Where does the Development of Road Transport Emission Macro

Modelling Lead?

Mohammad Maghrour Zefreh and Adam Torok

Department of Transport Technology and Economics, Budapest University of Technology and Economics,

Muegyetem rkp 3., Budapest, Hungary

Keywords: Road Transport, Emission Modelling, Comparative Analysis.

Abstract: In recent years, road transport models have developed for better estimation of road traffic emissions with

higher and higher temporal and spatial resolution, to be used as a tool in air quality management for the

better living. Road transport related emission models are becoming more and more complex. In this paper,

the key research question is how the improvement in modelling influences the results? The authors

compared three different macro emission modelling system with the dataset of Hungary for 2010. One must

notice that more precise model has larger data requirement. Firstly, the consumption-based model was run

with 31 needed input data secondly, EURO standard based model was run with 261 needed data and finally,

speed dependent model was run with 1060 needed input data. According to the results, it can be stated that a

more complex model could cause significant differences in emission compared to simpler one. The

differences can be caused by old Hungarian vehicle fleet or differences in estimation error.

1 INTRODUCTION

The emission of greenhouse gases (GHGs) and the

consequential climate change impacts is one of the

greatest challenges facing the global community

(Stern, 2007). The transport sector contributes

significantly to society and the economy (Wismans

et. al., 2011). It also can cause substantial adverse

impacts on the environment, global climate and

human health in different ways. This main source of

environmental noise leads to emissions of

greenhouse gases (GHG) and air pollutants, and

consequently habitat fragmentation. Some air

pollutants persist in the environment for long periods

of time and they may accumulate in the environment

and in the food chain, affecting humans and animals

not only via air intake but also via water and food

intake. Air pollution is, therefore, a complex

problem that poses multiple challenges in terms of

management and mitigation and has been studied

several times in the case of air quality, emissions etc.

(Brand et. al., 2012), (Samaras et al., 2012).

Effective action to reduce the impacts of air

pollution requires a good understanding of the

sources that cause it, as well as up-to-date

knowledge of air quality status and its impact on

humans and on ecosystems (European

Environmental Agency, 2015). The European Union

has a wide range of policies of nature protection,

noise and fuel quality to air quality which have

resulted in some significant improvements in

environmental performance. In this paper, authors

have only dealt with emissions of greenhouse gases

(GHG) and air pollutants. Although European air

quality is projected to improve in future with a full

implementation of existing legislation, further

efforts to reduce emissions of air pollutants are

necessary to assure full compliance with EU air

quality standards set for the protection of human

health and the environment.

In recent years, road transport models have

developed for better estimation of road traffic

emissions on macro level with higher and higher

temporal and spatial resolution (PTV Group, 2015),

(Liu, 2007), (Verkehr, 2011), (Axhausen and

Garling, 1992), (McNally and Rindt, 2008) to be

used as a tool in air quality management for the

better living. It is recognised that road transport is

one of the major pollution sources for urban

dwellers (Ahrens, 2003), (Fenger, 1999), (Andreoni

and Galmarini, 2012). In some European countries

estimates of road transport emissions have been

made on a national basis, and more locally as part of

pollution impacts studies, since the 1970s. The

methods used have been improved and developed

100

Zefreh, M. and Torok, A.

Where does the Development of Road Transport Emission Macro Modelling Lead?.

In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 100-104

ISBN: 978-989-758-184-7

since then, mainly depending on the amount, type

and quality of data available (European

Commission, 1999). (Figure 1):

Figure 1: Change of Annual Harmful Emissions from

Road Transport – Hungary.

(http://www.kti.hu/uploads/images/Trends-9/3 Environme

nt/ GT09_3-350.JPG)

To develop a consistent approach for analysing

traffic-induced environmental emission, a precise

quantification of pollutant amount emitted by

vehicles to the atmosphere is essential (Azar et. al.,

2003). One of the approaches commonly used for

this purpose is emission modelling. In the literature,

emission models are often placed into two broad

categories: macro-scale and micro-scale models (e.g.

André et al., 2006; Zachariadis and Samaras 1997).

The macro-scale models estimate emissions in a

large area, e.g. an urban or national network (i.e. at

the vehicle fleet level) and give the average

emissions for a group of vehicles, while the micro-

scale models come down to the street level and

predict emissions at the individual vehicle level and

may involve a complete driving cycle for each

vehicle. Macro-scale emission models could also

produce inputs for an air quality model applied in an

urban region, but additional information would be

required to increase the accuracy (Zachariadis and

Samaras 1997).

In this paper, three different macro emission

modelling system have been compared with each

other. The key research question is: what the

increasing accuracy of macro emission modelling

in road transport could cause? How the models

are improved? The authors compared three

different emission modelling family. Some

preliminary results were already communicated

(Török Ádám, 2015):

Figure 2: Evolution of emission models (Török Ádám,

2015).

2 METHODOLOGY

In order to be able to compare the three different

evolutionary steps of road transport related emission

modelling the same modelling environment were

given (for instance, all models were run with

Hungarian vehicle dataset on 1 km of straight road

with the layout of double single lane road and only

spark ignition (Lakatos, 2015), and compression

ignition engines (Barabás, 2015), (Tutak et. al.,

2015), (Zöldy and Török, 2015) were considered

with the same free flow traffic conditions).

Top-down equation description is used in order

to see the decomposition of emissions on each

evolution stage.

At first, the consumption-based model is

surveyed (Szendrő and Török, 2014), (Astarita, et.

al., 2015), (1):







,

∙





















,

∙



∙



∙



∙













(1)

where:

i,j

: emission factor for vehicle group i for pollutant

j [kg emission/kg fuel]

: mass of consumed fuel for vehicle group i

[kg fuel]

: fuel consumption for vehicle group i [l/100 km]

: travelled distance for vehicle group i [km]

: fuel density for vehicle group i [kg/l]

: number of vehicles in EURO group [pcs]

Secondly, the EURO emission standard based

emission model is considered (Csikós, et. al., 2015)

(2):

Where does the Development of Road Transport Emission Macro Modelling Lead?

101



,

∙



∙









(2)

where:

i,j

: emission factor for vehicle group i for pollutant

j [kg emission/kg fuel]

: travelled distance for vehicle group i [km]

: number of vehicles in EURO group [pcs]

Thirdly, the velocity based emission model is

investigated (Toşa, et. al., 2015), (Li et. al., 2015). In

this model micro level velocities were considered

derived from macroscopic fundamental diagram

(Stamos, et. .al., 2015), (Husnjak et. al., 2015), (3):









,

 ∙



∙









(3)

where:

i,j

: polynomial approximation of speed based

emission in EURO group i for pollutant j

[kg emission/kg fuel]

: travelled distance for vehicle group i [km]

: number of vehicles in EURO group [pcs]

3 RESULTS

Three different models were programmed in the

same software environment. These three models

have different complexity and data requirements

(Figure 3). Firstly, the consumption-based model

needed 31 data secondly, EURO standard based

model needed 261 data and thirdly speed dependent

model needed 1060 data. Each case data for

Hungary for 2010 has been used.

Figure 3: Comparison of input parameters (own edition).

The comparison of emission modelling shows

that there is a direct relation between more detailed

model and the lower amount of emitted greenhouse

gases (GHG). Approximately 60 % of greenhouse

gases (GHG) emission can be theoretically reduced

only by using the velocity based modelling. In the

case of other pollutants such as CO, HC and PM

emission environmental class-based modelling had

the largest result with significant differences

compared to other models. This result probably

could be derived from the old vehicle fleet in

Hungary with the average 13.6 (2010) years. NO

emission is continuously increasing with the

increasing complexity.

Such phenomena could be derived from a more

precise description of burning process in the engine.

As it can be stated in these models the perfect

burning was considered in terms of CO

. With more

accuracy modelling the CO

emission decreased

while it leads to the increase of other pollutants.

Not only problem between different types of

emission modelling but considerable differences

were captured between laboratory tests and on-road

measurements recently. European Environmental

Agency reported in 2015 (European Environmental

Agency, 2015) that the road transport sector emits

around 40% of Europe's NO

emissions, comprising

a mixture of NO and NO

. Although NO is not

harmful to health at the concentrations typically

found in the atmosphere, NO

is associated with a

range of environmental and health problems. The

emitted NO

from vehicles, around 80 % comes

from diesel-powered vehicles, for which the

proportion of harmful NO

in the NO

is far higher

than the proportion found in the emissions from

petrol vehicles. Over the past years, increasingly

strict vehicle emission standards (i.e. the so-called

'Euro' standards) have been introduced in Europe in

order to limit the amount of air pollution emitted by

vehicles. In order to be placed on the EU market,

vehicle models are first tested under laboratory

conditions using a pre-defined 'test-cycle' and their

emissions are measured. Recently, there has been

increasing public attention on the current vehicle

emissions testing regime. It is clear that both the on-

road fuel consumption and emissions from European

cars can be significantly higher than the official

vehicle test measurements would indicate. The

amount of fuel consumption by cars on the road, and

subsequently the CO

emissions, can be 20–30 %

higher than the official measurements. The

differences are even higher for NO

emissions, in

particular for diesel vehicles. Real-life

measurements have shown that NO

emissions from

diesel vehicles can be, on average, as much as four

or five times higher under real driving conditions.

SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

102

Petrol vehicles broadly meet the 'EURO' standards

under real driving conditions. Such differences are

attributable to a variety of factors, including the fact

that the current laboratory test cycle used in Europe

is not very representative of how people drive their

cars in real life. Furthermore, current legislation

affords manufacturers a number of flexibilities,

which enable them to optimise vehicles for the

testing procedure. In order to address these issues,

Europe will change the test cycle used to measure

vehicle emissions in the future to ensure that it better

reflects real-world driving conditions and to remove

many of the existing testing flexibilities. In addition,

a new real driving emissions testing procedure will

shortly be implemented, which will also provide a

valuable assessment of the NOx levels during the

on-road performance of vehicles compared with

laboratory testing.

4 ANALYSIS AND DISCUSSION

Scientists using road transport related emission

modelling in the last couple of decades. New

methods are introduced not only in measurement but

in modelling as well. In this paper, three different

modelling approaches were compared to each other

in order to reveal the differences between model

families. According to the results, it can be stated

that the different emission model could cause

significant differences in emission.

European Environmental Agency stated

(European Environmental Agency, 2015) that

transport contribute to Europe's air pollution.

Emissions of the main air pollutants in Europe have

declined since 1990, resulting in generally improved

air quality across the region. However,

transportation has not sufficiently reduced its

emissions in order to meet air quality standards or

have even increased emissions of some pollutants.

For example, emissions of nitrogen oxides (NO

)

from road transport have not sufficiently decreased

to meet air quality standards in many urban areas.

The key question would be the way of estimation

behind this statement without questioning the

rightness of pollutant decreasing.

5 CONCLUSIONS

In this paper, three different road transport emission

model with different complexity and data

requirements were compared with each other using

Top-down equation description in order to find out

how the improvement in emission modelling

influences the results. This comparison showed that

there is a direct relation between more detailed

model and the lower amount of emitted greenhouse

gases (GHG). Using velocity based modelling

showed that around 60 % of greenhouse gases

emission can be theoretically reduced. On the other

hand, using environmental class-based modelling

showed the largest result with significant differences

relating to CO, HC and PM compared to the other

models. It should be mentioned that NO

emission

increased continuously with increasing complexity

of the models.

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