Showing papers by "Margaret Bell published in 2012"

Toward reconciling instantaneous roadside measurements of light duty vehicle exhaust emissions with type approval driving cycles.

Abstract: A method is proposed to relate essentially instantaneous roadside measurements of vehicle exhaust emissions, with emission results generated over a type approval driving cycle. An urban remote sensing data set collected in 2008 is used to define the dynamic relationship between vehicle specific power and exhaust emissions, across a range of vehicle ages, engine capacities, and fuel types. The New European Driving Cycle is synthesized from the remote sensing data using vehicle specific power to characterize engine load, and the results compared with official published emissions data from vehicle type approval tests over the same driving cycle. Mean carbon monoxide emissions from gasoline-powered cars ≤ 3 years old measured using remote sensing are found to be 1.3 times higher than published original type approval test values; this factor increases to 2.2 for cars 4-8 years old, and 6.4 for cars 9-12 years old. The corresponding factors for diesel cars are 1.1, 1.4, and 1.2, respectively. Results for nitric oxide, hydrocarbons, and particulate matter are also reported. The findings have potential implications for the design of traffic management interventions aimed at reducing emissions, fleet inspection and maintenance programs, and the specification of vehicle emission models.

Application of ADMS and AERMOD models to study the dispersion of vehicular pollutants in urban areas of India and the United Kingdom

Abstract: Urban air pollution poses a significant threat to human health, the environment and the quality of life of people throughout the world. In the United Kingdom 103 areas have been declared as local air quality management areas (LAQMA). While in India, 72 cities have been identified as cities having poor air quality/non-attainment area, i.e., the air quality in these cities are exceeding prescribed National Ambient Air Quality Standards (NAAQS). The transport sector is the principal source of local air pollution in urban areas, because of the increased vehicular population, vehicle kilometres travelled (VKT) and lack of infrastructure development. Many mathematical models have been widely used as tools in local air quality management in developed countries. Among them, ADMS [1] and AERMOD [2] models have been widely used for urban air quality management in Europe and the US, respectively. However, their applications are limited in developing countries like India due to the lack of readily available input data, time and the cost involved in collecting the required model input data. In this paper the performance evaluation of ADMS and AERMOD in predicting particulate matter (PM) concentrations at road sides in Chennai, India and Newcastle, UK is discussed. Air Pollution XX 3 doi:10.2495/AIR120011 www.witpress.com, ISSN 1743-3541 (on-line) WIT Transactions on Ecology and The Environment, Vol 1 , © 2012 WIT Press 57 The statistical parameters such as Index of Agreement (IA), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias (MG) and Geometric Mean Variance (VG) have been used to evaluate the ADMS and AERMOD model performance. Results indicated that both the models are able to predict the pollutant concentrations with reasonable accuracy. The IA values for ADMS and AERMOD are found to be 0.39 and 0.37 and 0.48 and 0.44, respectively, for the Chennai and Newcastle study sites.

Analysis of Electric Vehicle Driver Charging Behaviour and Use of Charging Infrastructure

Abstract: Estimates suggest that there will be between 0.5 and 12.8 million electric vehicles (EVs) on UK roads by 2030. Power grids could overload if large numbers of these EVs are not recharged off peak, between midnight and early morning. This study investigates the recharging patterns of 12 Private, 21 Organization Individual and 32 Organization Pool users over two successive six month periods as part of the Switch EV trials. EVs were monitored using a data loggers and GPS devices. Recharging locations were identified as Home, Work, Public and Other. It was found less than 10% of recharging took place off peak. Work was the most popular location to recharge, and demand peaked between 09:00am and 10:00am. Private individuals peak recharging occurred on an evening at Home, while Organization Individuals users recharged mostly early morning at Work. Organization Pool users recharging peaked at Work, late afternoon. Smart Meters can delay recharging until off peak hours at Home and Work locations. It is recommended that pay as you go access to non-domestic infrastructure is used for individual drivers. This will encourage them to recharge at Home or Work, where Smart Meters can then shift their recharging to off peak.

Investigating the Potential to Influence the Behaviour of Electric Vehicle Users’ Recharging Behaviour to Reduce Well to Wheel Carbon Emissions

Abstract: The need to cut carbon emissions from cars and small vans is becoming an increasingly important issue. In the UK, it is anticipated that the electric vehicle (EV) will play a key role in meeting the 80% emissions reduction target in the Climate Change Act 2008. Although there are no emissions at their point of use, the equivalent emissions from an electric vehicle are dependent on the electricity used to recharge the EV’s battery. This electricity is generated from coal (910gCO2/kWh), natural gas (400gCO2/kWh), nuclear (zero emissions) and renewables (zero emissions). The contribution of these power sources to the overall energy mix varies depending on the time of day; meaning that the average carbon content varies from an ‘off peak’ minimum of 366gCO2/kWh at 03:00am to an ‘on peak’ 466gCO2/kWh at 18:00pm. Therefore, depending on when an EV is recharged, the effective carbon content of the electricity stored in the battery varies. This study aims to quantify the carbon emissions and power demands of electric vehicles when in everyday use, by correlating the times of day when drivers recharge their cars with the carbon content of electricity at that time. Data was collected through the Switch EV trial in North East England, which see 44 electric vehicles employed in the region for three years. Analysis of the behaviour of these drivers over a six month period indicates that the average carbon content of the electricity transferred into an EV during recharging is 436gCO2/kWh. Changes in charging behaviour could lead to a 70gCO2/kWh reduction in emissions.

Analysis and modelling of effects of traffic light operations variability to violation rates at junction

Abstract: Previous research, based on potential conflicts analysis, has provided a quantitative evaluation of ‘proneness’ to red-light running (RLR) behaviour at urban signalised intersections (Giuffre & Rinelli, 2006) varying geometric, traffic flow and driver characteristics. Recent study (Bell, Galatioto, Giuffre, & Tesoriere, 2012) demonstrated the potential to use micro-simulation models to evaluate the ‘proneness’ to RLR behaviour at urban signalised intersections in Italian cities, varying flow characteristics and stop line distances. The micro-simulation, although at its early stages of development, has shown promise in its ability to model unintentional RLR behaviour and to evaluate alternative junction designs on proneness. In order to make more robust the new modelling framework, the need to demonstrate the transferability of the modelling approach has been addressed in this paper by using a four arms junction in Enna (Italy), where an extensive period of continuous video recording has been carried out. Moreover, in collaboration with the local Police, different cycle and green time settings have been implemented in order to measure the effects on the RLR rates. Then the measured RLR rates have been evaluated and compared to the modelling results as a validation exercise. In this way the prediction capability of the proposed potential conflict model has been extended and improved.

Performance evaluation of air quality dispersion models in Delhi, India

Abstract: Several attempts have been made worldwide to understand the gravity of the air pollution problem. Researchers and policy-makers have been trying to understand it through scientific, regulatory and non-regulatory options. This study reports on the applicability and performance of some well known air quality dispersion models like AERMOD and ISCST3 models for Indian conditions. The evaluation of these models has been carried out in Delhi, India using the historical air quality, meteorological and traffic data for the year 2007. The models have been evaluated using standard performance indicators like fractional bias, mean bias, normalized root mean square error, index of agreement and geometric mean bias.

Using Integrated Air Quality Data through UTMC to Better Inform Traffic Operators and other Stakeholders

Abstract: The Newcastle University Integrated Database and Assessment Platform (NUIDAP), developed to integrate pervasive air quality sensors with other intelligent transportation system (ITS) data sources (Galatioto 2011 [1]) is used to provide real-time traffic management tools for control room based traffic operators to manage traffic related air pollution. In conjunction with other data sources and control mechanisms, co-ordinated through UTMC, stakeholders are provided with information of the effect on the environment of traffic related congestion and their potential to lead to pollution episodes by identifying those streets vulnerable to pollution hot spot formation; operators can develop appropriate mitigation strategies to reduce the impact of network events. This paper describes the deployment of the motes in Medway and the implementation of NUIDAP. The motes were installed on street furniture in spring 2011 and system stability was reached within a month. The challenges in achieving system integration were of a logistical nature managing installations, upgrading technology and third party software rather than academic or technical. The systems architecture and tools resulting from the integration are described. The installation of the NUIDAP and development of the interface with the common database required enhancements of the UTMC common database data standards specification which are outlined and a glimpse of the visualisation is given.

The Role of Electronic Vehicle and Fare Data for Carbon Accounting in the Delivery of Sustainable Bus Operation

Abstract: The deregulation of the public transport industry (creating competition) has tended to disconnect services rather than encourage better provision through integration, which requires co-operation between different transport companies. In the United Kingdom (UK) the Local Transport Act (2008), the creation of Quality Bus Partnership, aimed to promote and facilitate mode shift, increase bus patronage, lowering emissions per passenger on the bus and consequential reduction in carbon emissions from private cars. Furthermore, the reduced congestion leads to more reliable bus journey times and reduced emissions from all vehicles. Given the pressures on Governments to reduce the contribution of transport to climate change (Stern (2006), White Paper, ECCP (2005)) economic criteria can no longer be considered the only metric for planning and managing bus networks. This paper presents evidence from the literature that potential savings in carbon dioxide are possible with driver training, of typically 20-30%. The vehicle type, bus operation practices and traffic control systems affect the carbon emissions per person generated by their need to travel. Intelligent Transport and Vehicle Systems generate, as a bye product of their control function, data that can potentially be used to improve the sustainability of transport operation. In particular the carbon emissions benefits from the introduction of smart ticketing and second by second vehicle tracking coupled with eco-driving training are demonstrated.

Evaluation of Carbon Reduction Traffic Measures Employing a Novel Approach to Micro-simulation Modelling of Real-world Emissions

Abstract: Local Authorities and Governments are under pressure to meet air quality and carbon emissions targets, however in several cases high level scale modelling outputs do not truly represent traffic conditions in local urban environments and associated hot spot pollutant concentrations which are determined by the built environment (canyons, road orientation, etc.). To address this gap in knowledge, Newcastle University recently developed an Integrated Database and Assessment Platform (NUIDAP), to bring together data from pervasive environmental and traffic monitoring systems with existing information, such as intelligent transportation system (ITS) and UTMC sources and traffic models. This paper presents the preliminary analysis and results that the Newcastle Team, with the support of AMEY, made using the software platform which integrates different data sources in order to identify problems, understand causes and formulate the solutions to air quality and manage traffic to alleviate, even prevent, pollution hotspots. This paper describes and presents evidence and results of the use of two types of microscale approaches for air quality assessment, the first using novel pervasive sensors, and the second employing modelling using an enhanced instantaneous emission model within the AIMSUN microsimulator. Scenarios modelled, including speed reduction and mode shift to public transport with reduction of traffic volumes, will be described and the outputs with and without the enhanced emission model are compared.

Decision support system monitoring and modelling platform to inform policy and management strategies to reduce environmental impacts of traffic

Abstract: Local Authorities, National and European Governments are under pressure to meet air quality and carbon emissions targets. Appropriate ways to monitor and model environmental impacts of traffic in networks are becoming increasingly important. Traffic related emissions create pollution “hot spots” which are governed by the built environment (canyons, street orientation, gradient etc.) as well as the prevailing meteorological conditions. There is a need to provide traffic operators with a tool kit to allow traffic interventions that are designed to manage air quality not just delay and stops. This paper presents an over view of the systems architecture developed by Newcastle University to deliver the next generation of environmental assessment and management of traffic. Details of the on-line component, namely the Newcastle University Integrated Database and Assessment Platform, NUIDAP, in an UTMC environment are elaborated. This is followed by examples of three NUIDAP components, namely the real-time data/status processing, historic analysis and canyon concentration estimator, which have been analysed by the Newcastle Team, using the software platform which has integrated different legacy system data sources. (6 pages)

Creating an Evaluation Platform to Deliver Sustainable Urban Networks using Bluetooth Technology

Abstract: Current research (Cairns, 2012) demonstrates that vehicle emissions reduction technologies alone will neither deliver carbon emissions targets (CCA, 2008) nor European Union (EU) air quality objectives. Therefore, modal shift, demand management and behavioural changes need to be at the heart of future policies with emphasis on efficient and effective movement of people rather than single occupancy vehicles. By reporting network problems, by way of APPs, to mobiles and iPhones drivers are informed of less congested routes, and are given directions to car parks, while passengers receive information on availability of bus services in real-time, thereby reducing vehicle-miles, emissions, speeding up journeys and unnecessary delay at bus stop, potentially this can be delivered by Bluetooth technology operating in an Intelligent Transport Systems (ITS) environment. This paper focuses on demonstrating the usefulness of Bluetooth sensors in delivering sustainability. It is aimed at enhancing network efficiency by developing an insight into monitored Bluetooth signals from a network of sensors distributed over part of an urban area of Birtley, UK, employing a post-processing data analysis technique. From the analysis, the study of spatial and temporal status monitoring of the network helped to assess journey times and gain knowledge of routes irrespective of the mode. The preliminary results and analysis indicate that Bluetooth can establish the distribution of journey times to a level of accuracy capable of differentiating the proportion of vehicles in different traffic conditions. This enables more realistic estimates of pollutant emissions to be made link by link. The ability to identify routes through the network (origins and destinations) offers the potential to considerably enhance decision making with regard to managing traffic demand and providing information to users of the network across modes. Future work includes system calibration, validation and real time deployment to enable traffic optimisation and emissions reduction to be achieved.