Publication Detail
A Research Plan Designed to Reduce Uncertainty in the Emission Inventory for Heavy-Duty Diesel-Powered Trucks
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UCD-ITS-RR-92-01 Research Report |
Suggested Citation:
Guensler, Randall L., Daniel Sperling, Paul P. Jovanis (1992) A Research Plan Designed to Reduce Uncertainty in the Emission Inventory for Heavy-Duty Diesel-Powered Trucks. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-92-01
Regulatory agencies are beginning to focus on the implementation of transportation control measures (TCMs) and economic incentives as means for reducing emissions from heavy-duty trucks. Rational implementation of heavy-duty vehicle emission control measures hinges upon our ability to assess complex transportation and air quality relationships in specific corridors. To determine the effects of these proposed measures, changes in vehicle activity and emission rates that result must be modelable/estimable. However, existing emission inventory methodologies were designed to produce bulk estimates of the emission inventory (i.e. crude estimates, based upon aggregate parameters). The existing models were never designed to provide estimates of emission rates in specific transportation corridors, making current methodologies extremely imprecise for local impact analysis.
As described in our recent report, the methodologies used to estimate the emission contribution of heavy-duty trucks need significant improvement. The emission factors are based upon insufficient testing, vehicle activity estimates are sketchy at best, and a number of potentially critical activity/emission relationships are omitted from the existing models. If, through additional laboratory testing, the emission relationships prove to be significant (which appears likely), we believe that a complete overhaul of the methodologies will be required. Even though it is clear that all mobile source emission inventory methodologies require significant improvement, the deficiencies are so significant for heavy-duty trucks that a concerted extra effort will be required even to catch up to the current state of the practice.
When a large amount of uncertainty exists in the relationships between variables of a physical system, determining what research projects should be undertaken to better understand these relationships is a difficult task.
Interested parties must determine which research projects are likely to result in the most useful information, but they often face this task without certain knowledge of which existing methodologies and assumptions result in the greatest uncertainty. Determining which projects should be implemented and in what order proves to be as much an art as a science.
In a resource constrained environment, the task becomes even more daunting, as any errors in research hierarchy can limit the pursuit of subsequent projects.
This paper describes a proposed research framework to reduce uncertainty in heavy-duty truck emission inventory methodologies. Sixteen research projects that are consistent with the proposed framework are identified and discussed, and a relative timetable for project implementation is presented.
As described in our recent report, the methodologies used to estimate the emission contribution of heavy-duty trucks need significant improvement. The emission factors are based upon insufficient testing, vehicle activity estimates are sketchy at best, and a number of potentially critical activity/emission relationships are omitted from the existing models. If, through additional laboratory testing, the emission relationships prove to be significant (which appears likely), we believe that a complete overhaul of the methodologies will be required. Even though it is clear that all mobile source emission inventory methodologies require significant improvement, the deficiencies are so significant for heavy-duty trucks that a concerted extra effort will be required even to catch up to the current state of the practice.
When a large amount of uncertainty exists in the relationships between variables of a physical system, determining what research projects should be undertaken to better understand these relationships is a difficult task.
Interested parties must determine which research projects are likely to result in the most useful information, but they often face this task without certain knowledge of which existing methodologies and assumptions result in the greatest uncertainty. Determining which projects should be implemented and in what order proves to be as much an art as a science.
In a resource constrained environment, the task becomes even more daunting, as any errors in research hierarchy can limit the pursuit of subsequent projects.
This paper describes a proposed research framework to reduce uncertainty in heavy-duty truck emission inventory methodologies. Sixteen research projects that are consistent with the proposed framework are identified and discussed, and a relative timetable for project implementation is presented.