Are HOV/eco-lanes a sustainable option to reducing emissions in a medium-sized European city?

Innovative traffic management measures are needed to reduce transportation-related emissions. While in Europe, road lane management has focused mainly on introduction of bus lanes, the conversion to High Occupancy Vehicles (HOV) and eco-lanes (lanes dedicated to vehicles running on alternative fuels) has not been studied comprehensively. The objectives of this research are to: (1) Develop an integrated microscopic modeling platform calibrated with real world data to assess both traffic and emissions impacts of future Traffic Management Strategies (TMS) in an urban area; (2) Evaluate the introduction of HOV/eco-lanes in three different types of roads, freeway, arterial and urban routes, in an European medium-sized city and its effects in terms of emissions and traffic performance. The methodology consists of three distinct phases: (a) Traffic and road inventory data collection; (b) Traffic and emissions simulation using an integrated platform of microscopic simulation; and (c) Evaluation of scenarios. For the baseline scenario, the statistical analysis shows valid results. The results show that HOV and eco-lanes in a medium European city are feasible, and when the Average Occupancy of Vehicles (AOV) increases, on freeways, the majority of vehicles can reduce their travel time (2%) with a positive impact in terms of total emissions (−38% NO_x, −39% HC, −43% CO and −37% CO₂). On urban and arterial corridors, the reduction in emissions could be achieved only if the AOV increases from 1.50 to 1.70 passengers/vehicle. Total emissions of the corridor with an AOV of 1.70 passengers/vehicle can be reduced up to 35–36% for the urban route while the values can be reduced by 36–39% for the arterial road. With the introduction of Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV) it is possible to reduce emissions, although the introduction of eco-lanes did not show significant reductions in emissions. When both policies are simulated together, an emissions improvement is observed for the arterial route and for two of the scenarios.     

Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

Use of Bayesian inference method to model vehicular air pollution in local urban areas

Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO₂, NO₂ and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

An assessment of pollutant emissions due to air traffic at Nikola Tesla International Airport,Belgrade, and the link between local air quality and weather types

The assessment of local air pollution due to air traffic is an important issue from the standpoint of human health. An advanced approach using a landing and take-off cycle method is employed to assess emissions of nitrogen oxides (NO_x), carbon monoxide (CO), hydrocarbon (HC), and sulphur oxides (SO_x). Emissions of both volatile and non-volatile particulate matter are estimated using the new First-Order Approximation methodology. As synoptic situations (weather) determine the transport of pollutants in the air as well as their scavenging, the weather type for each day is classified for the study period (2008–2015).Due to a significant increase in air traffic at Nikola Tesla International Airport, Belgrade, in the last eight years, emissions of all considered pollutants have also increased. Emissions of NO_x and CO were the highest (totals for eight years were 2976.03 and 2875.66 tons, respectively). An analysis of weather types showed that the most frequent were warm, dry, anticyclones (AWD) and cyclones (low-pressure systems) including the passage of a cold front (CCF). The frequency of occurrence of AWDs and CCFs was 28.3% and 21.6%, respectively. An AWD is very unfavourable from the viewpoint of local air pollution, especially during the cold part of year, due to a shallow temperature inversion and fog formation in the morning confining the pollutants to the emission location for a longer time span. CCFs are also adverse due to the prevailing westerly and north-westerly winds that transport pollutants toward the city.     

Urban roadside monitoring and prediction of CO,NO2 and SO2 dispersion from on-road vehicles in megacity Delhi

The study inspects the traffic-induced gaseous emission dispersion characteristics from the urban roadside sites in Delhi, India. The concentration of pollutants viz. CO, NO₂ and SO₂ along with traffic and ambient atmospheric conditions at five selected local urban road sites were simultaneously measured. A developed General Finite Line Source Model (GFLSM) was used to predict the local roadside CO, NO₂ and SO₂ concentrations. A comparison of the observed and predicted values emission parameters using GFLS model has shown that the predicted values for SO₂, CO and NO₂ at all the selected local urban roadside locations are found to lie within the error bands of 5%, 6%, and 7% respectively. A high level of agreement was found between the monitored and estimated CO, NO₂ and SO₂ concentration data. From the study, it has also been established that the developed model exhibits the capability of reasonably predicting the characteristics of gaseous pollutants dispersion from on-road vehicles for the urban city air quality.     

Atmospheric impact of ship traffic in four Adriatic-Ionian port-cities: Comparison and harmonization of different approaches

Shipping is a growing transport sector representing a relevant share of atmospheric pollutant emissions at global scale. In the Mediterranean Sea, shipping affects air quality of coastal urban areas with potential hazardous effects on both human health and climate. The high number of different approaches for investigating this aspect limits the comparability of results. Furthermore, limited information regarding the inter-annual trends of shipping impacts is available. In this work, an approach integrating emission inventory, numerical modelling (WRF-CAMx modelling system), and experimental measurements at high and low temporal resolution is used to investigate air quality shipping impact in the Adriatic/Ionian area focusing on four port-cities: Brindisi and Venice (Italy), Patras (Greece), and Rijeka (Croatia). Results showed shipping emissions of particulate matter (PM) and NOx comparable to road traffic emissions at all port-cities, with larger contributions to local SO₂ emissions. Contributions to PM_2.5 ranged between 0.5% (Rijeka) and 7.4% (Brindisi), those to PM₁₀ were between 0.3% (Rijeka) and 5.8% (Brindisi). Contributions to particle number concentration (PNC) showed an impact 2–4 times larger with respect to that on mass concentrations. Shipping impact on gaseous pollutants are larger than those to PM. The contribution to total polycyclic aromatic hydrocarbon (PAHs) concentrations was 82% in Venice and 56% in Brindisi, with a different partition gas-particle because of different meteorological conditions. The inter-annual trends analysis showed the primary contribution to PM concentrations decreasing, due to the implementation of the European legislation on the use of low-sulphur content fuels. This effect was not present on other pollutants like PAHs.     

Real-world gaseous emission characteristics of Euro 6b light-duty gasoline- and diesel-fueled vehicles

To accurately investigate vehicle emissions that have become major contributors to global air pollutants and greenhouse gases, test conditions have been transferred from laboratory type approval test cycles to real-world driving conditions. In this study, the real-world driving emissions of carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NO_x), and carbon dioxide (CO₂) from one gasoline and two diesel Euro 6b light-duty passenger vehicles were investigated by a portable emission measurement system (PEMS) in Lyon, France. NO_x and CO₂ emission controls remain critical to addressing the real-world driving emissions of Euro 6b vehicles. Notably, the tested gasoline vehicle emitted higher CO₂ emissions than diesel vehicles on all types of roads, especially on the urban road with an excess of 29.3–48.3%. The highest emission factors of gaseous pollutants generally occurred on the motorway for the gasoline vehicle, while on the urban road for diesel vehicles. In particular, for high-speed driving conditions, the gasoline vehicle gaseous emissions, especially NO_x emissions, were more affected by acceleration than diesel vehicle emissions. In addition, the CO emissions, especially THC emissions, for the gasoline vehicle, were more influenced by warm-start, especially cold-start, than those for diesel vehicles.     

Influence of coordinated traffic lights parameters on roadside pollutant concentrations

The paper examines the effects of coordinated traffic lights on CO and C₆H₆ roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C₆H₆ roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.     

How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions?

In recent years, several studies show that people who live, work or attend school near the main roadways have an increased incidence and severity of health problems that may be related with traffic emissions of air pollutants. The concentrations of near-road atmospheric pollutants vary depending on traffic patterns, environmental conditions, topography and the presence of roadside structures. In this study, the vertical and horizontal variation of nitrogen dioxide (NO₂) and benzene (C₆H₆) concentration along a major city ring motorway were analysed. The main goal of this study is to try to establish a distance from this urban motorway considered “safe” concerning the air pollutants human heath limit values and to study the influence of the different forcing factors of the near road air pollutants transport and dispersion. Statistic significant differences (p = 0.001, Kruskal–Wallis test) were observed between sub-domains for NO₂ representing different conditions of traffic emission and pollutants dispersion, but not for C₆H₆ (p = 0.335). Results also suggest significant lower concentrations recorded at 100 m away from roadway than at the roadside for all campaigns (p < 0.016 (NO₂) and p < 0.036 (C₆H₆), Mann–Whitney test). In order to have a “safe” life in homes located near motorways, the outdoor concentrations of NO₂ must not exceed 44–60.0 μg m⁻³ and C₆H₆ must not exceed 1.4–3.3 μg m⁻³. However, at 100 m away from roadway, 81.8% of NO₂ receptors exceed the annual limit value of human health protection (40 μg m⁻³) and at the roadside this value goes up to 95.5%. These findings suggest that the safe distance to an urban motorway roadside should be more at least 100 m. This distance should be further studied before being used as a reference to develop articulated urban mobility and planning policies.     

Assessing greenhouse gas and related air pollutant emissions from road traffic counts: A case study for Mauritius

The road transport sector is one of the major contributors of greenhouse gases and other air pollutants emissions. Regional emissions levels from road vehicles were investigated, in Mauritius, by applying a fuel-based approach. We estimated fuel consumption and air emissions based on traffic counts on the various types of classified roads at three different regional set ups, namely urban, semi urban and rural. The Relative Development Index (RDI), a composite index calculated from socio-economic and environmental indicators was used to classify regions. Our results show that the urban motorways were the most polluting due to heavy traffic. Some rural areas had important pollution levels as well. Our analysis of variance (ANOVA), however, showed little difference in emissions among road types and regions. The study can provide a simple tool for researchers in countries where data are very scarce, as is the case for many developing countries.     

Impacts of freeway accidents on CO2 emissions: A case study for Orange County,California, US

Traffic congestion caused by traffic accidents contributes to CO₂ emissions. Generally, more efficient and prompt responses to accidents lead to reduced traffic congestion as well as CO₂ emissions. Here we assess the CO₂ emissions impacts of freeway accidents, applies an existing model to capture spatio-temporally congested regions caused by freeway accidents. A case study for the assessment of CO₂ emissions impacts of based on the results from the model is presented.     

Traffic assignment considering air quality

This paper presents a methodology of assigning traffic in a network with the consideration of air quality. Traffic assignment is formulated as an optimization problem considering travel cost and on-road emissions. It introduces a cell-based approach to model emission concentrations so that either the average or maximum emissions in a network can be considered in the optimization process. The emissions in a cell are modeled taking into consideration the influence of the emission sources from all cells in the network. A case study demonstrates that minimizing travel cost and reducing air pollutants may not be always achieved simultaneously. The traffic assignment procedure can effectively reduce emission concentrations at those locations with the worst air quality conditions, with only a marginal increase in travel time and average emission concentration in the network.     

Simulating the environmental effects of isolated and area-wide traffic calming schemes using traffic simulation and microscopic emission modeling

This study focuses on the development of a microscopic traffic simulation and emission modeling system which aims at quantifying the effects of different types of traffic calming measures on vehicle emissions both at a link-level and at a network-level. It also investigates the effects of isolated traffic-calming measures at a corridor level and area-wide calming schemes, using a scenario analysis. Our study is set in Montreal, Canada where a traffic simulation model for a dense urban neighborhood is extended with capabilities for microscopic emission estimation. The results indicate that on average, isolated calming measures increase carbon dioxide (CO₂), carbon monoxide (CO), and nitrogen oxides (NO_x) emissions by 1.5, 0.3, and 1.5 %, respectively across the entire network. Area-wide schemes result in a percentage increase of 3.8 % for CO₂, 1.2 % for CO, and 2.2 % for NO_x across the entire network. Along specific corridors where traffic calming measures were simulated, increases in emissions of up to 83 % were observed. We also account for the effect of different measures on traffic volumes and observe moderate decreases in areas that have undergone traffic calming. In spite of traffic flow reductions, total emissions do increase.     

Estimating transboundary economic damages from climate change and air pollution for subnational incentives for green on-road freight

Subnational incentives to adopt zero emission vehicles (ZEVs) are critical for reducing the external economic damages posed by transportation to air quality and the climate. Few studies estimate these damages for on-road freight, especially at scales relevant for subnational policies requiring cross-border cooperation. Here, we assess the damages to US receptors from emissions of air pollutants (PM_2.5, NO_x, SO₂, NH₃), and greenhouse gases (CO₂, CH₄, N₂O) from medium and heavy duty freight trucking, and the benefits of ZEV adoption by census division in the Province of Ontario. We develop an integrated modelling framework connecting a travel demand model, a mobile emissions simulator, and a regression based marginal damages model of air pollutants and climate change. We estimate $1.9 billion (2010 USD) in annual cross-border damages, or $0.16/VKT, resulting from scaled up atmospheric emissions from a ‘typical day’ of medium and heavy duty truck traffic volume for Ontario in 2012. This implies approximately $8000 per truck per year in damages, which could inform an economic incentive for emission reduction. The provincial goal of 5% ZEV adoption would reduce GHG emissions in 2012 by 800 ktCO₂e, yielding $89 Million (2010 USD) in cross-border benefits annually, with air quality co-benefits of $83/tCO₂e. This result varies between −19% and 22% based on sensitivity analysis for travel and emissions models, though economic damages are likely the largest uncertainty source. Such advances in subnational scale integrated modeling of the environmental impacts of freight can offer insights into the sustainable design of clean freight policy and programs.     

The contribution of road traffic to particulate matter and metals in air pollution in the vicinity of an urban road

A detailed investigation was conducted to study the sources of particulate matter in the vicinity of an urban road in Žilina. To determine the amount of particulate matter (PM₁₀, PM_2.5 and PM₁) present in the ambient air, a reference gravimetric method was used. The main objective of this contribution was to identify the sources of these particles by means of statistical methods, specifically principal component analysis (PCA), factor analysis (FA), and absolute principal component scores (APCS), as well as using the presence of 17 metals in the particulate matter (Na, Mg, Al, Ca, V, Cr, Fe, Mn, Ni, Cu, Zn, As, Mo, Sb, Cd, Ba, Pb). To identify the metals in the particulate matter samples and to determine their abundances, spectroscopic methods were used, specifically inductively coupled plasma mass spectrometry (ICP-MS). Each of these metals may come from a specific source, such as the burning of fossil fuels in fossil fuel power plants; local heating of households; the burning of liquefied fossil fuels in the combustion engines of vehicles; the burning of coal and wood; non-combustion related emissions resulting from vehicular traffic; resuspension of traffic-related dust; and industry. Diesel vehicles and non-combustion emissions from road traffic have been identified as two key sources of the particulate matter. The results reveal that non-combustion emissions, which are associated with the elements Na, Fe, Mn, Ni, Zn, Mo, Sb, Cd, and Pb, are the major contributors, followed by combustion emissions from diesel vehicles, which are associated with the elements Mg, Ca, and Ba.     

Costs of air pollutants from shipping: a meta-regression analysis

ABSTRACT

This study estimated the external cost of air pollution from shipping by means of a meta-regression analysis, which has not been made before. Three pollutants, which were included in most of the primary studies, were considered: nitrogen oxides (NO_x), sulphur dioxides (SO₂) and particulate matters with a diameter of max 2.5 micrometres (PM_2.5). All primary studies included damages of health and a majority added impacts on agriculture and estimated the cost of air pollutants by transferring cost estimates from studies on costs of air emissions from transports in Europe. Different regression models and estimators were used and robust results were found of statistically significant emission elasticities of below one, i.e. total external costs increase by less than 1% when emissions increase by 1%. There was a small variation between the pollutants, with the highest elasticity for PM_2.5 and lowest for NO_x. Calculations of the marginal external cost of the pollutants showed the same pattern, with this cost being approximately six times higher for PM_2.5 than for the other pollutants. Common to all pollutants was that the marginal external cost decreases when emission increases. Another robust result was a significant increase in the cost of studies published in journals compared with other publication outlets. These findings point out some caution when transferring constant external unit cost of air pollutant from shipping, which is much applied in the literature, and the cost functions estimated in this study could thus provide a complementary transfer mechanism.     

Estimation of road transport related air pollution in Saint Petersburg using European and Russian calculation models

The paper analyzes Russian and European emission and dispersion models aimed at the estimation of road transport related air pollution on street and regional scale as exemplified with St. Petersburg, Russia. It demonstrates the results of model calculations of peak concentrations of main harmful substances (NО_X, CO and PM₁₀) along the St. Petersburg Ring Road at high traffic volume and adverse meteorological conditions (calm, temperature inversion) executed by means of a Russian street pollution model, and it evaluates the computed results against the measurements from monitoring stations. The paper also examines the ways of adaptation of the COPERT IV model – a software tool for calculation of air pollutant and greenhouse gas emissions from road transport on regional or country scale – to the inventory conditions of the Russian Federation, compares the COPERT IV numerical estimates with the national inventory data. It also reveals the obstacles and possibilities in the harmonization of the Russian and European approaches.