Trucks driving at night and their effect on local air pollution
Abstract
Avoiding congestion is seen as beneficial for the haulage sector and also for the general economy because of the reduction of time losses from private cars queuing during the day. In addition congestion dramatically increases the amount exhaust emissions. Avoiding congestion is therefore seen as an environmental benefit by most policy makers. On the other hand it is evident that when more trucks drive at night, that would obviously have negative impacts related to night time noise exposure and possibly accidents. In this paper we use a simple modelling scheme to demonstrate that changing the timing of the transport activity also has effects on the dispersion of the exhaust gases. Shifting the emission of pollutants to the late evening or early morning, when the atmosphere is relatively more stable can cause an unwanted increase in air pollution even when emissions are constant. Such an effect is easy to demonstrate, but has been overlooked by researchers because the focus of most studies has been either on episodes of severe air pollution or on estimating the annual average concentrations for exposure and analysis of health effects. At the time of the political discussion, information on this effect was unavailable. We provide this analysis to demonstrate the important contribution that analysis of transport problems from an activity-based perspective can offer to policy makers.
European Transport Conference - 1(1)p.9(2007) AET, London(UK), 2007 ISBN: 978-1-905701-02-5 ISSN: 1 474-9122
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Available from: Luc LR Int PanisTRUCKS DRIVING AT NIGHT AND THEIR EFFECT ON LOCAL AIR
POLLUTION
Luc Int Panis
Flemish Institute for Technological Research (VITO, Belgium)
Carolien Beckx
Transportation Research Institute, University of Hasselt (IMOB, Belgium)
1. INTRODUCTION
In January 2007 the Flemish transport minister proposed to improve the
opportunities for loading/unloading goods in the port of Antwerp (Belgium) by
extending the working hours at container terminals. The main intention of this set
of measures is to increase the capacity for moving goods from the harbour out to
the hinterland. This implies that the capacity of both the terminals and the
motorway system is increased because it would enable trucks to avoid day-time
congestion by driving at night.
Avoiding congestion is seen as beneficial for the haulage sector and also for the
general economy because of the reduction of time losses from private cars
queuing during the day. In addition congestion dramatically increases the amount
exhaust emissions. Avoiding congestion is therefore seen as an environmental
benefit by most policy makers. On the other hand it is evident that when more
trucks drive at night, that would obviously have negative impacts related to night
time noise exposure and possibly accidents.
In this paper we use a simple modelling scheme to demonstrate that changing
the timing of the transport activity also has effects on the dispersion of the
exhaust gases. Shifting the emission of pollutants to the late evening or early
morning, when the atmosphere is relatively more stable can cause an unwanted
increase in air pollution even when emissions are constant. Such an effect is
easy to demonstrate, but has been overlooked by researchers because the focus
of most studies has been either on episodes of severe air pollution or on
estimating the annual average concentrations for exposure and analysis of health
effects.
At the time of the political discussion, information on this effect was unavailable.
We provide this analysis to demonstrate the important contribution that analysis
of transport problems from an activity-based perspective can offer to policy
makers.
2. METHODOLOGY
2.1 The dispersion model
Using a simple 1-dimensional Gaussian plume model, we studied the effect of
shifting the timing of the emissions from day to night. The following equation was
applied (Vlarem II, 2005):
With
x,y,z (in m): Cartesian coordinates indicating the location of the
immission, in the direction of dispersion (x), horizontal (y) and vertical (z)
to the dispersion direction
C
x,y,z
(in mg/m
3
): mass concentration of the air pollutant at the immission
location with the coordinates x, y, z
Q (in kg/h): emission mass stream from the emission source
z (in m): height of the point of immission occurrence
h (in m): actual exhaust height
σ
y
, σ
z
(in m): horizontal and vertical dispersion parameter
u
h
(in m/s): wind speed
This model is valid for the calculation of concentrations of gaseous pollutants
when chemical transformation can be ignored as well as for fine particles when
local deposition velocities are small. Both conditions are met for the generalized
local conditions presented in this paper.
2.2. High resolution meteorological data
In this study we have used high resolution meteorological data on a 10 minute
basis for an entire year. Information about wind speed, wind direction and
temperature was provided for different measuring heights at a measuring point in
the city of Mol. Every 10 minute the stability of the meteorological situation was
determined based on wind speed and temperature information and a stability
class was determined for every 10-minute record, ranging from 1 (= very stable
situation) to 7 (= very unstable situation). Based on this information, the
dispersion parameters (see equation above) could be calculated as:
σ
y
= A.x
a
σ
z
= B.x
b
with x: distance from the source (in m)
The coefficients A and B and the exponents a and b in these equations are
subject to the stability class and derived from the following Table 1:
Table 1. Coefficients and exponents to calculate dispersion parameters (Vlarem II,
2005)
Stability class
Description
A
a
B
b
E1
Very stable
0.235
0.796
0.311
0.711
E2
Stable
0.297
0.796
0.382
0.711
E3
Neutral
0.418
0.796
0.520
0.711
E4
Slightly unstable
0.586
0.796
0.700
0.711
E5
Unstable
0.826
0.796
0.950
0.711
E6
Very unstable
0.946
0.796
1.321
0.711
E7
High wind speed
1.043
0.698
0.819
0.669
3. RESULTS
3.1. General
Wind speed and atmospheric stability classes at 10 minute time resolution were
used to calculate the effect of truck emissions on pollutant concentrations at
different distances from the road. A line source with an emission strength of 1
kg/hour was used to derive resulting concentrations for receptor points at a
distance of 10 meters, 100 meters and 1 km from the emission source. The
general result is presented in Figure 1.
0
20
40
60
80
100
120
140
160
1
2
7
8
9
10
11
12
14
15
16
17
18
19
20
21
23
24
25
26
27
28
29
30
Day of month
Conc (µg/m3)
Figure 1 : Predicted concentrations for a receptor at a distance of 100 meters from the
source (based on a continuous emission source strength and 1 month of high resolution
meteo data, April 2005).
3.2. Effect of time of day
We have averaged the time-series discussed in the previous paragraph to obtain
a mean concentration for each 10 minute interval of a twenty-four hours' period
(based on 30 results, 1 for each day of the month). The results are shown in
Figure 2 using meteo data for the month of April.
It is clear that that a constant source of emission causes concentrations that are
higher at night than during the day for the same receptor. The effect is most
pronounced at short distances from the road. At a distance of 10 meters, local
concentrations will be up to four times higher during the night than during the day
although the emissions are the same (assuming equal speed and traffic
dynamics). A distance of 10 meters is a typical distance between the center of
major roads and the facades of buildings.
At a distance of 100 meter from the road average night time concentrations
equaled 30 µg/m
3
(21:00h-8:30h). Concentrations resulting from the same
emissions source (constant strength of 1 kg per hour) are only about 10 µg/m
3
during the afternoon (12:00h – 18:00h).
0
5
10
15
20
25
30
35
40
45
00 10
01 00
01 50
02 40
03 30
04 20
05 10
06 00
06 50
07 40
08 30
09 20
10 10
11 00
11 50
12 40
13 30
14 20
15 10
16 00
16 50
17 40
18 30
19 20
20 10
21 00
21 50
22 40
23 30
Time of day
Conc (µg/m3)
Figure 2: Average concentration for all 10-minute intervals (meteo data for April 2005,
constant emission source at a distance of 100 meter).
The resulting concentrations at a distance of 1000 meters are an order of
magnitude smaller. Hence any relative difference in concentration will be
negligible in absolute concentrations (Figure 3).
0.1
1
10
100
1000
10000
00 10
01 10
02 10
03 10
04 10
05 10
06 10
07 10
08 10
09 10
10 10
11 10
12 10
13 10
14 10
15 10
16 10
17 10
18 10
19 10
20 10
21 10
22 10
23 10
Time of day
Conc (µg/m3)
10 m
100 m
1000 m
Figure 3: Comparative graphs for the concentration at receptors located at different
distances from the emission source (logarithmic scale)
3.2. Effect of month of year
We repeated calculations for different months of the year, as far as high
resolution meteo data was available. Because of the importance of average wind
speed and atmospheric stability on the results, we show the results for the
months of January and April, for receptor points at a distance of 10 meters
(Figure 4). Differences between daytime and nighttime concentrations are more
distinct in April than in January.
0
200
400
600
800
1000
1200
1400
00 10
01 10
02 10
03 10
04 10
05 10
06 10
07 10
08 10
09 10
10 10
11 10
12 10
13 10
14 10
15 10
16 10
17 10
18 10
19 10
20 10
21 10
22 10
23 10
Time of day
Conc (µg/m3)
April
January
Figure 4: Average concentration for all 10-minute intervals (meteo data for January 2006
and April 2005, constant emission source at a distance of 10 meter).
4. DISCUSSION
In this paper we have demonstrated that identical emissions will cause local
impacts that are higher at night than during the day. The magnitude of the
difference is a factor of about three.
At first glance this result seems strange and it certainly needs an explanation.
Everyone familiar with air quality measurements and time series knows that
concentrations of primary pollutants are far lower at night than during the day.
Peaks are usually seen during peak hours and concentration time-series follow
the general daily pattern of the traffic flows, especially when measuring near busy
roads.
In this study we have deliberately discarded the effect of changing traffic volumes
to focus on the local dispersion. We have taken this approach because we are
interested in the marginal effect of one unit of pollutant emitted at different times
during the day.
Our results can be explained by differences in average wind speed and
atmospheric stability at different times of the day. Meteorological conditions
certainly appear quite random, but on average, there are important differences.
Figure 5 shows the average wind speed data during 10 minute time intervals,
corresponding to the concentrations that were calculated in Figure 4. Setting both
figures side by side clearly shows the impact of wind speed on pollutant
concentration. Lower wind speeds result in higher concentrations whereas higher
wind speeds will dilute these concentrations. In January variations in wind speed
are rather small compared to the wind speed variability in April, resulting in more
concentration variability in April.
0
0.5
1
1.5
2
2.5
3
3.5
4
00 10
01 00
01 50
02 40
03 30
04 20
05 10
06 00
06 50
07 40
08 30
09 20
10 10
11 00
11 50
12 40
13 30
14 20
15 10
16 00
16 50
17 40
18 30
19 20
20 10
21 00
21 50
22 40
23 30
Time of day
Wind speed (m/s)
April
January
Figure 5: Average wind speed data for all 10-minute intervals (meteo data for April 2005
and January 2006).
It is clear that we cannot possibly capture the whole phenomenon in one simple
model and some comments or objections can be made. Emissions (even from
the same truck) may well be different because of different speeds or different
driving behaviour (no congestion) during the night. We have also neglected the
possible effects of lower temperature on the engine and differences in the use of
headlights and comfort systems (heating and airco), but keeping the emission
strength constant allowed us to focus on the diurnal changes in local dispersion.
On the other hand we may even have underestimated the effect because the
highest concentrations are likely to occur during calm episodes with no wind at
all. Unfortunately the dispersion under such conditions cannot be modeled with a
Gaussian dispersion model.
The result presented in this paper have serious implications for air quality and
transport policy. PM air quality targets prove very hard to comply with in areas
affected by residential ribbon development. It has been shown that PM
concentrations in 2010 will not meet air quality standards in the vicinity of many
important roads. Partly changing the timing of the emissions on these roads from
day to night will likely offset whatever benefits are gained from improved traffic
flows. The unexpected magnitude of this difference in dispersion dwarfs recent
improvements in European PM emissions standards for heavy duty. This effect is
also large enough to offset remaining differences in PM emissions between
advanced diesel cars and petrol fueled cars (Int Panis et al., 2001).
In addition our results also highlight the fact that annual average impacts (e.g.
annual exposure of urban populations routinely used for health impact
assessment) may hide important seasonal differences between summer months
when days are long and winter time when rush hours occur either before sunrise
or after sunset. Such aspects need to be studied in much more detail before the
environmental consequences of specific transport policy measures can be
assessed.
At this point we can only speculate what the impact of this effect is for the
exposure of people living near busy roads. In many countries a significant
fraction of the population lives next to a busy road and distances of 10 meter
between the central axis of the road and the facades of the houses are common.
One option to prevent negative environmental effects is to allow truck to
load/unload at night but restrict driving to motorways. This would reduce
emissions resulting from motorway congestion while preventing a
disproportionate increase in urban concentrations.
Since traditional travel demand models mainly focus on peak hour information,
only peak emissions and concentrations can be modeled. To overcome this
problem and obtain hourly trip information, new travel demand models have been
developed. The activity-based model treats travel demand as derived from the
activities that individuals and households need or wish to perform (Ettema and
Timmermans, 1997). The model aims at predicting which activities are
conducted, where, when, for how long, with whom and the transport mode
involved. This travel demand model predicts activity-travel patterns of individuals
in a population based on activity diary information and provides very detailed
information on the personal travel behaviour including very detailed trip departure
data. This information is very useful when focusing at differences in travel
behaviour, emissions or concentrations per time of day.
5. CONCLUSIONS
The time of day at which a specific emission takes place is very important for its
impact on local air quality and exposure.
Activity-based models are capable of accurately predicting trip departure times
and therefore they provide the necessary data to achieve greatly improved
assessments of personal exposure.
The intention to increase night-time traffic (emissions) should be weighed against
the negative effect on air quality that was demonstrated in this paper.
Bibliography
Ettema, K., and H. Timmermans (1997) Theories and models of activity patterns,
in Ettema, K. and H. Timmermans (eds), Activity-based approaches to travel
analysis, Pergamon, Oxford.
Luc Int Panis, Leo De Nocker, Ina De Vlieger and Rudi Torfs (2001) Trends and
uncertainty in air pollution impacts and external costs of Belgian passenger car
traffic. International Journal of Vehicle Design, 27 (1-4), 183-194.
Vlarem II (2005) Bijlagen bij het besluit van de Vlaamse regering van 1 juni 1995
houdende algemene en sectorale bepalingen inzake milieuhygiëne.
Gecoördineerde versie 31 augustus 2005, 75-80.
- CitationsCitations6
- ReferencesReferences1
- "Two previous studies use concepts of atmospheric science, which will be a focus of this paper, to show that exhaust concentrations can increase as a result of off-peak operations. One study provides estimates of the change in pollutant concentrations resulting from trucks, pointing out the potential problems resulting from off-peak policies (Panis and Beckx, 2007). An experimental study in Southern California shows that pollutant concentrations, resulting from aggregate traffic, can be higher during pre-sunrise than daylight hours, despite lower total traffic levels (Hu et al., 2009). "
[Show abstract] [Hide abstract] ABSTRACT: In recent years, the reduction of freight vehicle trips during peak hours has been a common policy goal. To this end, policies have been implemented to shift logistics operations to nighttime hours. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, the atmospheric boundary layer is generally more stable during the night than the day. Consequently, shifting logistics operations to the night would increase the 24-hour average concentrations of diesel exhaust pollutants in many locations. This paper presents case examples of this phenomenon, which provide concentration and human intake estimates after temporal redistributions of daily logistics operations.- "The presence of slightly worse results for O 3 during night time has been reported previously (e.g. by Sokhi et al., 2006). There are various possible factors which could cause these discrepancies, but they can often be explained by errors in the meteorological parameters (especially wind speeds) during the night (Int Panis and Beckx, 2007). Since the results for O 3 and PM 10 did not significantly change across al subsets of monitoring data, only the spatial and temporal results for NO 2 will be discussed further. "
[Show abstract] [Hide abstract] ABSTRACT: The potential advantages of using activity-based transport models for air quality purposes have been recognized for a long time but models that have been developed along these lines are still scarce. In this paper we demonstrate that an activity-based model provides useful information for predicting hourly ambient pollutant concentrations. For this purpose, the traffic emissions obtained in a previous application of the activity-based model ALBATROSS were used as input for the AURORA air quality model to predict hourly concentrations of NO(2), PM(10) and O(3) in the Netherlands. Predicted concentrations were compared with measured concentrations at 37 monitoring stations from the Dutch air quality monitoring network. A statistical analysis was performed to evaluate model performance for different pollutants, locations and time periods. Results confirm that modelled and measured concentrations present the same geographical and temporal variation. The overall index of agreement for the prediction of hourly pollutant concentrations amounted to 0.64, 0.75 and 0.57 for NO(2), O(3) and PM(10) respectively. Concerning the predictions for NO2, a major traffic pollutant, a more thorough analysis revealed that the ALBATROSS-AURORA model chain yielded better predictions near traffic locations than near background stations. Further, the model performed better in urban areas, on weekdays and during the day, consistent with the emission results obtained in a previous study. The results in this paper demonstrate the ability of the activity-based model to predict the contribution of traffic sources to local air pollution with sufficient accuracy and confirms the usefulness of activity-based transport models for air quality purposes. The fact that the ALBATROSS-AURORA chain provides reliable pollutant concentrations on hourly basis for the whole Netherlands instead of using only daily averages near traffic stations is a plus for future exposure studies aiming at more realistic exposure analyses and health impact assessments.- "The morning concentrations were more than three times higher in comparison to the afternoon concentration levels (Fig. 6b). This suggest that meteorological conditions are also plausible cause for the observed differences (Int Panis and Beckx, 2007). In future studies, a fixed background station will be used to allow a better discrimination between source related and meteorological factors that affect concentrations. "
[Show abstract] [Hide abstract] ABSTRACT: Estimating personal exposure to air pollution is a crucial component in identifying high-risk populations and situations. It will enable policy makers to determine efficient control strategies. Cycling is again becoming a favorite mode of transport both in developing and in developed countries due to increasing traffic congestion and environmental concerns. In Europe, it is also seen as a healthy sports activity. However, due to high levels of hazardous pollutants in the present day road microenvironment the cyclist might be at a higher health risk due to higher breathing rate and proximity to the vehicular exhaust. In this paper we present estimates of the exposure of a cyclist to particles of various size fractions including ultrafine particles (UFP) in the town of Mol (Flanders, Belgium). The results indicate relatively higher UFP concentration exposure during morning office hours and moderate UFP levels during afternoon. The major sources of UFP and PM(10) were identified, which are vehicular emission and construction activities, respectively. We also present a dust mapping technique which can be a useful tool for town planners and local policy makers.
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