Real-World Energy Use and Emission Rates for Idling Long-Haul Trucks and Selected Idle Reduction Technologies

Abstract

Long-haul freight trucks typically idle for 2000 or more hours per year, motivating interest in reducing idle fuel use and emissions using auxiliary power units (APUs) and shore-power (SP). Fuel-use rates are estimated based on electronic control unit (ECU) data for truck engines and measurements for APU engines. Engine emission factors were measured using a portable emission measurement system. Indirect emissions from SP were based on average utility grid emission factors. Base engine fuel use and APU and SP electrical load were analyzed for 20 trucks monitored for more than 1 yr during 2.76 million mi of activity within 42 U.S. states. The average base engine fuel use varied from 0.46 to 0.65 gal/hr. The average APU fuel use varied from 0.24 to 0.41 gal/hr. Fuel-use rates are typically lowest in mild weather, highest in hot or cold weather, and depend on engine speed (revolutions per minute [RPM]). Compared with the base engine, APU fuel use and emissions of carbon dioxide (CO2) and sulfur dioxide (SO2) are lower by 36-47%. Oxides of nitrogen (NO(x)) emissions are lower by 80-90%. Reductions in particulate matter (PM), carbon monoxide (CO), and hydrocarbon emissions vary from approximately 10 to over 50%. SP leads to more substantial reductions, except for SO2. The actual achievable reductions will be lower because only a fraction of base engine usage will be replaced by APUs, SP, or both. Recommendations are made for reducing base engine fuel use and emissions, accounting for variability in fuel use and emissions reductions, and further work to quantify real-world avoided fuel use and emissions.

Escenarios de contaminación atmosférica generados por la gestión de residuos sólidos: un reto actual en el departamento de Boyacá, Colombia

“…Individually, by 2050, emissions of CH 4 reach up to 578886.17 metric tons, BC emissions reach 410376.38 metric tons and CO 2 emissions up to 1498164.01 metric tons. A comparison between these results and known atmospheric projected values, showed that these models probed a well representation of global pollution budget previously reported (Haro et al, 2019;Vitolo, Scutari, Ghalaieny, Tucker & Russell, 2018;Fuglestvedt et al, 2010;Frey & Kuo, 2009). 2 2 5 6 -1 5 3 6 • 2 8 7 • Stockwell, C., Yokelson, R., Kreidenweis, S.,…”

Multipollutant Air Quality Management

“…Flaws in the accountability measures were revealed by real-world emission testing (e.g., source-dominated studies using in-plume and cross-plume measurements in source-dominated environments such as roadsides, parking garages, tunnels, etc.). [117][118][119][120][121][122][123][124][125][126][127] An example of AQM for multipollutants and multiple effects is the long-term regional strategy for health, visibility, and ecological benefits in the U.S. Midwest, U.S. Northeast, and Canadian southeast regions. The O 3 Transport Assessment Group (OTAG), 128 NO x SIP call, [129][130][131][132][133]51,134,135 and its failure to reduce emissions from grandfathered power generators, the Regional Haze Rule, 10,47,94,136 and the CAIR and Clean Air Mercury Rules 12,134,137-142 all address multipollutants and in some cases required continuous emission monitors (CEMs) for accountability.…”

Atmospheric dispersion modelling with AERMOD for comparative impact assessment of different pollutant emission sources in an Alpine valley

“…In the present case the following hypotheses were made: all the idling vehicles are heavy-duty diesel vehicles (HDDV); all of them are standing with an auxiliary power unit (APU) in use; all the vehicles are always idling for the whole period of breaking in the parking lot, no matter the season of the year nor the hour of the day. The emission rate of each idling vehicle was determined according to standard values for this type of engine idling with the APU in use [7,10]: given the annual mean number of parked trucks, known from the available traffic data, the annual mean emission of the parking lot was easily calculated and modulated with data-based seasonal and hourly patterns. Table 5 summarises the emission rates of the three sources representing the parking.…”