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Construction Emissions: Using Project Data to Improve Regional Inventories

Construction Emissions: Using Project Data to Improve Regional Inventories Douglas Eisinger, Ph.D. Deb Niemeier, Ph.D., P.E. UC Davis-Caltrans Air Quality Project Presented at the 86 th Annual Meeting of the Transportation Research Board, Workshop 135

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Construction Emissions: Using Project Data to Improve Regional Inventories

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  1. Construction Emissions: Using Project Data to Improve Regional Inventories Douglas Eisinger, Ph.D. Deb Niemeier, Ph.D., P.E. UC Davis-Caltrans Air Quality Project Presented at the 86th Annual Meeting of the Transportation Research Board, Workshop 135 Guidelines on Conducting Project Level Air Quality Analysis Washington, D.C. January 21, 2007

  2. Outline • Motivation: Need Local Activity Data • Setting the Context: Example Project • Top-Down Regional Emissions • Bottom-up Regional Emissions • Number and type of construction projects per year • Equipment and use per project • Emissions • Sacramento Example: Bottom-up Inventory • Comparison: Top-Down vs. Bottom-up • Some Observations • Conclusions

  3. 1. Motivation: Need Local Activity Data Powerpoint From Caltrans Training Exercise

  4. 2. Setting the Context: Example Project • 1.7 mile long I-80 auxiliary lane addition in Sacramento • 6 month project, four construction stages: • Land clearing • Roadway grading and excavation • Drainage/utilities/sub-grade • Paving

  5. Sacramento Project:Year 2008 NOx Emissions • Analysis inputs • Total disturbed project area: 8 acres • Soil moved: 300 yd3 per day (imports and exports) • Model-Assigned Equipment by phase

  6. Sacramento Project:Year 2008 NOx Emissions • Modeled with Sacramento area air district tool • Total project NOx emissions: 3.5 tons • But… emissions would be about 10-15% less with solar sign boards (instead of diesel-powered sign boards) Let’s look at building a regional inventory…

  7. 3. Sacramento NOx Emissions: Top-Down • Diesel-powered construction is 8% of annual average NOx emissions (2008) • Basis: regional equipment populations, activity, and OFFROAD emission factors • Note: transportation construction is not separated

  8. Top Down: Estimating Transportation Construction NOx • Need to disaggregate total construction emissions • Possible approaches… • Road construction PM emissions are 58% of all construction and demolition PM emissions • ARB construction equipment population surveys: • SIC Group 161 (highway / street construction) is 12% • SIC Group 162 (includes bridge, tunnel, elevated highway) is 38% SIC 161 + 162 equal 50% of all construction equipment • Possible range… 12-58% of all construction = transportation construction

  9. 4. “Bottom Up” Regional Emissions Data needs: • Equipment and use per project • Number of construction projects • Emission factors

  10. Equipment and Use Per Project (1 of 2):California Statewide Data • UCD created data set of Caltrans projects for years 2000-2005 • Established six major construction categories

  11. Equipment and Use Per Project (2 of 2) • Collected daily reports (diaries) for 30 projects • Built data set of equipment activity by project

  12. Year 2008 NOx Emissions for “Average” Project *EFs: gms/equip piece/hr (from OFFROAD2007) Wide range by project type: 0.3 – 10.7 tons

  13. 5. Sacramento Example: Bottom-Up Inventory a. Number of Projects • Caltrans data: 57 Caltrans projects / year • SACOG data: 63 other transportation projects / year • Total: 120 projects per year Source: average of 2004-2006 project data from Caltrans and SACOG.

  14. 5. Sacramento Example: Bottom-Up Inventory b. 2008 Regional NOx Emissions Estimate 289 NOx tons/yr X = 120 projects/yr Ave. project: 2.7 tons NOx 1.1 NOx tons/day = Ave. project: 253 days

  15. Sacramento Example: Caveats Lots of assumptions! • Caltrans data set is representative • “Other” Caltrans projects are similar to top six categories • SACOG projects are similar to Caltrans projects • “Average” project represents typical project mix for a given year Main point – illustrate concept, give sense of scale to emissions

  16. 6. Comparison: Top-Down vs. Bottom-Up Sacramento 2008 NOx Emissions • Top-Down (OFFROAD2007) All NOx = 238 tpd All construction = 19 tpd IFTHEN 58% = trans. 11 tpd (PM %) 50% = trans. 9.5 tpd (SIC 161 + 162) 12% = trans. 2.3 tpd (SIC 161 only) • Bottom-Up 1.1 tpd = trans. In this illustration, top-down emissions are 2-10 X higher than bottom-up

  17. 7. Some Observations Activity varies by project type in Caltrans data • Project duration: 181 – 394 days • Total project NOx emissions: 0.3 – 10.7 tons • Caveat: data set needs to be expanded to better represent individual project types Main point: estimating project emissions requires activity data specific to project type

  18. Some Observations: Off-Road Modeling Not as Mature as On-Road Modeling Nov. 2006: California ARB releases OFFROAD2007 • 8% less equipment than prior model • Construction equipment useful life doubled from prior model Example year 2000 equipment populations (statewide)

  19. 8. Conclusions • Activity assumptions are central to emissions estimates • Inventory tools are still in early development stages • “Bottom-up” can quality-check “top-down” inventories • Sacramento illustration includes lots of assumptions – but results imply: • Lots of uncertainty in regional inventories • Need to disaggregate sources to quality-check findings • Need better project and regional activity data (spatial, temporal, fleet mix)

  20. Acknowledgments The study team thanks the following individuals for their assistance: • Song Bai, U.C. Davis • Mike Brady, Caltrans • José Luis Cáceres, SACOG • Zhen Dai, California Air Resources Board/UCD • Gordon Garry, SACOG • Justin Kable, Port of Oakland/UCD • Robert O’Loughlin, Federal Highway Administration • Jeff Pulverman, Caltrans • Sharon Tang, Caltrans • Ru Wang, U.C. Davis

  21. For Q & A Top Five NOx Emitters by Construction Equipment Type OFFROAD2007Caltrans Data Rollers √ Graders √ Scrapers √ Excavators √ Generator Set √ Crawler Tractors √ Skid Steer Loaders √ Rubber Tire Loaders √ √ Tractors/Loaders/Backhoes √ Main point: construction activity in model does not characterize road construction per se

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