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CALIFORNIA CASE STUDIES WRAP Implementation Working Group Meeting San Diego, California ♦ April 17-19, 2007
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Population Distribution San Francisco Los Angeles San Francisco Los Angeles
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California Context DATA 17 IMPROVE monitors, 4 with substituted data, for 29 Class 1 areas AIR QUALITY All but one small county non-attainment for state ozone or PM standards 21 Class 1 Areas are, or have been, in federal non-attainment areas REGULATORY CONTROL State (ARB) controls on and off-road mobile sources, toxics, and consumer products Local districts control stationary and area sources (including BART) In-State Transport Mitigation: requires BARCT rules in upwind districts Legislative initiatives (agricultural control, global warming, Carl Moyer) Executive initiatives (goods movement) REASONABLE PROGRESS GOALS vs. LONG TERM STRATEGY On-the-books vs. continuous improvements
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Geographic Overview Far Northern California (Inland) Sierra Nevada Southern California Coastal Units TRANSPORT
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Reasonable Progress Demonstration Case Study: LABE LABE (Lava Beds) Monitor –Lava Beds Wilderness Area (28,460 acres) –South Warner Wilderness Area (70,385 acres) Far Northern California (inland) Lava Beds Source: all images from COHA LABE South Warner
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Nearby Transportation Pattern Source: www.wrapair.org In-And-Near Forum
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Vegetative Land Cover Type Source: www.wrapair.org In-And-Near Forum
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Nearby Population Density Source: www.wrapair.org In-And-Near Forum
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Lava Beds / South Warner Baseline Extinction Budget Source: http://matar.cira.colostate.edu/tss/
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Best 3.9 Mn-1 Worst 39.3 Mn-1 15.06 dv Actual Species Seasonal Contribution Source: TSS >> Resources >> Monitoring >> Aerosol Composition Tool & Trend Analysis
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Source: TSS >> Reasonable Progress Demonstration >> Modeling >> Visibility Modeling Time Series BEST WORST CMAQ Species Input Comparison LABE Best 3.1 Mn-1 Worst 33.8 Mn-1 14.15 dv Best 3.9 Mn-1 Worst 39.3 Mn-1 15.06 dv LAVO
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PMF PM2.5 Modeling Results PMF Modeling Results (All Sampling Days) PMF Modeling Results (20% Worst Visibility Days) Source: COHA >> PMF Modeling y = 0.601x + 1.4867 R2 = 0.6778 y = 0.3647x + 3.9964 R2 = 0.4793
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Source Apportionment: LAVO Source: TSS>> Demonstrating Reasonable Progress>> Emissions & Source Apportionment >>WEP >> Worst days Spread Sheet available for nearby Lassen Volcanic OMC SOx NOx EC
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Worst Months: Non-Fire PS4 Contributions Source: TSS>> Emissions & Attribution >> WEP >> NOx & SOX Tracer Results
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Worst Months: Non-Fire PN3 Contributions Source: TSS>> Emissions & Attribution >> WEP >> NOx & SOX Tracer Results
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* Emission maps not yet available on TSS. Hence, the above map is used as a placeholder and is for illustration purposes only. This map was obtained from the Causes of Haze website. Illustration Only SOx 1996 Point Source Emissions NOx Evaluate Large Point Sources in Surrounding Region
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Worst Months: Fire Contributions Source: TSS>> Emissions & Attribution >> WEP >> NOx & SOX Tracer Results
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TO DO: Emission Inventory Analysis Questions to Ask What is local inventory for surrounding CA counties (Lassen, Siskiyou, Modoc, Shasta) Which county inventories to use for Oregon and Nevada? What are projections for 2018 by source type and by species? Any significant BART reductions in CA, OR, WA, NV? What percentage of local CA inventory are the out-of state reductions?
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Source: WRAP Technical Support System >> Resources >> Emissions Example: Look at nearby OR, NV, WA
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NOx Sources > 500 tpy in the 2018 Oregon Point Source Pivot Table Source: WRAP website: Emissions Forum pivot tables: http://www.wrapair.org/forums/ssjf/pivot.htmlhttp://www.wrapair.org/forums/ssjf/pivot.html Look at nearby CA, OR, NV and WA sources, but make judgment on size and distance, e.g Q/D > 10
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Source CategoryPSATWEPNotes Pacific Boundary conditions Outside state authority. High uncertainty. CaliforniaPrimarily MV reductions OregonMaybe 10% SOx reduction in stationary sources (pivot tables) but minor impact for all worst days WashingtonMaybe minor benefit from BART reductions NevadaAre there sources of concern? Smoke? Most Likely ---- Sources Significantly Contributing to ---- at LABE On the 20% Worst Visibility Days Attribution & Potential Consultation
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For SO4, NOx, EC… Is the Base+BART projection better than URP? –CMAQ base case projections for 2018 show ----% reduction in extinction due to ___, ___, ____. Sources outside CA have some influence Is WRAP anthro reduction > 20%? –PSAT apportionment shows only a ---% reduction from WRAP anthro sources BART not yet included, but only one source remaining in northern CA Reductions from BART sources in WA, OR, NV not known, minor impact currently Mobile source reductions are key contributors
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Draft Summary Table for Worst Visibility Days LABE: Lava Beds and South Warner Wilderness Areas 2000-04 Baseline Conditions (Mm -1 ) 2064 Natural Conditions (Mm -1 ) 2018 URP (Mm -1 ) 2018 Base Case (Mm -1 ) 2018 Control Case (Mm -1 ) Change In Statewide Emissions 1 (tons / %) Change In Upwind Weighted Emissions 1 (%) Change In WRAP Anthro Contribution 1 (%) Other Apportion- ment Results SO4/PSAT PMF NO3/PSAT PMF OC/ Not Applicable PMF EC/PMF FS/PMF CM/PMF DV 3 Not Applicable 1 Represents change between control case and 2000-04 baseline condition. 2 Visibility projections not available due to model performance issues. 3 Unitless value. 4 This becomes the reasonable progress goal for the worst visibility days.
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Final Analysis Are there any important uncontrolled upwind sources? –Which species do they contribute and when? –Are there any technologies to control them? –Are there legal opportunities in place? –What is the likely timeline for additional controls or reductions?
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Lava Beds / South Warner Species Trends and URP Glidepaths Source: http://matar.cira.colostate.edu/tss/ ParameterBaselineURP-2018Base-2018RPG-2018Target 2064 LABE (EPA)15.0513.3715.10 7.86 LABE (quarter)15.0513.3715.33 7.86 LABE (Monthly)15.0513.3715.41 7.86 Best Days3.21(2.76)3.03< 3.21(1.29)
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Reasonable Progress Demonstration Case Study: JOSH JOSH (Joshua Tree) Monitor (not JOTR) –Joshua Tree Wilderness Area (429,690 acres/~10 sq. mi.) –Southern California (inland desert) –Influence from LA Basin through San Gorgonio –Influence from Mexico and the Gulf Source: all images from http://www.nps.gov/jotr/ WebCam 11/15/06 From Belle Mountain
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Nearby Transportation Pattern Source: www.wrapair.org In-And-Near Forum NV Arizona AZ
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Land Use / Land Cover Source: www.wrapair.org In-And-Near Forum NV AZ Arizona
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Nearby Population Density Source: www.wrapair.org In-And-Near Forum NV AZ Arizona
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Joshua Tree Baseline Extinction Budget Source: http://matar.cira.colostate.edu/tss/
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Species Contribution to Baseline TSS: Methods>> Monitoring >> Select extinction individually 2001-2004, show best and worst days to get seasonal patterns and relationships
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PMF PM2.5 Modeling Results PMF Modeling Results (All Sampling Days) PMF Modeling Results (20% Worst Visibility Days) Source: COHA >> PMF Modeling y = 0.8762x + 0.8179 R2 = 0.9067 y = 0.6545x + 3.5958 R2 = 0.674
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NOx & SOx Tracers and Other Apportionment Needs Not currently available for JOTR…what about JOSH? Look at the fire / non-fire break out? Look at inventory for nearby counties: Riverside, San Bernardino, Imperial, Los Angeles, Orange, San Diego Mexico, Pacific, what other possible source areas? Look at SAGO or AGTI although both are on the other side of a mountain pass Organic Matter and Coarse Mass info also needed
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Check PSAT at Surrogate Monitors Nitrates: CA, PO BC Sulfates: CA, BC, PO MX NV, CAN, OR, WA
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Check Surrogate Sources: OM, CM ExRT (Plan 02 PMC Worst 20%)Residence Time (Worst 20%) COARSE MASS at SAGO ORGANIC CARBON MATTER at SAGO Residence Time (Worst 20%) ExRT (Plan 02 POA Worst 20%)
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JOTR – Not JOSH Organic Aerosol base 02b TSS: Source Apportionment, Organic Aerosol Tracer, Monthly, all days, (worst days not available), 2002 base case
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TSS: Source Apportionment, Organic Aerosol Tracer, Monthly, all days, (worst days not available) JOTR- not JOSH Organic Aerosol Attribution 2000-2004 Baseline plan 02c 2018 Base Case plan 18b
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CMAQ OC Species at JOSH CMAQ Species Species DefinitionAnnual Average Modeled Concentration (ug/m3) Clean02aPlan02bBase18b (% change) AORGASecondary Organic Aerosols from Anthropogenic Sources AORGBSecondary Organic Aerosols from Biogenic Sources AORGPAPrimary Organic Aerosols from All Sources
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TO DO: Emission Inventory Analysis What is local inventory for surrounding CA counties: Riverside, San Bernardino, Imperial, Los Angeles, Orange, San Diego? What are projected reductions for 2018 by source type and by species for nearby CA sources? What percentage are the out-of state reductions? –Which sources for Mexico NV, OR, WA really matter? –Any significant BART reductions in CA, OR, WA, NV? Is there a correlation between the coarse mass peaks and wind events? What is the correlation for the organic matter elevated periods? Anthro VOC, fire events, biogenics?
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2018 Changes to California Inventory
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Source CategoryPSATWEPNotes Boundary conditions Outside state authority. SECA, Asian transport CA mobile sources Look at local inventory changes CA Stationary/Area sources Look at local changes MexicoReally need the JOSH analysis Other statesReally need the JOSH analysis Most Likely NOx, SOX, OC, CM Sources Significantly Contributing to these top drivers at JOSH on the 20% Worst Visibility Days
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Draft Summary Table for Worst Visibility Days Joshua Tree Wilderness Area – JOSH IMPROVE Monitor 2000-04 Baseline Condition s (Mm -1 ) 2064 Natural Conditions (Mm -1 ) 2018 URP (Mm -1 ) 2018 Base Case (Mm -1 ) 2018 Control Case (Mm -1 ) Change In Statewide Emissions 1 (tons / %) Change In Upwind Weighted Emissions 1 (%) Change In WRAP Antrho Contribution 1 (%) Other Apportion- ment Results SO4 NO3 OC EC FS CM DV 3 1 Represents change between control case and 2000-04 baseline condition. 2 Visibility projections not available due to model performance issues. 3 Unitless value. 4 This becomes the reasonable progress goal for the worst visibility days.
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Joshua Tree (JOSH) Species Baseline and URP Glidepaths
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Are these still valid? Eleven California IMPROVE sites http://wrapair.org/forums/aoh/ meetings/061102m/index.html
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Source: U.S. Census Bureau Year 2000 Each dot represents 7500 people
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