Atmosphere (Jun 2022)

Development of an Engine Power Binning Method for Characterizing PM<sub>2.5</sub> and NOx Emissions for Off-Road Construction Equipment with DPF and SCR

  • Qi Yao,
  • Seungju Yoon,
  • Yi Tan,
  • Liang Liu,
  • Jorn Herner,
  • George Scora,
  • Robert Russell,
  • Hanwei Zhu,
  • Tom Durbin

DOI
https://doi.org/10.3390/atmos13060975
Journal volume & issue
Vol. 13, no. 6
p. 975

Abstract

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Aftertreatment technologies in Tier 4 off-road engines have resulted in significant emission reductions compared to older tier engines without aftertreatments. The appropriate characterization of Tier 4 engine emissions in consideration of aftertreatment operation is important for projecting emissions and developing mitigation strategies. The current method of aggregating emissions over an entire duty cycle and averaging them by engine load has a limitation in developing emission profiles over various duty cycles of Tier 4 engines, especially at low-load operations, where aftertreatment control for NOx may not be effective. In this study, an engine power binning method was developed to characterize emissions for Tier 4 construction equipment with aftertreatment systems, especially at low-power operating conditions. This binning method was applied to real-time emissions and activity data for four different types of Tier 4 construction equipment. Results show that low-power operations (2.5 emissions depending on the equipment types. These results underscore the need for controlling NOx emissions during low-power operations. PM2.5 EFs for non-DPF backhoes were one to two orders of magnitude greater than all the other equipment due to the lack of a DPF, despite being certified to the same PM2.5 standard. This shows the benefits of DPFs on construction equipment and that they are substantial in reducing PM2.5 emissions. Estimated emission differences between using the binning and the averaging methods were 49–86% and 16–82% for NOx and PM2.5, respectively. These differences may change once the binning method is applied to larger emission datasets obtained from real-world vocational activities.

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