New WVU Study Provides Road Map to Lower Methane Emissions for Future Heavy-Duty Natural Gas Vehicle Fleet
Research identifies technology and management practices most likely to provide the largest reduction in methane emissions
MORGANTOWN, W.Va.—A new study published today (August 23, 2017) in the Journal of Air and Waste Management Association builds upon recent heavy-duty natural gas vehicle methane emission measurements to model methane emissions from a future, much larger vehicle fleet. The predicted methane emissions rates from a 2035 natural gas fleet cover a wide range depending on technologies adopted and best management practices employed.
This study, conducted by researchers at West Virginia University’s Center for Alternative Fuels, Engines, and Emissions, comes as the price of natural gas has decreased, leading to interest in natural gas as a cleaner replacement for diesel in heavy-duty vehicles. Natural gas vehicles currently make up a small market share but are predicted to grow significantly over the next few decades.
Methane, the main component of natural gas, is a cleaner burning, lower carbon intensity fuel. However, it is also a powerful greenhouse gas, and leaks from vehicles and fueling stations have the potential to undermine the climate benefits of using natural gas over diesel fuel.
The paper entitled, Future Methane Emissions from the Heavy-Duty Natural Gas Transportation Sector for Stasis, High, Medium, and Low Scenarios in 2035, used data from a prior study to project various scenarios in order to evaluate potential emissions reductions of technological advances and best management practices. The study did not look at the full suite of vehicles on the road today but rather focused on vehicles and engines currently under production as these represented those most likely to populate the fleet in 2035. For this reason, the study does not estimate emissions from the current fleet.
“We considered both liquefied natural gas and compressed natural gas technologies employed in a future fleet and considered a range of engine applications including over-the-road and refuse trucks and buses,” said Nigel Clark, professor of mechanical and aerospace engineering and George Berry Chair at WVU. “Our first study served to highlight fuel losses meriting future attention and we assess the impacts of their potential reductions within this study.”
This study found that the biggest reduction in emissions would come from implementing closed crankcase ventilation systems on heavy-duty natural gas spark ignition engines. Adherence to best practices during fueling and fuel station management could also have a significant impact on the amount of methane leaked through reduction in manual venting of LNG tanks and proper design of station and fleet combinations. In addition to current and new technologies, regulation and policy may lead to further developments that could reduce methane emissions.
“While the models in this paper provide valuable insights on technological and management practice improvements to reduce methane emissions as the natural gas fleet grows, the study only looks at the emissions from the fleet and associated infrastructure, or the ‘pump-to-wheels’ emissions. However, to understand fully the climate benefits of an industry swing from diesel to natural gas, the full ‘well-to-wheels’ emissions must be considered,” said Joe Rudek, lead senior scientist, Environmental Defense Fund.
Support for this paper was provided by the Environmental Defense Fund, Cummins, Cummins Westport, Royal Dutch Shell, the American Gas Association, Chart Industries, Clean Energy, the International Council on Clean Transportation, PepsiCo, Volvo Group, Waste Management and Westport Innovations. Support was also provided by West Virginia University’s George Berry Chair endowment and the WVU Transportable Chassis Testing Laboratory personnel.
A Scientific Advisory Panel comprised of academic experts in the fields relevant to the study served as independent advisors, reviewing the appropriateness of the methodologies, results and statistical methods.