Developing Emission Factors from Real-World Emissions of Euro VI Urban Diesel, Diesel-Hybrid, and Compressed Natural Gas Buses

Abstract

Urban transportation is a key contributor to air pollution in cities. While urban buses impact air quality, they also promote sustainable mobility. In the Paris region, buses account for approximately 4% of traffic emissions. This study addresses the gap in real-world emissions data for Euro VI diesel, diesel-hybrid, and compressed natural gas (CNG) urban buses by developing speed-dependent emission factors for CO, NO_X, SPN₂₃, and energy consumption. An optimized methodology was applied to portable emission measurement system data collected from 28 urban buses across various routes in the Paris metropolitan area, capturing emissions across different speeds and traffic conditions. Results showed that diesel buses emit around 2 g/km of NOx at low speeds, compared to 1.4 g/km for diesel hybrids and 0.6 g/km for CNG. CO emissions reached approximately 1 g/km for CNG and 0.5 g/km for diesel, while SPN₂₃ emissions for all powertrains were in the order of 10¹² particles/km. The resulting speed-dependent emission factors were incorporated into COPERT version 5.8, the European Union’s standard emission inventory software, improving the inventory accuracy. The findings underscore the need for additional reductions in air pollutant emissions to meet Euro 7 standards and provide a robust framework for improving air quality management.

1. Introduction

Despite improvements in air quality, many areas in Europe, especially cities, still do not meet current European Union (EU) standards. In 2021, 38.9% of EU citizens were living in cities and 35.9% in towns and suburbs [1]. Recent data shows that 96% of the urban population in Europe was exposed to fine PM levels above the World Health Organization (WHO) guidelines [2]. This exposure contributed to around 238,000 premature deaths in the EU in 2020, according to the WHO. To address this, the European Parliament and the Council have recently agreed on new air quality standards [3] for 2030 that are more aligned with the WHO’s global guidelines. Measuring the emissions levels of urban transport is the first step in tackling the imperative of improving the air quality in urban areas.

Road transport air pollutant emissions in the European Union have substantially decreased over the past 30 years due to vehicle emission standards, the increasing uptake of electric vehicles, and fuel quality requirements [4,5]. This continuous drop will be further enhanced by the recently adopted regulation [6] 2024/1257 for Euro 7 emission limits which establish rules not only for exhaust emissions of road vehicles but also, for the first time worldwide, for non-exhaust emissions from brake and tire wear.

Greenhouse gas emissions (GHGs) are also one of the main targets of EU legislations [7], with legislation aiming for at least a 55% reduction from all sectors by 2030 compared to 1990 levels in order to meet carbon neutrality by 2050 [8]. Road transport is the highest emitter among the transport sectors, accounting for 76% of all the EU’s transport GHGs in 2021 [9], 25% of which comes from heavy-duty vehicles (HDVs) [10]. However, if no further actions are taken, road transport emissions are projected not to drop below 1990 levels before 2032 [9]. In response to this, in May 2024, the European Parliament adopted regulation strengthening the existing CO₂ standards [11] (of regulation 2019/1242) from HDVs following the European Commission’s proposal.

Promoting sustainable transport modes such as public transport, specifically buses, can significantly reduce air pollutants and greenhouse gas emissions. The EU Sustainable and Smart Mobility Strategy emphasizes the need to shift towards more public passenger transport like buses [12]. Although the sales of battery electric buses are continuously increasing in Europe, their share among the total fleet remains low [13]. Therefore, understanding emissions from real-world measurements of current urban buses that operate in cities is necessary before moving to the Euro 7 emission standards.

Several research studies have focused on emissions from road transport due to their significant contribution to air pollution and greenhouse gas emissions. The Euro VI emission standard, introduced to limit harmful emissions from heavy-duty vehicles, has spurred a range of studies examining the performance of buses under real-world conditions [14,15,16,17,18,19,20,21,22]. Real driving emissions (RDE) testing has emerged as a critical method for understanding vehicle emissions in everyday operations, but there are still notable gaps in the available literature.

Indeed, most existing studies on road-based emissions of urban buses in Europe rely on a limited number of vehicles (up to 5 buses), lack coverage of a wide variety of routes as specific bus routes are selected that rarely cover both urban and suburban areas, and have a limited temporal sampling resolution which may not accurately reflect the diverse operating conditions of urban environments [14,15,20,23]. Although these studies measure the impact of different parameters, such as road slope, vehicle weight, traffic conditions, and ambient temperature, they often provide static average emission factor estimates and fail to offer speed-dependent emission factors, which are essential for accurately modeling emissions over different driving conditions, such as stop-and-go traffic, idling, and the varied speeds typical of urban routes.

While laboratory-based, simulation, and remote sensing studies have contributed valuable data, they often under- or overestimate real-world emissions, especially for pollutants like NOx and CO, as mentioned in several studies [14,24,25]. Furthermore, comparisons of Euro VI diesel, diesel-hybrid, and compressed natural gas (CNG) buses under real urban driving conditions are still limited [14,15,20].

To address these shortcomings, the present study provides an optimized methodology for developing speed-dependent emission factors based on real-world emission measurements from a diverse set of urban buses’ measurement data, allowing for a more dynamic and precise understanding of emissions across different driving speeds and conditions. By analyzing speed as a key variable in emissions variability, this study also aligns with established inventory methodologies, such as the EMEP/EEA Guidebook [26] and COPERT [27], which rely heavily on mean traveling speed for their calculations.

Our work uses an optimized methodology tailored to the operational characteristics of urban buses, which are characterized by frequent stops, varied speeds, and high idling times. By collecting portable emission measurement systems (PEMSs) data from 28 buses (1600 journeys) operating in the Paris region, this paper offers a robust dataset of emissions under real-world conditions. Journeys within the Paris region were made in both the densely populated urban area and in the suburbs. All the measured data (emissions, engine data, and location) has been temporally synchronized for analysis. This approach not only improves the accuracy of the emission factors but also provides valuable insights into the effectiveness of Euro VI technology across different powertrains and bus technologies. The experimental campaign employed a field-based approach using portable emission measurement systems (PEMSs) to capture real-time, high-frequency emission data during regular bus operations. The autonomous measurement system was designed for in-service measurements with passengers onboard, ensuring accurate insights into emission profiles under actual driving conditions and diverse traffic scenarios

The findings of this research are integrated into national emissions inventories through tools such as COPERT [27], enhancing the precision of urban transportation emissions modeling in Europe. Utilizing the total average energy demand and powertrain efficiency, the emission factors derived from this study can be compared to the Euro 7 emission limits [6].

2. Measurements

The materials and methods used in the measurement campaigns have already been described in detail [28]. A field-based approach was employed using PEMSs to measure emissions during the regular operation of buses across urban routes. For this, an autonomous measurement system has been developed to measure in real-world conditions with passengers. The use of PEMSs allows for real-time, high-frequency data collection under actual driving conditions, providing accurate insights into emission profiles across varied traffic and environmental contexts.

2.1. Vehicle Specifications

The measurement campaigns consist of a sample of 28 urban buses. The test fleet included a mix of diesel, diesel-hybrid, and CNG buses from four different manufacturers operating in the Paris metropolitan area. Differences also exist in the vehicles’ aftertreatment systems, which vary from three-way catalysts (TWC) for CNG buses to buses fueled with diesel and equipped with various combinations of exhaust gas recirculation (EGR), selective catalyst reduction (SCR), and diesel particulate filter (DPF) systems. The vehicles selected are therefore intended to represent typical vehicle technology in urban public transport systems.

Table 1. Main specifications and testing conditions of vehicles selected in the measuring campaigns. Testing conditions are described with the columns exhaust temperature, ambient temperature, and speed, where the average (and standard deviation in parenthesis).

Powertrain	Euro Standard	Number of Vehicles and Aftertreatment System	Odometer (km)	Curb Weight to MPLW (t)	Exhaust Temperature [°C]	Ambient Temperature [°C]	Speed [km/h]
Diesel	Euro IV	2 with EGR & 6 with SCR and DPF	500 K–600 K	11.8 to 18.2	214 (54)	14 (6)	16 (16)
Diesel	Euro VI	4 with SCR, EGR and DPF 4 with SCR and DPF	100 K–350 K	11.5 to 18.2	191 (52)	13 (7)	12 (13)
Diesel-Hybrid	Euro VI	2 with SCR, EGR and DPF 4 with SCR and DPF	100 K–200 K	13.0 to 18.7	187 (66)	13 (7)	13 (15)
CNG	Euro VI	6 with TWC	80 K–150 K	12.0 to 18.2	399 (85)	9 (5)	15 (17)

summarizes the key specifications of the tested vehicles, including information about the powertrain types, Euro standard, aftertreatment system, odometer reading, curb, and maximum permissible laden weight (MPLW).

2.2. Testing Routes and Conditions

In total, 16 experimental campaigns were conducted over a span of two years, covering a diverse range of operating and climate conditions (e.g., covering both summer and winter climate conditions, varying times of day, congested and non-congested traffic situations), which targeted typical commuting hours to ensure the results reflect real-world operation. The geographic range spanned several urban and suburban areas of Paris, capturing environmental variations to further refine the emission factors.

The campaigns consist of routes from all of the buses’ operations, including the bus rides between two services, or from/to the bus depot, and of trips on defined bus lines. Figure 1 illustrates an example of the instantaneous speed along a bus route that was part of the experimental campaign.

Table 1 presents the test conditions across the different powertrains and Euro standards. Notably, average speed differences are evident among the bus trips, influenced by the operating environment. Indeed, some buses primarily operate in city centers, while others serve suburban routes, leading to speed variations. Additionally, the differences in aftertreatment systems introduce some variation within diesel powertrains, particularly in emissions control and exhaust temperature. For CNG buses, exhaust temperatures are notably higher due to the single TWC without supplementary systems. Seasonal differences were also observed: CNG buses circulated slightly more kilometers in winter compared to diesel buses, resulting in a marginally lower mean ambient temperature across their routes. This variety of vehicles, powertrains, operating environments, and seasonal conditions provides a comprehensive representation of urban public transport operations.

2.3. Instruments and Procedures

Two PEMS devices were involved in this work, i.e., AVL MOVE GAS PEMS 492 iS and AVL MOVE PN PEMS 496 iS, and they were employed to record second-by-second data for carbon monoxide (CO) and carbon dioxide (CO₂), nitrogen oxides (NO and NO₂), and the particle number (PN, from 23 nm to 2.5 µm). A series of deep-cycle absorbent glass mat (AGM) batteries were used for providing power to the PEMS devices offering autonomy for up to 8 h. A modular design adaptive to different vehicle configurations was created, consisting of two separate boxes for batteries and devices.

The sampling probe was positioned 20 cm upstream of the exhaust to ensure laminar flow. The sampling line for the GAS PEMS had a length of 5 m and a diameter of 6 mm, while the one for the PN PEMS had a length of 5 m and diameter of 4 mm with a 6:1 dilution rate. Additionally, the buses were equipped with an exhaust flow meter (AVL MOVE EFM) 31 cm upstream from the sampling probe to collect information about the exhaust temperature and flow.

Vehicle-related data, such as vehicle speed, coolant temperature, and fuel rate, were gathered using the on-board diagnostics interface (OBD) for Euro IV buses and the fleet management system (FMS) interface for Euro VI and retrofitted Euro VI buses. Environmental conditions, including ambient temperature and humidity, were monitored with sensors positioned on the buses’ rooftop supplied with the AVL MOVE system. An advanced global navigation satellite system (GNSS) receiver (NEO-M8U, u-blox) was used to collect positional information, including coordinates and the road gradient for each record. The ALV Concerto version 5 software was used for synchronization of all the signals as well as dry–wet correction and humidity–temperature correction [28].

3. Results

3.1. The Importance of Speed as an Estimator for Emission Factors

In previous research performed by Cavellin et al. [28], emissions from buses with varying powertrains were assessed by calculating the average emission factors across all trips, covering pollutants like CO, NO_X, PN, and CO₂. While these comparisons provide a useful overview of differences between powertrains and Euro standards, they lack detail concerning speed’s role in emission variability, a critical factor in emission inventories.

Our study addresses this need by introducing a methodology that generates speed-dependent emission factors from real-world data, making it well-suited for integration into emission inventory models like COPERT. This seven-step methodology builds on approaches applied in previous studies [29,30] but optimizes them for the unique operational characteristics of urban buses. By basing emission factors on speed, this methodology enhances the accuracy of emission inventories without requiring granular data on every trip, a distinct advantage in large-scale modeling.

The role of mean traveling speed in emission modeling is well established [14,31]. Established models, such as COPERT [27], MOVES [32], and EMFAC [33], rely on mean traveling speed as a key input variable to link emissions to traffic conditions. Furthermore, speed-dependent emissions have been mapped at a road-link level within urban networks, enabling more precise emissions estimates on a city-wide scale [22,34]. The use of PEMS data in our study enables us to bridge the gap between theoretical modeling and real-world emissions, offering a robust framework for understanding emissions under actual operating conditions in urban areas.

The procedure guide of Figure 2 illustrates the seven-step method followed for developing the speed-dependent equation for the energy consumption and the pollutant emissions of the Euro VI urban buses. The same methodology can be applied to Euro IV urban buses. However, Euro IV buses, being over 15 years old, have largely been phased out and replaced across Europe. As a result, they are no longer relevant for future emission inventories, which focus on current and upcoming vehicle technologies.

3.1.1. Step 1: Data Cleaning

The initial dataset, comprising 6,500,000 data points with 1 Hz time resolution, included parameters such as trip data, fuel rate, pollutant emissions, and environmental conditions. Data transformation was performed to standardize units and formats. The data cleaning process then excluded trips with missing data or erroneous records, specifically removing records lacking vehicle speed or coolant temperature readings. A coolant temperature threshold of 80 °C was applied to filter out segments influenced by engine cold starts. Additionally, continuous periods exceeding two minutes with missing records were removed from further analysis.

3.1.2. Step 2: Subtrips Segmentation

To generate emission factors across a range of speeds, the trips were segmented into subtrips. The average speed of a whole bus trip varies mostly between 10 and 20 km/h, as Figure 3 shows. It is important to note that higher speeds are poorly represented in such real journey measurements.

Time- or distance-based integrations are more suitable to implement for creating emission factors as a function of speed in the case of available PEMS data which provide a wide variety of representative real-world conditions. Papadopoulos [30] and Keramydas [29] demonstrated that 500 m segments provide an optimal balance between uncertainty and data availability after evaluating the average coefficient of variance (CV) of emission levels, which is defined as the ratio of the standard deviation to mean value. However, a 200 m integration was selected here as it best captures the operational speed range of Paris buses, aligning with typical stop intervals (280–400 m in urban Paris and 450–830 m in suburban areas). The trend of the increase in resolution leads to an increase in the variability of energy consumption (EC) and emissions. This is something which was confirmed in this study, as

Table 2. Coefficient of variance (CV) and mean emission levels (in parenthesis) for different distance-based integration approaches. The values are the average CV within each 10 km/h speed bin while the values in parentheses are the average emission levels in the corresponding unit (g/km for CO, NOx, #/km for SPN₂₃, and MJ/km for EC).

Distance-Based Integration Method	EC	CO	NOX	SPN23
200 m	0.66 (13.47)	1.92 (0.36)	1.24 (2.00)	1.09 (9.88 × 1011)
500 m	0.48 (12.98)	1.63 (0.30)	1.06 (1.97)	0.92 (9.89 × 1011)
1000 m	0.38 (13.44)	1.4 (0.23)	0.96 (2.18)	0.85 (9.46 × 1011)

shows as it illustrates the CV (and their mean emission levels in a parenthesis) for each case. Energy consumed has been calculated from the total fuel consumed utilizing its energy content, and has been validated through the CO₂ emissions data based on carbon mass balance method.

Figure 4 illustrates that the decrease in variability exists at the same time regarding the average speed of the subtrips of the same distance integration, with the 200 m approach offering the wider speed variation which is required for the creation of speed-dependent emission factors. In all cases, the share of subtrips for which the average speed is above 45 km/h is cumulatively less than 7%, no matter of the distance integration method, since urban buses rarely circulate at high speeds.

A minimum distance of 100 m was imposed on each subtrip to avoid distortions, while idle times were retained to ensure a realistic representation of real-world emissions. However, it should be highlighted that the emissions during all idle times (e.g., during bus stops or at the traffic lights) were included in the calculations to better capture real-world emissions and avoid inserting any bias. Figure 5 presents an example of distance-based integration in a bus route.

3.1.3. Step 3: Emission Factor Calculation per Subtrip

After splitting each route into subtrips, pollutant emissions and distance covered within each subtrip were totaled. The emission factor for each pollutant was calculated by dividing the total emissions by the distance covered for that subtrip, as shown in the following equation:

$${\mathrm{EF}}_{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t} [\mathrm{g}/\mathrm{km}]}={\displaystyle \frac{\sum \mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\left[\mathrm{g}\right]}{\sum \mathrm{d}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\left[\mathrm{k}\mathrm{m}\right]}}$$

(1)

Equivalently, for energy consumption the numerator is the sum of energy instead of emissions. This calculation was conducted across all subtrips, routes, and buses, resulting in emission factor distributions for each pollutant across speed conditions. Figure 6 presents an example of the resulting energy consumption distribution by subtrip average speed for the case of a Euro VI diesel bus.

3.1.4. Step 4: Classification of Subtrips by Speed

The emission factors were grouped into 10 km/h speed bins, from 0 to 10 km/h up to 60–70 km/h, reflecting the bus fleet’s typical speed range in Paris. Since speeds above 45 km/h represent cumulatively less than 7% of the subtrips, data points above this speed are shown as dashed lines in Figure 7, which demonstrates the mean and standard deviation of energy consumption across speed for the three different powertrains.

3.1.5. Step 5: Calculation of Average Emission Factor per Speed Bin

The average emission factor was calculated as the ratio of the total emissions over the overall distance driven within each speed bin for each bus as a function of powertrains. The mean speed bin (30–40 km/h) of each bus served as the baseline, and each bin’s emission factor was normalized against the baseline speed bin to allow for consistent comparison across powertrains and Euro standards. This normalization reduces variability due to the specific characteristics of each vehicle, isolating the effect of speed on emissions. The need for normalization is further illustrated in Step 7.

3.1.6. Step 6: Aggregation of Speed-Dependent Emission Factors Across All Buses

The average emission factor for all buses (and its mean speed bin) was used as a reference value and multiplied with the average normalized ratios per speed bin from the previous step, producing points for use in the subsequent regression analysis.

3.1.7. Step 7: Regression Line

Finally, a regression curve was fitted to the derived emission factor data points using a rational function with a non-polynomial numerator, as proposed by EEA [26] and employed by COPERT [27], which is as follows:

$$\mathrm{E}\mathrm{F}={\displaystyle \frac{(\mathrm{A}\mathrm{l}\mathrm{p}\mathrm{h}\mathrm{a}\times {\mathrm{V}}^2+\mathrm{B}\mathrm{e}\mathrm{t}\mathrm{a}\times \mathrm{V}+\mathrm{G}\mathrm{a}\mathrm{m}\mathrm{m}\mathrm{a}+\mathrm{D}\mathrm{e}\mathrm{l}\mathrm{t}\mathrm{a}/\mathrm{V})}{\mathrm{E}\mathrm{p}\mathrm{s}\mathrm{i}\mathrm{l}\mathrm{o}\mathrm{n}\times {\mathrm{V}}^2+\mathrm{Z}\mathrm{e}\mathrm{t}\mathrm{a}\times \mathrm{V}+\mathrm{E}\mathrm{t}\mathrm{a})}}\times (1-\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{t}\mathrm{a})$$

(2)

where

• EF [g/km] ([MJ/km] for energy consumption, or [#/km] for SPN₂₃ emissions) is the emission factor,
• V [km/h] is the velocity of the vehicle,
• Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Zeta, and Theta are the equation parameters.

The application of the method is shown in Figure 8, which presents the energy consumption factor of the diesel-hybrid buses. This curve-fitting approach yields emission factors that are both consistent with previous studies [30] and reflective of Paris-specific operational conditions.

As Figure 9 shows, the application of normalized emission factors reduces variability and enhances model reliability, particularly in the example of NOx emissions for diesel buses where outlier behavior in underrepresented speed bins of high velocities is avoided. In the case of not applying the normalization with the mean speed bin, the extracted equation would have resulted in misleading conclusions (as indicated in blue line). This methodology thus produces robust, speed-dependent emission factors suitable for inventory applications and aligns well with COPERT-based emission modeling [27].

3.2. Resulting Emission Factors

The methodology developed in this study was successfully applied across all powertrains and pollutants, resulting in a comparative set of emission factors for Euro VI urban buses. The following sections describe the findings for energy consumption and key pollutant emissions, highlighting the impact of speed and powertrain type on emission patterns.

Figure 10 presents the energy consumption curves of the different powertrains of buses as a function of speed. Across all powertrains, higher energy demands are observed at lower speeds, which decrease until approximately 50 km/h in all cases except for CNG, where emissions are still reducing until 70 km/h. This trend aligns with expectations for urban driving, where frequent stops, accelerations, and idling increase energy usage at lower speeds. Among the three powertrains, CNG buses exhibited the highest energy consumption, followed by diesel, while diesel-hybrid buses demonstrated the lowest energy demand. This efficiency at lower speeds for diesel hybrids is largely attributable to their reliance on battery-supplied electricity during frequent start–stop events, with energy recuperated from regenerative braking. For speeds above 50 km/h, a slight increase in energy consumption is noted, likely due to increased aerodynamic drag; however, data above this threshold were limited, so these findings are preliminary. These results are consistent with previous research, which also shows that energy consumption generally decreases with speed up to a threshold and then rises slightly at higher speeds due to external drag forces [21,22,27,29,30,31].

The NO_X emission factors, shown in Figure 11, reveal a clear trend of elevated emissions at lower speeds, corresponding to congestion or stop-and-go traffic. Across all powertrains, NO_X emissions peak at lower speeds due to inefficient fuel combustion in stop–start scenarios. Notably, CNG buses emit significantly lower NO_X levels—about 3–4 times less than their diesel and diesel-hybrid counterparts. This reduction aligns with the expected cleaner combustion characteristics of CNG compared to diesel, making CNG a favorable option for reducing NO_X emissions in urban areas.

Figure 12 displays CO emission factors for each powertrain. The results indicate that CNG buses emit approximately twice as much CO as diesel buses and three times more than diesel hybrids. This difference in CO emissions is related to the nature of fuel combustion in CNG engines, where incomplete combustion at lower speeds can lead to higher CO emissions. Diesel-hybrid buses, in contrast, show the lowest CO emissions across speeds, likely due to the supplementary electric power during low-speed operations, which mitigates CO production in start–stop driving.

Figure 13 depicts the solid particle number per kilometer traveled as a function of speed. Diesel buses have the highest emission factor at almost every speed, surpassing 10¹² particles per km in speeds below 40 km/h. CNG and diesel-hybrid buses produce fewer particles overall, and the highest emissions for these powertrains occur only at speeds below 20 km/h, where the number of particles for CNG vehicles exceeds the one for diesel.

3.3. Impact of This Study on Emission Inventories

The speed-dependent emission factors developed in this study for the Paris urban bus fleet have been integrated into the COPERT software, beginning with versions 5.7 and 5.8 (released in 2023 and 2024, respectively) [35]. As COPERT serves as the primary emission inventory model for national inventories across most EU countries, the addition of these updated emission factors has implications beyond Paris, enhancing national inventories with more accurate emissions data for urban buses.

The relative changes introduced by these new emission factors, compared to the previous COPERT version (v5.6.5) which lacked these updates, are summarized in

Table 3. Impact of the results of this study to national emission inventories of urban buses at EU level for 2023. Values are provided as relative growth of emissions calculated using the resulting emission factors of this study compared to the emission factors.

Buses	CO	NOX	EC	SPN23
Diesel	7%	14%	3%	0%
Diesel Hybrid	−6%	144%	−20%	105%
CNG	−3%	−63%	−9%	−74%
All Urban buses	7%	10%	2%	−21%

. The table highlights the overall relative effect on emissions estimates across pollutants for an average EU urban bus fleet in 2023, using consistent activity data, fleet composition, and environmental parameters from EMISIA’s database [36]. To allow a fair comparison, all fleet and activity data as well as other parameters of COPERT are the same in both cases. The results show that the total NO_X, CO emissions as well as energy consumption (EC) from urban buses had been underestimated previously by 10%, 7%, and 2% while the solid particle number was overestimated by 21%. Specifically, NO_X and SPN₂₃ from diesel-hybrid buses were found to be almost twice the prior estimates while NO_X and SPN₂₃ emissions from CNG buses had been previously overestimated. Diesel buses, which constitute over 90% of the EU urban bus fleet, showed minimal discrepancies. These refined emission factors enhance COPERT’s ability to accurately reflect real-world emissions, especially as previous Euro VI emission factors relied primarily on simulations rather than real-world data.

3.4. Compliance of Euro VI Urban Buses with Euro 7 Limits

In previous research by Cavellin et al. [28], the average emission factors across all trips of Euro IV diesel buses in Paris had been compared against the ones of Euro VI buses of the same fuel type. The aim was to calculate the emission reductions from the advanced aftertreatment technologies (e.g., SCR) used in Euro VI buses, covering pollutants like CO, NO_X, PN, and CO₂. Building upon this, the current study examines potential emission reductions achievable under the new Euro 7 standards set to be enforced by 2028 for urban buses with regulation (EU) 2024/1257 [6].

The resulting emission factors of urban buses presented in this study can be compared to the Euro 7 emission limits. Different limits are defined for real driving emissions (RDE) and the worldwide harmonized driving cycles.

Table 4. NO_X, CO, and PN₁₀ Euro 7 emission limits for real driving emissions (RDE) for urban buses.

Pollutant	Emission Limit per kWh
NOX	260 mg
CO	1950 mg
PN10	9 × 1011 #

presents the emission limits of the Euro 7 buses for real driving conditions. Since the emission factors of this study have been developed from real-world data, the derived values are compared against the emission limits under real driving emissions so the limits according to the worldwide harmonized driving cycles are omitted. The regulation does not differentiate the emission limits for the powertrains and aftertreatment technologies, so all urban buses have to comply with the same limit, which is expressed in emissions per engine power.

To allow a fair comparison, the first step consists of the transformation into a common unit. For this reason, the distance-based emission levels (per km) were transformed into energy-based emission levels (per kWh) based on the engine power demand and its powertrain efficiency. Figure 14 shows the median and standard error limit per pollutant and powertrain, which are presented and compared with the respective emission limit. Results reveal that all powertrains meet the CO emission limits by a significant margin, while diesel and diesel-hybrid buses exceed the NO_X limits, suggesting the need for enhanced aftertreatment technologies to meet the Euro 7 requirements. While the regulation specifies a particle limit for particles down to 10 nm (PN₁₀), a fair comparison to our SPN₂₃ values cannot be performed since the cut-off criterion of the measurement device used is much higher at 23 nm. This would be very important not only to verify compliance with Euro 7 specifications but also for emission inventories and air quality compliance studies.

3.5. Uncertainty and Future Steps

Several sources of uncertainty remain in COPERT’s emission factors, particularly regarding factors like bus load (i.e., number of passengers) and road slope. Vehicle load directly affects fuel consumption and emissions in heavy-duty vehicles [21,29]. While the buses in this study have comparable weights under similar conditions, passenger load data were not recorded. Future urban emission studies should incorporate passenger count data as load impacts could further refine the results.

Road slope is a significant parameter taken into account when studying energy consumption and pollutant emissions. The impact of grade in fuel consumption and NO_X emissions has been investigated in similar studies [29,37] where distance-based emission factors per speed were developed for a variety of road slope conditions. The influence of the high uphill grades in mountainous areas has also been investigated [24] providing insights into the level of increase in fuel consumption and CO₂ emissions. Data availability is a restriction in the case of Paris as it is a flat city and only a small fraction of emission points (less than 2%) had uphill or downhill driving, which is not enough for deriving conclusions. Once more data are available from other cities, further updates can be performed.

Speed range limitations also exist since the average speed of bus trips in the examined dataset was less than 20 km/h. Despite the implementation of a high-resolution distance-based integration of 200 m, the uncertainty of the emission factor for an average high speed is still significant due to the limited sample size. Expanding data collection to include a broader range of speeds and operating conditions will improve the robustness of emission factors.

Another limitation is the exclusion of volatile organic compounds (VOCs) and their role in atmospheric chemistry. VOCs, including aldehydes like formaldeydes, contribute to the formation of the tropospheric ozone and pose a significant threat to human health and environment. Additionally, non-regulated pollutants, such as N₂O (contributing also to the greenhouse effect) and NH₃, which are increasingly relevant to air quality assessments, were not measured in this measurement campaign. Future measurement campaigns should aim to quantify these pollutants to provide a more comprehensive assessment of urban bus emissions.

Non-exhaust emissions from brake and tire wear are another important source of urban air pollution and should also be included in future studies. These emissions are heavily influenced by the weight of the vehicle [38], emphasizing the importance of taking them into account when measuring urban buses total emissions.

Although this study does not directly model pollutant dispersion or residential air quality impacts, the derived real-world emission factors can be integrated into air quality models such as COPERT to estimate pollutant concentrations in urban environments. Coupling these emission factors with atmospheric dispersion models could enhance assessments of human exposure and health risks. Future work should explore such integrations to provide a more complete understanding of the environmental and public health effects of urban transportation emissions.

Real-world PEMS data are invaluable in accurately quantifying urban emissions, providing a foundation for more precise predictions of emission trends. The study showed that it was technically feasible, although the automation of the measurement process, data synchronization, and technical guarantees for installation in the presence of passengers would have to be developed. The methodology developed in this study is adaptable and applicable to future datasets, facilitating continuous improvement of emission inventory models as new data becomes available. This study highlights the benefits of speed-based emission factor methodologies while acknowledging that diverse modeling approaches may suit various scenarios.

4. Conclusions

This study aimed to develop a methodology to generate speed-dependent emission factors for urban buses, specifically addressing the emission characteristics of Euro VI diesel, diesel-hybrid, and CNG buses operating in the Paris metropolitan area. Using a structured, data-driven approach, real-world PEMS data were transformed into emission factors across varied speed intervals to reflect the unique operational patterns of urban buses.

The main findings reveal that this speed-dependent methodology accurately characterizes emissions under real driving conditions, with notable variations between powertrains. Diesel and diesel-hybrid buses exhibit substantially higher NO_X emissions at lower speeds, with average values around 2 g/km at low speeds, compared to 1.4 g/km for diesel-hybrid and 0.6 g/km for CNG buses. Low-speed urban driving also results in elevated emissions for other pollutants, with CO measured at around 1 g/km for CNG and 0.5 g/km for diesel buses, while for SPN₂₃ all powertrains are in the order of 10¹² particles/km. These findings highlight the need for advanced aftertreatment solutions in the upcoming Euro 7 standards. Additionally, the methodology demonstrated that low-speed urban driving results in elevated emissions of NO_X, CO, and particulate numbers (SPN₂₃) due to frequent stops and idling.

Incorporating these real-world emission factors into the COPERT model not only enhances EU-wide emission inventories but also underscores the relevance of urban-specific emission data for air quality management simulations that estimate pollutant levels in urban environments, including residential areas. While this study primarily focuses on direct emissions, it lays the groundwork for future research that integrates emission factors with atmospheric dispersion models. Such models could provide more accurate assessments of population exposure and health impacts, particularly in relation to NO_X-driven ozone formation and secondary pollutants.

By providing a detailed methodology and robust dataset for urban bus emissions, this study contributes to ongoing efforts in improving air quality management and emission regulation policies. The developed approach, adaptable to various urban settings and pollutant types, sets a foundation for further research and data collection, ultimately supporting policy decisions aimed at reducing emissions from urban transportation.