Transcript Presentation: St. Louis Pm25 Modeling Update

St. Louis PM2.5 SIP Modeling Update
Air Quality Advisory Committee Meeting
May 24, 2007
East-West Gateway Board Room
Calvin Ku, Ph.D.
Missouri Department of Natural Resources
Air Pollution Control Program
Topics
• Background
• 2002 Base 4 Model Performance Evaluation
• PM Source Apportionment
• 2009 Base 4 “on the Book” Control Modeling
Proposed NAA Boundary
Map Area
Timeline for PM2.5 NAAQS Implementation
39 areas designated for 1997 standards
Emission Inventory
EPA PM2.5 Implementation Rule
Initial Photochemical Modeling
Attainment Demonstration
Control Strategy Selection
Contingency Measure Selection
Initiate Rulemaking
Rule Filed
SIP Document
Public Hearing
MACC Adoption
PM2.5 SIP Submittal
Attainment date for 1997 standards
April 2005
Completed, Sep 2006
Published, March, 2007
Completed, Dec 2006
TBD, 2007
TBD, 2007
TBD, 2007
July15, 2007
November 30, 2007
December 31, 2007
February 7, 2007
March 28, 2008
April, 2008
2010 - 2015
St Louis PM2.5 SIP Modeling
• PM2.5 modeling performed by MDNR and IEPA
– 2002 Base 4 emissions
– CMAQ and CAMx air quality models
– 36/12 km grid
• Model performance evaluation by ENVIRON
– STL area FRM data
– STL area speciation data
• four speciation network sites, 1-in-3 or 1-in-6 day
frequency
• STL Supersite (East St. Louis), daily frequency
PM 2.5 In Ambient Air: A Complex Mixture
Secondary Particles
(From Precursor Gases)
Primary Particles
(Directly Emitted)
VOC
Condensed
Organics
Secondary
Organics
Elemental
Carbon
SO2
Ammonium
Sulfate
Crustal
Ammonia
Metals
Ammonium
Nitrate
Crustal
Other
NOx
June 2000 / tgp
2002 Model Performance Evaluation
• Evaluated PM2.5 species include, but are not limited to:
– total PM2.5 mass
– sulfate, nitrate, ammonium
– organic carbon, elemental carbon
– “Other PM2.5” (e.g. crustal material and metals oxides)
• Modeled and measured PM2.5 mass agrees reasonably well at
most sites
– However, at all site the organic carbon is significantly underpredicted and the Other PM2.5 is significantly over-predicted
– From a control strategy standpoint, the reasonably good
model performance for PM2.5 mass is unacceptable if the
major species are not adequately modeled
• Using the Supersite (East St. Louis) as an example…
St. Louis PM2.5 Model Domain
CMAQ V4.4
SOAmods run on 36
km and larger 12 km
grid (Domain 2) using
one-way nesting
500
CAMx run on 36 km
grid and smaller 12
km grid (Domain 3)
using two-way nesting
0
-500
IEPA to evaluate
effects of smaller and
larger 12 km grid on
model estimates?
Domain 3 (92x113)
-1000
IEPA to run CAMx
V4.31 w/ SOAmods?
Domain 2 (128x149)
-1500
Domain 1 (68x68)
-500
0
500
1000
1500
SO4
OC
NO3
PM2.5
Example
Blair St.
STN
CMAQ
and CAMx
Evaluation
for 2002
Q2 and
PM
Species
Summary of Performance Evaluation

CAMx and CMAQ performed reasonably well for PM2.5
sulfate (CAMx better than CMAQ)

Both models showed poor performance for PM2.5
nitrate (under-prediction; CMAQ better than CAMx)

Organic Carbon is mostly under-predicted and other
PM2.5 is significantly over-predicted by both models

PM2.5 ammonium and Element Carbon performances
are reasonable
STL PM2.5 SIP - Monitoring Data Analysis
• OBJECTIVES: Examine monitoring data (PM2.5 mass and
species, allied air quality and weather data) towards building a
scientific weight-of-evidence to support the PM2.5 SIP
– Photochemical model performance evaluation and
diagnostic testing
– Additional insights into PM2.5 sources and source
contributions (complement the modeling effort)
• METHODS: Including, but not limited to…
– Spatial-temporal trends analysis (e.g. day of week trends,
urban/rural contrast)
– Modulation of PM burdens by synoptic weather patterns
– Source apportionment (PMF)
Grant awarded to Washington University in St. Louis
(with subcontracts to Sonoma Technology and U. Wisconsin)
PM2.5 Mass monitored at East St. Louis
(June 2001 – May 2003)
East St. Louis
Measured Species Contributions to PM2.5
unaccounted
8%
other
3%
organic matter
(OC
OM x 1.8)
31%
crustal
3%
+
NH4
11%
NO312%
EC
9%
SO4223%
QUESTIONS:
• What are the emission sources
responsible for these observed PM
species?
• What are the relative roles of
locally-generated emissions versus
regionally transported materials?
Are the source contributions similar
across the metropolitan area?
[analysis in progress]
APPROACH: Refine the PM2.5
mass apportionment of Lee et al.
(2006) by conducting model
sensitivity studies and using ancillary
data not typically available or used
PM2.5 Mass Apportionment for East St. Louis
20
Copper processing - 0.21 (1.2%)
Zinc smelting - 0.29 (1.6%)
18
Based on analysis by Lee, Hopke
and Turner (2006); rerun with
different version of PMF to be
consistent with subsequent work
Lead smelting - 0.31 (1.7%)
Steel production - 1.35 (7.6%)
16
Mobile - Diesel - 0.62 (3.5%)
QUESTIONS:
• Are the number of apportioned
factors optimal?
Mobile - Gasoline - 2.63 (14.8%)
•WWhat source(s) does the
Soil - 1.26 (7.1%)
PM2.5 mass, ug/m
3
14
12
“Carbon + Sulfate” factor
represent?
10
"Carbon + Sulfate" - 2.77 (15.6%)
• Is the mobile source split
8
Secondary Nitrate - 2.66 (14.9%)
(gasoline versus diesel)
representative?
6
• What are the local versus
4
Secondary Sulfate - 5.71 (32.1%)
2
0
0
1
2
all concentration
values in mg/m3
regional contributions to carbon
within the PM2.5 mass
apportionment?
Preliminary 2009 Modeling
“On the Book” Controls
–
–
–
–
CAIR/CAMR
NOx SIP Call
MACT standards
Tier 2 rule (light-duty vehicle engine standards and lowsulfur gasoline)
– Heavy-duty diesel engine standards and low-sulfur
diesel
– Tier 4 rule (offroad mobile engine standards)
– Vehicle emission controls
Area and Point Growth and Control
• Area and non-EGU point
– Growth and control factors provided by Alpine
Geophysics applied within SMOKE
– Control factors account for federal regulations such as
Maximum Achievable Control Technology (MACT), New
Source Performance Standards (NSPS), standards for
locomotives and commercial marine vessels and
state/local rules including NOx SIP call for non-EGU
boilers and cement kilns
• EGU point based on IPM model run from multi-RPO
process
Mobile Growth and Control
• Onroad mobile
– EPA default VMT growth factors (~1.7 %/year
within St. Louis nonattainment counties)
– Emission factors from MOBILE6 -- accounts for
federal Tier 2 rule and heavy-duty diesel engine
standards and state/local regs such as I/M
program
• Offroad mobile
– NONROAD2004 model output provided by
Midwest RPO -- accounts for federal regulations
such as Tier 4 offroad diesel rule
2002-2009 Annual NOx Emissions - Totals for
Missouri and Illinois
600,000
500,000
Tons/Year
400,000
300,000
200,000
100,000
0
Point
Area
Offroad Mobile
2002
2009
Source: DRAFT St. Louis base 5 emissions inventory; onroad mobile from St. Louis base 4 inventory.
Onroad Mobile
2002-2009 Annual SO2 Emissions - Totals
for Missouri and Illinois
1,000,000
800,000
Tons/Year
600,000
400,000
200,000
0
Point
Area
Offroad Mobile
2002
Source: DRAFT St. Louis base 5 emissions inventory; onroad mobile from St. Louis base 4 inventory.
2009
Onroad Mobile
Comparison 2002-2009
CAMx (February)
PM2.5
Comparison 2002-2009
CMAQ (July)
MDNR STL 2002 Design Value (Left) and
2009 CAMx BaseD 12k projected DVF (Middle Left) and
2009 CMAQ BaseD 12k projected DVF (Middle Right) and
2009 CMAQ BaseG2 12k projected DVF (Right)
25
20
PBW
NH4
15
ug/m3
Crustal
EC
OC
10
NO3
SO4
5
STL
ar
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Fe
rg
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on
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W
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Lo
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Ea
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St
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Al
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0
Conclusions
• The preliminary 2009 modeling shows that St
Louis will not meet the annual PM2.5 standard
based only on “on-the-books” controls.
• Additional emission reductions from local
sources and/or regional transported will be
needed.
• From a control strategy standpoint, need to
improve 2002 model performance for organic
carbon, nitrate, and other PM2.5 species (i.e.
fugitive emissions)
PM2.5 Carbon Apportionment
• Carbonaceous matter is ~40% of the PM2.5 mass at
East St. Louis
• PM2.5 mass apportionment cannot adequately resolve local
carbon sources (from a control strategy perspective)
• Apportion PM2.5 carbon using organic molecular marker data
(every sixth day for two years at East St. Louis)
• Organic carbon (OC) apportionment by chemical mass
balance (CMB) and positive matrix factorization (PMF)
– Jamie Schauer group (University of Wisconsin)
– PMF identified several OC sources not used in the CMB
(CMB requires knowing the sources and having
representative emissions source profiles)