Article
citation information:
Goyal, Y.,
Meena, S., Singh, S.K., Kulshrestha, M. Real-time emissions of gaseous
pollutants from vehicles under heterogeneous traffic conditions. Scientific Journal of Silesian University of
Technology. Series Transport. 2023, 118,
55-75. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2023.118.5.
Yuvraj GOYAL[1],
Sanu MEENA[2],
Suresh Kumar SINGH[3],
Mukul KULSHRESTHA[4]
REAL-TIME EMISSIONS OF GASEOUS
POLLUTANTS FROM VEHICLES UNDER HETEROGENEOUS TRAFFIC CONDITIONS
Summary. Air quality problems in
cities are often a cause for worry. The air quality index is increasing daily,
leading to an increase in cancer and many respiratory problems. Road transport
in an urban area is a significant cause of air pollution. The vehicles must
meet Indian emission regulations for which the emissions are measured using
legally mandated standard driving cycles that did not accurately reflect
real-world driving emissions because of varying traffic conditions,
meteorological conditions, driving behaviour, vehicle power, performance, etc.
This study focuses on real-time emissions of gaseous pollutants hydrocarbon
(HC), carbon dioxide (CO2), carbon monoxide (CO), and nitric oxide (NO) from
vehicle exhaust pipes under heterogeneous traffic conditions. The emissions
were measured using a Portable Emission Measurement System (PEMS). The PEMS
used was an AVL MDS 450 analyser mounted on the vehicle, and on-road emissions
were captured. The test sample consists of four passenger vehicles with varying
engine sizes, manufacturers, and fuel. The test route comprises city and
highway areas, and it was discovered that the emissions were reduced by 40 to
70% on highways compared to the city. In petrol BSIV and BSVI engines, the
emission was reduced to 41.73% for CO, 46.90% for HC, and 64% for NO in the
city area. Speed and emissions scatter graphs were plotted for the vehicles,
and it was found that in the city area, the optimum speed for less emission is
between 30-40 km/h, and on highways, the optimum speed is 80-90 km/h. The
emissions were also sensitive to the rate and frequency of acceleration and
decelerations. This type of study is very limited in India, and more such
studies are required for the assessment of air quality in metropolitan areas
and successful traffic management strategies, as well as for determining
instantaneous projections of pollutant emissions.
Keywords: real-world driving emissions, portable emission
measurement system, heterogeneous traffic condition
1. INTRODUCTION
The air quality index and
respiratory problems faced throughout the world are increasing and with grave
health implications.
Air quality issues in India have reached an alarming
proportion. According
to the World Air Quality report, Bhiwadi in Rajasthan is the most polluted city
in the world, followed by Ghaziabad in Uttar Pradesh [1]. In
2021, New Delhi was named the world's most polluted capital city for the fourth
year in a row. The decline in air quality is
attributable to many reasons, among which pollution due to vehicular emissions
is becoming a serious concern. In India, the
Compound Annual Growth Rate (CAGR) of road length is 4.2%, as the total road
length has increased from 3.99 lakh km to 63.86 lakh km from the year 1951 to
2019, respectively [2]. As the connectivity in the country and the quality
of roads, the accident rates have declined, and the growth in the number of
vehicles has also increased significantly. The Compound
Annual Growth Rate (CAGR) for growth in the total number of registered vehicles
was 9.91% as 67,007 were registered in the year 2003, and it increased to 2,95,772 vehicles in
the year 2019 [2]. The compound annual growth in this period
for registered two-wheelers was 10.38%, for cars jeeps and taxis 9.64%, for
buses 3.26%, for goods vehicles 8.59%, and for others 7.63% [2]. As the
number of vehicles on the road increase, the emissions increase. This increase in emissions has become a global concern and needs
a solution, as there has been an increase in average temperatures, melting
glaciers, climate change, global warming, etc. As per the
International Energy Agency database in India itself, it was found that CO2
emissions from coal, oil, and natural gas burning were reported as 1628.0 Mt,
595 Mt, and 83 Mt, respectively, in the year 2018. In
the year 2018, a total of 33,514 Mt emission was reported from different
sectors in India, of which 8258 Mt, that is, 25% of total emissions was from
the transport sector [3].
The emissions released from fuel
combustion contain major pollutants such as carbon monoxide (CO), oxides of
Sulphur (SO2), particulate matter (PM), nitrogen oxides (NOx)
and hydrocarbon (HC). The principal pollutants in diesel-powered vehicles are nitrogen oxides
and particulates, whereas the main pollutants in petrol/gasoline-powered
vehicles are hydrocarbons and carbon monoxide. These
emissions have severe direct and indirect impacts on human health and ecology [4,
5, 6]. The effects of these pollutants on human health are summarized in Table 1.
Tab. 1
Effects of pollutants on human health
|
Pollutants |
Effect on Health |
|
CO |
|
|
NOx |
|
|
SO2 |
It has a serious effect on lung function. |
|
SPM and RPM (Suspended particulate
matter and respirable particulate matter) |
|
|
HC |
Cancer-causing potential. |
2. LITERATURE REVIEW
The remote
sensing technique is commonly used as it can monitor emissions from a large
number of vehicles, up to 1000 vehicles per day [8]. In this technique, the detectors are
calibrated and placed at the sampling location. The
sensor detects changes in the concentration of pollutants in the air.
The
tunnel measurement technique works on the principle of change in the
concentration of pollutants at entry and exit points of tunnels, which also
depends on tunnel characteristics and traffic flow conditions. The emissions are reported by
estimating the airflow in the tunnel and multiplying it by the difference in
the concentrations of pollutants obtained [8, 9].
Portal
emission measurement systems are widely used, as they measure with good
accuracy instantaneous emissions from vehicles, in addition, they also provide
various parameters of vehicles such as revolution per minute, engine speed and
temperature, speed, etc. They directly measure emissions such as HC, NOx, and CO from the
tailpipes of vehicles [7].
The
emissions are also predicted by several models like COPERT and Motor Vehicle
Emission Simulator (MOVES), which are modelled using past emissions data. The use of
artificial neural networks is becoming popular as the nonlinear relationships
can be modelled, whereas Gaussian models are unable to explain vehicle
emission's non-linearity nature
[14]. Artificial Neural Network (ANN) models can parallel process, self-learn, and self-correct, making
them more suitable for the prediction of vehicle emissions [17].
In the
study conducted by Wyatt et al. [12], a PHEM model was used for CO2
emissions for the 5 road grades (coefficients 0,0.5,1,2,3), and there were 48 test
runs for each lap and section. It was observed that for 0 grade coefficient (which implies
considering the area as flat), the average lap CO2 emissions
reported was between 400 to 513 gCO2/km, which were increased by
1.4, 4.0, and 7.6% when the road grade coefficient was changed to 1, 2 and 3,
respectively. It was also pointed out that for a route that has several
changes in elevation over the length, the average flat road grade cannot
be taken as the assumption of balancing the emissions from uphill sections and
downhill sections would give wrong results. This is because the model indicates an
increase in emissions with the increase in steepness of the grade.
Wang [13]
conducted a study to understand the effect of altitude on emissions. It was
conducted in China (it has 65% of its territory at an altitude higher than 1000
m, among which 33% have an altitude above 2000 m) on diesel vehicles and
emissions were noted at different altitudes of 30, 1330, 1910, 2240, 2400, and
2990 m. The CO, NOx, and PN emissions
increased with altitude. The CO emissions increased by
209% at 2990 m altitude compared to an altitude of 30 m, and PN emissions were
3 times at 2990 m altitudes compared to 30 m altitude but NOx emissions show an
increasing trend from an altitude of 30 m to altitude 2400 m, and after that,
at altitude 2990 m, a decline in emission was observed. The decline was believed to be because of less oxygen
concentration and an extremely long delay in the ignition of an engine at a
high altitude.
Jaikumar et al.
[14] reported that emissions quantified with the help of models or inventories
are much lower than the actual emissions from vehicle exhaust to local air. The study was conducted on 10
different vehicle passenger cars using an AVL Digas analyser, and the observed
data was used to prepare a prediction model. The best model was observed in the
nonlinear autoregressive exogenous input (NARX) model, which has 4 inputs
(speed, RPM, engine speed, VSP, and acceleration). The results of the model were compared to those
of the COPERT model and the emission factors of the Automotive Research
Association of India (ARAI), and it was reported that these models predict the
emissions while the developed model results were accurate with 0.9 as an index
of agreement.
Furthermore,
the study conducted by Jaikumar et al. [15] on 10 vehicles included buses,
three-wheelers, passenger cars, and two-wheelers and the route consisted of
roads of different categories such as 2 lanes, 4 lanes, and 6 lanes with
separated and mixed flow traffic characteristics. It was reported that
due to larger engines and higher exhaust flow rates, buses emitted higher NO
emissions than other vehicles. For two-wheeler
vehicles, the CO emissions were the lowest; however, compared to other
vehicles, two-wheelers had the highest HC emissions. The results
obtained were compared with the COPERT model and the Comprehensive Modal
Emission Model (CMEM), which under-predicted emissions.
Mahesh et
al. [16] studied the real-world emissions of motorcycles of engine size varying
from 70 cm3 to 124 cm3 by AVL Ditset gas 1000 on the four
urban arterial roads near the IIT Madras campus. The emission factors for CO for vehicles were
found to be greater than Bharat Stage (BS) emission standard values. The lowest value reported was 3.81 times, and the highest
value was reported as 12.3 times greater than BS Standard emissions.
An ANN (Artificial Neural Network)
based model was developed representing CO and NO2 concentrations at
traffic intersections and arterial roads in Delhi. The
model for NO2 concentrations used a 2-year data from 1 January 1997
to 31 December 1998 to train and develop a model, and then it predicted
emissions for a 1-year period (1 January to 31 December 1999) and predicted 76%
error-free at the intersection and 59% at arterial roads [17]. The model for CO concentrations under-predicted the
concentrations values and concluded that univariate time-series models do not
consider inversion conditions; thus, time-based models fail to forecast CO
concentrations in poor meteorological conditions [18].
3. STUDY AREA AND METHODOLOGY
3.1.
Study area
This
study was conducted in Jodhpur, a city in Rajasthan, India, as shown in Figure
1. The route selected consisted of a city area and a highway.
The route consists of varying traffic conditions, traffic lights,
pedestrian crossings, and road conditions. The change
in road type and traffic conditions throughout the route was marked by
waypoints ranging from M1 to M3. This divides the test route into two
segments. The total length of the route is 14.7 km,
which consists of a city area of 4.4 km and a 10.3 km highway area. The route considered for the emission test is shown below
in Figure 2, and the grade of the roads measured by the GPS device is reported
in Table 2.
Fig. 1. Study area
Fig. 2. Test route for data collection
Tab.
2
Description of segments in the route
|
Road Identification |
Segment
Length (km) |
Grade
(%) |
|
M1-M2 (City) |
4.4 |
-0.20% |
|
M2-M3 (Highway) |
10.3 |
0.10% |
3.2.
Instrument set-up
This study used the following
equipment: (a) AVL MDS 450, (b) Garmin Etrex 10, (c) 2 kW inverter, and (d)
portable battery as shown in Figure 3.
(a)
(b)
(c)
Fig. 3. (a) AVL MDS 450 (b) Garmin Etrex 10 GPS
Device,
(c) 2 kW inverter and portable battery
The AVL MDS 450 was used to measure
RPM, CO, HC, CO2, O2 and NO. The range and accuracy of
measuring these parameters are presented in Table 3.
Tab.
3
Range and accuracy of AVL MDS 450 (Source: AVL handbook [27])
|
Measured |
Measuring |
Resolution |
Accuracy |
|
|
CO |
0-15 % vol. |
0.01 % vol. |
<
0.6 % vol.: ≥
0.6 % vol.: |
±
0.03 % vol. ±
5 % o. M. |
|
CO2 |
0-20 % vol. |
0.01 % vol. |
<
10 % vol.: ≥
10 % vol.: |
±
0.5 % vol. ±
5 % o. M. |
|
HC |
0-30 ppm vol. |
≤ 2.000: 1 ppm vol. |
<
200 ppm vol.: ≥
200 ppm vol.: ≥
10000 ppm vol.: |
±
10 ppm vol. ±
5 % o. M. ±
10 % o. M. |
|
O2 |
0-25 % vol. |
0.01 % vol. |
< 2 % vol.: ≥ 2 % vol.: |
± 0.1 % vol. ± 5 % o. M. |
|
NO |
0-5 ppm vol. |
1 ppm vol. |
< 500 ppm vol.: ≥ 500 ppm vol.: |
± 50 ppm vol. ± 10 % o. M. |
Fig. 4.
Calibration of the AVL MDS 450 machine
3.3. Sampling procedure
The
sampling was not done in cold start conditions as vehicles were picked from the
source and taken to MBM University, marked as waypoint M1 in the test route, as
shown in Figure 5, where the machine was installed in the vehicles. For every run, the HC test and leak
test were done before sampling. The speed and
location of the vehicle were recorded by hand using the GPS device Garmin Etrex
10. The AVL MDS 450 used the inverter and the portable
battery as power sources. The tailpipe of the vehicle was connected to the
sampling hose. The RPM sensor was then connected to the engine. Thereafter, the
vehicle was run on the test route. The laptop was
connected to the AVL MDS 450, and the data for emission was recorded. For all the vehicles, the driver was the same. The
data was collected from 13:30 to 15:30 hours.
Fig. 5. Flow
chart of preparation of the vehicle for testing
3.4. Test vehicles
Tab.
4
Details of vehicles used for data collection
|
Specifications |
V1 |
V2 |
V3 |
V4 |
|
Displacement(cc) |
10866 |
1197 |
1248 |
1497 |
|
Curb Weight (kg) |
952 |
950 |
975 |
1173 |
|
No. of Cylinders |
4 |
4 |
4 |
4 |
|
Emission Standard |
BSIV |
BSVI |
BSIV |
BSIII |
|
Transmission |
Manual |
Automatic |
Manual |
Manual |
|
Model |
2015 |
2021 |
2018 |
2010 |
|
Fuel |
Petrol |
Petrol |
Diesel |
Diesel |
|
Odometer Reading |
23,581 |
7,217 |
61,938 |
1,61,399 |
3.5. Data collection
The
vehicle was parked at waypoint M1, and the analyser, GPS device, and vehicle
were started simultaneously. The AVL MDS 450 analyser measured RPM, CO, CO2,
HC, NO, and O2, as shown in the figure, and the GPS device measured
the coordinates, elevation profile, and speed-time profile of the test route,
as shown in the figure. The data obtained from the
gas analyser and GPS device were correlated based on the time stamp, then the
unrelated data was removed, and an excel sheet was formed, which was then used
for the analysis.
The data
recorded by AVL MDS 450 for the pollutants CO, CO2, and O2
were recorded in % vol unit, while the data recorded for HC and NO was in ppm. The unit of
the pollutants was converted into a standard unit of g/s using an empirical
equation, which is been also studied by [16, 19].
Where,
E = emission rate of pollutants in g/s
P = pollutant concentration measured in % vol
or ppm
EFR = exhaust flow rate measured in L/s
ρ = density of pollutant in g/L
The EFR
is measured from an exhaust flow metre device, but without this device, the
study by Mahesh et al. [16] suggested that “For a four-stroke engine, the
exhaust flow rate (in L/s) equals half the engine size (in litres) times the
number of revolutions per second; for a two-stroke engine, the exhaust flow
rate (in L/s) equals the engine size (in litres) times the number of
revolutions per second.” This assumption is used in this study because of the absence of an
exhaust flow metre device.
4. RESULTS AND DISCUSSIONS
4.1 Comparison of Modified Indian
Driving Cycle with speed-time profile
The driving cycle (DC) is a
speed-time profile representing the normal driving behaviour of a given vehicle
type in a given city or region, specifically the speed and acceleration
characteristics. Due to factors such as topography,
infrastructure, and vehicle type, it has been observed that the driving cycle
differs by region. In India, the Modified Indian Driving Cycle
(MIDC) is used as the standard driving cycle for passenger vehicles, as shown
in Figure 6. The driving cycle covers a distance
of approximately 10.647 km in 1180 seconds with a maximum speed of 90 km/h. The driving cycle is designed in a way to represent normal
driving conditions, but due to the increase in the road network, traffic, and
vehicles categories, it has failed to represent the driving conditions, which
can be seen by the speed-time profile of the four vehicles recorded by the GPS
device. When compared with the MIDC cycle, it can be
seen that according to the MIDC cycle, the vehicle accelerates and
deaccelerates 4 times in a span of 195 seconds, and this pattern repeats itself
four times in the driving cycle until approximately 780 seconds, whereas in the
speed-time profile of vehicles V1, V2, V3 recorded by the GPS device. The speed-time profile of vehicles
V1, V2 and V3 are shown in Figures 7, 8 and 9.
Fig. 6. Modified
Indian Driving Cycle
It was
observed that for vehicle V1, there were 6 phases of acceleration and
deceleration in 195 seconds and stopped for 43 seconds, for vehicle V2, there
were 5 phases of acceleration and deceleration, for vehicle V3, there were 4
phases of acceleration and deceleration, but unlike other vehicles, the initial
accelerations were very low compared to other vehicles. The MIDC cycle
consists of two phases, an Elementary Cycle of Emission (ECE) and an
Extra-Urban Driving Cycle (EUDC), which represents driving conditions in the
city and highway, respectively.
Similarly, the test route also consists of the city area and highway area. In
the ECE phase, the distance covered is approximately 4.053 km in 780 seconds. The average speed for the cycle is 19 km/h, while in the
city area, vehicles V1, V2, V3, and V4 are 26.5, 22.2, 38.9 and 25.5 km/h,
respectively. In the EUDC phase, the distance covered is 6.594 km in 400
seconds. The average speed for the cycle is 59.3 km/h,
while in highway areas, vehicles V1, V2, V3, and V4 are 42.4, 45.1, 43.7 and
47.8 km/h, respectively. Hence, it can be clearly observed that there is a huge variation
in the MIDC driving cycle because real-world driving involves varied
horsepower, average speeds, traffic congestion, road grades, and maximum
acceleration rates compared to the authorized driving cycle. There is a strong need for updating this cycle or introducing
new cycles, as it is used to test the emissions in the Chasis Dyanometer test.
Fig. 7. Speed-Time
profile of vehicle V1
Many
researchers have suggested and developed a local driving cycle, which would
help for a better understanding and designing of local traffic conditions,
field conditions, and evaluation of vehicle performance that would help
policymakers in making decisions [20-23]. The new driving cycle should be more
representative of real-world driving characteristics, such as higher speed and
acceleration, than the current one. Instead of aggregating to a single
driving cycle, it should discriminate between different types of roads and
locales. Apart from regulatory driving cycles, which
involve extreme driving circumstances, countries like Australia, the United
Kingdom, Germany, and France have devised driving cycles to estimate emissions
and fuel usage [24].
India has heterogeneous traffic conditions and should
consider taking a similar approach.
4.2 Comparison of emissions in the city and highway areas
In this
study, it was observed that the emissions in highway areas were reduced to
40-70% compared to the emissions in the city areas. This is because of the traffic and road
condition variations in the city and highway areas. The
traffic in the highway area is more homogeneous than the traffic in the city
areas. Also, the roads in the city area are in much better condition than the
highway area consisting of few or zero potholes and rough patches. The steady condition is obtainable in the highway area as
there are less frequent acceleration and deceleration compared to the city
area, which plays a vital role in the release of emissions. The variation in power requirement in vehicles in highway
areas is mostly constant compared to city areas because of congestion in city
areas.
4.2.1 CO emission
It was
observed that CO emission was reduced to 44.08, 60.23, 71.76 and 42.97% for
vehicles V1, V2, V3 and V4, respectively, as shown in Figure 10. This gas is the consequence of an
incomplete combustion reaction, which occurs when the amount of oxygen
available is inadequate to burn the raw material injected into the combustion
system.
(a) (b)
(c)
(d)
Fig. 10. (a) CO emissions from
vehicle V1, (b) CO emission from vehicle V2,
(c) CO emission from vehicle V3, (d) CO emission from vehicle V4
This gas also constitutes major exhaust gases that are released from
vehicle tailpipes. Apart from the vehicle tailpipe, another important component
in the generation of CO in gasoline engines is the chamber. In some regions of
the chamber, there is a lack of oxygen, which encourages partial oxidation of
the fuel, which is why designing chambers has always been a challenge and a
constant area of research.
Due to
the action of temperature, secondary oxidation might occur. CO generated by
partial fuel oxidation can react with oxygen molecules in the cylinder's zones.
In CO emissions, there are two primary pathways. The first is a rich mixture,
and the second is inefficient combustion, which occurs when all of the
hydrocarbon fuel is not entirely burned; for example, if the mixture does not
reach equilibrium, partially burned fuel and incomplete oxidation would result
in increased CO emissions. This is usually due to a variety of factors. The
first reason is a lack of sufficient residence time, which prevents the
equilibrium from being reached. The second cause is poor fuel/air mixing, which
results in rich local patches with high equilibrium CO, which causes a lack of
oxygen inside the mixing zone to be burned out.
4.2.2 NO and HC emission
(a) (b)
(c) (d)
Fig. 11. (a) HC and NO emissions
from vehicle V1, (b) HC and NO from vehicle V2,
(c) HC and NO from vehicle V3, (d) HC and NO from vehicle V4
NO is
created in the environment through a high-temperature (over 1600 degrees
Celsius) chain reaction that starts with nitrogen and oxygen. An early burning mixture
frequently creates more NO than a late burning mixture due to the higher
temperatures at which it is compressed. NO is formed at
the back of the flame, inside the area where the gas has been burned; it is
also formed within the flame itself but to a lesser level.
The production of HC emissions is
caused by a lack of air or a rich mixture during combustion. In the engine, an incomplete reaction results in the
emission of unburned hydrocarbon. It is worth mentioning
another source of hydrocarbon emissions at this point because it is
considerable. Evaporation allows more volatile and
lighter fuels, typically gasoline, to escape through the seals of a vehicle's
fuelling system [25]. Secondary impacts such as global warming result from
the emission of exhaust gases into the environment. Furthermore,
hydrocarbons can combine with nitrogen oxide species to produce ozone
molecules, a reaction that is catalysed by sunshine.
4.2.3 CO2 emission
(a)
(b)
(c)
(d)
Fig. 12. (a) CO2
emissions from vehicle V1, (b) CO2 emission from vehicle V2,
(c) CO2 emission from vehicle V3, (d) CO2 emission from
vehicle V4
CO2
is released as a result of the complete combustion of fuel. It is one
of the greenhouse gases (GHG), and as fossil fuels are depleted, the amount of
CO2 produced rises, resulting in global warming. According to scientists, the global temperature will rise by 1
degree Celsius by 2030, affecting most agricultural patterns.
4.3. Comparison of emissions from BSIV and BSVI
vehicles
The Indian Ministry of Road
Transport and Highways (MoRTH) prepared a draft notification of Bharat Stage
(BS) VI emission criteria for all major on-road vehicle categories in India on
February 19, 2016. All light- and heavy-duty vehicles, as well as two- and
three-wheeled vehicles, manufactured on or after April 1, 2020, were subject to
the BS VI standards.
(a)
(b)
(c)
Fig. 13. Emission measured for BSIV
and BSVI vehicles in the city area:
(a) CO, (b) HC, and (c) NO
It was observed that there was a significant decrease in the emissions
in the city area, whereas, in the highway area, the reduction in emissions was
nominal compared to the reduction in the city areas. In the
city areas, CO, HC and NO emissions were reduced to 41.73, 46.90 and 64%, as
shown in Figure 13, whereas, in the highway areas, CO, HC and NO emissions were
reduced to 17.46, 32.25 and 13.83%, as shown in Figure 14.
(a)
(b)
(c)
Fig. 14. Emission
measured for BSIV and BSVI vehicles in the highway area:
(a) CO, (b) HC, and (c) NO
The
reduction observed is due to modification in engine design, optimization of
power requirement by vehicle and modification in the catalytic converter. Also, the reduction of Sulphur content in the fuel is another
reason for reduced emission.
The Sulphur concentration in BS4 fuel is 50
parts per million; it is five times lower in BS6 fuel, which has a Sulphur
value of 10 parts per million. Selective Catalytic
Reduction (SCR) and Diesel Particulate Filter (DPF) were incorporated into the
BSVI emission norms to analyse and reduce the emission levels of BS6 vehicles;
however, this was not part of the BS4 emission norms.
4.4 Effect of speed on emission
Fig. 15. CO
emissions with speed scatter diagram of vehicles V1 and V2 in the city area
Fig. 16. CO2 emissions with speed scatter diagram of
vehicles V1 and V2 in the city area
Fig. 17. HC
emissions with speed scatter diagram of vehicles V1 and V2 in the city area
Fig. 18. NO
emissions with speed scatter diagram of vehicles V1 and V2 in the city area
It is
also suggested that in city areas, designing the traffic flow in such a way
that there is free flow and vehicle speed is between 30-40 km/h would help in
reducing emissions. In highway areas, there was a two-speed range in which peaks and variation
were observed to be 20-30 km/h and 50-60 km/h, as shown in Figures 19, 20, 21
and 22.
Fig. 19. CO
emissions from vehicles on the highway area
Fig. 20. CO2
emissions from vehicles on the highway area
Fig. 21. HC
emissions from vehicles on the highway area
Fig. 22. HC and NO
emissions from vehicles on the highway area
Fig. 19. Speed
emission scatter graph for vehicle V4
5. CONCLUSION AND POLICY RECOMMENDATION
There is a need to upgrade the Modified Indian Driving Cycle as it does
not reflect present driving conditions since real-world driving involves varied
horsepower, average speeds, traffic congestion, road grades, and maximum
acceleration rates compared to the authorized driving cycle. The
emissions were reduced by 40 to 70% on highways compared to the city area. This is because
of the steady driving conditions on the highways, whereas, in the city areas,
there are frequent accelerations and decelerations. Compared to BS1V vehicles, the emissions in BSVI vehicles were
found to decrease significantly in city areas, while in the highway areas, the
reduction in emissions was nominal compared to the city areas. In the city
areas, CO, HC, and NO emissions were reduced to 41.73, 46.90, and 64%, as shown
in Figures 7 and 8, whereas in the highway areas, CO, HC, and NO emissions were
reduced to 17.46, 32.25, and 13.83%. The
reduction observed is due to modification in engine design, optimization of
power requirement by vehicle, and modification in the catalytic converter. The reduction
of the Sulphur content in the fuel is another reason for reduced emission. In the city, the speed range was found to be 40-50 km/h, for
which the highest values of emissions were observed, and in the highway areas,
there was a two-speed range in which peaks and variation were observed to be
20-30 km/h and 50-60 km/h. The peak and variation observed were due to sudden
acceleration and deceleration and changes in the power demand of the vehicle.
The first recommendation will be the updating of the
Modified Indian Driving Cycle as it was seen that they do not reflect present
driving conditions. The new driving cycle should be more
representative of real-world driving characteristics, such as higher speed and
acceleration, than the current one. Instead of aggregating to a single driving
cycle, it should discriminate between different types of roads and locales. Apart from standard driving cycles for a country or
region, the driving cycle for local areas should be developed for a better
understanding of emissions from the area and effective designing of traffic
regulations. There is a need for strategies such as
congestion mitigation, a technique that can be used for reducing severe traffic
congestion, shock wave suppression techniques that eliminate acceleration and
deceleration events, both of which are linked to the stop-and-go behaviour seen
in crowded traffic, and permitting free flow and using traffic speed control
strategies to reduce overly high free-flow speeds to more acceptable levels. The traffic in the city should be designed with proper
synchronization of traffic signals so that homogenous conditions can be
achieved. The speed breakers result in sudden
deceleration and acceleration, leading to rising emissions; therefore, unwanted
or illegally made speed breakers should be removed. A real-time Air
Fuel Ratio indicator should be installed in vehicles so that the user can track
the car’s performance and if it crosses a range that depicts some problem
in the combustion chamber that would lead to higher emissions than normal
therefore prompting the user to get the car serviced for a better life and
performance.
At the time of service, especially for taxi vehicles,
the catalytic converter should be inspected and changed every 100,000
kilometres. Engine issues, such as misfiring, can
elevate the temperature above 1400 degrees, causing the substrate to melt and
the converter to fail. Also, the deposits of lead
on the active substrate limit the surface area available for reaction, thereby
reducing the efficiency of the catalytic converter. The
government urgently needs to establish an independent regulatory body at the
state and national levels to monitor the air quality levels and enforce
standards/norms to protect the health of its citizen.
References
1.
IQAir. 2021. “2021 World Air Quality
Report”. Region & City PM2.5
Ranking. Available at: http://www.indiaenvironmentportal.org.in/files/file/2021%20world%20air%20quality%20report.pdf.
2.
Ministry of Road Transport & Highways
(MORTH). 2021. „Annual
Report 2020-21”. New Delhi.
3.
International Energy Agency. 2021. „Global
Energy Review: CO2 Emissions in 2021”. Available at: https://www.iea.org/reports/global-energy-review-2021/co2-emissions.
4.
Manisalidis Ioannis, Elisavet Stavropoulou,
Agathangelos Stavropoulos, Eugenia Bezirtzoglou. 2020. „Environmental And
Health Impacts Of Air Pollution: A Review”. Frontiers In Public Health
8. DOI: 10.3389/fpubh.2020.00014.
5.
Jacyna M., J. Merkisz. 2014. “Proecological approach to modelling
traffic organization in national transport system”. Archives of
Transport 2(30): 43-56.
6.
Merkisz Jerzy,
Marianna Jacyna, Agnieszka Merkisz-Guranowska, Jacek Pielecha. 2014. “The
parameters of passenger cars engine in terms of real drive emission
test”. Archives of Transport
32(4): 43-50. DOI: 10.5604/08669546.1146998.
7.
Liu Huan, Matthew Barth, George
Scora, Nicole Davis, James Lents. 2010. „Using Portable Emission
Measurement Systems For Transportation Emissions Studies”. Transportation
Research Record: Journal Of The Transportation Research Board 2158(1):
54-60. DOI: 10.3141/2158-07.
8.
Franco Vicente, Marina Kousoulidou,
Marilena Muntean, Leonidas Ntziachristos, Stefan Hausberger, Panagiota
Dilara. 2013. „Road Vehicle Emission Factors Development:
A Review”. Atmospheric Environment 70: 84-97. DOI: 10.1016/j.atmosenv.2013.01.006.
9.
Smit R., P. Kingston, D.H.
Wainwright, R. Tooker. 2017. „A Tunnel Study To
Validate Motor Vehicle Emission Prediction Software In Australia”. Atmospheric
Environment 151: 188-199. DOI: 10.1016/j.atmosenv.2016.12.014.
10.
Shiva Nagendra S.M., Mukesh Khare.
2006.
„Artificial Neural Network Approach For Modelling
Nitrogen Dioxide Dispersion From Vehicular Exhaust Emissions”. Ecological
Modelling 190(1-2): 99-115. DOI: 10.1016/j.ecolmodel.2005.01.062.
11.
Shiva Nagendra S.M., Mukesh Khare.
2010.
„Artificial Neural Network Based Vehicular
Pollution Prediction Model: A Practical Approach For Urban Air Quality
Prediction”. International Journal Of Environment And Waste Management
5(3/4): 303. DOI: 10.1504/ijewm.2010.032010.
12.
Wyatt David W., Hu Li, James E.
Tate. 2014. „The Impact Of Road Grade On Carbon Dioxide
(CO2) Emission Of A Passenger Vehicle In Real-World Driving”. Transportation
Research Part D: Transport And Environment 32: 160-170. DOI: 10.1016/j.trd.2014.07.015.
13.
Wang Haohao, Yunshan Ge, Lijun Hao,
Xiaoliu Xu, Jianwei Tan, Jiachen Li, Legang Wu, Jia Yang, Dongxia Yang, Jian
Peng, Jin Yang, Rong Yang. 2018. „The
Real Driving Emission Characteristics Of Light-Duty Diesel Vehicle At Various
Altitudes”. Atmospheric Environment 191: 126-131. DOI: 10.1016/j.atmosenv.2018.07.060.
14.
Jaikumar Rohit, S.M. Shiva
Nagendra, R. Sivanandan. 2017. „Modeling Of Real
Time Exhaust Emissions Of Passenger Cars Under Heterogeneous Traffic
Conditions”. Atmospheric Pollution Research 8(1): 80-88. DOI: 10.1016/j.apr.2016.07.011.
15.
Jaikumar Rohit, S.M. Shiva
Nagendra, R. Sivanandan. 2017. „Modal Analysis Of
Real-Time, Real World Vehicular Exhaust Emissions Under Heterogeneous Traffic
Conditions”. Transportation Research Part D: Transport And Environment
54: 397-409. DOI: 10.1016/j.trd.2017.06.015.
16.
Mahesh Srinath, Gitakrishnan
Ramadurai, S.M. Shiva Nagendra. 2019. „Real-World
Emissions Of Gaseous Pollutants From Motorcycles On Indian Urban
Arterials”. Transportation Research Part D: Transport And Environment
76: 72-84. DOI: 10.1016/j.trd.2019.09.010.
17.
Nagendra Shiva S.M., Mukesh Khare.
2006. „Artificial Neural Network Approach For Modelling Nitrogen
Dioxide Dispersion From Vehicular Exhaust Emissions”. Ecological
Modelling 190(1-2): 99-115. DOI: 10.1016/j.ecolmodel.2005.01.062.
18.
Nagendra Shiva S.M., Mukesh Khare.
2009. „Univariate Stochastic Model For Predicting Carbon Monoxide On
An Urban Roadway”. International Journal Of Environmental Engineering
1(3): 223. DOI: 10.1504/ijee.2009.027802.
19.
Lau Jason, W.T. Hung, C.S. Cheung.
2011. „On-Board Gaseous Emissions Of LPG Taxis And Estimation Of Taxi
Fleet Emissions”. Science Of The Total Environment 409(24):
5292-5300. DOI: 10.1016/j.scitotenv.2011.08.054.
20.
Ho Sze-Hwee, Yiik-Diew Wong, Victor
Wei-Chung Chang. 2014. „Developing Singapore
Driving Cycle For Passenger Cars To Estimate Fuel Consumption And Vehicular
Emissions”. Atmospheric Environment 97: 353-362. DOI: 10.1016/j.atmosenv.2014.08.042.
21.
Donateo Teresa, Mattia Giovinazzi.
2017. „Building A Cycle For Real Driving Emissions”. Energy
Procedia 126: 891-898. DOI: 10.1016/j.egypro.2017.08.307.
22. Stasinopoulos Peter,
Nirajan Shiwakoti, Tobias Seidl, Alan Wong. 2018. „Comparison of
Melbourne driving characteristics with the NEDC and WLTC”. In: Proceedings
of the 40th Australasian Transport Research Forum (ATRF 2018): 1-11. Australasian Transport Research
Forum (ATRF).
23.
Nowak Mateusz, Jacek Pielecha.
2017. „Comparison Of Exhaust Emission On The Basis Of Real Driving
Emissions Measurements And Simulations”. MATEC Web Of Conferences
118: 00026. DOI: 10.1051/matecconf/201711800026.
24.
Nesamani K.S., K.P. Subramanian. 2006.
„Impact Of Real-World Driving Characteristics On Vehicular
Emissions”. JSME International Journal Series B 49 (1): 19-26. DOI: 10.1299/jsmeb.49.19.
25. Kishan S., T. DeFries,
et al. 1993. „A Study of Light-Duty Vehicle Driving Behavior: Application
to Real-World Emission Inventories”. SAE
Technical Paper Series. Paper 932659.
26. Nakhawa
Husein Adam, S.S. Thipse. 2015. „Characterization Of The Ultrafine And
Nano Particle Emissions On Modified Indian Driving Cycle For Passenger Cars
Operating On CNG Phase Wise Analysis”. Advanced Engineering Forum 14:
86-96. DOI: 10.4028/www.scientific.net/aef.14.86.
27. AVL
DiTest. 2022. „AVL Ditest MDS 450 – Modular Diagnostic
System”. Available at: https://www.avlditest.com/en/emt-mds-450.html.
Received 02.10.2022; accepted in revised form 16.12.2022
Scientific
Journal of Silesian University of Technology. Series Transport is licensed
under a Creative Commons Attribution 4.0 International License
[1] M.Tech Scholar, Department of Civil
Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya
Pradesh, India. Email:
yuvrajkumargoyal2020@gmail.com.
ORCID: https://orcid.org/0000-0002-1471-8190
[2] Department of Civil
Engineering, M.B.M. University, Jodhpur 342011 Rajasthan, India.
Email: sanu.iitb@gmail.com.
ORCID: https://orcid.org/0000-0003-0898-051X
[3] Department of Civil
Engineering, M.B.M. University, Jodhpur 342011 Rajasthan, India.
Email: sksingh.jnvu@gmail.com. ORCID: https://orcid.org/0000-0003-1143-3285
[4] Department of Civil
Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya
Pradesh, India. Email: mukul.kuls@gmail.com.
ORCID: https://orcid.org/0000-0002-3917-632