Hickory and Triad Hickory and Triad PM2.5 SIP Development PM2.5 SIP Development Stakeholder Meeting Stakeholder Meeting Presented By: Presented By: NC Division Of Air Quality NC Division Of Air Quality Attainment Planning Branch Attainment Planning Branch Hosted At: Hosted At: P P iedmont iedmont A A uthority uthority for for R R egional egional T T ransportation ransportation Offices Offices November 14, 2007 November 14, 2007
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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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Hickory and TriadHickory and TriadPM2.5 SIP Development PM2.5 SIP Development
Meeting OutlineMeeting Outline Fine Particulate Matter BackgroundFine Particulate Matter Background Air Quality Modeling OverviewAir Quality Modeling Overview Emissions Inventory DevelopmentEmissions Inventory Development Model PerformanceModel Performance Attainment TestAttainment Test General Insignificance of PM2.5 SpeciesGeneral Insignificance of PM2.5 Species Clean Air Act RequirementsClean Air Act Requirements Motor Vehicle Emissions BudgetsMotor Vehicle Emissions Budgets Summarize / Next StepsSummarize / Next Steps
((10µm10µm)) M. Lipsett, California Office of Environmental Health Hazard Assessment
A complex mixture of extremely small A complex mixture of extremely small particles and liquid droplets particles and liquid droplets
Particulate Matter: What is It?Particulate Matter: What is It?
Public Health Risks Are SignificantPublic Health Risks Are Significant
Particles are linked to:Particles are linked to: Premature death from heart and lung diseasePremature death from heart and lung disease Aggravation of heart and lung diseasesAggravation of heart and lung diseases
Hospital admissions Hospital admissions Doctor and ER visits Doctor and ER visits Medication useMedication use School and work absencesSchool and work absences
And possibly toAnd possibly to Lung cancer deathsLung cancer deaths Infant mortalityInfant mortality Developmental problems in children, such as Developmental problems in children, such as
low birth weightlow birth weight
Primary Particles(Directly Emitted)
Secondary Particles(From Precursor Gases)
Elemental Carbon
OtherCrustal Ammonium
Nitrate
NOx
AmmoniumSulfate
SO2
SecondaryOrganics
VOC
Ammonia
Crustal
June 2000 / tgp
Condensed Organics
Typical PM Size DistributionTypical PM Size Distribution
National Ambient Air Quality National Ambient Air Quality Standard (NAAQS)Standard (NAAQS)
Annual PM2.5 NAAQSAnnual PM2.5 NAAQS A monitor is violating the annual standard, if the A monitor is violating the annual standard, if the
annual design value is > 15.0 annual design value is > 15.0 µµg/mg/m33
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 NAAQSDaily PM2.5 NAAQS A monitor is violating the daily standard, if the daily A monitor is violating the daily standard, if the daily
design value is > 35 design value is > 35 µµg/mg/m33
The daily design value is defined as:The daily design value is defined as:• Annual 98Annual 98thth percentile concentrations averaged over 3 years percentile concentrations averaged over 3 years
North Carolina Areas Designated North Carolina Areas Designated Nonattainment for PM2.5Nonattainment for PM2.5
* Or as early as possible* Or as early as possible
VISTAS / ASIPVISTAS / ASIP VVisibility isibility IImprovement mprovement SState and tate and TTribal ribal
AAssociation of the ssociation of the SSoutheastoutheast
AAssociation of ssociation of SSoutheastern outheastern IIntegrated ntegrated PPlanninglanning
Collaborative effort of States and Tribes to Collaborative effort of States and Tribes to support management of regional haze, and support management of regional haze, and attainment demonstrations for fine particulate attainment demonstrations for fine particulate matter and ozone nonattainment areas in the matter and ozone nonattainment areas in the Southeastern USSoutheastern US
No independent regulatory authority and no No independent regulatory authority and no authority to direct or establish State or Tribal law authority to direct or establish State or Tribal law or policy.or policy.
NC / SC SIP CoordinationNC / SC SIP Coordination Working together in VISTAS / ASIPWorking together in VISTAS / ASIP
Making use of VISTAS 2002 Making use of VISTAS 2002 meteorological, emissions and air quality meteorological, emissions and air quality modelingmodeling
Future year (2009) work completed through Future year (2009) work completed through ASIP ASIP
Control strategies for the Metrolina area Control strategies for the Metrolina area developed through a consultation process developed through a consultation process involving NCDAQ, SCDHEC and involving NCDAQ, SCDHEC and appropriate stakeholdersappropriate stakeholders
Air Quality Modeling SystemAir Quality Modeling System
Air Quality ModelAir Quality Model Community Multiscale Air Quality (CMAQ) Community Multiscale Air Quality (CMAQ)
modelmodel
Modeling Season / EpisodeModeling Season / Episode Full YearFull Year of 2002 selected for VISTAS / ASIP of 2002 selected for VISTAS / ASIP
modelingmodeling Regional Haze / Fine Particulate: Full YearRegional Haze / Fine Particulate: Full Year
The “higher” portion of the 2002 ozone season The “higher” portion of the 2002 ozone season selected for the Attainment Demonstration selected for the Attainment Demonstration modelingmodeling No exceedances in April or October No exceedances in April or October Used modeling for May through SeptemberUsed modeling for May through September
Area sources:Area sources: gas stations, dry cleaners, farming gas stations, dry cleaners, farming practices, fires, etc.practices, fires, etc.
On-road mobile sources:On-road mobile sources: cars, trucks, buses, etc. cars, trucks, buses, etc.
Nonroad mobile sources:Nonroad mobile sources: agricultural equipment, agricultural equipment, recreational marine, lawn mowers, construction recreational marine, lawn mowers, construction equipment, etc.equipment, etc.
Emissions Inventory DefinitionsEmissions Inventory Definitions ActualActual = the emissions inventory developed to simulate = the emissions inventory developed to simulate
what happened in 2002what happened in 2002 Used for model performance evaluation only.Used for model performance evaluation only.
TypicalTypical = the emissions inventory developed to = the emissions inventory developed to characterize the “current” emissions… It does not characterize the “current” emissions… It does not include specific events, but rather averages or typical include specific events, but rather averages or typical conditions conditions Only effects emissions from electric generating units and Only effects emissions from electric generating units and
FutureFuture = the emissions inventory developed to simulate = the emissions inventory developed to simulate the attainment year 2009the attainment year 2009
submittals for calendar year 2002submittals for calendar year 2002 Point, Area and select Nonroad mobile sourcesPoint, Area and select Nonroad mobile sources Augment State data where pollutants missingAugment State data where pollutants missing
Generate large forest management/wild fires as specific Generate large forest management/wild fires as specific daily eventsdaily events
Utility Emissions refined using actual Continuous Utility Emissions refined using actual Continuous Emissions Monitor (CEM) distributionsEmissions Monitor (CEM) distributions
On-road mobile processed through MOBILE6 module of On-road mobile processed through MOBILE6 module of SMOKE emissions systemSMOKE emissions system
Majority of Nonroad mobile emissions estimated using Majority of Nonroad mobile emissions estimated using NONROAD2005c modelNONROAD2005c model
Biogenic emissions estimated with BEIS3 modelBiogenic emissions estimated with BEIS3 model
Nonroad Mobile, On-road Mobile & Biogenic Nonroad Mobile, On-road Mobile & Biogenic SourcesSources Same as Actual 2002 InventorySame as Actual 2002 Inventory
Area SourcesArea Sources Only forest management/wild fires changedOnly forest management/wild fires changed Worked with Forest Service to develop typical fire Worked with Forest Service to develop typical fire
inventoryinventory
Point SourcesPoint Sources Only utility emissions changedOnly utility emissions changed Used 2000 – 2004 average heat input from CEM data Used 2000 – 2004 average heat input from CEM data
to adjust 2002 emissions to adjust 2002 emissions
VISTAS / ASIP Typical 2009 InventoryVISTAS / ASIP Typical 2009 Inventory Nonroad Mobile SourcesNonroad Mobile Sources
Re-ran NONROAD2005c model for 2009Re-ran NONROAD2005c model for 2009 Grew aircraft, locomotive and commercial marine engine Grew aircraft, locomotive and commercial marine engine
emissionsemissions On-road Mobile SourcesOn-road Mobile Sources
Re-ran MOBILE module in SMOKE for 2009Re-ran MOBILE module in SMOKE for 2009 Used transportation partners speed, vehicle miles traveled, etcUsed transportation partners speed, vehicle miles traveled, etc
Area SourcesArea Sources Grew all sources except forest management/wild fire emissionsGrew all sources except forest management/wild fire emissions Forest management/wild fire typical emissions kept constantForest management/wild fire typical emissions kept constant
Point SourcesPoint Sources Grew all sources except utility emissions Grew all sources except utility emissions Ran Integrated Planning Model (IPM) for projected utility Ran Integrated Planning Model (IPM) for projected utility
emissionsemissions Biogenic – same as 2002 emissionsBiogenic – same as 2002 emissions
Expanded from 9 to 48 Counties; Expanded from 9 to 48 Counties; All of the North Carolina Metrolina counties have I/M All of the North Carolina Metrolina counties have I/M
Run at both 36km Run at both 36km (Nationwide)(Nationwide)and 12km and 12km (Southeastern US)(Southeastern US) resolutions for 2002 resolutions for 2002
Modeling DomainsModeling Domains
36 km
12 km
Grid StructureGrid Structure
Horizontal: 36 km & 12 kmHorizontal: 36 km & 12 km
Vertical: Vertical:
MM5 = 34 layersMM5 = 34 layers
SMOKE & CMAQ = 19 layersSMOKE & CMAQ = 19 layers
Layer 1 = 36 m deepLayer 1 = 36 m deep GroundGround
~48,000 ft
Met Model PerformanceMet Model Performance Model Performance For Key Variables:Model Performance For Key Variables:
TemperatureTemperature Moisture (Mixing Ratio & Relative Humidity)Moisture (Mixing Ratio & Relative Humidity) WindsWinds PrecipitationPrecipitation
Summary Of Met Model PerformanceSummary Of Met Model Performance
Overall diurnal pattern captured very wellOverall diurnal pattern captured very well Slight cool bias in the daytimeSlight cool bias in the daytime Slight warm bias overnightSlight warm bias overnight
TemperatureTemperature
Little bias in summer, low bias in winterLittle bias in summer, low bias in winter Lower error in summer, greater error in winterLower error in summer, greater error in winter
TemperatureTemperature
1.5m Temperature Bias & Error
-1.5
-1-0.5
0
0.51
1.5
22.5
3
Jan
Feb Mar Apr
May Ju
n Jul
Aug Sep Oct NovDec
Kel
vin Bias
Error
Moisture (Mixing Ratio)Moisture (Mixing Ratio) Tracks observed trends fairly wellTracks observed trends fairly well Low bias in the morning through the early afternoonLow bias in the morning through the early afternoon High bias in the late afternoon and at nightHigh bias in the late afternoon and at night
Moisture (Mixing Ratio)Moisture (Mixing Ratio) Negligible bias most of year; lowest in Sep/OctNegligible bias most of year; lowest in Sep/Oct Higher error in summerHigher error in summer
Mixing Ratio Bias & Error
-1
-0.5
0
0.5
1
1.5
2
Jan
Feb Mar Apr
May Ju
n Jul
Aug Sep Oct NovDec
g/k
g Bias
Error
High bias in the daytimeHigh bias in the daytime Low bias at nightLow bias at night
RH is linked to temperature and moisture biasesRH is linked to temperature and moisture biases
~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 Partly due to relative inability of winds in the model to go calm
(There is always “some” wind)(There is always “some” wind) Also due to “Also due to “starting thresholds”starting thresholds” of observation network… of observation network…
network can’t measure winds < 3 mph, so winds < 3 mph are network can’t measure winds < 3 mph, so winds < 3 mph are reported as “calm”reported as “calm”
Wind SpeedWind Speed
Improved performance when factoring out calm windsImproved performance when factoring out calm winds Bias and error fairly steady throughout the yearBias and error fairly steady throughout the year
Wind SpeedWind Speed
Wind Speed Bias & Error
00.2
0.40.6
0.81
1.2
1.41.6
1.82
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
m/s Bias
Error
Wind Speed (no calm) Bias & Error
00.2
0.40.6
0.81
1.21.4
1.61.8
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
m/s Bias
Error
ObservedObserved
PrecipPrecip
JanuaryJanuary
ObservedObserved
PrecipPrecip
AprilApril
ModeledModeled
PrecipPrecip
JanuaryJanuary
ModeledModeled
PrecipPrecip
AprilApril
ObservedObserved
PrecipPrecip
JULYJULY
ObservedObserved
PrecipPrecip
OctoberOctober
ModeledModeled
PrecipPrecip
JULYJULY
ModeledModeled
PrecipPrecip
OctoberOctober
Model Performance StatisticsModel Performance StatisticsMeteorology In North CarolinaMeteorology In North Carolina
Quarterly MeteorologicalMeteorological Statistics
Bias Absolue Error R SquaredDaily Met Stats JFM AMJ JAS OND JFM AMJ JAS OND JFM AMJ JAS ONDTMP-1.5m_(K) -0.78 -0.11 -0.11 -0.69 2.06 1.62 1.53 1.79 0.87 0.88 0.80 0.85
Met Model PerformanceMet Model Performance Model Performance For Key Variables:Model Performance For Key Variables:
TemperatureTemperature Moisture (Mixing Ratio & Relative Humidity)Moisture (Mixing Ratio & Relative Humidity) WindsWinds PrecipitationPrecipitation
Summary Of Met Model PerformanceSummary Of Met Model Performance
Take Away MessagesTake Away Messages The 2002 meteorological model performance:The 2002 meteorological model performance:
Compares favorably to the performance in similar modeling Compares favorably to the performance in similar modeling projects / studies, including that of EPAprojects / 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 The precipitation biases would tend to inversely affect PM2.5 peaks in the AQ model:PM2.5 peaks in the AQ model: Under-predicted precip -> Under-predicted precip -> over-predicted PM2.5 (Fall)over-predicted PM2.5 (Fall) Over-predicted precip -> Over-predicted precip -> under-predicted PM2.5 (Apr-Sep)under-predicted PM2.5 (Apr-Sep) Slightly higher wind speeds -> Slightly higher wind speeds -> dispersion of pollutants, under-dispersion of pollutants, under-
predictionprediction Low temp bias in winter -> Low temp bias in winter -> more Nitrate formation???more Nitrate formation??? Moisture biases may impact secondary aerosol formationMoisture biases may impact secondary aerosol formation
Met Model PerformanceMet Model Performance
Brief questions before we proceed?Brief questions before we proceed?
Please reference Appendix I of the PM2.5 Please reference Appendix I of the PM2.5 Attainment Demonstration documentation for more Attainment Demonstration documentation for more exhaustive model performance metrics.exhaustive model performance metrics.
Air Quality ModelingAir Quality Modeling Community Multiscale Air Quality Model (CMAQ)Community Multiscale Air Quality Model (CMAQ)
Version 4.5 Version 4.5 (With SOA Modifications)(With SOA Modifications)
Widely used in the research & regulatory communitiesWidely used in the research & regulatory communities VISTAS Contracted With UC-Riverside, Alpine VISTAS Contracted With UC-Riverside, Alpine
Geophysics LLC, and ENVIRON International CorpGeophysics LLC, and ENVIRON International Corp Run at both 36kmRun at both 36km
(Nationwide) (Nationwide) and 12kmand 12km(Southeastern US)(Southeastern US)resolutionsresolutions
PM2.5 Non-Attainment Area MonitorsPM2.5 Non-Attainment Area Monitors
PM2.5 Non-Attainment Area MonitorsPM2.5 Non-Attainment Area Monitors
AQ Model PerformanceAQ Model Performance VISTAS, NC Modeled PM2.5 PerformanceVISTAS, NC Modeled PM2.5 Performance
Statistical Tables and PlotsStatistical Tables and Plots Scatter PlotsScatter Plots Time Series Time Series ((Selected ExamplesSelected Examples))
PM2.5 Spatial PlotsPM2.5 Spatial Plots
Stacked Bar Charts (Speciation)Stacked Bar Charts (Speciation)
Summary Of AQ Summary Of AQ (PM2.5)(PM2.5) Model Performance Model Performance
•Poor NOPoor NO33 performance due to low predicted performance due to low predicted values. Worst performance is in summer.values. Worst performance is in summer.
FRM Monitoring Sites within the VISTAS 12km DomainFRM Monitoring Sites within the VISTAS 12km Domain ..
All VISTAS FRM Sites January February March April May June July August September October November DecemberStatistical Measure Abbrev. PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25Mean Fractionalized Bias (Fractional Bias)
FRM: 37-035-0004 January February March April May June July August September October November DecemberStatistical Measure Abbrev. PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25Mean Fractionalized Bias (Fractional Bias)
Lexington (Davidson County)Lexington (Davidson County)FRM: 37-057-0002 January February March April May June July August September October November DecemberStatistical Measure Abbrev. PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25Mean Fractionalized Bias (Fractional Bias)
Mendenhall (Guilford County)Mendenhall (Guilford County)FRM: 37-081-0013 January February March April May June July August September October November DecemberStatistical Measure Abbrev. PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25Mean Fractionalized Bias (Fractional Bias)
AQ Model PerformanceAQ Model Performance VISTAS, NC Modeled PM2.5 PerformanceVISTAS, NC Modeled PM2.5 Performance
Statistical TablesStatistical Tables Scatter PlotsScatter Plots Time Series Time Series ((Selected ExamplesSelected Examples))
PM2.5 Spatial PlotsPM2.5 Spatial Plots
Stacked Bar Charts (Speciation)Stacked Bar Charts (Speciation)
Summary Of AQ Summary Of AQ (PM2.5)(PM2.5) Model Performance Model Performance
Summary Of AQ (PM2.5) Model Summary Of AQ (PM2.5) Model PerformancePerformance
Under-predictions of the summer modeled total Under-predictions of the summer modeled total PM2.5 concentrations account for the majority PM2.5 concentrations account for the majority of the negative bias and error.of the negative bias and error.
Overall performance was reasonably good for Overall performance was reasonably good for Sulfate (SOSulfate (SO44) and Organic Carbon (OC), the ) and Organic Carbon (OC), the
largest constituents of PM2.5.largest constituents of PM2.5.
Summary Of AQ (PM2.5) Model Summary Of AQ (PM2.5) Model PerformancePerformance
There are not significant spatial or temporal There are not significant spatial or temporal errors with the modeled PM2.5 that held errors with the modeled PM2.5 that held consistently throughout the 2002 PM2.5 consistently throughout the 2002 PM2.5 Season.Season.
Episodic air quality Episodic air quality (PM2.5)(PM2.5) cycles are well cycles are well captured by the CMAQ air quality model with captured by the CMAQ air quality model with reasonable buildup and clean-out of PM2.5 reasonable buildup and clean-out of PM2.5 concentrations.concentrations.
Thinking ahead to “Typical” and “Future” year Thinking ahead to “Typical” and “Future” year modeling, Relative Reduction Factor (RRF) calculations, modeling, Relative Reduction Factor (RRF) calculations, and the Modeled Attainment Test:and the Modeled Attainment Test: The The relativerelative sense of the SIP modeling will make the summer sense of the SIP modeling will make the summer
under-predictions of PM2.5 less significant and not influence under-predictions of PM2.5 less significant and not influence strategy decisions.strategy decisions.
With the annual modeling strategy, there are a sufficient With the annual modeling strategy, there are a sufficient number of modeled days in this “Base” or “Actual” year number of modeled days in this “Base” or “Actual” year modeling at each monitoring site throughout the year that modeling at each monitoring site throughout the year that contribute to the annual average >15 contribute to the annual average >15 µµg without the need for g without the need for additional or alternative modeling.additional or alternative modeling.
Summary Of AQ (PM2.5) Model Summary Of AQ (PM2.5) Model PerformancePerformance
AQ Model PerformanceAQ Model Performance
Questions, comments, and discussion?Questions, comments, and discussion?
Please reference Appendix J of the PM2.5 Please reference Appendix J of the PM2.5 Attainment Demonstration documentation for the Attainment Demonstration documentation for the exhaustive list of model performance metrics for all exhaustive list of model performance metrics for all scales/sites and relevant time periods.scales/sites and relevant time periods.
What is a Modeled What is a Modeled Attainment Demonstration?Attainment Demonstration?
Analyses which estimate whether selected Analyses which estimate whether selected emissions reductions will result in ambient emissions reductions will result in ambient concentrations will meet NAAQSconcentrations will meet NAAQS
Identifies the set of control measures which will Identifies the set of control measures which will result in the required emissions reductionsresult in the required emissions reductions
Use the Use the Modeled Attainment TestModeled Attainment Test to estimate future to estimate future design valuesdesign values
Additional weight of evidence analyses as needed Additional weight of evidence analyses as needed to demonstrate attainmentto demonstrate attainment
What is the Modeled What is the Modeled Attainment Test ?Attainment Test ?
An exercise in which an air quality model is used to An exercise in which an air quality model is used to simulate current and future air quality near each simulate current and future air quality near each monitoring site. monitoring site.
Model estimates are used in a “relative” rather than Model estimates are used in a “relative” rather than “absolute” sense. “absolute” sense.
Future design values are estimated at existing Future design values are estimated at existing monitoring sites by multiplying a modeled relative monitoring sites by multiplying a modeled relative response factor at locations “near” each monitor response factor at locations “near” each monitor times the observed monitor-specific design value. times the observed monitor-specific design value.
The resulting projected site-specific “future design The resulting projected site-specific “future design value” is compared to NAAQS. value” is compared to NAAQS.
RRF is basedRRF is basedon modeled dataon modeled data DVB is basedDVB is based
on observed dataon observed data Future modeled valuesFuture modeled values Current modeled valuesCurrent modeled values
Attainment Test For PM2.5Attainment Test For PM2.5 The DVF calculation is done for each component of The DVF calculation is done for each component of
PM2.5 (Sulfates, Nitrates, Ammonium, Elemental PM2.5 (Sulfates, Nitrates, Ammonium, Elemental and Organic Carbon, Crustal, and Particle Bound and Organic Carbon, Crustal, and Particle Bound Water), for each quarter. Water), for each quarter.
Since this test utilizes both PM2.5 and individual Since this test utilizes both PM2.5 and individual PM2.5 component species, it is referred to as PM2.5 component species, it is referred to as Speciated Modeled Attainment Test, or SMAT.Speciated Modeled Attainment Test, or SMAT.
The quarterly components are then summed for a The quarterly components are then summed for a quarterly mean PM2.5 value. quarterly mean PM2.5 value.
The four quarterly mean values are then averaged The four quarterly mean values are then averaged to get the future annual average PM2.5 estimate for to get the future annual average PM2.5 estimate for each FRM site.each FRM site.
Attainment Test For PM2.5Attainment Test For PM2.5 If the future annual average PM2.5 estimate is less If the future annual average PM2.5 estimate is less
than 15.0 than 15.0 µµg/mg/m33 , then the attainment test is passed. , 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 < 14.5 µµg/mg/m33 the test is passed; Basic supplemental the test is passed; Basic supplemental analyses should be completed to confirm the outcome of analyses should be completed to confirm the outcome of the modeled attainment testthe modeled attainment test
Between 14.5 Between 14.5 µµg/mg/m33 and 15.5 and 15.5 µµg/mg/m33 ; A weight of evidence ; A weight of evidence demonstration should be conducted to determine if demonstration should be conducted to determine if aggregate supplemental analyses support the modeled aggregate supplemental analyses support the modeled attainment testattainment test
15.5 15.5 µµg/mg/m33 , attainment test failed; More qualitative , attainment test failed; More qualitative results are less likely to support a conclusion differing results are less likely to support a conclusion differing from the outcome of the modeled attainment test; from the outcome of the modeled attainment test; additional controls are neededadditional controls are needed
SMATSMAT Step 1: Compute observed quarterly mean PM2.5 and quarterly Step 1: Compute observed quarterly mean PM2.5 and quarterly
mean composition for each monitor (DVB) mean composition for each monitor (DVB)
Step 2: Use air quality modeling results to derive component-Step 2: Use air quality modeling results to derive component-specific relative response factors (RRF) at each monitor for each specific relative response factors (RRF) at each monitor for each quarterquarter
Step 3: Apply the component specific RRFs obtained in step 2 to Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1the component-specific design value in step 1
Step 4: Calculate the the future year annual average PM2.5 Step 4: Calculate the the future year annual average PM2.5 estimateestimate
DVF = RRF * DVF = RRF * DVBDVB
Step 1: Step 1: Calculating the DVBCalculating the DVB The first part of the process is to calculate the quarterly The first part of the process is to calculate the quarterly
mean PM2.5 concentration at the FRM sites:mean PM2.5 concentration at the FRM sites:
A mean concentration is calculated for each quarter, A mean concentration is calculated for each quarter, and then a 5-year weighted quarterly average is and then a 5-year weighted quarterly average is calculated using the following weight scheme:calculated using the following weight scheme:
Values are average based on calendar quarters, where:Values are average based on calendar quarters, where: Q1 = January, February, MarchQ1 = January, February, March Q2 = April, May, JuneQ2 = April, May, June Q3 = July, August, September Q3 = July, August, September Q4 = October, November, DecemberQ4 = October, November, December
Mean Quarterly PM2.5 values for the Mean Quarterly PM2.5 values for the PM2.5 Nonattainment AreasPM2.5 Nonattainment Areas
AIRS ID County Site Name 2002-2004 Q1 2002-2004 Q2 2002-2004 Q3 2002-2004 Q437-035-0004 Catawba Hickory 13.1 15.1 20.0 12.337-057-0002 Davidson Lexington 13.8 15.6 18.8 13.537-081-0013 Guilford Mendenhall 11.7 13.7 17.1 12.2
Step 1: Step 1: Calculating the DVBCalculating the DVB
The second part of the process is to calculate the The second part of the process is to calculate the component quarterly mean PM2.5 concentration at the component quarterly mean PM2.5 concentration at the FRM sites, which necessitates speciated data at these FRM sites, which necessitates speciated data at these sites.sites.
Two issues: Two issues:
1.1. Not all FRM monitoring sites have co-located STN Not all FRM monitoring sites have co-located STN speciation monitors. speciation monitors.
2.2. FRM measurements and speciated PM2.5 FRM measurements and speciated PM2.5 measurements do not always measure the same massmeasurements do not always measure the same mass
Step 1: Step 1: Calculating the DVBCalculating the DVB
Issue 1: FRM sites without co-Issue 1: FRM sites without co-located STN Siteslocated STN Sites
EPA Guidance suggests:EPA Guidance suggests:1.1. Use of concurrent data from a near by Use of concurrent data from a near by
speciated monitorspeciated monitor
2.2. Use of representative data (from a different Use of representative data (from a different time period)time period)
3.3. Use of interpolation techniques to create a Use of interpolation techniques to create a spatial field using ambient speciation dataspatial field using ambient speciation data
4.4. Use of interpolation techniques to create Use of interpolation techniques to create spatial fields, and gridded modeling outputs to spatial fields, and gridded modeling outputs to adjust the species concentrationsadjust the species concentrations
Issue 1: FRM sites without co-Issue 1: FRM sites without co-located STN Siteslocated STN Sites
The EPA developed software called The EPA developed software called “Modeled Attainment Test Software” (or “Modeled Attainment Test Software” (or MATS) will actually perform the spatial MATS) will actually perform the spatial analysis of number 3 and 4.analysis of number 3 and 4.
However, MATS has not been delivered at However, MATS has not been delivered at this time.this time.
As an alternative, we have used the As an alternative, we have used the speciated profiles from the CAIR SMAT speciated profiles from the CAIR SMAT tool, which is the predecessor for the tool, which is the predecessor for the MATS program. MATS program.
CAIR SMAT ToolCAIR SMAT Tool
CAIR SMAT ToolCAIR SMAT Tool
Issue 2: FRM Mass Issue 2: FRM Mass STN Mass STN Mass
Issue is that by design, FRM monitors do Issue is that by design, FRM monitors do not retain all ammonium nitrate and other not retain all ammonium nitrate and other semi-volatile materials (negative artifact) semi-volatile materials (negative artifact) and FRM samples include particle bound and FRM samples include particle bound water associated with sulfates, nitrates, water associated with sulfates, nitrates, and other hygroscopic species (positive and other hygroscopic species (positive artifact)artifact)
Neil Frank (2006) developed the Neil Frank (2006) developed the “sulfate, adjusted nitrate, derived “sulfate, adjusted nitrate, derived water, inferred carbonaceous water, inferred carbonaceous material balance approach”material balance approach”
SANDWICHSANDWICH
Issue 2: FRM Mass Issue 2: FRM Mass STN Mass STN Mass
Adjust nitrate to account for volatilizationAdjust nitrate to account for volatilization Calculate quarterly average nitrate, sulfate, EC, Calculate quarterly average nitrate, sulfate, EC,
Degree of Neutralization (DON) of sulfate, and Degree of Neutralization (DON) of sulfate, and crustalcrustal
Calculate quarterly average NHCalculate quarterly average NH44 from adjusted from adjusted NONO33, SO, SO44, and DON of sulfate, and DON of sulfate
Calculate particle bound water from DON, sulfate, Calculate particle bound water from DON, sulfate, nitrate, and ammonium valuesnitrate, and ammonium values
Calculate OC by difference from PM2.5 mass, Calculate OC by difference from PM2.5 mass, adjusted nitrate, ammonium, sulfate, water, EC, adjusted nitrate, ammonium, sulfate, water, EC, crustal, and passive (blank) masscrustal, and passive (blank) mass
Issue 2: FRM Mass Issue 2: FRM Mass STN Mass STN Mass
Nitrates - Nitrates - Adjusted use hourly temperatures Adjusted use hourly temperatures and 24-hour average nitrate measurementsand 24-hour average nitrate measurements
NH4NH4FRMFRM = DON * SO4 + 0.29*NO3 = DON * SO4 + 0.29*NO3FRMFRM
Issue 2: FRM Mass Issue 2: FRM Mass STN Mass STN Mass
Step 1: Compute observed quarterly mean PM2.5 and quarterly Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB)mean composition for each monitor (DVB)
Step 2: Use air quality modeling results to derive component-Step 2: Use air quality modeling results to derive component-specific relative response factors (RRF) at each monitor for each specific relative response factors (RRF) at each monitor for each quarterquarter
Step 3: Apply the component specific RRFs obtained in step 2 to Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1the component-specific design value in step 1
Step 4: Calculate the the future year annual average PM2.5 Step 4: Calculate the the future year annual average PM2.5 estimateestimate
DVF =DVF = RRFRRF * DVB* DVB
SMATSMAT
Step 2: Calculating the relative Step 2: Calculating the relative reduction factor (RRF)reduction factor (RRF)
RRF = RRF = the ratio of the model’s future to the ratio of the model’s future to current projections “near” monitor current projections “near” monitor
“x”“x”
(quarterly mean component concentration “near"monitor “x”)(quarterly mean component concentration “near"monitor “x”) futurefuture
== (quarterly mean component concentration “near” monitor “x”)(quarterly mean component concentration “near” monitor “x”)presentpresent
Step 2: Calculating the RRFStep 2: Calculating the RRF
Definition of “near a monitor” Definition of “near a monitor” EPA guidance recommends considering an array of values EPA guidance recommends considering an array of values
“near” each monitor“near” each monitor Assume a monitor is at the center of the grid cell in which it is 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” cellslocated 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 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 cell3 x 3 array of cells, with the monitor located in the center cell
Days used in RRF calculationDays used in RRF calculation The entire year of modeling is used to The entire year of modeling is used to
calculate the component RRFs calculate the component RRFs All 365 days are used in the calculation, and All 365 days are used in the calculation, and
there is no concentration limit like with there is no concentration limit like with Ozone Ozone
Step 2: Calculating the RRFStep 2: Calculating the RRF
For the base year:For the base year:
A daily average mass of one of the component A daily average mass of one of the component species of PM2.5 is calculated for each of the species of PM2.5 is calculated for each of the cells in the 3x3 grid array near the monitorcells in the 3x3 grid array near the monitor
These 9 cells are then averaged to produce a These 9 cells are then averaged to produce a mean daily value for the component for the 3x3 mean daily value for the component for the 3x3 arrayarray
All of the days in the each quarter are then All of the days in the each quarter are then averaged together to produce the quarterly mean averaged together to produce the quarterly mean component concentrationcomponent concentration
Step 2: Calculating the RRFStep 2: Calculating the RRF
This is then repeated for the future year.This is then repeated for the future year. The whole process is repeated for each component The whole process is repeated for each component
of PM2.5 (Sulfates, Nitrates, EC, OC, Crustal. of PM2.5 (Sulfates, Nitrates, EC, OC, Crustal. Ammonium and PBW are calculated based on the Ammonium and PBW are calculated based on the DVF of the other components)DVF of the other components)
Step 2: Calculating the RRFStep 2: Calculating the RRF
Step 1: Compute observed quarterly mean PM2.5 and quarterly Step 1: Compute observed quarterly mean PM2.5 and quarterly mean composition for each monitor (DVB) mean composition for each monitor (DVB)
Step 2: Use air quality modeling results to derive component-Step 2: Use air quality modeling results to derive component-specific relative response factors (RRF) at each monitor for each specific relative response factors (RRF) at each monitor for each quarterquarter
Step 3: Apply the component specific RRFs obtained in step 2 to Step 3: Apply the component specific RRFs obtained in step 2 to the component-specific design value in step 1the component-specific design value in step 1
Step 4: Calculate the the future year annual average PM2.5 Step 4: Calculate the the future year annual average PM2.5 estimateestimate
DVFDVF = RRF * DVB= RRF * DVB
SMATSMAT
Step 3: Compute the DVFStep 3: Compute the DVF Compute the quarterly component future design Compute the quarterly component future design
value (DVF)value (DVF) Calculate the mass due to Ammonium and PBWCalculate the mass due to Ammonium and PBW Components are summed for each quarter to Components are summed for each quarter to
achieve quarterly future year PM2.5 massachieve quarterly future year PM2.5 mass The four quarters are then averaged to get a final The four quarters are then averaged to get a final
future year annual average, which is compared to future year annual average, which is compared to the NAAQSthe NAAQS
ResultsResults
AIRS ID County DVC (2002) DVF (2009)37-035-0004 Catawba 15.6 13.037-057-0002 Davidson 16.0 13.337-081-0013 Guilford 13.5 11.4
< 14.5 < 14.5 µµg/mg/m33 the test is passed; Basic supplemental analyses the test is passed; Basic supplemental analyses
Between 14.5 Between 14.5 µµg/mg/m33 and 15.5 and 15.5 µµg/mg/m33 ; A weight of evidence ; A weight of evidence demonstration should be conducteddemonstration should be conducted
15.5 15.5 µµg/mg/m33 ; attainment test failed, need more controls ; attainment test failed, need more controls
Supplemental AnalysisSupplemental Analysis Modeling Metrics Modeling Metrics Results from other modeling studiesResults from other modeling studies Observational analysesObservational analyses Emissions analysesEmissions analyses
Results from Other StudiesResults from Other Studies Clean Air Interstate Rule (CAIR) modelingClean Air Interstate Rule (CAIR) modeling
EPA modeling done to quantify the benefits of EPA modeling done to quantify the benefits of CAIR CAIR
Modeling based on 2001 meteorologyModeling based on 2001 meteorology DVB was a 5yr weight DV centered around 2001 DVB was a 5yr weight DV centered around 2001
General Insignificance General Insignificance of PM2.5 Speciesof PM2.5 Species
Chris Misenis, NCDAQ Meteorologist IChris Misenis, NCDAQ Meteorologist I
OverviewOverview
NONOxx Insignificance Insignificance
NHNH44 Insignificance Insignificance
VOC InsignificanceVOC Insignificance
General Insignificance General Insignificance of PM2.5 Speciesof PM2.5 Species
Pollutants must be evaluated that Pollutants must be evaluated that contribute to PM2.5 attainment issue.contribute to PM2.5 attainment issue.
Included constituents are SOIncluded constituents are SO22, NO, NOxx, and , and
Direct PM2.5. NHDirect PM2.5. NH33 and VOCs are deemed and VOCs are deemed
insignificant.insignificant.
Technical demonstrations are permitted to Technical demonstrations are permitted to reverse the presumptions made about reverse the presumptions made about certain species.certain species.
OverviewOverview
SOSO22, NO, NOxx, and Direct PM2.5 , and Direct PM2.5 MUSTMUST be be
evaluated.evaluated.
Inclusion of NOInclusion of NOxx can be reversed if can be reversed if
Evidence may include:Evidence may include: Modeling Sensitivity StudiesModeling Sensitivity Studies Speciated DataSpeciated Data Emissions InventoriesEmissions Inventories Monitoring or Data AnalysisMonitoring or Data Analysis
30% reduction more significant during 30% reduction more significant during winter season, leading to large annual winter season, leading to large annual decrease.decrease.
However, 30% reduction in NHHowever, 30% reduction in NH33 emissions emissions
across entire domain reduces PM by less across entire domain reduces PM by less than 1 than 1 μg mμg m-3-3..
Agree with EPA that NHAgree with EPA that NH33 is insignificant to is insignificant to
VOCs have a significant impact on PM VOCs have a significant impact on PM formation in NC.formation in NC.
However, biogenic VOCs are significantly However, biogenic VOCs are significantly more influential to PM formation than more influential to PM formation than anthropogenic.anthropogenic.
Given current controls and inability to Given current controls and inability to curtail all biogenic emissions, agree with curtail all biogenic emissions, agree with EPA that VOCs are insignificant.EPA that VOCs are insignificant.
VOC InsignificanceVOC Insignificance
Clean Air Act RequirementsClean Air Act RequirementsMotor Vehicle Emissions BudgetsMotor Vehicle Emissions Budgets
Summary / Next StepsSummary / Next StepsGeorge Bridgers, NCDAQ Meteorologist IIGeorge Bridgers, NCDAQ Meteorologist II
Acting Chief of Attainment PlanningActing Chief of Attainment Planning
Clean Air Act RequirementsClean 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) PlanReasonable Further Progress (RFP) Plan Emission Inventory RequirementsEmission Inventory Requirements Permit RequirementsPermit Requirements Contingency MeasuresContingency Measures Transportation Conformity / Motor Vehicle Transportation Conformity / Motor Vehicle
To ensure Federal transportation actions To ensure Federal transportation actions occurring in nonattainment and maintenance occurring in nonattainment and maintenance areas do not hinder the area from attaining areas do not hinder the area from attaining and/or maintaining the NAAQSand/or maintaining the NAAQS
MVEBs set a level of emissions that cannot be MVEBs set a level of emissions that cannot be exceeded by expected emissions in exceeded by expected emissions in Transportation Improvement Plans (TIPs) and Transportation Improvement Plans (TIPs) and Long Range Transportation Plans (LRTP)Long Range Transportation Plans (LRTP)
Both SOBoth SO22 and Direct PM2.5 must be addressed and Direct PM2.5 must be addressed
and controls measures evaluated in the PM2.5 and controls measures evaluated in the PM2.5 attainment SIP.attainment SIP.
NCDAQ is working with EPA to potentially have NCDAQ is working with EPA to potentially have On-Road Mobile SOOn-Road Mobile SO22 and Direct PM2.5 found and Direct PM2.5 found
insignificant to the PM2.5 concentrations in the insignificant to the PM2.5 concentrations in the respective non-attainment areas.respective non-attainment areas.
Having either or both found insignificant would Having either or both found insignificant would remove them from consideration when setting the remove them from consideration when setting the MVEBs in the SIP.MVEBs in the SIP.
Mobile SOMobile SO22 & Direct PM2.5 & Direct PM2.5
InsignificanceInsignificance
2002 Emissions Summary by Source for North Carolina
0
100,000
200,000
300,000
400,000
500,000
VOC NOx PM2.5 PM10 NH3 SO2
Ton
s
Point
Area
On-Road Mobile
Non-Road Mobile
Biogenics
On-Road Mobile On-Road Mobile is ~2.4% of the is ~2.4% of the
Total SOTotal SO22
On-Road Mobile is On-Road Mobile is ~4.5% of the Total ~4.5% of the Total
Direct PM2.5Direct PM2.5
2009 Emissions Summary by Source for North Carolina
0
100,000
200,000
300,000
400,000
500,000
VOC NOx PM2.5 PM10 NH3 SO2
Ton
s
Point
Area
On-Road Mobile
Non-Road Mobile
Biogenics
On-Road Mobile On-Road Mobile is ~0.5% of the is ~0.5% of the
Total SOTotal SO22
On-Road Mobile is On-Road Mobile is ~3.3% of the Total ~3.3% of the Total
Direct PM2.5Direct PM2.5
2009 Emissions Summary by Source for Guilford County
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
VOC NOx PM2.5 PM10 NH3 SO2
Ton
s
Point
Area
On-Road Mobile
Non-Road Mobile
Biogenics
On-Road Mobile is On-Road Mobile is ~8.0% of the Total ~8.0% of the Total
Direct PM2.5Direct PM2.5
Currently, it appears that NCDAQ will be able to Currently, it appears that NCDAQ will be able to successfully declare Mobile SOsuccessfully declare Mobile SO22 insignificant in both insignificant in both
Hickory and the Triad.Hickory and the Triad.
Mobile Direct PM2.5 is more tenuous given higher Mobile Direct PM2.5 is more tenuous given higher percentages with respect to Total Direct PM2.5. Only percentages with respect to Total Direct PM2.5. Only Hickory appears possible for an insignificance Hickory appears possible for an insignificance determination.determination.
Thus, MVEBs in the Triad will likely be set of Thus, MVEBs in the Triad will likely be set of Direct PM2.5.Direct PM2.5.
Mobile SOMobile SO22 & Direct PM2.5 & Direct PM2.5
InsignificanceInsignificance
Motor Vehicle Emissions Motor Vehicle Emissions BudgetsBudgets
Geographic ExtentGeographic Extent The MVEBs will be set at the county levelThe MVEBs will be set at the county level
Primary PM2.5 MVEBsPrimary PM2.5 MVEBs Established for the attainment year 2009Established for the attainment year 2009 Set in kilograms/yearSet in kilograms/year
Motor Vehicle Emissions Motor Vehicle Emissions BudgetsBudgets
Estimated MVEB emissions outside of Air Estimated MVEB emissions outside of Air Quality modelingQuality modeling Used updated speeds, VMT, vehicle mix and vehicle Used updated speeds, VMT, vehicle mix and vehicle
age distribution supplied by the transportation age distribution supplied by the transportation partnerspartners
Used average 2002 July temperatures Used average 2002 July temperatures OBD-II Inspection/Maintenance Program in all countiesOBD-II Inspection/Maintenance Program in all counties RVP of 7.8 for Guilford and Davidson Counties andRVP of 7.8 for Guilford and Davidson Counties and
9.0 for Catawba County9.0 for Catawba County Diesel fuel sulfur content of 43 ppm for all countiesDiesel fuel sulfur content of 43 ppm for all counties
Motor Vehicle Emissions Motor Vehicle Emissions BudgetsBudgets
Placeholder For MVEBs:Placeholder For MVEBs: Catawba CountyCatawba County -- Direct PM2.5???Direct PM2.5??? Davidson CountyDavidson County -- Direct PM2.5Direct PM2.5 Guildford CountyGuildford County -- Direct PM2.5Direct PM2.5
NCDAQ Mobile Team has calculated the various NCDAQ Mobile Team has calculated the various MVEBs and is in the process of quality assuring MVEBs and is in the process of quality assuring the work this week.the work this week.
Significant Emissions Reductions Significant Emissions Reductions Occurring Or On The BooksOccurring Or On The Books
State LevelState Level Clean Smokestacks ActClean Smokestacks Act Open Burning RegulationsOpen Burning Regulations Control of Visible EmissionsControl of Visible Emissions NC Senate Bill 953 (Expanded I&M / OBD)NC Senate Bill 953 (Expanded I&M / OBD) NONOxx SIP Call Rule SIP Call Rule State School Bus Idling PoliciesState School Bus Idling Policies
Federal LevelFederal Level Clean Air Interstate Rule (CAIR)Clean Air Interstate Rule (CAIR) Heavy-Duty Engine and Vehicle Standards and Highway Heavy-Duty Engine and Vehicle Standards and Highway
Diesel Fuel Sulfur Control RequirementsDiesel Fuel Sulfur Control Requirements Anti-idling EffortsAnti-idling Efforts Standards of Performance for Stationary Compression Standards of Performance for Stationary Compression
Ignition Internal Combustion EnginesIgnition Internal Combustion Engines Clean Air Diesel Nonroad RuleClean Air Diesel Nonroad Rule
Close To Attaining Now And Plenty OfClose To Attaining Now And Plenty OfSOSO22 Reductions Yet To Come… Reductions Yet To Come…
…Prior to the end of 2009…Prior to the end of 2009
Allen Steam Station (Gaston County)Allen Steam Station (Gaston County) 5 units to get Scrubber controls installed in 20095 units to get Scrubber controls installed in 2009
• ~13,314 tons SO~13,314 tons SO22 per year to be reduced per year to be reduced Belews Creek (Stokes County)Belews Creek (Stokes County)
2 units to get Scrubber controls installed in 20082 units to get Scrubber controls installed in 2008• ~85,347 tons SO~85,347 tons SO22 per year to be reduced per year to be reduced
Marshall Steam Station (Catawba County)Marshall Steam Station (Catawba County) 4 units had Scrubber controls installed in 2006/074 units had Scrubber controls installed in 2006/07
• ~74,533 tons SO~74,533 tons SO22 per year to be reduced per year to be reduced Progress Energy (Mayo and Roxboro)Progress Energy (Mayo and Roxboro)
5 units to get Scrubber controls installed by 20095 units to get Scrubber controls installed by 2009• ~105,522 tons SO~105,522 tons SO22 per year to be reduced per year to be reduced
Annaul PM2.5 Averages In The Hickory And Triad Nonattainment Areas
PM2.5 Attainment Demonstration SIPPM2.5 Attainment Demonstration SIPTimeline From Here…Timeline From Here…
Development of the draft PM2.5 SIP package is well Development of the draft PM2.5 SIP package is well underway.underway.
NCDAQ will share portions of the draft SIP with EPA for NCDAQ will share portions of the draft SIP with EPA for preliminary comments.preliminary comments.
Draft SIP made available to public ~January 18Draft SIP made available to public ~January 18thth, 2008., 2008. 43 day comment period through February 2943 day comment period through February 29thth.. Notice of Request for Public Hearing (Week of February 25Notice of Request for Public Hearing (Week of February 25 thth))
NCDAQ will address all comments and prepare final NCDAQ will address all comments and prepare final PM2.5 Attainment Demonstration SIP during March.PM2.5 Attainment Demonstration SIP during March.
Final SIP submittal no later than April 5Final SIP submittal no later than April 5thth, 2008., 2008.
George Bridgers, Acting Chief of Attainment PlanningGeorge Bridgers, Acting Chief of Attainment Planning919-715-6287919-715-6287George.Bridgers@[email protected]
Presentation AcronymsPresentation AcronymsNCDAQNCDAQ North Carolina Division Of Air QualityNorth Carolina Division Of Air QualitySCDHECSCDHEC South Carolina Department Of Health And Environmental ControlSouth Carolina Department Of Health And Environmental ControlPARTPART Piedmont Authority For Regional TransportationPiedmont Authority For Regional TransportationUSEPAUSEPA U.S. Environmental Protection AgencyU.S. Environmental Protection AgencyVISTASVISTAS Visibility Improvement State And Tribal Association Of The Visibility Improvement State And Tribal Association Of The
SoutheastSoutheastASIPASIP Association Of Southeastern Integrated PlanningAssociation Of Southeastern Integrated Planning
SIPSIP State Implementation PlanState Implementation PlanCAACAA Clean Air ActClean Air ActAQAQ Air QualityAir QualityNAAQSNAAQS Nation Ambient Air Quality StandardNation Ambient Air Quality StandardRPORPO Regional Planning OrganizationRegional Planning OrganizationCAIRCAIR Clear Air Interstate Rule (USEPA)Clear Air Interstate Rule (USEPA)CSACSA Clean Smokestacks Act (NC)Clean Smokestacks Act (NC)
DVDV Design ValueDesign ValueDVBDVB Base Design ValueBase Design ValueDVFDVF Final Design ValueFinal Design ValueRRFRRF Relative Reduction FactorRelative Reduction Factor
Presentation AcronymsPresentation AcronymsMM5MM5 Mesoscale Meteorological Model - Version 5Mesoscale Meteorological Model - Version 5SMOKESMOKE Sparse Matrix Operator Kernel EmissionsSparse Matrix Operator Kernel EmissionsCMAQCMAQ Community Multiscale Air QualityCommunity Multiscale Air QualityMOBILEMOBILE Mobile Emission ModelMobile Emission ModelCERRCERR Consolidated Emissions Reporting Rule Consolidated Emissions Reporting Rule CEMCEM Continuous Emissions MonitorContinuous Emissions MonitorNONROADNONROAD Nonroad Mobile Emissions ModelNonroad Mobile Emissions ModelBEISBEIS Biogenic Emissions ModelBiogenic Emissions ModelIPMIPM Integrated Planning ModelIntegrated Planning Model
I&MI&M Inspection And MaintenanceInspection And MaintenanceOBD-IIOBD-II On-Board DiagnosticsOn-Board DiagnosticsVMTVMT Vehicle Miles TraveledVehicle Miles TraveledRVPRVP Reid Vapor Pressure Reid Vapor Pressure (Normally Expressed In Pounds Per Square Inch Or PSI)(Normally Expressed In Pounds Per Square Inch Or PSI)
MVEBMVEB Motor Vehicle Emission BudgetMotor Vehicle Emission Budget
STNSTN Speciated Trends Network (Speciated PM2.5 Monitor)Speciated Trends Network (Speciated PM2.5 Monitor)FRMFRM Federal Reference Method (Mass Only PM2.5 Monitor)Federal Reference Method (Mass Only PM2.5 Monitor)µµgg MicrogramsMicrogramsµµg/m3g/m3 Micrograms Per Cubic MeterMicrograms Per Cubic Meterppmppm Parts Per MillionParts Per Million
Presentation AcronymsPresentation AcronymsPMPM Particulate MatterParticulate MatterPM2.5PM2.5 Particulate Matter With A Diameter Less Than 2.5 µmParticulate Matter With A Diameter Less Than 2.5 µmPM10PM10 Particulate Matter With A Diameter Less Than 10 µmParticulate Matter With A Diameter Less Than 10 µmDirect PM2.5Direct PM2.5 Directly Emitted And Not Secondarily Formed PM2.5Directly Emitted And Not Secondarily Formed PM2.5
Also Known As Primary PM2.5Also Known As Primary PM2.5