1. Introduction
Road traffic is a major source of PM in urban air [
1,
2,
3]. However, the quantitative evaluation of its contribution to airborne PM concentrations encounters significant challenges, and the contribution of traffic emissions to air pollution has always been different in the literature. Different approaches have been employed to characterize road traffic emissions [
1,
4,
5]. Research that is based on emission models is common in China. According to the estimations by Wang et al. [
6] in Beijing, the ratio of PM
10 emission factors from road dust (EF
d) to the emission factors from vehicle exhaust (EF
e) in 2012 ranged from 19.2 to 184.1, depending on the vehicle type. In 2004, road dust emissions in Shanghai occurred at nearly 146.7 times the rate of PM
10 from the vehicle’s exhaust according to a PM
10 emissions inventory that was compiled in a previous study [
7], in which the authors used the Compilation of Air Pollutant Emission Factors (Ap-42) to evaluate road dust emissions. However, the road dust emissions in this study may have been overestimated because the AP-42 data are based on measurements near dusty roads [
8]. EF
d derived from field measurements in other countries has not been shown to be significantly greater than EF
e. Harrison et al. [
9] reported that vehicle-induced resuspension provided a source of strength for road dust (represented by PM
2.5-10) approximately equal to that of exhaust emissions (represented by PM
2.5) at a roadside in London. According to positive matrix factorization outputs, the PM
10 EF
d to EF
e ratios for LDV and HDV in an urban street canyon in Weststrasse, Zürich were 0.1 and 1.7, respectively [
10]. In addition, Abu-Allaban et al. [
11] reported that the EF
d to EF
e ratios for PM
10 varied from 1.0 to 18.8 for heavy-duty diesel vehicles and varied from 0.7 to 45.8 for light-duty spark ignition vehicles in Reno when estimated using the chemical mass balance. These studies suggest that the measured emission factors on real roads significantly vary from the results estimated by AP-42. In addition to the difference of applied approaches, the differences of ratios between China and other countries also resulted from many other factors such as the climate and road conditions [
12,
13,
14].
Current research has shown that back-calculations based on concentration measurements can be used to assess emission factors or source strength [
15,
16,
17,
18]. Primary back-calculation methods include dispersion models [
17,
19] and receptor models [
20,
21,
22]. The tracer method has been one of the most popular methods used to back-calculate traffic-related emissions [
5,
10,
23,
24]. Gas tracers such as NOx, CO
2, and SF
6 have been commonly used in studies, whereas reference emission factors for these tracers are typically estimated by emission models. As a result, the accuracy of the emission model is critical to the accuracy of the derived PM
10 emission factors. Therefore, the use of the tracer method is not recommended if no proper emission model exists for the tracer. This study addressed back-calculations using dispersion models because dispersion models can be easily modified for use in other locations compared with emission models. Several dispersion models are available for mobile sources, including American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD), California Puff Model (CALPUFF), Advanced air dispersion model (ADMS) [
25]; Highway air pollution model (HIWAY), Third California Line Source Dispersion Model with queuing and hot spot calculations (CAL3QHC) [
26,
27]; Fourth California Line Source Dispersion Model (CALINE4) [
28]; and Operational Street Pollution Model (OSPM) [
29].
However, research that employs back-calculations has often failed to estimate emission factors for roadside emissions or has yielded unclear results. For example, Thorpe et al. [
5] reported that roadside PM
10 concentrations were lower than background concentrations in some cases. Bukowiecki et al. [
10] needed to omit many unqualified samples to ensure the accuracy of their results. The PM
2.5 emission factors obtained by Ferm et al. [
30] contained great uncertainties as a result of the small PM
2.5 concentration increments at the roadside. Generally, uncertainties in the back-calculation of emission factors or source strengths come from measurement and modeling errors [
31,
32,
33]. To estimate traffic-related PM
10 emission factors, the ideal concentration increments must be obtained. Particles in urban air come from not only road transport, but also many other sources. Concentration increments must be large enough to ensure the accuracy of the back-calculation [
17,
34]. Therefore, factors such as the location of the background site, traffic volume, and the diffusion conditions must be considered in the design of any experiment. The impact of these factors has been revealed in previous studies, although this has not been deliberately examined. Many researchers have tended to select background sites near their location of interest. For example, the background site in the study performed by Ketzel et al. [
35] was established approximately 500 m from the sampling site. Bukowiecki et al. [
10] set their background site approximately 600 m from the sampling site and only kept those samples whose roadside NOx increments were greater than 20 μg/m
3. Generally, a nearby site is always superior, unless it is excessively biased. For example, Thorpe et al. [
5] found that a greater number of negative roadside PM
10 increments appeared when a nearby background site was used. Theoretically, concentration increments are sensitive to road structure, traffic volume, and meteorological conditions. Wang et al. [
6] measured PM
10 concentrations in a street canyon, an open road, and an intersection in Beijing; the PM
10 levels in the street canyon were often significantly greater than those along the open road and in the intersection, while the traffic volume in the street canyon was less than half that along the open road. Previous studies have back-calculated traffic-related emission factors for both open roads and street canyons. In measurements carried out by Abu-Allaban et al. [
36], the PM
10 levels in a street canyon were found to be higher than those on an open road, while the vehicle volume in the street canyon was lower than that on the open road. Bukowiecki et al. [
10] carried out measurements on a freeway and in a street canyon. The freeway was an open road with a large traffic volume of approximately 2083 veh/h, whereas the street canyon was a narrow, occlusive road with only 833 veh/h. Ketze et al. [
35] and Amato et al. [
23] also examined two street canyons, which registered 1083 veh/h and 792 veh/h, respectively. Conducting measurements in a street canyon appears to support the measurement of high concentration increments under a low traffic volume due to the occlusion of the street canyon. Nevertheless, there is still a dearth of knowledge related to the experimental conditions necessary for successful back-calculations.
In 2014, shortly before this study was conducted, the Ministry of Environmental Protection of China (MEP) released two technical guides for compiling emission inventories of air pollutants associated with road transport. Two emission models were provided to estimate the EF
d and EF
e of PM
10. Compared with exhaust emissions, particles from road dust are more difficult to handle in emission modelling for many reasons [
37]. Previous studies have shown that particles from non-exhaust emissions are the predominant source of PM at roadside locations [
38,
39], whereas the value of dust emission factors was extremely variable. For example, Rauterberg-wulff et al. [
40] discovered that the Swedish Meteorological and Hydrological Institute model (SMHI) [
41] predicted significantly lower emission factors for road dust than the emission factors obtained by the AP-42 method; Gustafsson et al. [
37] summarized the studies of resuspension PM emission factors related to roads. The available studies and emission models provide emission factors that vary over several orders of magnitude, from less than 100.0 mg/km to several thousand mg/km for passenger cars, and a maximum of several tens of thousands mg/km for HDV; Nicholson et al. [
42] obtained an estimated PM
10 value for resuspended material in the UK of 40.0 mg/km for the vehicle fleet composition; and Luhana et al. [
43] recorded significantly lower PM
10 emission factors for resuspension: 0.8 mg/km for LDV and 14.4 mg/km for HDV in the UK based on measurements in a tunnel. Therefore, these emission models, especially the dust emission model, need to be examined. However, few published papers have employed emission models released by the MEP to calculate the emission factors.
In this study, traffic-related PM10 emission factors were estimated by both emission models and back-calculation based on roadside concentration measurements, and the results of these two methods were examined by a dispersion simulation. The back-calculation of emission factors was investigated to obtain additional information related to the experimental conditions that are necessary to perform successful back-calculations.
4. Conclusions
In this study, traffic-related PM10 emission factors for an urban road were back-calculated. The study was performed using data from a street canyon to ensure relatively high concentration increments from local road transportation. Samples collected on clean days were used to successfully back-calculate emission factors. Background concentrations from the background site were not representative of the background concentrations in the street canyon on polluted days. Therefore, the samples collected on polluted days were not utilized in our final back-calculation. Road traffic emission factors were back-calculated using samples from clean days. The mean value of the obtained EFf was 0.138 g/km. The specific emission factors for LDV, MDV, HDV, and motorcycles were approximately 0.121, 0.427, 0.445, and 0.096 g/km, respectively. The EFf,ne value was approximately 0.121 g/km, indicating that road dust was responsible for 87.7% of the concentration increment at the sampling site. The back-calculated emission factors were found to be consistent with observed emission levels on the road, as validated using the reserved samples. The back-calculated emission factors were much smaller than the modeled emission factors. Emission models released by the MEP may have overestimated the road traffic emission levels. Actually, wear emissions were not included in the emission models. The modeled emission factor will be greater if the wear is taken into account.
It is not recommended to measure the PM10 concentrations on polluted days when using the back-calculation method to experimentally determine traffic-related emission factors because there are more errors in the background concentrations on polluted days. The results showed that the success rate of back-calculations could be enhanced by improving the experimental design of a study, particularly by choosing an appropriate background site and optimizing the meteorological conditions under which samples are taken. Some models such as CFD can be carried out to present an indication of the potential appropriate background site and favorable weather conditions before the field measurements are conducted. In addition, the measurements in this study are relatively limited. Therefore, the back-calculation method needs to be tested on more types of streets and roads where the road and climate conditions may be significantly different in the future.