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Michael Kleeman Deb Niemeier Susan Handy Jay Lund

RD-83184201. Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions in the San Joaquin Valley. Michael Kleeman Deb Niemeier Susan Handy Jay Lund With Song Bai, Sangho Choo, Shengyi Gao, and Julie Ogilvie University of California Davis

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Michael Kleeman Deb Niemeier Susan Handy Jay Lund

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  1. RD-83184201 Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions in the San Joaquin Valley Michael Kleeman Deb Niemeier Susan Handy Jay Lund With Song Bai, Sangho Choo, Shengyi Gao, and Julie Ogilvie University of California Davis Dana Coe Sullivan Sonoma Technology, Inc.

  2. Project Objectives • Develop a system of models for evaluating the impact of local and regional policies and trends on air quality • Global variables from sources like IPCC, California Department of Finance • Apply this system to the San Joaquin Valley to evaluate the sensitivity of air quality to different policy scenarios.

  3. PM10 Trends Summary: San Joaquin Valley Air Basin Source: CARB, http://www.arb.ca.gov/adam/php_files/aqdphp/graphtrendpm10bb.php

  4. Ozone Trends Summary: San Joaquin Valley Air Basin Source: CARB, http://www.arb.ca.gov/adam/php_files/aqdphp/graphtrendo3bb.php

  5. Project Schedule

  6. Policy Scenarios Pop& employ growth Land-use policy (e.g. density) Power generation Policy scenarios Global factors Water policy Transportation infrastructure Transport policy (e.g. pricing) Agricultural activities Tech adoption (e.g. fuel cells)

  7. Overall Modeling Procedure Policy scenarios Land-use modeling (UPLAN) Water management modeling (SWAP, CUP, CALVIN, SIMETO) Travel demand modeling (TP+/Viper) On-road mobile source emissions modeling (UCDrive) Stationary source and off-road mobile source emissions modeling (STI) Future-year emissions inventory Ambient air quality modeling Ambient concentration

  8. Land-use modeling (UPLAN) Socioeconomic forecast: Increase in pop & employment Residential distribution (%): HD, MD, LD, VLD Employment distribution (%): industrial, commercial (HD, LD) Policy scenarios Lot size (acres) by residential type: HD, MD, LD, VLD Sq. ft by employment type: industrial, commercial (HD, LD) Land-use demand Vacancy proportion (%) Floor area ratio by employment type: industrial, commercial (HD, LD) Attraction factors/weights by land-use type (e.g. highways, freeway ramps, major & minor arterials, SOI) Land-use supply Discouragement factors/weights by land-use type (e.g. floodplains, wetlands, habitats, slopes) Mask (exclusion) factors/widths (e.g. lakes, rivers, public lands, existing urban areas) Land-use allocation (maps & tables): resid (HD, MD, LD, VLD), ind, com (HD, LD) Traffic Analysis Zone data: no. HHs & emp by type Outputs Stationary source and off-road mobile source emissions modeling Travel demand modeling

  9. Travel demand modeling (TP+/Viper) Transportation network data: nodes, links, characteristics Land use data: households, employees by zone Socio-demographic data: autos, income by zone Trip generation Trip distribution Mode split Traffic assignment Travel activity data: link volumes & speed, trips, VMT On-road mobile source emissions modeling (UCDrive)

  10. Mobile source emissions modeling (UCDrive) EMFAC/MOBILE regulatory or modal emission factors activity data Travel demand model activity data: link volumes and speed, no. of trips Interpolation or internal functional speed correction Link-based interzonal data Intrazonal activity data Interzonal trip-end data Intrazonal spatial allocation factor Interzonal trip-end spatial allocation factors Interzonal link disaggregation Zone emissions to grid cell: Intrazonal running emissions Intrazonal start emissions Intrazonal hotsoak emissions Intrazonal diurnal emissions (partial-day) Zone emissions to grid cell: Interzonal start emissions Interzonal hotsoak emissions Interzonal diurnal emissions (multi-day) Gridded interzonal running emissions Gridded mobile source emission inventory Regional mobile source emission inventory Source: Niemeier, et al. (2004)

  11. Scenario Development • Initial list of variables • Background research and preparation of white papers • Initial levels and combinations of variables • Expert panel review – April 2005 • Caltrans, California High Speed Rail Authority • California Air Resources Board • Additional experts in economics and agriculture • Finalization of variables, levels, combinations • Translation of variables into model inputs

  12. Scenarios

  13. Scenarios: continued

  14. Stanislaus County Results to Date • Land use modeling • Travel demand modeling

  15. Stanislaus County Growth

  16. Baseline

  17. Controlled

  18. Uncontrolled

  19. As Planned

  20. New Households by Residential Density Stanislaus County

  21. Travel Demand Modeling ResultsStanislaus County

  22. Network Link V/C Ratio (Scenario # 1 – Baseline)

  23. Network Link V/C Ratio (Scenario # 2 – Controlled)

  24. Network Link V/C Ratio (Scenario # 3 – Uncontrolled)

  25. Network Link V/C Ratio (Scenario # 4 – As Planned)

  26. Questions • How will results differ by county? • How will differences in travel demand translate into differences in vehicle emissions? • How will differences in land use patterns translate into differences in stationary and off-road source emissions?

  27. STI: Area, Non-road Mobile, and Point Sources • Focus on the most important area, non-road mobile, and point sources (large emitters and categories of research interest for this project). • Assess Growth • Review existing tools and establish improvements or beneficial alternatives. • Demonstrate use and synthesis of area-specific and source-specific data to estimate growth. • Assess Spatial Allocation • Evaluate spatial surrogates for future-year conditions and establish a recommended spatial allocation scheme. • Demonstrate application of spatial allocation techniques.

  28. Preliminary Results for Growth

  29. Spatial Allocation—Surrogate Gridding Procedure

  30. Spatial Allocation—Gridding Example Calculation

  31. PM Source Profiles

  32. The Source Oriented External Mixture Model Tracks Particles Separately in the Atmosphere Crustal Material Other than Paved Road Dust Diesel Engines Meat Cooking Paved Road Dust

  33. The Source Oriented External Mixture Model Tracks Particles Separately in the Atmosphere Non-cat Gas Engines Cat Gas Engines Acidic Industrial Fireplace Combustion

  34. Example of Final Results:PM2.5 Mass 24-hr average for January 6, 1996Major concentration peaks around urban centers and Hwy 99 corridor 118mg m-3

  35. Acknowledgements • United States Environmental Protection Agency Science to Achieve Results (STAR) Grant # RD-83184201

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