Hickory and Triad PM2'5 SIP Development Stakeholder Meeting

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Hickory and TriadPM2.5 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

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Meeting Outline

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

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Fine Particulate Matter BackgroundAir Quality
Modeling OverviewEmissions Inventory Development

• George Bridgers, NCDAQ Meteorologist II
• Acting Chief of Attainment Planning

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Particulate Matter What is It?
A complex mixture of extremely small
particles and liquid droplets
Hair cross section (70 mm)
Human Hair (70 µm diameter)
M. Lipsett, California Office of Environmental
Health Hazard Assessment
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Public Health Risks Are Significant

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

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Typical PM Size Distribution
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2002
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2002
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2002
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National Ambient Air Quality Standard (NAAQS)

• Annual PM2.5 NAAQS
• A monitor is violating the annual standard, if
  the annual design value is gt 15.0 µg/m3
• The annual design value is defined as
• Annual mean concentration averaged over 3 years
• Daily PM2.5 NAAQS
• A monitor is violating the daily standard, if the
  daily design value is gt 35 µg/m3
• The daily design value is defined as
• Annual 98th percentile concentrations averaged
  over 3 years

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North Carolina Areas Designated Nonattainment for
PM2.5
2001 2003 Design Value Catawba 15.5
µg/m3 Davidson 15.8 µg/m3 Guilford 14.0 µg/m3
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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 2007-2009 2009 TBD
Or as early as possible
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VISTAS / ASIP

• Visibility Improvement State and Tribal
  Association of the Southeast
• 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
• No independent regulatory authority and no
  authority to direct or establish State or Tribal
  law or policy.

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NC / SC SIP Coordination

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

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Air Quality Modeling System
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Model Selection

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

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Modeling Season / Episode

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

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Emission Processing
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Emission Source Categories

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

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Emissions Inventory Definitions

• Actual the emissions inventory developed to
  simulate what happened in 2002
• 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
• Only effects emissions from electric generating
  units and forest management/wild fires
• Future the emissions inventory developed to
  simulate the attainment year 2009

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VISTAS / ASIP Actual 2002 Inventory

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

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VISTAS / ASIP Typical 2002 Inventory

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

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VISTAS / ASIP Typical 2009 Inventory

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

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Controls Applied

• NOx SIP Call
• Seasonal NOx emission caps large industrial
  boilers
• Clean Smokestacks Act
• Effects North Carolina Duke Energy Progress
  Energy sources
• Year-round caps of NOx (2007 2009) andSO2
  (2009 2013)
• No trading allowed to meet caps
• Required to submit compliance plan annually
• Clean Air Interstate Rule (CAIR)
• Year-round NOx (2009 2015) and SO2 (2010
  2015) caps for utilities
• Allows for trading credits

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Controls Applied (continued)

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

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Model Performance Evaluation

• Nick Witcraft, NCDAQ Meteorologist I

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Meteorological Modeling

• Penn State / NCAR MM5 meso-scale meteorological
  model
• Version 3.6.1
• Widely used in theresearch and
  regulatorycommunities
• VISTAS Contracted WithBarons AdvancedMeteorologi
  cal Systems(BAMS)
• Run at both 36km (Nationwide)and 12km
  (Southeastern US) resolutions for 2002

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Modeling Domains
12 km
36 km
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Grid Structure
Vertical MM5 34 layers SMOKE CMAQ 19
layers
48,000 ft
Horizontal 36 km 12 km
Layer 1 36 m deep
Ground
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Met Model Performance

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

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Temperature

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

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Temperature

• Little bias in summer, low bias in winter
• Lower error in summer, greater error in winter

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Moisture (Mixing Ratio)

• Tracks observed trends fairly well
• Low bias in the morning through the early
  afternoon
• High bias in the late afternoon and at night

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Moisture (Mixing Ratio)

• Negligible bias most of year lowest in Sep/Oct
• Higher error in summer

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Moisture (Relative Humidity)

• High bias in the daytime
• Low bias at night
• RH is linked to temperature and moisture biases

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Moisture (Relative Humidity)

• Slight high bias most of year
• Low bias Sep-Nov
• RH is linked to temperature and moisture biases

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Wind Speed

• 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)
• Also due to starting thresholds of observation
  network network cant measure winds lt 3 mph, so
  winds lt 3 mph are reported as calm

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Wind Speed

• Improved performance when factoring out calm
  winds
• Bias and error fairly steady throughout the year

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Observed Precip January
Modeled Precip January
Observed Precip April
Modeled Precip April
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Observed Precip JULY
Modeled Precip JULY
Observed Precip October
Modeled Precip October
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Model Performance StatisticsMeteorology In North
Carolina
Quarterly Meteorological Statistics
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Met Model Performance

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

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Take Away Messages

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

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Met Model Performance

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

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Air Quality Modeling

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

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PM2.5 Non-Attainment Area Monitors
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PM2.5 Non-Attainment Area Monitors
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AQ Model Performance

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

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Model Performance StatisticsPM2.5 STN sites
Hickory (Catawba County)
Hattie Ave (Forsyth County)
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Model Performance StatisticsPM2.5 Hickory STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75

• Good SO4, Total PM2.5 performance
• Poor NO3 performance

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Model Performance StatisticsPM2.5 Hickory STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75

• Good SO4, Total PM2.5 performance
• Poor NO3 performance

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Model Performance StatisticsPM2.5 Hickory STN

• Poor NO3 performance due to low predicted values.
  Worst performance is in summer.

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Model Performance StatisticsPM2.5 Hattie STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75

• Good SO4, Total PM2.5 performance
• Poor NO3 performance

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Model Performance StatisticsPM2.5 Hattie STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75

• Good SO4, Total PM2.5 performance
• Poor NO3 performance

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Model Performance StatisticsPM2.5 Hattie STN

• Good SO4, Total PM2.5 performance
• Poor NO3 performance

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Model Performance StatisticsPM2.5 FRM sites
FRM Monitoring Sites within the VISTAS 12km
Domain.
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Model Performance StatisticsPM2.5 FRM sites
FRM Monitoring Sites within the VISTAS 12km
Domain.
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Model Performance StatisticsPM2.5 FRM sites
Hickory (Catawba County)
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Model Performance StatisticsPM2.5 FRM sites
Hickory (Catawba County)
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Model Performance StatisticsPM2.5 FRM sites
Lexington (Davidson County)
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Model Performance StatisticsPM2.5 FRM sites
Lexington (Davidson County)
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Model Performance StatisticsPM2.5 FRM sites
Mendenhall (Guilford County)
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Model Performance StatisticsPM2.5 FRM sites
Mendenhall (Guilford County)
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AQ Model Performance

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

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Model Performance Scatter PlotsVISTAS STN SO4
January
July
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Model Performance Scatter PlotsVISTAS STN NO3
January
July
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Model Performance Scatter PlotsVISTAS STN OC
January
July
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Model Performance Scatter PlotsVISTAS STN EC
January
July
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Model Performance Scatter PlotsVISTAS STN NH4
January
July
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Model Performance Scatter PlotsVISTAS STN Total
PM2.5
July
January
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Model Performance Scatter PlotsNC STN SO4
January
July
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Model Performance Scatter PlotsNC STN NO3
January
July
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Model Performance Scatter PlotsNC STN OC
January
July
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Model Performance Scatter PlotsNC STN EC
January
July
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Model Performance Scatter PlotsNC STN NH4
January
July
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Model Performance Scatter PlotsNC STN Total PM2.5
January
July
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Model Performance Scatter PlotsHickory STN Total
PM2.5
January
July
Speciated performance similar to all NC
performance
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Model Performance Scatter PlotsHattie Ave STN
Total PM2.5
January
July
Speciated performance similar to all NC
performance
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Model Performance Scatter PlotsVISTAS FRM Total
PM2.5
January
July
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Model Performance Scatter PlotsNC FRM Total PM2.5
January
July
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Model Performance Scatter PlotsHickory FRM Total
PM2.5
January
July
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Model Performance Scatter PlotsLexington FRM
Total PM2.5
January
July
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Model Performance Scatter PlotsMendenhall FRM
Total PM2.5
January
July
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AQ Model Performance

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

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Hickory STN Time Series
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Model Performance Time SeriesTotal PM2.5
Obs Model
Hickory STN
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Model Performance Time SeriesSulfate (SO4)
Obs Model
Hickory STN
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Model Performance Time SeriesNitrate (NO3)
Obs Model
Hickory STN
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Model Performance Time SeriesElemental Carbon
(EC)
Obs Model
Hickory STN
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Model Performance Time SeriesOrganic Carbon (OC)
Obs Model
Hickory STN
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Model Performance Time SeriesAmmonium (NH4)
Obs Model
Hickory STN
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Hattie Ave STN Time Series
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Model Performance Time SeriesTotal PM2.5
Obs Model
Hattie Ave STN
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Model Performance Time SeriesSulfate (SO4)
Obs Model
Hattie Ave STN
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Model Performance Time SeriesNitrate (NO3)
Obs Model
Hattie Ave STN
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Model Performance Time SeriesElemental Carbon
(EC)
Obs Model
Hattie Ave STN
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Model Performance Time SeriesOrganic Carbon (OC)
Obs Model
Hattie Ave STN
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Model Performance Time Series Ammonium (NH4)
Obs Model
Hattie Ave STN
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Model Performance Time SeriesHickory FRM
January
July
Obs Model 36km, 12km
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Model Performance Time SeriesLexington FRM
January
July
Obs Model 36km, 12km
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Model Performance Time SeriesMendenhall FRM
January
July
Obs Model 36km, 12km
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AQ Model Performance

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

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Example July 16
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Example July 16
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Example August 3
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Example August 3
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Example February 25
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Example February 25
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AQ Model Performance

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

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Stacked Bar ChartsHickory STN
Jan-March
April-June
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Stacked Bar ChartsHickory STN
July-Sep
Oct-Dec
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Stacked Bar ChartsHattie Ave STN
Jan-March
April-June
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Stacked Bar ChartsHattie Ave STN
July-Sep
Oct-Dec
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AQ Model Performance

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

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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.
• Overall performance was reasonably good for
  Sulfate (SO4) and Organic Carbon (OC), the
  largest constituents of PM2.5.

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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.
• 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.

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Summary Of AQ (PM2.5) Model Performance

• 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.
• 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 gt15 µg without the need for additional or
  alternative modeling.

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AQ Model Performance

• 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.

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Attainment Test

• Bebhinn Do, NCDAQ Meteorologist II

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What is a Modeled Attainment Demonstration?

• 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
• Use the Modeled Attainment Test to estimate
  future design values
• Additional weight of evidence analyses as needed
  to demonstrate attainment

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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.
• 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.
• The resulting projected site-specific future
  design value is compared to NAAQS.

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Attainment Test

• DVF RRF DVB
• DVF Future Design Value
• RRF Relative Response Factor
• DVB Baseline Design Value

RRF is based on modeled data
DVB is based on observed data
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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.
• 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 four quarterly mean values are then averaged
  to get the future annual average PM2.5 estimate
  for each FRM site.

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Attainment Test For PM2.5

• If the future annual average PM2.5 estimate is
  less than 15.0 µg/m3 , then the attainment test
  is passed.
• If all such future site-specific design values
  are
• lt 14.5 µg/m3 the test is passed Basic
  supplemental analyses should be completed to
  confirm the outcome of the modeled attainment
  test
• Between 14.5 µg/m3 and 15.5 µg/m3 A weight of
  evidence demonstration should be conducted to
  determine if aggregate supplemental analyses
  support the modeled attainment test
• ? 15.5 µg/m3 , 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

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SMAT

• 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 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
• DVF RRF DVB

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Step 1 Calculating the DVB

• 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
• DVB (2000) 2(2001) 3(2002) 2(2003)
  (2004)
• Values are average based on calendar quarters,
  where
• Q1 January, February, March
• Q2 April, May, June
• Q3 July, August, September
• Q4 October, November, December

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Step 1 Calculating the DVB

• Mean Quarterly PM2.5 values for the PM2.5
  Nonattainment Areas

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Step 1 Calculating the DVB

• 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
• Not all FRM monitoring sites have co-located STN
  speciation monitors.
• FRM measurements and speciated PM2.5 measurements
  do not always measure the same mass

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Issue 1 FRM sites without co-located STN Sites

• EPA Guidance suggests
• Use of concurrent data from a near by speciated
  monitor
• Use of representative data (from a different time
  period)
• Use of interpolation techniques to create a
  spatial field using ambient speciation data
• Use of interpolation techniques to create spatial
  fields, and gridded modeling outputs to adjust
  the species concentrations

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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.
• 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.

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CAIR SMAT Tool
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CAIR SMAT Tool
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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)

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Issue 2 FRM Mass ? STN Mass

• Neil Frank (2006) developed the sulfate,
  adjusted nitrate, derived water, inferred
  carbonaceous material balance approach

143
Issue 2 FRM Mass ? STN Mass

• Adjust nitrate to account for volatilization
• Calculate quarterly average nitrate, sulfate, EC,
  Degree of Neutralization (DON) of sulfate, and
  crustal
• Calculate quarterly average NH4 from adjusted
  NO3, SO4, and DON of sulfate
• 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
• PM2.5FRM OCMmb EC SO4 NO3FRM
  NH4FRM water crustal material 0.5

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Issue 2 FRM Mass ? STN Mass

• Nitrates - Adjusted use hourly temperatures and
  24-hour average nitrate measurements
• NH4FRM DON SO4 0.29NO3FRM
• Particle Bound Water PBW (-0.002618)
  (0.980314nh4) (-0.260011no3)
  (-0.000784so4) (-0.159452nh42)
  (-0.356957no3nh4) (0.153894no32)
  (0.212891so4nh4) 0.0444366so4no3)
  (-0.048352so42)
• Crustal/Soil 3.73 Si 1.63Ca
  2.42Fe 1.94Ti
• Organic carbon mass by difference
• (OCmb) PM2.5FRM - SO4 NO3FRM NH4FRM
  water crustal material EC 0.5

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SMAT

• 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 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
• DVF RRF DVB

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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)present

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Step 2 Calculating the RRF

• Definition of near a 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
• 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

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Step 2 Calculating the RRF

• Days used in RRF calculation
• 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

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Step 2 Calculating the RRF

• 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
• 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

150
Step 2 Calculating the RRF

• 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)

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SMAT

• 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 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
• DVF RRF DVB

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Step 3 Compute the DVF

• Compute the quarterly component future design
  value (DVF)
• Calculate the mass due to Ammonium and PBW
• 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

153
Results

• lt 14.5 µg/m3 the test is passed Basic
  supplemental analyses
• Between 14.5 µg/m3 and 15.5 µg/m3 A weight of
  evidence demonstration should be conducted
• ? 15.5 µg/m3 attainment test failed, need more
  controls

154
Supplemental Analysis

• Modeling Metrics
• Results from other modeling studies
• Observational analyses
• Emissions analyses

155
Results from Other Studies

• Clean Air Interstate Rule (CAIR) modeling
• EPA modeling done to quantify the benefits of
  CAIR
• Modeling based on 2001 meteorology
• DVB was a 5yr weight DV centered around 2001
  (1999-2003)
• For 2010 Catawba 14.07 Davidson 14.36
• For 2015 Catawba 13.45 Davidson 13.61
• http//www.epa.gov/interstateairquality/pdfs/fina
  ltech02.pdf
• Modeling from other RPOs

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Observational Analyses
Design Values Trends
157
General Insignificance of PM2.5 Species

• Chris Misenis, NCDAQ Meteorologist I

158
General Insignificance of PM2.5 Species

• Overview
• NOx Insignificance
• NH4 Insignificance
• VOC Insignificance

159
Overview

• Pollutants must be evaluated that contribute to
  PM2.5 attainment issue.
• Included constituents are SO2, NOx, and Direct
  PM2.5. NH3 and VOCs are deemed insignificant.
• Technical demonstrations are permitted to reverse
  the presumptions made about certain species.

160
Technical Demonstrations

• SO2, NOx, and Direct PM2.5 MUST be evaluated.
• Inclusion of NOx can be reversed if sufficient
  evidence exists.
• Evidence may include
• Modeling Sensitivity Studies
• Speciated Data
• Emissions Inventories
• Monitoring or Data Analysis

161
NOx Insignificance
162
NOx Insignificance
163
NOx Insignificance
164
NOx Insignificance
165
NOx Insignificance
166
NOx Insignificance
167
NOx Insignificance

• More prevalent in cooler seasons.
• Less than 0.2 µg m-3 decrease annually at all
  three sites.
• Based on evidence, claiming NOx as insignificant
  to PM2.5 attainment.

168
NH3 Insignificance
169
NH3 Insignificance
170
NH3 Insignificance

• 30 reduction more significant during winter
  season, leading to large annual decrease.
• However, 30 reduction in NH3 emissions across
  entire domain reduces PM by less than 1 µg m-3.
• Agree with EPA that NH3 is insignificant to PM2.5
  attainment.

171
VOC Insignificance
172
VOC Insignificance
173
VOC Insignificance

• VOCs have a significant impact on PM formation in
  NC.
• 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.

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 Measures (RACM)
• Reasonable Further Progress (RFP) Plan
• Emission Inventory Requirements
• Permit Requirements
• Contingency Measures
• 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
• 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
Mobile SO2 Direct PM2.5Insignificance

• Both SO2 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 SO2 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.

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181
Mobile SO2 Direct PM2.5Insignificance

• Currently, it appears that NCDAQ will be able to
  successfully declare Mobile SO2 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.
• Thus, MVEBs in the Triad will likely be set of
  Direct PM2.5.

182
Motor Vehicle Emissions Budgets

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

183
Motor Vehicle Emissions Budgets

• Estimated MVEB emissions outside of Air Quality
  modeling
• Used updated speeds, VMT, vehicle mix and vehicle
  age distribution supplied by the transportation
  partners
• Used average 2002 July temperatures
• OBD-II Inspection/Maintenance Program in all
  counties
• RVP of 7.8 for Guilford and Davidson Counties
  and9.0 for Catawba County
• Diesel fuel sulfur content of 43 ppm for all
  counties

184
Motor Vehicle Emissions Budgets

• Placeholder For MVEBs
• Catawba County - Direct PM2.5???
• Davidson 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.

185
Significant Emissions Reductions Occurring Or On
The Books

• State Level
• Clean Smokestacks Act
• Open Burning Regulations
• Control of Visible Emissions
• NC Senate Bill 953 (Expanded IM / OBD)
• NOx SIP Call Rule
• State School Bus Idling Policies
• Federal Level
• Clean Air Interstate Rule (CAIR)
• Heavy-Duty Engine and Vehicle Standards and
  Highway Diesel Fuel Sulfur Control Requirements
• Anti-idling Efforts
• Standards of Performance for Stationary
  Compression Ignition Internal Combustion Engines
• Clean Air Diesel Nonroad Rule

186
Close To Attaining Now And Plenty OfSO2
Reductions Yet To ComePrior to the end of 2009

• Allen Steam Station (Gaston County)
• 5 units to get Scrubber controls installed in
  2009
• 13,314 tons SO2 per year to be reduced
• Belews Creek (Stokes County)
• 2 units to get Scrubber controls installed in
  2008
• 85,347 tons SO2 per year to be reduced
• Marshall Steam Station (Catawba County)
• 4 units had Scrubber controls installed in
  2006/07
• 74,533 tons SO2 per year to be reduced
• Progress Energy (Mayo and Roxboro)
• 5 units to get Scrubber controls installed by
  2009
• 105,522 tons SO2 per year to be reduced

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PM2.5 Attainment Demonstration SIPTimeline From
Here

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

189
Questions/Comments

• http//ncair.org
• George Bridgers, Acting Chief of Attainment
  Planning
• 919-715-6287
• George.Bridgers_at_ncmail.net
• Bebhinn Do, Meteorologist II
• 919-715-0921
• Bebhinn.Do_at_ncmail.net
• Nick Witcraft, Meteorologist I
• 919-715-2106
• Nick.Witcraft_at_ncmail.net

190
Questions/Comments

• http//ncair.org
• Chris Misenis, Meteorologist I
• 919-715-9773
• Chris.Misenis_at_ncmail.net
• Janice Godfrey, Environmental Engineer II
• 919-715-7647
• Janice.Godfrey_at_ncmail.net
• Phyllis Jones, Environmental Engineer II
• 919-715-1246
• Phyllis.D.Jones_at_ncmail.net

191
Thank You!
192
Presentation Acronyms

• NCDAQ North Carolina Division Of Air Quality
• SCDHEC South Carolina Department Of Health And
  Environmental Control
• PART Piedmont Authority For Regional
  Transportation
• USEPA U.S. Environmental Protection Agency
• VISTAS Visibility Improvement State And Tribal
  Association Of The Southeast
• ASIP Association Of Southeastern Integrated
  Planning
• SIP State Implementation Plan
• CAA Clean Air Act
• AQ Air Quality
• NAAQS Nation Ambient Air Quality Standard
• RPO Regional Planning Organization
• CAIR Clear Air Interstate Rule (USEPA)
• CSA Clean Smokestacks Act (NC)
• DV Design Value
• DVB Base Design Value
• DVF Final Design Value
• RRF Relative Reduction Factor

193
Presentation Acronyms

• MM5 Mesoscale Meteorological Model - Version 5
• SMOKE Sparse Matrix Operator Kernel Emissions
• CMAQ Community Multiscale Air Quality
• MOBILE Mobile Emission Model
• CERR Consolidated Emissions Reporting Rule
• CEM Continuous Emissions Monitor
• NONROAD Nonroad Mobile Emissions Model
• BEIS Biogenic Emissions Model
• IPM Integrated Planning Model
• IM Inspection And Maintenance
• OBD-II On-Board Diagnostics
• VMT Vehicle Miles Traveled
• RVP Reid Vapor Pressure (Normally Expressed In
  Pounds Per Square Inch Or PSI)
• MVEB Motor Vehicle Emission Budget
• STN Speciated Trends Network (Speciated PM2.5
  Monitor)
• FRM Federal Reference Method (Mass Only PM2.5
  Monitor)
• µg Micrograms

194
Presentation Acronyms

• PM Particulate Matter
• PM2.5 Particulate Matter With A Diameter Less
  Than 2.5 µm
• PM10 Particulate Matter With A Diameter Less Than
  10 µm
• Direct PM2.5 Directly Emitted And Not Secondarily
  Formed PM2.5
• Also Known As Primary PM2.5
• SO2 Sulfur Dioxide
• SO4 Sulfate
• NO Nitrogen Oxide
• NO2 Nitrogen Dioxide
• NO3 Nitrate
• NOx Nitrogen Oxides
• OC Organic Carbon
• EC Elemental Carbon
• VOC Volatile Organic Carbons
• NH3 Ammonia
• NH4 Ammonium
• NH4SO4 Ammonium Sulfate
• NH4NO3 Ammonium Nitrate
• CM Crustal Mass