Designations for the 2006 PM2.5 Standards Evaluating the Nine

Designations for the 2006 PM2.5 Standards: Evaluating the Nine Factors in Setting Nonattainment Area Boundaries Part 2 – Conceptual Model for Evaluating High PM2.5 Days and its Influencing Emission Sources Neil Frank EPA Office of Air Quality Planning and Standards For Presentation at EPA State / Local / Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process June 20-21, 2007 1 The 9 Designation Factors To Help Determine Nearby Area of Influence for 24-hr NAAQS Violations Population and Urbanization Emissions Traffic & Commuting Air Quality Non Attainment Boundaries Growth Meteorology Political and Other Boundaries Current Emission Controls Topography Air Quality is one of the most Important Designation Factors 2 Topics to be Covered • • • • Conceptual model for high PM days Seasons when exceedances occur Composition of the high days Analytical tools – SLICE technique - for evaluating urban contributions to high days – Residence time analysis – for assessing nearby contributing source regions using back trajectories and emissions data – Gradient analysis – for identifying days with potential high source-oriented impacts 3 Conceptual Model for High PM2.5 Days • How to define high PM2.5 days? • What is the typical “daily increment” for high PM days in relation to the annual average? • What is the urban contribution above regional levels? 4 Conceptual Model for High PM2.5 Days What high PM2.5 days to consider? • “High PM2.5 Days” Associated with the 98th percentile  Not just one day per year  Select all candidate days  e.g. top 5% or days > 30 - 35ug/m3  Summarize by season to distinguish varying conditions 5 Conceptual Model for High PM2.5 Days High Daily PM2.5 has Urban and Regional Components 40ug/m3 Typical Daily Increment - Example 24 16 Example + PM2.5 High Day Value Seasonal Average Typical Daily Increment • The annual average PM2.5 (urban background) is the stuff that is there on a day-today basis. –Comes from nearby and more distant areas –Can be estimated by seasonal average PM2.5 concentration of non-high days –Includes contributions from all nearby surrounding counties –Can be estimated using the traditional urban increment approach • The daily increment (on top of annual average urban background) also has regional and local contributions. - Key issue: what counties and sources from the urban area contribute to the typical daily increment? 6 Conceptual Model for High PM2.5 Days An approach to partition typical levels into urban and regional components • • – Urban Increment Analyses as used in 2004/2005 PM2.5 Designations Urban sources in the Eastern US contribute at least 4-6 ug/m3 to annual average PM2.5 Probably even larger urban contribution in western US cities • – Carbon is significant component of average PM2.5 mass, but metro area emissions typically are much less than SO 2 and NOx Weighted emissions score developed to give additional weight to nearby direct carbon emissions as they contribute to the urban background 7 Air Quality - Annual Average PM2.5 Conceptual Diagram PM [µg/m³] Annual Average PM2.5 40 35 30 25 20 Urban areas ~14-20 ug/m3 Countryside ~10-12 ug/m3 15 10 urban increment regional contribution natural background 8 Air Quality - High Daily PM2.5 Concentrations Conceptual Diagram PM [µg/m³] High Daily PM2.5 Concentrations 40 Urban areas ~30-40 ug/m3 urban increment Countryside ~18-30 ug/m3 Larger “urban island” on peak days 35 30 25 20 15 10 natural background Focus of new analyses: understanding what emissions contribute to urban increment 9 regional contribution Conceptual Model for High PM2.5 Days Emissions Population and Urbanization Traffic & Commuting Source region considerations • Air Quality Air Quality Growth Non Attainment Boundaries Meteorology Current Emission Controls Political and Other Boundaries Topography Role of Regional vs Urban vs Micro-scale Influences – – – On high days particularly in the east, regional emissions often provide a “base” amount of pollution Urban-wide and nearby emissions also contribute significantly to high days: “urban island” effect In some cases, there may be a micro-scale effect from a single source or small group of sources • Does not help define NA boundaries, unless it is the only contributing source (Note: “urban” can mean large metropolitan area or smaller city) 10 Conceptual Model for High PM2.5 Days Seasons when exceedances occur • Time of Year for Exceedances- varies by Geographic Region – SE: Mostly summer – Industrial Midwest (IMW), Mid-Atlantic, So. CA: Winter and summer – NW, UT, NM, Middle CA: Mostly or exclusively Winter 11 % 50 Percent of 2003-05 Days > 35 ug/m3, by Month (NW) 50 Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain Percent of 2003-05 Days > 35 ug/m3, by Month (UT) 50 Percent of 2003-05 Days > 35 ug/m3, by Month (IMW) Based on all FR M Site-days throughout the R egional D om ain 50 Based on all FR M Site-days throughout the R egional D om ain NW 40 40 UT 30 40 IMW 30 Percent of 2003-05 FRM Days > 35 ug/m3 by Month 30 25 20 Based on all sites which violate 24-hr NAAQS 20 20 10 10 10 0 0 J 0 0 PLOT J F M A M J J A S O N D GT 35 ug/ m 3 at F R M s it es Pv X Based on all FR M Site-days throughout the R egional D om ain F M A M J J A S O N D J J PLOT F M A M J J PvX A S O N D F M A M J J PvX A S O N D PLOT GT 35 ug/m 3 at FR M sites J F M A M J J A S O N D GT 35 ug/m 3 at FR M sites Based on all FR M Site-days throughout the R egional D om ain Percent of 2003-05 Days > 35 ug/m3, by Month (Mid. Cal) Percent of 2003-05 Days > 35 ug/m3, by Month (N.Eng-MidAtl) 50 60 50 Mid CA 40 MidAtl 40 30 30 20 20 10 10 0 J F M A M J J A S O N D 0 J PLOT F M A M J J PvX A S O N D PLOT J F M A M J J A S O N D GT 35 ug/m 3 at FR M sites PvX J F M A M J J A S O N D GT 35 ug/m 3 at FR M sites Based on all FR M Site-days throughout the R egional D om ain % 50 Percent of 2003-05 Days > 35 ug/m3, by Month (S. Cal) 50 Percent of 2003-05 Days > 35 ug/m3, by Month (Las Cruces) Based on all FR M Site-days throughout the R egional D om ain 50 Based on all FR M Site-days throughout the R egional D om ain Percent of 2003-05 Days > 35 ug/m3, by Month (SE) 50 S.CA 40 40 La Cruces, NM 40 30 SE 30 30 25 20 20 20 10 10 10 12 J F M A M J J A S O N D 0 0 J F M A M J J A S O N D 0 J F M A M J J A S O N D 0 Conceptual Model for High PM2.5 Days Population and Urbanization Emissions Composition data are important 1) Composition Air Quality Traffic & Commuting Air Quality Growth Non Attainment Boundaries Meteorology Current Emission Controls Political and Other Boundaries Topography – Indicate which sources are contributing to average and high PM2.5 values • Varies across country – Warm season exceedances: Mostly sulfate + organic carbon – Cold season exceedances: Nitrate (at higher latitudes and in Western US) + sulfate + carbon; Carbon may dominate in some locations (e.g. MT, ID) Gaps in speciation data for certain areas – 13 Air Quality Composition on Annual Average and High PM2.5 Days (From PM Staff Paper) Some source categories and regional influences may be Birm more important for high concentration days High PM2.5 days have: S More Sulfate 14 More Nitrate • • • • Comparing average of 5 highest days during 2003, regional sources of sulfates and nitrates are larger contributors to peak day concentrations than to annual average (selected city analysis) Composition can vary from high day to high day Carbon can be smaller as % -- but still larger in absolute concentration values -compared to the average Note: All the new analyses present “FRM” composition with the peer-reviewed “SANDWICH” Technique – As used in CAIR and PM2.5 RIA Nitrate Atlanta NE NYC Cleveland Chicago MW St .Louis SLC UT TCM This analysis shows Sulfate PM2.5 Composition of the ambient aerosol (not adjusted to represent FRM mass) Crustal Fresno CA From PM Staff Paper (Rao et al) % 50 Percent of 2003-05 Days > 35 ug/m3, by Month (NW) 50 Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain Percent of 2003-05 Days > 35 ug/m3, by Month (UT) 50 Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain Percent of 2003-05 Days > 35 ug/m3, by Month (IMW) Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain 50 40 NW Cold 40 UT Cold Avg High 3 Cold 40 IMW Cold Avg High 3 Cold Warm Avg High 3 Warm 30 or 30 30 25 20 20 20 10 10 10 0 0 J F M A M J J A S O N D 0 J F M A M J J A S O N D 0 J F M A M J J A S O N D “Example” Composition for High Days [“Warm” Season (May-Sept) & “Cold”] PLOT GT 35 ug/ m 3 at F R M s it es Pv X PLOT GT 35 ug/ m 3 at F R M s it es Pv X PLOT GT 35 ug/ m 3 at F R M s it es Pv X Percent of 2003-05 Days > 35 ug/m3, by Month (Mid. Cal) Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain 60 50 Mid CA Cold But sites can be different within each “domain” MidAtl 50 40 30 Percent of 2003-05 Days > 35 ug/m3, by Month (N.Eng-MidAtl) Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain Cold Warm 40 or 30 20 20 10 10 0 0 J F M A M J J A S O N D PLOT J F M A M J J A S O N D GT 35 ug/ m 3 at F R M s it es Pv X Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain J PLOT F M A M J J Pv X A S O N D % 50 Percent of 2003-05 Days > 35 ug/m3, by Month (S. Cal) 50 J F M A M J J A S O N D Pies represent average of 3 highest days per year per season, using SANDWICH GT 35 ug/ m 3 at F R M s it es Percent of 2003-05 Days > 35 ug/m3, by Month (Las Cruces) Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain 50 Percent of 2003-05 Days > 35 ug/m3, by Month (SE) 50 Bas ed on all F R M Sit e-day s t hroughout t he R egional D om ain S.CA Cold Avg High 3 Cold Warm Avg High 3 Warm 40 40 La Cruces NM Cold Avg High 3 Cold SE 40 30 Warm Avg High 3 Warm 30 30 25 20 20 El Paso STN 20 10 10 10 15 J F M A M J J A S O N D 0 0 J F M A M J J A S O N D 0 J F M A M J J A S O N D 0 Composition is often similar among the high days IM W - 5 5 1 3 3 0 0 2 7 W is c on s in M ilw a u k ee - W a u k es h a ,W I 2 4 - h r D V = 3 6 u g /m 3 A n n u al D V = 1 3 .5 u g /m 3 " c o ld " d a y s > 3 0 = 5 To ta l 3 y r ob s = 1 6 1 " w a rm " d ay s > 3 0 = 1 A vg c onc . - top 3 values/yr: 39.0 ug/m 3 41.1 ug/m 3 S .C A Milwaukee, 2003-05 24 Average Cool Season Warm Season AVG A vg H igh 3 C old A vg H igh 3 W arm 22 20 18 3 highest PM2.5 days > 30ug/m3 Per season, Milwaukee, WI (2003-05) P M 2 .5 a n d c o m p o n e n t m a s s , u g /m 3 16 14 12 10 8 .0 6 .0 4 .0 2 .0 0 .0 -2 2002 2003 EC P as s ive S ulfate_m as s OCMmb N itrate_m as s C rus tal PM2.5 days > 30ug/m3 Measured PM2.5 4 mass, 2 0 0 5 ug/m3 200 N itra te _ m a s s C ru s ta l 2006 16 EC P a s s ive Black line is difference between OCMmb and OCM14 Chem S u lfa te _ m a s s OCMm b Conceptual Model for High PM2.5 Days An approach to partition total daily increment into urban and regional components 5 5 0 7 9 0 0 2 6 [To t c o ld d a ys = 1 8 3 ] W is co n s in M ilw aIM We -W0 7 9 0 0 2 6 ,W It [H a rm dd a ys = 81 4 2 ] W is co n s in M ilw a u ke e -W a u k e s h a ,W I [H ig h d a ys = 3 ] u ke 5 5 a u k e s h a [To w ig h a ys = ] 2 4 -h r D V = . u g /m 3 A n n u a l D V = . u g /m 3 2 4 -h r D V = . u g /m 3 A n n u a l D V = . u g /m 3 Cool Warm 65 60 55 65 P M 2.5 a nd co m po nen t m as s , ug/m 3 50 45 40 35 30 25 20 15 10 5 .0 0 .0 1 2 . 4 3 6 1 2 . 6 4 6 3 2 . 3 4 4 3 2 . 7 0 5 P M 2.5 a nd c om p one nt m a ss , ug /m 3 Cool Daily composition 5 5 Warm minus Seasonal0 avg. season season 5 45 40 35 30 25 20 15 10 5 .0 0 .0 60 Next subtract the daily composition from the seasonal average PM2.5 Use resident time weighted emissions to partition each component of total daily Increments into urban & regional contributions (% of RTWE in local area) AS SA 3 3 . 9 6 4 3 5 . 0 4 3 3 5 . 7 4 4 4 0 . 0 5 7 4 1 . 0 2 0 4 1 . 5 3 8 Measured PM2.5 mass 1 1 . 8 9 9 1 2 . 4 2 0 3 4 . 8 4 2 4 5 . 6 0 7 4 6 . 6 6 4 u n d e r o th e r b a rs a re th e d a ily co n c e n tra tio n . --E s tim a te d IN C R E M E N T C O M P O S IT IO N is sh o w n he a S _m N itra te _ m a ss EC N itra te _ mm ss backgroundCPM2.5 can be u lfa teE C a s s O C M mP a s sive b C ru s ta l P s 30ug/m3 PM2.5 daysa>sive C ru s ta l 17 estimated using seasonal average PM2.5 Pera ta is c o m p le te ) year, Milwaukee, WI (2003-05) season & s F irst b a rs a re s e a so n a l a n d a n of anon-high (la tte r if d a ta is bc o m a rete ) a so n a l a n d a n n u a l a ve ra g e s (la tte r if d concentration n u l a ve ra g e sa ludaysd e r F irst r baars p le the d a ily co n c e n tra tio n . --E s tim a te d IN C R E M E N T C O M P O S IT IO N is sh o w n V es un o th e rs a re e S u lfa te _ m a surban The s OCMm b Analytical Tools to help identify boundaries and develop SIPs • SLICE technique - for evaluating urban contributions to high days • Residence time analysis – for assessing nearby contributing source regions using back trajectories and emissions data • Urban gradient analysis – for identifying whether there are any sites predominantly affected by a single source 18 Analytical Tools Identify urban PM2.5 and gradients • Air Quality Population and Urbanization Emissions Traffic & Commuting Air Quality Growth Non Attainment Boundaries Meteorology Current Emission Controls Political and Other Boundaries Topography “SLICE” to identify “urban island” days and relative urban amount of PM2.5 mass • Evidence of urban source contributions • Air Quality Urban “gradient” technique • Met Emissions Evidence of predominant strong nearby source influence Daily urban portion of PM2.5 19 Analytical Tools - Residence Time Analysis Emissions Population and Urbanization Traffic & Commuting Where did the air parcel come from on high concentration days? Met 1) Transport patterns producing a potential source region Air Quality Air Quality Growth Non Attainment Boundaries Meteorology Current Emission Controls Political and Other Boundaries Topography • • Use trajectories and “Residence-Time Analysis” to find upwind probability fields. For PM2.5 mass or its components  Focus on the ensemble of “High PM2.5 days”, by season for subsequent linking to composition pattern.  Days with identified “urban islands” are more important • Local pollution roses (annual vs. high days) would also be helpful to identify nearby sources. Residence time probability plots with HYSPLIT trajectories have been used by Kinski, Poirot and others to identify potential source regions. 20 Analytical Tools- Residence time weighted emissions What are the most likely contributing emissions? Emissions Population and Urbanization Emissions Traffic & Commuting Air Quality Growth Non Attainment Boundaries Meteorology Current Emission Controls Political and Other Boundaries Topography 1) Spatial distribution of emissions by season • • • • Developed from monthly emissions for precursors and direct PM: (SO2, NOx, Carbon, Crustal ) The importance of each precursor pollutant can be guided by the composition of the high PM2.5 day. consider monthly emissions corresponding to the affected PM component according to typical composition by season. Some precursors will not be considered or could be downweighted. e.g. crustal (year-round) and NOX (summer). Met • Residence time weighted emissions • Use probability that air parcel passed over an area to weight emissions as potential contributors to the high day concentration impacts High probability nearby contributing emissions can be identified for each PM2.5 contributor 21 Emissions Air Quality • Summary • Identifying the area of emission influence considers contributions for – each “high PM2.5 day” and – urban average background on top of which are the daily impacts • High concentration days with evidence of urban influence (i.e. with urban islands) are more important – The magnitude of urban island can help define the daily urban contributions. • In combination with daily and average speciation data, by season of the year – Emissions with high probability of trajectory residence time are important to assess high day impacts. – Average emissions and typical wind patterns help understand the sources contributing to the urban “background” – Both used to understand the relative importance of the various nearby contributing emissions (e.g. direct PM vs SO2 vs NOx). 22