Data Needs for Updating and Improving the U.S. Environmental Protection Agency NONROAD Model February 18, 2019 Phil Lewis Texas A&M University Carl Fulper, U.S. EPA Sarah Roberts, U.S. EPA Reza Farzaneh, Texas A&M Transportation Institute Jeremy Johnson, Texas A&M Transportation Institute
Cooperative and Research Development Agreement (CRADA) This work is part of a Cooperative Research and Development Agreement (CRADA) between Texas A&M Transportation Institute (TTI) and U.S. EPA. Allows EPA and TTI to work together; Leverage shared resources and expertise; and Gather data through this and other project initiatives. 2
Objective Collect and analyze real-world activity data from a variety of nonroad vehicles and equipment in order to update and improve the United States Environmental Protection Agency s (EPA) NONROAD model. 3
Nonroad Vehicles and Equipment NONROAD estimates emissions from a variety of off-road vehicles and equipment: Construction Agriculture Lawn & Garden Recreation Airport Support Portable Engines Calculates fuel consumption and emissions of over two dozen pollutants for every county in the U.S. 4
Source Data NONROAD uses a large array of source data including: Estimates of equipment & engine populations for each U.S. county; Equipment usage levels (e.g., hours of use per year and seasonal variations in usage patterns); Rates of engine scrappage and population growth; and Engine-specific emission factors, load factors, & horsepower levels. This is what we are trying to update and improve! 5
Data Sources These data traditionally have come from a variety of government and industry sources, such as: U.S. Department of Energy; U.S. Department of Agriculture; U.S. Census Bureau; U.S. Forest Service; International Snowmobile Manufacturers Association; National Marine Manufacturers Association; Motorcycle Industry Council; and Power Systems Research 6
Data Deficiencies The majority of NONROAD source data dates back to the model s first public release over twenty years ago. These data do not account for: Impacts of large-scale economic trends (e.g., the Great Recession); Regional differences in economic trends that drive nonroad equipment activity; and Recent advances in nonroad engine technology. Hence, many engine-specific parameters in NONROAD are based on predictions rather than actual measurements. 7
New Data Sources On-board telematics and fleet management software collect vast amounts of data from nonroad vehicles and equipment. These data include: Fuel consumption; Daily hours of operation (both at idle and non-idle); Equipment turnover; and Weekday, weekend, and seasonal usage patterns. These data inform EPA s current efforts to update NONROAD source data; therefore, it improves the ability to estimate emissions from off-road engines and equipment. 8
Current Data Sources Currently data is coming from 3 data sources, Texas (TxDOT), California (CALTRANS), and one private fleet. Data is updated daily via usage reports for the fleets. Usage patterns may be analyzed on various timescales including daily, weekly, monthly, quarterly, annually, etc. 9
Sample Data Analysis 10
Vehicle and Equipment Data Needs Data Description Priority Fleet ID Number Unique fleet identification number High VIN Vehicle Identification Number High Vehicle Model Year Model Year (YYYY) High Vehicle Manufacturer Manufacturer (CAT, John Deere, etc.) High Vehicle Model Model number assigned by manufacturer High Hour Meter at Purchase Hour Meter reading when purchased Medium Mileage at Purchase Odometer reading when purchased Medium Vehicle Weight Pounds (lb) or Kilograms (kg) Medium Date Purchased YYYY:MM:DD or Year (YYYY) Medium 11
Engine Data Needs Data Description Priority Engine Serial Number Serial Number found on engine label High Engine Model Year Model Year (YYYY) High Engine Manufacturer Manufacturer (CAT, Cummings, etc.) High Engine Model Model number assigned by manufacturer High Engine Power Rating (Gross) Horsepower (HP) or Kilowatts (kw) High Engine Power Rating (NET) Horsepower (HP) or Kilowatts (kw) High Engine HP at reference RPM Ex: 100 HP @ 2000 RPM High Engine Displacement Liters or Cubic Inches High Aspiration Natural, turbo, etc. Medium 12
Location and Usage Data Needs Data Description Priority State State & date where equipment is used High County County & date where equipment is used High District District & date where equipment is used High Zip Code Zip code & date where equipment is used High Hours Used Total hours operated annually or quarterly High Odometer Used Total miles operated annually or quarterly High Fuel Used Gallons/Liters used annually or quarterly High Number of Trips Number of trips taken annually or quarterly High State State & date where equipment is used High 13
Benefits Potential benefits of an updated and improved NONROAD model based on real-world data include: Improved usage patterns of equipment within the fleet; Improved equipment maintenance practices within the fleet; Right-sizing of fleet based on seasonal usage and geographic location; More accurate estimates of idle and non-idle time; Improved characterization of the relationship between fuel consumption and engine load; More accurate estimates of equipment productivity & operating costs; and Improved overall evaluation of the economic, energy, and environmental impacts of off-road vehicles and equipment. 14
Questions/Contact Name Organization Email Phone Phil Lewis Texas A&M University plewis@arch.tamu.edu 979-458-4457 Carl Fulper U.S. EPA Fulper.CarlR@epa.gov 734-214-4400 Sarah Roberts U.S. EPA Roberts.Sarah@epa.gov 734-214-4615 Reza Farzaneh Texas A&M Transportation Institute Reza.Farzaneh@tamu.edu 512-407-1118 Jeremy Johnson Texas A&M Transportation Institute j-johnson@tti.tamu.edu 979-317-2670 15