions Run at both 36km (Nationwide) and 12km (Southeastern US) resolutions

54 PM2.5 Non-Attainment Area Monitors

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56 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables and Plots Statistical Tables and Plots Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

57 Model Performance Statistics PM2.5 – STN sites Hattie Ave (Forsyth County) Hickory (Catawba County)

58 Model Performance Statistics PM2.5 – Hickory STN Good SO 4, Total PM2.5 performanceGood SO 4, Total PM2.5 performance Poor NO 3 performancePoor NO 3 performance Goal Thresholds Bias: +-30% Error: 50% Criteria Thresholds Bias: +-60% Error: 75%

59 Model Performance Statistics PM2.5 – Hickory STN Good SO 4, Total PM2.5 performanceGood SO 4, Total PM2.5 performance Poor NO 3 performancePoor NO 3 performance Goal Thresholds Bias: +-30% Error: 50% Criteria Thresholds Bias: +-60% Error: 75%

60 Model Performance Statistics PM2.5 – Hickory STN Poor NO 3 performance due to low predicted values. Worst performance is in summer.Poor NO 3 performance due to low predicted values. Worst performance is in summer.

61 Model Performance Statistics PM2.5 – Hattie STN Good SO 4, Total PM2.5 performanceGood SO 4, Total PM2.5 performance Poor NO 3 performancePoor NO 3 performance Goal Thresholds Bias: +-30% Error: 50% Criteria Thresholds Bias: +-60% Error: 75%

62 Model Performance Statistics PM2.5 – Hattie STN Good SO 4, Total PM2.5 performanceGood SO 4, Total PM2.5 performance Poor NO 3 performancePoor NO 3 performance Goal Thresholds Bias: +-30% Error: 50% Criteria Thresholds Bias: +-60% Error: 75%

63 Model Performance Statistics PM2.5 – Hattie STN Good SO 4, Total PM2.5 performanceGood SO 4, Total PM2.5 performance Poor NO 3 performancePoor NO 3 performance

64 Model Performance Statistics PM2.5 – FRM sites FRM Monitoring Sites within the VISTAS 12km Domain.

65 Model Performance Statistics PM2.5 – FRM sites FRM Monitoring Sites within the VISTAS 12km Domain.

66 Model Performance Statistics PM2.5 – FRM sites Hickory (Catawba County)

67 Model Performance Statistics PM2.5 – FRM sites Hickory (Catawba County)

68 Model Performance Statistics PM2.5 – FRM sites Lexington (Davidson County)

69 Model Performance Statistics PM2.5 – FRM sites Lexington (Davidson County)

70 Model Performance Statistics PM2.5 – FRM sites Mendenhall (Guilford County)

71 Model Performance Statistics PM2.5 – FRM sites Mendenhall (Guilford County)

72 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables and Plots Statistical Tables and Plots Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

73 Model Performance Scatter Plots VISTAS STN SO 4 JanuaryJuly

74 Model Performance Scatter Plots VISTAS STN NO 3 JanuaryJuly

75 Model Performance Scatter Plots VISTAS STN OC JanuaryJuly

76 Model Performance Scatter Plots VISTAS STN EC JanuaryJuly

77 Model Performance Scatter Plots VISTAS STN NH 4 JanuaryJuly

78 Model Performance Scatter Plots VISTAS STN Total PM2.5 January July

79 Model Performance Scatter Plots NC STN SO 4 JanuaryJuly

80 Model Performance Scatter Plots NC STN NO 3 JanuaryJuly

81 Model Performance Scatter Plots NC STN OC JanuaryJuly

82 Model Performance Scatter Plots NC STN EC JanuaryJuly

83 Model Performance Scatter Plots NC STN NH 4 JanuaryJuly

84 Model Performance Scatter Plots NC STN Total PM2.5 JanuaryJuly

85 Model Performance Scatter Plots Hickory STN Total PM2.5 JanuaryJuly ***Speciated performance similar to all NC performance

86 Model Performance Scatter Plots Hattie Ave STN Total PM2.5 JanuaryJuly ***Speciated performance similar to all NC performance

87 Model Performance Scatter Plots VISTAS FRM Total PM2.5 JanuaryJuly

88 Model Performance Scatter Plots NC FRM Total PM2.5 JanuaryJuly

89 Model Performance Scatter Plots Hickory FRM Total PM2.5 JanuaryJuly

90 Model Performance Scatter Plots Lexington FRM Total PM2.5 JanuaryJuly

91 Model Performance Scatter Plots Mendenhall FRM Total PM2.5 JanuaryJuly

92 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables and Plots Statistical Tables and Plots Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

93 Hickory STN – Time Series

94 Model Performance Time Series Total PM2.5 Hickory STN ObsModel

95 Model Performance Time Series Sulfate (SO 4 ) Hickory STN ObsModel

96 Model Performance Time Series Nitrate (NO 3 ) Hickory STN ObsModel

97 Model Performance Time Series Elemental Carbon (EC) Hickory STN ObsModel

98 Model Performance Time Series Organic Carbon (OC) Hickory STN ObsModel

99 Model Performance Time Series Ammonium (NH 4 ) Hickory STN ObsModel

100 Hattie Ave STN – Time Series

101 Model Performance Time Series Total PM2.5 Hattie Ave STN ObsModel

102 Model Performance Time Series Sulfate (SO 4 ) Hattie Ave STN ObsModel

103 Model Performance Time Series Nitrate (NO 3 ) Hattie Ave STN ObsModel

104 Model Performance Time Series Elemental Carbon (EC) Hattie Ave STN ObsModel

105 Model Performance Time Series Organic Carbon (OC) Hattie Ave STN ObsModel

106 Model Performance Time Series Ammonium (NH 4 ) Hattie Ave STN ObsModel

107 Model Performance Time Series Hickory – FRM JanuaryJuly Obs Model – 36km, 12km

108 Model Performance Time Series Lexington – FRM JanuaryJuly Obs Model – 36km, 12km

109 Model Performance Time Series Mendenhall – FRM JanuaryJuly Obs Model – 36km, 12km

110 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables and Plots Statistical Tables and Plots Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

111 Example – July 16

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113 Example – August 3

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115 Example – February 25

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117 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables and Plots Statistical Tables and Plots Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

118 Stacked Bar Charts Hickory STN Jan-MarchApril-June

119 July-SepOct-Dec

120 Stacked Bar Charts Hattie Ave STN Jan-MarchApril-June

121 July-SepOct-Dec

122 AQ Model Performance VISTAS, NC Modeled PM2.5 Performance VISTAS, NC Modeled PM2.5 Performance Statistical Tables Statistical Tables Scatter Plots Scatter Plots Time Series (Selected Examples) Time Series (Selected Examples) PM2.5 Spatial Plots PM2.5 Spatial Plots Stacked Bar Charts (Speciation) Stacked Bar Charts (Speciation) Summary Of AQ (PM2.5) Model Performance Summary Of AQ (PM2.5) Model Performance

123 Summary Of AQ (PM2.5) Model Performance Under-predictions of the summer modeled total PM2.5 concentrations account for the majority of the negative bias and error. Under-predictions of the summer modeled total PM2.5 concentrations account for the majority of the negative bias and error. Overall performance was reasonably good for Sulfate (SO 4 ) and Organic Carbon (OC), the largest constituents of PM2.5. Overall performance was reasonably good for Sulfate (SO 4 ) and Organic Carbon (OC), the largest constituents of PM2.5.

124 Summary Of AQ (PM2.5) Model Performance There are not significant spatial or temporal errors with the modeled PM2.5 that held consistently throughout the 2002 PM2.5 Season. There are not significant spatial or temporal errors with the modeled PM2.5 that held consistently throughout the 2002 PM2.5 Season. Episodic air quality (PM2.5) cycles are well captured by the CMAQ air quality model with reasonable buildup and clean-out of PM2.5 concentrations. Episodic air quality (PM2.5) cycles are well captured by the CMAQ air quality model with reasonable buildup and clean-out of PM2.5 concentrations.

125 Thinking ahead to Typical and Future year modeling, Relative Reduction Factor (RRF) calculations, and the Modeled Attainment Test: Thinking ahead to Typical and Future year modeling, Relative Reduction Factor (RRF) calculations, and the Modeled Attainment Test: The relative sense of the SIP modeling will make the summer under-predictions of PM2.5 less significant and not influence strategy decisions. The relative sense of the SIP modeling will make the summer under-predictions of PM2.5 less significant and not influence strategy decisions. With the annual modeling strategy, there are a sufficient number of modeled days in this Base or Actual year modeling at each monitoring site throughout the year that contribute to the annual average >15 µg without the need for additional or alternative modeling. With the annual modeling strategy, there are a sufficient number of modeled days in this Base or Actual year modeling at each monitoring site throughout the year that contribute to the annual average >15 µg without the need for additional or alternative modeling. Summary Of AQ (PM2.5) Model Performance

126 AQ Model Performance Questions, comments, and discussion? Questions, comments, and discussion? Please reference Appendix J of the PM2.5 Attainment Demonstration documentation for the exhaustive list of model performance metrics for all scales/sites and relevant time periods. Please reference Appendix J of the PM2.5 Attainment Demonstration documentation for the exhaustive list of model performance metrics for all scales/sites and relevant time periods.

127 Attainment Test Bebhinn Do, NCDAQ Meteorologist II

128 What is a Modeled Attainment Demonstration? Analyses which estimate whether selected emissions reductions will result in ambient concentrations will meet NAAQS Analyses which estimate whether selected emissions reductions will result in ambient concentrations will meet NAAQS Identifies the set of control measures which will result in the required emissions reductions Identifies the set of control measures which will result in the required emissions reductions Use the Modeled Attainment Test to estimate future design values Use the Modeled Attainment Test to estimate future design values Additional weight of evidence analyses as needed to demonstrate attainment Additional weight of evidence analyses as needed to demonstrate attainment

129 What is the Modeled Attainment Test ? An exercise in which an air quality model is used to simulate current and future air quality near each monitoring site. An exercise in which an air quality model is used to simulate current and future air quality near each monitoring site. Model estimates are used in a relative rather than absolute sense. Model estimates are used in a relative rather than absolute sense. Future design values are estimated at existing monitoring sites by multiplying a modeled relative response factor at locations near each monitor times the observed monitor-specific design value. Future design values are estimated at existing monitoring sites by multiplying a modeled relative response factor at locations near each monitor times the observed monitor-specific design value. The resulting projected site-specific future design value is compared to NAAQS. The resulting projected site-specific future design value is compared to NAAQS.

130 Attainment Test DVF = RRF * DVB DVF = RRF * DVB DVF = Future Design Value DVF = Future Design Value RRF = Relative Response Factor RRF = Relative Response Factor DVB = Baseline Design Value DVB = Baseline Design Value RRF is based on modeled data DVB is based on observed data Future modeled values Future modeled values Current modeled values Current modeled values

131 Attainment Test For PM2.5 The DVF calculation is done for each component of PM2.5 (Sulfates, Nitrates, Ammonium, Elemental and Organic Carbon, Crustal, and Particle Bound Water), for each quarter. The DVF calculation is done for each component of PM2.5 (Sulfates, Nitrates, Ammonium, Elemental and Organic Carbon, Crustal, and Particle Bound Water), for each quarter. Since this test utilizes both PM2.5 and individual PM2.5 component species, it is referred to as Speciated Modeled Attainment Test, or SMAT. Since this test utilizes both PM2.5 and individual PM2.5 component species, it is referred to as Speciated Modeled Attainment Test, or SMAT. The quarterly components are then summed for a quarterly mean PM2.5 value. The quarterly components are then summed for a quarterly mean PM2.5 value. The four quarterly mean values are then averaged to get the future annual average PM2.5 estimate for each FRM site. The four quarterly mean values are then averaged to get the future annual average PM2.5 estimate for each FRM site.

132 Attainment Test For PM2.5 If the future annual average PM2.5 estimate is less than 15.0 µg/m 3, then the attainment test is passed. If the future annual average PM2.5 estimate is less than 15.0 µg/m 3, then the attainment test is passed. If all such future site-specific design values are: If all such future site-specific design values are: < 14.5 µg/m 3 the test is passed; Basic supplemental analyses should be completed to confirm the outcome of the modeled attainment test < 14.5 µg/m 3 the test is passed; Basic supplemental analyses should be completed to confirm the outcome of the modeled attainment test Between 14.5 µg/m 3 and 15.5 µg/m 3 ; A weight of evidence demonstration should be conducted to determine if aggregate supplemental analyses support the modeled attainment test Between 14.5 µg/m 3 and 15.5 µg/m 3 ; A weight of evidence demonstration should be conducted to determine if aggregate supplemental analyses support the modeled attainment test 15.5 µg/m 3, attainment test failed; More qualitative results are less likely to support a conclusion differing from the outcome of the modeled attainment test; additional controls are needed 15.5 µg/m 3, attainment test failed; More qualitative results are less likely to support a conclusion differing from the outcome of the modeled attainment test; additional controls are needed

133 SMAT Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 4: Calculate the the future year annual average PM2.5 estimate Step 4: Calculate the the future year annual average PM2.5 estimate DVF = RRF * DVB DVF = RRF * DVB

134 Step 1: Calculating the DVB The first part of the process is to calculate the quarterly mean PM2.5 concentration at the FRM sites: The first part of the process is to calculate the quarterly mean PM2.5 concentration at the FRM sites: A mean concentration is calculated for each quarter, and then a 5-year weighted quarterly average is calculated using the following weight scheme: A mean concentration is calculated for each quarter, and then a 5-year weighted quarterly average is calculated using the following weight scheme: DVB = (2000) + 2*(2001) + 3*(2002) + 2*(2003) + (2004) Values are average based on calendar quarters, where: Values are average based on calendar quarters, where: Q1 = January, February, March Q1 = January, February, March Q2 = April, May, June Q2 = April, May, June Q3 = July, August, September Q3 = July, August, September Q4 = October, November, December Q4 = October, November, December

135 Mean Quarterly PM2.5 values for the PM2.5 Nonattainment Areas Mean Quarterly PM2.5 values for the PM2.5 Nonattainment Areas Step 1: Calculating the DVB

136 The second part of the process is to calculate the component quarterly mean PM2.5 concentration at the FRM sites, which necessitates speciated data at these sites. The second part of the process is to calculate the component quarterly mean PM2.5 concentration at the FRM sites, which necessitates speciated data at these sites. Two issues: 1. Not all FRM monitoring sites have co-located STN speciation monitors. 2. FRM measurements and speciated PM2.5 measurements do not always measure the same mass Step 1: Calculating the DVB

137 Issue 1: FRM sites without co- located STN Sites EPA Guidance suggests: 1. Use of concurrent data from a near by speciated monitor 2. Use of representative data (from a different time period) 3. Use of interpolation techniques to create a spatial field using ambient speciation data 4. Use of interpolation techniques to create spatial fields, and gridded modeling outputs to adjust the species concentrations

138 Issue 1: FRM sites without co- located STN Sites The EPA developed software called Modeled Attainment Test Software (or MATS) will actually perform the spatial analysis of number 3 and 4. The EPA developed software called Modeled Attainment Test Software (or MATS) will actually perform the spatial analysis of number 3 and 4. However, MATS has not been delivered at this time. However, MATS has not been delivered at this time. As an alternative, we have used the speciated profiles from the CAIR SMAT tool, which is the predecessor for the MATS program. As an alternative, we have used the speciated profiles from the CAIR SMAT tool, which is the predecessor for the MATS program.

139 CAIR SMAT Tool

140

141 Issue 2: FRM Mass STN Mass Issue is that by design, FRM monitors do not retain all ammonium nitrate and other semi-volatile materials (negative artifact) and FRM samples include particle bound water associated with sulfates, nitrates, and other hygroscopic species (positive artifact) Issue is that by design, FRM monitors do not retain all ammonium nitrate and other semi-volatile materials (negative artifact) and FRM samples include particle bound water associated with sulfates, nitrates, and other hygroscopic species (positive artifact)

142 Neil Frank (2006) developed the sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance approach Neil Frank (2006) developed the sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance approachSANDWICH Issue 2: FRM Mass STN Mass

143 Adjust nitrate to account for volatilization Adjust nitrate to account for volatilization Calculate quarterly average nitrate, sulfate, EC, Degree of Neutralization (DON) of sulfate, and crustal Calculate quarterly average nitrate, sulfate, EC, Degree of Neutralization (DON) of sulfate, and crustal Calculate quarterly average NH 4 from adjusted NO 3, SO 4, and DON of sulfate Calculate quarterly average NH 4 from adjusted NO 3, SO 4, and DON of sulfate Calculate particle bound water from DON, sulfate, nitrate, and ammonium values Calculate particle bound water from DON, sulfate, nitrate, and ammonium values Calculate OC by difference from PM2.5 mass, adjusted nitrate, ammonium, sulfate, water, EC, crustal, and passive (blank) mass Calculate OC by difference from PM2.5 mass, adjusted nitrate, ammonium, sulfate, water, EC, crustal, and passive (blank) mass PM2.5 FRM = { [OCMmb] + [EC] + [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [crustal material] + [0.5] } PM2.5 FRM = { [OCMmb] + [EC] + [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [crustal material] + [0.5] } Issue 2: FRM Mass STN Mass

144 Nitrates - Adjusted use hourly temperatures and 24-hour average nitrate measurements Nitrates - Adjusted use hourly temperatures and 24-hour average nitrate measurements NH4 FRM = DON * SO *NO3 FRM NH4 FRM = DON * SO *NO3 FRM Particle Bound Water = PBW =( ) + ( *nh4) + ( *no3) + ( *so4) + ( *nh4**2) + ( *no3*nh4) + ( *no3**2) + ( *so4*nh4) *so4*no3) + ( *so4**2) Particle Bound Water = PBW =( ) + ( *nh4) + ( *no3) + ( *so4) + ( *nh4**2) + ( *no3*nh4) + ( *no3**2) + ( *so4*nh4) *so4*no3) + ( *so4**2) Crustal/Soil = 3.73 * [Si] *[Ca] *[Fe] *[Ti] Crustal/Soil = 3.73 * [Si] *[Ca] *[Fe] *[Ti] Organic carbon mass by difference Organic carbon mass by difference (OCmb) = PM2.5 FRM - { [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [crustal material] + [EC] + [0.5] } Issue 2: FRM Mass STN Mass

145 Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 4: Calculate the the future year annual average PM2.5 estimate Step 4: Calculate the the future year annual average PM2.5 estimate DVF = RRF * DVB DVF = RRF * DVB SMAT

146 Step 2: Calculating the relative reduction factor (RRF) RRF = the ratio of the models future to current projections near monitor x (quarterly mean component concentration near"monitor x) future (quarterly mean component concentration near"monitor x) future = (quarterly mean component concentration near monitor x) present (quarterly mean component concentration near monitor x) present

147 Step 2: Calculating the RRF Definition of near a monitor Definition of near a monitor EPA guidance recommends considering an array of values near each monitor EPA guidance recommends considering an array of values near each monitor Assume a monitor is at the center of the grid cell in which it is located and that cell is the center of an array of nearby cells Assume a monitor is at the center of the grid cell in which it is located and that cell is the center of an array of nearby cells Using a grid with 12 km grid cells, nearby is defined by a 3 x 3 array of cells, with the monitor located in the center cell Using a grid with 12 km grid cells, nearby is defined by a 3 x 3 array of cells, with the monitor located in the center cell

148 Days used in RRF calculation The entire year of modeling is used to calculate the component RRFs The entire year of modeling is used to calculate the component RRFs All 365 days are used in the calculation, and there is no concentration limit like with Ozone All 365 days are used in the calculation, and there is no concentration limit like with Ozone Step 2: Calculating the RRF

149 For the base year: A daily average mass of one of the component species of PM2.5 is calculated for each of the cells in the 3x3 grid array near the monitor A daily average mass of one of the component species of PM2.5 is calculated for each of the cells in the 3x3 grid array near the monitor These 9 cells are then averaged to produce a mean daily value for the component for the 3x3 array These 9 cells are then averaged to produce a mean daily value for the component for the 3x3 array All of the days in the each quarter are then averaged together to produce the quarterly mean component concentration All of the days in the each quarter are then averaged together to produce the quarterly mean component concentration Step 2: Calculating the RRF

150 This is then repeated for the future year. This is then repeated for the future year. The whole process is repeated for each component of PM2.5 (Sulfates, Nitrates, EC, OC, Crustal. Ammonium and PBW are calculated based on the DVF of the other components) The whole process is repeated for each component of PM2.5 (Sulfates, Nitrates, EC, OC, Crustal. Ammonium and PBW are calculated based on the DVF of the other components) Step 2: Calculating the RRF

151 Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 2: Use air quality modeling results to derive component- specific relative response factors (RRF) at each monitor for each quarter Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1 Step 4: Calculate the the future year annual average PM2.5 estimate Step 4: Calculate the the future year annual average PM2.5 estimate DVF = RRF * DVB DVF = RRF * DVB SMAT

152 Step 3: Compute the DVF Compute the quarterly component future design value (DVF) Compute the quarterly component future design value (DVF) Calculate the mass due to Ammonium and PBW Calculate the mass due to Ammonium and PBW Components are summed for each quarter to achieve quarterly future year PM2.5 mass Components are summed for each quarter to achieve quarterly future year PM2.5 mass The four quarters are then averaged to get a final future year annual average, which is compared to the NAAQS The four quarters are then averaged to get a final future year annual average, which is compared to the NAAQS

153 Results < 14.5 µg/m 3 the test is passed; Basic supplemental analyses < 14.5 µg/m 3 the test is passed; Basic supplemental analyses Between 14.5 µg/m 3 and 15.5 µg/m 3 ; A weight of evidence demonstration should be conducted Between 14.5 µg/m 3 and 15.5 µg/m 3 ; A weight of evidence demonstration should be conducted 15.5 µg/m 3 ; attainment test failed, need more controls 15.5 µg/m 3 ; attainment test failed, need more controls

154 Supplemental Analysis Modeling Metrics Modeling Metrics Results from other modeling studies Results from other modeling studies Observational analyses Observational analyses Emissions analyses Emissions analyses

155 Results from Other Studies Clean Air Interstate Rule (CAIR) modeling Clean Air Interstate Rule (CAIR) modeling EPA modeling done to quantify the benefits of CAIR EPA modeling done to quantify the benefits of CAIR Modeling based on 2001 meteorology Modeling based on 2001 meteorology DVB was a 5yr weight DV centered around 2001 ( ) DVB was a 5yr weight DV centered around 2001 ( ) For 2010: Catawba 14.07; Davidson For 2010: Catawba 14.07; Davidson For 2015: Catawba 13.45; Davidson For 2015: Catawba 13.45; Davidson 13.61http://www.epa.gov/interstateairquality/pdfs/finaltech02.pdf Modeling from other RPOs Modeling from other RPOs

156 Observational Analyses Design Values Trends

157 General Insignificance of PM2.5 Species Chris Misenis, NCDAQ Meteorologist I

158 Overview Overview NO x Insignificance NO x Insignificance NH 4 Insignificance NH 4 Insignificance VOC Insignificance VOC Insignificance General Insignificance of PM2.5 Species

159 Pollutants must be evaluated that contribute to PM2.5 attainment issue. Pollutants must be evaluated that contribute to PM2.5 attainment issue. Included constituents are SO 2, NO x, and Direct PM2.5. NH 3 and VOCs are deemed insignificant. Included constituents are SO 2, NO x, and Direct PM2.5. NH 3 and VOCs are deemed insignificant. Technical demonstrations are permitted to reverse the presumptions made about certain species. Technical demonstrations are permitted to reverse the presumptions made about certain species. Overview

160 SO 2, NO x, and Direct PM2.5 MUST be evaluated. SO 2, NO x, and Direct PM2.5 MUST be evaluated. Inclusion of NO x can be reversed if sufficient evidence exists. Inclusion of NO x can be reversed if sufficient evidence exists. Evidence may include: Evidence may include: Modeling Sensitivity Studies Modeling Sensitivity Studies Speciated Data Speciated Data Emissions Inventories Emissions Inventories Monitoring or Data Analysis Monitoring or Data Analysis Technical Demonstrations

161 NO x Insignificance

162

163

164

165

166

167 More prevalent in cooler seasons. More prevalent in cooler seasons. Less than 0.2 μg m -3 decrease annually at all three sites. Less than 0.2 μg m -3 decrease annually at all three sites. Based on evidence, claiming NO x as insignificant to PM2.5 attainment. Based on evidence, claiming NO x as insignificant to PM2.5 attainment. NO x Insignificance

168 NH 3 Insignificance

169

170 30% reduction more significant during winter season, leading to large annual decrease. 30% reduction more significant during winter season, leading to large annual decrease. However, 30% reduction in NH 3 emissions across entire domain reduces PM by less than 1 μg m -3. However, 30% reduction in NH 3 emissions across entire domain reduces PM by less than 1 μg m -3. Agree with EPA that NH 3 is insignificant to PM2.5 attainment. Agree with EPA that NH 3 is insignificant to PM2.5 attainment. NH 3 Insignificance

171 VOC Insignificance

172

173 VOCs have a significant impact on PM formation in NC. VOCs have a significant impact on PM formation in NC. However, biogenic VOCs are significantly more influential to PM formation than anthropogenic. However, biogenic VOCs are significantly more influential to PM formation than anthropogenic. Given current controls and inability to curtail all biogenic emissions, agree with EPA that VOCs are insignificant. Given current controls and inability to curtail all biogenic emissions, agree with EPA that VOCs are insignificant. VOC Insignificance

174 Clean Air Act Requirements Motor Vehicle Emissions Budgets Summary / Next Steps George Bridgers, NCDAQ Meteorologist II Acting Chief of Attainment Planning

175 Clean Air Act Requirements Reasonably Available Control Technology (RACT) Reasonably Available Control Technology (RACT) Reasonably Available Control Measures (RACM) Reasonably Available Control Measures (RACM) Reasonable Further Progress (RFP) Plan Reasonable Further Progress (RFP) Plan Emission Inventory Requirements Emission Inventory Requirements Permit Requirements Permit Requirements Contingency Measures Contingency Measures Transportation Conformity / Motor Vehicle Emissions Budgets (MVEBs) Transportation Conformity / Motor Vehicle Emissions Budgets (MVEBs)

176 Transportation Conformity To ensure Federal transportation actions occurring in nonattainment and maintenance areas do not hinder the area from attaining and/or maintaining the NAAQS To ensure Federal transportation actions occurring in nonattainment and maintenance areas do not hinder the area from attaining and/or maintaining the NAAQS MVEBs set a level of emissions that cannot be exceeded by expected emissions in Transportation Improvement Plans (TIPs) and Long Range Transportation Plans (LRTP) MVEBs set a level of emissions that cannot be exceeded by expected emissions in Transportation Improvement Plans (TIPs) and Long Range Transportation Plans (LRTP)

177 Both SO 2 and Direct PM2.5 must be addressed and controls measures evaluated in the PM2.5 attainment SIP. Both SO 2 and Direct PM2.5 must be addressed and controls measures evaluated in the PM2.5 attainment SIP. NCDAQ is working with EPA to potentially have On- Road Mobile SO 2 and Direct PM2.5 found insignificant to the PM2.5 concentrations in the respective non- attainment areas. NCDAQ is working with EPA to potentially have On- Road Mobile SO 2 and Direct PM2.5 found insignificant to the PM2.5 concentrations in the respective non- attainment areas. Having either or both found insignificant would remove them from consideration when setting the MVEBs in the SIP. Having either or both found insignificant would remove them from consideration when setting the MVEBs in the SIP. Mobile SO 2 & Direct PM2.5 Insignificance

178 On-Road Mobile is ~2.4% of the Total SO 2 On-Road Mobile is ~4.5% of the Total Direct PM2.5

179 On-Road Mobile is ~0.5% of the Total SO 2 On-Road Mobile is ~3.3% of the Total Direct PM2.5

180 On-Road Mobile is ~8.0% of the Total Direct PM2.5

181 Currently, it appears that NCDAQ will be able to successfully declare Mobile SO 2 insignificant in both Hickory and the Triad. Currently, it appears that NCDAQ will be able to successfully declare Mobile SO 2 insignificant in both Hickory and the Triad. Mobile Direct PM2.5 is more tenuous given higher percentages with respect to Total Direct PM2.5. Only Hickory appears possible for an insignificance determination. Mobile Direct PM2.5 is more tenuous given higher percentages with respect to Total Direct PM2.5. Only Hickory appears possible for an insignificance determination. Thus, MVEBs in the Triad will likely be set of Direct PM2.5. Thus, MVEBs in the Triad will likely be set of Direct PM2.5. Mobile SO 2 & Direct PM2.5 Insignificance

182 Motor Vehicle Emissions Budgets Geographic Extent Geographic Extent The MVEBs will be set at the county level The MVEBs will be set at the county level Primary PM2.5 MVEBs Primary PM2.5 MVEBs Established for the attainment year 2009 Established for the attainment year 2009 Set in kilograms/year Set in kilograms/year

183 Motor Vehicle Emissions Budgets Estimated MVEB emissions outside of Air Quality modeling Estimated MVEB emissions outside of Air Quality modeling Used updated speeds, VMT, vehicle mix and vehicle age distribution supplied by the transportation partners Used updated speeds, VMT, vehicle mix and vehicle age distribution supplied by the transportation partners Used average 2002 July temperatures Used average 2002 July temperatures OBD-II Inspection/Maintenance Program in all counties OBD-II Inspection/Maintenance Program in all counties RVP of 7.8 for Guilford and Davidson Counties and 9.0 for Catawba County RVP of 7.8 for Guilford and Davidson Counties and 9.0 for Catawba County Diesel fuel sulfur content of 43 ppm for all counties Diesel fuel sulfur content of 43 ppm for all counties

184 Motor Vehicle Emissions Budgets Placeholder For MVEBs: Placeholder For MVEBs: Catawba County-Direct PM2.5??? Catawba County-Direct PM2.5??? Davidson County-Direct PM2.5 Davidson County-Direct PM2.5 Guildford County-Direct PM2.5 Guildford County-Direct PM2.5 NCDAQ Mobile Team has calculated the various MVEBs and is in the process of quality assuring the work this week. NCDAQ Mobile Team has calculated the various MVEBs and is in the process of quality assuring the work this week.

185 Significant Emissions Reductions Occurring Or On The Books State Level State Level Clean Smokestacks Act Clean Smokestacks Act Open Burning Regulations Open Burning Regulations Control of Visible Emissions Control of Visible Emissions NC Senate Bill 953 (Expanded I&M / OBD) NC Senate Bill 953 (Expanded I&M / OBD) NO x SIP Call Rule NO x SIP Call Rule State School Bus Idling Policies State School Bus Idling Policies Federal Level Federal Level Clean Air Interstate Rule (CAIR) Clean Air Interstate Rule (CAIR) Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements Anti-idling Efforts Anti-idling Efforts Standards of Performance for Stationary Compression Ignition Internal Combustion Engines Standards of Performance for Stationary Compression Ignition Internal Combustion Engines Clean Air Diesel Nonroad Rule Clean Air Diesel Nonroad Rule

186 Close To Attaining Now And Plenty Of SO 2 Reductions Yet To Come… …Prior to the end of 2009 Allen Steam Station (Gaston County) Allen Steam Station (Gaston County) 5 units to get Scrubber controls installed in units to get Scrubber controls installed in 2009 ~13,314 tons SO 2 per year to be reduced~13,314 tons SO 2 per year to be reduced Belews Creek (Stokes County) Belews Creek (Stokes County) 2 units to get Scrubber controls installed in units to get Scrubber controls installed in 2008 ~85,347 tons SO 2 per year to be reduced~85,347 tons SO 2 per year to be reduced Marshall Steam Station (Catawba County) Marshall Steam Station (Catawba County) 4 units had Scrubber controls installed in 2006/07 4 units had Scrubber controls installed in 2006/07 ~74,533 tons SO 2 per year to be reduced~74,533 tons SO 2 per year to be reduced Progress Energy (Mayo and Roxboro) Progress Energy (Mayo and Roxboro) 5 units to get Scrubber controls installed by units to get Scrubber controls installed by 2009 ~105,522 tons SO 2 per year to be reduced~105,522 tons SO 2 per year to be reduced

187

188 PM2.5 Attainment Demonstration SIP Timeline From Here… Development of the draft PM2.5 SIP package is well underway. Development of the draft PM2.5 SIP package is well underway. NCDAQ will share portions of the draft SIP with EPA for preliminary comments. NCDAQ will share portions of the draft SIP with EPA for preliminary comments. Draft SIP made available to public ~January 18 th, Draft SIP made available to public ~January 18 th, day comment period through February 29 th. 43 day comment period through February 29 th. Notice of Request for Public Hearing (Week of February 25 th ) Notice of Request for Public Hearing (Week of February 25 th ) NCDAQ will address all comments and prepare final PM2.5 Attainment Demonstration SIP during March. NCDAQ will address all comments and prepare final PM2.5 Attainment Demonstration SIP during March. Final SIP submittal no later than April 5 th, Final SIP submittal no later than April 5 th, 2008.

189 Questions/Comments George Bridgers, Acting Chief of Attainment Planning Bebhinn Do, Meteorologist II Nick Witcraft, Meteorologist I

190 Questions/Comments Chris Misenis, Meteorologist I Janice Godfrey, Environmental Engineer II Phyllis Jones, Environmental Engineer II

191 Thank You!

192 Presentation Acronyms NCDAQNorth Carolina Division Of Air Quality SCDHECSouth Carolina Department Of Health And Environmental Control PARTPiedmont Authority For Regional Transportation USEPAU.S. Environmental Protection Agency VISTASVisibility Improvement State And Tribal Association Of The Southeast ASIPAssociation Of Southeastern Integrated Planning SIPState Implementation Plan CAAClean Air Act AQAir Quality NAAQSNation Ambient Air Quality Standard RPORegional Planning Organization CAIRClear Air Interstate Rule (USEPA) CSAClean Smokestacks Act (NC) DVDesign Value DVBBase Design Value DVFFinal Design Value RRFRelative Reduction Factor

193 Presentation Acronyms MM5Mesoscale Meteorological Model - Version 5 SMOKESparse Matrix Operator Kernel Emissions CMAQCommunity Multiscale Air Quality MOBILEMobile Emission Model CERRConsolidated Emissions Reporting Rule CEMContinuous Emissions Monitor NONROADNonroad Mobile Emissions Model BEISBiogenic Emissions Model IPMIntegrated Planning Model I&MInspection And Maintenance OBD-IIOn-Board Diagnostics VMTVehicle Miles Traveled RVPReid Vapor Pressure (Normally Expressed In Pounds Per Square Inch Or PSI) MVEBMotor Vehicle Emission Budget STNSpeciated Trends Network (Speciated PM2.5 Monitor) FRMFederal Reference Method (Mass Only PM2.5 Monitor) µgMicrograms µg/m3Micrograms Per Cubic Meter ppmParts Per Million

194 Presentation Acronyms PMParticulate Matter PM2.5Particulate Matter With A Diameter Less Than 2.5 µm PM10Particulate Matter With A Diameter Less Than 10 µm Direct PM2.5Directly Emitted And Not Secondarily Formed PM2.5 Also Known As Primary PM2.5 SO2Sulfur Dioxide SO4Sulfate NONitrogen Oxide NO2Nitrogen Dioxide NO3Nitrate NOxNitrogen Oxides OCOrganic Carbon ECElemental Carbon VOCVolatile Organic Carbons NH3Ammonia NH4Ammonium NH4SO4Ammonium Sulfate NH4NO3Ammonium Nitrate CMCrustal Mass PBWParticle Bound Water


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