Modelling the on-road traffic emissions
from Catalonia (Spain) for
photochemical air pollution research:
weekday/weekend differences
R. Parra & J. M. Baldasano
Environmental Modelling Laboratory,
Universitat Politècnica de Catalunya (UPC), Spain
Abstract
On-road traffic can vary between weekdays and weekends, both in timing and
magnitude. It influences atmospheric emissions behaviour and tropospheric
ozone concentrations. Giving priority to the bottom-up approach, we developed
the high spatial (1 km2) and temporal (1 hour) resolution EMICAT2000 model,
to estimate atmospheric emissions for the year 2000 from Catalonia (Spain). Its
on-road traffic component was built with a high-resolution digital road-map and
considered the fleet of vehicles, traffic intensity and profiles for weekdays and
weekends; for every highway and the most important roads and urban streets
(daily average traffic > 5000). Most of the emission factors were taken from the
top-down COPERTIII model. We used specific Carbon Bond 4 profiles for
NMVOC speciation. During the year 2000, emissions of NOx were 62.4 kt yr-1,
VOCs 50.5 kt yr-1, CO 259.0 kt yr-1, SO2 1.3 kt yr-1, PST 15.7 kt yr-1. Emissions
are mainly located on the Metropolitan Area of Barcelona and on the axis of
highways following the coastline. Gasoline vehicles represent 70% of the park,
but they emit ~57% of NOx and ~92% of VOC. Due to the drop of heavy duty
vehicle traffic, NOx emissions on weekends are ~22% lower and emission of
VOC are only slightly higher. Influences of these changes on ozone pollution
could be deeply understood using a chemical transport model. Development of
high spatial resolution emission is expensive both in time and resources, but the
benefits and potential uses completely justify the investment.
Keywords: traffic, emission, speciation, weekend effect, Catalonia.
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
4 Air Pollution XII
1
Introduction
Ozone is a secondary air pollutant whose behaviour is difficult to understand and
control. It is formed by photochemical reactions of nitrogen oxides (NOx) and
volatile organic compounds (VOC). Also, ozone could be transported from the
stratosphere. Its measures at any given location may have contributions from
different sources as background concentration, long-range and regional transport,
or local photochemistry. On-road traffic, among other sources, is recognized as a
significant source of ozone precursors.
In Catalonia (Spain), located in the NE of the Iberian Peninsula, more than
1390 cases of excedances in the legislative hourly concentration threshold for
people information (180 µg m-3) were recorded in the period 1991 – 2002, and
almost 80% of these happened into the period June – August.
Mathematical modelling could be used to explain this source of pollution
(Rusell and Dennis [7]) using a chemical transport model (CTM). Before this
activity, it is necessary to know the magnitude, superficial and temporal
distribution of ozone precursors. Focussing in this objective for Catalonia, we
developed the first version of the EMICAT2000 emission model. It includes the
biogenic, on-road traffic and most important industrial sources.
Previously, and toward on-road traffic emission, Delgado et al. [3] made the
most important work, presenting results for the year 1994. Being both on-road
traffic and knowledge under continuous changes over time, (e.g. park vehicles
evolution, emission level reductions, more reliable emission factors, more
developed emission models), on-road traffic is one of the sources demanding
continuously improvements and updating.
We describe the model basis and results obtained using the on-road traffic
emissions component actually built into EMICAT2000.
2
Method
EMICAT2000 was built taking the year 2000 as reference. Primary pollutants
included are NOx, VOC, carbon monoxide (CO), sulphur dioxide (SO2) and total
suspended particles (PST).
2.1 Mathematical model
Using the bottom-up approach, we built an updated digital map of all the
highways and most important roads and streets. Using high spatial (territory was
divided with cells of 1 km2) and temporal (1 hour) resolutions; we included three
kinds of emissions: (1) hot exhaust emissions, occurring under thermally
stabilised engine and exhaust after treatment conditions, (2) cold exhaust
emissions, occurring during transient thermal engine operation (cold start), and
(3) evaporative emissions, non-exhaust VOC emission relevant for gasoline fleet.
Also, we included non-exhaust particles emissions (tire and brake wear, road
abrasion).
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
Air Pollution XII
5
2.1.1 Hot exhaust emissions
They were estimated using equation (1):
E ihot
r (k, d) =
n
∑ Clf.Crd.DAT (k).L (k).F
rj
r
ihot
(s r )
j
(1)
j=1
Parameters:
road section (urban, rural or highway type) into the cell k.
pollutant (NOx, COV, CO, SO2, PST, CO2, CH4 y N2O).
vehicle category
total vehicle type number considered (36 for Catalonia).
Term:
E ihot
hot exhaust emission of the pollutant i during one day
r (k, d) :
(weekday or weekend) in the road section r. It is expressed in
g d-1.
Data:
ihot
Fj (s r ) :
hot emission factor of the pollutant i, for the vehicle category
r:
i:
j:
n:
L r (k) :
DATrj (k) :
j (g km-1). It is a function of the typical speed sr (km h-1).
length of the road section r (km).
daily average traffic of the category j on the road type r
(number o vehicles j per day)
Crd :
relation between daily traffic for a specific month and DAT.
Clf :
coefficient for daily traffic (weekday or weekend).
Hourly emissions profiles were estimated using equation (2):
 Crh  ihot
E ihot
(2)
.E r (k, d)
r (k, h) = 
 100 
Parameter:
h:
hour (0,1,...........23).
Term:
E ihot
hot emission of the pollutant i during the hour h (g h-1).
r (k, h) :
Data:
C rh :
percentage of the traffic during the hour h in relation to the
daily traffic.
Monthly emissions were obtained adding up the respective daily values. The
same procedure was used for annual emission.
2.1.2 Cold exhaust emissions
They seem to be most likely for urban driving. They occur for all vehicle
categories, but emission factors now can be reasonable estimated for gasoline
and diesel passenger cars (Ntziachristos and Samaras [8]). Hourly cold emissions
were established as additional emissions using equation 3:
n
 Fjicold



−
E icold
(k, h) =
E ihot
(at)
1
(3)
r
r (k, h).β (ltrip, at).
 F ihot

j=1
 j

∑
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
6 Air Pollution XII
Term:
E icold
(k, h)
r
:
hourly cold emission of the pollutant i (only for urban roads)
(g h-1).
Data:
β:
fraction of the mileage driven with cold engines or catalyst
operated below the light-off temperature. It is established as
function of the average trip length (ltrip) and the ambient
temperature (at).
Fjicold
Fjihot
:
cold over hot ratio of pollutant i emissions.
2.1.3 Evaporative VOC emissions
There are three primary sources of evaporative emissions. (1) diurnal (daily)
emissions, associated with daily variation in ambient temperature that result in
vapour expansion inside the gasoline tank; (2) hot soak emissions, caused when a
hot engine is turned off, increasing the temperature of the fuel (as into the
carburettor) no longer flowing; and (3) running losses, that result of vapour
generated in gasoline tanks during vehicle operations.
Total daily diurnal emissions were estimated using equation (4):
E devap
(daily) = N m .(e dm (t min , t max , RVP))
m
(4)
Parameter:
m:
number of vehicle categories (gasoline passenger cars with and
without control, motorcycles < 50 cc and motorcycles > 50cc).
Term:
E devap
(daily) :
m
total daily VOC diurnal evaporative emission produced by the
vehicles of the category m (g d-1).
Data:
Nm:
number of vehicles of the fleet of Catalonia belonging to the
category m
e dm :
diurnal emission factor (g VOC d-1) for vehicles of the category
m. It is a function of the minimum/maximum ambient
temperatures (tmax, tmin) and the gasoline volatility (measured
by its reid vapour pressure (RVP).
Total daily hot soak emissions were estimated using equation (5):
swarm
E sevap
(daily) = N m .(p.x m .e shot
(at, RVP))
m
m (at, RVP) + w.x m .e m
Term:
E sevap
(t) :
total daily VOC hot soak emission (g d-1).
m
(5)
Data:
p:
fraction of trips finished with hot engine. It depends on the
average monthly ambient temperature.
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
Air Pollution XII
7
w:
fraction of trips finished with cold or warm engine or with
catalyst below its light-off temperature.
xm:
mean number of trips of the vehicles type m.
shot
e m (at, RVP) : emission factor for hot soak emissions (g trip-1).
e swarm
(at, RVP) : emission factor for cold and warm soak emissions (g trip-1).
m
Last two emission factors depend on the ambient temperature and on gasoline
volatility (RVP).
Total hourly diurnal and soak emissions were established, dealing the daily
value with averaged hourly values toward the average daily ambient temperature.
Hourly diurnal emissions were spatially disaggregated according to the traffic
volume of each vehicle category and population density.
Running losses emission factors were estimated using equation (6). Daily and
hourly running losses were estimated using equivalents equations as the hot
exhaust emissions.
rwarm
Fjrevap = (p.x j .e rhot
(at, RVP))
j (at, RVP) + w.x j .e j
(6)
Parameter:
j:
vehicle category. The same considered for cold emissions.
Term:
Fjrevap :
running losses VOC emission factor (g km-1).
Data:
e rhot
j (at, RVP) :
emission factor for hot running losses (g km-1)
e rwarm
(at, RVP) : emission factor for warm running losses (g km-1).
j
Last two emission factors depend on the ambient temperature and on gasoline
volatility (RVP).
Equations (4) to (6) were taken from the top-down model COPERTIII
(Ntziachristos and Samaras [8]), developed for be used in Europe.
2.2 Base information
Daily average traffic information, vehicle park composition by type kind of road
(differencing between weekday and weekend profiles), average speeds,
temperature information, timing traffic profiles (monthly, daily and hourly) and
fuel properties; were collected from different sources. Exhaust emission factors
for NOx, VOC, CO, PST (for diesel vehicles), were obtained from Ntziachristos
and Samaras [8]. Exploitation of these and other information required by the
equations, and modelling hypothesis are fully described in Parra [5].
3
Results
3.1 Annual emissions
Table 1 shows the contribution on fleet, mileage driven and ozone precursors
emission. Total mileage driven of the EMICAT2000 road net adds up 32 202
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
8 Air Pollution XII
millions km yr-1. NOx were emitted mainly by duty diesel vehicles (37%),
gasoline passenger cars without catalyst (30%) and heavy duty gasoline cars
(20%). VOC were emitted mainly by gasoline passenger cars without catalyst
(38%), motorcycles (30%) and heavy duty gasoline cars (14%). Figure 1 shows
the structure of the annual emission of primary pollutants: 62.4 kt yr-1 of NOx
(16%), 50.5 kt yr-1 of VOC (13%), 259 kt yr-1 of CO (66.7%), 1.3 kt yr-1 of SO2
(0.3%) and 15.7 kt yr-1 (4%) of PST.
Table 1:
Contribution on fleet, mileage driven and ozone precursors emission
from on-road traffic in Catalonia for the year 2000.
Mileage driven
Type of vehicle
-1
NOx
VOC
-1
-1
Fleet (%) (Mkm yr ) (%) (t yr ) (%) (t yr )
(%)
Gasoline passenger cars
without catalyst
Gasoline passenger cars
with catalyst
24
6 987
22
18 555
30
19 095
38
33
7 252
23
4 104
7
5 020
10
Diesel passenger car
17
6 358
20
3 722
6
1 034
2
Heavy duty gasoline vehicles
4
1 533
5
12 577
20
7 051
14
37
Duty diesel vehicles
12
8 095
25
23 181
Motorcycles
10
1 978
6
270
Total:
100
32 202
3 110
6
15 201
30
100 62 409 100 50 511
100
Figure 1: On-road traffic annual emission by type of vehicles of primary
pollutant in Catalonia during the year 2000.
Heavy duty gasoline vehicles and motorcycles contribute with low mileage
driven percents (5 and 6%, respectively), but their emission factors are high,
providing important contributions of primary pollutants (31 and 18%). Gasoline
vehicles mean 71% of the fleet, but they emit 57% of NOx and 92% of VOC.
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
Air Pollution XII
9
3.2 Weekday – weekend emission profiles
At weekend there is a reduction of traffic from heavy duty vehicles (60% in
average), and also variations of traffic on roads, both imply differences in
emission profiles. Figure 2 depicts the evolution of ozone precursors emissions
during weekday and weekend of August. Both NOx and VOC profiles are
bimodal. At weekday, NOx peaks occur about 9:00 and 18:00, but at weekend
first peak is lower and displaced to the right (about 12:00). Total NOx emission
on weekend is 22% lower than weekday.
For VOC, similar NOx timing is observed, but first peaks have equivalent
magnitudes and the second peak of weekend is higher. Total VOC emissions at
weekend are slightly higher (4%) than weekday.
Figure 2:
Hourly emission of ozone precursors during weekday and weekend
of August from on-road traffic in Catalonia.
Figure 3 shows the position of Catalonia in Europe, the distribution of NOx
emission for weekday and the difference between weekday and weekend. In
cells, the relation of emission values NOx/VOC for weekday could rise until 2.7,
but for weekend, it is no larger than 1.5. Also, for weekend CO and PST
reductions were 13 and 12% respectively.
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
10 Air Pollution XII
Figure 3: Location of Catalonia and distribution of NOx emission for weekday
and weekend of August from on-road traffic in Catalonia. At
weekend, NOx emission is 22% lower than weekday, mainly due to
drop in traffic of heavy duty vehicles.
Emissions are mainly located on the Metropolitan Area of Barcelona and on
the axis of highways following the coastline, precisely in front of the sea breeze
(Baldasano et al. [1]).
4
VOC speciation
Vehicles were classified in 5 groups. Using profiles provided by Ntziachristos
and Samaras [8], and according to the categories of the Carbon Bond 4 chemical
mechanism, we obtained the profiles of the Table 2. Those are actually built into
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
Air Pollution XII
11
EMICAT2000, which also produces the data files using the netCDF interface for
the third generation Models-3 Air Quality Model System.
Table 2:
Profiles used in EMICAT2000 for Carbon Bond 4 species (mol h-1)
due to 1 g h-1 NMVOC emitted by on-road traffic.
Cat.
(1)
(2)
(3)
(4)
(5)
PAR
ETH
OLE
0.0191370
0.0291000
0.0633813
0.0454749
0.0523800
0.0013200
0.0010673
0.0000000
0.0015391
0.0008834
0.0021989
0.0017019
0.0009258
0.0016681
0.0016143
Carbon Bond 4 species
TOL
XYL
0.0027566
0.0020294
0.0001424
0.0001894
0.0000718
0.0023353
0.0020733
0.0000712
0.0002820
0.0003768
FORM
ALD2
NR
0.0003152
0.0002486
0.0000000
0.0016836
0.0010586
0.0007971
0.0007793
0.0014243
0.0029799
0.0022116
0.0076106
0.0057482
0.0007121
0.0036278
0.0019215
(1) Exhaust emission of conventional vehicles: passenger cars without catalyst, heavy duty gasoline
cars and motorcycles. (2) Exhaust emission of gasoline passenger cars with catalyst. (3) Evaporative
emissions of gasoline passenger cars and motorcycles. (4) Exhaust emission of diesel passenger cars.
(5) Exhaust emissions of heavy duty diesel vehicles.
5
Discussion
We developed the on-road traffic component of the EMICAT2000 emission
model prioritizing the bottom-up approach for hot emissions. Nevertheless, being
very difficult to get all the information for each cell of the domain, or due to the
methodologies themselves, for cold and evaporative emissions we used the topdown approach.
Main applications are related with photochemical pollution modelling and
dynamic of the atmosphere. Considering the morphology of Catalonia and its
complex wind fields, high spatial resolution of emissions become necessary.
Successful results are being obtained (Barros et al. [2]).
Differences and similarities on ozone precursors emissions were found
between weekday and weekend, both in magnitude and timing. Their effects
originate the phenomenon knew as the “weekend effect” that nowadays is
demanding proof analysis, particularly in U.S. Detailed studies indicate that drop
in NOx emissions mean higher ozone concentrations on weekends in a VOC
limited regime (as urban zones). In contrast, lower ozone is probably produced in
locations with NOx limited regime (Heuss et al. [4]). The first kind of this
research is under developing for Catalonia using the emissions data provided by
EMICAT2000. To identify the kind of regime (NOx or VOC limited) it is
essential to employ high spatial resolution on emissions.
Implementation of the emission model was both time-and resourcesconsuming. Despite it is not complete (every emission source nowadays is not
included), but with biogenic (Parra et al. [6]), on-road traffic and most important
industrial emissions, the principal emitter features of Catalonia are described.
The gradual inclusion of minor sources as small pollutant industries, aircraft and
ship traffic, station services or secondary roads (with low daily average traffic),
will contribute mainly in details.
Nowadays, Models-3 is one of the most powerful air quality models, under
constant improvement. It integrates different U.S. emission models, as
GLOBEIS for biogenic emissions or MOBILE for traffic emissions, using an
Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1
12 Air Pollution XII
auxiliary processor called SMOKETOOL. These emission models are not
adequate for being used directly in other regions. Being EMICAT2000
developed explicitly for Catalonia and proved with Models-3, it provides total
control, freedom and flexibility on the emission topic. It could be easily applied
and exploited for other regions with similar characteristics
On-road traffic model into EMICAT2000 could be the basis for forecasting
the future air quality of Catalonia over different scenarios, as probably or
hypothetical changes in fleet vehicles or new quality of fuels.
The emission model demands a bigger effort between the elements required
for air quality modelling, but the benefits and potential applications are obvious.
References
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474-479.
[2] Barros N., I. Toll, C. Soriano, P. Jiménez, C. Borrego and J. M. Baldasano
(2003) Urban Photochemical Pollution in the Iberian Peninsula: Lisbon and
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Air Pollution XII, C. A. Brebbia (Editor)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-722-1