This study demonstrates through a case study that detailed analyses, even after the construction of a project, are feasible using current technologies and available data. A case study of highway 25 is used to illustrate the method and verify the levels of air contaminants from additionally induced traffic during and after the construction of highway. Natural traffic growth was removed from the effect of observed gas emissions by comparing observed levels on other further locations in the same metropolitan area. This study estimates air pollution from the additional traffic during and after the construction of A-25 extension project. NO2 levels were spatially interpolated during peak and off-peak hour traffic and traffic density simulated on the road network for four scenarios. Comparing the four scenarios, it was found that levels of NO2 concentrations were reduced at neighbor areas due to less traffic during the construction period. Levels of NO2 after the construction were higher than those in 2008. The simulated traffic density for four scenarios revealed that traffic density was significantly increased on both arterial and access roads within the close vicinity of the extension project during and after its construction.
The construction of highway 25 (A-25) started in 1966 with the goal of enhancing the commercial trade among Montreal, Laval, and Longueuil. This highway is part of the TransCanada corridor and connects two major highways (A-40 with A-20) traversing Montreal. The Ministère des Transports du Québec (MTQ) postponed the construction of A-25 on the locality of
The government decided to complete the extension of A-25 on 2007 and the project was opened to traffic on May 21, 2011. The project had three main components:
This study presents a framework for future analysis and to understand and illustrate air pollution from the additional traffic during and after the construction of A-25 extension project in Montreal. This study has two objectives:
Nitrogen Oxides (NOx), fine Particulate Matter (PM2.5), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), and Sulphur Dioxide (SO2) are the most common air pollutants emitted by vehicles. These air pollutants are also emitted from other anthropogenic sources in urban areas. For example, gasoline and diesel combustions only contribute at most to 16% and 9% of PM2.5 in urban areas. In urban and industrial areas, many VOCs are emitted from anthropogenic sources, such as transportation, fossil fuel-burning power plants, chemical plants, petroleum refineries, certain construction activities, solid waste disposal, and slash burning [
Several studies [
Spatial techniques are applied to estimate the concentrations of traffic-related pollutants within the buffer zone of the project sites [
The air pollution of additional traffic on surrounding areas of new road infrastructure was not addressed in these studies. It is complicated to estimate the air pollution from additional traffic during and after the construction of road infrastructure projects. Huang et al. [
The long-term modeling of traffic-related pollutants requires integration and reliable interface of two separate modeling processes:
This study spatially interpolates the levels of NO2 concentrations in the years 2003, 2008, and 2013 to determine changes in air quality during and after the construction of the extension of A-25. There are eighteen air record stations located within the island of Montreal and ten stations have records on NO2 concentrations in the years 2003, 2008, and 2013. This study uses the hourly levels of NO2 concentrations (parts per billion, ppb) that were collected from the
Geostatistics tools of ArcGIS are applied to spatially interpolate NO2 concentrations assuming that levels of NO2 are absolutely spatial. The geostatistics incorporate different statistical techniques to determine the relationship between spatially distributed values that estimates values at unsampled locations. The fundamental concept of spatial interpolation is that an unknown interpolation parameter is the function of certain statistical parameters [
The inverse distance weighting (IDW) and Kriging are most widely used methods for spatial interpolation. The IDW gives more weight to the closest samples and less weight to samples located farther away. The weight for each estimate is inversely proportionate to the power of distance between the sample points [
This study simulates the traffic flow on each road segment of the road network in Montreal applying a four-step travel demand model [
The traffic density of each road segment of Montreal’s road network is estimated based on the simulated traffic flow and average travel speed. The average travel speed (km/hr) of each road segment is estimated following [
This study has considered two primary factors for simulating traffic density during and after the construction of the A-25 extension project, such as traffic growth during the period of 2003–2013 and additional traffic induced by the A-25 extension. The travel demand model estimates the traffic flow and traffic density on each road link of the road network for four scenarios:
Data on the levels of NO2 at stations 1, 3, 7, and 29 were analyzed for both peak hour traffic and off-peak hour traffic. These stations are located within the close proximity of the extension project. The levels of NO2 that vehicles emit vary according to time of day, season, and meteorological conditions. Levels are also higher in winter season than in other seasons because of the increased use of heating fuels.
As shown on Tables
Average hourly concentration of nitrogen dioxide (ppb) at four stations near construction site during off-peak hour traffic.
Months | Off-peak hour traffic | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Station 1 | Station 3 | Station |
Station 29 | ||||||||
2003 | 2008 | 2013 | 2003 | 2008 | 2013 | 2008 | 2013 | 2003 | 2008 | 2013 | |
January | 22.6 | 17.47 | 16.9 | 18.5 | 14 | 14.6 | |
15.6 | 17.9 | 19.4 | 20.3 |
February | 24.6 | 19.10 | 22.10 | 20.2 | 15.2 | 14 | 16.8 | 18.4 | 21.6 | 17.8 | 18.7 |
March | 26 | 18.37 | 21.37 | 22.6 | 14.9 | 13.6 | 17.9 | 18.4 | 24.7 | 19 | 20.4 |
April | 17.3 | 13.29 | 16.29 | 14.6 | 11.9 | 11.4 | 15.3 | 16.7 | 19.6 | 16.1 | 16.6 |
May | 16.1 | 12.57 | 15.57 | 13.3 | 10.9 | 14.3 | 10.8 | 12.3 | 21.6 | 12.3 | 15 |
June | 17.5 | 10.46 | 13.46 | 15.4 | 10.3 | 11.3 | 10 | 12 | 16.6 | 10.9 | 12.6 |
July | 14.5 | 10.86 | 13.86 | 13.6 | 9.86 | 11.3 | 9.78 | 10.9 | 13.4 | 8.74 | 10.3 |
August | 14.2 | 11.53 | 14.53 | 11.6 | 9.23 | 11.5 | 9.61 | 10.9 | 13.5 | 10.6 | 11.3 |
September | 15.7 | 11.27 | 14.27 | 12.2 | 9.81 | 10.7 | 9.89 | 10.1 | 15.1 | 11.1 | 12.1 |
October | 17.2 | 14.40 | 17.40 | 13.6 | 10.9 | 11.7 | 13.1 | 14.2 | 16.3 | 12.5 | 14.1 |
November | 19.2 | 15.31 | 18.31 | 12.6 | 10.5 | 11.5 | 12.5 | 13.1 | 16.5 | 14.3 | 16.1 |
December | |
17.75 | 20.75 | 18.1 | 12.2 | 13.7 | 13.6 | 17.1 | 20.4 | 14.8 | 17.8 |
|
|||||||||||
Average | 18.6 | 14.37 | 17.1 | 15.5 | 11.7 | 12.5 | 12.7 | 14.1 | 18.1 | 14 | 15.4 |
Average hourly concentration of nitrogen dioxide (ppb) at four stations near to construction site during peak-hour traffic.
Months | Peak hour traffic | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Station 1 | Station 3 | Station |
Station 29 | ||||||||
2003 | 2008 | 2013 | 2003 | 2008 | 2013 | 2008 | 2013 | 2003 | 2008 | 2013 | |
January | 27.3 | 20.7 | 20.1 | 21.9 | 17.1 | 20.4 | |
19.2 | 22.3 | 22.5 | 24.4 |
February | 27 | 20 | 26 | 22.7 | 17 | 15.7 | 19.5 | 20 | 23.3 | 21.2 | 22.1 |
March | 26.6 | 18.6 | 24.6 | 22.5 | 14.5 | 14.5 | 17.1 | 18.2 | 26.9 | 19.3 | 23.2 |
April | 17.6 | 12.7 | 18.7 | 14.9 | 11 | 11.6 | 13.3 | 16.1 | 21 | 15.7 | 18 |
May | 13.6 | 10.5 | 16.5 | 12.2 | 9.34 | 12.8 | 8.52 | 9.21 | 21.8 | 11.3 | 14.8 |
June | 15.2 | 9.4 | 15.4 | 14.2 | 9.67 | 10.6 | 8.84 | 10 | 16.5 | 10.3 | 12.3 |
July | 13.1 | 9.18 | 15.2 | 11.9 | 9.2 | 9.91 | 8.4 | 9.32 | 14.2 | 8.8 | 10 |
August | 14.3 | 9.37 | 15.4 | 11.7 | 7.78 | 11.3 | 8.16 | 9.57 | 15 | 10.2 | 11.3 |
September | 16.4 | 12.6 | 18.6 | 12.9 | 10.2 | 11.2 | 10.6 | 12.7 | 18 | 13.1 | 13.6 |
October | 20.2 | 16.1 | 22.1 | 16.1 | 24.5 | 13.5 | 14.4 | 15.8 | 20.5 | 15.1 | 16.4 |
November | 23 | 18.9 | 24.9 | 14.8 | 12.2 | 14.6 | 14.4 | 16.8 | 20.9 | 17.5 | 21.5 |
December | |
20 | 26 | 19.1 | 14.8 | 16.4 | 16.4 | 21 | 23.9 | 18.5 | 21.4 |
|
|||||||||||
Average | 19.5 | 14.8 | 20.3 | 16.2 | 13.1 | 13.6 | 12.7 | 14.8 | 20.3 | 15.3 | 17.4 |
During the winter season, the levels of NO2 that vehicles emit were significantly increased at all stations during the selected years (Tables
This temporal analyses help to understand the levels of NO2 before, during and after the construction of the A-25 at the selected stations. At all stations, the levels of NO2 were decreased during the period of 2003–2008 and increased during the period of 2008–2013 (Tables
There is a probability that the similar changes in NO2 levels were observed at the remaining areas of Montreal during the selected years. This study analyzes the spatial patterns of NO2 levels in Montreal during the peak hour and off-peak hour traffic by applying the IDW and Kriging spatial interpolation methods. The IDW is identified as the best fitted model for the spatial interpolation of NO2 levels by applying the cross-validation tests.
The spatial patterns of NO2 levels during peak hour traffic in 2003 explains that the levels of NO2 were between 18.29 and 25.07 ppb within 5 km distance of the A-25 at the north-eastern area between A-25 and A-40 (Figure
Spatial interpolation of average hourly concentration (ppb) of NO2 during peak hour traffic in 2003.
During the peak hours in 2008, the levels of NO2 lay to 9.3–14 ppb within 2 km distance of the project site at the north-western and south-western sides between A-25 and A-40 (Figure
Spatial interpolation of average hourly concentration (ppb) of NO2 during peak hour traffic in 2008.
Traffic flow was increased in close proximity to the project site after the opening of highway 25 extension on 2011. Higher traffics resulted in higher levels of NO2 concentrations. The levels of NO2 were up to 17 ppb within 1 km distance of the project site at the north-western side between A-25 and A-40 (Figure
Spatial interpolation of average hourly concentration (ppb) of NO2 during peak hour traffic in 2013.
The findings from the spatial analyses of NO2 levels support the hypothesis that NO2 concentrations resulted from the additional traffic after the construction of A-25 expansion project.
The coefficients of a logistic discrete choice model of trip generation estimated the increase of working and business trips with increasing number of persons per households, but education oriented trips decreased with more people per households during both peak and off-peak hours [
Traffic density (vehicles per km) and level of service of road network in the City of Montreal, 2003 (scenario 1).
Traffic density on each road link was simulated for four scenarios (Figures
Traffic density (vehicles per km) and level of service of road network in the City of Montreal, 2003 (scenario 2).
Traffic density (vehicles per km) and level of service of road network in the City of Montreal, 2008 (scenario 3).
Traffic density (vehicles per km) and level of service of road network in the City of Montreal, 2013 (scenario 4).
Percentage change in traffic density (vehicles per km) at scenario 2 comparing to scenario 1.
Significant changes in traffic density (2–5%) were observed along the highway that connects Laval and Longueuil for scenario 2 as compared to scenario 1 (Figure
For scenario 2, traffic density was increased by 5% at the southern part and up to 4% at the northern part of the borough of
Different types of land uses in Montreal Island.
Percentage change in traffic density (vehicles per km) at scenario 3 comparing to scenario 1.
Percentage change in traffic density (vehicles per km) at scenario 4 comparing to scenario 1.
Traffic density was increased on all roads of Montreal for traffic growth during the period 2003–2008. The construction of the A-25 extension influenced the traffic density of surrounding road links for scenario 3 (Figure
In the year 2013, traffic density was significantly increased on the A-25, especially on the segments between
Significant increase of traffic was observed in
It is difficult to argue that increases in the levels of NO2 concentrations were a consequence of the additional traffic resulting from the construction of A-25 extension project. The major limitation of this study is to integrate the simulated traffic and vehicle emissions. The mobile-source data could be an effective solution; however, estimation of traffic-related pollutants during a long-period of time for different scenarios on a large geographical area is very difficult to attain. Nitrogen Oxides (NOx), fine Particulate Matter (PM2.5), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), and Sulphur Dioxide (SO2) are the most common air pollutants emitted by vehicles but there are other anthropogenic sources that generate these pollutants. During the construction period, air pollutants were also emitted from construction vehicles. Moreover, the levels of NO2 that vehicles emit vary according to time of day, season, and meteorological conditions. For example, levels of NO2 are higher in winter season than in other seasons because of the increased use of heating fuels.
The A-25 is the connecting highway between East Montreal and Laval and a significant number of freight traffic transit this highway; however, this study only simulated urban traffic ignoring the fright traffic during different scenarios. This study observes the reduction of levels of NO2 in 2013 comparing to that in 2003 despite the increase of traffic volume on the Montreal road network. This is because of increased modal share of public transit resulting from government’s ongoing initiative to promote mass transit and reduce vehicle emissions as a part of Greater Montreal Area Transport Plan, 2000. Future studies can address this issue to identify the impact of government’s strategy on vehicle emissions before, during and after the construction of A-25 extension project. Future studies can also calibrate the vehicle emissions from simulated traffic and actual traffic counts.
This study finds out two outcomes from the spatial analyses of the levels of NO2 and traffic density for four scenarios. First, the levels of NO2 were increased in close proximity to the A-25 extension after its construction. Increase of NO2 concentrations resulted from the additional traffic. Second, the simulated traffic density for four scenarios explains that the traffic density was significantly increased, on both arterials and access roads within the close vicinity of the A-25 extension project during and after its construction.
This study justifies the concerns of the environmentalists on the potential air pollution from the additional traffic resulting from the construction of A-25 extension project. The outcomes of this study urge MTQ and other transport authorities to conduct environmental impact assessment before the construction of new road infrastructure. This study also suggests that transport authorities should assess other alternative solutions in order to reduce environmental degradation. Future studies require more detailed data on air pollutants emitted from the vehicles to understand the impact of additional traffic on the air quality rather than depending on the spatial interpolation of air records from available stations.
There are no conflicts of interest regarding this paper.