1 / 23

St. Louis PM 2.5 SIP Modeling Update

St. Louis PM 2.5 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

jayme
Télécharger la présentation

St. Louis PM 2.5 SIP Modeling Update

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Topics • Background • 2002 Base 4 Model Performance Evaluation • PM Source Apportionment • 2009 Base 4 “on the Book” Control Modeling

  3. Proposed NAA Boundary Map Area

  4. Timeline for PM2.5 NAAQS Implementation 39 areas designated for 1997 standardsApril 2005 Emission Inventory Completed, Sep 2006 EPA PM2.5 Implementation Rule Published, March, 2007 Initial Photochemical Modeling Completed, Dec 2006 Attainment Demonstration TBD, 2007 Control Strategy Selection TBD, 2007 Contingency Measure Selection TBD, 2007 Initiate Rulemaking July15, 2007 Rule Filed November 30, 2007 SIP Document December 31, 2007 Public Hearing February 7, 2007 MACC Adoption March 28, 2008 PM2.5 SIP SubmittalApril, 2008 Attainment date for 1997 standards 2010 - 2015

  5. 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

  6. PM 2.5 In Ambient Air: A Complex Mixture Metals

  7. 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 under-predicted 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…

  8. 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 CAMx run on 36 km grid and smaller 12 km grid (Domain 3) using two-way nesting IEPA to evaluate effects of smaller and larger 12 km grid on model estimates? IEPA to run CAMx V4.31 w/ SOAmods? 500 0 -500 Domain 3 (92x113) -1000 Domain 2 (128x149) -1500 Domain 1 (68x68) -500 0 500 1000 1500

  9. Example Blair St. STN CMAQ and CAMx Evaluation for 2002 Q2 and PM Species NO3 SO4 OC PM2.5

  10. 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

  11. 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)

  12. PM2.5 Mass monitored at East St. Louis(June 2001 – May 2003) 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 East St. Louis Measured Species Contributions to PM2.5 organic matter (OC x 1.8)

  13. PM2.5 Mass Apportionment for East St. Louis Based on analysis by Lee, Hopke and Turner (2006); rerun with different version of PMF to be consistent with subsequent work QUESTIONS: • Are the number of apportioned factors optimal? • WWhat source(s) does the “Carbon + Sulfate” factor represent? • Is the mobile source split (gasoline versus diesel) representative? • What are the local versus regional contributions to carbon within the PM2.5 mass apportionment? all concentration values in mg/m3

  14. Preliminary 2009 Modeling“On the Book” Controls • CAIR/CAMR • NOx SIP Call • MACT standards • Tier 2 rule (light-duty vehicle engine standards and low-sulfur gasoline) • Heavy-duty diesel engine standards and low-sulfur diesel • Tier 4 rule (offroad mobile engine standards) • Vehicle emission controls

  15. 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

  16. 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

  17. Source: DRAFT St. Louis base 5 emissions inventory; onroad mobile from St. Louis base 4 inventory.

  18. Source: DRAFT St. Louis base 5 emissions inventory; onroad mobile from St. Louis base 4 inventory.

  19. Comparison 2002-2009CAMx (February) PM2.5

  20. Comparison 2002-2009 CMAQ (July)

  21. 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)

  22. 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)

More Related