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Studies on the Formation of Secondary Organic Aerosol from Vehicle Exhaust

Jiliang Guo , Jianfei Peng , Ainan Song , Jinsheng Zhang , Zhuofei Du , Hongjun Mao

Progress in Chemistry ›› 2023, Vol. 35 ›› Issue (1) : 177 -188.

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Progress in Chemistry ›› 2023, Vol. 35 ›› Issue (1) : 177-188 DOI: 10.7536/PC220605
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Studies on the Formation of Secondary Organic Aerosol from Vehicle Exhaust
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Abstract

Secondary organic aerosol (SOA) is an important component of fine particulate matter (PM2.5), which significantly impacts on atmospheric visibility, human health and regional/global climate change. In urban air, high level of SOA is formed from the atmospheric oxidation of gaseous organic precursors emitted from vehicles, becoming an important factor for the decline of urban air quality. This review summarizes recent studies on the SOA formation from vehicle exhaust, focusing on the identification of key precursors and their emission characteristics, as well as SOA formation, evolution, and influencing factors. In addition, SOA production factors are compared among studies. New measuring techniques, new mechanisms and new parametric method will be the key research direction in the future.

Contents

1 Introduction

2 Identification of SOA precursors from vehicle exhaust and their emission characteristics

2.1 Measurement methods

2.2 Emission characteristics and influencing factors

3 Laboratory simulation of SOA formation from vehicle exhaust

3.1 Smog chamber and flow tube experiments

3.2 Influencing factors of SOA formation

3.3 Evolution and product composition of SOA

3.4 Model simulation of SOA formation

4 Conclusion and prospect

Graphical abstract

Keywords

vehicle exhaust / secondary organic aerosol / semi/intermediate volatility organic compounds / aging process / influencing factor

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Jiliang Guo, Jianfei Peng, Ainan Song, Jinsheng Zhang, Zhuofei Du, Hongjun Mao. Studies on the Formation of Secondary Organic Aerosol from Vehicle Exhaust. Progress in Chemistry, 2023, 35(1): 177-188 DOI:10.7536/PC220605

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1 Introduction

In recent years, the frequent occurrence of haze weather in China and the significant air pollution problem have become an important challenge for the production and life of our people. Atmospheric fine particulate matter (P M 2 . 5, particles with aerodynamic equivalent diameter less than or equal to 2.5 μm) is the "culprit" of haze weather, which has an important impact on atmospheric visibility, human health, and regional or global climate change[1]. The composition of PM2.5 is complex, mainly composed of black carbon, inorganic salts, organic matter, heavy metals and so on. Organic aerosols (OAs) are an important component of Primary Organic Aerosol, accounting for 20% ~ 90% of submicron aerosols globally. According to their sources, OAs can be divided into Primary Organic Aerosols (POAs) directly emitted from pollution sources and Secondary Organic Aerosols (SOAs) produced by gaseous precursors such as Volatile Organic Compounds (VOCs) through atmospheric physical and chemical processes[2]. Compared with POA, SOA has more complex structure, more functional groups, stronger polarity, solubility, hygroscopicity and so on, which has a greater impact on the environment and climate. A large number of studies have shown that the occurrence of haze is often accompanied by the formation of high concentrations of SOA[3⇓⇓⇓~7]. SOA also contributes significantly to the formation of cloud condensation nuclei in the atmosphere, which can produce regional or global climate effects by changing the microphysical properties of clouds[8⇓~10]. Therefore, accurately calculating the generation of SOA in the atmosphere plays an important role in ensuring environmental air quality and predicting global climate change.
In urban areas, frequent industrial production and human activities (such as coal combustion, road/non-road vehicle exhaust, household emissions, etc.) lead to high intensity of precursor emissions and a large number of SOA, which pose a great threat to the urban environment and the health of residents. Vehicle exhaust is one of the important sources of urban SOA, and the SOA generated by its transformation can contribute 40% to 60% of OA in urban areas, so it is very important to control vehicle exhaust emissions for improving urban air quality, but the formulation of relevant standards requires a deep understanding of the composition of exhaust and the generation mechanism of SOA[11]. In recent years, with the upgrading of engine technology, after-treatment devices and emission standards, the composition and concentration of vehicle exhaust are constantly changing, which affects the formation of SOA. At home and abroad, a large number of studies have been carried out on the formation of SOA in vehicle exhaust, including the establishment of precursor measurement methods, the discussion of the formation and transformation mechanism of SOA, and the analysis of key influencing factors, but there is a lack of summary of existing studies. This paper systematically reviews the research progress of SOA generation in vehicle exhaust, in order to provide a reference for future research and policy formulation.

2 Identification and Emission Characteristics of SOA Precursors in Exhaust

In the study of SOA formation in vehicle exhaust, what kind of oxidation products of gaseous precursors contribute significantly is an important scientific issue. Early studies suggested that aromatics were the main gaseous precursors in gasoline vehicle exhaust. Odum et al. Obtained the SOA yields of 17 aromatics based on smoke chamber experiments, and explained the SOA formation of reformed gasoline with different components[12]. With the increase of the content of aromatic components in gasoline, the formation of SOA increases exponentially[13]. The results of smog chamber experiments of different gasoline vehicle exhaust show that the monocyclic aromatic hydrocarbons of C6-C9 have an important contribution to SOA[14⇓~16]. However, photooxidation experiments on diesel engine exhaust show that traditional aromatic precursors can only explain less than 10% of SOA formation, and it is proposed that Semi-Volatile Organic Compounds (SVOCs) and Intermediate Volatility Organic Compounds (IVOCs) may make an important contribution to SOA[17⇓~19]. Based on the definition of Volatility Basis Set (VBS), SVOCs are organic compounds with saturated vapor concentration at Volatility Basis Set and n-alkanes with Volatility range close to C23-C32, which mainly exist in the particulate phase and can be directly partitioned between the gas phase and the particulate phase. IVOCs are organic compounds with saturated vapor concentration at 103~106μg/m3, which mainly exist in the gas phase and are easy to react in the gas phase to form products with lower volatility, and then to form SOA[20]. The IVOCs in the tail gas are mainly composed of resolvable n-alkanes, branched alkanes, naphthenes, polycyclic aromatic hydrocarbons (Polycyclic Aromatic Hydrocarbons,PAHs), tetralin, and unresolved branched alkanes and naphthenes[21⇓~23]. The study shows that the IVOCs in the exhaust have a higher yield of SOA than the monocyclic aromatic hydrocarbon precursors[21,24,25]. Zhao et al. Found that the SOA generated by IVOCs in California was 4 times that generated by traditional aromatic hydrocarbon precursors[21]. When POA aging and S/IVOCs atmospheric oxidation processes are considered in the PMCAMx model, the simulation results are very close to the real measurements (Figure 1)[17]. In addition, the results of smoke chamber and flow tube experiments also confirmed the important role of S/IVOCs on SOA generation[14,16,26⇓ ~28]. At present, aromatics and S/IVOCs have been widely recognized as the main precursors of SOA in tail gas, but with the continuous upgrading of emission standards, the proportion of aromatics in tail gas is gradually decreasing, and S/IVOCs will play an increasingly important role in the formation of SOA in tail gas.

2.1 Precursor measurement method

The measurement of SOA precursor in exhaust gas is mostly based on off-line method. Because the precursor exists in both gas and solid phases, it is necessary to collect gas and particle phase samples separately. Particulate phase samples are mainly sampled by membrane, while gas phase samples are mainly sampled by Summa tank, Tedlar bag and solid adsorption tube[29]. Among them, Summa tank and Tedlar bag are suitable for the collection of non-polar and highly volatile components (such as alkane, alkene, alkyne, aromatic hydrocarbon, etc.); The solid adsorption tube has good applicability for the collection of polar and low volatile components, and can be used to collect different target species by adding different adsorbents, such as 2,4-dinitrophenylhydrazine (DNPH), which is often used to collect carbonyl compounds, and Tenax-TA adsorbent, which is widely used to collect S/IVOCs samples[30,31][32,33]. The collected samples were mainly extracted by thermal desorption or solvent extraction. The thermal desorption method is simple to operate, only needs to program the temperature of the sample, and has no selectivity, but the high temperature is easy to cause the cracking of organic matter, resulting in the lack of measurement. Solvent extraction methods are selective, mainly for some specific species, so it is necessary to select the appropriate pretreatment method according to the purpose of the experiment. Gas chromatography-mass spectrometry (GC-MS) and two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) are often used to identify species at the molecular level. One-dimensional gas chromatography is suitable for the measurement of most VOCs components, but due to the limitation of peak capacity, only a small fraction of S/IVOCs species (~ 15%) can be separated, and most of the substances flow out in the form of Unresolved complex mixture (UCM)[34,35]. Two-dimensional gas chromatography can effectively solve the problem that organic compounds can not be effectively separated due to insufficient peak capacity. It connects two chromatographic columns with different separation mechanisms in series.It can separate organic compounds in two dimensions, achieve higher sensitivity and resolution through programmed temperature and change of chromatographic column polarity, and realize effective separation of complex components such as isomers. Based on GC × GC-TOF-MS, Alam et al. Realized the measurement of C9-C37 components in diesel vehicle exhaust, and identified n-alkanes, branched alkanes, cycloalkanes, tetrahydronaphthalenes, alkylnaphthalenes, alkylcyclohexanes, alkylbenzenes, PAHs, n-aldehydes and other substances from the molecular point of view, providing more detailed information on the chemical composition of S/IVOCs in exhaust[36].
In the process of motor vehicle driving, the actual road conditions are complex and changeable, and the operating conditions will change constantly, so it is necessary to explore the transient emission characteristics of precursors in the exhaust gas by means of online mass spectrometry testing technology with high time resolution. Proton Transfer Reaction Mass Spectrometry (PTR-MS) and Chemical Ionization Mass Spectrometry (CIMS) can reflect the emission change of exhaust precursors with a time resolution of seconds. For example, Zhang et al. Used PTR-MS to study the transient emission of precursors during the cold start of gasoline vehicles, and measured higher precursor concentration and longer peak duration at low ambient temperature[29,37][38]. On-line testing technology is also commonly used to analyze the conversion mechanism of precursors, which can provide high time-resolved information on the molecular composition and concentration changes of precursors and products.It is of great significance for the study of atmospheric chemical transformation processes, but its species identification ability and quantitative accuracy for precursors are weaker than those of off-line methods, and it requires a lot of correction and processing of huge amounts of data[39,40]. In general, the off-line method can identify more precursor species, while the on-line method is more suitable for discussing the instantaneous change of precursor emission and the secondary transformation process.

2.2 Emission Characteristics and Influencing Factors of Exhaust Precursors

Controlling the emission of exhaust precursors is of great significance to reduce the formation of SOA, but the composition and concentration of precursors are affected by many factors, including fuel type, oil quality, vehicle emission standards, driving conditions and after-treatment technology.
The analysis of gasoline and diesel components in California shows that most of the organic compounds in gasoline belong to VOCs, and a few are S/IVOCs. However, most of the organic matter in diesel belongs to the range of S/IVOCs, and VOCs only account for 30% of the total organic matter[41]. The results of PMF analysis show that the species distribution of gas-phase SVOCs in diesel engine exhaust is similar to that of diesel fuel, while the species distribution of particle-phase SVOCs is closely related to lubricating oil[42]. Oil products also affect the emission of precursors. Diesel with high aromatics content will increase the emission of PAHs in the tail gas and decrease the content of unresolved naphthenes[21,43]. Gasoline vehicles in the United States were divided into Pre-LEV (before 1994), LEVⅠ (between 1994 and 2003) and LEVⅡ (after 2004) according to their production years, and it was found that the emission factor of Non-methane organic gas (NMOG) in the exhaust decreased from methane organic gas to <1 g·kg·fuel-1(LEVⅡ), the content of resolved precursors decreased from 26% to 20%, and the content of unresolved precursors increased from 12% to about 30%[14,26]. The test results of gasoline vehicle exhaust precursors with different emission standards in China show that the VOCs content decreases from 61% of the national II standard to 46% of the national V standard, and the IVOCs content increases from 2% to 8%. Compared with the gasoline vehicles with national II and III standards, the concentrations of UCM components in the exhaust of gasoline vehicles with national IV and V standards decreased by 3% and 11%, respectively, which was consistent with the results of Zhao et al. And Tang et al[44][27][23]. Different driving conditions also show significant differences in precursor emissions, and the emissions of gas-phase and particle-phase SVOCs at high load are 2. 74 and 2. 02 times higher than those at low load, respectively[45]. At low load, IVOCs are mainly composed of saturated hydrocarbons, which are similar to unburned diesel, while at high load, IVOCs are mainly composed of unsaturated hydrocarbons and oxygenated organics, which may be formed by incomplete combustion[43,46]. In addition, the use of a reprocessing device has a significant effect. Diesel oxidation catalysts (DOC) and Diesel Particulate Filter (DPF) can reduce the precursor by 50% ~ 74% under different load and driving conditions. The average reduction of C11-C18 component in gas phase by DOC was 40%, and the average reduction of 34%;C18-C26 component by DPF was 20% and 53% respectively[36]. The n-alkanes, n-alkylcyclohexanes and PAHs in SVOCs were reduced by 54%, 55% and 37%, respectively, at high load, and by 14%, 16% and 7%, respectively, at low load. The removal efficiency of SVOCs was mainly determined by the working state of DOC[45].
In recent years, with the continuous improvement of oil quality, the updating of engine and after-treatment technology and the gradual upgrading of vehicle emission standards, the emission of exhaust precursors has shown a downward trend, at the same time, the composition of exhaust precursors has also changed, and the proportion of IVOCs has increased, which will have a negative impact on reducing the formation of SOA. The emission of IVOCs in the tail gas should be focused on in subsequent studies to control their contribution to SOA generation. Table 1 summarizes the exhaust IVOCs emission factors obtained by previous studies under different conditions.

3 Laboratory Simulation Study on SOA Generation of Vehicle Exhaust

Compared with the complex atmospheric conditions and many influencing factors in field observation, the research based on laboratory simulation can carry out experiments under stable and controllable conditions. At present, the commonly used laboratory simulation research methods are smoke chamber and flow tube simulation, which are used to simulate the oxidation process of precursors in the atmosphere under certain conditions of light, free radical concentration, temperature and humidity, and precursor concentration, and to explore the formation potential, key products, growth and aging mechanism of SOA[51]. The smog chamber has smaller specific surface area and poor mobility, the concentration of free radicals in the reactor is slightly higher or similar to the environmental level, the residence time of precursors is long, and the photooxidation process can be simulated for hours to days, so the smog chamber is suitable for the study of the conversion process and intermediate products of SOA with photochemical reaction, and can effectively explore the generation and conversion mechanism of SOA. The wall effect of the flow tube is obvious, the residence time of the precursor is very short, generally between a few seconds and tens of seconds, and the concentration of radicals in the reactor is much higher than the environmental level (the concentration of OH radicals can reach 1012molecules·cm-3), which is suitable for the discussion of SOA formation potential, but the higher oxidation exposure will lead to a different reaction mechanism from the oxidation process in the actual atmosphere.

3.1 Research on SOA Generation of Exhaust Gas Based on Smoke Chamber and Flow Tube

Unburned fuel vapor, diluted exhaust from a single vehicle, and ambient air from a tunnel or roadside were introduced into a smog chamber or flow tube to simulate the formation of SOA in the exhaust, and the formation of SOA was observed to be much higher than that of POA emissions. The SOA formation factors obtained by Nordin et al. And Gordon et al. Based on multiple laboratory simulations of light-duty gasoline vehicle exhaust are in good agreement, about 5~60 mg·kg·fuel-1; The result obtained by Platt et al. Was 345 mg·kg·fuel-1, which was 5 times higher than that of other studies[14,16,52]. Most studies have shown that the SOA generated by gasoline vehicle exhaust exceeds the POA emission, but the SOA/POA obtained by different experiments is quite different. For example, the ratio of SOA/POA generated by photochemical oxidation of the exhaust gas of Euro V gasoline vehicles under the New European Driving Cycle (NEDC) is 9 ~ 15, while the ratio of SOA/POA generated by Liu et al. Using Euro I and Euro IV gasoline vehicles at idle speed is 12 ~ 259[52][15]. The results of flow tube simulation of roadside ambient atmosphere in Hong Kong show that the SOA generated is 3.5 times of the primary PM2.5 emission[53]. In addition, some studies have shown that the SOA generation factor of diesel vehicles equipped with DOC is lower than the POA emission factor[54].
Figure 2 compares the SOA generation factors of gasoline and diesel vehicle exhaust in China and the United States, and the selected data meet the similar working conditions and OH exposure conditions as far as possible. Fig. 2a shows the SOA formation factors of gasoline vehicles with different emission standards (or different production years) after exposure to (3~5)×106molecules cm-3·h OH under idling and cold start conditions. The existing literature only reports the SOA formation factors of gasoline vehicles with national IV standards in China, and the median value is 32.1 mg·kg·fuel-1, which is between LEV1 (produced between 1994 and 2004) and LEV2 (produced after 2004) vehicles in the United States. With the upgrading of emission standards, the composition and concentration of precursors in exhaust gas have changed, but there is still a lack of research on SOA formation factors for vehicles with higher emission standards in China. The OH exposure of the diesel vehicle experiment is between 2×106~2×107molecules cm-3·h, and it can be found from Fig. 2b that the SOA formation factor of the exhaust of medium/heavy-duty diesel vehicles in China is significantly higher than that of diesel vehicles in the United States. The main reason is that the research in China is carried out without after-treatment devices and under idling conditions, and the precursor emissions are significantly higher than those in the United States with after-treatment and urban driving cycle conditions.

3.2 Influencing factors of SOA formation

The engine type, emission standards, oil, driving conditions and after-treatment technology of motor vehicles will affect the generation of SOA[55⇓⇓⇓⇓⇓⇓⇓~63]. The research results of Nordin et al. On light gasoline vehicles using Euro II, Euro III and Euro IV at idle speed show that with the continuous upgrading of vehicle emission standards, the amount of SOA generated in the exhaust gas gradually decreases, which is 51, 9 and 5μg/m3[16], respectively. Gordon et al. Found that the SOA generated by LEVⅡ gasoline vehicle exhaust is about three times lower than that of Pre-LEV gasoline vehicle[14]. Zhao et al. Also found that the generation of SOA in exhaust gas decreased with the upgrading of emission standards[47]. Du et al. And Zhao et al. Have studied the SOA of gasoline vehicle exhaust with different engine types (GDI and PFI) in China and the United States respectively, and the results show that GDI engine emissions have a higher SOA generation potential in China, while different engine types have no significant effect on SOA generation in the United States[64][47]. Oil products also affect the formation of SOA, and with the increase of aromatics content in gasoline from 29% to 37%, the formation of SOA increases by 3 to 6 times[13]. Chen et al. Found similar experimental results that the formation of SOA increased by 4 to 7 times when the aromatic content increased from 23% to 50%, and proposed that toluene and C2- benzene played an important role in the formation of SOA[65]. The addition of ethanol to fuel may affect the formation of SOA. Roth et al. Found that the use of E78 gasoline produces less SOA than the use of E30 gasoline. However, in another study, it was found that for gasoline with high aromatic content, higher ethanol content produces more SOA. Some studies also showed that the use of ethanol gasoline does not affect the formation of SOA[66][62][67]. The generation of SOA under different working conditions is significantly different, and the cold start gasoline vehicle will produce higher SOA emissions, which is 3 to 7 times higher than the hot start. The SOA of diesel vehicle during idling is 2 ~ 5 times of that of city driving cycle and constant speed cruise[68]. In terms of exhaust after-treatment, the SOA generation of diesel vehicles equipped with DOC is 50% lower than that of diesel vehicles without after-treatment devices, and the combination of DOC + DPF further reduces the SOA generation potential of diesel vehicles[69]. The use of particulate filter instead of three-way catalytic converter in gasoline vehicles significantly reduces POA emissions and SOA, but the value of SOA/POA increases[70].
The concentration level of VOCs/NOx and inorganic gases in the reaction system of the tail gas also have a significant effect on the formation of SOA in the tail gas. The SOA yield of aromatics under "high NOx” condition is significantly lower than that under" low NOx” condition. This is due to the fact that NOx can change the fate of RO2 radicals. When the concentration of NO in the system is high, RO2 radicals mainly react with NO to form peroxyacyl nitrates (PANs) with high volatility, which reduces the formation of SOA. When the concentration of NO in the system is low, RO2 radicals mainly react with HO2 or RO2 radicals to form less volatile substances, which promote the formation of SOA. As the NMOG:NOx in the reaction system increases from 4 to 16, the amount of SOA in the tail gas increases exponentially[71]. Other inorganic gases in the exhaust gas can also participate in the formation of SOA, and the promotion of SOA formation was observed in the presence of NH3 and SO2 in the exhaust gas photooxidation experiment, and the formation of SOA was inhibited after removing NH3 from the exhaust gas[72][73]. The addition of high concentration of SO2 to the smog chamber can increase the yield of SOA. It is believed that the Criegee radical generated by the reaction of olefins with ozone oxidizes SO2 to sulfuric acid, which promotes the formation of SOA through acid catalysis[74].
In addition to the above factors, OH exposure is also an important factor affecting the formation of precursor SOA. Lambe et al. Found that the SOA yield of diesel vapor and several alkane precursors increased first and then decreased with the increase of OH exposure, and Liao et al. Found similar results based on the flow tube test of actual road environment[75][28]. In the initial oxidation stage of the precursor, the number of oxygen-containing functional groups of the product molecules increases and the volatility decreases, which can promote the movement of low-volatility products to the particle phase and improve the yield of SOA. However, with the increase of OH exposure, the molecules on the formed SOA will be decomposed and produce high-volatility products, which will re-enter the gas phase, resulting in the decrease of SOA yield[76]. Therefore, it is necessary to consider the influence of OH exposure on the results when studying the formation of SOA in exhaust gas. Some studies have found that the yield of SOA in tail gas reaches the highest value at the equivalent photooxidation age of 1 to 4 days, but due to the differences between different experiments and the uncertainty of OH exposure calculation, this result can only be used as a rough estimate, and more in-depth discussion is needed[28,67,77].
To sum up, there are many factors affecting the generation of SOA from vehicle exhaust, and there are great differences in the generation of SOA under different experimental conditions. However, the existing studies are based on a single factor to carry out variable experiments, and it is difficult to comprehensively evaluate the impact of various factors on the formation of SOA. It is necessary to carry out systematic research on the impact of different vehicle types, working conditions, and post-processing conditions on the generation of SOAs in the future.

3.3 The Evolution Process and Product Composition of SOA

During the formation and aging of SOA, the particle size, number concentration and mass concentration of particulate matter are constantly changing. The mass concentration of SOA generally increases gradually with the photooxidation age, and then decreases with the increase of the photooxidation age after reaching the maximum value at a certain time. The maximum concentration of SOA in the conversion process is called the maximum formation potential of SOA, which represents the formation ability of SOA in vehicle exhaust under certain driving conditions and environmental conditions[28,77]. In order to eliminate the influence of different vehicle exhaust emissions, the amount of SOA produced/organic precursors reacted at a certain time is defined as the effective yield of SOA. However, due to the different types of precursors identified in different studies, the selection of "reacted organic precursors" is not the same, which leads to great differences in the calculation results of SOA effective yield. In addition to the above reasons, it is also affected by the selected conditions of the experiment, so it is still necessary to explore the representative methods to characterize the SOA generated by the exhaust gas.
Aerosol Mass Spectrometer (AMS) is often used to explore the chemical process of SOA conversion in vehicle exhaust. The information of organic aerosol is obtained by ionizing the gasified molecules in AMS. The oxidation degree of particulate matter is usually characterized by the ion fragments of m/z 43(C3 H 7 +) and m/Z 44 (C O 2 +), and the signals of m/Z 43 and m/Z 44 at different time periods are plotted in the triangle defined by the environmental AMS data. As the reaction proceeds, the components of the organic aerosol gradually move from the lower right of the triangle with high volatility and low oxidation state (SV-OOA) to the upper left of the triangle with low volatility and high oxidation state (LV-OOA), indicating that the organic aerosol is continuously aging (Figure 3A)[70,78⇓ ~80]. Heald et al also analyzed the aging mechanism through the "paradigm diagram". With the increase of OH exposure, the O/C of organic matter gradually increased and the H/C gradually decreased. By fitting the ΔO/C and ΔH/C, the slope of the curve can characterize the mechanism of organic aerosol oxidation[81]. The results of different studies show that the SOA slope of vehicle exhaust varies between -1 and 0, indicating that the main oxidation mechanism is the formation of oxygen-containing organic compounds such as carboxylic acids, alcohols, peroxides, etc. (Fig. 3B)[16,67,80,82]. However, because AMS uses an electron impact ion source, it can not characterize the product at the molecular level, and it is difficult to infer the specific chemical process from the molecular level.
Breton et al. Of the University of Gothenburg used Filter Inlet for Gases and AEROsols-Chemical Ionization Mass Spectrometry,FIGAERO-CIMS was used to determine the particulate phase species at the molecular level in the primary emission of 13 buses and after flow tube aging, and 61 acids were determined based on the ionization method of acetate.Its mass concentration accounts for 4% and 46% of the total mass of the particulate phase in the first emission and after aging, respectively. The concentrations of nitric acid, cresol and carboxylic acid increase significantly during the reaction, indicating that the oxidation of precursors to carboxylic acid is an important reaction pathway for the formation of SOA in diesel exhaust[84]. Further comparing the actual traffic emissions of Euro VI standard heavy-duty diesel trucks with those of Euro V standard heavy-duty diesel trucks, the analysis results of FIGAERO-I-CIMS show that for the measured species,Inorganic acids and small molecular carboxylic acids are the main components in the gas and particle phases, and the concentrations of most gas and particle phase compounds (nitric acid, organic acids, and carbonyl compounds) after the secondary conversion exceed those of the primary emission, indicating that the photooxidation process enhances the content of small molecular acids[85]. These studies provide valuable experience for future related experiments, but there are also some shortcomings in the experiments themselves. Firstly, some key precursors (aromatics, alkanes) and products in the tail gas conversion process are ignored according to the ionization technology based on acetate ions and iodide ions, and secondly, the discussion on the intermediate products and chemical processes of the secondary conversion is lost due to the high oxidation atmosphere in the flow tube. In addition, these two studies are aimed at the SOA product composition of diesel vehicle exhaust, and more comprehensive discussions on the SOA product composition and chemical process of more types and emission standards of vehicle exhaust are needed.

3.4 Model Simulation of SOA Generation in Exhaust Gas

The simulation methods of SOA generation are mainly divided into chemical mechanism model and yield model. Chemical Mechanism models mainly include MCM (Master Chemical Mechanism), GECKO-A (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere) and other Chemical reaction modules, which can directly simulate the chemical reactions and products of each precursor[86⇓⇓~89]. Because the chemical mechanism model needs a large number of smoke chamber experimental results as a basis to obtain the reaction process and transformation mechanism of different precursors, and the composition of vehicle exhaust precursors is complex, the structural properties and transformation mechanism of a large number of unresolved compounds are unknown, so it is not suitable for simulating the formation of SOA in exhaust.
The yield model is based on the reaction results of smoke chamber or flow tube, and is fitted by mathematical statistics. The classical model is the two-product model proposed by Odum et al., which is based on the gas-particle partition theory and replaces all products with two components of different volatility. The formula is as follows:[90]
Y = M o ∑ a i K o m , i 1 + K o m , i M o
In the above formula, Y is the SOA yield, M o is the OA concentration, a i is the gas-phase stoichiometric fraction of species I, and K o m , i is the gas-particle partition coefficient of species I. According to the experimental results, a series of a i 、 K o m , i can be obtained respectively, and the SOA yield can be calculated. In the two-product model, POA is regarded as an unreactive and non-volatile component, but recent studies have shown that POA is volatile and can participate in the atmospheric chemical aging process to form SOA, which is an important source of SOA, and the two-product model will significantly underestimate the formation of SOA.
In order to consider the gas-particle distribution and the formation and aging process of POA and SOA at the same time, Donahue et al. Developed a VBS unit model based on volatile distribution (1D-VBS), which divides the saturated vapor concentration of organic matter at 300 K into different volatile intervals, and then defines the saturated vapor concentration of each interval. Organic matter will move from the high volatility range to the low volatility range after oxidation, so the oxidation process of organic matter can be simulated only by inputting the parameters of organic matter concentration, volatility, OA concentration, reaction rate and so on[20]. Compared with the two-product model, the VBS model can effectively improve the underestimation of SOA and improve the accuracy of simulation results. The key assumption of D-VBS is that the chemical properties of the organic matter in each interval are similar, and the properties of a single species do not affect the whole. However, in practice, the chemical properties of species in the same volatile range are significantly different, and 1D-VBS can not effectively distinguish species with similar volatility but different reactivity. Donahue further developed the two-dimensional VBS model (2D-VBS) by adding the dimension of average oxidation state (OSc ≈ 2 (O/C)- (H/C)), which can simulate the change of SOA in both oxidation state and volatility, so as to better describe the atmospheric transformation process of organic aerosols[91,92].
At present, the VBS model has been widely used to simulate the generation of SOA and explore the chemical conversion mechanism of organic aerosols. For example, Chan et al. Used 1D-VBS to simulate the SOA yield of α-pinene ozonation under different OA loading conditions and experimental conditions, and the fitting results were in good agreement with the measured values[93]. 2D-VBS was used to simulate the formation and aging of SOA in the free radical oxidation of α-pinene-OH, and the results showed that the mass concentration of organic aerosol and O ∶ C were consistent with the laboratory measurements within the uncertainty range[94]. Ye et al. Used 2D-VBS to simulate the results of pinene diol-OH radical oxidation smoke chamber experiment, and found that low volatile organic compounds contributed a large part of SOA formation[95]. The 1D-VBS was embedded into the CMAQ model to simulate the generation of SOA from non-traditional precursors (S/IVOCs) at Atlanta Airport due to aircraft activity, and the results showed that the SOA generated by the oxidation of S/IVOCs explained about 10% of the contribution to P M 2 . 5 generation due to aircraft activity[96]. Esmaeilirad et al. established a numerical model based on the VBS method to predict the SOA formation of vehicle exhaust after OH radical oxidation, integrated the smog chamber experimental data with vehicle emission data, and calculated the contribution of traditional (aromatic hydrocarbons) and non-traditional precursors (S/IVOCs, etc.) to SOA. According to the model prediction results, the average yield of VBS for each type of vehicle is obtained, and the obtained yield matrix can reproduce the SOA generation observed in the smoke box[97].
In general, the VBS model has good applicability for simulating the generation of SOA from complex precursors containing a large number of unknown species. Because it takes into account the semi-volatility and aging of POA and the oxidation process of S/IVOCs, the accuracy of the simulation is improved compared with the traditional two-product model, but the VBS model still has some shortcomings.For example, 1D-VBS can not distinguish species with the same volatility but different chemical activity, while 2D-VBS classifies species in two dimensions, but it is still insufficient to describe the evolution of SOA.Different research results show that there is a difference between the O ∶ C ratio obtained based on the 2D-VBS model and the measured results, so further research is needed to improve the accuracy of the model simulation[94,98].

4 Conclusion and prospect

The generation of SOA from vehicle exhaust through atmospheric transformation has an important contribution to urban PM2.5, and in-depth understanding of its secondary transformation law and formulating reasonable control measures are the key links of air pollution control in China in the future. At present, researchers at home and abroad have made some achievements in the identification of key precursors, the influencing factors of vehicle emission precursors, the formation of SOA, aging and influencing factors, but due to the limitations of analysis technology and experimental conditions, there are still some shortcomings in the existing research results. First of all, the composition of precursors, especially S/IVOCs, still needs further study. Secondly, there is a lack of parameterization scheme for precursor emission and SOA generation under a series of influencing factors such as vehicle type, oil product, driving behavior and so on. Finally, the understanding of the product composition and chemical process of SOA in exhaust gas is very limited at present. The interaction in the photochemical reaction process of complex precursors will lead to the complexity of product composition and concentration. The existing models have some errors between the simulation results and the actual values due to the insufficient description of the intermediate chemical process[99]. To sum up, this paper puts forward the following prospects for the research of SOA generation in exhaust gas.
1) Fine development of SOA precursor emission source spectrum of motor vehicle exhaust: develop and improve new all-species testing methods for precursors, realize the measurement and quantification of gas-phase and particle-phase precursors at the molecular level, establish a more perfect motor vehicle emission source spectrum, and provide a basis for the research of SOA transformation mechanism and the compilation of emission inventory.
2) In-depth study on the formation mechanism of SOA in vehicle exhaust: carry out simulation experiments on the formation of SOA in vehicle exhaust, and use advanced instruments such as CIMS to obtain key parameters in the formation process of SOA, such as product composition and concentration changes, SOA formation rate and volatility, so as to explore the formation mechanism of SOA at the molecular level.
3) Improvement of vehicle exhaust SOA generation model: carry out comprehensive exhaust emission and SOA simulation experiments to obtain the parameterization scheme of precursor emission and SOA generation under different types of vehicles, oil products, driving behavior and other conditions, and combine with the exhaust SOA generation mechanism to supplement and improve the existing SOA generation model and improve the accuracy and applicability of the model.

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Funding

National Natural Science Foundation of China(42175123)

National Natural Science Foundation of China(42107125)

Natural Science Foundation of Tianjin(20JCYBJC01270)

Tianjin Research Innovation Project for Postgraduate Students(2021YJSS013)

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