Real-world emissions of gaseous pollutants from motorcycles on Indian urban arterials

Transportation Research Part D 76 (2019) 72–84

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Real-world emissions of gaseous pollutants from motorcycles on Indian urban arterials

T

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Srinath Mahesh , Gitakrishnan Ramadurai, S.M. Shiva Nagendra Department of Civil Engineering, Indian Institute of Technology, Madras, Chennai 600036, India

A R T IC LE I N F O

ABS TRA CT

Keywords: Motorcycles Two-wheelers Real-world emissions Driving cycle Emission factor Pollution

Motor vehicles contribute significantly to the total emissions in a city. Several studies have attempted to quantify emissions from cars, buses, and trucks using laboratory-based or real-world emission tests. However, real-world emissions from motorcycles (or motorized two-wheelers) has not received adequate attention. Unlike developed countries where cars are predominantly used for personal transport, motorcycles are widely used in developing countries. In this study, we quantify emissions of carbon monoxide (CO), hydrocarbons (HC), and nitric oxide (NO) from four typical motorcycles (MC 1, MC 2, MC 3, and MC 4) using real-world emission measurements and develop emission models. We also compare the observed real-world speed-time profiles with the laboratory test cycles and the emission factors developed using real-world emission measurements with the Bharat Stage (BS) emission standards. The real-world emission factors of CO for MC 1, MC 2, MC 3, and MC 4 were 12.3 times, 3.18 times, 9.71 times, and 5.84 times above the respective BS emission standard values. Also, the CO and [HC + NO] emissions from a two-stroke motorcycle (MC 1) were higher than the four-stroke motorcycles. The models presented in this paper can be used to accurately quantify real-world emissions from motorcycles on Indian urban arterials and to formulate policies for air quality improvement.

1. Introduction Motorcycles (or motorized two-wheelers) are widely used as a means of personal transport in many Asian countries including India due to their affordability, convenience, and ease of operation. Consequently, the number of motorcycles in India has grown in the last few years. As per the data from the Ministry of Statistics and Programme Implementation, Government of India, the number of registered two-wheelers in India stood at 92 million in 2010 and rose to 169 million in 2016 (MoSPI, 2019). In the state of Tamil Nadu, the number of newly registered two-wheelers was 11.2 million in 2010 and 19.5 million in 2016 (Fig. 1). In terms of the number of vehicles sold in India during the financial year 2017–18, 20.19 million (81%) of the 24.97 million total vehicles sold were motorcycles (SIAM, 2019). With a high percentage of young population, inadequate public transportation infrastructure, and a growing economy, the number of motorcycles is expected to grow further. Since almost all of these two-wheelers run on gasoline, emissions from these vehicles would have a drastic impact on air quality. The increase in the number of motor vehicles has led to severe air pollution in many cities in India (Nesamani, 2010; Gurjar et al., 2016). Nesamani (2010) estimated that about 64% of the total CO emissions in Chennai city was contributed by two and threewheelers and heavy-duty vehicles contributed to 60% of the total NOx emissions. In a study in Delhi, Nagpure et al. (2016) estimated exhaust and non-exhaust emissions from motor vehicles, and predicted that the contribution of emissions from private cars and two-

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Corresponding author. E-mail address: [email protected] (S. Mahesh).

https://doi.org/10.1016/j.trd.2019.09.010

1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Growth in the number of registered two-wheelers in Tamil Nadu. Source: Ministry of Statistics and Programme Implementation (MoSPI, 2019), Government of India.

wheelers in 2020 would be 2–18 times as compared to 1991. Pandey and Venkataraman (2014) estimated emissions of particulate matter, black carbon, and organic carbon for all modes of transportation using locally available emission factor values. Heavy-duty diesel vehicles and two-stroke vehicles were found to be the largest emitters of particulate matter and organic carbon, respectively. Emissions from motor vehicles are known to depend on the speed and acceleration of vehicles. The speed and acceleration of a vehicle, in turn, depends on the prevailing traffic conditions. Unlike the western countries, the traffic composition in India is heterogeneous with vehicles having different static (size and weight) and dynamic characteristics (speed, acceleration, braking, etc.) sharing the same road space. Motorcycles, cars, buses, trucks, and non-motorized vehicles ply on the same road at different desired speeds causing complex traffic flow conditions (Arasan and Koshy, 2005; Kanagaraj et al., 2015; M. and Verma, 2016). This has been identified as a cause of frequent and sudden acceleration and deceleration maneuvers, thereby leading to higher emissions (Kumar Pathak et al., 2016; Choudhary and Gokhale, 2016; Jaikumar et al., 2017; Arun et al., 2017). Traditionally, emissions from motor vehicles have been measured using laboratory-based tests on chassis or engine dynamometers. These tests are conducted using standardized driving cycles that are meant to represent the real-world operation of the vehicles. However, the representativeness of the driving cycles has been questioned by several researchers (Hung et al., 2007; Tong and Hung, 2010; Ho et al., 2014; Kumar Pathak et al., 2016). Further, several recent studies have attempted to quantify emissions from passenger cars and motorcycles using either laboratory tests or on-board emission measurements (Momenimovahed et al., 2014; Iodice and Senatore, 2015; Li et al., 2015; Hassani and Hosseini, 2016; Pandey et al., 2016; Choudhary and Gokhale, 2016; Tsai et al., 2017; Jaikumar et al., 2017; Mahesh et al., 2018; Jaiprakash and Habib, 2018). However, the emission tests on motorcycles have been laboratory-based and real-world emissions from motorcycles have been neglected. Hence, there is a need to develop emission models for motorcycles based on real-world emission measurements. Thus, the objective of this paper is to quantify and model the emissions of CO, HC and NO from motorcycles using real-world emission measurements. This paper contributes to the existing literature in the following aspects: 1. To the best of the authors’ knowledge, this is the first study to quantify emissions of CO, HC, and NO from motorcycles of different sizes using real-world emission measurements and develop emission models specifically for motorcycles in India. 2. The unique driving behavior of motorcycles in the real-world is captured which is not replicated in laboratory-based emission tests on standard driving cycles. Thus, the developed emission models better represent real-world emissions. 2. Experimental method 2.1. Study area and data collection This study was conducted in the city of Chennai, India. Chennai is located in the southern part of India and is the capital of the state of Tamil Nadu. It is the sixth largest city in India in terms of population (Census, 2011). Although the city has a well-developed public transportation system consisting of buses, suburban trains and metro rail system, the major arterials of the city experience severe traffic congestion during the peak periods. The route considered for the emission tests is shown in Fig. 2. The route is of 14 km 73

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Fig. 2. Study area. Source: Google maps.

length and consists of four urban arterials around the Indian Institute of Technology Madras (IIT M) campus with Sardar Patel Road in the north, Velachery Road in the west, Tharamani Road in the south, and IT corridor in the east. These arterial roads were chosen as they carry a significant volume of traffic throughout the day. During the peak-hour, the traffic volumes on the Sardar Patel Road, Velachery Road, Tharamani Road, and IT corridor were 16,907, 4,580, 7,690, and 20,545, respectively (Jaikumar et al., 2017). These four arterial roads are of different configurations (divided/undivided and number of lanes) and hence representative of the arterial road network in Chennai city. The motorcycles in India are of different engine sizes (ranging from 70 cm3 to 1100 cm3). In this study, we select the low power (<150 cm3 ) motorcycles which are commonly used and do not consider high power motorcycles. Four common motorcycles (represented as MC 1, MC 2, MC 3, and MC 4) of different engine sizes (ranging from 70 cm3 to 124 cm3 ) were selected for emission monitoring. All the motorcycles were in-use before the emission testing. MC 1 is a moped (light-weight motorcycle) and MC 3 is a scooter (wheels smaller than a motorcycle). The specifications of the tested motorcycles including displacement, emission standard, and odometer reading are shown in Table 1. These motorcycle models are popular for short-distance commuting within the city limits

Table 1 Specifications of the tested motorcycles. Specification

MC 1

MC 2

MC 3

MC 4

Displacement (cm3 ) Curb weight (kg) Wheel size (cm) No. of cylinders Strokes Fuel Emission standard Aftertreatment device Model year Odometer reading (km)

70

100

109

124

66 40.64 1 2 Gasoline BS II CC 2011 9243

109 45.72 1 4 Gasoline BS II CC 2008 17,321

110 25.40 1 4 Gasoline BS III CC 2013 14,345

118 43.18 1 4 Gasoline BS III CC 2010 12,147

Note: CC – Catalytic convertor. 74

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Table 2 Specifications of the AVL Ditest Gas 1000 (AVL, 2015). Pollutant CO HC NO

Measuring range

Resolution

Accuracy

0–15% vol 0–30,000 ppm vol 0–5000 ppm vol

0.01% vol 1 ppm vol 1 ppm vol

<0.6% vol: ± 0.03% vol ⩾0.6% vol: ± 5% v. M. <200 ppm vol: ± 10 ppm vol ⩾200 ppm vol: ± 5% v. M. <500 ppm vol: ± 50 ppm vol ⩾500 ppm vol: ± 10% v. M.

due to their low cost and high fuel efficiency. Among the four motorcycles, three of them had four-stroke engines, and one of them (MC 1) had a two-stroke engine. Two motorcycles (MC 1 and MC 2) conformed to BS II emission standards and the other two motorcycles (MC 3 and MC 4) conformed to BS III emission standards. The fuel used in the motorcycles were of BS IV standard having maximum lead and sulphur content of 0.005 g/m3 and 0.015% by mass, respectively (CPCB, 2017). All the motorcycles used carburetor for fuel delivery. The instruments used in this study include: (a) AVL Ditest Gas 1000 gas analyzer, (b) Garmin eTrex 10 hand-held GPS receiver, (c) laptop computer, and (d) a portable battery. The AVL Ditest Gas 1000 was used to measure CO, CO2, HC, NO and O2 at a frequency of 1 hertz. This gas analyzer uses the non-dispersive infrared technique (NDIR) to measure CO, CO2, and HC and uses an electrochemical sensor to measure the concentration of NO (AVL, 2015). The measuring range and accuracy of the instrument is given in Table 2. To ensure accuracy of the emission measurements, the instrument was calibrated using a known concentration of gases as specified by the manufacturer before the start of data collection. Leak test and HC residue test was also performed before the start of every test. Further, to test the accuracy of the gas analyzer in following emission peaks, we compared the AVL Ditest Gas 1000 with Horiba’s Mexa One gas analyzer in a laboratory setting using a transient driving cycle. The emission peaks of HC and NO measured by the Horiba instrument were also captured by the AVL gas analyzer. The speed and acceleration of the motorcycle was obtained using a hand-held GPS receiver. The laptop computer was connected to the gas analyzer to record the concentrations of the gaseous pollutants. Fig. 3 shows the test setup for collecting the real-world emission data from the motorcycles. The AVL Ditest Gas 1000 analyzer, laptop computer, GPS receiver, and the battery for powering the gas analyzer were placed inside the bag. The bag had a small opening at the top through which the sampling hose was taken to the tailpipe. The probe attached to the sampling hose was inserted into the tailpipe of the motorcycle. The emission tests were conducted during the off-peak periods (14:00–16:00 h) as driving during peak periods with the bag was cumbersome. The emissions measured were hot start emissions since the tested motorcycles were inuse before the emission test. Each motorcycle was run twice on the test route, thus covering a distance of about 28 km. We

Fig. 3. Test setup for collecting real-world emission data from motorcycle. 75

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Table 3 Travel time (in minutes) for each test in the study area. Test No.

MC 1

MC 2

MC 3

MC 4

1 2

42.70 53.61

25.23 27.73

36.25 40.64

37.82 44.90

determined the number of runs based on the following requirements/limitations: (1) Obtain sufficient quantity of emissions and speed/acceleration data, (2) Ensure traffic conditions remain the same during the data collection, and (3) Duration of data collection was within the capacity of the battery used for powering the gas analyzer. Considering these aspects, we decided to conduct two runs for each motorcycle. The time taken for each test in the study area is given in Table 3. The same rider drove the motorcycles to minimize the effect of driver bias.

2.2. Data synchronization and analysis Since the GPS data and the emissions data are collected separately by using two different devices, synchronization is necessary to determine the effect of speed and acceleration on the emissions from the motorcycles. The GPS data and the emission data were synchronized by matching the time stamps. Also, there is a time lag between the emission at the tailpipe and the detection by the gas analyzer. To account for the lag in sensing the exhaust gas, the time shifts of 10 s, 2 s, and 10 s for CO, HC, and NO, respectively, were assumed (Mahesh and Ramadurai, 2018). Also, before beginning the test, the time in the computer used to record the emission measurements was set to match the time in the GPS device. The gas analyzer recorded the concentrations of CO, CO2 and O2 in % volume and HC and NO in ppm. The measured concentrations were converted to emission rate in grams/s (g/s) using the following equation:

ER[P ] = P × FR × ρ[P ]

(1)

where ER[P ] is the emission rate of pollutant P in g/s, P is the concentration of the pollutant P in % volume or ppm, FR is the exhaust flow rate in L/s, and ρ[P ] is the density of the pollutant P in g/L. The exhaust flow rate from the motorcycle was calculated based on the engine size and the average engine speed (rpm) values. The exhaust flow rate (in litres/s or L/s) is equal to half the engine size (in litres) times the number of revolutions per second for a four-stroke engine. For a two-stroke engine, the exhaust flow rate (in L/s) is equal to the engine size (in litres) times the number of revolutions per second. The average engine speed was assumed to be 3000 rpm for all the motorcycles. We assumed the value of 3000 rpm based on the visual observation of the tachometer by the motorcycle rider during data collection. Besides, we also collected the rpm data of one of the motorcycles (MC 4) using an engine module (called AVL combo sensor) provided with the gas analyzer and found the average engine speed to be around 3000 rpm. However, this module could not be connected to other motorcycles due to the differences in the design of the motorcycle and the safety concerns during data collection. Since we used the same driver for all the emission tests, we believe that the assumption of the same rpm value for all motorcycles would not introduce significant errors. The emission factor in grams/km (g/km) was determined by dividing the total emissions by the total distance travelled during data collection.

3. Results and discussion 3.1. Observed real-world speed-time profiles for motorcycles The laboratory emission tests for motorcycles are conducted on the Indian Driving Cycle (IDC) for motorcycles (ARAI, 2016). The IDC consists of 6 sample cycles of 108 s each with a total test time of 648 s and a total distance of 3.9 km. The maximum and the average speeds in the IDC are 42 km/h and 22 km/h, respectively. Fig. 4 compares the IDC with a sample of the real-world speed-time profile of the motorcycles. The real-world speed-time profile for all the four motorcycles shows significant variation in speed. Also, the maximum speed of the IDC (42 km/h) is much lower than the maximum speeds observed in the real-world. Fig. 5 compares the acceleration of the IDC with that observed during real-world driving. The dotted rectangle shows the range of the speed-acceleration values of IDC. The acceleration/deceleration in the real-world has higher magnitudes than the IDC. This may lead to drastically different emissions in the real-world as compared to the laboratory tests using the IDC. The comparison of speed and acceleration during the real-world emission tests with IDC is shown in Table 4. The maximum speed of MC 1 is lower than the other motorcycles due to its smaller engine size. Although the average speeds are similar to the average speed of 22 km/h in the IDC, the maximum speeds are significantly higher than the maximum speed of IDC (42 km/h). The time spent idling and cruising in the real-world is higher than that of the IDC. 76

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Fig. 4. Comparison of the speed-time profiles of the motorcycles in the real-world with the Indian Driving Cycle (IDC).

3.2. Effect of speed on emission rate Fig. 6(a–c) shows the boxplot of the emission rates for the tested motorcycles. The median CO emission rate is highest for MC 1 and lowest for MC 2. Although MC 2 has lower emission standard than MC 3 and MC 4, the median CO emission rate of MC 2 is lower than the other motorcycles, perhaps due to proper maintenance at regular intervals. Similarly, the median HC emission rate was highest for MC 1 and lowest for MC 2. However, the median NO emission rates were similar for all the tested motorcycles. Since the tested motorcycles were of varying engine displacement, emission standard, and curb weight, we develop separate models for the motorcycles. The emission rate of the measured pollutants from the tested motorcycles show different trends due to the difference in their engine displacement, curb weight, odometer reading, and driving conditions. Although the same driver was used during emission testing, the speed and acceleration of the motorcycles are different due to the different engine specifications of the tested motorcycles. This difference in the speed and acceleration would affect the emission rate. To model the relationship between the emission rate of CO, HC and NO with speed, the average emission rate of the pollutants was plotted with respect to speed. The speed was divided into bins of 5 km/h range such that the bins included all the speed values observed during data collection. The details of the models including standard error, t-stat values, and overall significance value F are given in Table 5. The emission models for MC 1, MC 2, and MC 3 were second order polynomial regression models with speed as the 77

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Fig. 5. Speed versus acceleration for the Indian Driving Cycle (IDC) and the real-world driving. The dotted rectangle shows the range of the points of IDC.

Table 4 Comparison of speed and acceleration observed during real-world testing with IDC. Parameter

MC 1

MC 2

MC 3

MC 4

IDC

Average speed (kmph) Maximum speed (kmph) % Idle % Cruise % Accln. % Decln.

17.67 35 28.09 20.33 26.72 24.85

31.79 59.2 13.08 32.82 29.64 24.46

21.92 53 27.41 15.55 33.38 23.67

20.46 67.20 36.10 14.84 27.42 21.64

22 42 16.52 10.43 38.89 34.26

explanatory variable. The coefficient of determination (R2) of the models for MC 1 were above 0.7. In the case of MC 2, the R2 value was above 0.6 for CO and NO, and 0.352 for HC. The R2 values for the models developed for MC 3 were above 0.69. For MC 4, the R2 values of the quadratic models were relatively lower than for the other motorcycles and the independent variables (v and v2) were insignificant. Hence, we tested models with different specifications. The highest R2 values were obtained for the third order polynomial regression models. The quadratic term (v2) of the regression model was statistically significant (p < 0.05) in most of the models. The sign of the coefficient of the v2 term indicates whether the apex of the polynomial curve is at the top (coefficient is negative) or the bottom (coefficient is positive). In most of the models, the coefficient of the v2 term was negative indicating a polynomial curve with apex at the top. This is in contrast to the study by Lozhkina and Lozhkin (2016), where they found NOx emissions from petrol and diesel passenger cars following a quadratic polynomial trend with respect to average speed with the apex at the bottom (coefficient of v2 term is positive). The authors attributed the high emissions at low and at very high speeds to cold start and frequent acceleration/ deceleration, respectively. In our study, the emissions measured were hot start emissions since the tested motorcycles were in-use before the emission tests. Hence, the emissions at low-speeds were low and increased with increase in the speed of the motorcycle. 3.3. Application of the developed emission models and comparison with other studies The developed speed-based emission models are used to calculate the emissions from motorcycles from an urban arterial section in Chennai. To apply the developed models, we need the speed versus time data of the motorcycle. The speed versus time data of the motorcycles for an arterial road section was obtained from Kanagaraj et al. (2015). The data contains the speed, acceleration, and position of motorcycles, cars, buses, trucks, light commercial vehicles and auto-rickshaw at a frequency of 0.5 s for a duration of 30 min. Kanagaraj et al. (2015) extracted the data from video recordings using a Trajectory Extractor software. The location of the video recording was a six-lane divided urban arterial road having a length of 245 m. The speed versus time data of the motorcycles were used as input to the developed emission models. Since the type of the motorcycle was not provided, we assumed all the motorcycles to be similar to MC 4 (displacement = 124 cm3 , four-stroke and BS III). Kanagaraj et al. (2015) provide the dataset for a 78

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Fig. 6. Comparison of CO, HC and NO emission rate for the tested motorcycles.

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Table 5 Emission models for different motorcycles as a function of speed (kmph). MC

Pollutant

Variable

Coefficients

SE

t-stat

p-value

R2

F

CO

Constant v v2 Constant v v2 Constant v v2

78.143 5.618 −0.097 22.408 0.427 −0.017 0.762 0.010 −2.47E−04

19.750 2.300 0.056 0.707 0.082 0.002 0.022 0.003 6.24E−05

3.956 2.443 −1.744 31.710 5.189 −8.744 34.522 3.445 −3.962

0.011 0.058 0.142 0.000 0.003 0.000 0.000 0.018 0.011

0.716

0.042

0.981

0.000

0.786

0.021

Constant v v2 Constant v v2 Constant v v2

24.168 0.206 −0.006 1.193 −0.015 3.06E−04 1.390 −0.044 9.62E−04

3.856 0.255 0.004 0.217 0.014 1.99E−04 0.399 0.026 3.65E−04

6.268 0.806 −1.829 5.492 −1.024 1.541 3.485 −1.682 2.636

0.000 0.437 0.094 0.000 0.328 0.151 0.005 0.121 0.023

0.628

0.004

0.352

0.092

0.638

0.004

Constant v v2 Constant v v2 Constant v v2

11.293 3.958 −0.054 1.553 0.129 −1.98E−03 0.134 0.112 −1.50E−03

12.028 0.796 0.011 0.393 0.026 3.6E−04 0.306 0.020 2.8E−04

0.939 4.973 −4.934 3.952 4.977 −5.504 0.437 5.559 −5.374

0.368 0.000 0.000 0.002 0.000 0.000 0.671 0.000 0.000

0.694

0.001

0.746

0.000

0.737

0.000

Constant v v2 v3 Constant v v2 v3 Constant v v2 v3

26.591 −0.114 0.058 −7.7E−04 0.371 0.179 −0.006 6.48E−05 0.820 0.194 −6.10E−03 5.66E−05

19.255 2.396 0.079 7.48E−04 0.434 0.054 0.002 1.68E−05 0.725 0.090 3.00E−03 2.81E−05

1.381 −0.048 0.733 −1.029 0.856 3.318 −3.538 3.852 1.132 2.149 −2.039 2.012

0.197 0.963 0.480 0.328 0.412 0.008 0.005 0.003 0.284 0.057 0.069 0.072

0.510

0.058

0.768

0.002

0.422

0.125

MC 1

HC

NO

MC 2

CO

HC

NO

MC 3

CO

HC

NO

MC 4

CO

HC

NO

Note: Emissions are in mg/s.

period of 30 min (14:45–15:15). Three thousand motorcycles passed through the selected road section during this period. The emissions calculated for the urban arterial section using the emission models developed in this study are compared with the emissions estimated using the Automotive Research Association of India (ARAI) emission factors (ARAI, 2007) and the emission factors given by Hassani and Hosseini (2016). The emission factors of ARAI were developed based on the emission tests of different vehicle types on a chassis dynamometer using the Indian Driving Cycle. These emission factors have been used in several studies (Sahu et al., 2011; Pandey and Venkataraman, 2014; Nagpure et al., 2016) for estimating emission inventories. The emission factors of CO, HC, and NOx given by ARAI (2007) for BS II fuel four-stroke motorcycles between 100–200 cm3 were 1.48 g/km, 0.50 g/km, and 0.54 g/km. Meanwhile, Hassani and Hosseini (2016) tested 64 motorcycles of different engine sizes in a chassis dynamometer and obtained their emission factors. From their study, the emission factors for 125 cm3 motorcycles were 15.625 g/km, 0.504 g/km, and 0.074 g/km for CO, HC, and NOx, respectively. The mean emission factors for CO were 6–8 times higher than Euro 3 emission limits.

Table 6 Comparison between the developed emission model with other studies. Pollutant Speed-based model Real-world EF ARAI (2007) Hassani and Hosseini (2016)

CO (g)

HC (g)

NOx (g)

1347.0 4293.6 1088.1 11485.3

63.2 213.2 367.6 370.5

82.6 132.3 397.0 54.4

80

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Fig. 7. Emission standards for motorcycles. Source: ARAI (2016).

Table 6 shows the comparison between the total emissions of CO, HC, and NOx estimated by our model with the emissions obtained using ARAI emission factors and those from Hassani and Hosseini (2016). The total emissions from ARAI model and Hassani and Hosseini (2016) are obtained by multiplying (1) the emission factor given in those studies, (2) the total number of motorcycles passing the road section during the period of video data, and (3) the length of the road Section (245 m). The emissions estimated by our speed-based model is significantly lower than the emissions obtained using the real-world emission factor, ARAI emission factors, and the emission factors given by Hassani and Hosseini (2016). This is because of the low variation in speed and no stoppage of the motorcycles in the 245 m section of the trajectory data. The emissions of CO, HC, and NOx estimated using the real-world emission factors is 3.18 times, 3.37 times, and 1.6 times the emissions estimated using the developed speed-based emission model. The ARAI model based emission estimates of CO, HC, and NOx were 0.81, 5.81, and 4.81 times the emissions estimated by our model. Meanwhile, the CO emission factor of 15.625 g/km given by Hassani and Hosseini (2016) increases the total CO emissions. However, the NOx emissions obtained using the emission factors given by Hassani and Hosseini (2016) were similar to the emissions obtained using our model. In summary, the emissions of HC and NOx estimated using laboratory-based emission factors given by ARAI were significantly higher than the emissions obtained from our model. This may be because the ARAI emission factor values are for BS II motorcycles. Also, the ARAI provides a single emission factor whereas our model is a speed-based emission model. Thus, the studies which use laboratory-based emission factors to estimate emission inventories may overestimate the total emissions of CO and HC from motorcycles. 3.4. Comparison of the real-world emission factors with emission standards Fig. 7 shows the change in the emission standards for gasoline powered motorcycles since the Bharat Stage emission standards were implemented. As seen from the figure, a significant reduction in the emission limits have taken place since 1996. Table 7 shows the comparison between the measured real-world emission factor and the emission standards given by ARAI (2016). The ARAI provides the emission standard values for CO and [HC + NOx] in g/km. The real-world emission factors were different for the tested motorcycles with MC 1 having the highest emission factor for both CO and [HC + NOx]. The values in bold face in Table 7 indicate emission factor values above the emission standards. The real-world emission factors from all the motorcycles are higher than the CO Table 7 Comparison between the real-world emission factor and emission standards. Motorcycle

MC MC MC MC

1 2 3 4

Emission standard

BS BS BS BS

II II III III

CO (g/km)

[HC + NOx] (g/km)

Real-world

BS standard

18.44 4.77 9.71 5.84

1.50 1.50 1.00 1.00

81

Real-world 3.23 (3.12 0.48 (0.24 0.69 (0.46 0.47 (0.29

+ 0.11) + 0.24) + 0.23) + 0.18)

BS standard 1.50 1.50 1.00 1.00

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Table 8 Comparison of the developed real-world emission factor with previous studies. Region Taiwan Taiwan Taipei, Taiwan Taichung, Taiwan Kaohsiung, Taiwan URB, Taiwan Pingtung, Taiwan Taiwan Taiwan Taiwan Hanoi, Vietnam Macao, China Dhanbad, India Dhanbad, India Tehran, Iran Delhi, India Chennai, India Chennai, India

Vehicle specification

Test cycle

CO (g/km)

HC (g/km)

NOx (g/km)

2-S, In-use, with CC 4-S, In-use, with CC 2-S & 4-S 2-S & 4-S 2-S & 4-S 2-S & 4-S 2-S & 4-S 2-S, Cb 4-S, Cb 4-S, FI

ECE ECE Real-world Real-world Real-world Real-world Real-world ECE ECE ECE Real-world

21.72 ± 13.86 36.10 ± 13.58 8.24 ± 2.22 7.81 ± 1.86 6.53 ± 0.70 7.41 ± 1.57 6.79 ± 3.39 4.45 ± 0.41 3.84 ± 0.20 2.90 ± 0.12 8.86

15.16 ± 12.36 3.20 ± 0.56 2.53 ± 3.09 2.28 ± 2.06 1.62 ± 0.60 2.36 ± 2.06 1.63 ± 1.27 3.94 ± 0.85 1.38 ± 0.19 0.94 ± 0.19 0.79

0.016 ± 0.006 0.250 ± 0.130 0.120 ± 0.130 0.120 ± 0.100 0.130 ± 0.110 0.110 ± 0.090 0.130 ± 0.140 0.050 ± 0.002 0.230 ± 0.010 0.390 ± 0.060 0.130

–

8.95 ± 6.83

0.93 ± 1.27

0.390 ± 0.300

Zhou et al. (2014)a

IDC Real-world Real-world

0.41 1.60 15.63 1.00 ± 0.60 18.44 6.77 ± 2.60

0.14 0.45 0.50

0.510 0.460 0.074

Adak et al. (2016)b Adak et al. (2016)b Hassani and Hosseini (2016)

– 3.12 0.33 ± 0.12

0.070 ± 0.010 0.110 0.217 ± 0.032

Jaiprakash and Habib (2018) This study This study

4-S, 100–125 cm3

>50 cm3 , 2-S & 4-S 4-S 4-S 4-S, 125 cm3 4-S, with CC 2-S, with CC 4-S, with CC

MIDC – –

Reference Tsai Tsai Chen Chen Chen Chen Chen Yang Yang Yang Tung

et et et et et et et et et et et

al. al. al. al. al. al. al. al. al. al. al.

(2000) (2000) (2003) (2003) (2003) (2003) (2003) (2005) (2005) (2005) (2011)

Note: 2-S represents 2-stroke, CC-catalytic convertor, ECE-Economic Commission for Europe, Cb-carburetor, FI-fuel injection, DC-driving cycle, IDCIndian Driving Cycle, MIDC-Modified Indian Driving Cycle, CMEM-Comprehensive Modal Emission Model. Emission factor values are given in mean ± standard deviation. a Emissions were measured using remote sensing technique. b Used CMEM to estimate emissions.

emission standards. The highest emission factors of 18.44 g/km (CO) and 3.23 g/km (HC + NOx) were observed for MC 1 which has a two-stroke engine. The real-world emission factors of CO for MC 1, MC 2, MC 3, and MC 4 were 12.3 times, 3.18 times, 9.71 times, and 5.84 times above the respective BS emission standard values. However, only MC 1 exceeded the [HC + NOx] BS II emission standard of 1.5 g/km. Thus, HC and NOx emissions for BS III motorcycles are within the permissible limits whereas CO emissions are significantly higher than the prescribed standards. 3.5. Comparison with previous studies Table 8 shows the comparison of our emissions factors with previous studies on motorcycles. The region of the study and specifications of the tested motorcycles are also given in Table 8. Most of the previous studies were laboratory-based studies using either a legislative driving cycle such as the ECE (Economic Commission for Europe) or using a driving cycle developed from real-world GPS data. Tsai et al. (2000) tested 19 motorcycles (7 new and 12 in-use) in the laboratory using the ECE driving cycle. The studies of Yang et al. (2005) and Jaiprakash and Habib (2018) also used legislative driving cycle. Since the legislative driving cycles do not capture the frequent acceleration and deceleration observed in the real-world, some of the studies (Chen et al., 2003; Tung et al., 2011; Adak et al., 2016; Hassani and Hosseini, 2016) used real-world driving cycles for testing vehicles in the laboratory or as input to an emission model. Adak et al. (2016) used the real-world driving cycles as input into the Comprehensive Modal Emission Model (CMEM) to estimate the emission factors for motorcycles in Dhanbad, India. However, Zhou et al. (2014) used remote sensing technique to determine real-world emission factors in Macao, China. The emissions of CO, HC, and NOx were measured at 19 sites. Unlike the above studies, our work is based on on-board emission measurement. As seen in Table 8, the emission factor values reported in the previous studies varied significantly. The emission factor values of CO, HC, and NOx in g/km were in the range of 0.41–36.10, 0.33–15.16, and 0.016–0.510, respectively. The variation in emission factor values may be due to the differences in vehicle type, fuel type, emission standard, mileage, and maintenance history of the tested vehicles. Comparing with similar studies in India, the highest CO emission factor for four-stroke motorcycle was 1.60 g/km as reported by Adak et al. (2016), which was lower than the real-world emission factor of 6.77 g/km found from our real-world tests. However, the real-world emission factors of HC and NOx from our tests were lower than the values reported by Adak et al. (2016). Thus, using the CO emission factor values given by Adak et al. (2016) and Jaiprakash and Habib (2018) would underestimate the real-world CO emissions. 4. Challenges and limitations In developing countries, a significant share of traffic consists of motorcycles. These motorcycles squeeze through the gaps in the traffic and do not follow lane discipline. Thus, conducting real-world emission measurements of motorcycles is a challenging task. To the best of our knowledge, this is the first study in India that has conducted real-world emission measurements on motorcycles. This study has several limitations. Firstly, since the emission tests were expensive and time-consuming, we could test only a limited number of motorcycles. Testing more vehicles would provide further evidence to substantiate the observed trends. Secondly, the tests 82

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were restricted to urban arterials. Future tests could include other road types such as residential roads and highways, thus providing a complete understanding of emissions from motorcycles on different roads. Finally, particulate matter emissions were not considered. Since particulate matter (including fine and ultra-fine particulates) is one of the most toxic pollutants to human health, further study could focus on particulate matter emissions from motorcycles. 5. Conclusions The emissions of gaseous pollutants from motorcycles in urban areas is a serious problem that has not received adequate attention from researchers. In this paper, we measured real-world emissions of CO, HC, and NO from four motorcycles of different sizes in Chennai, India using a portable gas analyzer. Unlike the chassis dynamometer tests, the real-world tests conducted in this study measure the emissions in real-time, thus capturing the emissions during typical driving conditions on urban roads. The results show that CO emission from motorcycles is significantly higher than the emission standards. Also, on comparing the emission rates of the tested motorcycles, we found the emission rates to be significantly different from each other. Thus, separate speed-based emission models were developed for each of the tested motorcycles which can be used to accurately estimate emissions from motorcycles in urban arterials. In summary, the following results were obtained. Firstly, the speed-time profiles observed during the real-world tests were different from the Indian Driving Cycle (IDC) used for the laboratory testing of motorcycles. The maximum speed in the real-world for three motorcycles was higher than the maximum speed of the IDC (42 km/h). Secondly, the average CO emission factor for the BS II and BS III motorcycles was 7.74 times and 7.78 times higher than the emission standard. Thus, CO emissions is a major problem from motorcycles. The [HC + NOx] emission factors were within the emission standards for three of the motorcycles with only the twostroke motorcycle having a higher emission factor. Thirdly, the emission from two-stroke motorcycle was higher than the four-stroke motorcycles. However, we tested only one two-stroke motorcycle and hence more vehicles should be tested in future to further validate this result. Finally, the ARAI emission factors are dated and hence newer emission factors are required for motorcycles. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.trd.2019.09.010. References Adak, P., Sahu, R., Elumalai, S.P., 2016. Development of emission factors for motorcycles and shared auto-rickshaws using real-world driving cycle for a typical Indian city. Sci. Total Environ. 544, 299–308. https://doi.org/10.1016/j.scitotenv.2015.11.099. ARAI, 2007. 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