AIMS Geosciences, 2017, 3(2): 239-267. doi: 10.3934/geosci.2017.2.239.

Research article

Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Spatio-temporal Variations in on-road CO2 Emissions in the Los Angeles Megacity

1 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109
2 School of Natural Resources and Environment, University of Michigan, Ann Arbor, Michigan 48109
3 School of Life Sciences, Global Institute of Sustainability, Arizona State University, Tempe, Arizona 85287

We quantify hourly on-road fossil fuel carbon dioxide (FFCO2) emissions at the road segment level for the Los Angeles (LA) megacity based on observed traffic data, and characterize emission patterns across space and time. This on-road FFCO2 emissions dataset for LA (from Hestia version 1.0), based on actual traffic volume, provides emissions per vehicle kilometer travelled (VKT)—an important metric for greenhouse gas (GHG) reductions. We further identify emissions hotpots that can help state and local policy makers plan the most effective GHG reduction strategies. On-road vehicle traffic accounts for half of the FFCO2 emissions in LA, of which 41% is from arterials (intermediate road type). Arterials also have the largest C emissions intensity—FFCO2 per VKT—possibly from high traffic congestion and fleet composition. Non-interstate emissions hotspots (> 419 tC lane-km-1) are equally dominated by arterials and collectors (the lowest road type) in terms of FFCO2 emissions though collectors have a higher VKT. These hotspots occur in densely populated areas and developed landuse classes, largely in LA (67%) and Orange (18%) counties, and provide specific targets for emissions reduction efforts. The estimated uncertainties for interstate, arterial and collector emissions per road length are ± 2.1, ± 0.5 and ± 18.0%, respectively. Our overall estimates compare reasonably well with other products, DARTE and FIVE but with substantial differences in spatial distribution. The method for developing this dataset is easily replicable in other urban landscapes, and represents a powerful tool for carbon cycle science and regional policy makers.
  Figure/Table
  Supplementary
  Article Metrics

Keywords Fossil fuel; emissions; carbon dioxide; transportation; on-road; urban; spatio-temporal

Citation: Preeti Rao, Kevin R. Gurney, Risa Patarasuk, Yang Song, Charles E. Miller, Riley M. Duren, Annmarie Eldering. Spatio-temporal Variations in on-road CO2 Emissions in the Los Angeles Megacity. AIMS Geosciences, 2017, 3(2): 239-267. doi: 10.3934/geosci.2017.2.239

References

  • 1. IEA (2014) IEA Statistics, CO2 emissions from fuel combustion–highlights Rep. International Energy Agency, Paris.
  • 2. CARB (2015a) Sources of Carbon Dioxide in California for 2013, edited, California Air Resources Board, Sacramento, CA.
  • 3. CARB (2015b) SB 375 Implementation, edited, California Air Resources Board, Sacramento, CA.
  • 4. ILO (2010) Understanding California's Sustainable Communities and Climate Protection Act of 2008 (SB 375): A Local Official's Guide Rep., Institute for Local Government, Sacramento, CA.
  • 5. Deakin E (2011) Climate change and sustainable transportation: The case of California. J Transp Eng 137: 372-382.    
  • 6. Kennedy C, Steinberger J, Gasson B, et al. (2009) Greenhouse gas emissions from global cities. Environ sci technol 43: 7297-7302.    
  • 7. Duren RM, Miller CE (2012) Measuring the carbon emissions of megacities. Nat Clim Change 2: 560-562.    
  • 8. Gurney KR (2014) Recent research quantifying anthropogenic CO2 emissions at the street scale within the urban domain. Carbon Manage 5: 309-320.    
  • 9. Gurney KR, Razlivanov I, Song Y, et al (2012), Quantification of fossil fuel CO2 emissions on the building/street scale for a large US City. Environ sci technol 46: 12194-12202.
  • 10. Beckx C, Arentze T, Panis LI, et al. (2009), An integrated activity-based modelling framework to assess vehicle emissions: approach and application. Environ Plan B: Plan Des 36: 1086-1102.
  • 11. Hatzopoulou M, Miller EJ (2010) Linking an activity-based travel demand model with traffic emission and dispersion models: Transport's contribution to air pollution in Toronto. Transp Res Part D: Transp Environ 15: 315-325.    
  • 12. Marr LC, Harley RA (2002) Modeling the effect of weekday-weekend differences in motor vehicle emissions on photochemical air pollution in central California. Environ sci technol 36: 4099-4106.    
  • 13. Marr LC, Black DR, Harley RA (2002) Formation of photochemical air pollution in central California 1. Development of a revised motor vehicle emission inventory. J Geophys Res Atmos 107: ACH 5-1–ACH 5-9.
  • 14. Enting IG (2002) Inverse problems in atmospheric constituent transport, Cambridge University Press.
  • 15. CARB (2014) The California Almanac of Emissions and Air Quality - 2013 Edition Rep. California Air Resources Board, Sacramento, CA.
  • 16. BEA (2014) Economic growth widespread across metropolitan areas in 2013, edited, U.S. Department of Commerce, Bureau of Economic Analysis (BEA), Washington, DC.
  • 17. Wunch D, Wennberg PO, Toon GC, et al. (2009) Emissions of greenhouse gases from a North American megacity. Geophys res lett 36: 139-156.
  • 18. CARB (2011) EMFAC, edited, California Air Resources Board.
  • 19. TxDOT (2012) Appendix A: Vehicle Classification Using FHWA 13-Category Scheme, in Traffic Recorder Instruction Manual, edited, Texas Department of Transportation.
  • 20. FHWA (2012a) Highway Statistics 2010, edited, Policy and Governmental Affairs Office of Highway Policy Information U.S. Department of Transportation Federal Highway Administration (FHWA), Washington, DC.
  • 21. SCAG (2015) Transportation, edited, Southern California Association of Governments, Los Angeles, CA.
  • 22. Caltrans (2014a) Caltrans Performance Measurement System (PeMS), edited, California Department of Transportation, Sacramento, CA.
  • 23. SCAG (2010) Regional Screenline Traffic Count Program for the 2008 Regional Travel Model Validation Rep, Los Angeles, CA.
  • 24. SCAG (2012) SCAG Regional Travel Demand Model and 2008 Model ValidationRep, Southern California Association of Governments, Los Angeles, CA.
  • 25. Bell S (2001) A beginner's guide to uncertainty of measurement Rep. 1368-6550, National Physical Laboratory, Teddington, Middlesex, United Kingdom.
  • 26. Taylor BN, CE Kuyatt (1994) Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results Rep, 24 pp, United States Department of Commerce Technology Administration, National Institute of Standards and Technology, US Government Printing Office, Washington DC.
  • 27. Efron B, Tibshirani RJ (1994) An introduction to the bootstrap, CRC press.
  • 28. Caltrans (2014b) Caltrans GIS Data, edited, California Department of Transportation, Sacramento, CA.
  • 29. TAMU (2012) Mobility investment priorities, edited, Texas A & M Transportation Institute, College Station, TX.
  • 30. LACO (2016) Metro GIS Data (Transit Data for Los Angeles County), edited, LA County GIS Portal.
  • 31. McDonald BC, McBride ZC, Martin EW, et al. (2014) High‐resolution mapping of motor vehicle carbon dioxide emissions. J Geophys Res: Atmos 119: 5283-5298.    
  • 32. Yarwood G, Stoeckenius TE, Heiken JG, et al (2003) Modeling weekday/weekend ozone differences in the Los Angeles region for 1997. J Air Waste Manage Assoc 53: 864-875.    
  • 33. Barth M, Boriboonsomsin K, Cervero R (2009) 2 Traffic congestion and greenhouse gases. University of California Transportation Center Working Papers 1.
  • 34. Mendoza D, Gurney KR, Geethakumar S, et al (2013), Implications of uncertainty on regional CO2 mitigation policies for the US onroad sector based on a high-resolution emissions estimate. Energy Policy 55: 386-395.
  • 35. Gately CK, Hutyra LR, Wing IS (2015), Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships. Proc Natl Acad Sci 112: 4999-5004.
  • 36. Kinnee EJ, Touma JS, Mason R, et al (2004) Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area. Transp Res Part D: Transp Environ 9: 139-150.    
  • 37. FHWA (2012b) Flexibility in Highway Design Chapter 3: Functional ClassificationRep., Office of Planning, Environment, & Realty (HEP), U.S. Department of Transportation Federal Highway Administration (FHWA), Washington, DC.
  • 38. Hudda N, Fruin S, Delfino RJ et al (2012) Cost effective determination of vehicle emission factors using on-road measurements. Atmos Chem Phys Discuss 12: 18715-18740.    
  • 39. Harley RA, Lunden MM. (2008) Long-term changes in emissions of nitrogen oxides and particulate matter from on-road gasoline and diesel vehicles. Atmos Environ 42: 220-232.    

 

Reader Comments

your name: *   your email: *  

Copyright Info: © 2017, Preeti Rao, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved