Particle effective density and mass during steady-state operation of GDI, PFI, and diesel passenger cars
Introduction
Exposure to particulate matter (PM) is associated with increased cardiopulmonary morbidity and mortality (Pope & Dockery, 2006) and is influenced by mobile source emissions (EPA, 2002, Hill et al., 2009, Lloyd and Cackette, 2001). The California Air Resources Board (ARB) recently adopted the Low Emissions Vehicle (LEV) III regulations which, by 2025, will reduce the light-duty vehicle PM emission standards for the Federal Test Procedure (FTP) from 10 to 3 and ultimately to 1 mg/mi (6.2–0.62 mg/km) (CARB, 2012a). Recently, ARB and U.S. EPA demonstrated measurement of PM emissions below 1 mg/mi (0.62 mg/km) using the existing filter-based gravimetric method (Hu et al., 2014). However, alternative measurement approaches are still of great interest to better understand the characteristics of PM at very low levels.
One alternative method for PM mass measurement is the Integrated Particle Size Distribution (IPSD) method, a phrase first used by Liu et al. (2009) referring to the general method for estimating PM concentrations from particle size distribution and effective density. Defined as mass divided by electrical mobility equivalent volume (Kelly & McMurry, 1992), particle effective density enables rapid conversion between number and mass distributions, or between mobility and aerodynamic diameters, without assuming bulk density or morphology. Several studies have reported particle effective density of emissions from gasoline, natural gas, and diesel engines (e.g. Barone et al., 2011, Bullock and Olfert, 2014, Maricq and Xu, 2004, Olfert et al., 2007, Park et al., 2003, Ristimäki et al., 2002, Van Gulijk et al., 2004, Virtanen et al., 2004, Zelenyuk et al., 2005). Except for one study reporting constant effective density of emissions from a natural gas engine (Bullock & Olfert, 2014), the majority of studies on gasoline and diesel engines show particle effective density decreases as a function of particle size, in general agreement with the power fit model for fractal aerosols using a mass–mobility scaling exponent (Sorensen, 2011). However, to the best of our knowledge, only one study has evaluated gasoline direct injection (GDI) emissions (i.e. Maricq & Xu, 2004), and no previous studies have measured particle effective density from port-fuel injected (PFI) gasoline or light-duty diesel vehicles equipped with a diesel particulate filter (DPF). Therefore, redefining the particle effective density functions over the breadth of current vehicle technologies is needed to properly evaluate IPSD.
Kelly and McMurry (1992) first measured effective density of laboratory aerosol using a Differential Mobility Analyzer (DMA) and an inertial cascade impactor. Subsequently, several studies used a DMA placed upstream of an Electrical Low Pressure Impactor (ELPI, Ahlvik et al., 1998, Keskinen et al., 1992, Maricq et al., 2000, Maricq and Xu, 2004) providing real-time aerodynamic size distribution for a given DMA set point. A DMA has also been operated as part of a Scanning Mobility Particle Sizer (SMPS) in parallel to, rather than in series with, an ELPI for making density measurements more quickly by fitting size distributions (Ristimäki et al., 2002, Virtanen et al., 2004, Virtanen et al., 2002). A DMA has also been placed upstream of an Aerosol Particle Mass Analyzer (APM, Ehara, Hagwood, & Coakley, 1996) and Centrifugal Particle Mass Analyzer (CPMA, Olfert & Collings, 2005), which both classify according to mass to charge ratio using two rotating concentric cylinders to balance electrostatic and centrifugal forces. McMurry, Wang, Park, and Ehara (2002) used a DMA and APM to measure the effective density of atmospheric aerosol, and the approach has been subsequently applied to characterize engine exhaust particles (e.g. Barone et al., 2011, Park et al., 2003). Later, the CPMA was designed to improve the transfer function of the APM by using slightly different angular velocities for the two rotating cylinders (Olfert and Collings, 2005, Olfert et al., 2006). There are other methods for measuring particle effective density, such as using a DMA and Single Particle Laser Ablation Time-of-flight Mass Spectrometer (SPLAT, Zelenyuk et al., 2005). However, the system measures vacuum aerodynamic diameter and has low (<0.1%) detection efficiencies for particles below 50 nm (Zelenyuk, Yang, Choi, & Imre, 2009) and is not commercially available. The Dekati Mass Monitor (DMM, Lehmann, Niemelä, & Mohr, 2004) uses yet another approach by combining one mobility with six aerodynamic channels measuring size distribution to estimate particle effective density in real time. The DMM is used widely to measure PM mass, but density values are not reported but are used to directly report mass concentration based on a unimodal size distribution, mass median diameter, and geometric standard deviation (GSD), which have all shown to largely deviate from accepted reference methods (Mamakos et al., 2006, Quiros et al., 2014).
In this study, we use the DMA–CPMA method to measure particle effective density from two gasoline direct injected (GDI-1 and GDI-2) and two port fuel injected (PFI and PFI-E85) gasoline vehicles, and one turbo direct injection (TDI) light-duty diesel vehicles on a chassis dynamometer. Because the DMA–CPMA approach requires several minutes to complete each measurement, steady-state testing was conducted. The primary objective of this study was to determine particle effective density functions that can be used to evaluate the capability of IPSD to estimate PM mass emitted from light-duty vehicles meeting the LEV III standards. A secondary objective is to compare size distributions measured by the TSI Engine Exhaust Particle Sizer (EEPS, 5.6–560 nm) and SMPS (8.7–365 nm). The SMPS is regarded as the reference method for measuring size distribution; however, it requires one to two minutes to complete a scan, and therefore the EEPS was developed to measure transient particle size distributions. Therefore, SMPS-to-EEPS ratio is calculated under controlled steady-state conditions, and is used to correct EEPS measurements. Finally, this study also aims to measure the contribution of larger particle sizes using a TSI Aerodynamic Particle Sizer (APS, 0.54–2.5 µm), in order to compare total suspended real-time mass with the filter-based gravimetric standard method.
Section snippets
Laboratory, instruments, and quality assurance
All data were collected at ARB׳s Haagen-Smit Laboratory (HSL) in El Monte, CA in one of the light-duty test cells equipped with a 48-in. single-roll electric chassis dynamometer, a constant volume sampler (CVS), and sampling systems meeting certification requirements defined by 40 CFR 1066 (U.S. EPA, 2012). A cyclone upstream of all PM sampling was used to remove particles larger than 2.5 µm, an optional requirement listed in the CFR.
Figure 1 shows the instrumentation and sampling setup. The
Particle mass–mobility scaling exponent and effective density
Figure 3(A) shows the lognormal fitting procedure used to determine average particle mass of 140-nm particles selected by the DMA during the 16-kW test of the GDI-1 vehicle. In this case, the CPMA scan reported three peaks: a primary peak centered at 0.633 fg with ρeff=441 kg/m3, a secondary peak centered at 0.375 fg with ρeff=261 kg/m3, and a doubly charged peak centered at 0.95 fg that was subtracted from the distribution before calculating average mass. In many cases, only two peaks were
Discussion and conclusion
Particle effective density functions were derived from measurements between 55 and 270 nm. Measured data for larger sizes (i.e. 350 nm) fit to the power law model well, and therefore, the parameters can be used to estimate particle effective density for larger mobility diameters. For smaller particles, effective density typically plateaus, and in some cases, increases between 30 and 55 nm. Further investigation could help better understand this increase; for example a thermodenuder could be used
Acknowledgments
The authors thank the ARB management and staff who assisted during the project plan development and provided laboratory assistance during testing including Mark Fuentes, Wayne McMahon, M.-C. Oliver Chang, Sherry Zhang, Bruce Frodin, William Robertson, Henry Toutoundjian, Manuel Cruz, Derrick Lee, Huy Khou, and Inna Dzhema. The authors also thank Matti Maricq of Ford Motor Company for his feedback in the design of the test program. The statements and opinions expressed in this paper are solely
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