hickory and triad pm2.5 sip development stakeholder meeting presented by: nc division of air quality...

Hickory and TriadHickory and TriadPM2.5 SIP Development PM2.5 SIP Development

Stakeholder MeetingStakeholder MeetingPresented By:Presented By:

NC Division Of Air QualityNC Division Of Air Quality

Attainment Planning BranchAttainment Planning Branch

Hosted At:Hosted At:

PPiedmontiedmont AAuthorityuthority forfor RRegionalegional TTransportationransportation OfficesOffices

November 14, 2007November 14, 2007

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

Fine Particulate Matter BackgroundFine Particulate Matter BackgroundAir Quality Modeling OverviewAir Quality Modeling Overview

Emissions Inventory DevelopmentEmissions Inventory DevelopmentGeorge Bridgers, NCDAQ Meteorologist IIGeorge Bridgers, NCDAQ Meteorologist II

Acting Chief of Attainment PlanningActing Chief of Attainment Planning

Human Hair (70 Human Hair (70 µµm diameterm diameter))

Hair cross section (70 Hair cross section (70 m)m)

PMPM2.52.5

(2.5 (2.5 µµm)m)PMPM1010

((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

PM2.5 Speciated Mass Contributions at Hickory

0

5

10

15

20

25

30

35

40

4 28 40 52 70 82 94 106 118 130 142 154 166 178 196 208 220 232 244 256 268 280 292 304 316 328 340 352 364 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4

2002

PM2.5 Speciated Mass Contributions at Lexington

0

5

10

15

20

25

30

35

40

16 28 40 52 64 76 94 106 118 130 142 154 166 178 190 202 214 226 238 250 262 274 286 298 310 322 334 346 358 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4

2002

PM2.5 Speciated Mass Contributions at Mendenhall

0

5

10

15

20

25

30

35

40

4 16 28 40 52 64 76 100 112 124 136 154 166 178 190 202 214 226 238 250 262 274 286 310 322 334 346 358 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4

2002

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

2001 – 2003 Design Value2001 – 2003 Design Value

Catawba – Catawba – 15.515.5 µµg/m3g/m3

Davidson – Davidson – 15.815.8 µµg/m3g/m3

Guilford – 14.0 Guilford – 14.0 µµg/m3g/m3

PM2.5 Nonattainment TimelinePM2.5 Nonattainment Timeline

Effective date = Effective date =

SIP submittal date =SIP submittal date =

Attainment date = Attainment date =

Data used to determineData used to determine attainment attainment ==

(Modeling) Attainment year = (Modeling) Attainment year =

Maintenance years =Maintenance years =

April 5, 2005April 5, 2005April 5, 2008April 5, 2008April 5, 2010*April 5, 2010*2007-20092007-200920092009TBDTBD

* 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

Meteorological Model Emissions Processor

Air Quality Model

MM5MM5 SMOKESMOKE

CMAQCMAQ

SSparseparseMMatrixatrixOOperatorperatorKKernelernelEEmissionsmissions

CCommunityommunity

MMultiscale ultiscale

AAir ir

QQualityuality

SystemSystem

Temporally and Temporally and Spatially Gridded Spatially Gridded Air Quality Output Air Quality Output

predictionspredictions

Model SelectionModel Selection Meteorological Model Meteorological Model

Mesoscale Meteorological Model (MM5)Mesoscale Meteorological Model (MM5) Emissions ModelEmissions Model

Sparse Matrix Operator Kernel Emissions Sparse Matrix Operator Kernel Emissions (SMOKE)(SMOKE)

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

Emission ProcessingEmission Processing

Gridding

Speciation

Temporal

Emission Inventory

SMOKE Emission

Model

Emission Source CategoriesEmission Source Categories Point sources:Point sources: utilities, refineries, industrial utilities, refineries, industrial

sources, etc.sources, etc.

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.

Biogenic:Biogenic: trees, vegetation, crops trees, vegetation, crops

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

forest management/wild fires forest management/wild fires

FutureFuture = the emissions inventory developed to simulate = the emissions inventory developed to simulate the attainment year 2009the attainment year 2009

VISTAS / ASIP Actual 2002 InventoryVISTAS / ASIP Actual 2002 Inventory Utilized Consolidated Emissions Reporting Rule (CERR) Utilized Consolidated Emissions Reporting Rule (CERR)

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

VISTAS / ASIP Typical 2002 InventoryVISTAS / ASIP Typical 2002 Inventory

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

Controls AppliedControls Applied NONOxx SIP Call SIP Call

Seasonal NOSeasonal NOxx emission caps large industrial boilers emission caps large industrial boilers Clean Smokestacks ActClean Smokestacks Act

Effects North Carolina Duke Energy & Progress Energy Effects North Carolina Duke Energy & Progress Energy sourcessources

Year-round caps of NOYear-round caps of NOxx (2007 & 2009) and (2007 & 2009) andSOSO22 (2009 & 2013) (2009 & 2013)

No trading allowed to meet capsNo trading allowed to meet caps Required to submit compliance plan annuallyRequired to submit compliance plan annually

Clean Air Interstate Rule (CAIR)Clean Air Interstate Rule (CAIR) Year-round NOYear-round NOxx (2009 & 2015) and SO (2009 & 2015) and SO22 (2010 & 2015) (2010 & 2015)

caps for utilitiescaps for utilities Allows for trading creditsAllows for trading credits

Controls Applied (continued)Controls Applied (continued) Vehicle emissions testingVehicle emissions testing

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

programprogram Ultra-Low sulfur fuelsUltra-Low sulfur fuels

Both diesel and gasolineBoth diesel and gasoline Cleaner enginesCleaner engines

Tier 2 vehicle standardsTier 2 vehicle standards Heavy duty gasoline & diesel highway vehicle Heavy duty gasoline & diesel highway vehicle

standardsstandards Large nonroad diesel engine standardsLarge nonroad diesel engine standards Nonroad spark engine & recreational engine Nonroad spark engine & recreational engine

standards standards

2002 - 2009 Emissions Summary for North Carolina

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

VOC NOx PM2.5 PM10 NH3 SO2

Ton

s

2002 2009

2002 - 2009 Emissions Summary for Catawba County

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000


Ton

s

2002 2009

2002 - 2009 Emissions Summary for Davidson County

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000


Ton

s

2002 2009

2002 - 2009 Emissions Summary for Guildford County

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000


Ton

s

2002 2009

Model Performance EvaluationModel Performance EvaluationNick Witcraft, NCDAQ Meteorologist INick Witcraft, NCDAQ Meteorologist I

Meteorological ModelingMeteorological Modeling Penn State / NCAR MM5 meso-scale Penn State / NCAR MM5 meso-scale

meteorological modelmeteorological model Version 3.6.1+Version 3.6.1+ Widely used in theWidely used in the

research and regulatoryresearch and regulatorycommunitiescommunities

VISTAS Contracted WithVISTAS Contracted WithBarons AdvancedBarons AdvancedMeteorological SystemsMeteorological Systems(BAMS)(BAMS)

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

Moisture (Relative Humidity)Moisture (Relative Humidity)

Slight high bias most of yearSlight high bias most of year Low bias Sep-NovLow bias Sep-Nov

RH is linked to temperature and moisture biasesRH is linked to temperature and moisture biases

Moisture (Relative Humidity)Moisture (Relative Humidity)

Relative Humidity Bias & Error

-4

-2

0

2

4

6

8

10

12

14

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

%

Bias

Error

~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


m/s Bias

Error

Wind Speed (no calm) Bias & Error

00.2

0.40.6

0.81

1.21.4

1.61.8

2


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

QV_(g/kg) 0.15 0.04 -0.27 -0.25 0.75 1.36 1.64 0.85 0.89 0.77 0.58 0.85RH_(%) 2.11 0.44 -0.49 -1.56 12.53 11.07 9.88 11.72 0.57 0.60 0.59 0.48

WSPD-regular_(m/s) 0.82 0.90 0.83 0.96 1.50 1.49 1.45 1.54 0.47 0.46 0.32 0.42WSPD-nocalms_(m/s) 0.33 0.45 0.19 0.36 1.24 1.23 1.11 1.21 0.41 0.41 0.28 0.36WSPD-mincalm_(m/s) 0.62 0.71 0.58 0.71 1.31 1.31 1.21 1.30 0.48 0.48 0.34 0.43

SPD-lyr1_(m/s) 1.34 1.42 1.19 1.38 1.70 1.74 1.58 1.69 0.48 0.46 0.30 0.43CLD_(%) 0.30 2.01 3.31 -1.89 19.13 25.12 29.05 21.72 0.50 0.26 0.21 0.46CLD2_(%) 3.70 6.63 9.31 3.19 18.53 26.03 29.83 20.52 0.50 0.26 0.22 0.46

TMP-lyr1_(K) -0.33 -0.20 -0.13 -0.26 2.17 1.86 1.73 1.95 0.85 0.85 0.76 0.81Wdir_stats

WDIR_(deg) -17.51 -37.84 14.90 -1.87 22.37 25.29 32.74 25.38 0.12 -0.06 -0.19 0.16

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

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

Model Performance StatisticsModel Performance StatisticsPM2.5 – STN sitesPM2.5 – STN sites

37-035-0004Statistical Measure Abbrev. SO4 NO3 OC NH4 EC PM25Mean Fractionalized Bias (Fractional Bias)

MFB -22.6710 -45.6240 -45.9420 0.0470 -38.6880 -24.0680

Mean Fractional Error MFE 36.5230 95.2820 50.8140 33.8510 47.6100 36.2260

Accuracy of Paired Peak Ap -14.3780 3.6960 -32.9480 11.8600 -26.0520 -15.7440

Normalized Mean Bias NMB -14.3260 10.0740 -32.7020 -3.0160 -34.4990 -20.9890Normalized Mean Error NME 31.7830 72.3460 39.7650 32.3020 39.7050 30.4840Mean Biased MB -14.3780 3.6960 -32.9480 11.8600 -26.0520 -15.7440

Mean Absolute Gross Error MAGE 1.5430 0.8410 2.1080 0.5290 0.2870 4.9020

Root Mean Square Error RMSE 2.1480 1.1900 2.5920 0.7480 0.3870 6.4110

Coefficient of Determination R2 0.6570 0.3660 0.3890 0.4180 0.4300 0.4610

Number of Points used n 50 50 50 50 50 49

Pollutant

37-067-0022Statistical Measure Abbrev. SO4 NO3 OC NH4 EC PM25Mean Fractionalized Bias (Fractional Bias)

MFB -14.1840 -35.3550 -48.2610 0.6820 -3.8770 -19.2570

Mean Fractional Error MFE 31.7940 76.7290 53.0330 29.6960 30.4620 26.5040

Accuracy of Paired Peak Ap -6.9750 -1.8550 -34.4750 10.0430 7.1710 -14.1970

Normalized Mean Bias NMB -10.8610 15.6900 -32.5150 -4.8180 -2.1740 -17.7670Normalized Mean Error NME 25.7980 66.8970 38.3890 29.3730 30.6660 24.2430Mean Normalized Bias MNBMean Biased MB -6.9750 -1.8550 -34.4750 10.0430 7.1710 -14.1970

Mean Absolute Gross Error MAGE 1.3160 0.6870 1.9260 0.5020 0.1660 3.8390

Root Mean Square Error RMSE 1.7440 1.0030 2.1940 0.7330 0.2550 5.4080

Coefficient of Determination R2 0.7450 0.5130 0.5840 0.4730 0.3000 0.6080

Number of Points used n 54 54 54 54 54 54

Pollutant

Hattie AveHattie Ave(Forsyth County)(Forsyth County)

HickoryHickory(Catawba County)(Catawba County)

Model Performance StatisticsModel Performance StatisticsPM2.5 – Hickory STNPM2.5 – Hickory STN

STN Hickory CMAQ 12km - 2002 Monthly

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0

Fractional Bias

Mea

n F

ract

ion

al E

rro

r

SulfateNitrateAmmon.OrganicsECSoilsPM2.5PM10CMCriteriaGoal

•Good SOGood SO44, Total PM2.5 performance, Total PM2.5 performance•Poor NOPoor NO33 performance performance

Goal ThresholdsGoal ThresholdsBias: +-30%Bias: +-30%Error: 50%Error: 50%

Criteria ThresholdsCriteria ThresholdsBias: +-60%Bias: +-60%Error: 75%Error: 75%


0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Mea

n F

ract

ion

al E

rro

r







•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.


-200.0

-150.0

-100.0

-50.0

0.0

50.0

100.0

150.0

200.0

0.0 4.0 8.0 12.0 16.0 20.0

Average Concentration (g/m3)

Mea

n F

ract

ion

al B

ias

SulfateNitrateAmmon.OrganicsECSoilsPM2.5PM10CM(+) Criteria(+) Goal(-) Goal(-) Criteria


0.0

50.0

100.0

150.0

200.0

0.0 4.0 8.0 12.0 16.0 20.0


Mea

n F

ract

ion

al E

rro

r


Model Performance StatisticsModel Performance StatisticsPM2.5 – Hattie STNPM2.5 – Hattie STN


STN Hattie CMAQ 12km - 2002 Monthly

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0

Fractional Bias

Mea

n F

ract

ion

al E

rro

r





0.0

10.0

20.0

30.0

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Model Performance StatisticsModel Performance StatisticsPM2.5 – FRM sitesPM2.5 – FRM sites

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)

MFB3.87179 -5.00249 -11.5124 -18.60138 -32.29198 -34.01488 -37.5508 -17.17965 -17.81556 12.57825 9.42611 -1.15629

Mean Fractional Error MFE 32.51936 29.65687 32.76715 30.39688 38.81785 38.97467 45.33094 32.1642 35.16821 32.31407 32.17262 36.62851Accuracy of Paired Peak

Ap -0.25745 0.16006 -0.13917 -0.10491 0.87539 -0.22932 -0.4327 -0.06662 -0.20342 0.01004 -0.11037 -0.41067Normalized Mean Bias NMB 7.79789 -3.21695 -5.75633 -13.48394 -24.85635 -26.42513 -29.84115 -15.11038 -10.97409 19.69164 16.98349 0.68683Normalized Mean Error

NME33.0415 28.14266 29.60649 26.23081 30.89238 30.72728 36.10852 26.68481 26.80111 35.01287 34.78851 34.8323

Mean Normalized Bias MNB 16.9901 4.42162 4.39775 -10.90071 -20.80268 -24.63417 -21.6425 -9.69121 -7.93415 25.43418 19.19293 10.52794Mean Normalized Gross Error

MNGE39.72529 31.85955 38.45982 28.23872 33.76054 31.89422 40.49355 29.04517 31.4553 41.14046 37.26213 39.56016

Mean Biased MB 0.91415 -0.36289 -0.63763 -1.48697 -3.24962 -3.87066 -5.48763 -2.34163 -1.56093 2.24939 1.90059 0.07785Mean Absolute Gross Error

MAGE3.87349 3.17467 3.2795 2.89266 4.03875 4.50082 6.64017 4.13531 3.81213 3.99954 3.89312 3.94814

Root Mean Square Error

RMSE5.21913 4.38938 4.45683 3.74948 5.49243 6.04268 8.86405 5.606 5.25377 5.47428 5.32453 5.26488

Coefficient of Determination

R2

0.33012 0.45595 0.42787 0.43043 0.45697 0.67614 0.50262 0.65574 0.6596 0.54024 0.45193 0.41422Bias Factor BF 1.16947 1.04422 1.04398 0.89099 0.79169 0.75366 0.78358 0.90275 0.92032 1.25434 1.19193 1.10528Number of Points used n 2740 2707 2789 2778 2758 2710 2925 2709 2685 2788 2699 2661


FRM Monitoring Sites within the VISTAS 12km DomainFRM Monitoring Sites within the VISTAS 12km Domain ..

FRM VISTAS Sites CMAQ 12km - 2002 Monthly

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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)

MFB 10.28557 -24.51066 -25.30801 -15.82477 -17.78711 -34.29187 -45.3679 -15.78621 -17.24198 25.14946 -10.92016 3.08583


Ap 0.04317 -0.09879 -0.1763 -0.00499 -0.19553 -0.42383 -0.36958 -0.14559 -0.01765 0.27824 -0.04994 -0.09921

Normalized Mean Bias NMB 5.40073 -18.72328 -23.38787 -13.18737 -16.96025 -32.53042 -38.38965 -17.807 -16.67013 27.92665 -9.00896 3.654Normalized Mean Error

NME 15.72311 19.9611 28.46699 18.91983 18.18236 32.53042 38.38965 22.99225 25.84471 30.13704 19.00118 20.12219

Mean Normalized Bias MNB 14.27531 -20.74026 -18.62383 -13.1912 -15.13653 -28.18097 -35.00442 -11.03096 -10.96486 33.69843 -6.76357 9.81511Mean Normalized Gross Error

MNGE 22.16478 22.23697 25.8631 18.57948 16.84285 28.18097 35.00442 25.06525 28.3495 35.92392 21.56552 27.7277

Mean Biased MB 0.74868 -2.3092 -3.17607 -1.43479 -2.73399 -5.24824 -9.32171 -3.6629 -3.03063 3.21715 -1.09279 0.4721Mean Absolute Gross Error

MAGE 2.17962 2.46187 3.86582 2.05848 2.931 5.24824 9.32171 4.72951 4.69857 3.47179 2.30484 2.59979

Root Mean Square ErrorRMSE 2.72987 2.65671 5.5412 2.44399 4.07019 7.01467 10.84356 5.52071 5.84947 4.46585 2.82757 3.69954

Coefficient of Determination R2 0.78737 0.92657 0.44631 0.71435 0.6854 0.80312 0.30351 0.83091 0.41104 0.72664 0.69705 0.79278

Bias Factor BF 1.14275 0.7926 0.81376 0.86809 0.84863 0.71819 0.64996 0.88969 0.89035 1.33698 0.93236 1.09815Number of Points used n 8 9 10 10 10 9 11 10 10 10 10 10

Hickory (Catawba County)Hickory (Catawba County)


Hickory (Catawba County)Hickory (Catawba County)FRM Hickory CMAQ 12km - 2002 Monthly

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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)

MFB 4.63564 -18.0252 -9.9917 -9.35937 -26.63161 -42.23228 -40.24946 -29.80245 -10.85414 12.6755 -12.31272 -19.06711


Ap 0.17986 -0.11705 0.15716 0.02694 -0.03313 -0.2793 -0.35558 -0.24467 -0.0794 0.21105 0.23135 -0.32285

Normalized Mean Bias NMB 8.43928 -14.79351 -18.88724 -7.37745 -20.72336 -35.10524 -35.79996 -26.31458 -13.34432 14.89208 -0.13561 -18.53893Normalized Mean Error

NME 20.27853 17.44602 35.13404 13.43276 23.21198 35.10524 36.51237 26.31458 24.45658 19.64973 23.4158 37.13708

Mean Normalized Bias MNB 8.04976 -14.5975 292.0322 -7.82412 -21.11925 -33.73975 -31.93295 -24.98397 -6.21985 16.7945 -4.45061 -3.56587Mean Normalized Gross Error

MNGE 21.48891 18.37526 337.8194 13.02814 24.18666 33.73975 33.58529 24.98397 26.12053 23.59218 24.17365 45.82681

Mean Biased MB 1.27602 -2.10068 -2.61693 -0.82381 -3.37169 -5.62386 -8.13733 -5.03924 -2.01666 1.96129 -0.01946 -2.76045Mean Absolute Gross Error

MAGE 3.06611 2.47733 4.86802 1.49999 3.77659 5.62386 8.29926 5.03924 3.696 2.58787 3.36017 5.52971


RMSE 3.81138 2.96724 6.62947 1.98443 4.78826 6.79069 9.37234 6.2034 4.26848 2.98761 4.32064 6.82838


R2 0.7756 0.78917 0.40266 0.66008 0.71797 0.78242 0.61825 0.83332 0.38543 0.92932 0.81825 0.66368



Lexington (Davidson County)Lexington (Davidson County)FRM Davidson CMAQ 12km - 2002 Monthly

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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)

MFB 19.1454 -1.12418 -7.666 -24.20169 -24.93979 -32.97002 -36.445 -21.66835 -4.68628 16.90341 5.42927 4.05433


Ap 0.09011 0.12577 0.14355 -0.09023 -0.17322 -0.24689 -0.20378 -0.39376 -0.18011 0.36109 0.49883 -0.48266

Normalized Mean Bias NMB 25.46292 0.66019 -0.45107 -19.24879 -17.68747 -29.28373 -34.12104 -25.16558 -7.95848 26.58833 15.66082 -2.36134Normalized Mean Error

NME 40.13498 18.97885 26.0331 23.6201 23.88124 29.87053 36.78232 28.89808 25.28658 34.81678 30.02136 36.26746

Mean Normalized Bias MNB 31.81679 1.53086 -1.45199 -18.83038 -18.81816 -27.13435 -27.45204 -16.63162 3.12702 24.03073 10.56236 13.36104Mean Normalized Gross Error

MNGE 45.70157 18.22748 27.00923 22.93126 25.16462 27.92454 34.14149 25.61538 32.50039 33.82903 28.2018 38.90338

Mean Biased MB 3.04737 0.07394 -0.05381 -2.1033 -2.26072 -4.50481 -7.70365 -4.36443 -1.14101 3.0182 1.81091 -0.27164Mean Absolute Gross Error

MAGE 4.8033 2.12563 3.1055 2.58095 3.05238 4.59508 8.3045 5.01175 3.62535 3.95227 3.47147 4.17215


RMSE 5.96173 2.90943 4.00003 3.35457 3.81557 5.55912 10.02389 6.91199 4.19206 5.2867 4.5967 6.08506


R2 0.30967 0.65173 0.62773 0.34213 0.56949 0.7936 0.60927 0.72854 0.59994 0.80392 0.83195 0.50524



Mendenhall (Guilford County)Mendenhall (Guilford County)FRM Guilford CMAQ 12km - 2002 Monthly

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Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN SOVISTAS STN SO44

JanuaryJanuary JulyJuly

Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN NOVISTAS STN NO33


Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN OCVISTAS STN OC


Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN ECVISTAS STN EC


Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN NHVISTAS STN NH44


Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS STN Total PM2.5VISTAS STN Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN SONC STN SO44


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN NONC STN NO33


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN OCNC STN OC


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN ECNC STN EC


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN NHNC STN NH44


Model Performance Scatter PlotsModel Performance Scatter PlotsNC STN Total PM2.5NC STN Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsHickory STN Total PM2.5Hickory STN Total PM2.5

JanuaryJanuary JulyJuly***Speciated performance similar to all NC performance***Speciated performance similar to all NC performance

Model Performance Scatter PlotsModel Performance Scatter PlotsHattie Ave STN Total PM2.5Hattie Ave STN Total PM2.5

JanuaryJanuary JulyJuly***Speciated performance similar to all NC performance***Speciated performance similar to all NC performance

Model Performance Scatter PlotsModel Performance Scatter PlotsVISTAS FRM Total PM2.5VISTAS FRM Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsNC FRM Total PM2.5NC FRM Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsHickory FRM Total PM2.5Hickory FRM Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsLexington FRM Total PM2.5Lexington FRM Total PM2.5


Model Performance Scatter PlotsModel Performance Scatter PlotsMendenhall FRM Total PM2.5Mendenhall FRM Total PM2.5


Hickory STN – Time SeriesHickory STN – Time Series

Model Performance Time SeriesModel Performance Time SeriesTotal PM2.5Total PM2.5

Hickory STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesSulfate (SOSulfate (SO44))

Hickory STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesNitrate (NONitrate (NO33))

Hickory STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesElemental Carbon (EC)Elemental Carbon (EC)

Hickory STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesOrganic Carbon (OC)Organic Carbon (OC)

Hickory STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesAmmonium (NHAmmonium (NH44))

Hickory STNObsObs

ModelModel

Hattie Ave STN – Time SeriesHattie Ave STN – Time Series

Model Performance Time SeriesModel Performance Time SeriesTotal PM2.5Total PM2.5

Hattie Ave STNObsObs

ModelModel

Model Performance Time SeriesModel Performance Time SeriesSulfate (SOSulfate (SO44))


ModelModel

Model Performance Time SeriesModel Performance Time SeriesNitrate (NONitrate (NO33))


ModelModel

Model Performance Time SeriesModel Performance Time SeriesElemental Carbon (EC)Elemental Carbon (EC)


ModelModel

Model Performance Time SeriesModel Performance Time SeriesOrganic Carbon (OC)Organic Carbon (OC)


ModelModel

Model Performance Time SeriesModel Performance Time Series Ammonium (NH Ammonium (NH44))


ModelModel

Model Performance Time SeriesModel Performance Time SeriesHickory – FRMHickory – FRM


ObsObsModel – Model – 36km36km, , 12km12km

Model Performance Time SeriesModel Performance Time SeriesLexington – FRMLexington – FRM



Model Performance Time SeriesModel Performance Time SeriesMendenhall – FRMMendenhall – FRM



Example – July 16Example – July 1637-035-004 37-035-004 37-057-0002 37-081-0013

Date Jday Quarter Hickory (STN) Hickory (FRM) Lexington (FRM) Mendenhall (FRM)07/16/02 197 Q3 33.5 33.1 34.8

Example – August 3Example – August 337-035-004 37-035-004 37-057-0002 37-081-0013

Date Jday Quarter Hickory (STN) Hickory (FRM) Lexington (FRM) Mendenhall (FRM)08/03/02 215 Q3 30.0 19.5 17.4

Example – February 25Example – February 2537-035-004 37-035-004 37-057-0002 37-081-0013

Date Jday Quarter Hickory (STN) Hickory (FRM) Lexington (FRM) Mendenhall (FRM)02/25/02 56 Q1 24.5 21.4 20.3 18.8

Stacked Bar ChartsStacked Bar ChartsHickory STNHickory STN

JFM Obs (left) vs 2002gt2a (right) at Hickory STN

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2 14 20 26 32 38 44 50 56 62 68 74 86

Julian Date

ug

/m^

3

EC

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AMJ Obs (left) vs 2002gt2a (right) at Hickory STN

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92 98 104 110 116 134 176

Julian Date

ug

/m^

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Jan-MarchJan-March April-JuneApril-June

Stacked Bar ChartsStacked Bar ChartsHickory STNHickory STN

JAS Obs (left) vs 2002gt2a (right) at Hickory STN

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182 188 194 200 206 212 218 224 230 236 242 248 254 260 266 272

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OND Obs (left) vs 2002gt2a (right) at Hickory STN

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278 284 290 296 302 308 314 320 326 332 338 344 350 362

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ug

/m^

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NH4

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July-SepJuly-Sep Oct-DecOct-Dec

Stacked Bar ChartsStacked Bar ChartsHattie Ave STNHattie Ave STN

JFM Obs (left) vs 2002gt2a (right) at Hattie STN

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2 14 20 26 32 38 44 50 56 62 68 74 80 86

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/m^

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AMJ Obs (left) vs 2002gt2a (right) at Hattie STN

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92 98 104 110 116 122 128 134 140 146 152 158 164 170 176

Julian Date

ug

/m^

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EC

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Jan-MarchJan-March April-JuneApril-June

Stacked Bar ChartsStacked Bar ChartsHattie Ave STNHattie Ave STN

JAS Obs (left) vs 2002gt2a (right) at Hattie STN

0

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OND Obs (left) vs 2002gt2a (right) at Hattie STN

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278 284 290 296 302 308 314 320 326 332 338 344 350 362

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/m^

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EC

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July-SepJuly-Sep Oct-DecOct-Dec


Statistical TablesStatistical Tables Scatter PlotsScatter Plots Time Series Time Series ((Selected ExamplesSelected Examples))




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.


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.


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.

Attainment TestAttainment TestBebhinn Do, NCDAQ Meteorologist IIBebhinn Do, NCDAQ Meteorologist II

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.

Attainment TestAttainment Test

DVF = RRF * DVBDVF = RRF * DVB

DVF = Future Design ValueDVF = Future Design Value RRF = Relative Response FactorRRF = Relative Response Factor DVB = Baseline Design ValueDVB = Baseline Design Value

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:

DVB = (2000) + 2*(2001) + 3*(2002) + 2*(2003) + (2004)DVB = (2000) + 2*(2001) + 3*(2002) + 2*(2003) + (2004)

Values are average based on calendar quarters, where:Values are average based on calendar quarters, where: Q1 = January, February, 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

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


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

PM2.5PM2.5FRMFRM = { [OCMmb] + [EC] + [SO4] + = { [OCMmb] + [EC] + [SO4] +

[NO3[NO3FRMFRM] + [NH4] + [NH4FRMFRM] + [water] + [crustal material] ] + [water] + [crustal material]

+ [0.5] }+ [0.5] }


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

Particle Bound Water = PBWParticle Bound Water = PBW =(-0.002618) + =(-0.002618) + (0.980314*nh4) + (-0.260011*no3) + (-0.000784*so4) + (-(0.980314*nh4) + (-0.260011*no3) + (-0.000784*so4) + (-0.159452*nh4**2) + (-0.356957*no3*nh4) + 0.159452*nh4**2) + (-0.356957*no3*nh4) + (0.153894*no3**2) + (0.212891*so4*nh4) + (0.153894*no3**2) + (0.212891*so4*nh4) + 0.0444366*so4*no3) + (-0.048352*so4**2) 0.0444366*so4*no3) + (-0.048352*so4**2)

Crustal/SoilCrustal/Soil = 3.73 * [Si] + 1.63*[Ca] + 2.42*[Fe] + = 3.73 * [Si] + 1.63*[Ca] + 2.42*[Fe] + 1.94*[Ti]1.94*[Ti]

Organic carbon mass by difference Organic carbon mass by difference (OCmb)(OCmb) = PM2.5 = PM2.5FRMFRM - { [SO4] + [NO3 - { [SO4] + [NO3FRMFRM] + [NH4] + [NH4FRMFRM] + ] +

[water] + [crustal material] + [EC] + [0.5] }[water] + [crustal material] + [EC] + [0.5] }


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)




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


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


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 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)




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

(1999-2003)(1999-2003) For 2010: Catawba 14.07; Davidson 14.36For 2010: Catawba 14.07; Davidson 14.36 For 2015: Catawba 13.45; Davidson 13.61For 2015: Catawba 13.45; Davidson 13.61

http://www.epa.gov/interstateairquality/pdfs/finaltech02.pdfhttp://www.epa.gov/interstateairquality/pdfs/finaltech02.pdf

Modeling from other RPOsModeling from other RPOs

Observational AnalysesObservational Analyses

Design Values Design Values TrendsTrends

Hickory Design Values

12.00

13.00

14.00

15.00

16.00

17.00

18.00

1999-2001

2000-2002

2001-2003

2002-2004

2003-2005

2004-2006

2005-2007

2006-2008

2007-2009

Hickory

2009 DVF

Linear (Hickory)

Lexington Design Values

12.00

13.00

14.00

15.00

16.00

17.00

18.00

1999-2001

2000-2002

2001-2003

2002-2004

2003-2005

2004-2006

2005-2007

2006-2008

2007-2009

Lexington

2009 DVF

Linear (Lexington)

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

sufficient evidence exists.sufficient evidence exists.

Evidence may include:Evidence may include: Modeling Sensitivity StudiesModeling Sensitivity Studies Speciated DataSpeciated Data Emissions InventoriesEmissions Inventories Monitoring or Data AnalysisMonitoring or Data Analysis

Technical DemonstrationsTechnical Demonstrations

PM2.5 Speciated Mass Contributions at Mendenhall

0

5

10

15

20

25

30

35

40

4 16 28 40 52 64 76 100 112 124 136 154 166 178 190 202 214 226 238 250 262 274 286 310 322 334 346 358 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4



0

5

10

15

20

25

30

35

40

4 28 40 52 70 82 94 106 118 130 142 154 166 178 196 208 220 232 244 256 268 280 292 304 316 328 340 352 364 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4


PM2.5 Speciated Mass Contributions at Lexington

0

5

10

15

20

25

30

35

40

16 28 40 52 64 76 94 106 118 130 142 154 166 178 190 202 214 226 238 250 262 274 286 298 310 322 334 346 358 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4


Greensboro, NC (370810013) Summer-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Greensboro, NC (370810013) Winter-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3 )

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Greensboro, NC (370810013) Annual-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


Hickory, NC (370350004) Summer-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Hickory, NC (370350004) Winter-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Hickory, NC (370350004) Annual-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3 )

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


Lexington, NC (370570002) Summer-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_

EGU

SO2_

nonEGU

NOx_

Ground

NOx_

Point

NH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Lexington, NC (370570002) Winter-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_non

EGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fires

P

M2.

5 (

g/m

3 )

Bio.

Antro.

BCsMRPO

M-VU

CENVISTAS

WV

VATN

SC

NC

MSKY

GA

FLAL

Lexington, NC (370570002) Annual

-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2_EGU

SO2_nonEG

U

NOx_

Gro

und

NOx_

Point

NH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


More prevalent in cooler seasons.More prevalent in cooler seasons.

Less than 0.2 Less than 0.2 μg mμg m-3-3 decrease annually at decrease annually at all three sites.all three sites.

Based on evidence, claiming NOBased on evidence, claiming NOxx as as

insignificant to PM2.5 attainment.insignificant to PM2.5 attainment.



0

5

10

15

20

25

30

35

40

4 28 40 52 70 82 94 106 118 130 142 154 166 178 196 208 220 232 244 256 268 280 292 304 316 328 340 352 364 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4



-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3 )

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


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

PM2.5 attainment.PM2.5 attainment.



0

5

10

15

20

25

30

35

40

4 28 40 52 70 82 94 106 118 130 142 154 166 178 196 208 220 232 244 256 268 280 292 304 316 328 340 352 364 _ Avg.

Julian Day

Mas

s (μ

g m

-3) Crustal

EC

OC

H2O

NH4

NO3

SO4



-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEG

U

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

P

M2.

5 ( g

/m3)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2_EGU

SO2_nonEGU

NOx_Gro

und

NOx_Poin

tNH3

VOCs

PC_Gro

und

PC_Poin

t

PC_Fire

s

PM

2.5 ( g

/m3 )

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL


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.


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

Emissions Budgets (MVEBs)Emissions Budgets (MVEBs)

Transportation ConformityTransportation Conformity

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


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


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



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


Ton

s

Point

Area

On-Road Mobile

Non-Road Mobile

Biogenics



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


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


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

10

11

12

13

14

15

16

17

18

19

1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

PM

2.5

Co

nce

ntr

atio

n (

ug

/m3)

Catawba 3703500041 Davidson 3705700021 Guilford 3708100131

Annual PM2.5 NAAQS

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.

Questions/CommentsQuestions/Commentshttp://ncair.orghttp://ncair.org

George Bridgers, Acting Chief of Attainment PlanningGeorge Bridgers, Acting Chief of Attainment Planning919-715-6287919-715-6287George.Bridgers@[email protected]

Bebhinn Do, Meteorologist IIBebhinn Do, Meteorologist [email protected]@ncmail.net

Nick Witcraft, Meteorologist INick Witcraft, Meteorologist [email protected]@ncmail.net

Questions/CommentsQuestions/Commentshttp://ncair.orghttp://ncair.org

Chris Misenis, Meteorologist IChris Misenis, Meteorologist [email protected]@ncmail.net

Janice Godfrey, Environmental Engineer IIJanice Godfrey, Environmental Engineer II919-715-7647919-715-7647Janice.Godfrey@[email protected]

Phyllis Jones, Environmental Engineer IIPhyllis Jones, Environmental Engineer II919-715-1246919-715-1246Phyllis.D.Jones@[email protected]

Thank You!Thank You!

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

SO2SO2 Sulfur DioxideSulfur DioxideSO4SO4 SulfateSulfateNONO Nitrogen OxideNitrogen OxideNO2NO2 Nitrogen DioxideNitrogen DioxideNO3NO3 NitrateNitrateNOxNOx Nitrogen OxidesNitrogen OxidesOCOC Organic CarbonOrganic CarbonECEC Elemental CarbonElemental CarbonVOCVOC Volatile Organic CarbonsVolatile Organic CarbonsNH3NH3 AmmoniaAmmoniaNH4NH4 AmmoniumAmmoniumNH4SO4NH4SO4 Ammonium SulfateAmmonium SulfateNH4NO3NH4NO3 Ammonium NitrateAmmonium NitrateCMCM Crustal MassCrustal MassPBWPBW Particle Bound WaterParticle Bound Water

hickory and triad pm2.5 sip development stakeholder meeting presented by: nc division of air quality...

Documents

gm3 slide

annual design value

daily design value

possible slide

annual standard

naaqs daily pm2

years daily pm2

daily standard