Gasoline direct injection vehicles exceed port fuel injection ones in both primary aerosol emission and 1 secondary aerosol formation 2

16 Gasoline vehicles greatly contribute importantly to urban particulate matter (PM) pollution. Gasoline direct 17 injection (GDI) engines, known as their higher fuel efficiency than that of port fuel injection (PFI) engines, have 18 been increasingly employed in new gasoline vehicles. However, the impact of this trend on air quality is still poorly 19 understood. Here, we investigated both primary emissions and secondary organic aerosol (SOA) formation from 20 GDI and PFI vehicles under urban-like condition, using combined approaches involving chassis dynamometer 21 measurement and environmental chamber simulation. The PFI vehicle emits slightly more volatile organic 22 compounds, e.g., benzene and toluene, whereas the GDI vehicle emits more particulate components, e.g., the total 23 PM, elemental carbon, primary organic aerosols and polycyclic aromatic hydrocarbons. Strikingly, a much higher 24 SOA production (by a factor of approximately 2.7) is found from the exhaust of the GDI vehicle than that of the 25 Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-776 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 16 October 2017 c © Author(s) 2017. CC BY 4.0 License.


Introduction
Organic aerosols account for approximately 20-50 % of ambient fine particulate matter (PM2.5), with significant environment and health effects (Kanakidou et al., 2005).Primary organic aerosol (POA) is emitted directly by sources, while secondary organic aerosol (SOA) is mainly formed via oxidation of gaseous precursors in the atmosphere and account for about 30-90 % of the organic aerosol (OA) mass worldwide (Zhang et al., 2007;Hu et al., 2016).A recent study revealed that 15-65 % of SOA was contributed by fossil fuel consumption (i.e., traffic and coal burning) in megacities, which indicates the important contribution of vehicles to ambient SOA in urban areas (Huang et al., 2014).An ambient organic aerosol measurement in the Los Angeles Basin demonstrated that SOA contributed from gasoline vehicles was significant in the urban air, much larger than that from diesel vehicles (Bahreini et al., 2012).Meanwhile, several chamber simulation studies concluded that exhausts of gasoline vehicles could form substantial SOA (Jathar et al., 2014).Thus, gasoline vehicles exhaust is highly associated with ambient SOA formation.
Gasoline vehicles can be categorized into two types based on the fuel injection technologies in their engine, i.e., port fuel injection (PFI) vehicles and gasoline direct injection (GDI) vehicles.Unlike a PFI engine, in which gasoline is injected into intake port, gasoline is sprayed into cylinder directly in a GDI engine.With the increased atomization and vaporization rate of fuel, and more accurate control of fuel volume and injection time, a GDI engine improves fuel efficiency and reduces CO2 emissions (Myung et al., 2012;Liang et al., 2013).In past decades, PFI vehicles dominated the market share of gasoline cars in the world.However, in recent years, GDI vehicles have been increasingly employed, due to their higher fuel efficiency.The market share of GDI vehicles in 2016 reached about 25 %, 50 % and 60 % in China, the US and Europe, respectively (Wen et al., 2016;Zimmerman et al., 2016).
Several previous studies investigated the emissions of GDI and PFI vehicles, in terms of concentrations of gaseous pollutants, particle numbers and mass concentrations, and evaluated the reduction of emissions with the upgrading emission standards (Ueberall et al., 2015;Zhu et al., 2016;Saliba et al., 2017).These studies show that GDI vehicles emit more primary particles than PFI vehicles (Zhu et al., 2016;Saliba et al., 2017), and even diesel vehicles equipped with diesel particulate filter (DPF) (Wang et al., 2016), which is likely due to insufficient time allowed for gasoline fuel to be mixed with air thoroughly, as well as gasoline droplets impinging onto pistons and surfaces of combustion chamber in GDI engine (Chen et al., 2017;Fu et al., 2017).However, in most studies, vehicles were tested under the driving cycles of the US or European standards, indicating that those results are not representative of China's traffic conditions.SOA production from gasoline vehicle exhaust was previously simulated in smog chambers and potential aerosol mass (PAM) flow reactors.SOA formed from gaseous pollutants exceeds the related POA emissions and having much more contribution to air quality degradation.These studies mostly focused on the impacts of SOA formation by the model year (Gordon et al., 2014;Jathar et al., 2014;Liu et al., 2015), fuel formulations (Peng et al., 2017), driving cycles (including idling) (Nordin et al., 2013) and start-up modes of the gasoline vehicles (Gentner et al., 2017).Few studies, however, have investigated SOA formation from vehicles with different engine technologies (GDI and PFI) under the same working condition.
In this study, both primary emissions and secondary aerosol formation from GDI and PFI vehicles were investigated.To represent typical urban driving patterns in megacities such as Beijing, the vehicles were tested using gasoline fuel meeting the China Phase V fuel standard, and were operated with the cold-start Beijing cycle (BJC).The SOA formations from both the PFI and GDI vehicle exhausts were then simulated using a smog chamber.
Finally, the overall contributions of the GDI and PFI gasoline vehicles to ambient particulate matter (PM) were evaluated.This study is part of a project that investigates the relationship between vehicle (engine) emissions and ambient aerosols, including potential of SOA formation from a PFI engine (Du et al., 2017) and the effects of gasoline aromatics on SOA formation (Peng et al., 2017).

Vehicles
One PFI vehicle and one GDI vehicle were tested in this study to investigate their primary emissions and SOA formations.In this study, the selected PFI and GDI vehicles were certified to the China Phase IV Emissions Standard (equivalent to Euro IV) and the China Phase V Emissions Standard (equivalent to Euro V), respectively.
More information of the vehicles is shown in Table 1.The fuel used in the experiments was a typical Phase V gasoline on the China market.Cold-start BJC, characterized by a higher proportion of idling periods and lower acceleration speeds than the New European Driving Cycle (NEDC), was performed to simulate the repeated braking and acceleration on road in megacities such as Beijing.The BJC lasted approximately 17 minutes, with a maximum speed of 50 km h -1 (Peng et al., 2017).

Experimental setup
The chamber experiments were carried out in the summer at the State Key Laboratory of Automotive Safety and Energy of Tsinghua University in Beijing.The tested vehicles were placed on a chassis dynamometer system (Burke E. Porter Machinery Company) with a controlled room temperature and absolute humidity of 26.4±2.5 °C and 11.5±2.4g m -3 , respectively.The exhaust emitted by the vehicle tailpipe was diluted in a constant volume sampler (CVS) system, where the flow was maintained at 5.5 m 3 min -1 using filtered ambient air, achieving about 20 times dilution of the exhaust.Several instruments, including an AVL CEBII gas analyzer, a Cambustion Differential Mobility Spectrometer (DMS500) and a particle sampler, were connected to the CVS (detailed in Figure 1 and section 2.3) to characterize the primary gas-and particulate-phase pollutants.The diluted exhausts produced by the CVS system were injected into an outdoor chamber, where secondary aerosol formation from gasoline vehicle exhausts was simulated.This was the second dilution step of the exhausts and had a dilution factor of approximately 15.A schematic illustration of the outdoor experimental setup is shown in Figure 1.The photochemical oxidation experiments were carried out in a quasi-atmospheric aerosol evolution study (QUALITY) outdoor chamber.More details of the setup and performance of the QUALITY chamber were introduced by Peng et al. (2017).Prior to each experiment, the chamber was covered with a double-layer antiultraviolet (anti-UV) shade to block sunlight and was cleaned with zero air for about 15 h to create a clean environment.Approximately 120 ppb O3 were injected into the chamber prior to the injection of vehicle exhaust to make the oxidation environment similar to the mean O3 peak concentration in the ambient atmosphere.Before the chamber was exposed to sunlight, about 15-minute period was left to ensure that the pollutants mixed sufficiently in the chamber, then the initial concentrations were characterized in the dark.Subsequently, the anti-UV shade were removed from the chamber and photo-oxidation was initiated.A suite of high time resolution instruments was utilized to track the evolution of pollutants during the chamber experiments.Zero air was added into the chamber when sampling to maintain a constant pressure.

Instrumentation
Primary gases and aerosols were measured by the instruments connected to the CVS.The concentrations of gaseous pollutants, including CO, CO2, NOx and total hydrocarbon (THC) were monitored with a gas analyzer AVL Combustion Emissions Bench II (CEB II, AVL, Austria).Primary aerosols were measured with both on-line and off-line instruments.A DMS500 (Cambustion, UK) was implemented to monitor the real-time number size distribution and total number concentration of primary particles.The aerosols were also collected on Teflon and quartz filters by AVL Particulate Sampling System (SPC472, AVL, Austria) to analyze the mass, organic carbon (OC) and elemental carbon (EC) emission factors using a balance and OC/EC analyzer (Sunset Lab, USA).
During the chamber experiments, a suite of real-time instruments was utilized to characterize the evolutions of the gas and particulate-phase pollutants.CO analyzer, NO-NO2-NOx analyzer and O3 analyzer (Thermo Fisher Scientific Inc., USA) were employed to measure the concentrations of CO, NOx (including NO and NO2) and O3, respectively.The evolutions of volatile organic compounds (VOCs) were monitored with a proton transfer reaction mass spectrometer (PTR-MS, IoniconAnalytik, Austria) (Lindinger et al., 1998).H3O + was used as the reagent ion, which reacted with the target compounds.The resulting ions were detected by a quadruple mass spectrometer.
Meanwhile, the particles size distribution was characterized using a scanning mobility particle sizer system (SMPS, TSI, USA), which consisted of a differential mobility analyzer (DMA, TSI, USA) and a condensation particle counter (CPC, TSI, USA).This system can measure aerosols with a diameters ranging from 15 nm to 700 nm.A high-resolution time-of-flight aerosol mass spectrometer (HR-Tof-AMS, Aerodyne Research, USA) was applied to obtain mass concentrations and size distributions of submicron, non-refractory aerosols, including sulfate, nitrate, ammonium, chloride and organic (DeCarlo et al., 2006).Table 2 lists the instruments used to measure the primary emissions and their evolutions in the chamber experiments.

Gaseous pollutant emissions
Emission factors (EFs) of CO2, THC, benzene and toluene from the GDI and PFI vehicles are listed in Table 3.The EFs of CO2 and THC are derived from measured concentrations in CVS, while the EFs of benzene and toluene were calculated from the initial concentrations in the chamber.The THC emission factor was reported in units of carbon mass, g C kg -fuel -1 .
The GDI vehicle emitted less CO2 and THC than the PFI vehicle due to their different fuel injection strategies and mixing features (Liang et al. 2013;Gao et al., 2015).The EF of THC from the GDI vehicle met the standard of the China Phase V Emission Standard (0.1 g km -1 ), but that from the PFI vehicle was slightly beyond the standard limit.The PFI vehicle used in this study met lower emission standard (the China Phase IV), which might cause additional THC emission when compared to the China Phase V Emission Standard.In addition, BJC and NEDC were applied in this study and emission standard, respectively.More repeated braking and acceleration in BJC might cause incomplete combustion and consequently higher THC emission from the PFI vehicle in this study.As typical VOC species emitted by vehicles, benzene and toluene were measured in this study.For both vehicles, the EFs of toluene were higher than those of benzene.Consistent with the feature of THC emission, the PFI vehicle emitted more benzene and toluene than the GDI vehicle, and the enhancement of toluene was much larger than that of benzene.
The EFs of the gaseous pollutants in this study had similar magnitudes to those in previous studies in which gasoline vehicles met comparable levels of emission standards and were tested under cold-start driving condition, while the results in this study were slightly higher, as shown in Table 3.This difference might be because the California ultralow-emission vehicles (ULEV) (Saliba et al., 2017) and most LEV II vehicles (manufactured in 2004 or later) (May et al., 2014) meet the US certification gasoline emission standards for the ULEV category, which has a lower limit of gaseous pollutants than the China Phase V Emission Standard.In addition, the different driving cycles of our study and those other studies (listed in Table 3) might be another explanation for the difference in the EFs of gaseous pollutants.

Primary particle emissions
The EFs of PM, elemental carbon (EC), POA and particulate polycyclic aromatic hydrocarbons (PAHs) are shown in Table 4.The EF of PM2.5 from the GDI vehicle was about 1.4 times higher than that of the PFI vehicle.
Both vehicles met the China Phase V Emission Standard for PM emission (4.5 mg km -1 ).The GDI vehicle emitted about 3.3 times more EC and 1.2 times more POA than the PFI vehicle.The primary carbonaceous aerosols (EC+POA) accounted for 85 % and 82 % of the PM in the GDI and PFI vehicles respectively, suggesting that carbonaceous aerosols were the major contributors in the PM from gasoline vehicles, especially for the GDI vehicle.
PAHs account for a small fraction of particulate organic matter in the atmosphere, but the molecular signature of PAHs can be utilized in source identification of vehicle emissions (Kamal et al., 2015).The GDI vehicle emitted about 1.5 times the PAHs of the PFI vehicle.It should be noted that the PAHs were tested under warm-start cycles.
A higher EF of PAHs would be obtained under cold-start cycle, since the lower temperature led to inefficient catalyst at the beginning of cold-start (Mathis et al., 2005).The higher PM2.5 and carbonaceous aerosol emissions from GDI vehicle were also found in Saliba's and Zhu's studies (Zhu et al., 2016;Saliba et al., 2017).The EC emissions were in the range of those of previous studies but on the lower level.The EF of the POA measured in this study was higher than those of other studies, leading to a higher OC/EC ratio, which could be attributed to the less strict emission standard of our vehicles and the different driving cycles applied in the experiments.
The bimodal number size distributions of the primary PM from the vehicles measured by the DMS500 are shown in Figure 2. The particle distributions of the exhausts of the GDI and PFI vehicles illustrated similar patterns, with two peaks located at about 10 nm for nucleation mode and at 60-90 nm for accumulation mode, respectively, which are consistent with the results of previous studies (Maricq et al., 1999;Chen et al., 2017).The bimodal particle distributions from gasoline vehicles verified the traffic emission related sources resolved from ambient aerosols in Beijing (Wang et al., 2013), indicating that vehicle emissions significantly contribute to the small particles in the ambient air, especially those under 100 nm.The particle number size distribution of the exhausts of the GDI vehicle showed a similar pattern to that of the PFI vehicle, with a much higher number concentration that is consistent with the emission of more particle mass.

SOA formation from gasoline vehicle exhaust
The time-resolved concentrations of gases and particles during the chamber experiments are illustrated in Figure 3. Before removing the anti-UV shade, the initial concentrations of NOx, benzene and toluene from the PFI and GDI vehicles were 80 ppb, 3 ppb, 5 ppb and 100 ppb, 4 ppb, 14 ppb respectively.
After the aging experiment started (t=0 in Figure 3), NO was formed from NO2 photolysis, and then reacted with O3 to form NO2. The O3 concentration increased rapidly to a maximum within 2-3 h and then decreased via reactions and dilution.Benzene and toluene decayed during the aging process at different rates.
New particle formation was found inside the chamber 15 minutes after the exhaust was exposed to sunlight, providing substantial seeds for secondary aerosol formation.Significant growths of particles in both size and mass were observed in the chamber, indicating that a large amount of secondary aerosol was formed during the photochemical oxidation.The chemical compositions of the secondary aerosols were measured continuously by HR-Tof-AMS.Organic was the dominant composition of the secondary aerosol, accounting for 88-95 % of the total particle mass inside the chamber (Figure S1), which is consistent with our previous research (Peng et al.,

2017
).The SOA mass exhibited different growth rate for the two types of vehicles.After a 4 h oxidation in the chamber, the SOA formed from the exhaust of the GDI vehicle was approximately double that of the PFI vehicle.
The solar radiation conditions significantly influenced the SOA formation.Thus, OH exposure was used to characterize the photochemical age as a normalization, instead of the experiment time.Two VOC species with noticeable differences in their reaction rate constants with OH radicals could be utilized to calculate the OH exposure ([OH] Δt) based on Equation 1 (for benzene and toluene, as used in this study) (Yuan et al., 2012).
The SOA concentrations as a function of OH exposure are illustrated in Figure 4. Wall-loss correction and dilution correction, including both particles and gaseous pollutants, were taken into consideration in the calculation of the SOA mass concentration in the chamber (Peng et al., 2017).Assuming the mean OH concentration was 1.6×10 6 molecular cm -3 in Beijing (Lu et al., 2013), the whole aging procedure in the chamber experiments was equal to a 6-10 h atmospheric photochemical oxidation.The average SOA concentrations were 9.25±1.80and 4.68±1.32µg m -3 for the GDI and PFI vehicles, respectively, when the OH exposure was 5×10 6 molecular cm -3 h in the chamber.Considering the driving cycle mileage and fuel consumption, the SOA productions were 54.77±10.70mg kg -fuel -1 or 3.06±0.60mg km -1 for the GDI vehicle and 20.57±5.82mg kg-fuel -1 or 1.55±0.44mg km -1 for the PFI vehicle.Compared with the PFI vehicle, the GDI vehicle exhaust exhibited a higher potential of SOA formation, even though the PFI vehicle emitted more VOCs, which are considered as dominant class of SOA precursors.This result indicates that higher concentrations of some other SOA precursors exist in the exhaust of GDI vehicles, which will be further discussed in section 3.3.
The results from chamber simulation of SOA formation from individual gasoline vehicles are illustrated in Figure 5.The SOA production from the both vehicles in this study is in the range of the results of previous studies (Nordin et al., 2013;Platt et al., 2013;Jathar et al., 2014;Liu et al., 2015;Peng et al., 2017).The variation of the SOA production among these studies might be caused by several factors: the model years of vehicles (corresponding to emission standards), their driving cycles, the initial concentrations of gaseous pollutants in the chamber, and the NOx condition of the SOA formation in the chamber experiments.
To investigate the dominant contributors to ambient PM from the GDI and PFI vehicles, Figure 6 illustrates the EFs of EC and POA as well as the production factors of SOA in this study.The SOA production from the GDI vehicle was approximately 2.7 times higher than that from the PFI vehicle.At 5×10 6 molecular cm -3 h OH exposure, the SOA/POA ratio was approximately 1. Figure 4 illustrates that the SOA production increased with photochemical age rapidly (within 2×10 7 molecular cm -3 h).Thus, SOA would exceed POA at higher OH exposure, e.g., the SOA/POA ratio reached about 4 at 10 7 molecular cm -3 h OH exposure, becoming the major PM contributor.
In terms of the POA and EC emissions as well as the SOA formation, the GDI vehicle contributed 2.2 times more than the PFI vehicle.
Although wall-loss correction as well as particle and gas dilution corrections were considered in this study, the SOA productions were still underestimated.First, the concentration of aerosols in the ambient air in Beijing was higher than that inside the chamber.Semi-volatile vapors may prefer to be partitioned into particle phase rather than gas phase in ambient condition, increasing the SOA formation (Odum et al., 1996).Second, the loss of semivolatile vapors to the chamber walls was not considered in the calculation of the SOA production.Previous study found that semi-volatile vapor loss could lead to the underestimation of SOA formation with a factor of 1.1-4.1 (Zhang et al., 2014).Therefore, the SOA production from vehicle exhaust would be enhanced under atmospheric condition.

SOA mass closure
SOA production (ΔOApredicted) estimated from VOC precursors can be defined as Eq. ( 2): where Δi is the concentration change of precursor VOCi measured with PTR-MS in the chamber experiments, and Yi is the SOA yield of the VOCi.In this study, benzene, toluene, C8 benzene and C9 benzene were involved in the estimation of SOA production, and alkanes and alkenes were not considerated because much lower declines of concentrations were observed than those of aromatics during chamber experiments.
The SOA yield is sensitive to VOCs/NOx ratio (Song et al., 2005).In this study, the VOCs/NOx ratio was in the range of 0.5-1.0,thus, the SOA formation from the vehicle exhaust was determined under high NOx conditions.
The high NOx SOA yields of benzene and toluene were taken from Ng et al. (2007).The C8 and C9 benzene used the SOA yield of m-xylene from Platt et al. (2013).
The increased predicted SOA contribution from the VOC precursors as a function of OH exposure accumulation is demonstrated in Figure 7.At the end of the experiments, the SOA estimated from these speciated VOCs accounted for about 25 % and 53 % of the measured SOA formation from the GDI and PFI vehicle exhausts, respectively.Similar to the results of previous studies (Platt et al., 2013;Nordin et al., 2013;Gordon et al., 2014), single-ring aromatics played an important role in the SOA formation, especially for the PFI vehicle which shows higher predicted SOA fraction.
The unpredicted fraction of the measured SOA in the chamber experiments was in the range of 47-75 %.
Contributions from intermediate volatility organic compounds (IVOCs) and semivolatile organic compounds (SVOCs), e.g., long branched and cyclic alkanes and gas-phase polycyclic aromatic hydrocarbons could be a possible explanation for this underestimation.The SOA formed by oxidation of IVOCs and SVOCs is found to dominate over that from single-ring aromatics (Robinson et al., 2007).The unpredicted SOA ratio exhibited a maximum value at the beginning of the experiment, indicating that the IVOCs and SVOCs with low volatilities produced SOA much more efficiently than the single-ring aromatics with high volatilities, as the first generation products of photo-oxidation of these precursors form SOA (Robinson et al., 2007).
The larger fraction of the unpredicted SOA from the GDI vehicle exhaust might be associated with higher IVOCs and SVOCs emissions.Gas-phase PAH is one of the main component of speciated IVOCs (Zhao et al., 2016).The particulate-phase PAHs from the GDI vehicle were more abundant than those from the PFI vehicle by a factor of 1.5 (section 3.1).Based on gas-particle equilibrium, this indicates that more gas-phase PAHs, including some aromatic IVOCs, might be emitted by the GDI vehicles, contributing to the SOA enhancement.

Discussions and conclusions
GDI and PFI vehicles have different fuel injection technologies in their engines, which affects their emissions of gaseous and particulate pollutants.In GDI engine, the fuel is directly injected into cylinder, which benefits the fuel atomization and vaporization and provides better control of fuel volume and the combustion process (Liang et al. 2013;Gao et al., 2015).Thus, in this study, the tested GDI vehicle has higher fuel economy and lower THC emission than the PFI vehicle.However, the insufficient mixing time allowed for the fuel and air leads to incomplete combustion in the GDI engine (Fu et al., 2014).In addition, direct fuel injection leads to fuel impingement onto surfaces of combustion chamber, where liquid pools form, favoring soot-like particulate formation (Ueberall et al., 2015;Chen et al., 2017).Consequently, larger particle mass and number are emitted by the GDI vehicle than from the PFI vehicle.The particles emitted by the GDI vehicle have higher EC mass fraction, leading to lower OC/EC ratio.The considerable particle number emitted by gasoline vehicles, especially in GDI vehicles exhaust, makes a significant contribution to particle number concentration as well as seeds for further reactions in the atmosphere, and needs to be controlled in the future emission standards.
Our results show that the GDI vehicle contributes more to both primary and secondary aerosol than the PFI vehicle, and has greater impact on environment and air quality.In recent years, the market share of GDI vehicles exerts a continuous growth in China because they provide better fuel economy and lower CO2 emissions.In 2016, GDI vehicles accounted for 25 % of China's market share, and this proportion is expected to reach 60 % by 2020 (Wen et al., 2016).The PM enhancement of GDI vehicles with increasing population could potentially offset any PM emission reduction benefits, including the development of gasoline emission and fuel standards and the advanced engine technologies of gasoline vehicles.Therefore, our results highlight the necessity of further research and regulation of GDI vehicles.
Primary emissions and secondary organic formation from one GDI vehicle and one PFI vehicle were investigated when driving under cold-start BJC.The primary PM emitted by the GDI vehicle was 1.4 times greater than that from the PFI vehicle and the SOA formation from the GDI vehicle exhaust was 2.7 times greater than that from the PFI vehicle exhaust for the same OH exposure.The SOA production factors were 54.77±10.70mg kg - fuel -1 or 3.06±0.60mg km -1 for the GDI vehicle and 20.57±5.82mg kg-fuel -1 or 1.55±0.44mg km -1 for the PFI vehicle at an OH exposure of 5×10 6 molecular cm -3 h, which is consistent with the values seen in previous studies.
Considering the higher amounts of OA derived from primary emission and secondary formation, the GDI vehicle contribute considerably more to particle mass concentrations in the ambient air than the PFI vehicle.

high
NOx condition.Single-ring aromatic VOCs could explain only 25-53 % of the measured SOA formation in the chamber experiments.The GDI vehicle exhibited higher fraction of unexplained SOA.More IVOCs and SVOCs were inferred as being emitted by the GDI vehicle.With increasing population of GDI vehicles, any benefits of the aerosol emission reduction of gasoline vehicles are substantially offset, because GDI vehicles have significant contributions to ambient aerosols.More work is needed to improve the understanding of GDI vehicle emissions and to provide information for the regulation of gasoline vehicles.Foundation (2015M580929).We also thank the State Key Lab of Automotive Safety and Energy at Tsinghua University for the support to experiments.Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2017-776Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 16 October 2017 c Author(s) 2017.CC BY 4.0 License.Liang, B., Ge, Y., Tan, J., Han, X., Gao, L., Hao, L., Ye, W., and Dai, P.: Comparison of PM emissions from a gasoline direct injected (GDI) vehicle and a port fuel injected (PFI) vehicle measured by electrical low pressure impactor (ELPI) with two fuels: Gasoline and M15 methanol gasoline, Journal of Aerosol Science, 57, 22-31, 10.1016/j.jaerosci.2012.11.008, 2013.Lindinger, W., Hansel, A., and Jordan, A.: On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS) -Medical applications, food control and environmental research, International Journal of Mass Spectrometry . Phys.Discuss., https://doi.org/10.5194/acp-2017-776Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 16 October 2017 c Author(s) 2017.CC BY 4.0 License.

Figure 1 .
Figure 1.Schematic diagram of the outdoor chamber set up for the experiments.

Figure 2 .
Figure 2. Number size distributions of primary PM emitted from the GDI (red line) and PFI (blue line) gasoline vehicles.

Figure 3 .
Figure 3.Time series of the gases and particle evolutions over the photochemical age in the chamber experiments from the GDI vehicle exhaust (a, c, e) and PFI vehicle exhaust (b, d, f).(a, b): NO, NO2 and O3 concentration; (c, d): benzene and toluene concentration; (e, f): corrected SOA concentration.

Figure 4 .
Figure 4. SOA productions from the GDI vehicle exhaust (red markers) and the PFI vehicle exhaust (blue markers) as functions of OH exposure in the chamber experiments.

Figure 5 .
Figure 5. Fuel-based SOA production as a function of OH exposure in the chamber.The SOA production data are from published studies of chamber simulation of gasoline vehicle exhaust.From the study of Jathar et al. (2014), the SOA production of vehicles manufactured in 2004 or later is selected, which is a model year that is more close to those of the vehicles in this study.The driving cycles are also noted in the legend of each study.

Figure 6
Figure 6 EC and POA EFs as well as corrected SOA production factors from the GDI and PFI vehicle exhausts in this study (OH exposure = 5×10 6 molecular cm -3 h).

Figure 7 .
Figure 7. Measured and predicted SOA concentration as a function of OH exposure from GDI vehicle exhaust (a) and PFI vehicle exhaust (b) in the chamber experiments.The black line is the measured SOA concentration with wall-loss and particle dilution correction during the experiment.The red, blue, yellow and pink areas are predicted SOA concentration estimated from benzene, toluene, C8 benzene and C9 benzene, respectively.The green markers are the ratios of the predicted SOA to the measured SOA.

Table 3
Emission factors (EFs) of gaseous pollutants from the gasoline direct injection (GDI) and port fuel injection (PFI) vehicles in this study and those of previous studies.
b UC: Unified Cycle; c WLTC: Worldwide-harmonized Light-duty Test Cycle