Skip to content

Advertisement

  • Original Paper
  • Open Access

Investigation and sensitivity analysis of air pollution caused by road transportation at signalized intersections using IVE model in Iran

  • 1Email author,
  • 2 and
  • 3
European Transport Research ReviewAn Open Access Journal201710:7

https://doi.org/10.1007/s12544-017-0275-3

  • Received: 19 November 2016
  • Accepted: 23 November 2017
  • Published:

Abstract

Introduction

The development of urbanization has had many negative outcomes in different societies. Population growth and the increase in the transportation are the consequences of urban growth which has resulted into problems. Vehicles are responsible for 90% of air pollution in Iran and it is essential to use authentic models of traffic emissions in accordance with the current conditions to predict this and future emissions. Iran has a lot of different air pollution dispersion parameters compared with the developed countries.

Method

In this paper air pollution emission parameters in signalized intersections have been modelled and results have been compared to measured concentrations of air pollution in intersections. For this purpose the use of IVE (International Vehicle Emission) model that is common air pollution modelling and besides, Sensitivity Analysis has been performed to show modeling accuracy in comparison with current emissions.

Result

By modeling and measurement results, it’s easily understood in warm seasons emission concentration is more than cold seasons. Minimum and maximum rate of Carbon-monoxide (CO) and Nitrogen-oxides (NOX) has been evaluated.

Conclusion

IVE model has shown a bit difference amount of pollutions by comparison with field measurement emission. It could be said it is appropriate for model vehicle emission in Iran.

Keywords

  • Air pollution
  • Emission
  • Transportation
  • IVE
  • Pollutants

1 Introduction

The development of urbanization in different societies has had many negative outcomes. Population growth result increases in the transportation which has resulted into problems. Transportation causes lots of problems contrary to its benefits. Two major problems are noise and air pollution. Noise pollution in airports is more important. Lots of studies on noise pollution reduction of aircraft around airport have been done recently by introducing optimal model to decrease noise pollution and fuel use of commercial aircraft [13]. Another problems is the air pollution that significantly visible in metropolitan areas. It has been estimated that air pollution is responsible for the death of 3.1 million people in the world every year [4]. Among the sources of air pollution motor vehicles movement in transport network is known as the main cause of air pollution to such an extent that it has obtained a share of 60 to 90% of total emissions [5]. Environmental stress, fuel consumption and transport industry pollutions augment when the traffic flow is stopped and delayed and stop-motion phenomenon occurs frequently. These phenomena are often seen at intersections and road junctions, especially when the traffic signals are used. Thus, the highest concentration of pollutants produced by transportation occurs near the signalized intersections and squares and urban air quality near these areas is of lower condition compared to other areas which is due to the changes in vehicle speed when approaching and moving away from the intersection [6, 7]. In other words, there are high traffic dynamics near signalized intersections.

High dynamics for congestion, i.e. stop-and-go traffic and low dynamics for free-flowing traffic. Traffic with high dynamics has significantly higher emissions than traffic with low dynamics [8, 9]. Also Mustafa et al. (1993) revealed that the traffic signals at intersections generate about 50% more emissions than roundabouts and during heavy traffic, signals lead to larger emissions of HC, almost double of that at roundabouts [10]. Coelho et al. by modelling and laboratory studies displayed that existence of signalized intersections in urban areas increases pollution 15 to 40% [11].

Road vehicle emissions depend on many parameters like transportation and weather factors. Emission models are used to perform the measurments of road transport emissions [4]. Vafa-Arani et al. (2014) have studied on air pollution in Tehran metropolitan city and have shown two source of air pollution affect the weather of this city. They have examined transportation and industrial pollution effect [12]. Sivacoumar et al. (2001) investigated Jamshedpur region in India by using a mathematical programming method to anticipate the air pollution of this region. He illustrated the portion of NOX concentration from vehicles, domestic and industrial [13]. Hong and Shen (2013) conveyed the residential density on CO2 in comparison with vehicle using emission factors based on vehicle and trip characteristics [14]. Wang et al. (2008) used a system dynamic model to predict air pollution in Dalian. They utilized system dynamic model based on the cause-and-effect analysis and feedback loop structures. Their model comprises 7 sub-models like population, economic development, number of vehicles, environmental influence, travel demand, transport supply, and traffic congestion. They suggest Dalian should have been restricted in quantity of vehicle in aim of reducing air pollution [15] and Anh (2003) by using a dynamic model showed air pollution is the main result of traffic congestion. He thinks development in public transportation system and road network expanding can help to reduce pollutant [16].

2 Defining the problem

The field study of the transportation related projects in the field of air pollution is not completely possible and would not provide satisfactory results because this can be performed by a specific devices or tool such as a portable spectrometer device connected to the exhaust which also create some drawbacks and limitations, such as:
  • These devices are associated with errors.

  • Using this device a few parameters such as instantaneous velocity can be measured with limits and it is not possible to consider factors, as well as the entire fleet of transportation.

  • Preparation and maintenance of these tools is very expensive and not applicable in this country [4].

Hence it is necessary to use authentic models of traffic emissions in accordance with the current conditions. On the other hand, despite the traffic emissions models used to evalute pollution from mobile sources, no steps have been taken to examine their suitability in the countries’ conditions. In this study IVE model have been chosen because of its ability in covering lots kind of vehicle (about 1372) and estimating emission factors based on spot speed instead of average speed. In this research IVE model is evaluated and validated using field studies and measurements to introduce a reliable and convenient model for its eligibility. In this study the IVE model is evaluated based on the sensitivity to changing parameters. To examine this model all conditions of the intersection of the municipality of Najafabad - Esfahan have been applied. Accordingly, to enter the input parameters needed by the model the data collected on some winter and spring days of 2015 at a certain hour (11 to 12 AM) were used. One of the factors that cause changes in the calculated concentrations of pollutants causing an error in the calculations is wind. That’s why among the days of study the selected days did not vary in terms of wind speed and direction.

Hui et al. (2007) implemented IVE model in Guangzhou China city streets in 2005 and observed correlation coefficients of 0.90 and 0.81 for CO and NOX [17].

3 Research

3.1 Intersection profile

The area under study is a signalized intersection with a fixed schedule both sides of which end to a one-way path. Phasing of this intersection traffic signal is presented in the Fig. 1.
Fig. 1
Fig. 1

Phases of the intersection

3.2 The volume of vehicles

In this study, video recording was used as a tool for counting the vehicles on the road at 11–12 within the mentioned days that the results of which are shown in Appendix Table 5. The counts are multiplied by 10 to be rounded. Also the left turn movements of east-west were 19–22% and right turn movements of north-south were 10–12%.

3.3 The average speed of the fleet

According to the software user manual of IVE, the speed of a fleet of vehicles that as an input parameter in this model, is movement speed not travel speed [18]. For this reason, the fleet velocity of this intersection, the stop time of the vehicles caused by the red lights was ignored. These numbers show the dominant speed of the cars 50 m before and after the intersections at two intersecting streets obtained by the distance and time (minus the red lights interval). The results of the average approximate speed of vehicles on research days are presented in Appendix Table 6.

3.4 Driving cycle

The driving style means the acceleration and vehicle speed during driving. The type of driving is the result of the driving culture and traffic conditions of the area. IVE model, in order to define the style of driving, uses a driving factor related to the type of driving associated with vehicle specific power (VSP) and engine stress. VSP is a function of instantaneous velocity, gradient, vehicle weight, air density and …, IVE uses the Eq. (1) which is valid for light vehicles with good accuracy.
$$ \mathrm{VSP}={V}^{\ast}\left[1.1a+9.81\left({a}^{\ast}\kern0.5em \tan \left(\sin (grade)\right)\right)+0.132\right]+0.000302{\mathrm{V}}^3 $$
(1)

Where, V is the velocity (m/s) a stands for acceleration (m/s2) and grade as the slope of the road. In IVE 60 modes (indices) are considered for specific power and engine stress. Specific power and engine stress parameters related to each of these 60 modes in the model has been shown in Appendix Table 7. By setting these parameters in a period, the percentage of time that the fleet exists in each index is determinded. Engine stress is also visible in minimum and maximum periods in Appendix Table 7 too. According to the instantaneous and average velocity as well as VSP, the engine stress of most vehicles is within (−1.6–3.1) and a very small number of them (some imported cars) has the engine stress of 7 or more.

As previously mentioned among the countries that use this model, China has highest similarity with Iran’s cities driving style. In this study, first the urban cycle of China’s Beijing has been used and it has been applied to the study area by repeatedly driving situations under the intended traffic and recording time, velocity and acceleration within continious and short intervals. Also the condition of those vehicles with right or left turns and not-stop before traffic signal have been included in this cycle. Figure 2 presents the cycle has been defined in the IVE related to this study.
Fig. 2
Fig. 2

Index values related to the cycle defined within the region in IVE

3.5 Meteorological data

Among lots of meteorological factors, most important ones are air temperature, humidity, wind speed and wind direction [19]. Humidity and temperature are the most important meteorological variable that are effective in engine performance and emissions and have been performed in IVE model for this study. Appendix Table 8 shows the humidity and temperature in the area of this study.

3.6 Variables

3.6.1 Fleet technology

In IVE model, 1372 predefined technologies exist for vehicles and it is possible to define additional capabilities by the user. The classifications are based on engine size, fuel type, fuel system, pollution standards, pollution control of exhaust system, the engine operation and etc. In this study for specifying the fleet technology, by analyzing of field video recorded during this period, all vehicle types have been determined and the technological features have been found by the manufacturer information and other authoritative sources and have been entered into the model. Also to determine the age and kilometers usage of vehicle the information on transport and energy performance of vehicles have been used [20]. Appendix Table 9 shows the fleet technology of this study.

3.6.2 Air conditioning systems

This defined parameter shows a percentage of time in which the fleet uses air conditioner at 27 °C or higher temperature. In designing this model, it is assumed that regardless of the user defined amount of parameters at 15° and below none of the fleet uses the parameter and at a temperature of 32 °C or above the whole fleet having an air conditioning system uses this system. Given the field studies and the fact that this study is done in winter and early spring, this parameter is considered to be 0.

3.6.3 Fuel parameters

Indicating to reliable sources and studies about the quality of gasoline and diesel in the country and the amount of material in them, corresponding parameters to fuel have been determined and entered into the model. Some of these parameters are as follows:
  • The overall quality of fuel: Medium

  • The amount of lead in fuel: zero (negligible)

  • The amount of sulfur in the fuel: between 50 and 300 ppm

  • The benzene in the fuel: between 5.0 and 5.1% (average)

  • The oxygen content of gasoline (fuel additive that increases the oxygen content of fuel and improves pollution): zero

4 Results

4.1 The results of modelling

After entering all the parameters mentioned in the previous section, IVE has been run and the results are presented in Table 1.
Table 1

The amount of emission factor of the IVE

Spring 2015

Winter 2015

NOX (G/Km)

CO (G/Km)

Day 2015

NOX (G/Km)

CO (G/Km)

Day 2015

0.63

7.47

April 15

0.7

8.35

January 1

0.57

6.76

April 16

0.61

7.32

January 3

0.57

6.76

April 17

0.64

7.71

January 5

0.66

7.89

April 19

0.55

6.65

January 6

0.54

6.45

April 20

0.7

8.35

January 10

0.6

7.61

April 21

0.77

9.78

January 11

0.57

6.76

April 22

0.72

8.59

January 12

0.7

8.35

April 23

0.62

7.75

January 20

0.79

9.46

April 24

0.75

8.87

January 21

0.57

6.77

April 25

0.54

6.45

January 23

0.83

12.7

April 30

0.6

7.1

January 24

0.6

7.1

May 1

0.6

7.61

January 25

0.7

8.35

May 3

0.63

7.47

January 26

0.63

7.47

May 4

0.7

8.35

January 30

0.6

7.1

May 7

0.66

7.89

January 31

0.59

9.06

May 8

0.75

8.87

February 4

0.69

10.6

May 12

0.6

7.61

February 6

0.63

7.47

May 13

0.61

7.32

February 7

0.54

8.27

May 14

0.64

7.71

February 8

0.79

9.46

May 15

0.61

7.32

February 12

0.66

7.89

May 16

0.58

6.97

February 13

0.66

7.89

May 17

0.6

7.1

February 14

0.63

7.47

May 18

0.6

7.61

February 16

0.63

7.47

May 19

0.49

6.29

February 17

0.54

6.45

May 20

0.53

6.36

February 18

0.63

7.47

May 21

0.57

7.23

February 22

0.6

7.1

May 25

0.7

8.35

February 24

0.63

7.47

February 25

0.63

7.47

February 26

0.6

7.1

February 27

0.6

7.1

March 2

0.54

6.45

March 3

4.2 The results of field measurement

In this study at the intersection, fixed air pollution monitoring device from “Signal group” is installed by Esfahan Organization of Environmental Protection at the height of 2.5 m ground above that the sensors of which are able to measure and record air pollution at the intersection at any moment. Therefore, in order to perform this study, the data provided by this device have been applied and among the urban emissions, NOX and CO are more than any other emissions which are presented in Table 2.
Table 2

The concentration of pollutants measured at 11 to 12

Spring 2015

Winter 2015

NO [ppb]

NO2 [ppb]

CO [ppm]

Day 2015

NO [ppb]

NO2 [ppb]

CO [ppm]

Day 2015

32.6

53.4

3.05

April 15

72.22

48.42

3.6

January 1

53.46

36.15

2.35

April 16

34.74

41.57

3.22

January 3

47.09

36.4

2.38

April 17

31.73

53.5

3.45

January 5

49.4

43.41

2.92

April 19

20.78

59.96

2.89

January 6

52.69

31.5

2.03

April 20

31.8

73.82

4.1

January 10

39.86

51.57

2.86

April 21

32.2

79.69

4.3

January 11

42.54

42.72

2.55

April 22

55.41

64.61

3.77

January 12

11.2

68.6

3.64

April 23

44.9

63.4

3.63

January 20

24.78

71.45

3.99

April 24

51.25

75.5

4.4

January 21

37.72

41.71

2.37

April 25

25.81

38.91

2.87

January 23

19.8

70.12

4.65

April 30

38.21

38.3

3.3

January 24

37.8

39.81

2.4

May 1

53.07

34

3.33

January 25

27.13

59.7

3.59

May 3

63.9

46.72

3.1

January 26

22:25

56.5

3.32

May 4

42.7

51.1

3.79

January 30

19.7

57.41

2.85

May 7

22.3

65.69

3.49

January 31

25.97

43.5

2.53

May 8

54.4

76.85

3.78

February 4

29.74

60.03

3.2

May 12

11.6

57

3.38

February 6

38.1

53.17

2.78

May 13

57.81

33.75

3.39

February 7

55.35

33.3

2.15

May 14

16.93

53.4

3.74

February 8

17.98

77.51

3.54

May 15

36.69

51.1

3.57

February 12

32.6

52.12

2.41

May 16

28.8

53.1

2.9

February 13

27.7

63.4

2.75

May 17

33.2

57.1

3.05

February 14

21.2

64.4

3.33

May 18

34.3

61.1

3.22

February 16

37.5

45.2

2.88

May 19

39.1

35.5

2.81

February 17

29.1

47.3

1.92

May 20

37.1

32.1

2.78

February 18

21.1

64.3

3.08

May 21

41.1

51.2

3.51

February 22

25.2

57.7

2.1

May 25

58

66.6

4.01

February 24

55.3

42.1

3.7

February 25

59.1

38.7

3.61

February 26

42.2

35.5

3.2

February 27

18.9

49.8

3.5

March 2

49.1

32.2

2.67

March 3

4.3 Data comparison and results evaluation

Environmental pollution measuring devices are able to measure the concentration in ppm, volume percentage or the weight of the pollutants based on a determinded volume of the air (such as g/m3), while IVE model shows the emissions in grams and finally by using the kilometers usage of vehicle and travel time by fleet shows emissions on grams per traveled distance or grams per time unit. However, for comparing the pollutants in both methods of modelling and field data collection, having a similar method of measuring is necessary. To solve this problem data have been converted to unit of the pollutants into g/l using the Eqs. (2) and (3) [4, 17].

To convert the concentration units achieved by the environmental pollution gauge device into g/l the following equations are used:
$$ {EF}_{CO}=\frac{C_{CO}}{C_{CO2}+{C}_{CO}+4{C}_{HC}}{\rho}_f{w}_c\frac{M_{CO}}{12} $$
(2)
$$ {EF}_{NOx}=\frac{C_{NOx}}{C_{CO2}+{C}_{NOx}+4{C}_{HC}}{\rho}_f{w}_c\frac{M_{NOX}}{12} $$
(3)

Where M represents the molar mass (28.1 g/mol for CO, 30 g/mol for NO and 46.01 g/mol for NO2), the fuel density (740 g per liter for gasoline), is the share of fuel carbon (Usually 0.85) and, CCO, CCO2 CHC, CNOX are the concentration of pollutants in terms of percent. These equations are mostly used for spectrometer systems that have a function similar to environmental devices and assuming that the ratio of the pollutants in a mass is preserved that these equations can be used in environmental assessments.

For concentrations derived from modelling the Eq. (4) is used as well:
$$ {F}_E\left(\frac{g}{l}\right)={F}_E\left(\frac{g}{km}\right)\ast F\left(\frac{km}{l}\right) $$
(4)

Where, presents the emission factors in terms of g/km and g/lit and F is the average km per consumed liter of fuel [17].

By converting and standardization of the concentration of pollutants resulting from the two methods of field data collection and modelling, the difference between this data is compared based on the Tables 3 and 4 and the correlation between the concentrations of the models have been calculated and measured (Figs. 3, 4, 5, and 6) . It can be seen that:
  • The difference between the concentrations of the average CO pollutants has been 1.2 and 1.6 times for winter and spring repectively.
    • According to Table 4 this difference for NOX emissions has been 1.7 and 1.9 times for winter and spring respectively.

    • The differences are for the fact that the measuring device of this study is the environmental gauge device and evaluate the pollutant after withdrawal of supply and decreased with ambient air and it cannot measure it as it comes out of the exhaust. The device used in this study was installed on the edge of the intersection and the pollutants are spreaded and decreased before reaching the sensor. It is observed that the difference in both pollutants is higher in spring, the reason of which might be the wind and increased dispersion of pollutants.

  • Since the objective of using this model in this research is descriptive analysis and in order to evaluate it, the model sensitivity should be analyzed and the amount of following changes by the model must be estimated. When the results of field measurements differ from different parameters, the model must follow these alterations as well. One of the best ways for this sensitivity analysis is measuring the correlation of the model results and real perceptions. The correlation coefficient is applied to specify the sensitivity of two data series versus each other in face of changes as it presents the severity of relation, correlation and proportionality of the data. As the value approaches 1 it showes higher sensitivity of the data in a direct manner. In this study the correlation between CO concentrations in both methods was 0.86 in spring and 0.88 in winter. Also correlation between NOX concentrations in both methods was 0.84 in spring and 0.85 in winter. The results of the model were connected with the results of measurement and change by converting different parameters with a harmonized, linear trend with high regression coefficient.

Table 3

Comparison of CO emissions concentration of the model and Field data

Winter

Spring

Day 2015

Modelling

Field data

Data difference of models (equal)

Day 2015

Modelling

Field data

Data difference of models (equal)

CO g/l

CO g/l

CO g/l

CO g/l

January 1

47.11

33.969

1.39

April 15

42.998

28.881

1.49

January 3

33.839

30.457

1.11

April 16

32.731

22.354

1.46

January 5

42.473

32.585

1.3

April 17

33.632

22.635

1.49

January 6

29.716

27.394

1.08

April 19

48.539

27.673

1.75

January 10

51.747

38.563

1.34

April 20

33.69

19:35

1.74

January 11

60.639

40.392

1.5

April 21

43.15

27.115

1.59

January 12

50.766

35.534

1.43

April 22

36.485

24.225

1.51

January 20

46.173

34.245

1.35

April 23

51.19

34.337

1.49

January 21

55.785

41.305

1.35

April 24

63.302

37.554

1.69

January 23

27.238

27.208

1

April 25

36.698

22.541

1.63

January 24

33.116

31.198

1.06

April 30

83.993

43.582

1.93

January 25

39.935

31.476

1.27

May 1

38.478

22.822

1.69

January 26

39.512

29.345

1.35

May 3

52.118

33.876

1.54

January 30

52.674

35.718

1.47

May 4

44.492

31.383

1.42

January 31

47.488

32.954

1.44

May 7

34.693

27.022

1.28

February 4

53.617

35.626

1.5

May 8

43.692

24.038

1.82

February 6

38.92

31.938

1.22

May 12

62.948

30.272

2.08

February 7

36.279

32.03

1.13

May 13

43.496

26.37

1.65

February 8

36.137

35.258

1.02

May 14

40.622

20.478

1.98

February 12

33.839

33.692

1

May 15

61.409

33.415

1.84

February 13

30.821

27.487

1.12

May 16

46.086

22.915

2.01

February 14

34.22

28.881

1.18

May 17

47.488

26.091

1.82

February 16

34.012

30.457

1.12

May 18

44.824

31.476

1.42

February 17

27.676

26.65

1.04

May 19

42.168

27.301

1.54

February 18

28.995

26.37

1.1

May 20

33.69

18.314

1.84

February 22

38.745

33.139

1.17

May 21

43.662

29.16

1.5

February 24

52.303

37.738

1.39

May 25

38.793

20.008

1.94

February 25

40.508

34.89

1.16

February 26

41.504

34.061

1.22

February 27

35.009

30.272

1.16

March 2

31.855

33.046

0.96

March 3

28.242

25.345

1.11

r = 0.88

r = 0.86

Table 4

Comparison of NOX emissions concentration of the model and Field data

Winter

Spring

Day 2015

Modelling

Field data

Data difference of models (equal)

Day 2015

Modelling

Field data

Data difference of models (equal)

NOX g/l

NOX g/l

NOX g/l

NOX g/l

January 1

2.461

1.48

1.66

April 15

2.264

1.161

1.95

January 3

1.759

0.998

1.76

April 16

1.719

1.109

1.55

January 5

2.215

1.151

1.92

April 17

1.766

1.048

1.69

January 6

1.547

1.144

1.35

April 19

2.553

1.177

2.17

January 10

2.704

1.461

1.85

April 20

1.764

1.031

1.71

January 11

2.995

1.553

1.93

April 21

2.108

1.208

1.75

January 12

2.653

1.559

1.7

April 22

1.916

1099

1.74

January 20

2.291

1.436

1.6

April 23

2.674

1176

2.27

January 21

2.93

1.679

1.75

April 24

3.317

1.354

2.45

January 23

1.426

0.868

1.64

April 25

1.924

1.036

1.86

January 24

1.736

0.982

1.77

April 30

3.426

1.278

2.68

January 25

1.951

1.065

1.83

May 1

2.017

1.007

2

January 26

2.08

1.374

1.51

May 3

2.723

1199

2.27

January 30

2.752

1.222

2.25

May 4

2.342

1.102

2.12

January 31

2.497

1.244

2.01

May 7

1.818

1.094

1.66

February 4

2.817

1.739

1.62

May 8

1.773

0.943

1.88

February 6

1.901

1.002

1.9

May 12

2.571

1.234

2.08

February 7

1.885

1.109

1.7

May 13

2.29

1.215

1.88

February 8

1.884

0.998

1.89

May 14

1.659

1.085

1.53

February 12

1.759

1.163

1.51

May 15

3.218

1.383

2.33

February 13

1.607

1.119

1.44

May 16

2.424

1.146

2.11

February 14

1.794

1225

1.46

May 17

2.497

1.269

1.97

February 16

1.661

1.297

1.28

May 18

2.36

1.214

1.94

February 17

1.354

0.95

1.43

May 19

2.22

1.084

2.05

February 18

1.519

0.877

1.73

May 20

1.764

1.038

1.7

February 22

1.9

1.209

1.57

May 21

2.299

1.214

1.89

February 24

2.733

1.614

1.69

May 25

2.033

1.16

1.75

February 25

2.133

1.21

1.76

February 26

2.185

1.197

1.83

February 27

1.835

0.979

1.87

March 2

1.67

0.963

1.73

March 3

1.478

1.001

1.48

r = 0.85

r = 0.84

Fig. 3
Fig. 3

The regression between CO concentration in modelling and field measurement- winter 2015

Fig. 4
Fig. 4

The regression between CO concentration in modelling and field measurement- Spring 2015

Fig. 5
Fig. 5

The regression between nitrogen oxides concentration in modelling and field measurement - winter 2015

Fig. 6
Fig. 6

The regression between nitrogen oxides concentration in modelling and field measurement- Spring 2015

The high correlation values for the existing data refers to high levels of proportionality of IVE model calculations with field data collections and indicates that the function of this model is appropriate for the conditions stated in the changes, and present a proper calculation response against changes. As a result, the model is suitable for the assessment and interpretation of emissions behavior in different parameters and it is possible to analyze different transportation policies in these areas before and after implementation in terms of environment and make the best decisions.

5 Conclusion

In this research, a comprehensive field study has been performed at an intersection of Najafabad city in Isfahan province in winter and spring over a period of 59 days. The terms of the intersection entered IVE model and sensitivity analysis was performed and the results were compared to field measurement data for the NO and NO2 and CO pollutants. The outcomes of the correlation between the concentrations of CO pollutant in both methods were 0.86 in the spring and 0.88 in winter.

Also in intersection suburbs and in low traffic hours with the determined fleet combination, 7.58 g/l CO and 0.63 g/km NOX have been produced. This value equals 7.91 g/l for CO and 0.64 g/km for NOX.

Because of the volume of resulting traffic during study period and considering environmental pollution produced by vehicles, only within 50 m from the intersection, the emission share of 1 h under light traffic conditions at this intersection for CO is minimum 1225 g and maximum 2730 g in winter and minimum 1474 g and maximum 3785 g in spring, for NOX is minimum 97 g and maximum 215 g in winter and minimum 119 g and maximum 247 g in spring. It shows the more pollutant production and propagation in spring in comparison with winter. It suggests to study in summer and autumn also do this research in intersections located in other city by different weather and traffic characteristic. By the result of this study and future study in this field can have a unique emission forecasting in intersection.

Notes

Declarations

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
(2)
Ph.D. Candidate of Road and Transportation Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
(3)
M.S of Road and Transportation Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran

References

  1. Shafabakhsh G, Hadjihosseinlou M, Taghizadeh SA (2014) Selecting the appropriate public transportation system to access the Sari International Airport by fuzzy decision making. Eur Transp Res Rev 6(3):277–285View ArticleGoogle Scholar
  2. Khardi S (2009) Reduction of commercial aircraft noise emission around airports. A new environmental challenge. Eur Transp Res Rev 1(4):175–184View ArticleGoogle Scholar
  3. Khardi S (2014) Environmental impact reduction of commercial aircraft around airports. Less noise and less fuel consumption. Eur Transp Res Rev 6(1):71–84View ArticleGoogle Scholar
  4. Franco V, Kousoulidou M, Muntean M, Ntziachristos L, Hausberger S, Dilara P (2013) Road vehicle emission development factors: a review. Atmos Environ 70:84–97View ArticleGoogle Scholar
  5. Afandizadeh S, Mostoufi K (2005) The role of culture of traffic to reduce air pollution. Air pollution and its effects on health conference, clean environment institute. https://www.civilica.com/Paper-NAP01-NAP01_041=%D9%86%D9%82%D8%B4-%D9%81%D8%B1%D9%87%D9%86%DA%AF%E2%80%8C%D8%B3%D8%A7%D8%B2%DB%8C-%D8%AA%D8%B1%D8%A7%D9%81%DB%8C%DA%A9-%D8%AF%D8%B1-%DA%A9%D8%A7%D9%87%D8%B4-%D8%A2%D9%84%D9%88%D8%AF%DA%AF%DB%8C-%D9%87%D9%88%D8%A7.html
  6. Pandian S, Gokhale S, Ghoshal A (2009) Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections. Transp Res D 14:180–196View ArticleGoogle Scholar
  7. Zito P (2009) Influence of coordinated traffic signals parameters on roadside pollutant concentrations. Transp Res D 14:604–609View ArticleGoogle Scholar
  8. Gense NLJ, Wilmink IR, Van de Burgwal HC (2001) Emission and congestion—estimation of emissions on road sections and the Dutch motorway network. TNO Report 2001-R044. TNO, DelftGoogle Scholar
  9. Coelho MC, Farias TL, Rouphail NM (2009) A numerical tool for estimating pollutant corridors. Int J Sustain Transp 3(4):246–262View ArticleGoogle Scholar
  10. Mustafa S, Mohammed A, Vougias S (1993) Analysis of pollutant emissions and concentrations at urban intersections. Institute of Transportation Engineers, Compendium of Technical Papers, Washington, DCGoogle Scholar
  11. Coelho MC, Fariasa TL, Rouphail NM (2005b) Impact of speed control traffic signals on pollutant emissions. Transp Res D 10:323–340View ArticleGoogle Scholar
  12. Vafa-Arani H, Jahani S, Dashti H, Heydari J, Moazen S (2014) A system dynamics modelling for urban air pollution: a case study of Tehran, Iran. Transp Res D 31:21–36View ArticleGoogle Scholar
  13. Sivacoumar R, Bhanarkar AD, Goyal SK, Gadkari SK, Aggarwal AL (2001) Air pollution modelling for an industrial complex and model performance evaluation. Environ Pollut 111:471–477View ArticleGoogle Scholar
  14. Hong J, Shen Q (2013) Residential density and transportation emissions: examining the connection by addressing spatial autocorrelation and self-selection. Transp Res Part D: Transp Environ 22:75–79View ArticleGoogle Scholar
  15. Wang J, Lu H, Peng H (2008) System dynamics model of urban transportation system and its application. J Transp Syst Eng Inf Technol 8(3):83–89Google Scholar
  16. Anh TT (2003) System dynamic applied to study the urban traffic. East Asia Soc Transp Stud 4:1693–1697Google Scholar
  17. Hui G, Qing-yu ZH, Yao SH, Da-hui W (2007) Evaluation of the International Vehicle Emission (IVE) model with on-road remote sensing measurements. J Environ Sci 19:818–826View ArticleGoogle Scholar
  18. (2008) IVE model user’s manual version 2.0. ISSRC, La HabraGoogle Scholar
  19. Arhami M, Kamali N, Rajabi M (2013) Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environ Sci Pollut Res 20:4777–4789View ArticleGoogle Scholar
  20. (2011) Transport and energy Information of the country. The Institute of Applied Science (SID). http://www.saba.org.ir/saba_content/media/image/2013/06/5406_orig.pdf

Copyright

© The Author(s) 2017

Advertisement