Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood

In this study, the effects of isolated traffic calming measures and area-wide calming schemes on air quality in a dense neighborhood were estimated using a combination of microscopic traffic simulation, emission, and dispersion modeling. Results indicated that traffic calming measures did not have as large an effect on nitrogen dioxide (NO₂) concentrations as the effect observed on nitrogen oxide (NO_x) emissions. Changes in emissions resulted in highly disproportional changes in pollutant levels due to daily meteorological conditions, road geometry and orientation with respect to the wind. Average NO₂ levels increased between 0.1% and 10% with respect to the base-case while changes in NO_x emissions varied between 5% and 160%. Moreover, higher wind speeds decreased NO₂ concentrations on both sides of the roadway. Among the traffic calming measures, speed bumps produced the highest increases in NO₂ levels.     

Traffic control oriented impact on the persistence of urban air pollutants: A causeway bay revelation during emergency period

The continuous traffic flow is always considered to take a great extent responsibility for the air quality deterioration in urban areas. Meanwhile, traffic control is assumed to be one of the most effective ways to mitigate the high concentration situation as this may cut off the emission directly and satisfy the air quality objectives. Unfortunately, the overdevelopment of central business district area in megacities not only complicates the control plan, but also troubles the process of plan assessment. Because of the road blockages caused by the radical behavior during the Hong Kong protest in 2014, it offers an unexpected chance to evaluate the influence of traffic control oriented plan on urban (i.e., Causeway Bay) air pollution. Hence, we here investigated the six air pollutants concentrations that measured in the time series before, during and after the Hong Kong Protest period. The impact of traffic flow restriction on pollutants’ persistence has been quantified both qualitatively and quantitatively in this study. The results showed that the persistence of pollutants was a general property in Causeway Bay which dominated by the traffic flow pattern. The road blockages, considered as one kind of extreme traffic control plan, would strengthen the persistence of most pollutants (except ozone). Moreover, it also indicated that comprehensive consideration and further balance among different pollutants were necessary when try to reduce pollution in urban area by traffic control.     

Substitution of bus for car travel in urban Britain: an economic evaluation of bus and car exhaust emission and other costs

Car exhaust emissions cause serious air pollution problems in many regions and, at a global level, contribute to climate change. Car use is also an important factor in other problems including traffic congestion, road accidents, noise pollution, community severance, and loss of countryside from road building. Forecasts of further increases in car ownership and use have prompted calls for policy-makers to encourage car users to switch to other forms of transport, particularly the bus. The effects of substituting bus for car travel in urban areas are simulated by specifying a spreadsheet model incorporating two types of car (petrol and diesel engine) and three types of bus (mini-, midi- and large bus). Six types of exhaust emission are considered for each vehicle type for the years 1992, 1995 and 1999: carbon monoxide, volatile organic compounds, nitrogen oxides, sulphur dioxide, (small) particulate matter and carbon dioxide. The paper provides a synthesis of monetary estimates of these exhaust emission and other costs. The other costs considered are traffic congestion, fuel consumption, noise pollution, road accidents and road damage. The exhaust emission monetary cost estimates, mainly from the United States and the United Kingdom, are discussed within the context of a sensitivity analysis which allows for changes in parameters such as load factors, emission factors and the individual exhaust emission cost estimates. The simulation results show that substitution of bus for car travel generally decreases the overall costs, particularly the costs of congestion, but increases exhaust emission costs if bus load factors are insufficiently high. In order to reduce exhaust emission costs from car to bus transfer at given load factors, the most effective policy option is to encourage the reduction of particulate emissions from bus engines. In terms of the overall costs, increasing bus load factors by relatively modest amounts can lead to substantial reductions in these overall costs. These results should be regarded as illustrative rather than definitive, given the uncertainties in a number of parameter estimates and the need for further research in areas not covered by the paper.     

Simulating the impacts of household travel on greenhouse gas emissions,urban air quality,and population exposure

This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution.     

香港典型山区道路汽车行驶工况污染物排放研究

通过安装车载测试系统收集香港港岛山区路段正常行驶工况下尾气中的CO、NOx、HC等污染物和油耗并辅助计算机软件进行分析。研究得出,山区道路设计、地形地貌和驾驶习惯对车辆油耗以及CO、NOx和HC排放有直接关系。可以通过坡道加宽、坡道延长、减少坡道红绿灯等措施减少车辆在山区道路行驶过程中速度变化频率,从而减少油耗以及CO、NOx和HC排放。     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Traffic and emissions impact of congestion charging in the central Beijing urban area: A simulation analysis

Congestion charging is being considered as a potential measure to address the issue of substantially increased traffic congestion and vehicle emissions in Beijing. This study assessed the impact of congestion charging on traffic and emissions in Beijing using macroscopic traffic simulation and vehicle emissions calculation. Multiple testing scenarios were developed with assumptions in different charging zone sizes, public transit service levels and charging methods. Our analysis results showed that congestion charging in Beijing may increase public transit use by approximately 13%, potentially reduce CO and HC emissions by 60–70%, and reduce NOx emissions by 35–45% within the charging zone. However, congestion charging may also result in increased travel activities and emissions outside of the charging zone and a slight increase in emissions for the entire urban area. The size of charging zone, charging method, and charging rate are key factors that directly influence the impact of congestion charging; improved public transit service needs to be considered as a complementary approach with congestion charging. This study is used by Beijing Transportation Environment and Energy Center (BTEC) as reference to support the development of Beijing’s congestion charging policy and regulation.     

A model for evaluating the environmental and functional benefits of “innovative” roundabouts

This study looks at the singling out of a multi-parameter criterion for choosing conventional or innovative roundabout layouts, by taking functional, environmental and economic aspects into consideration. The performances of three conventional roundabouts (with different lane number at entries and through the ring), turbo-roundabouts and roundabouts with right-turn bypass lane on all the arms (flower roundabouts) have been compared in terms of vehicle delays and pollutant (carbon dioxide, nitrogen oxides, particle pollution (PM₁₀ and PM_2.5)) emissions. By means of closed-form capacity models and with the help of COPERT IV^© software, several traffic simulations have been carried out, referred to yearly peak flow values Q_max and ranging between 1300 and 3300 veh/h, starting from a typical annual traffic demand curve in urban areas. The estimation of cumulative vehicle delays and annual pollutant emissions, together with construction and maintenance costs has allowed working out overall costs for each roundabout under consideration, depending on the traffic demand. Thus, the proposed model allows finding the most cost-effective geometric solution as to overall costs for a comprehensive case record of traffic values.     

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.     

The impact of coordinated policies on air pollution emissions from road transportation in China

Improving air quality across mainland China is an urgent policy challenge. While much of the problem is linked to China’s broader reliance on coal and other fossil fuels across the energy system, road transportation is an important and growing source of air pollution. Here we use an energy-economic model, embedded in a Regional Emissions Air Quality Climate and Health (REACH) integrated assessment framework, to analyze the impacts of implementing vehicle emissions standards (ES) together with a broader economy-wide climate policy on total air pollution in five species and 30 Chinese provinces. We find that full and immediate implementation of existing vehicle ES at China 3/III level or tighter will significantly reduce the contribution of transportation to degraded air quality by 2030. We further show that road transport ES function as an important complement to an economy-wide price on CO₂, which delivers significant co-benefits for air pollution reduction that are concentrated primarily in non-transportation sectors. Going forward, vehicle emissions standards and an economy-wide carbon price form a highly effective coordinated policy package that supports China’s air quality and climate change mitigation goals.     

Modeling regional mobile source emissions in a geographic information system framework

Suburban sprawl, population growth, and automobile dependency contribute directly to air pollution problems in US metropolitan areas. As metropolitan regions attempt to mitigate these problems, they are faced with the difficult task of balancing the mobility needs of a growing population and economy, while simultaneously lowering or maintaining levels of ambient pollutants. Although ambient air quality can be directly monitored, predicting the amount and fraction of the mobile source components presents special challenges. A modeling framework that can correlate spatial and temporal emission-specific vehicle activities is required for the complex photochemical models used to predict pollutant concentrations. This paper discusses the GIS-based modeling approach called the Mobile Emission Assessment System for Urban and Regional Evaluation (MEASURE). MEASURE provides researchers and planners with a means of assessing motor vehicle emission reduction strategies. Estimates of spatially resolved fleet composition and activity are combined with activity-specific emission rates to predict engine start and running exhaust emissions. Engine start emissions are estimated using aggregate zonal information. Running exhaust emissions are predicted using road segment specific information and aggregate zonal information. The paper discusses the benefits and challenges related to mobile source emissions modeling in a GIS framework and identifies future GIS mobile emissions modeling research needs.     

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.     

Reducing emissions at land border crossings through queue reduction and expedited security processing

Vehicle border crossings between Mexico and the United States generate significant amounts of air pollution, which can pose health threats to personnel at the ports of entry (POEs) as well as drivers, pedestrians, and local inhabitants. Although these health risks could be substantial, there is little previous work quantifying detailed emission profiles at POEs. Using the Mariposa POE in Nogales, Arizona as a case study, light-duty and heavy-duty vehicle emissions were analyzed with the objective of identifying effective emission reduction strategies such as inspection streamlining, physical infrastructure improvements, and fuel switching. Historical traffic information as well as field data were used to establish a simulation model of vehicle movement in VISSIM. Four simulation scenarios with varied congestion levels were considered to represent real-world seasonal changes in traffic volume. Four additional simulations captured varying levels of expedited processing procedures. The VISSIM output was analyzed using the EPA’s MOVES emission simulation software for conventional air pollutants. For the highest congestion scenario, which includes a 200% increase in vehicle volume, total emissions increase by around 460% for PM_2.5 and NO_x, and 540% for CO, SO₂, GHGs, and NMHC over uncongested conditions for a two-hour period. Expedited processing and queue reduction can reduce emissions in this highest congestion scenario by as much as 16% for PM_2.5, 18% for NO_x, 20% for NMHC, 7% for SO₂ and 15% for GHGs and CO. Other potential mitigation strategies examined include fleet upgrades, fuel switching, and fuel upgrades. Adoption of some or all of these changes would not only reduce emissions at the Mariposa POE, but would have air-quality benefits for nearby populations in both the US and Mexico. Fleet-level changes could have far-reaching improvements in air quality on both sides of the border.     

Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?

Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network.This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level.Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.     

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

Short trips: An opportunity for reducing mobile-source emissions?

This paper examines how conversion of automobile trips of less than 3 miles to other transportation modes reduces emissions. Short trips contribute disproportionately to emissions because of cold starts. An analysis is conducted of short-trip behavior across the US using the 1995 Nationwide Personal Transportation Survey. The data is used to develop likely scenarios of mode conversions for short trips, which are then applied to estimate emission savings using MOBILE6 cold start and running emission factors for volatile organic compounds, nitrogen oxides, carbon monoxide, and carbon dioxide. The results suggest that reducing short auto trips would modestly reduce mobile source air pollution, but emission reductions are high compared to most federally-funded surface transportation interventions aimed at improving air quality. Enhanced the community pedestrian environment to encourage short trip mode conversion also produces co-benefits such as increased physical activity and subsequent reductions in chronic diseases.     

The impacts of inter-vehicle spacing on in-vehicle air pollution concentrations in idling urban traffic conditions

The influence of inter-vehicle spacing on the in-vehicle air pollution exposure of car commuters in heavy traffic conditions was investigated, both experimentally and numerically. An experimental investigation was carried out into the effect, on in-vehicle air pollution exposure, of maintaining a distance of approximately 2 m to the preceding vehicle in congested idling traffic conditions compared to that of an identical vehicle maintaining a distance of approximately 1 m. In-vehicle VOC and PM_2.5 concentrations revealed that a 19–31% reduction in exposure at the larger inter-vehicle spacing. A computational fluid dynamics model was calibrated using the experimental data and used to prediction car exposure under different conditions by varying certain key parameters. Agreement between the experimental and predicted data of 82% was achieved. The results show a significant drop in pollutant concentrations occurred within the first 2 m of their emission from the preceding vehicles exhaust.     

Studying the benefits of carpooling in an urban area using automatic vehicle identification data

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode.