An instrumental trifecta now exists for aerosol separation and classification by aerodynamic diameter (D_ae), mobility diameter (D_m) and mass (m) utilizing an aerodynamic aerosol classifier (AAC), differential mobility analyzer (DMA) and aerosol particle mass analyzer (APM), respectively. In principle, any combination of two measurements yields the third. These quantities also allow for the derivation of the particle effective density (ρ_eff) and dynamic shape factor (χ). Measured and/or derived deviations between tandem measurements are dependent upon the configuration but are generally less than 10%. Notably, non-physical values of χ (< 1) and ρ_eff (> bulk) were determined by the AAC-APM. Harmonization of the results requires the use of χ in the determination of m and D_m from the AAC-DMA and AAC-APM requiring either a priori assumptions or determination from another method. Further errors can arise from assuming instead of measuring physical conditions – e.g. temperature and pressure affect the gas viscosity, mean free path and the Cunningham slip correction factor therefore impacting D_m, D_ae – but are expected to have a smaller impact than χ. Utilizing this triplet of instrumentation in combination allows for quantitative determination of χ and the particle density (ρ_p). If the bulk density is known or assumed, then the packing density can be determined. The χ and ρ_p were determined to be 1.10 ± 0.03 and (1.00 ± 0.02) g cm⁻³, respectively, for a water stabilized black carbon mimic that resembles aged (collapsed) soot in the atmosphere. Assuming ρ_bulk = 1.8 g cm⁻³, a packing density of 0.55 ± 0.02 is obtained.

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 (<20% engine power) are responsible for 38–60% NOx and 11–51% of PM_2.5 emissions depending on the equipment types. These results underscore the need for controlling NOx emissions during low-power operations. PM_2.5 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 PM_2.5 standard. This shows the benefits of DPFs on construction equipment and that they are substantial in reducing PM_2.5 emissions. Estimated emission differences between using the binning and the averaging methods were 49–86% and 16–82% for NOx and PM_2.5, respectively. These differences may change once the binning method is applied to larger emission datasets obtained from real-world vocational activities.