# Determination of gaseous and particulate emission factors from road transport in a Middle Eastern capital

Charbel Abdallah, C. Afif, S. Sauvage, Agnès Borbon, T. Salameh, A. Kfoury, T. Leonardis, C. Karam, P. Formenti, J.F. Doussin, et al.

► To cite this version:

Charbel Abdallah, C. Afif, S. Sauvage, Agnès Borbon, T. Salameh, et al. Determination of gaseous and particulate emission factors from road transport in a Middle Eastern capital. Transportation Research Part D: Transport and Environment, 2020, 83, pp.102361. 10.1016/j.trd.2020.102361. hal-02913603

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# Determination of Gaseous and Particulate Emission Factors from Road Transport in a Middle Eastern capital

Abdallah, C.<fnref n="1" />,<fnref n="2" />, Afif, C.*<fnref n="1" />,<fnref n="3" />, Sauvage, S.<fnref n="4" />, Borbon, A.<fnref n="5" />, Salameh, T.<fnref n="4" />, Kfoury, A.<fnref n="6" />, Leonardis, T.<fnref n="4" />, Karam, C.<fnref n="1" />, Formenti, P.<fnref n="7" />, Doussin, J.F.<fnref n="7" />, Locoge, N.<fnref n="4" />, Sartelet K.<fnref n="2" />

*Corresponding author: charbel.afif@usj.edu.lb

## Abstract

Road transport is a major source of anthropogenic emissions especially in the Middle East where the regulations enforcement is generally poor. This study aims to quantify the Emission Factors (EF) of traffic-related gaseous and particulate pollutants inside the Salim Slam urban tunnel in Beirut, Lebanon. The fuel-based emission factors of measured pollutants were from the carbon mass balance model. The EF determined showed general higher values than those reported in recent studies from European and American countries, even for speciated NMVOC. The average CO and NOx emission factors for the mixed fleet (HDV + LDV) were determined to be 10.52 ± 3.00 g km⁻<fnref n="1" /> and 2.20 ± 0.57 g km⁻<fnref n="1" /> respectively, while the EF for PM₂.₅ 55±27 mg km⁻<fnref n="1" />. Moreover, IVOC species from gaseous phase were reported for the first time in the region. A reduction trend was observed in comparison with the previous tunnel study from Lebanon, however there is still a need to have tougher regulations to control the local practices such as removal of catalytic converter, adjustment of engine parameters for inspection, etc. The comparison of the EF to those calculated through EMEP or IPCC methodologies shows the need to take local practices while establishing national emission inventories.

## Keywords

Emission factor, NMVOC emissions, Tunnel study, Middle East, East Mediterranean, Lebanon

<footnote n="1">¹ Emissions, Measurements, and Modeling of the Atmosphere (EMMA) Laboratory, CAR, Faculty of Sciences, Saint Joseph University, Beirut, Lebanon.</footnote>

<footnote n="2">² Cerea, Joint laboratory école des Ponts ParisTech / EDF R&D, Université Paris-Est, Marne-la-Vallée, France.</footnote>

<footnote n="3">³Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus</footnote>

<footnote n="4">⁴IMT Lille Douai, Univ. Lille, SAGE - Département Sciences de l'Atmosphère et Génie de l'Environnement, 59000 Lille, France</footnote>

<footnote n="5">⁵Laboratoire de Météorologie Physique, CNRS-Université Clermont Auvergne, UMR6016, 63117, Clermont Ferrand, France.</footnote>

<footnote n="6">⁶Department of Environmental Sciences, University of Balamand, Al Kourah, Lebanon</footnote>

<footnote n="7">⁷LISA, UMR CNRS 7583, Université Paris Est Créteil et Université Paris Diderot, Institut Pierre Simon Laplace, Créteil, France.</footnote>
# 1. Introduction

Road transportation is an essential and recurring activity of humans across the globe. The increasing need of short to medium range travels for personal and professional activities has prompted a growth of the number of vehicles across the globe especially in urban areas when public transport is underdeveloped (Waked and Afif, 2012). Since most vehicles currently rely on fuel combustion technologies, road transport exhaust emissions are a major source of pollution at both the global and urban/city scales. They are characterized by a wide variety of toxic emissions (CO₂, CO, NO, NO₂, volatile organic compounds, particulate matter, etc.), affecting air quality and climate, resulting in different health impacts, i.e. higher cancer risk, etc. (Dhaini et al., 2017). In an effort to limit these impacts of road transport, studies in developed countries have focused on identifying the emission factors from the different vehicle categories/models and on implementing reduction strategies. Currently, the European Union (EU) and the United States (US) have been implementing more stringent emissions standards, enforced on both of their locally manufactured vehicles and those imported from abroad; thus, controlling the worldwide evolution of exhaust emissions. Also, in these developed countries, highly accurate data are compiled to quantify emissions and build emission inventories, such as the EMEP guidebook, AP-42, etc. This is not the case in the Middle East in general, and more specifically in Lebanon where the regulations are not being enforced and detailed data related to road transport are not collected systematically by the public authorities to estimate reliable quantities of emissions or determine real world emissions directly (Waked and Afif, 2012).

To quantify the road-transport sector emissions, two kinds of data are needed: 1) Emission Factors (EF) which represent the amount of pollutants released relatively to the sector activity; and 2) Activity Data (AD) which define the magnitude of human activity resulting in emissions over a period of time. The former requires local field studies as a result of the different parameters taken into consideration, and the latter can be estimated using statistical analysis and observations. In the Middle-East, road transport emissions are a major contributor to anthropogenic emissions (Waked and Afif, 2012) and in the absence of local measurements, a common practice to estimate road-transport emissions is to use EF provided for developed countries. Similarly, Waked et al. (2012) applied this technique to develop a national emission inventory over Lebanon and its capital Beirut based on the EMEP Tier 3 method for the on-road transport sector that requires extensive data like vehicle speed,
technologies used in the fleet, etc. but gives more accurate results. Using this national emission inventory, Abdallah et al. (2016, 2018) pointed out the need for up-to-date local emission inventories in air quality studies. In Lebanon, a recent report published in 2017 provided an estimation of road transport emissions for the year 2013 (MoE/UNDP/GEF, 2017). The results suggested that road transport amounted 371.06 Gg of CO and 54.23 Gg NOₓ. Also, in the national emission inventory for the year 2010, Waked et al. (2012) estimated road transport to contribute to 93% of CO emissions, 67% of NMVOC, and 56% of NOₓ. Salameh et al. (2016) reported based on their Positive Matrix Factorization study that the major NMHC sources in Greater Beirut Area were traffic-related emissions (combustion and gasoline evaporation) in winter and in summer accounting for 51 and 74 wt%, respectively. Moreover, the PMF analysis finds reasonable differences on emission rates, of 20–39 % higher than the national road transport inventory (Waked et al. 2012, Waked and Afif, 2012). However, global inventories (ACCMIP, EDGAR, MACCity) underestimate the emissions up to a factor of 10 for the transportation sector (Salameh et al., 2016). Furthermore, air quality data collected within the capital Beirut and Tripoli showed that NO₂, PM₁₀, and PM₂.₅ concentrations exceed the WHO limits over the years (MoE, 2017), and are mainly emitted from road transport (Afif et al., 2009). Additionally, in a recent study of Non-Methane Volatile Organic Compounds (NMVOC) profiles in Lebanon, Salameh et al. (2015) found high levels of the measured NMVOC even exceeding concentrations encountered in the megacities Paris and Los Angeles (Salemeh et al., 2015). Moreover, Salameh et al. (2014) found that local profiles for on-road transport differed greatly from those adopted from EMEP in terms of the potential of ozone formation and Secondary Organic Aerosols (SOA) formation, which led them to conclude that the measurement of specific EF is needed locally for a better representation of the local emissions and atmospheric chemistry. Similarly, the modeling study of Waked et al. (2013) pointed out the need to review the current EF and replace them by local measurements. With the road transport sector being one of the most emitting sectors in Lebanon and the Middle East and the different studies pointing to discrepancies between road transport emissions inventories and reality, better characterizing this sector is essential to simulate air quality and hence decide on policy making and environmental management. Finally, it is noted that EF based on experimental data are very scarce over the Middle-East; the only tunnel study for Lebanon dates back to 2000 (El-Fadel
and Hashisho, 2000). Therefore, a new determination of the local EF from road transport is of
utmost importance to better characterize the sector.

To measure road-transport EF, different approaches exist of which (Franco et al., 2013): (1) dynamometer studies that carry out measurements on individual vehicles for different test cycles using a chassis dynamometer, (2) chase or follower car studies and on-board measurements using online mobile equipment, (3) roadside measurement also called remote sensing method and tunnel studies where an averaged EF of the passing fleet is calculated from the measurements made by roadside stations or inside tunnels.

Consequently, the TRANSEM (Transport and Emissions: the case of Beirut) project aimed at determining the emission factors of the Lebanese fleet by using the tunnel method. Indeed, this method allows the source to be very dominant and since it is applied in a confined space within tunnels, it provides an environment with a very limited photolysis activity in the absence of sunlight, consequently allowing the capture of accurate primary emissions of PM and VOC. The importance of such studies is that they provide an overview of the on-road transport emissions under real-world conditions and cover not only exhaust emissions but the ones from tire and brake wears, etc. In this case, category specific EF might only be determined if multiple bores exist for the different vehicle categories (Geller et al., 2005).. However, since tunnels are travelled at almost constant speed, tunnel studies are not considered as perfect real-world observations (El-Fadel and Hashisho, 2001; Franco et al., 2013). The main pollutants from road transport (CO, NOx, PM2.5, a selection of NMVOC) were measured in the Salim Slam tunnel in Beirut (Lebanon), and the local EF have then been calculated and compared with those of other countries and/or those used in emission inventories.

**2. Material and Methods**

**2.1. Tunnel description**

The Salim Slam Tunnel is 526 m long and is located in a very dense residential and commercial area at the heart of the capital of Lebanon, Beirut. This tunnel connects the administrative center of the capital to Beirut International Airport, and it is considered a major route for road trips between the northern and southern regions of the country. It has an average annual daily traffic (AADT) of around 90 000 vehicles based on the counting
campaign results for the year 2010. It is one of the three main entrances of the capital; thus, it
is considered as an appropriate site to characterize the national and regional on-road traffic
emissions. The tunnel consists of one bi-directional bore with four lanes and a concrete
barrier at the middle separating both directions. It is equipped with a ventilation system
which was shut-down during the whole sampling period. The average speed within the tunnel
was varied during the campaign from 10 to around 70 km h⁻¹ with an average of 30 km h⁻¹.
Measurements inside the tunnel were carried out at an emergency parking area located in the
middle of the tunnel, while background measurements, where carried out outside the tunnel.
The sampling was performed between 8 a.m. and 6 p.m. from July 17 to 21, 2014.

**2.2. Experimental strategy**

Various strategies exist to measure EF inside tunnels all relying on the mass balance model (simple box model) whether considering Pierson’s method (Pierson and Brachanzek, 1983, Pierson et al., 1996) where measurements are carried out at two points inside the tunnel, mainly the entrance and the exit, or one point measurement where one monitoring station is located inside the tunnel and the second one outside to report on background concentrations entering the box (Ait-Helal et al., 2015, Kirchstetter et al., 1999). This study considered the latter method where identical measuring instruments for NMVOC and PM<sub>2.5</sub> were installed inside and outside the tunnel, whereas a mobile station measured alternatively CO, CO<sub>2</sub>, and NO<sub>x</sub> at both observation sites due to the lack of the another set of analyzers. Hence, CO/CO<sub>2</sub> and NO<sub>x</sub> measurements were carried out alternatively at both observation sites (inside and outside the tunnel using a mobile station) with a 30-minute stop at each site (during which measurements are performed) and a 5-minute travel in-between. This setup enabled the measurement of inside tunnel and outside tunnel (background) concentrations.

Emission factors for individual pollutants were calculated from measured pollutant
concentrations using the carbon balance method (Singer and Harley, 1996), which was
developed for remote sensing measurements, yet it is also applicable to tunnel studies
(Kirchstetter et al., 1999; Miguel et al., 1998). Since the conversion efficiency of elemental
carbon in fuel to CO₂ is considered to be higher than 90% for both diesel and gasoline
vehicles (Heywood, 1988), it is assumed that the concentrations of CO and CO₂ observed
within the tunnel represent the amount of fuel burned. Thus, the EF of a pollutant P can be
measured proportionally to the amount of fuel burned using Equation (1):
$$E_P = \frac{\text{Estimated mass emission of P}}{\text{Estimated fuel consumption}} = 10^6 \cdot w_c \cdot \left( \frac{\Delta[P]/[\Delta CO_2]}{1 + \Delta[CO]/[\Delta CO_2]} \right) \quad (1)$$

where $E_P$ is the emission factor of P given in mg of P per kg of fuel burned, $\Delta[P]$ is the increase in the concentration of the pollutant P above background levels (in µg of P per m³), $\Delta[CO]$ and $\Delta[CO_2]$ are the increase in the concentrations of CO and $CO_2$ (in µg of C per m³) above background levels, and $w_c$ is the weight fraction of carbon in the fuel considered. Based on the values reported in other studies (Baalbaki et al., 2013; Grieshop et al., 2006; Kirchstetter et al., 1999; Kittelson et al., 2004; Miguel et al., 1998; Sánchez-Ccoyllo et al., 2009; Singer and Harley, 1996), the carbon weight fraction varies between 0.83 and 0.87, with an average of 0.85 for gasoline fuel which was used in this study since Beirut fleet is 98.5% constituted by gasoline vehicles.

At the time of this study, security measures were very restrictive in Beirut; as a result, further traffic data could not be collected. Consequently, the EF apportionment could not be achieved, only some gross specific EFs were measured for LDV during the periods when HDV circulation was restricted (c.a. morning and evening hours during weekdays from Monday to Friday, and weekends from Saturday noon to midnight Sunday/Monday).

## 2.3. Fleet characteristics

In the absence of developed public transport and infrastructure for railway or other urban or national public networks, Lebanon fleet consists mainly of passenger cars. Only a few bus lines currently exist and are mainly managed by the private sector (individuals or companies). Based on field measurements, Mansour (2011) reported that 25% of stops are below 2 seconds and the total stop time per trip corresponds to more than 15% of travel time with high congestion rate. In addition to that, the author indicates the internal combustion engines work inefficiently due to the continuous stop-and-go driving patterns resulting in high fuel consumption and pollutant emissions (Mansour et al., 2011). Based on the data of the Car Registration Office, the operational fleet distribution at a national level for the year 2014 consists of 1 697 919 vehicles with 84.9% passenger cars (PC), 6.8% Light-Commercial Vehicles (LCV), 2.4% Heavy-Duty Vehicles (HDV), and 5.9% Motorcycles. It should be noted that in Greater Beirut area HDV are subject to a circulation restriction schedule which was used to segregate the LDV (Light Duty Vehicles) EF (which encompasses PC and LCV) from the total fleet (LDV+HDV). The main fuel type used for road transport in Lebanon is
gasoline, only HDV and a small sub-category of LDV (minibuses that carry 17+ persons) run on diesel since 15 July 2002, when a law had been in effect to ban diesel fuel for light and medium-duty engines (Afif et al., 2008). The average fleet age as calculated by Waked and Afif (2012) during a survey realized in 2010 for Beirut fleet is 10 years. The fleet is also characterized by a high percentage of vehicles that are not equipped with catalysts (approx. 35%), of which a fraction that originally had a catalyst. The catalyst was probably removed without any replacement as part of a common practice to avoid high maintenance costs in the absence of a law banning such practices (Waked and Afif, 2012). The fuel consumption is estimated to be 12.55 L/100km for LDV and 28.97 L/100km for HDV as based on the fuel consumption reported in MoE/UNDP/GEF (2015).

Lebanon, a relatively small country, has its centralized administration in Greater Beirut which has a population of more than 1.3 million inhabitants (Salameh et al., 2015, 2016). Hence, a great number of persons living outside Beirut head to their work there which results in more than 600,000 vehicles circulating inside Beirut daily. The share of HDV is between 2 and 3% (Waked et al., 2012) which is in line with the data from the National Car Registration Office. Moreover, surveys related to the characteristics of the active fleet conducted over different years in different regions showed homogeneous geographical results (Waked et al., 2012, and unpublished data). Consequently, no significant heterogeneity in the fleet characteristics is expected across the territory.

**2.4. Instruments**

During this tunnel campaign, the major pollutants measured were CO, CO₂, NOₓ, PM₂.₅, and speciated NMVOC.

Table 1 summarizes the properties of the sampling procedure. To measure CO and CO₂, we used an infrared GFC (Gas Filter Correlation) online analyzer: the CO12M analyzer from Environnement S.A. NOₓ were measured with a chemiluminescence based online analyzer: the AC32M. These analyzers are operated with a 1 min resolution. CO/CO₂ and NOₓ analyzers were calibrated on-site with a certified gas cylinder prior to the measurements. To measure hydrocarbons from C5 to C16 including alkanes, alkenes, aromatics, aldehydes, we used multi-adsorbent cartridges (Carbopack B and C) as described in Detournay et al. (2011). The cartridges were pre-conditioned at high temperatures for 24 hours. The samples were collected using a flow-controlled sampler, operating at a flow rate of 200 mL.min⁻¹ for 2
hours for each sample. The NMVOC set up was duplicated for inside and outside measurements. Analysis was then carried out using a thermodesorption system coupled with GC/FID/MS. To measure PM<sub>2.5</sub>, we used the gravimetric method: 150 mm quartz-fiber filters with a high-volume sampler and PM<sub>2.5</sub> impactor head. The sampler was operated at 30 m<sup>3</sup> h<sup>-1</sup> for 2 hours per sample. The filters used are conditioned for 24 hours under controlled temperature (20 ± 2°C) and humidity (50 ± 5%) before being weighted blank. After the sampling ended, the filters were conditioned again and then weighted.

<table><caption>Table 1. Summary of the sampling method properties.</caption><thead><tr><th>Pollutants</th><th>Analyzer</th><th>Sampling Resolution</th><th>Number of Samples</th></tr></thead><tbody><tr><td>CO and CO<sub>2</sub></td><td>CO12M<br>(Environnement S.A.)<br>IR Absorption</td><td>1 min</td><td>17<sup>a</sup></td></tr><tr><td>NO<sub>x</sub></td><td>AC32M<br>(Environnement S.A.)<br>Chemiluminescence</td><td>1 min</td><td>17<sup>a</sup></td></tr><tr><td>NMVOCs</td><td>Active Sampling onto Thermal desorption tube</td><td>2 hrs.</td><td>12</td></tr><tr><td>PM<sub>2.5</sub></td><td>High Volume Sampler with Quartz Filters</td><td>2 hrs.</td><td>11</td></tr></tbody></table>

### 3. Results

The EFs (± the standard deviation) of all the pollutants measured in the Salim Slam tunnel varied considerably with lower values on Sunday morning when vehicles were running at nearly 70 km h<sup>-1</sup> since the traffic was the lowest with only a flow of few cars per minute and the highest values during high traffic flows on Monday and Friday which explains the high standard deviations of many compounds.

For CO (Table 2), the measured EF of the total fleet is 10.52 ± 3 g km<sup>-1</sup> while LDVs EF is estimated to be 9.15 ± 1.25 g km<sup>-1</sup>. These values are in the same range and highlight the

<footnote><sup>a</sup> The number of samples of CO, CO<sub>2</sub> and NO<sub>x</sub> consists of the total number of measurement cycles performed inside the tunnel during the campaign.</footnote>
insignificant contribution of HDV to CO in the presence of large numbers of gasoline vehicles in the tunnel. In comparison to studies conducted recently in the US and China, this EF is up to 7 times higher (Dallmann et al., 2013; Zhang et al., 2015a). Such differences could mainly be related to the vehicle fleet age and technology, as well as the different fleet composition between countries. In comparison to a previous study performed by El-Fadel and Hashisho (2000) at the same location during the year 1999, the results suggest an important decrease (up to a factor 3) in the EFs which were 27.6 g km⁻¹ at that time. This decrease could be attributed to the decrease of the HDV fraction in the fleet distribution, and the evolution of emission reduction technologies installed in recent cars with respect to the stricter emission standards enforced on most renown car manufacturers by developed countries.

<table><caption>Table 2. Traffic EFs for CO (g km⁻¹) from different tunnel studies (± the standard deviation)</caption><thead><tr><th>Study</th><th>Country</th><th>Fleet Type</th><th>Year</th><th>EF (g km<sup>-1</sup>)</th><th>Fleet composition</th></tr></thead><tbody><tr><td rowspan="2">This study</td><td rowspan="2">Lebanon</td><td>LDV</td><td>2014</td><td>9.15 ± 1.25</td><td>Gasoline</td></tr><tr><td>LDV + HDV</td><td>2014</td><td>10.52 ± 3.0</td><td>Gasoline, 1.5% Diesel</td></tr><tr><td>(El-Fadel and Hashisho, 2000)</td><td>Lebanon</td><td>LDV + HDV</td><td>1999</td><td>27.66</td><td>Gasoline, 3% Diesel</td></tr><tr><td>(Kristensson et al., 2004)</td><td>Sweden</td><td>LDV + HDV</td><td>1999</td><td>5.27 ± 0.1</td><td>Gasoline, 5% Diesel</td></tr><tr><td>(Sánchez-Ccoyllo et al., 2009)</td><td>Brazil</td><td>LDV</td><td>2004</td><td>15</td><td>Gasoline, 5-10% HDV Diesel</td></tr><tr><td>(Park et al., 2011)</td><td>US</td><td>LDV</td><td>2010</td><td>4.7</td><td>Gasoline</td></tr><tr><td>(Dallmann et al., 2013)</td><td>US</td><td>LDV</td><td>2010</td><td>1.43 ± 0.07</td><td>Gasoline</td></tr><tr><td>(Deng et al., 2015)</td><td>China</td><td>LDV + HDV</td><td>2012</td><td>6.05 ± 5.94</td><td>Gasoline</td></tr><tr><td>(Brimblecombe et al., 2015)</td><td>Honk-Kong</td><td>LDV</td><td>2014</td><td>3.1 ± 0.39</td><td>Gasoline</td></tr><tr><td>(Zhang et al., 2015b)</td><td>China</td><td>LDV + HDV</td><td>2014</td><td>3.1</td><td>61% LDV gasoline, 12% HDV Diesel, and 27% LPG</td></tr></tbody></table>

In the case of NOₓ (Table 3), the values also show a decrease compared to the values of El-Fadel and Hashisho (2000). The mean EF from this study is 2.2 ± 0.57 g km⁻¹ for the total fleet and 1.46 ± 0.21 g km⁻¹ for LDVs. However, compared to values from developed
countries, this EF is higher by a factor up to 4 compared to other studies (Brimblecombe et al., 2015, Deng et al., 2015, Park et al., 2011). The highest difference to other published values is a factor of 10 when compared to Dallmann et al. (2013). Such high NOₓ EFs may seem surprising as LDV running on gasoline contributed to 99% of the fleet. The high NOₓ EF observed in this study compared to others in different countries could be partially attributed to differences in the average vehicle age, and most importantly to the local maintenance practices. In fact, common practices in Lebanon consist in the removal of a defective catalyst because of its high maintenance cost, regardless of its purpose to reduce NOₓ exhaust emissions (Waked and Afif, 2012). Also, it is common to have the EGR (Exhaust Gas Recirculation) valves blocked or removed to increase the oxygen in the cylinder for a lower CO level at the exhaust because of the yearly inspection, regardless of the increase in NOₓ since it is not controlled at the inspection.

<table><caption>Table 3. Traffic EFs (g km⁻¹) for NOₓ from different tunnel studies (± the standard deviation).</caption><thead><tr><th>Study</th><th>Country</th><th>Fleet Type</th><th>Year</th><th>EF (g km<sup>-1</sup>)</th><th>Fleet composition</th></tr></thead><tbody><tr><td rowspan="2">This study</td><td rowspan="2">Lebanon</td><td>LDV</td><td>2014</td><td>1.46 ± 0.21</td><td>Gasoline</td></tr><tr><td>LDV + HDV</td><td>2014</td><td>2.20 ± 0.57</td><td>Gasoline, 1.5% Diesel</td></tr><tr><td>(El-Fadel and Hashisho, 2000)</td><td>Lebanon</td><td>LDV + HDV</td><td>1999</td><td>2.64</td><td>Gasoline, 3% Diesel</td></tr><tr><td>(Kristensson et al., 2004)</td><td>Sweden</td><td>LDV + HDV</td><td>1999</td><td>1.36 ± 0.03</td><td>Gasoline, 5% Diesel</td></tr><tr><td>(Sánchez-Ccoyllo et al., 2009)</td><td>Brazil</td><td>LDV</td><td>2004</td><td>1.6 ± 0.3</td><td>Gasoline, 5-10% HDV Diesel</td></tr><tr><td>(Park et al., 2011)</td><td>US</td><td>LDV</td><td>2010</td><td>0.94</td><td>Gasoline</td></tr><tr><td>(Dallmann et al., 2013)</td><td>US</td><td>LDV</td><td>2010</td><td>0.19 ± 0.008</td><td>Gasoline</td></tr><tr><td>(Deng et al., 2015)</td><td>China</td><td>LDV + HDV</td><td>2012</td><td>0.818 ± 0.755</td><td>Gasoline</td></tr><tr><td>(Brimblecombe et al., 2015)</td><td>Hong-Kong</td><td>LDV</td><td>2014</td><td>0.47 ± 0.27</td><td>Gasoline</td></tr><tr><td>(Zhang et al., 2015b)</td><td>China</td><td>LDV + HDV</td><td>2014</td><td>1.23</td><td>61% LDV gasoline, 12% HDV Diesel</td></tr></tbody></table>
For PM<sub>2.5</sub> (Table 4), the emission factor for the total fleet is 55 ± 27 mg km<sup>-1</sup>; this factor includes non-exhaust emissions such as tire wear, break wear, road surface wear, and resuspension. Compared to the LDV only measurement (37.17 ± 10 mg km<sup>-1</sup>), the impact of diesel emissions is significant; a 40% increase in PM<sub>2.5</sub> emissions is observed. The EFs are in good agreement with the results from the remote sensing study conducted by Baalbaki et al. in 2011 on different road segments in Beirut which reported an EF ranging between 41.6 and 94.2 mg km<sup>-1</sup> during the afternoon and the evening respectively (Baalbaki et al., 2013). The results are higher than those of other recent studies dominated by gasoline fueled vehicles such as Mancilla and Mendoza (2012), and Ning et al. (2008), which is related not only to the fleet characteristics but also to the driving conditions. Park et al. (2011) reported an increase of PM<sub>2.5</sub> exhaust emissions by 20% between low speed cruising (< 50 km h<sup>-1</sup>) and high-speed cruising (> 50 km h<sup>-1</sup>) for LDV, with the most emissions during engine idling. In addition to the exhaust emissions increase, brake wear emissions are expected to be an important characteristic of the Lebanese driving condition when based on Mansour (2011) field measurements. On the other hand, tire wear and surface wear quality, conditions and maintenance play an important role in the particles emissions especially when these are not maintained regularly and up to the international standards. Also, in comparison to developed countries (US, Sweden), road cleaning is rarely performed in Lebanon and consequently accounts to a certain amount of the excessive PM observed due to the resuspension process.

<table><caption>Table 4. Traffic EFs (mg km<sup>-1</sup>) for PM2.5 from different tunnel studies (± the standard deviation).</caption><thead><tr><th>Study</th><th>Country</th><th>Fleet Type</th><th>Year</th><th>EF (mg km<sup>-1</sup>)</th><th>Fleet composition</th></tr></thead><tbody><tr><td rowspan="3">This study</td><td rowspan="3">Lebanon</td><td>LDV</td><td>2014</td><td>37 ± 10</td><td>Gasoline</td></tr><tr><td>LDV + HDV</td><td>2014</td><td>55 ± 27</td><td>Gasoline, 1.5% Diesel</td></tr><tr><td>HDV</td><td></td><td></td><td></td></tr><tr><td>(Baalbaki et al., 2013)</td><td>Lebanon</td><td>LDV + HDV</td><td>2011</td><td>41.6</td><td>-</td></tr><tr><td>(Kristensson et al., 2004)</td><td>Sweden</td><td>LDV + HDV</td><td>1999</td><td>67 ± 5</td><td>Gasoline, 5% Diesel</td></tr><tr><td>(Laschober et al., 2004)</td><td>Austria</td><td>LDV + HDV</td><td>2002</td><td>46.8</td><td>39% Diesel LDV, 12.6% HDV</td></tr></tbody></table>
<table><tr><td>(Ning et al., 2008)</td><td>US</td><td>LDV</td><td>2004</td><td>11.3 ± 2.59</td><td>Gasoline</td></tr><tr><td>(Ning et al., 2008)</td><td>US</td><td>LDV + HDV</td><td>2004</td><td>21.9 ± 3.9</td><td>80% Gasoline LDV, 20% Diesel HDV</td></tr><tr><td>(Sánchez-Ccoyllo et al., 2009)</td><td>Brazil</td><td>LDV (gasohol)</td><td>2004</td><td>92 ± 20</td><td>Gasohol, 5-10% HDV Diesel</td></tr><tr><td>(Mancilla and Mendoza, 2012)</td><td>Mexico</td><td>LDV</td><td>2009</td><td>22.8 ± 7.4</td><td>Gasoline</td></tr><tr><td>(Hung-Lung et al., 2007)</td><td>Taiwan</td><td>LDV + HDV</td><td>2005</td><td>38 ± 11</td><td>>80% Gasoline LDV, 9.5-20% Diesel HDV</td></tr><tr><td>(Zhang et al., 2015b)</td><td>China</td><td>LDV + HDV</td><td>2014</td><td>82.7</td><td>61% LDV gasoline, 12% HDV Diesel, and 27% LPG</td></tr></table>
<table><caption>Table 5. Traffic EFs (mg km⁻¹) for individual VOC from different tunnel studies (± the standard deviation).</caption><thead><tr><th rowspan="4"></th><th colspan="2">2014</th><th colspan="2">1999</th><th colspan="2">2000</th><th colspan="2">2001</th><th colspan="2">2002</th><th colspan="2">2003</th><th colspan="2">2003</th><th colspan="2">2004</th><th colspan="2">2004</th><th colspan="2">2005</th><th colspan="2">2009</th><th colspan="2">2012</th><th colspan="2">2011-2013</th></tr><tr><th>This study</th><th></th><th>(Kristensson et al., 2004)</th><th>(Hwa et al., 2002)</th><th>(Petrea, 2007)</th><th>(Stemmler et al., 2005)</th><th>(Ho et al., 2009)</th><th>(Petrea, 2007)</th><th>(Legreid et al., 2007)</th><th>(Martins et al., 2006)</th><th>(Petrea, 2007)</th><th>(Stemmler et al., 2005)</th><th>(Ho et al., 2009)</th><th>(Petrea, 2007)</th><th>(Legreid et al., 2007)</th><th>(Martins et al., 2006)</th><th>(Petrea, 2007)</th><th>(Hung-Lung et al., 2007)</th><th>(Araizaga et al., 2013)</th><th>(Ammoura et al., 2014)</th><th>(Ait-Helal et al., 2015)</th><th>(Ait-Helal et al., 2015)</th><th>(Ait-Helal et al., 2015)</th><th>(Ait-Helal et al., 2015)</th></tr><tr><th>Lebanon</th><th></th><th>Sweeden</th><th>Taiwan</th><th>Austria</th><th>Switzerland</th><th>China</th><th>UK</th><th>Switzerland</th><th>Brazil</th><th>Taiwan</th><th>Switzerland</th><th>China</th><th>UK</th><th>Switzerland</th><th>Brazil</th><th>Taiwan</th><th>Switzerland</th><th>Brazil</th><th>Taiwan</th><th>Switzerland</th><th>Brazil</th><th>Taiwan</th><th>Switzerland</th><th>Brazil</th></tr><tr><th>LDV</th><th></th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th></tr></thead><tbody><tr><td>Isoprene</td><td>0.7 ± 0.4</td><td>0.9 ± 0.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Benzene</td><td>14.1 ± 11.1</td><td>13.7 ± 11.7</td><td>17.3 ± 0.3</td><td>12.21 ± 3.3</td><td>6.8 ± 0.8</td><td>3.3 ± 0.3</td><td>4.5 ± 0.9</td><td>5.9 ± 2.6</td><td>2.3 ± 0.1</td><td>78.3 ± 72</td><td>5.9</td><td>78.3 ± 72</td><td>81.3 ± 112.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cyclohexane</td><td>0.4 ± 0.5</td><td>0.7 ± 0.4</td><td></td><td></td><td></td><td></td><td>0.3 ± 0.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hexene</td><td>1.7 ± 1.4</td><td>5.3 ± 3.5</td><td></td><td></td><td></td><td></td><td>0.4 ± 0.3</td><td></td><td></td><td>6.8 ± 7.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hexane</td><td>14.2 ± 13.8</td><td>30.6 ± 26.2</td><td></td><td></td><td></td><td></td><td>1.3 ± 0.5</td><td>2.4 ± 1</td><td></td><td>60.1 ± 62.7</td><td>5.7</td><td>60.1 ± 62.7</td><td>4.0 ± 4.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,2-Dimethylbutane</td><td>2.7 ± 1.3</td><td>1.7 ± 1.5</td><td></td><td></td><td></td><td></td><td>0.2 ± 0.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2-Methylpentane</td><td>25.5 ± 21.3</td><td>20.3 ± 16.4</td><td></td><td></td><td></td><td></td><td>1.8 ± 0.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3-Methylpentane</td><td>8.7 ± 10.9</td><td>10.1 ± 8.5</td><td></td><td></td><td></td><td></td><td>1.2 ± 0.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Toluene</td><td>101.4 ± 58.1</td><td>92 ± 51.9</td><td>67.3 ± 1</td><td>29.02 ± 5</td><td>12.6 ± 3.4</td><td>8.7 ± 0.5</td><td>12 ± 3.9</td><td>15.8 ± 7</td><td>5.7 ± 0.4</td><td>134.5 ± 135.4</td><td>29</td><td>134.5 ± 135.4</td><td>4.2 ± 2.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hexanal</td><td>0.012 ± 0.006</td><td>1.5 ± 1.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,2,3-Trimethybutane</td><td>0.9 ± 0.5</td><td>1.0 ± 0.8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,2-Dimethylpentane</td><td>0.8 ± 0.6</td><td>1.8 ± 1.4</td><td></td><td></td><td></td><td></td><td>0.1 ± 0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,3-Dimethylpentane</td><td>2.2 ± 2.0</td><td>4.2 ± 3.3</td><td></td><td></td><td></td><td></td><td>0.1 ± 0.1</td><td></td><td></td><td>7.9 ± 7.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,4-Dimethylpentane</td><td>2.0 ± 0.5</td><td>4.2 ± 2.7</td><td></td><td></td><td></td><td></td><td>0.2 ± 0.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3,3-Dimethylpentane</td><td>0.8 ± 1.0</td><td>4.0 ± 4.9</td><td></td><td></td><td></td><td></td><td>0.1 ± 0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2-Methylhexane</td><td>10.1 ± 5.5</td><td>20.3 ± 13.5</td><td></td><td></td><td></td><td></td><td>0.7 ± 0.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Heptane</td><td>7.9 ± 3.2</td><td>19.2 ± 11.5</td><td></td><td></td><td></td><td></td><td>0.9 ± 0.4</td><td>10 ± 4</td><td></td><td>41.1 ± 39.9</td><td>1.6</td><td>41.1 ± 39.9</td><td>5.7 ± 6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Styrene</td><td>4.4 ± 1.9</td><td>9.2 ± 4.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ethylbenzene</td><td>22.1 ± 9.2</td><td>43.5 ± 22.4</td><td></td><td></td><td></td><td></td><td>1.3 ± 0.4</td><td>3.5 ± 1.6</td><td></td><td>31.1 ± 33</td><td>5.3</td><td>31.1 ± 33</td><td>6.1 ± 2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>m,p-Xylene</td><td>90.4 ± 55.3</td><td>101.4 ± 55.2</td><td>48.6 ± 0.8</td><td>8.95 ± 2.4</td><td>9.7 ± 3.4</td><td>4.2 ± 0.3</td><td>2.6 ± 0.9 | 1.1 ± 0.4</td><td>11.4 ± 2.5</td><td>3.3 ± 0.2</td><td>62 ± 62.8</td><td>8.4</td><td>62 ± 62.8</td><td>16.7 ± 3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>o-Xylene</td><td>30.6 ± 12.5</td><td>60.4 ± 31.0</td><td>19.6 ± 0.4</td><td>7.88 ± 2.1</td><td>6 ± 2</td><td>1.9 ± 0.1</td><td>1.6 ± 0.6</td><td>4.4 ± 2</td><td>1.3 ± 0.1</td><td>44.4 ± 39.8</td><td>6.4</td><td>44.4 ± 39.8</td><td>7.7 ± 1.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chlorobenzene</td><td>1.2 ± 0.7</td><td>2.4 ± 1.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2,3,4-Trimethylpentane</td><td>2.1 ± 2.0</td><td>5.2 ± 3.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 5. (Continued)</caption><thead><tr><th rowspan="3"></th><th colspan="2">2014</th><th colspan="2">1999</th><th colspan="2">2000</th><th colspan="2">2001</th><th colspan="2">2002</th><th colspan="2">2003</th><th colspan="2">2003</th><th colspan="2">2004</th><th colspan="2">2004</th><th colspan="2">2005</th><th colspan="2">2009</th><th colspan="2">2012</th><th colspan="2">2011-2013</th></tr><tr><th>This study</th><th></th><th>(Kristensson et al., 2004)</th><th>(Hwa et al., 2002)</th><th>(Petrea, 2007)</th><th>(Stemmler et al., 2005)</th><th>(Ho et al., 2009)</th><th>(Petrea, 2007)</th><th>(Austria)</th><th>(Switzerland)</th><th>(China)</th><th>(Petrea, 2007)</th><th>(Legreid et al., 2007)</th><th>(Martins et al., 2006)</th><th>(Petrea, 2007)</th><th>(Switzerland)</th><th>(Brazil)</th><th>(Hung-Lung et al., 2007)</th><th>(Araizaga et al., 2013)</th><th>(Ammoura et al., 2014)</th><th>(Ammoura et al., 2014)</th><th>(Ait-Helal et al., 2015)</th><th>(Ait-Helal et al., 2015)</th></tr><tr><th>Lebanon</th><th></th><th>Sweeden</th><th>Taiwan</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV+HDV</th><th>LDV</th></tr></thead><tbody><tr><td>Isooctane</td><td>6.7 ± 2.8</td><td>13.3 ± 7.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Octane</td><td>2.8 ± 1.5</td><td>6.5 ± 3.6</td><td></td><td>1.31 ± 0.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.5 ± 0.2</td><td></td><td></td><td></td><td>29.3 ± 28.1</td><td></td><td>0.78</td><td></td><td></td><td></td><td></td><td></td><td>0.29 ± 0.1</td><td></td></tr><tr><td>1,3,5-Trimethylbenzene</td><td>0.3 ± 0.2</td><td>1.0 ± 0.9</td><td></td><td>2.31 ± 0.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.8 ± 0.4</td><td></td><td></td><td></td><td>20.8 ± 17.7</td><td></td><td>3.7</td><td></td><td></td><td></td><td></td><td></td><td>0.63 ± 0.22</td><td></td></tr><tr><td>2-Ethyltoluene</td><td>6.7 ± 3.2</td><td>14 ± 7.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.0 ± 0.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3.2 ± 0.5</td><td></td><td></td><td></td><td></td></tr><tr><td>3-Ethyltoluene</td><td>20.3 ± 7.4</td><td>39 ± 20.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.4 ± 0.9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>4-Ethyltoluene</td><td>7.5 ± 3.0</td><td>14.6 ± 7.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.7 ± 0.8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Isopropylbenzene</td><td>1.1 ± 0.3</td><td>2.1 ± 1.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.1 ± 0.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nonane</td><td>1.1 ± 0.6</td><td>2.4 ± 1.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.7 ± 0.4</td><td></td><td></td><td></td><td></td><td>22.6 ± 22.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Isobutylbenzene</td><td>0.2 ± 0.1</td><td>0.2 ± 0.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Butylbenzene</td><td>1.7 ± 0.9</td><td>2.4 ± 2.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Limonene</td><td>0.3 ± 0.2</td><td>0.6 ± 0.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nonanal</td><td>0.8 ± 0.4</td><td>0.8 ± 0.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Decane</td><td>0.8 ± 0.5</td><td>2.1 ± 1.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>14 ± 15.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1,2-Dichlorobenzene</td><td>9.3 ± 3.5</td><td>18 ± 9.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1,3-Dichlorobenzene</td><td>1.2 ± 0.8</td><td>1.9 ± 1.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1,4-Dichlorobenzene</td><td>0.5 ± 0.4</td><td>0.6 ± 0.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Decanal</td><td>4.2 ± 3.6</td><td>4.0 ± 5.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Undecane<sup>a</sup></td><td>0.7 ± 0.5</td><td>2.0 ± 1.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>9.6 ± 10.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Undecanal</td><td>3.5 ± 2.8</td><td>1.7 ± 1.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dodecane<sup>a</sup></td><td>0.6 ± 0.3</td><td>2.7 ± 1.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>6.2 ± 5.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tridecane<sup>a</sup></td><td>0.6 ± 0.3</td><td>1.7 ± 0.8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tetradecane<sup>a</sup></td><td>1.1 ± 0.6</td><td>2.2 ± 1.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pentadecane<sup>a</sup></td><td>1.7 ± 1.9</td><td>1.8 ± 1.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hexadecane<sup>a</sup></td><td>1.2 ± 0.6</td><td>2.1 ± 1.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote>a IVOC Species, Fleet composition: Kristensson et al., 2004 (5% HDV Diesel), Hwa et al., 2002 (Gasoline), Petrea, 2007 (UK: Gasoline, Austria: 36.6% LDV Gasoline, 43.4% LDVDiesel, 20% HDV Diesel), Stemmler et al., 2005 (Gasoline), Ho et al., 2009 (10% LPG, 40% Gasoline, 50% Diesel), Legreid et al., 2007 (Gasoline), Martins et</footnote>
al., 2006 (Gasohol), Hung-Lung et al., 2007 (>80% LDV Gasoline, 9.5-20%HDV Diesel), Araizaga et al., 2013 (Gasoline), Ammoura et al., 2014 (78% Gasoline, 22% Diesel), Ait-Helal et al., 2015 (35.5% Gasoline, 64.2% Diesel).
In Table 5, the EFs for the measured speciated NMVOC are listed together with the EFs from other studies. Of the measured NMVOCs, alkanes account for 27.1%, aldehydes for 1.4%, aromatics for 70.5%, and alkenes for 0.47% when considering the entire fleet. In the case of isoprene, this compound was long considered as an indicator of biogenic emissions, until a significant anthropogenic origin, namely road-transport, was identified (Borbon et al., 2001). As shown in Table 5, very few studies reported EF for isoprene from road transport; the estimated EF from this study is 0.9 ± 0.7 mg km⁻¹ for the total fleet accounting for 0.15% of the total NMVOC measured. Considering the differences in the fleet characteristics, important discrepancies are observed for most NMVOCs: they highly exceed EFs from other studies. The EFs of toluene, m,p-xylene and ethylbenzene from this study are up to 20 times higher than the values reported by Ait-Helal et al. (2015) for Belgium, Ammoura et al. (2014) for France, where the fleet has a higher portion diesel-running vehicles. Also, the toluene/benzene ratio from this study (12 – 15) highly exceeds the ones determined from the near field measurements in Lebanon during March and April of Salameh et al. (2014) (exhaust when vehicle is idle: 2.75, evaporation: 4.8), as well as the data from Legreid et al. (2007) (up to 6.2). Based on these observations, it is assumed that evaporative emissions (diurnal evaporation and running loss emissions) might contribute to the EFs of organic compounds especially in summer (Sawyer et al., 2000). Due to poor Lebanese fleet conditions, the combustion reaction at the engine level is of poor quality yielding higher NMVOC emissions which might contribute to this observation. Similarly to other VOC, the EFs for benzene in this study are higher than those of European studies. Lebanon EF is 13.7 ± 11.7 mg km⁻¹ for the total fleet, while an EF between 2.3 and 6.8 mg km⁻¹ is observed in European studies past the year 2000. Benzene discrepancies could be related to the national legislation: while the Lebanese legislation tolerates a 5% benzene content for gasoline until now, European legislation has a threshold of 1% since 2000 (EEC, 2000).

IVOCs (NMVOC with ≥C11) are involved in the formation of secondary organic aerosols (SOA) (Robinson et al., 2007; Seinfeld and Pandis, 2006). In the case of linear alkane IVOC species in the East Mediterranean and the Middle East, EF were not reported in previous tunnel studies to our knowledge and these EFs are the first. However, these reported values for individual alkane IVOC in the gas phase are subject to significant uncertainty since these species may partition between the gas and particle phases (Kim et al., 2016). The fraction of alkanes in
the gas phase and in the particulate phase may be different at the background site and in the tunnel, because the temperature inside the tunnel was higher by almost 5 degrees compared to the outside. Because the partitioning between the gas and particle phases depends not only on temperature, but also on the mass of organics in the particles, the alkane IVOC EFs should be considered with caution. Of the measured VOCs, linear alkane IVOCs contribute to 2.1 %. This value is consistent with the chamber measurements of Zhao et al. (2016): they estimated that for hot-start gasoline vehicles about 17% of nonmethane hydrocarbons are IVOCs, and amongst those less than about 15% are linear alkanes.

These NMVOC have an impact on air quality and on the production of SOA since each NMVOC has a different SOA yield, which also varies depending on environmental conditions (Derwent et al., 2010). The values of the SOA yields are available in Table S1 of the Supplementary Information. The values of the SOA yields were chosen to best reflect the ambient conditions in Beirut, i.e. high NOₓ and OA concentrations of around 40 µg m⁻³ when available. Among the compounds chosen, IVOC have the highest SOA yields whereas aromatics have the highest EF. The product of SOA Yields (Y) (upper panel of Figure 1, grey colored with Sparse pattern) and EF (in mg kg⁻¹ of fuel) (upper panel of Figure 1, red colored with no pattern) shows that aromatics due to their high emissions dominate the SOA formation in Beirut when compared to IVOC up to C16 (lower panel of Figure 1). This is in line with the NMVOC measurements conducted by Salameh et al. (2015) where the concentrations of aromatics are considerably higher than the IVOC up to C16. Since on-road transport is the main source of NMVOC in Beirut (Salameh et al., 2016), it is very important to mitigate its emissions from this source to decrease not only the NMVOC concentrations but also the high concentrations of PM2.5 (Abdallah et al., 2018). Derwent et al. (2010) shows that road transport is the main anthropogenic source of SOA in the UK.
<figure><img src="image_2.png" /><figcaption>Figure 1. EF determined by this study and (SOA) yields (Y) of relevant VOC precursors (upper panel); products of EF and SOA yields (lower panel). Y are determined under the most relevant atmospheric conditions regarding the environmental conditions in Beirut (Organic mass concentration COA =40 µg.m⁻³ and high NOx).</figcaption></figure>

We reported in Table 6 the EFs estimated by EMEP Tier 3 and IPCC Tier 2 methodologies using the national vehicle fleet for 2014. The EMEP Tier 3 methodology was modified to take into account the local practice of catalyst removal based on the findings of Waked and Afif (2012). For the modified EMEP Tier 3, 35% of the post-ECE fleet was degraded by 4 generations to account for the emission changes from such practices. The presented method considers the annual travelled distance as activity data for the emissions calculation before calculating the average EF presented in Table 6. The modified IPCC Tier 2 EFs were already modified by MoE/UNDP/GEF (2017) to take into account the local practice of catalyst removal. When comparing the measured EFs to those estimated by EMEP and the IPCC, the EMEP Tier 3 EF exhibit constant lower values, whereas the Modified EMEP Tier 3 EF shows acceptable values for NOₓ but overestimates the CO. The modified IPCC Tier 2 (MoE/UNDP/GEF, 2017)
shows that the NOₓ EF is in good agreement with the measured one, but CO shows even higher overestimation than the Modified EMEP Tier 3. PM₂.₅ is underestimated by both EMEP proposed methodologies even when accounting for tire wear, surface wear, and break wear. These discrepancies to the EMEP Tier 3 methodologies could be explained mainly by the fleet characteristics (average vehicle mileage, average vehicle age, fuel consumption), the local maintenance practices presented previously (catalyst and EGR removal), the fuel quality, the absence of an effective enforcement of the regulations, the specific driving patterns in Beirut which are characterized by a high amount of traffic jams leading to low speed travels and recurrent deceleration-acceleration regimes (Mansour, 2011). Moreover, it is noted that the local reduction trends during the past 15 years, do not follow those suggested by other studies for other countries for CO and NOₓ. Ban-Weiss et al. (2008) reported a reduction of 67% and 36% for NOₓ and PM₂.₅ between 1997 and 2006 in the Caldecott tunnel in the US, McDonald et al. (2013) reported a reduction of up to 80% for CO. In Switzerland, Hueglin et al. (2006) reported a reduction of 36% for CO and NOₓ between 1992 and 2004. However, in Lebanon, CO was reduced by 62% and 17% for NOₓ between 1999 and 2014. This is due to the absence of a clear national transport policy that encompasses the air emissions from this sector and the most important is the absence of an effective enforcement of the existing regulations. The impact of these EFs on the national emissions were not evaluated thoroughly, yet the findings of this study show that the estimated EF used by Waked and Afif (2012) in the National Inventory for the year 2010 (Waked et al., 2012) based on the traffic volumes and origin-destination matrix as activity data before calculating the average EF for these three pollutants show acceptable values with the experimental ones determined in this study, however VOC emissions need further assessment in the future.

<table><caption>Table 6. Traffic EFs comparison between this study and other estimations for the year 2014.</caption><thead><tr><th></th><th></th><th>This study<br>(g.km<sup>-1</sup>)</th><th>EMEP<br>(Tier 3)*<br>(g km<sup>-1</sup>)</th><th>EMEP<br>(Tier 3 Modified)*<br>(g km<sup>-1</sup>)</th><th>IPCC 1996<br>(Tier 2 Modified)*<br>(g km<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="2">LDV +<br>HDV</td><td>CO</td><td>10.52</td><td>7.74<br>(-26%)</td><td>14.36<br>(+37%)</td><td>16.65<br>(+58%)</td></tr><tr><td>NO<sub>x</sub> (eq. NO<sub>2</sub>)</td><td>2.20</td><td>1.29</td><td>1.94</td><td>2.43</td></tr></tbody></table>
<table><tbody><tr><td></td><td></td><td>(-41%)</td><td>(-12%)</td><td>(+10%)</td></tr><tr><td>PM<sub>2.5</sub></td><td>0.055</td><td>0.035<br>(-36%)</td><td>0.035<br>(-36%)</td><td>-</td></tr><tr><td>CO</td><td>9.15</td><td>8.13<br>(-11%)</td><td>15.18<br>(+66%)</td><td>17.38<br>(+90%)</td></tr><tr><td>LDV</td><td></td><td></td><td></td><td></td></tr><tr><td>NO<sub>x</sub> (eq. NO<sub>2</sub>)</td><td>1.46</td><td>1.03<br>(-29%)</td><td>1.73<br>(+18%)</td><td>1.72<br>(+18%)</td></tr><tr><td>PM<sub>2.5</sub></td><td>0.037</td><td>0.024<br>(-35%)</td><td>0.024<br>(-35%)</td><td>-</td></tr></tbody></table>

Actions need to be taken to decrease the emissions from the road transport whether by decreasing the kilometers driven or emission factors at the national level. Different actions can be considered among which the increase of the share of the fuel-efficient vehicles, increase the share of hybrid electric vehicles and increase the share of mass transport (Haddad et al., 2017). The best approach would include all three by adopting a shift to mass transit through the expansion of the network and enhancing the reliability of the mass transport system, but also by improving the fleet efficiency through the renewal of the car fleet and establishing a car swap program. The introduction and use of plug-in hybrid vehicles are the most attractive in Lebanon on the medium term while gasoline hybrid electric vehicles are the most feasible and beneficial technologies on the short-term (Mansour and Haddad, 2017).

## 4. Conclusion

Road transport emissions of CO, NOx, PM2.5 and a selection of volatile organic compounds were determined in a tunnel study conducted at the Salim Slam tunnel, Lebanon. The carbon mass balance was used to quantify the EF of the different pollutants. This study highlights that emissions of CO and NOx were reduced during the past fifteen years when compared to the results of El-Fadel and Hashisho (2000). High discrepancies are observed when comparing the EF of CO, NOx, NMVOC to those measured in other tunnel experiments (over Europe, the US and China) where different fleet characteristics exist. Some countries had lower EF before the year 2000 than those measured in this study in 2014. The emissions of VOC showed high emissions in comparison to other studies. Due to the potential role of IVOC in the

<footnote>* Emission factor presented and in parenthesis the percentage of the variation compared to this study</footnote>
formation of secondary organic aerosols, this study reported emission factors of linear alkane IVOC species (C11 to C16) from the gaseous phase for the first time in the region even though they exhibit high uncertainties. The results of the comparison to EMEP and IPCC based EFs show that local practices and fleet characteristics lead to different EFs, suggesting the need to take them into account while establishing and reviewing national emission inventories.

There is still a need for more studies to provide further information on the EF of VOC. Moreover, due to security restrictions, this study was unable to present clear emission factors for individual vehicle categories, nor emission factors per speed category; future work should try to fill these gaps with longer periods of measurements, i.e. 2 weeks of continuous measurements or more, continuous speed measurements, vehicle categorization, real time traffic volume measurements, etc. However, this study is the first to report recent EFs for gaseous and particulate pollutants as well as individual VOC in the East Mediterranean and Middle East region. Furthermore, the results presented can be used to identify possible corrections to the EMEP EF specific to the country that could be implemented in the national emission inventory. International methodologies for emissions estimation, i.e. EMEP/EEA, AP-42, IPCC, etc., cannot be applied to the East Mediterranean and Middle East countries without further validation by measurements. Future work should also focus on the EF of PM number, as well as the speciation of the particulate matter to provide a better understanding of emissions.

## Acknowledgments

This project has been jointly funded with the support of the National Council for Scientific Research in Lebanon CNRS-L and Saint Joseph University (Faculty of Sciences and the Research Council), and the Partnerships for Enhanced Engagement in Research (PEER) program through USAID and NSF.

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