Gaseous and particulate emissions from non-road vehicles
The concentrations of CO, NOx, THC, PM2.5, PM10 and TPM were monitored for better understanding the characteristics of non-road vehicles. The gas pollutant concentrations of THC, NOx, CO of the three non-road vehicles were shown in Fig. 1. Concentrations of THC, NOx and CO were 12−87, 110−202 and 110−420 ppm, respectively, for E1; while the concentrations were 11−40, 155−375, and 210−850 ppm for B1. As to B2, the concentrations of THC, NOx and CO were 62−1860, 89−1100, 9−901 ppm, individually, that were slightly higher than the E1 and B1 due to the higher horsepower operation. The NOx emission from bulldozers were 89−1100 ppm which was close to the result of previous study (202−706 ppm) [21]. High NOx concentration was found in B2 that may be due to high exhaust gas temperature (100 ~ 350 oC) [22]. CO is emitted during engine start-up and continuous acceleration and lots of peak were found from three vehicles. Much high THC was measured from B2 due to by insufficient temperature near the engine cylinder wall [23].
The particulate matter concentrations of TPM, PM10 and PM2.5 emitted from the three non-road vehicles are shown in Fig. 2. Concentrations of TPM, PM10 and PM2.5 were 36−193, 17−152 and 14−113 mg/Nm3 for E1; 34−197, 17−163, and 11−102 mg/Nm3 for B1; 14−251, 12−181, 10−148 mg/Nm3 for B2, respectively. In addition, PM10 occupy 60−70% of TPM, while PM2.5 occupy 80−90༅ of PM10. The slightly higher PM emissions were observed in B2 that is attributed to the increased fuel consumed leading to the enhanced PM emission accordingly [24].
Metallic elements
The metallic concentrations from the exhaust of non-road vehicles, as excavator and bulldozers, were listed in Table 2. In idling condition, the total metallic concentrations were 0.523, 2.110, and 11.232, mg/m3 for E1, B1, and B2, respectively. In working condition, the total metallic concentrations were higher than that of idling as 2.711, 7.922, and 22.618 mg/m3 for E1, bulldozer B1, and B2, individually. Those values were similar to the emission concentration of metals (2.1 mg/m3) in diesel engine generator [25]. In addition, the ratio of W/I (working over idling ratio) was 2.01 − 5.12 indicating that higher metallic emissions in working condition because of higher engine load and more fuel consumption [2]. Furthermore, the higher metallic emissions were measured in B2 than that of E1 and B1 in both idling and working conditions, due to the higher non-road vehicle age of B2. Table 2 also shows the metallic concentration from different non-road vehicles at idling and working conditions. The sequence of the major species in the idling were Zn (6.881mg/m3) > Fe (3.825 mg/m3) > Al (0.468 mg/m3), while the sequences were Zn (15.55 mg/m3) > Fe (5.986 mg/m3) > Al (0.885 mg/m3) in the working condition for B2. The abundant elements in the idling were Fe (0.3895 mg/m3), Al (0.122 mg/m3) and Mn (0.0095 mg/m3), while the Zn (1.101 mg/m3), Al (0.868 mg/m3), Fe (0.729 mg/m3) in the working condition from E1. Regarding the B1, the dominant metals were Fe (1.5495 mg/m3), Al (0.4345 mg/m3), Mn (0.0745 mg/m3) and Pb (0.0545 mg/m3) for idling, while the main species were Fe (5.824 mg/m3), Zn (0.976 mg/m3), Al (0.826 mg/m3) and Pb (0.210 mg/m3) in the working condition. Preliminary, Fe, Zn and Al are the most abundant elements in those non-road vehicles that is consistent with the report [2]. Wang et al. (2003) also revealed that the concentration of the crustal elements Fe, Zn and Cu accounted 50% of total elements in diesel fuel [26]. In addition, Zn element may be originated from lubricating oil [27].
Table 2
The metallic concentrations from the non-road vehicles at idling and working conditions
Non-road vehicles
|
E1
|
B1
|
B2
|
mg/m3
|
Idling
|
Working
|
Idling
|
Working
|
Idling
|
Working
|
Zn
|
ND
|
1.101
|
ND
|
0.976
|
6.881
|
15.55
|
Cd
|
0.0015
|
0.002
|
0.0005
|
0.002
|
0.005
|
0.0015
|
Co
|
ND
|
ND
|
ND
|
ND
|
ND
|
ND
|
Cr
|
ND
|
ND
|
0.0005
|
0.0005
|
0.009
|
0.140
|
Cu
|
ND
|
ND
|
ND
|
ND
|
ND
|
ND
|
Fe
|
0.3895
|
0.729
|
1.5495
|
5.824
|
3.825
|
5.986
|
Mn
|
0.0095
|
0.012
|
0.0745
|
0.085
|
0.049
|
0.067
|
Ni
|
ND
|
ND
|
ND
|
ND
|
ND
|
ND
|
Pb
|
ND
|
ND
|
0.0545
|
0.210
|
ND
|
0.113
|
As
|
ND
|
ND
|
ND
|
ND
|
ND
|
ND
|
Al
|
0.122
|
0.868
|
0.4345
|
0.826
|
0.468
|
0.885
|
Total
|
0.523
|
2.711
|
2.110
|
7.922
|
11.232
|
22.618
|
W/I
|
5.12
|
3.75
|
2.01
|
The fraction of metallic elements from three non-road vehicles were shown in Fig. 3. In idling condition, the dominant species were Zn (61.3%), Fe (34.1%), and Al (4.2%) for E1, while the Fe (73.3 − 74.5%), Al (20.6 − 23.3%), and Mn (1.8 − 3.5%) were dominated for B1 and B2. In working condition, the most abundant elements were Zn (68.8%), Fe (26.5%), and Al (3.9%) for E1, while the proportions of Fe, Zn and Al occupied 26.9 − 73.5%, 12.3 − 40.6%, and 10.4 − 32.0%, respectively, for B1 and B2. Cui et al. [2] measured the exhausts for excavator and found that Fe, Zn, Cu were the most abundant elements. Previous studies have shown that Fe, Zn and Cu concentrations account for 50% of the total elements in diesel fuel [26].
Emission Factors
The power-based emission factors (EFs), expressed as mass rate emitted per brake engine power (g/bhp-hr), are listed in Table 1. The emission factors of CO from three non-on road vehicles ranged from 1.84 to 5.77 g/bhp-hr, which is lower than the emission standard of 10 g/bhp-hr for diesel and alternative clean fuel engine vehicles. The emission factors of NOx ranged from 2.50 to 7.92 g/bhp-hr, where only B1 EF is slightly higher than the standard of 5.0 g/bhp-hr. The emission factors of HC ranged from 0.13 to 2.92 g/bhp-hr, where only B2 EF is slightly higher than the standard of 1.3 g/bhp-hr. The EFs of three gas pollutants in this study are similar to the result from diesel locomotives that illustrating the EFs of 14.00 ± 10.44, 2.59 ± 5.15, and 1.05 ± 1.84 g/bhph for NOx, CO, and THC, respectively [28].
The TPM emission factors of three non-on road vehicles ranged from 0.64 to 0.94 g/bhp-hr, that is much higher than the diesel and alternative clean fuel engine vehicles standard of 0.1 g/bhp-hr, but lower than the PM EF (2.29 ± 2.06 g/bhph) from diesel locomotives [28]. Notably, the EFs were 0.53−0.79 and 0.32−0.49 g/bhp-hr for PM10 and PM2.5, respectively. In general, larger engines such as the bulldozer presented higher values of the emission factors than the smaller engines. In particular, the emission factor of TPM was high and need to continuous to monitor.
Simulations of the impact of non-road vehicles on ambient PM2.5
ISCST model was used to investigate the impact of emissions of non-road vehicles on atmospheric quality. The calculated PM2.5 concentration in different atmospheric stability was listed in Table 3. At atmospheric stability of B (unstable class), the pollutants could transport to downwind location 270 m away from the pollution source. The PM2.5 concentration was calculated as 2.5×10−4 µg/m3 at 90 m from source, while the max ground level concentration was 3.2 ×10−4 µg/m3. At atmospheric stability of C (unstable class), the calculated PM2.5 concentration was 6.0×10−4 µg/m3 at 60 m away from pollution source, while the max ground level concentration was 6.8 ×10−4 µg/m3. At D class with neutral air condition, the simulated PM2.5 concentration was 8.0×10−3 µg/m3 at 60 m away from pollution source, while max ground level concentration reached 1.3 ×10−2 µg/m3. At atmospheric stability of E (stable class), the PM2.5 concentration was 9.0×102 µg/m3, which is over air quality standards (35 µg/m3) at 60 m away from sources and the max ground level concentration reached to 1.8 ×103 µg/m3. According to the above results, the atmospheric stability plays an important role on ambient PM2.5 concentration. This phenomenon is similar to the higher PM concentrations were observed at ground-level in nighttime due to less turbulent [12]. Zafra-Mejía et al., (2020) also indicated that the PM10 concentrations were associated the highest degree of daytime atmospheric instability [29]. Furthermore, the enhancement of PM concentration under stable conditions depend on the distance between the source and the observation point [30]. Zoras et al. [31] illustrated that the PM10 worst-case episode is more likely to happen under neutral D to stable atmosphere F at the sampling station.
Table 3
The calculated PM2.5 concentration at 60 and 90 m away from three non-road vehicles at different atmospheric stability
Atmospheric stability
|
Max Con.
|
Con. at
60 m
|
Con. at
90 m
|
B
|
3.2⋅10−4
|
|
2.5⋅10−4
|
C
|
6.8⋅10−4
|
6.0⋅10−4
|
|
D
|
1.3⋅10−2
|
8⋅10−4
|
|
E
|
1.8⋅103
|
9.0⋅102
|
|
Unit: µg/m3 |
Health Risk Assessment
Health risk assessment of emissions of non-road vehicles were investigated according to previous ISCST results. The life-time average daily dose (LADD) and the average daily dose (ADD) from different metals at different downwind locations was listed in Table 4. The highest LADD and ADD were observed in Zn as three non-road vehicle operating simultaneously, followed by Fe, Al, Pb, Mn, Cr, and Cd. The LADD of Zn were 145, 126, 63 and 0 (10− 6 mg/kg-day) from the distance of vehicle sources at 0, 30, 90, and 150 m, respectively, while the ADD of Zn were 290, 252, 63 and 8 (10− 6 mg/kg-day). The LADD and ADD value of other metals also showed that those value decreased with increasing distance from the vehicle sources.
Table 4 The life-time average daily dose (LADD) and the average daily dose (ADD) from different metals at different downwind locations
Metals
|
LADD
|
ADD
|
LADD
|
ADD
|
LADD
|
ADD
|
LADD
|
ADD
|
0 m
|
|
30 m
|
|
90 m
|
|
150 m
|
|
Zn
|
145
|
290
|
126
|
252
|
63
|
63
|
0
|
8
|
Cd
|
0.014
|
0.028
|
0.11
|
0.024
|
0.007
|
0.011
|
0
|
0
|
Cr
|
0.13
|
0.261
|
0.11
|
0.225
|
0.06
|
0.112
|
0
|
0.026
|
Fe
|
54
|
108
|
47
|
96
|
38
|
37
|
6
|
7
|
Mn
|
0.766
|
1.532
|
0.628
|
1.256
|
0.129
|
0.606
|
0.034
|
0.083
|
Pb
|
1.89
|
3.78
|
1.55
|
3.09
|
1.37
|
1.49
|
0.029
|
0.11
|
Al
|
7.81
|
15.6
|
7.01
|
14.0
|
4.35
|
9.91
|
0.45
|
0.91
|
Unit: 10-6 mg/kg-day
Table 5 lists the carcinogenic risk of Cd and Pb at different downwind locations. Total carcinogenic risk of Cd and Pb decreased from 0.094×10−6 to 0.013×10−6 by increasing the distances from three non-road vehicles. The most cancer risk was attributed to Pb (> 99.9%). The results showed that the two selected elements had the carcinogenic risk lower than lifetime cancer risk (1.0×10−6). The results in this study were in consistent with previous report showing that the cancer risk of Pb is relatively low (< 1.0×10−6) for the subway passengers through inhalation [32]. Early report showed that the carcinogenic risk posed by Pb, and Cd in PM2.5 for children and adults at all the location were lower than the acceptable tolerance value 1.0×10−6 − 1.0×10−4 [33]. As for non-carcinogenic risk of Cd and Pb decreased from 3,243×10−5 to 0 by increasing the distances from three non-road vehicles. The results showed that the two selected elements had the non- carcinogenic risk within the limits (HQ = 1), which is considered an acceptable risk. In Seoul, HQ level were not significant in subway passengers through inhalation [32]. In addition, the HQ values of metallic elements, such as Cd and Pb in PM2.5 were less than one for both children and adults along the road network of Dhanbad, India [33]. Above results revealed that the carcinogenic risk and non-carcinogenic risk from three working vehicles were lower than the threshold which is no significant health effects on the surrounding residents.
Table 5
The carcinogenic risk of Cd and Pb at different downwind distances
|
0 m
|
30 m
|
90 m
|
150 m
|
Cd
|
6⋅10−12
|
5⋅10−12
|
2⋅10−12
|
6⋅10−12
|
Pb
|
0.094⋅10−6
|
0.085⋅10−6
|
0.037⋅10−6
|
0.013⋅10−6
|
total
|
0.094⋅10−6
|
0.085⋅10−6
|
0.037⋅10−6
|
0.013⋅10−6
|
TAPM simulation for the evaluation of PM2.5 contribution from non-road vehicles
The Air Pollution Model (TAPM) was employed to evaluate the contribution of non-road vehicles on PM2.5 concentration. Figure 3 shows the simulated wind vectors and the concentration contours of PM2.5 at AM 09:00 and PM 14:00, respectively, on 29 November 2018. The prevailing winds were north and east with a wind speed of 0.5−1 m/s at AM 9:00 and 5−6 m/s at PM 14:00 in the studied domain. Figure 3 shows the PM2.5 concentration were high (55 − 82 µg/m3) close to the coastal area, where several industrial processes are located, but were low (11 − 33 µg/m3) in mostly rural area. Figure 4 compares the three-day hourly simulations of surface PM2.5 concentration with the measurements at sampling site. The simulations generally agree well with the measurements, with a correlation coefficient of R = 0.61, and an index of agreement (IOA) = 0.77. The agreement between prediction and measurement is regard as good when IOA exceeds 0.5 [13, 15]. Wang et al. [18] applied TAPM to simulate the PM10 concentration with a comparison of IOA = 0.52 − 0.76, indicating a generally good agreement. Figure 5 presents the simulation of the incremental impact of non-road vehicles on ambient PM2.5 concentration at different scenarios, i.e., three, five, and seven non-road vehicles operated simultaneously. Three non-road vehicles increased PM2.5 by 22.52 µg/m3, with an increment percentage of about 1.1%, while 5 and 7 non-road vehicles increased PM2.5 by 105.1 µg/m3 and 177.33 µg/m3, respectively. The incremental percentages are approximately 5.0% and 8.5%. Therefore, the ambient PM2.5 concentration enhanced by increasing the number of non-road vehicles. Previous study reported that the same lane and street, the average PM2.5 concentration during the on-peak period for the number of motor vehicles is approximately 2.2–2.5 and 69.5–77.1 times higher than those during the flat peak and low ebb periods, respectively [34]. In addition, PM2.5 emission levels of motor vehicles on normal weekdays were overall higher than those on weekends [35]. In short, the number of vehicles is large at the peak period and weekdays, it contributed more PM2.5 concentration in ambient air.