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Johnson Matthey Technol. Rev., 2021, 65, (3), 404

doi:10.1595/205651320x15900542621515

On-Road Emission Characteristics of Volatile Organic Compounds from Light-Duty Diesel Trucks Meeting Different Emission Standards

Investigation on the characteristics of tailpipe volatile organic compound emissions with a portable emissions measurement system

  • Menglei Wang

  • National Engineering Laboratory for Mobile Source Emission Control Technology, China Automobile Technology & Research Center, 68 East Xianfeng Road, Dongli District, Tianjin 300300, China; School of Ecology and Environment, Zhengzhou University, 100 Science Avenue, Zhengzhou 450001, He’nan Province, China
  • Rencheng Zhu*, Ruiqin Zhang, Shunyi Li

  • School of Ecology and Environment, Zhengzhou University, 100 Science Avenue, Zhengzhou 450001, He’nan Province, China
  • Xiaofeng Bao

  • State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, 8 Dayangfang, Anwai Beiyuan, Chaoyang District, Beijing 100012, China; National Engineering Laboratory for Mobile Source Emission Control Technology, China Automobile Technology & Research Center, 68 East Xianfeng Road, Tianjin 300300, China
  • *Email: zhurc@zzu.edu.cn
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Article Synopsis

On-road tailpipe volatile organic compounds (VOCs) were sampled from light-duty diesel trucks (LDDTs) compliant with Euro III to V, and a total of 102 VOC species were quantified. The composition characteristics and carbon number distributions were investigated, and the contribution of individual VOC to ozone formation potentials (OFPs) was weighted. Results showed that alkanes were the major VOC species, accounting for approximately 65.5%. VOC emissions decreased significantly as the standards became stricter, especially for alkanes and aromatics; and the VOC emissions on highway were much lower than those on urban roads. Carbon number distribution of VOCs was mainly concentrated in C3–C4 and C10–C12. Aromatics were the major contributors to ozone formation, taking up 49.3–57.6% of the total OFPs, and naphthalene, 1-butene, dodecane, 1,2,3-trimethylbenzene and 2-propenal were the top five species. The information provided insight into the tailpipe VOC emission characteristics and may help decision makers drafting related emission policies.

1. Introduction

With the dramatic increase of motor vehicles in recent years, tailpipe emissions have become one of the primary anthropogenic air pollution sources in China, especially in large metropolises (1, 2). According to the data from Ministry of Ecology and Environment of People’s Republic of China (MEE), the total annual carbon monoxide (CO), hydrocarbon (HC) and nitric oxides (NOx) emissions from motor vehicles in 2018 were 28.6 million tonnes, 3.3 million tonnes and 5.2 million tonnes, respectively, and vehicles compliant with Euro II, III and IV emission standards contributed approximately 79.3–91.8%.

As important precursors of ozone and secondary organic aerosols (SOA), VOCs can cause severe photochemical smog and haze through a series of photochemical processes and consequent gas-to-particle condensations (35). On the other hand, a growing body of evidence indicates that some VOCs, such as benzene, 1,3-butadiene, toluene and xylene, are adverse to human health, including respiratory irritation, cancer and even death (68). Therefore, a better control of VOC emissions, especially those emitted by vehicles, is of great importance for the improvement of urban air quality.

In order to reduce tailpipe emissions, many measures have been employed by the China government, of which progressing the emission standards is of high efficiency. For example, China implemented China I (equal to Euro I) in 2000, and China VI emission standards have been partially implemented in China, which is deemed as one of the strictest standards in the world. Thus, despite the rapid growth of vehicle population in the past two decades, tailpipe pollutants only increased slightly (9, 10). In recent decades, a great number of studies on vehicle VOC emissions have been conducted. However, most of these studies mainly focused on gasoline vehicles due to the higher HC emissions compared to diesel vehicles (1113). With the development of engine technology and exhaust aftertreatment devices, HC emissions from gasoline vehicles have been dramatically reduced, and the problems caused by diesel vehicle emissions have become more prominent (14). Therefore, the HC emission limits have been set to the same level for both gasoline vehicles and light-duty diesel vehicles in the latest China VI standards.

To better understand the vehicular emission characteristics, many measurements have been conducted in recent years, such as traffic tunnel measurement, dynamometer tests and roadside sampling. Tunnel measurements and roadside sampling may be affected by many uncontrollable environmental conditions, and they are generally used to evaluate the average emission factors (EFs) of traffic fleets in an area (10, 15). Dynamometer measurement is often used to investigate the influence of certain factors (for example, fuel quality, engine technology, driving cycle) on vehicular emissions (16). However, results based on dynamometer measurements may not reflect the actual emissions, because it is mainly conducted in the laboratory and the test conditions are controlled very well. With the development of portable emission measurement systems (PEMS), an increasing number of researchers began to use these systems to investigate the vehicular emission characteristics because of their ability to quantify vehicle emission levels in real-world situations. However, PEMS was mainly used to detect regulated gaseous emissions from diesel vehicles in previous research (1719), and only a few studies investigated VOC emissions from motor vehicles based on PEMS (14, 20).

A series of policy documents aiming at pollution control for diesel trucks have been implemented to win the ‘Blue Sky Protection Campaign’ in China since 2017. According to the annual statistical report, nearly half of the total diesel vehicles in China were light-duty diesel vehicles, and most of them carry various cargoes for delivery in urban areas (21). Therefore, tailpipe emissions from light-duty diesel vehicles are closely associated with urban air quality. However, tailpipe emissions from diesel vehicles were mainly focused on NOx and particulate matter (PM). The understanding of the emission characteristics of VOCs, key precursors of SOA and ozone, from diesel trucks is still limited, which has become an obstacle for the establishment of stricter regulations in China.

The objective of this study was to investigate the on-road tailpipe VOC emission characteristics of LDDTs compliant with different emission standards. Effects of emission standards and driving conditions on the VOC profiles and carbon number distributions were analysed, and the contribution of each VOC species to OFPs was weighted with the maximum incremental reactivity (MIR) method. Results from this study present some interesting information regarding the emissions of a group of pollutants that play a key role in the chemistry of aerosols and ozone in the atmosphere, which will help decision makers drafting emission related policies.

2. Materials and Methods

2.1 Test Vehicles and Routes

Taking into account that more than 99.4% of the diesel vehicles currently in China are compliant with Euro III–V, three typical LDDTs compliant with Euro III, Euro IV and Euro V, respectively, were selected from the market and their specifications are provided in Table I. These trucks have similar dimensions and powers, and their biggest difference is their aftertreatment technology. To eliminate the impact of fuel quality, all the diesel fuel used in the study was from a specified filling station, conforming to the China VI standard.

Table I

Specifications of Tested Vehicles

LDDT-1 LDDT-2 LDDT-3
Intake type Turbocharging Charge intercooling Charge intercooling
Cylinder arrangement In-line In-line In-line
Displacement, ml 3660 2545 2982
Engine power, kW 83 65 85
Aftertreatment device _ DPFa SCRb + DOCc
Emission standard Euro III Euro IV Euro V
Kerb mass, kg 2700 2495 2720
Dimensions, mm × mm × mm 5995 × 2275 × 3040 5995 × 2060 × 2230 5995 × 2275 × 2420
Odometer, km 94,080 28,918 25,560
Manufacture year 2013 2016 2017

aThe diesel particulate filter

bThe selective catalytic reduction

cThe diesel oxidation catalyst

The test route was designed to simulate the real driving conditions of most diesel trucks in Zhengzhou, Henan province. The total length of the test route was approximately 68 km, including 14 km of urban roads, 18 km of connection roads and 36 km of highway. VOCs were sampled only when the trucks travelled on the urban and highway roads and cold start emissions of VOCs were not included during the whole test. Table II shows the driving condition parameters during each road type. The average speeds on urban and highway roads were 18.1–20.8 km h−1 and 72.8–76.5 km h−1, respectively. Driving conditions on urban roads are more aggressive than those on highway roads. The average accelerations on urban and highway roads were 0.22–0.26 m s−2 and 0.10–0.13 m s−2, respectively. During the measurement, the trucks were not in service and the load of each truck was approximately 500 kg during the experiment, containing the PEMS equipment, four batteries, two testers and one driver.

Table II

Driving Condition Parameters of Each Road Type

Road type Length, km Duration, min Average speed, km h−1 Maximum speed, km h−1 Average acceleration, m s−2
Urban 14 35–45 18.1–20.8 57.6 0.22–0.26
Highway 36 ~30 72.8–76.5 99.1 0.10–0.13

2.2 Volatile Organic Compounds Sampling and Analysis

Under real driving conditions, some gaseous emissions may transform to secondary fine particles when the exhaust is cooled or diluted with the ambient atmosphere. Thus, VOC emissions might be overestimated if sampled directly from the vehicle exhaust because the temperature is very high. Therefore, a combined PEMS (Sensors Inc, USA) was employed to sample the exhaust VOC emissions. The schematic diagram of the emission testing and sampling system is shown in Figure 1.

Fig. 1

Schematic diagram of VOCs sampling system (WP: weather probe; CPM: constant particle measurement; PFS: particle filter system; HTF: heated tube flowmeter; PDCM: power distribution control module)

Schematic diagram of VOCs sampling system (WP: weather probe; CPM: constant particle measurement; PFS: particle filter system; HTF: heated tube flowmeter; PDCM: power distribution control module)

The microproportional sample system (MPS), a partial flow dilution system, was used to dilute and cool the exhaust from tailpipe. After the MPS, the gas temperature decreased from about 120ºC to about 40ºC. Two 3.2 L SUMMA® canisters (Entech Instruments Inc, USA) were used to sample the VOCs during each test trip, one for the urban roads and the other for the highway roads. VOC emissions during the connection roads section were not sampled because the actual running speed could not meet the requirement due to unexpected road repairing. The sampling flow rate was controlled by a passive restrict valve at 0.1 L min−1. TeflonTM tubes were used to connect the canister and PEMS system to minimise the adsorption of VOCs. A laptop was used to control the system and collect data from the test module. It should be noted that these trucks were driven by their owners throughout the test to ensure these trucks were running under ordinary working conditions and each vehicle was tested twice to enhance the reliability of the results.

Analysis of the VOCs was carried out following the United States Environmental Protection Agency (US EPA) TO-15 method by a gas chromatography-mass selective detector (GC-MSD) (22). Samples collected in the SUMMA® canister were preconcentrated using an 8900DS preconcentrator (Nutech Instruments Inc, USA) with three cold traps and a canister autosampler (Nutech Instruments Inc, USA, mode 3600DS). The moisture, CO2 and methane would be removed through the traps. Then the concentration of the individual VOCs in the samples was determined by a GC-MSD system (7890A GC with a 5975 MSD, Agilent Technologies Inc, USA). Separation of the VOCs was achieved through a capillary column (60 mm × 0.25 mm internal diameter, 1.4 μm film thickness, DB-624 column, Agilent Technologies Inc). During sampling and analysis, strict quality assurance and quality control procedures were conducted to assure the data quality (22). The detection limits of the target non-methane hydrocarbons ranged from 7 parts per trillion by volume (pptv) to 141 pptv and the accuracy of the measurements was about 1–10%. Detailed description of the analysis procedures can be found in our previous study (23).

A total of 102 VOC species were identified and quantified, including 29 alkanes, 35 halocarbons, 17 aromatics, nine alkenes, five carbonyls and seven other compounds, which are presented in Table III. Due to the detection limitation of GC-MSD used in this study, some species (ethane, ethylene, propylene, acetylene, formaldehyde) were not detected and included.

Table III

Volatile Organic Compound Species Determined in Diesel Truck Emission Samples

NO. Species NO. Species
Alkanes (29) 52 1,2,3-trimethylbenzene
1 propane 53 m-diethylbenzene
2 i-butane 54 1,4-diethylbenzene
3 n-butane 55 naphthalene
4 i-pentane Carbonyl (5)
5 pentane 56 2-propenal
6 2,2-dimethylbutane 57 acetone
7 2,3-dimethylbutane 58 2-butanone
8 2-methylpentane 59 4-methyl-2-pentanone
9 3-methylpentane 60 2-hexanone
10 hexane Halocarbons (35)
11 cyclopentane 61 dichlorodifluoromethane
12 2,4-dimethypentane 62 1,2-dichloro-1,1,2,2-tetrafluoroethane
13 methylcyclopentane 63 chloromethane
14 i-heptane 64 chloroethene
15 cyclohexane 65 bromomethane
16 2,3-dimethylpentane 66 chloroethane
17 3-methylhexane 67 trichloromonofluoromethane
18 2,2,4-trimethylpentane 68 1,1-dichloroethene
19 heptane 69 1,1,2-trichilorotrifluoroethane
20 methylcyclohexane 70 dichloromethane
21 1,4-dioxane 71 cis-1,2-dichloroethene
22 2,3,4-trimethyl pentane 72 1,1-dichloroethane
23 2-methyl heptane 73 trans-1,2-dichloroethene
24 3-methyl heptane 74 trichloromethane
25 octane 75 1,1,1-trichloroethane
26 nonane 76 tetrachloromethane
27 decane 77 1,2-dichloroethane
28 n-hendecane 78 trichloroethylene
29 dodecane 79 1,2-dichloropropane
Alkenes (9) 80 bromodichloromethane
30 1-butene 81 cis-1,3-dichloro-1-propene
31 1,3-butadiene 82 trans-1,3-dichloropropene
32 2-butene 83 1,1,2-trichloroethane
33 cis-2-butene 84 tetrachloroethylene
34 1-pentene 85 dibromochloromethane
35 trans-2-pentene 86 1,1-dibromoethane
36 isoprene 87 chlorobenzene
37 cis-2-pentene 88 bromoform
38 1-hexene 89 1,1,2,2-tetrachloroethane
Aromatics (17) 90 1,3-dichlorobenzene
39 benzene 91 1,4-dichlorobenzene
40 toluene 92 benzyl chloride
41 ethylbenzene 93 1,2-diethylbenzene
42 m/p-xylene 94 1,2,4-trichlorobenzene
43 o-xylene 95 hexachlorobutadiene
44 styrene Other Compounds (7)
45 cumene 96 iso-propanol
46 propylbenzene 97 carbon disulfide
47 3-ethyltoluene 98 methyl tert-butyl ether
48 1-ethyl-4-methylbenzene 99 vinyl acetate
49 1,3,5-trimethylbenzene 100 ethyl acetate
50 2-ethyltoluene 101 tetrahydrofuran
51 1,2,4-trimethylbenzene 102 methyl methacrylate

2.3 Calculation of the Emission Factors and Ozone Formation Potential

EF per kilometre of a certain pollutant was calculated with the corresponding concentration, total exhaust volume and running distance during the test process. Prior to calculation, the results of the VOC measurements were time-aggregated. The total exhaust volumes in various driving conditions were the integration of the instantaneous exhaust flow rates, and the same for the total running distance. The EF of compound i was calculated as Equations (i)(iii):

(i)

 

(ii)

 

(iii)

where V (m3) is the total exhaust volume of the sampling process; Vins (m3 s−1) is the instantaneous exhaust flow rate; DRins is the instantaneous dilution ratio of MPS; S (km) is the distance that the test vehicle travelled during the sample period; Sj is the travel distance at j second, which is equal to the value of instantaneous speed at time j recorded by the global positioning system (m s−1); EFi (mg km−1) is the EF of compound i; Ci (parts per billion by volume) is the concentration of compound i; Mi (g mol−1) is the molar mass of compound i; and Vm (l mol−1) is the molar volume of compound i. The volumes and concentration data were all normalised to the standard ambient temperature and pressure condition (273.15 K, 101.33 kPa). The total EFs of the VOCs in a certain driving mode were summed by the individual VOC EFs in the driving mode.

The OFP refers to the amount of ozone generated by VOCs per unit mass (mg O3 mg−1 VOCs), which can reflect the ozone formation capacity of VOC species. In most cases, ratios of VOCs to NOx from the diluted exhaust were much higher than 20 in this study, which illustrated VOCs had the greater effect on the ozone formation (24). Therefore, the MIR scenarios developed by Cater (25) was applicable to evaluate the OFP of VOC species here. The OFP of a certain VOC is calculated according to Equation (iv) (26, 27):

(iv)

where OFPi (mg O3 km−1) is the ozone formation of compound i; and MIRi (mg O3 mg−1 VOCs) is the maximum incremental reactive of compound i obtained from Cater (25, 28). The total OFPs of a certain driving mode were summed by the individual VOC OFPs of the driving mode.

3. Results and Discussion

3.1 Regulated Gaseous Emissions

Figure 2 presents the EFs of regulated gaseous pollutants of three LDDTs compliant with different standards. Obviously, NOx, CO and HC emissions from LDDT-3 (Euro V) were the lowest and those from LDDT-1 (Euro III) were the highest, except for CO. In general, updated emission standards had a great effect on the reduction of regulated gaseous emissions. This is mainly because the three trucks adopted different aftertreatment technologies to meet different emission standards (29). For example, both selective catalytic reduction (SCR) and diesel oxidation catalyst (DOC) were utilised by LDDT-3 to be compliant with Euro V standards. SCR was often used to purify the NOx emissions and the DOC device could oxidise the CO and HC emissions efficiently (3032). It is not difficult to understand why LDDT-1 produced the worst emissions because there is no aftertreatment requirement for Euro III trucks in most of China.

Fig. 2

NOx, CO and HC emissions from three vehicles under urban and highway conditions

NOx, CO and HC emissions from three vehicles under urban and highway conditions

As shown in Figure 2, NOx, CO and HC emissions under urban conditions were significantly higher than those under highway conditions. To be specific, NOx, CO and HC emissions under urban conditions were 1.3–1.8 times, 1.4–2.2 times and 2.5–4.1 times those under highway conditions. This phenomenon could be explained by the fact that the combustion quality in the engine was associated with the operation speed and frequent acceleration and deceleration (18, 33, 34). During this experiment, no traffic signals were encountered on the highway and the average speed was up to 73.8 km h−1. However, there were 26 traffic signals on the urban roads and the average speed was only 19.4 km h−1. In this operating condition, the combustion was insufficient and the temperature of aftertreatments might not be high enough for proper function, which caused the emissions to deteriorate.

3.2 Volatile Organic Compound Speciation Profiles

Average weight percentage of individual VOC species of the entire trip was calculated based on the test trucks. On the whole, alkanes were the dominant group, accounting for 65.5 ± 10.3% of the total VOCs, followed by aromatics, carbonyls and alkenes, taking up 19.6 ± 5.0%, 5.4 ± 1.9% and 4.4 ± 1.8%, respectively. Additionally, though 35 halocarbons were quantified, they only took up 3.6 ± 1.5% of the VOCs. Thus, the following discussions on the VOCs are mainly focused on alkanes, aromatics, alkenes and carbonyls.

Weight percentages of the top 15 VOC species from the exhaust are presented in Table IV. These species accounted for approximately 83.4% of the total VOCs. Dodecane, n-undecane, naphthalene, n-decane and acetone were the major species, and their total weight percentages were over 80.1%. These results are partially consistent with the results obtained by Wang et al. (14), who indicated that n-decane, n-undecane and n-dodecane were the most abundant species. However, a study by Yao et al. (20) showed that carbonyls were the top group, which could account for 42.7–69.2% of the total VOCs. The difference was mainly attributed to the different VOC species quantified between the two studies. For example, Yao et al. (20) reported formaldehyde and acetaldehyde took up 47.9% and 21.0% of carbonyls, while these two species were not detected in this study.

Table IV

Weight Percentages of the Top 15 Volatile Organic Compound Species

No. Compounds Percentage, % No. Compounds Percentage, %
1 dodecane 44.9 ± 29.2 9 1-butene 1.9 ± 0.4
2 n-undecane 19.2 ± 13.7 10 1,2,3-trimethylbenzene 1.9 ± 0.2
3 n-decane 7.3 ± 4.3 11 1,4-diethylbenzene 1.8 ± 0.2
4 naphthalene 6.0 ± 2.5 12 benzene 1.7 ± 0.9
5 acetone 2.7 ± 0.4 13 3-ethyltoluene 1.7 ± 0.5
6 propane 2.5 ± 1.6 14 1,2,4-trimethylbenzene 1.6 ± 0.5
7 2-propenal 2.1 ± 1.2 15 2-ethyltoluene 1.2 ± 0.4
8 nonane 2.1 ± 0.9

3.3 Effect of Standards on Volatile Organic Compound Emissions

The mean EFs and weight percentages for each VOC group for the entire trip of the three test trucks are plotted in Figure 3. The total VOC EFs of LDDT-1 (Euro III), LDDT-2 (Euro IV) and LDDT-3 (Euro V) were 186.9 ± 34.9 mg km−1, 106.5 ± 26.2 mg km−1 and 61.1 ± 16.9 mg km−1, respectively. In other words, the VOC emissions decreased significantly as the standards tightened gradually from Euro III to Euro V. Most of the other species also showed a decreasing trend. Especially, dodecane and n-undecane presented the most significant decline, from 85.2 ± 3.7 mg km−1 and 38.6 ± 11.7 mg km−1 for Euro III to 16.7 ± 2.8 mg km−1 and 9.7 ± 3.0 mg km−1 for Euro V, respectively. The trend was partially consistent with that found by Zhang et al. (10), though the VOC EFs were a little higher than those in this work. This might be mainly attributed to the fact that Zhang et al. (10) employed tunnel measurement, which included evaporative emissions.

Fig. 3

EFs and weight percentages of the VOC groups under different emission standards: (a) LDDT-1; (b) LDDT-2; (c) LDDT-3

EFs and weight percentages of the VOC groups under different emission standards: (a) LDDT-1; (b) LDDT-2; (c) LDDT-3

Most VOC groups presented similar variation trends as the emission standards changed, especially for the dominant groups. For example, both alkane and aromatic emissions decreased noticeably as the standards varied from Euro III to Euro V. The progress in engine technology and application of aftertreatment devices played a major role in the subtraction of VOCs emissions. Additionally, there were no significant differences between emissions of carbonyls, alkenes and halocarbons from LDDT-1 (Euro III) and LDDT-2 (Euro IV), but they were much higher than those of LDDT-3 (Euro V). No coherent order was observed for other emissions among these diesel trucks, possibly because the absolute values of these species were too small to quantify accurately. On the whole, implementing stringent emissions standards could reduce most of the VOC species effectively in the freight transportation sector.

Figure 3 indicates that alkanes were the dominant group in tailpipe VOCs emissions from the test LDDTs, accounting for 57.2–80.0%, followed by aromatics (12.5–22.9%), carbonyls (3.1–7.7%) and alkenes (2.2–6.5%). This result was consistent with that observed by Wang et al. (14) (carbonyls < aromatics < alkanes) but inconsistent with that by Yao et al. (20) (alkenes < aromatics < alkanes < carbonyls). Discrepancy of the quantified VOC species was the main cause of the inconsistency. It can also be found that the proportion of alkanes decreased significantly, from 80.01% for LDDT-1 to 57.15% and 60.41% for LDDT-2 and LDDT-3, respectively. Additionally, LDDT-2 and LDDT-3 had similar VOC group distributions, while the aromatics weight percentage of LDDT-2 was significantly related to that of LDDT-3. This is probably due to the different aftertreatment used in LDDT-2 and LDDT-3 (as shown in Table III). Jung et al. (35) also observed that heavy-duty trucks equipped with DPF emitted higher quantities of aromatics compared with those with SCR.

Figure 4 shows the EFs of the top 15 VOC species from the exhaust of LDDTs. The EF of dodecane for LDDT-3 (Euro V) was 51.0% and for LDDT‐2 (Euro IV) it was only 19.6% relative to LDDT‐1 (Euro III). For several other species, LDDT-3 had the lowest EFs, while the EFs of LDDT-2 and LDDT-1 were comparable or even higher, such as naphthalene, acetone, 2-propenal. A hypothesis is that much higher temperatures and more oxidising conditions during the DPF regeneration process favour carbonyl formation (36). However, there is no direct evidence that DPF regeneration occurred. Additionally, there were several individual species whose emissions were not affected by the emission standards. Overall, most of the top 15 VOC species presented a decreasing trend as the emission standards tightened.

Fig. 4

EFs of the top 15 VOC species from the exhaust of the LDDTs

EFs of the top 15 VOC species from the exhaust of the LDDTs

3.4 Influence of Driving Conditions on Volatile Organic Compound Emissions

Figure 5 shows several VOC group emissions from the exhaust of LDDTs under urban and highway driving conditions, respectively. It can be seen that VOC emissions on highway roads were much lower than those on urban roads. EFs of each VOC group decreased significantly, especially for alkanes and aromatics. Specifically, EFs of alkanes under highway conditions were only 20.4–46.2% of those under urban conditions, which was mainly attributed to the sharp decline of the most abundant alkane species, such as dodecane, n-undecane and n- ecane. For aromatics, the significant reduction of the EFs during highway driving could be attributed to the sharp reductions of naphthalene, 1,2,3-trimethylbenzene and 1,2,4-trimethylbenzene. Lower average speed and more acceleration and declaration were found during the urban road episodes, causing more incomplete combustion on non-highway road driving, resulting in higher VOCs emissions than on highways (37). Caplain et al. (38) also reported that tailpipe emissions in urban driving cycles were approximately four times those in motorway driving cycles. In addition, the reduction degrees of VOC EFs (urban vs. highway) for LDDT-3 were highest while those for LDDT-2 were lowest. This discrepancy is mainly due to the different aftertreatment devices used. For instance, optimum conditions of a DOC + SCR system used for LDDT-3 could be maintained under highway conditions because of the high exhaust temperature, resulting in more efficient reduction.

Fig. 5

VOC EFs of the tested vehicles under different driving cycles: (a) LDDT-1; (b) LDDT-2; (c) LDDT-3

VOC EFs of the tested vehicles under different driving cycles: (a) LDDT-1; (b) LDDT-2; (c) LDDT-3

A breakdown of the C1–C12 VOCs for different driving conditions of the tested trucks is presented in Figure 6. There was no obviously consistent trend in the carbon number distribution of the VOC species between highway and urban road conditions except for C3 and C11, which showed a decreasing trend when driven on the highway compared to urban roads. This phenomenon illustrated that driving conditions had a weak correlation with carbon number distribution. On the whole, the carbon number of the VOCs was concentrated in C3–C4 and C10–C12, showing a distinct ‘double peak’ phenomenon. Lu et al. (39) summarised several previous studies and reached a similar conclusion. VOCs are expected to be a mixture of unburned and partially burned fuel species (40). Propane and acetone are the dominant species in C3–C4 group, and this portion of the VOCs is likely generated as a result of the high efficiency of the diesel engine. For the C10–C12 group, these species are considered to be components of diesel fuel. Durbin et al. (41) reported the C1–C3 species contributed most to non-methane organic gases (NMOG) and ethene, ethyne, acetaldehyde and formaldehyde made the largest contribution. The differences may be attributed to the VOCs species detected between these two studies.

Fig. 6

VOC distribution based on carbon number

VOC distribution based on carbon number

3.5 Ozone Formation Potential

According to the EFs of each VOC species, OFPs based on the travelled distance were calculated and the results are plotted in Figure 7. As expected, the magnitude of OFP based on emission rate presented a decreasing trend. To be specific, LDDT‐1 and LDDT-2 had the higher OFPs, approximately 239.6 ± 57.3 mg O3 km−1 and 227.7 ± 69.2 mg O3 km−1, respectively, and that for LDDT-3 was 124.8 ± 47.6 mg O3 km−1. The OFP values in this work were lower but comparable to those for diesel trucks in some studies (20, 37, 42). The lower OFP values in this study were mainly because DOC and SCR dramatically reduced VOCs emission. Additionally, engine technologies, driving cycles and fuel quality were also important factors.

Fig. 7

OFPs of the different VOC groups

OFPs of the different VOC groups

The chemical structure of OFPs was different from the trend of VOCs emissions based on distance travelled, shown in Figure 3. Aromatics were the primary contributor to OFP, accounting for 49.3–57.6% of the OFPs. It was noteworthy that although alkenes accounted for only approximately 5.0% of the VOC emissions, the OFP contribution of alkenes (13.4–22.3%) was comparable with that of alkanes (13.7–27.9%), which was attributed to the higher MIR scales of alkenes related to alkanes. Similar conclusions have been reached in previous studies. Therefore, priority measures should be taken to reduce the VOCs with high MIR values, such as aromatics and alkenes, to control the formation of ozone originated from diesel exhaust.

The top 20 VOC species ranked by their OFP are given in Figure 8. The contribution of these substances accounted for approximately 90.0% of the total measured OFPs. Naphthalene, 1-butene, dodecane, 1,2,3-trimethylbenzene, 2-propenal, 1,2,4-trimethylbenzene and 3-ethyltoluene were the dominant species in the photochemical ozone formation process, and their OFP values were over 10 mg O3 km−1. Among the top 20 species, 11 belonged to the aromatic group and four were alkenes, which accounted for a lower mass percentage but higher MIR values. This indicates that substances present in small amounts but with high MIR values should not be ignored.

Fig. 8

The top 20 VOC species ranked by their ozone formation potential

The top 20 VOC species ranked by their ozone formation potential

4. Conclusions

On-road VOC emissions from LDDTs compliant with different standards were sampled with a combined PEMS, and the effects of emission standards and driving conditions on both VOC characteristics and OFPs were analysed. Based on the results, the following conclusions could be drawn.

Alkanes were the most abundant species of exhaust VOC emissions from the test trucks, accounting for 57.2–80.0% of the total VOCs. Specifically, dodecane, n-undecane, decane, naphthalene and acetone were the top five species. The total VOC emissions decreased significantly as the emission standards tightened. EFs of LDDT‐2 (Euro IV) and LDDT-3 (Euro V) had reductions of 42.3% and 67.3% in related to LDDT-1 (Euro III). The reductions were mainly alkanes. Driving conditions had a great impact on the VOC emissions. VOC EFs on the highway were much lower than those on urban roads due to the sharp decrease of alkanes and aromatics. However, no consistent trend was found in the carbon number distribution of the VOC species between highway and urban conditions. The majority contributors of OFP were aromatics, accounting for 49.3–57.6% of the total OFPs. Naphthalene, 1-butene, dodecane, 1,2,3-trimethylbenzene, 2-propenal, 1,2,4-trimethylbenzene and 3-ethyltoluene were the dominant species in the photochemical ozone formation process. Priority measures should be taken to reduce VOCs with high MIR values, such as aromatics and alkenes.

The results of this study may provide insights into the VOC emission characteristics of diesel fleets, which will help decision makers drafting emission related policies. It should be noted that limited trucks were tested, which may not be sufficient for reflecting the general emission characteristics of diesel trucks. More studies should be conducted to validate the emission characteristics in further studies.

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References

  1. 1.
    R. Zhu, J. Hu, X. Bao, L. He, Y. Lai, L. Zu, Y. Li and S. Su, Transp. Res. Part D: Transp. Environ., 2017, 50, 305 LINK https://doi.org/10.1016/j.trd.2016.10.027
  2. 2.
    S. Chen, X. Zheng, H. Yin and Y. Liu, Transp. Res. Part D: Transp. Environ., 2020, 79, 102208 LINK https://doi.org/10.1016/j.trd.2019.102208
  3. 3.
    R. G. Derwent, M. E. Jenkin, S. R. Utembe, D. E. Shallcross, T. P. Murrells and N. R. Passant, Sci. Total Environ., 2010, 408, (16), 3374 LINK https://doi.org/10.1016/j.scitotenv.2010.04.013
  4. 4.
    P. J. Ziemann and R. Atkinson, Chem. Soc. Rev., 2012, 41, (19), 6582 LINK https://doi.org/10.1039/c2cs35122f
  5. 5.
    R. Suarez-Bertoa, A. A. Zardini, S. M. Platt, S. Hellebust, S. M. Pieber, I. El Haddad, B. Temime-Roussel, U. Baltensperger, N. Marchand, A. S. H. Prévôt and C. Astorga, Atmos. Environ., 2015, 117, 200 LINK https://doi.org/10.1016/j.atmosenv.2015.07.006
  6. 6.
    K. Zhang and S. Batterman, Sci. Total Environ., 2013, 450–451, 307 LINK https://doi.org/10.1016/j.scitotenv.2013.01.074
  7. 7.
    X. Hao, X. Zhang, X. Cao, X. Shen, J. Shi and Z. Yao, Sci. Total Environ., 2018, 645, 347 LINK https://doi.org/10.1016/j.scitotenv.2018.07.113
  8. 8.
    F. Golkhorshidi, A. Sorooshian, A. J. Jafari, A. N. Baghani, M. Kermani, R. R. Kalantary, Q. Ashournejad and M. Delikhoon, Atmos. Pollut. Res., 2019, 10, (3), 921 LINK https://doi.org/10.1016/j.apr.2018.12.020
  9. 9.
    H. Huo, Z. Yao, Y. Zhang, X. Shen, Q. Zhang, Y. Ding and K. He, Atmos. Environ., 2012, 49, 371 LINK https://doi.org/10.1016/j.atmosenv.2011.11.005
  10. 10.
    Q. Zhang, L. Wu, X. Fang, M. Liu, J. Zhang, M. Shao, S. Lu and H. Mao, Sci. Total Environ., 2018, 624, 878 LINK https://doi.org/10.1016/j.scitotenv.2017.12.171
  11. 11.
    J. Wang, L. Jin, J. Gao, J. Shi, Y. Zhao, S. Liu, T. Jin, Z. Bai and C.-Y. Wu, Sci. Total Environ., 2013, 445–446, 110 LINK https://doi.org/10.1016/j.scitotenv.2012.12.044
  12. 12.
    L. Li, Y. Ge, M. Wang, Z. Peng, Y. Song, L. Zhang and W. Yuan, Sci. Total Environ., 2015, 502, 627 LINK https://doi.org/10.1016/j.scitotenv.2014.09.068
  13. 13.
    X. Cao, Z. Yao, X. Shen, Y. Ye and X. Jiang, Atmos. Environ., 2016, 124, (Part B), 146 LINK https://doi.org/10.1016/j.atmosenv.2015.06.019
  14. 14.
    G. Wang, S. Cheng, J. Lang, S. Li and L. Tian, J. Environ. Sci., 2016, 46, 28 LINK https://doi.org/10.1016/j.jes.2015.09.021
  15. 15.
    K. Na, J. Environ. Manage., 2006, 81, (4), 392 LINK https://doi.org/10.1016/j.jenvman.2005.11.004
  16. 16.
    C.-S. Lim, J.-H. Lim, J.-S. Cha and J.-Y. Lim, J. Environ. Manage., 2019, 239, 103 LINK https://doi.org/10.1016/j.jenvman.2019.03.039
  17. 17.
    H. Liu, K. He, J. M. Lents, Q. Wang and S. Tolvett, Environ. Sci. Technol., 2009, 43, (24), 9507 LINK https://doi.org/10.1021/es902044x
  18. 18.
    C. Huang, D. Lou, Z. Hu, Q. Feng, Y. Chen, C. Chen, P. Tan and D. Yao, Atmos. Environ., 2013, 77, 703 LINK https://doi.org/10.1016/j.atmosenv.2013.05.059
  19. 19.
    T. Grigoratos, G. Fontaras, B. Giechaskiel and N. Zacharof, Atmos. Environ., 2019, 201, 348 LINK https://doi.org/10.1016/j.atmosenv.2018.12.042
  20. 20.
    Z. Yao, X. Shen, Y. Ye, X. Cao, X. Jiang, Y. Zhang and K. He, Atmos. Environ., 2015, 103, 87 LINK https://doi.org/10.1016/j.atmosenv.2014.12.028
  21. 21.
    “China Mobile Source Environmental Management Annual Report”, Ministry of Ecology and Environment of People’s Republic of China, Beijing, China, 2019 (in Chinese)
  22. 22.
    “Method TO-15: Determination of Volatile Organic Compounds (VOCs) in Air Collected in Specially-Prepared Canisters and Analyzed by Gas Chromatography/Mass Spectrometry (GC/MS)”, US EPA, Washington, USA, 1999 LINK https://www.epa.gov/sites/production/files/2019-11/documents/to-15r.pdf
  23. 23.
    M. Wang, S. Li, R. Zhu, R. Zhang, L. Zu, Y. Wang and X. Bao, Atmos. Environ., 2020, 223, 117294 LINK https://doi.org/10.1016/j.atmosenv.2020.117294
  24. 24.
    J. Sun, Z. Shen, Y. Zhang, Z. Zhang, Q. Zhang, T. Zhang, X. Niu, Y. Huang, L. Cui, H. Xu, H. Liu, J. Cao and X. Li, Environ. Sci. Pollut. Res., 2019, 26, (27), 27769 LINK https://doi.org/10.1007/s11356-019-05950-0
  25. 25.
    W. P. L. Carter, Air Waste, 1994, 44, (7), 881 LINK https://doi.org/10.1080/1073161x.1994.10467290
  26. 26.
    P. K. K. Louie, J. W. K. Ho, R. C. W. Tsang, D. R. Blake, A. K. H. Lau, J. Z. Yu, Z. Yuan, X. Wang, M. Shao and L. Zhong, Atmos. Environ., 2013, 76, 125 LINK https://doi.org/10.1016/j.atmosenv.2012.08.058
  27. 27.
    B. Li, S. S. H. Ho, Y. Xue, Y. Huang, L. Wang, Y. Cheng, W. Dai, H. Zhong, J. Cao and S. Lee, Atmos. Environ., 2017, 161, 1 LINK https://doi.org/10.1016/j.atmosenv.2017.04.029
  28. 28.
    W. P. L. Carter, “Development of the SAPRC-07 Chemical Mechanism and Updated Ozone Reactivity Scales”, Contracts No. 03-318, 06-408, and 07-730, California Air Resources Board, Sacramento, USA, 27th January, 2010, 396 pp LINK https://intra.engr.ucr.edu/~carter/SAPRC/saprc07.pdf
  29. 29.
    Q. Zhang, J. Fan, W. Yang, B. Chen, L. Zhang, J. Liu, J. Wang, C. Zhou and X. Chen, J. Air Waste Manage. Assoc., 2017, 67, (7), 814 LINK https://doi.org/10.1080/10962247.2017.1301275
  30. 30.
    Z. H. Zhang, C. S. Cheung, T. L. Chan and C. D. Yao, Sci. Total Environ., 2010, 408, (4), 865 LINK https://doi.org/10.1016/j.scitotenv.2009.10.060
  31. 31.
    L. Zhu, C. S. Cheung, W. G. Zhang, J. H. Fang and Z. Huang, Fuel, 2013, 113, 690 LINK https://doi.org/10.1016/j.fuel.2013.06.028
  32. 32.
    Y. S. Tadano, G. C. Borillo, A. F. L. Godoi, A. Cichon, T. O. B. Silva, F. B. Valebona, M. R. Errera, R. A. Penteado Neto, D. Rempel, L. Martin, C. I. Yamamoto and R. H. M. Godoi, Sci. Total Environ., 2014, 500–501, 64 LINK https://doi.org/10.1016/j.scitotenv.2014.08.100
  33. 33.
    A. Choudhary and S. Gokhale, Transp. Res. Part D: Transp. Environ., 2016, 43, 59 LINK https://doi.org/10.1016/j.trd.2015.12.006
  34. 34.
    S. Mahesh, G. Ramadurai and S. M. Shiva Nagendra, Sustain. Cities Soc., 2018, 41, 104 LINK https://doi.org/10.1016/j.scs.2018.05.025
  35. 35.
    S. Jung, S. Mun, T. Chung, S. Kim, S. Seo, I. Kim, H. Hong, H. Chong, K. Sung, J. Kim and Y. Hong, Aerosol Air Qual. Res., 2019, 19, (2), 431 LINK https://doi.org/10.4209/aaqr.2018.05.0195
  36. 36.
    I. J. George, M. D. Hays, R. Snow, J. Faircloth, B. J. George, T. Long and R. W. Baldauf, Environ. Sci. Technol., 2014, 48, (24), 14782 LINK https://doi.org/10.1021/es502949a
  37. 37.
    J.-H. Tsai, S.-Y. Chang and H.-L. Chiang, Atmos. Environ., 2012, 61, 499 LINK https://doi.org/10.1016/j.atmosenv.2012.07.078
  38. 38.
    I. Caplain, F. Cazier, H. Nouali, A. Mercier, J.-C. Déchaux, V. Nollet, R. Joumard, J.-M. André and R. Vidon, Atmos. Environ., 2006, 40, (31), 5954 LINK https://doi.org/10.1016/j.atmosenv.2005.12.049
  39. 39.
    Q. Lu, Y. Zhao and A. L. Robinson, Atmos. Chem. Phys., 2018, 18, (23), 17637 LINK https://doi.org/10.5194/acp-18-17637-2018
  40. 40.
    H. Wang, S. Jing, S. Lou, Q. Hu, L. Li, S. Tao, C. Huang, L. Qiao C. Chen, Sci. Total Environ., 2017, 607–608, 253 LINK https://doi.org/10.1016/j.scitotenv.2017.07.001
  41. 41.
    T. D. Durbin, X. Zhu and J. M. Norbeck, Atmos. Environ., 2003, 37, (15), 2105 LINK https://doi.org/10.1016/s1352-2310(03)00088-8
  42. 42.
    V. Bermúdez, J. M. Lujan, B. Pla and W. G. Linares, Biomass Bioenergy, 2011, 35, (2), 789 LINK https://doi.org/10.1016/j.biombioe.2010.10.034

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51808507); the National Key R&D Program of China (No. 2017YFC0212400); National Engineering Laboratory for Mobile Source Emission Control Technology, China (No. NELMS2018A16); the China Postdoctoral Science Foundation (No. 2018M632794); and the University Key Research Project of He’nan Province, China (No. 19A150010). The authors declare no conflict of interest.

The Authors


Menglei Wang received a bachelor’s degree from Central South University in China in 2017 and is currently studying for a master’s degree at Zhengzhou University, China. His research interests include the characteristics of automobile exhaust emissions.


Rencheng Zhu received his PhD from Nanjing University of Aeronautics and Astronautics, China, in 2017 and currently serves as an Associate Professor at Zhengzhou University. His research interests include the governance of VOCs and the characteristics of automobile exhaust emissions.


Ruiqin Zhang received her PhD from University of Tsukuba, Japan, and currently is Deputy Dean of the Institute of Environmental Science of Zhengzhou University, director of Henan Key Laboratory of Environmental Chemistry and Low-Carbon Technology. Her research interests include atmospheric environmental pollution and biomass energy.


Shunyi Li received his PhD from Sun Yat-sen University, China, in 2005. He is currently an Executive Director of the Henan Environmental Protection Federation and a Professor at Zhengzhou University. His research interests include the management of VOCs and the management of odorous gases.


Xiaofeng Bao is a research professor and national certified engineer on environmental protection, the director of Vehicle Emission Control Center (VECC) of Ministry of Environmental Protection of the People’s Republic of China (MEP), a deputy chief engineer of Chinese Research Academy of Environmental Sciences (CRAES) and the principal scientist of Mobile Source Emission Control Research Division of CRAES. His research interest concentrates on vehicle pollution prevention and control.

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