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Hickory and Triad PM2'5 SIP Development Stakeholder Meeting

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Title: Hickory and Triad PM2'5 SIP Development Stakeholder Meeting


1
Hickory and TriadPM2.5 SIP Development
Stakeholder Meeting
  • Presented By
  • NC Division Of Air Quality
  • Attainment Planning Branch
  • Hosted At
  • Piedmont Authority for Regional Transportation
    Offices
  • November 14, 2007

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

3
Fine Particulate Matter BackgroundAir Quality
Modeling OverviewEmissions Inventory Development
  • George Bridgers, NCDAQ Meteorologist II
  • Acting Chief of Attainment Planning

4
Particulate Matter What is It?
A complex mixture of extremely small
particles and liquid droplets
Hair cross section (70 mm)
Human Hair (70 µm diameter)
M. Lipsett, California Office of Environmental
Health Hazard Assessment
5
Public Health Risks Are Significant
  • Particles are linked to
  • Premature death from heart and lung disease
  • Aggravation of heart and lung diseases
  • Hospital admissions
  • Doctor and ER visits
  • Medication use
  • School and work absences
  • And possibly to
  • Lung cancer deaths
  • Infant mortality
  • Developmental problems in children, such as low
    birth weight

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8
Typical PM Size Distribution
9
2002
10
2002
11
2002
12
National Ambient Air Quality Standard (NAAQS)
  • Annual PM2.5 NAAQS
  • A monitor is violating the annual standard, if
    the annual design value is gt 15.0 µg/m3
  • The annual design value is defined as
  • Annual mean concentration averaged over 3 years
  • Daily PM2.5 NAAQS
  • A monitor is violating the daily standard, if the
    daily design value is gt 35 µg/m3
  • The daily design value is defined as
  • Annual 98th percentile concentrations averaged
    over 3 years

13
North Carolina Areas Designated Nonattainment for
PM2.5
2001 2003 Design Value Catawba 15.5
µg/m3 Davidson 15.8 µg/m3 Guilford 14.0 µg/m3
14
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15
PM2.5 Nonattainment Timeline
  • Effective date
  • SIP submittal date
  • Attainment date
  • Data used to determine attainment
  • (Modeling) Attainment year
  • Maintenance years

April 5, 2005 April 5, 2008 April 5,
2010 2007-2009 2009 TBD
Or as early as possible
16
VISTAS / ASIP
  • Visibility Improvement State and Tribal
    Association of the Southeast
  • Association of Southeastern Integrated Planning
  • Collaborative effort of States and Tribes to
    support management of regional haze, and
    attainment demonstrations for fine particulate
    matter and ozone nonattainment areas in the
    Southeastern US
  • No independent regulatory authority and no
    authority to direct or establish State or Tribal
    law or policy.

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

19
Air Quality Modeling System
20
Model Selection
  • Meteorological Model
  • Mesoscale Meteorological Model (MM5)
  • Emissions Model
  • Sparse Matrix Operator Kernel Emissions (SMOKE)
  • Air Quality Model
  • Community Multiscale Air Quality (CMAQ) model

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

22
Emission Processing
23
Emission Source Categories
  • Point sources utilities, refineries, industrial
    sources, etc.
  • Area sources gas stations, dry cleaners,
    farming practices, fires, etc.
  • On-road mobile sources cars, trucks, buses,
    etc.
  • Nonroad mobile sources agricultural equipment,
    recreational marine, lawn mowers, construction
    equipment, etc.
  • Biogenic trees, vegetation, crops

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

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

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

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

28
Controls Applied
  • NOx SIP Call
  • Seasonal NOx emission caps large industrial
    boilers
  • Clean Smokestacks Act
  • Effects North Carolina Duke Energy Progress
    Energy sources
  • Year-round caps of NOx (2007 2009) andSO2
    (2009 2013)
  • No trading allowed to meet caps
  • Required to submit compliance plan annually
  • Clean Air Interstate Rule (CAIR)
  • Year-round NOx (2009 2015) and SO2 (2010
    2015) caps for utilities
  • Allows for trading credits

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

30
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33
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34
Model Performance Evaluation
  • Nick Witcraft, NCDAQ Meteorologist I

35
Meteorological Modeling
  • Penn State / NCAR MM5 meso-scale meteorological
    model
  • Version 3.6.1
  • Widely used in theresearch and
    regulatorycommunities
  • VISTAS Contracted WithBarons AdvancedMeteorologi
    cal Systems(BAMS)
  • Run at both 36km (Nationwide)and 12km
    (Southeastern US) resolutions for 2002

36
Modeling Domains
12 km
36 km
37
Grid Structure
Vertical MM5 34 layers SMOKE CMAQ 19
layers
48,000 ft
Horizontal 36 km 12 km
Layer 1 36 m deep
Ground
38
Met Model Performance
  • Model Performance For Key Variables
  • Temperature
  • Moisture (Mixing Ratio Relative Humidity)
  • Winds
  • Precipitation
  • Summary Of Met Model Performance

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

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

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

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

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

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

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

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

47
Observed Precip January
Modeled Precip January
Observed Precip April
Modeled Precip April
48
Observed Precip JULY
Modeled Precip JULY
Observed Precip October
Modeled Precip October
49
Model Performance StatisticsMeteorology In North
Carolina
Quarterly Meteorological Statistics
50
Met Model Performance
  • Model Performance For Key Variables
  • Temperature
  • Moisture (Mixing Ratio Relative Humidity)
  • Winds
  • Precipitation
  • Summary Of Met Model Performance

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

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

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

54
PM2.5 Non-Attainment Area Monitors
55
PM2.5 Non-Attainment Area Monitors
56
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables and Plots
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

57
Model Performance StatisticsPM2.5 STN sites
Hickory (Catawba County)
Hattie Ave (Forsyth County)
58
Model Performance StatisticsPM2.5 Hickory STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75
  • Good SO4, Total PM2.5 performance
  • Poor NO3 performance

59
Model Performance StatisticsPM2.5 Hickory STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75
  • Good SO4, Total PM2.5 performance
  • Poor NO3 performance

60
Model Performance StatisticsPM2.5 Hickory STN
  • Poor NO3 performance due to low predicted values.
    Worst performance is in summer.

61
Model Performance StatisticsPM2.5 Hattie STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75
  • Good SO4, Total PM2.5 performance
  • Poor NO3 performance

62
Model Performance StatisticsPM2.5 Hattie STN
Goal Thresholds Bias -30 Error 50 Criteria
Thresholds Bias -60 Error 75
  • Good SO4, Total PM2.5 performance
  • Poor NO3 performance

63
Model Performance StatisticsPM2.5 Hattie STN
  • Good SO4, Total PM2.5 performance
  • Poor NO3 performance

64
Model Performance StatisticsPM2.5 FRM sites
FRM Monitoring Sites within the VISTAS 12km
Domain.
65
Model Performance StatisticsPM2.5 FRM sites
FRM Monitoring Sites within the VISTAS 12km
Domain.
66
Model Performance StatisticsPM2.5 FRM sites
Hickory (Catawba County)
67
Model Performance StatisticsPM2.5 FRM sites
Hickory (Catawba County)
68
Model Performance StatisticsPM2.5 FRM sites
Lexington (Davidson County)
69
Model Performance StatisticsPM2.5 FRM sites
Lexington (Davidson County)
70
Model Performance StatisticsPM2.5 FRM sites
Mendenhall (Guilford County)
71
Model Performance StatisticsPM2.5 FRM sites
Mendenhall (Guilford County)
72
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables and Plots
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

73
Model Performance Scatter PlotsVISTAS STN SO4
January
July
74
Model Performance Scatter PlotsVISTAS STN NO3
January
July
75
Model Performance Scatter PlotsVISTAS STN OC
January
July
76
Model Performance Scatter PlotsVISTAS STN EC
January
July
77
Model Performance Scatter PlotsVISTAS STN NH4
January
July
78
Model Performance Scatter PlotsVISTAS STN Total
PM2.5
July
January
79
Model Performance Scatter PlotsNC STN SO4
January
July
80
Model Performance Scatter PlotsNC STN NO3
January
July
81
Model Performance Scatter PlotsNC STN OC
January
July
82
Model Performance Scatter PlotsNC STN EC
January
July
83
Model Performance Scatter PlotsNC STN NH4
January
July
84
Model Performance Scatter PlotsNC STN Total PM2.5
January
July
85
Model Performance Scatter PlotsHickory STN Total
PM2.5
January
July
Speciated performance similar to all NC
performance
86
Model Performance Scatter PlotsHattie Ave STN
Total PM2.5
January
July
Speciated performance similar to all NC
performance
87
Model Performance Scatter PlotsVISTAS FRM Total
PM2.5
January
July
88
Model Performance Scatter PlotsNC FRM Total PM2.5
January
July
89
Model Performance Scatter PlotsHickory FRM Total
PM2.5
January
July
90
Model Performance Scatter PlotsLexington FRM
Total PM2.5
January
July
91
Model Performance Scatter PlotsMendenhall FRM
Total PM2.5
January
July
92
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables and Plots
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

93
Hickory STN Time Series
94
Model Performance Time SeriesTotal PM2.5
Obs Model
Hickory STN
95
Model Performance Time SeriesSulfate (SO4)
Obs Model
Hickory STN
96
Model Performance Time SeriesNitrate (NO3)
Obs Model
Hickory STN
97
Model Performance Time SeriesElemental Carbon
(EC)
Obs Model
Hickory STN
98
Model Performance Time SeriesOrganic Carbon (OC)
Obs Model
Hickory STN
99
Model Performance Time SeriesAmmonium (NH4)
Obs Model
Hickory STN
100
Hattie Ave STN Time Series
101
Model Performance Time SeriesTotal PM2.5
Obs Model
Hattie Ave STN
102
Model Performance Time SeriesSulfate (SO4)
Obs Model
Hattie Ave STN
103
Model Performance Time SeriesNitrate (NO3)
Obs Model
Hattie Ave STN
104
Model Performance Time SeriesElemental Carbon
(EC)
Obs Model
Hattie Ave STN
105
Model Performance Time SeriesOrganic Carbon (OC)
Obs Model
Hattie Ave STN
106
Model Performance Time Series Ammonium (NH4)
Obs Model
Hattie Ave STN
107
Model Performance Time SeriesHickory FRM
January
July
Obs Model 36km, 12km
108
Model Performance Time SeriesLexington FRM
January
July
Obs Model 36km, 12km
109
Model Performance Time SeriesMendenhall FRM
January
July
Obs Model 36km, 12km
110
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables and Plots
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

111
Example July 16
112
Example July 16
113
Example August 3
114
Example August 3
115
Example February 25
116
Example February 25
117
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables and Plots
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

118
Stacked Bar ChartsHickory STN
Jan-March
April-June
119
Stacked Bar ChartsHickory STN
July-Sep
Oct-Dec
120
Stacked Bar ChartsHattie Ave STN
Jan-March
April-June
121
Stacked Bar ChartsHattie Ave STN
July-Sep
Oct-Dec
122
AQ Model Performance
  • VISTAS, NC Modeled PM2.5 Performance
  • Statistical Tables
  • Scatter Plots
  • Time Series (Selected Examples)
  • PM2.5 Spatial Plots
  • Stacked Bar Charts (Speciation)
  • Summary Of AQ (PM2.5) Model Performance

123
Summary Of AQ (PM2.5) Model Performance
  • Under-predictions of the summer modeled total
    PM2.5 concentrations account for the majority of
    the negative bias and error.
  • Overall performance was reasonably good for
    Sulfate (SO4) and Organic Carbon (OC), the
    largest constituents of PM2.5.

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

125
Summary Of AQ (PM2.5) Model Performance
  • Thinking ahead to Typical and Future year
    modeling, Relative Reduction Factor (RRF)
    calculations, and the Modeled Attainment Test
  • The relative sense of the SIP modeling will make
    the summer under-predictions of PM2.5 less
    significant and not influence strategy decisions.
  • With the annual modeling strategy, there are a
    sufficient number of modeled days in this Base
    or Actual year modeling at each monitoring site
    throughout the year that contribute to the annual
    average gt15 µg without the need for additional or
    alternative modeling.

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

127
Attainment Test
  • Bebhinn Do, NCDAQ Meteorologist II

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

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

130
Attainment Test
  • DVF RRF DVB
  • DVF Future Design Value
  • RRF Relative Response Factor
  • DVB Baseline Design Value

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

132
Attainment Test For PM2.5
  • If the future annual average PM2.5 estimate is
    less than 15.0 µg/m3 , then the attainment test
    is passed.
  • If all such future site-specific design values
    are
  • lt 14.5 µg/m3 the test is passed Basic
    supplemental analyses should be completed to
    confirm the outcome of the modeled attainment
    test
  • Between 14.5 µg/m3 and 15.5 µg/m3 A weight of
    evidence demonstration should be conducted to
    determine if aggregate supplemental analyses
    support the modeled attainment test
  • ? 15.5 µg/m3 , attainment test failed More
    qualitative results are less likely to support a
    conclusion differing from the outcome of the
    modeled attainment test additional controls are
    needed

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

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

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

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

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

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

139
CAIR SMAT Tool
140
CAIR SMAT Tool
141
Issue 2 FRM Mass ? STN Mass
  • Issue is that by design, FRM monitors do not
    retain all ammonium nitrate and other
    semi-volatile materials (negative artifact) and
    FRM samples include particle bound water
    associated with sulfates, nitrates, and other
    hygroscopic species (positive artifact)

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

143
Issue 2 FRM Mass ? STN Mass
  • Adjust nitrate to account for volatilization
  • Calculate quarterly average nitrate, sulfate, EC,
    Degree of Neutralization (DON) of sulfate, and
    crustal
  • Calculate quarterly average NH4 from adjusted
    NO3, SO4, and DON of sulfate
  • Calculate particle bound water from DON, sulfate,
    nitrate, and ammonium values
  • Calculate OC by difference from PM2.5 mass,
    adjusted nitrate, ammonium, sulfate, water, EC,
    crustal, and passive (blank) mass
  • PM2.5FRM OCMmb EC SO4 NO3FRM
    NH4FRM water crustal material 0.5

144
Issue 2 FRM Mass ? STN Mass
  • Nitrates - Adjusted use hourly temperatures and
    24-hour average nitrate measurements
  • NH4FRM DON SO4 0.29NO3FRM
  • Particle Bound Water PBW (-0.002618)
    (0.980314nh4) (-0.260011no3)
    (-0.000784so4) (-0.159452nh42)
    (-0.356957no3nh4) (0.153894no32)
    (0.212891so4nh4) 0.0444366so4no3)
    (-0.048352so42)
  • Crustal/Soil 3.73 Si 1.63Ca
    2.42Fe 1.94Ti
  • Organic carbon mass by difference
  • (OCmb) PM2.5FRM - SO4 NO3FRM NH4FRM
    water crustal material EC 0.5

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

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

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

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

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

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

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

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

153
Results
  • lt 14.5 µg/m3 the test is passed Basic
    supplemental analyses
  • Between 14.5 µg/m3 and 15.5 µg/m3 A weight of
    evidence demonstration should be conducted
  • ? 15.5 µg/m3 attainment test failed, need more
    controls

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

155
Results from Other Studies
  • Clean Air Interstate Rule (CAIR) modeling
  • EPA modeling done to quantify the benefits of
    CAIR
  • Modeling based on 2001 meteorology
  • DVB was a 5yr weight DV centered around 2001
    (1999-2003)
  • For 2010 Catawba 14.07 Davidson 14.36
  • For 2015 Catawba 13.45 Davidson 13.61
  • http//www.epa.gov/interstateairquality/pdfs/fina
    ltech02.pdf
  • Modeling from other RPOs

156
Observational Analyses
Design Values Trends
157
General Insignificance of PM2.5 Species
  • Chris Misenis, NCDAQ Meteorologist I

158
General Insignificance of PM2.5 Species
  • Overview
  • NOx Insignificance
  • NH4 Insignificance
  • VOC Insignificance

159
Overview
  • Pollutants must be evaluated that contribute to
    PM2.5 attainment issue.
  • Included constituents are SO2, NOx, and Direct
    PM2.5. NH3 and VOCs are deemed insignificant.
  • Technical demonstrations are permitted to reverse
    the presumptions made about certain species.

160
Technical Demonstrations
  • SO2, NOx, and Direct PM2.5 MUST be evaluated.
  • Inclusion of NOx can be reversed if sufficient
    evidence exists.
  • Evidence may include
  • Modeling Sensitivity Studies
  • Speciated Data
  • Emissions Inventories
  • Monitoring or Data Analysis

161
NOx Insignificance
162
NOx Insignificance
163
NOx Insignificance
164
NOx Insignificance
165
NOx Insignificance
166
NOx Insignificance
167
NOx Insignificance
  • More prevalent in cooler seasons.
  • Less than 0.2 µg m-3 decrease annually at all
    three sites.
  • Based on evidence, claiming NOx as insignificant
    to PM2.5 attainment.

168
NH3 Insignificance
169
NH3 Insignificance
170
NH3 Insignificance
  • 30 reduction more significant during winter
    season, leading to large annual decrease.
  • However, 30 reduction in NH3 emissions across
    entire domain reduces PM by less than 1 µg m-3.
  • Agree with EPA that NH3 is insignificant to PM2.5
    attainment.

171
VOC Insignificance
172
VOC Insignificance
173
VOC Insignificance
  • VOCs have a significant impact on PM formation in
    NC.
  • However, biogenic VOCs are significantly more
    influential to PM formation than anthropogenic.
  • Given current controls and inability to curtail
    all biogenic emissions, agree with EPA that VOCs
    are insignificant.

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

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

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

177
Mobile SO2 Direct PM2.5Insignificance
  • Both SO2 and Direct PM2.5 must be addressed and
    controls measures evaluated in the PM2.5
    attainment SIP.
  • NCDAQ is working with EPA to potentially have
    On-Road Mobile SO2 and Direct PM2.5 found
    insignificant to the PM2.5 concentrations in the
    respective non-attainment areas.
  • Having either or both found insignificant would
    remove them from consideration when setting the
    MVEBs in the SIP.

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Mobile SO2 Direct PM2.5Insignificance
  • Currently, it appears that NCDAQ will be able to
    successfully declare Mobile SO2 insignificant in
    both Hickory and the Triad.
  • Mobile Direct PM2.5 is more tenuous given higher
    percentages with respect to Total Direct PM2.5.
    Only Hickory appears possible for an
    insignificance determination.
  • Thus, MVEBs in the Triad will likely be set of
    Direct PM2.5.

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

183
Motor Vehicle Emissions Budgets
  • Estimated MVEB emissions outside of Air Quality
    modeling
  • Used updated speeds, VMT, vehicle mix and vehicle
    age distribution supplied by the transportation
    partners
  • Used average 2002 July temperatures
  • OBD-II Inspection/Maintenance Program in all
    counties
  • RVP of 7.8 for Guilford and Davidson Counties
    and9.0 for Catawba County
  • Diesel fuel sulfur content of 43 ppm for all
    counties

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

185
Significant Emissions Reductions Occurring Or On
The Books
  • State Level
  • Clean Smokestacks Act
  • Open Burning Regulations
  • Control of Visible Emissions
  • NC Senate Bill 953 (Expanded IM / OBD)
  • NOx SIP Call Rule
  • State School Bus Idling Policies
  • Federal Level
  • Clean Air Interstate Rule (CAIR)
  • Heavy-Duty Engine and Vehicle Standards and
    Highway Diesel Fuel Sulfur Control Requirements
  • Anti-idling Efforts
  • Standards of Performance for Stationary
    Compression Ignition Internal Combustion Engines
  • Clean Air Diesel Nonroad Rule

186
Close To Attaining Now And Plenty OfSO2
Reductions Yet To ComePrior to the end of 2009
  • Allen Steam Station (Gaston County)
  • 5 units to get Scrubber controls installed in
    2009
  • 13,314 tons SO2 per year to be reduced
  • Belews Creek (Stokes County)
  • 2 units to get Scrubber controls installed in
    2008
  • 85,347 tons SO2 per year to be reduced
  • Marshall Steam Station (Catawba County)
  • 4 units had Scrubber controls installed in
    2006/07
  • 74,533 tons SO2 per year to be reduced
  • Progress Energy (Mayo and Roxboro)
  • 5 units to get Scrubber controls installed by
    2009
  • 105,522 tons SO2 per year to be reduced

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

189
Questions/Comments
  • http//ncair.org
  • George Bridgers, Acting Chief of Attainment
    Planning
  • 919-715-6287
  • George.Bridgers_at_ncmail.net
  • Bebhinn Do, Meteorologist II
  • 919-715-0921
  • Bebhinn.Do_at_ncmail.net
  • Nick Witcraft, Meteorologist I
  • 919-715-2106
  • Nick.Witcraft_at_ncmail.net

190
Questions/Comments
  • http//ncair.org
  • Chris Misenis, Meteorologist I
  • 919-715-9773
  • Chris.Misenis_at_ncmail.net
  • Janice Godfrey, Environmental Engineer II
  • 919-715-7647
  • Janice.Godfrey_at_ncmail.net
  • Phyllis Jones, Environmental Engineer II
  • 919-715-1246
  • Phyllis.D.Jones_at_ncmail.net

191
Thank You!
192
Presentation Acronyms
  • NCDAQ North Carolina Division Of Air Quality
  • SCDHEC South Carolina Department Of Health And
    Environmental Control
  • PART Piedmont Authority For Regional
    Transportation
  • USEPA U.S. Environmental Protection Agency
  • VISTAS Visibility Improvement State And Tribal
    Association Of The Southeast
  • ASIP Association Of Southeastern Integrated
    Planning
  • SIP State Implementation Plan
  • CAA Clean Air Act
  • AQ Air Quality
  • NAAQS Nation Ambient Air Quality Standard
  • RPO Regional Planning Organization
  • CAIR Clear Air Interstate Rule (USEPA)
  • CSA Clean Smokestacks Act (NC)
  • DV Design Value
  • DVB Base Design Value
  • DVF Final Design Value
  • RRF Relative Reduction Factor

193
Presentation Acronyms
  • MM5 Mesoscale Meteorological Model - Version 5
  • SMOKE Sparse Matrix Operator Kernel Emissions
  • CMAQ Community Multiscale Air Quality
  • MOBILE Mobile Emission Model
  • CERR Consolidated Emissions Reporting Rule
  • CEM Continuous Emissions Monitor
  • NONROAD Nonroad Mobile Emissions Model
  • BEIS Biogenic Emissions Model
  • IPM Integrated Planning Model
  • IM Inspection And Maintenance
  • OBD-II On-Board Diagnostics
  • VMT Vehicle Miles Traveled
  • RVP Reid Vapor Pressure (Normally Expressed In
    Pounds Per Square Inch Or PSI)
  • MVEB Motor Vehicle Emission Budget
  • STN Speciated Trends Network (Speciated PM2.5
    Monitor)
  • FRM Federal Reference Method (Mass Only PM2.5
    Monitor)
  • µg Micrograms

194
Presentation Acronyms
  • PM Particulate Matter
  • PM2.5 Particulate Matter With A Diameter Less
    Than 2.5 µm
  • PM10 Particulate Matter With A Diameter Less Than
    10 µm
  • Direct PM2.5 Directly Emitted And Not Secondarily
    Formed PM2.5
  • Also Known As Primary PM2.5
  • SO2 Sulfur Dioxide
  • SO4 Sulfate
  • NO Nitrogen Oxide
  • NO2 Nitrogen Dioxide
  • NO3 Nitrate
  • NOx Nitrogen Oxides
  • OC Organic Carbon
  • EC Elemental Carbon
  • VOC Volatile Organic Carbons
  • NH3 Ammonia
  • NH4 Ammonium
  • NH4SO4 Ammonium Sulfate
  • NH4NO3 Ammonium Nitrate
  • CM Crustal Mass
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