Geospatial Modeling of Ship Traffic and Air Emissions

1. Geospatial Modeling of Ship Traffic and Air Emissions Chengfeng Wang , Ph.D. California Air Resources Board Presentation for the US Environmental Protection Agency March 25, 2008

2. Background • Globally, ship emissions non-negligible • Regionally, ship emissions can be significant • Spatially resolved ship emissions inventory needed for • Emission impact modeling • Regulatory analysis • Producing spatially-resolved ship emissions has been challenging

3. ExistingApproaches • Bottom-up approach • Emissions estimated & allocated spatially based on historical ship movement data, ship particulars, emission factors, and assumed locations • Principally used for smaller scale inventory • Top-down approach • Assigns global ship emissions totals based on spatial proxy of emissions intensity • Used for larger scale emissions inventory

4. Top-downApproach • Strengths • Quicker, demands relatively fewer resources & costs less • Improved by updating global inventory, emissions intensity proxy • Multi-scale nature of the top-down approach • Weaknesses • Significant uncertainty of global totals • Statistical & spatial sampling bias proxy identified • Sources not sufficiently characterized

5. Sampling Bias of Spatial Proxies

6. ICOADS vs AMVER

7. Ship Traffic, Energy and Environment Model (STEEM) Reported Ship Positions Ship Movements Empirical Waterway Network Solved Shipping Routes Ship Attributes Ship Emissions Inventory

8. Overview of STEEM • Solves routes on an empirical global waterway network using ArcGIS Network Analyst • Estimates emissions based on: • Nearly complete historical ship movements • Individual ship engine installed power & ship speed • Hours of operation from ship speed & length of routes • Allocates emissions based on locations of routes

9. Data Sources • Ship position data • International Comprehensive Ocean-Atmosphere Data Set (ICOADS) • Automated Mutual-assistance Vessel Rescue System (AMVER) data set • Ship movement data • U.S. Army Corps of Engineers (USACE) Foreign Traffic Entrances & Clearances data set • Lloyd's Shipping Information Database • Ship attribute data • Lloyd's ship registry data

10. Relationship Among Data Sets of STEEM

11. TemporalDynamics of Traffic

12. Traffic Pattern from Independent Proxies

13. Building Empirical Waterway Network ~9000 segments & ~1700 ports ~170,000 ship trips/yr in North America

14. Illustration of Solved Routes

15. Illustration of Solved Asia-Europe Route

16. Illustration of Real World Route Captain Ed Page, The emerging world of vessel tracking

17. Segment ID Ship ID Trip ID Route ID Port ID Establish Relationships • Movements Data Set • ~170,000 North American trips • ~1,800 ports world wide • ~21,000 routes (unique pair of ports for a group of ships) Solve Waterway Network using ArcGIS Network Analyst

18. Ship ID Trip ID Route ID Segment ID Ship Attributes Segment Length & Area Emissions in Each Segment Estimate & Assign Emissions Emissions from Each Ship Trip

19. Spatial Distribution in Multimodal Context Spatial Distribution in Multimodal Context Base year 2002 inventory: applied network model (STEEM), activity based methods for “all” NA traffic

20. Validation: No systematic bias, but room to improve – toward convergence at all scales We are interested to learn how port-based adjustments contribute to these insights

21. Validation: No systematic bias, but room to improve – toward convergence at all scales We are interested to learn how port-based adjustments contribute to these insights

22. Future Improvement • Refine waterway network • Solve network by vessel type • Account for seasonality • Account for navigation constraints • Using ship tracking & monitoring data to: • Validate solved routes • Improve near port ship speed & load profiles • Improve estimation of hoteling emissions • Characterize temporal dynamics of ship traffic

23. Vessel Traffic Intensity Based on AIS Data Container Vessel Bulk Carrier Fishing Vessel Tug Boat

24. Acknowledgments: Collaborators, sponsors, colleagues • STEEM Model and North American Inventory: • California Air Resources Board; Council on Environmental Cooperation, EPA, other agencies • http://www.ocean.udel.edu/cms/jcorbett/sea/NorthAmericanSTEEM/ • Global inventory improvements and modeling: • Jeremy Firestone; James Winebrake; Clean Air Task Force; Prasad Kasibhatla • NOAA Right Whale Research Grant; ICTC 2k2 team; US DOT Center for Climate Change; US DOT Maritime Administration

25. References • Corbett, J.J., C. Wang, and J. Firestone, Estimation, Validation, and Forecasts of Regional Commercial Marine Vessel Inventories-Tasks 1 and 2: Baseline Inventory and Ports Comparison Final Report. 2006, Prepared for CARB. • Wang, C., J.J. Corbett, and J. Firestone, Modeling energy use and emissions from North American shipping: Application of the ship traffic, energy, and environment model. Environmental Science and Technology, 2007. 41(9): p. 3226-3232. • Wang, C., J.J. Corbett, and J. Firestone, Improving spatial representation of global ship emissions inventories. Environmental Science and Technology, 2008. 42(1): p. 193-199. • Wang, C., J. Callahan, and J.J. Corbett. Geospatial modeling of ship traffic and air emissions. in 2007 ESRI International User Conference. 2007. San Diego, California. • Wang, C. and J.J. Corbett, Geographical characterization of ship traffic and emissions, in Inland Waterways; Ports and Channels; and the Marine Environment. 2005. p. 90-99. • Wang, C., J.J. Corbett, and J.J. Winebrake, Cost-effectiveness of reducing sulfur emissions from ships. Environmental Science and Technology, 2007. 41(24): p. 8233-8239.

26. Contact Information Chengfeng Wang, Ph.D. California Air Resources Board 1001 I Street, Sacramento, CA 95812 Phone: (916) 322-1719 E-Mail: cwang@arb.ca.gov