Real-world PM, NOx, CO, and ultrafine particle emission factors for military non-road heavy duty diesel vehicles

Training on US military bases involves nonroad diesel vehicles with emissions that can affect base personnel, nearby communities, and attainment of air quality standards. Nonroad diesel engines contribute 44% of diesel PM and 12% of total NOx emissions from mobile sources nationwide. Although military sector fuel use accounts for only ≈0.4% of distillate fuel use in US, emissions factors measured for these engines improve the representation of the relatively small (as compared to onroad sources) database of nonroad emission factors. Heavy-duty multi-axle, all-wheel drive military trucks are not compatible with regular single-axle dynamometers and their emissions cannot be measured under standard laboratory conditions. We have developed a novel in-plume technique to measure in-use emissions from vehicles with elevated stack. Real-world gaseous and particulate matter (PM) emission factors (EFs) from ten 7-ton 6-wheel drive trucks and two 8-wheel drive heavy tactical Logistics Vehicle System (LVS) vehicles were measured using in-plume sampling. The EFs of these trucks are comparable to those of onroad trucks while the PM EFs of 2-stroke LVS are ≈10 times higher than those of onroad vehicles. Lower EC/PM ratio was observed for LVS compared with MTVR. PM number emission factors were 5.9 × 1014 particles km−1 for the trucks and 2.5 × 1016 particles km−1 for the LVSs, three orders of magnitude higher than the proposed European Union standard of 6 × 1011 particles km−1. The EFs sampled can be extended to engines used in the broader nonroad sector including agriculture and mining and used as inputs to the NONROAD model.

► Novel in-plume measurement of in-use emissions from heavy-duty nonroad vehicles.
► PM EFs of diesel 2-stroke engines are ≈10 times higher than 4-stroke engines.
► Nonroad trucks EFs are similar to those of onroad trucks of similar size.
► Measured EFs can be used as inputs for EPA’s NONROAD model where data is sparse.