Development of an emissions data base for air
pollutants from mobile sources in Portugal
C. Borrego, N. Barros, O. Tchepel, M. Lopes and A. Miranda
Department of Environment and Planning,
University of Aveiro, 3810 AVEIRO, Portugal
Email: borrego@ua.pt
Abstract
A methodology to elaborate a traffic emission inventory for Portugal, particularly
for urban areas, is presented. This methodology integrates bottom-up approach
for area sources and top-down approach for line sources. Time dependent
functions are used for temporal downscale of emission data.
The described methodology was applied to the Lisbon Region, for typical
Summer and Winter conditions. The obtained results were presented in a GIS
support that is also a fundamental tool to convert emission data in order to be
used by air quality models.
1 Introduction
The road traffic is continually increasing around the World. Portugal is
one of the European Union countries where the transport sector,
measured in terms of the number of vehicles multiplied by the number of
kilometres travelled, has grown much faster than gross domestic product,
while the population has remained stable 1. According to this source,
an unprecedented road-building process has been under way for the last
10 years in Portugal: from 1990 to 1994, the total length of motorway
increased from 318 to 587 km, an 84% increase! This trend is expected
to continue and emissions from the transport sector, which are mainly
related to gasoline and diesel combustion, are expected to increase 46%
between 1990 and 2000 and 78% by 2010 1.
The main pollutants emitted by automobile vehicles are carbon
monoxide (CO), nitrogen oxides (NOx) and volatile organic compounds
(VOC). At present, about 56.5% of CO, 48.3% of NOx, 12.6% of VOC
from the total annual emissions are produced, in Portugal, by the
transport sector 2. Attending to the expected increase of this activity’s
sector, emissions from automobile vehicles should be a source of large
concern.
This concern would be greater if one considers that 80% of the
Portuguese territory contains only 20% of the population, the remaining
population being concentrated on the coast, mainly in the large cities of
Lisbon and Oporto 1.
One of the main sources contributing to the deterioration of the air
quality in urban areas is road traffic. Micrometeorological conditions
preventing the dispersion of pollutants also contribute to the overall
deterioration of urban air quality and lead to especially acute episodes of
air pollution 3. To design an effective air pollution abatement strategy
it is necessary to identify pollution sources and to quantify their
emissions. This emission information shall be used afterwards as input
data to mathematical dispersion models that can be applied to assess the
effectiveness of control strategies.
Therefore, the elaboration of a road traffic emission’s inventory
constitute an important step for atmospheric modelling, models
validation, air pollution control strategies and other scientific
applications and political decision.
The main purpose of this paper is to describe the methodology
applied to develop an emission inventory for air pollutants from traffic
sources in Portugal, particularly in urban areas. The application of this
methodology to an urban case study will be presented.
2 A Case Study
Emission inventory developed for Portugal integrates the PolAr database
and includes emissions information covering almost all anthropogenic
and biogenic sources with high spatial and temporal resolution 4, 7. In
order to estimate traffic urban emissions, the developed methodology
was applied to the Lisbon Region, which includes 2 NUT III from
CORINAIR90: “Península de Setúbal” and “Grande Lisboa”. It covers 15
municipalities and 185 submunicipal administrative units (“freguesias”).
The Lisbon Region include important urban and industrial zones, with
almost 3 million of inhabitants and where the intensive traffic contributes
to high emission rates. The gaseous emissions under complex
geographical features and mesoscale atmospheric circulation (coastal
zone) that occurs on the region plays an important role in the air
pollution problems.
Emission inventories to be used for air quality and air quality
planning require appropriate spatial and temporal resolution 5, in
particular urban emission inventories. Generally, there are two
approaches for the establishment of temporally and spatially resolved
emission data: top-down and bottom-up approaches. The methodology
used for establishing urban traffic emission inventory is a combination of
these two approaches.
3 Top-down approach
The top-down approach uses available inventories for a greater emission
area and brake down the overall emissions to sub-units using actual data
for source strength and emission generating activities, or - if such is not
available - surrogate statistics 5.
The CORINAIR90 National Inventory constitutes the basis for the
spatial and temporal downscale of road traffic emissions in Portugal.
3.1 Spatial Downscale
The NUT III area emissions data, from the CORINAIR90 inventory, have
been downscaled to municipal level, using the fuel consumption. The
fuel and vehicle types were taken into account. The statistical
information was combined with the administrative map of Portugal.
However, the municipal level has still not enough resolution for
dispersion models. As a next step, the population data, such as total
population, population in age of economic activity or occupied in
industry and services, were used as indicator for downscaling of
emissions to sub-municipal level (“freguesia”). This data was obtained
from Census 91 of Institute of Statistics 6.
3.2 Temporal Downscale
The general idea of a temporal downscaling adheres to a similar
philosophy as the spatial downscale, being in general the temporal basis
annual emissions values 5. The standard for urban air modelling is a
time resolution of one hour.
The use of standardised fixed emission quotas is a very
straightforward approach to approximate the temporal emission patterns.
The method aims at modelling the ‘typical‘ rather than the ‘actual’
emission dynamics using surrogate data. The most important step in
creating a useful temporal downscale is the selection of adequate data.
In order to find the fixed emission quota to apply in Portugal, traffic
counts, from numerous roads of Portugal, were available from the Junta
Autónoma de Estradas (JAE). This database contains information about
daily mean traffic (DMT) for different periods: Summer/Winter and
Diurnal/Nocturnal.
With the purpose of finding a relation between Summer and Winter
traffic and between diurnal and nocturnal traffic, analysis of DMT for all
the count locations has been done 4. The results of analysis show
relations of 87/13 and 53/47 for diurnal/nocturnal and Summer/Winter
periods respectively. It was assumed that those relations are also applied
to the sub-municipal level.
Therefore, annual emissions have been downscaled to daily
emissions, considering the Summer period between April and September
(183 days) and the Winter period between October and March (182
days). Those daily emissions have also been broken down to hourly
emissions, assuming that the diurnal period corresponds to 16 hours and
the nocturnal to 8 hours. This procedure has been done for all the
“freguesias” of Portugal.
It should be noticed that there might be regional differences in the
emission dynamics that need special consideration and should be taken
into account in the near future.
3.3 Application
CO, NOx and VOC transport emissions from the CORINAIR90 inventory
where broken down, from the NUT III resolution to the municipal level
and then to the sub-municipal level using, respectively, statistical data of
fuel consumption and statistical data of population. The emissions rates
have been calculated within GIS, by dividing estimated values for submunicipal level to the administrative unit area.
The temporal downscale of those annual area emissions to a daily
basis was done using the DMT estimates provided by JAE, as described
before (see 3.2). Considering day and night emissions as constants, it was
possible to obtain hourly emissions, for each “freguesia” of study area.
As an example, in figure 1 the results for seasonal hourly CO
emissions in the “freguesia” of Amora are present. From the figure it is
possible to verify that Summer emissions are greater than Winter's one.
Diurnal emissions are, as foreseen, much greater than the nocturnal.
-1
Emissions (kg.h )
150
100
Winter
Night
Winter
Day
Summer
Night
0
Summer
Day
50
Figure 1: Road traffic CO emissions for the “freguesia of Amora”.
As an example of final result of spatial and temporal downscaling,
daily NOx emissions rate for typical Summer day on each “freguesia” of
the study area (kg/km2) is presented on figure 2. The obtained results
show a coherent distribution pattern with the greater emission level
associated to the urban centres.
Lisbon
Atlantic
Ocean
Setubal
Figure 2: Daily NOx traffic emissions.
NOx ( kg/km2)
0-5
5 - 100
100 - 1000
> 1000
4 Bottom-up approach
The bottom-up approach provides estimates for a particular region
(administrative units) by utilising local data set and applying emission
factors.
4.1 Methodology
Mobile sources include emissions from road transport, off-road vehicles
and machinery, railways, waterways and air traffic. There are three kinds
of emissions from road traffic 8: (i) hot emissions, emissions from the
vehicles after they warmed up and their engine is thermally stabilised;
(ii) cold start emissions take place while vehicles are warming up; and
(iii) evaporative emissions, that account for the evaporation of gasoline.
In the scope of this work, only road traffic hot emissions was considered.
Due to insufficient direct emission measurements, emissions rates are
usually estimated from mass balance calculations, being translated into
an emission factor 8. The emission model developed for principal roads
in Portugal is based on CORINAIR methodology.
Four vehicle categories have been considered: light-duty gasoline
vehicles (or passengers cars), light-duty diesel vehicles, heavy-duty
vehicles and two-wheeled vehicles. Concerning the driving modes,
following types have been distinguished: urban, rural and highway.
Principal roads was processed as a line sources and their emissions
were calculated on the basis of emission factors, daily mean traffic and
road length:
E i =  e ij . DMT j . L
4
j=1
where: E i – daily emission of pollutant i for road segment; eij - emission
factor for pollutant i and vehicle type j; DMT - daily mean traffic; L road segment length.
Highway emission factors have been applied for highways, principal
and complementary itineraries. For other roads the rural emission factors
has been used. Data related to the DMT was obtained from the JAE 9
and 57 measurement points were considered.
Geographical Information Systems were used for road segments
length calculation. Line sources have been subdivided, taking into
account the existent crossroads. Traffic data applied to each segment
resulted from the measurements realised along each segment or, in its
absence, along the nearest one.
4.2 Application
The result obtained by the application of described methodology is
presented on figure 3. The figure shows the considered line sources, the
measurements points and the NOx emission rate (kg/km) for a typical
Summer day for each road.
NOx (kg/km)
0-5
5 - 50
50 - 100
100 - 500
Lisbon
Atlantic
Ocean
Setubal
Figure 3: Daily NOx emissions from line sources on Lisbon Region.
It should be noted that greater emissions rates are associated with
highways, particularly those stretch near the Lisbon City. This fact was
expected attending that traffic flux increase within the urban zone
influence.
Daily pollutants emissions associated to principal roads were
aggregated for each NUT III area. Contribution of line sources to total
NUT III emissions were analysed. Figure 4 presents this contribution, for
Lisbon
a typical Summer1 day, for the considered NUT’s.
Setubal
0,8
0,6
0,4
0,2
0
CO
NOx
VOC
Figure 4: Contribution of line sources to total traffic emissions.
It is possible to verify that line sources contribute more to the total
emissions in “Península de Setúbal” than in “Grande Lisboa”. This
should be related to the fact that the road network of Lisbon
municipality, treated as area sources, has a significant contribution to the
total traffic emissions of the “Grande Lisboa” NUT III.
The same methodology described before has been applied to calculate
emissions from the road network of Lisbon’s city, using on this case
information provided by the traffic automatic counting system
GERTRUDES. In this paper, and as an example of application, the
hourly emissions from two specific roads located on the city centre are
presented: “R. do Ouro” and “Av. Da Liberdade”. To analyse the
emissions seasonal beaver two days have been chosen – a Summer
(95/08/23) and Winter (95/11/22) working days. There were available
hourly traffic flux data from 5 counters distributed along the considered
roads. Figure 5 presents the hourly CO emissions estimated for two
roads.
Both streets present a similar behaviour along the two chosen days:
CO emissions are smaller during the night, start to increase around 7:00
a.m., within maximum value among on the afternoon (between 11:0013:00 a.m. and 3:00-5:00 p.m.), and decrease again on the evening. This
behaviour is associated with the typical daily commercial/business
activity that occurs on the centre of a large city as Lisbon.
Emissions in the “Av. da Liberdade” are greater than in the “R. do
Ouro”, in agreement with the different road type of the streets. In fact,
“Av da Liberdade” is an avenue with 4 ways and “R. do Ouro” is a one
way street.
Av. Da Liberdade (95/08/23)
Av. Da Liberdade (95/11/22)
R. do Ouro (95/08/23)
R. do Ouro (95/11/22)
Hourly emissions of CO (kg)
120
100
80
60
40
20
23-24
22-23
21-22
20-21
19-20
18-19
17-18
16-17
15-16
14-15
13-14
12-13
11-12
10-11
8-9
9-10
7-8
6-7
5-6
4-5
3-4
2-3
1-2
0-1
0
Daily period
Figure 5: Hourly CO emissions variation for two urban streets and for two
different days (Summer and Winter days).
4 Conclusions
Detailed urban transport emissions were obtained by the temporal and
spatial downscale of the CORINAIR90 inventory and by the information
provided by PolAr database. The developed methodology involves two
different approaches: top-down for area sources and bottom-up for line
sources, where GIS constitutes an important tool for data processing.
The following analysis was performed:
 Spatial emissions distribution within the study area;
 Daily and seasonal variation of traffic emissions;
 Contribution of line sources to total traffic emission;
 Hourly emissions variation for specific roads located in Lisbon city
centre;
Acknowledgements
This work is supported by PRAXIS XXI grants and funded by
PEAM/P/AMA/603/95, Praxis/3/3.2/EMG/1949/95, Praxis/3/3.2/AMB/38/94
projects.
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