Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Sonntag, Darrell; Baldauf, Richard; Yanca, Catherine A.; Fulper, Carl

doi:10.1080/10962247.2013.870096

Cited by 15 publications

(13 citation statements)

References 35 publications

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“…The PM mass emissions of PFI vehicles (Figure 1, upper panel) are low and typically <2 mg/km for all regions. Higher emissions were measured for older vehicles (pre-2000) [91,92]. Some aggressive cycles, such as the Common Artemis Driving cycles (CADC), gave also emission levels around 10 mg/km (such cases not plotted in the figure).…”

Section: Pm Mass Emissionsmentioning

confidence: 99%

“…The following studies were considered in the figures of the main text. Figure 1 (upper panel): PM PFI: [42,91,[93][94][95][96]100,101,121, Figure 1 (lower panel): PM GDI: [37,42,51,68,74,[93][94][95]100,[102][103][104][105][106]108,109,120,121,132,[157][158][159][160][161][162][163][165][166][167]169,170,173,[175][176][177][178][179]181,…”

Section: Appendix Bmentioning

confidence: 99%

“…The following studies were considered in the figures of the main text. Figure 1 (upper panel): PM PFI: [42,91,[93][94][95][96]100,101,121, Figure 1 (lower panel): PM GDI: [37,42,51,68,74,93-95,100,102-106,108,109,120,121,132,157-163, 165-167,169,170,173,175-179,181,184,186,188-190,194-225] Figure 2 (upper panel): SPN PFI: [42,51,75,93,96,121,127,[158][159][160][161][162][163][164][165][168][169][170][171][172][173][174][175][176][177][178][179][180][181][182][183]194,224,[226][227][228]…”

Section: Appendix Bmentioning

confidence: 99%

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European Regulatory Framework and Particulate Matter Emissions of Gasoline Light-Duty Vehicles: A Review

et al. 2019

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The particulate matter (PM) emissions of gasoline vehicles were much lower than those of diesel vehicles until the introduction of diesel particulate filters (DPFs) in the early 2000s. At the same time, gasoline direct injection (GDI) engines started to become popular in the market due to their improved efficiency over port fuel injection (PFI) ones. However, the PM mass and number emissions of GDI vehicles were higher than their PFI counterparts and diesel ones equipped with DPFs. Stringent PM mass levels and the introduction of particle number limits for GDI vehicles in the European Union (EU) resulted in significant PM reductions. The EU requirement to fulfill the proposed limits on the road resulted to the introduction of gasoline particulate filters (GPFs) in EU GDI models. This review summarizes the evolution of PM mass emissions from gasoline vehicles placed in the market from early 1990s until 2019 in different parts of the world. The analysis then extends to total and nonvolatile particle number emissions. Care is given to reveal the impact of ambient temperature on emission levels. The discussion tries to provide scientific input to the following policy-relevant questions. Whether particle number limits should be extended to gasoline PFI vehicles, whether the lower limit of 23 nm for particle number measurements should be decreased to 10 nm, and whether low ambient temperature tests for PM should be included.

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Section: Pm Mass Emissionsmentioning

confidence: 99%

“…The following studies were considered in the figures of the main text. Figure 1 (upper panel): PM PFI: [42,91,[93][94][95][96]100,101,121, Figure 1 (lower panel): PM GDI: [37,42,51,68,74,[93][94][95]100,[102][103][104][105][106]108,109,120,121,132,[157][158][159][160][161][162][163][165][166][167]169,170,173,[175][176][177][178][179]181,…”

Section: Appendix Bmentioning

confidence: 99%

“…The following studies were considered in the figures of the main text. Figure 1 (upper panel): PM PFI: [42,91,[93][94][95][96]100,101,121, Figure 1 (lower panel): PM GDI: [37,42,51,68,74,93-95,100,102-106,108,109,120,121,132,157-163, 165-167,169,170,173,175-179,181,184,186,188-190,194-225] Figure 2 (upper panel): SPN PFI: [42,51,75,93,96,121,127,[158][159][160][161][162][163][164][165][168][169][170][171][172][173][174][175][176][177][178][179][180][181][182][183]194,224,[226][227][228]…”

Section: Appendix Bmentioning

confidence: 99%

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European Regulatory Framework and Particulate Matter Emissions of Gasoline Light-Duty Vehicles: A Review

et al. 2019

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“…It is possible for us to represent CMAQ aerosols in GOCART aerosol categories to calculate their optical properties. Table 1 shows how the CMAQ 5.1 Aero6 species (Sonntag et al, 2014) are mapped to CRTM's GOCART aerosol. CMAQ aerosols have three size modes: Aitken (i mode), accumulation (j mode) and coarse mode (k mode), representing super-fine nucleation particles, aged coagulated particles and coarse particles, respectively (Binkowski and Roselle, 2003).…”

Section: Calculations Of Aerosol Optical Depthsmentioning

confidence: 99%

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

et al. 2017

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Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM 2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM 2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM 2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM 2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM 2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM 2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

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“…It is possible for us to represent CMAQ aerosols in GOCART aerosol categories for calculating their optical properties. Table 1 shows how the CMAQ 5.1 Aero6 species (Sonntag et al, 2014) are mapped to CRTM's GOCART aerosol. CMAQ aerosols have 3 size modes: Aitken (i-mode), accumulation (j-mode) and coarse mode (k-mode) representing the super-fine nucleation particles, aged coagulated particles, and coarse particles, respectively (Binkowski and Roselle, 2003).…”

Section: Calculations Of Aerosol Optical Depthsmentioning

confidence: 99%

3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Study over the Contiguous United States

Tang

Pagowski

Chai

et al. 2017

Preprint

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Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. GSI results are compared with those obtained using the optimal interpolation (OI) method (Tang et al., 2015) for July, 2011 over CONUS. Both GSI and OI assimilate surface PM2.5 observations at 00, 06, 12, and 18 UTC, and MODIS AOD at 18 UTC. In the GSI experiments, assimilation of surface PM2.5 leads to stronger increments in surface PM2.5 compared to the MODIS AOD assimilation. In contrast, we find a stronger impact of MODIS AOD on surface aerosols at 18 UTC compared to the surface PM2.5 OI assimilation. The increments resulting from the OI assimilation are spread in 11×11 horizontal grid cells (12 km horizontal resolution) while the spatial distribution of GSI increments is controlled by its background error covariances, and the horizontal/vertical length scales. The assimilations of observations using both GSI and OI generally help reduce the prediction biases, and improve correlation between model predictions and observations. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). In this study, OI uses the relatively big model uncertainties, which helps yield better mean biases, but sometimes causes the RMSE increase. We also examine and discuss the sensitivity of the assimilation experiments results to the AOD forward operators

show abstract

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Cited by 15 publications

References 35 publications

European Regulatory Framework and Particulate Matter Emissions of Gasoline Light-Duty Vehicles: A Review

European Regulatory Framework and Particulate Matter Emissions of Gasoline Light-Duty Vehicles: A Review

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Study over the Contiguous United States

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