The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of research aimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work has begun to exploit the capability of a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect real time data on important aspects of driver and traffic behaviour, vehicle emissions, pollutant dispersion, concentration and human exposure. The variety, pervasiveness and scale of these sensor data will increase significantly in the future as a result of technological developments that will enable sensors to become cheaper, smaller and lower in power consumption. This will open up enormous opportunities to improve our understanding of urban air pollution and hence improve urban air quality. However, handing the vast quantities of real time data that will be generated by these sensors will be a formidable task and will require the application of advanced forms computing, communication and positioning technologies and the development of ways of combining and interpreting many different forms of data. Technologies developed in EPSRC's e-Science research programme offer many of the tools necessary to meet these challenges. The aim of the PMESG project is to take these tools and by extending them where necessary in appropriate ways develop and demonstrate practical applications of e-Science technologies to enable researchers and practitioners to coherently combine data from disparate environmental sensors and to develop models that could lead to improved urban air quality. The PMESG project is led by Imperial College London, and comprises a consortium of partners drawn from the Universities of Cambridge, Southampton, Newcastle and Leeds who will work closely with one another and with a number of major industrial partners and local authorities. Real applications will be carried out in London, Cambridge, Gateshead and Leicester which will build on the Universities' existing collaborative arrangements with the relevant local authorities in each site and will draw on substantial existing data resources, sensor networks and ongoing EPSRC and industrially funded research activities. These applications will address important problems that to date have been difficult or impossible for scientists and engineers working is this area of approach, due to a lack or relevant data. These problems are of three main types; (i) measuring human exposure to pollutants, (ii) the validation of various detailed models of traffic behaviour and pollutant emission and dispersion and (iii) the development of transport network management and control strategies that take account not just of traffic but also air quality impacts. The various case studies will look at different aspects of these questions and use a variety of different types of sensor systems to do so. In particular, the existing sensor networks in each city will be enhanced by the selective deployment of a number of new sensor types (both roadside and on-vehicle/person) to increase the diversity of sensor inputs. The e-Science technologies will be highly general in nature meaning that will have applications not only in transport and air quality management but also in many other fields that generate large volume of real time location-specific sensor data.Each institution participating in this project will be submitting their resource summary individually to Je-s. The resources listed within this Je-S Proposal are solely those of Imperial College with other institutions submitting their costs seperately, with one case for support.

We will mount sensors on pedestrians and cyclists to monitor their exposure to pollution from transport. This will be an addition to the TIME-EACM project, which is about to use Cambridge City as a test bed for a variety of ways to gather data about traffic flow, and is writing middleware to analyse the data in real time.The initial part of the study will be to confront the technical challenges associated with sensors that need to be highly portable. Sensor technologies are now advancing to the point where parts per billion sensitivities are becoming achievable in small low power devices for species relevant to local air quality including ozone, nitrogen dioxide and a range of hydrocarbons. The challenge will be to link such sensors to effective mobile systems to broadcast data back to central points for analysis and presentation, and to locate their wearers sufficiently accurately. The TIME-EACM project will log and store data and integrate databases with information flow from its sensors, and the data stream from the pervasive environmental sensors will be added to this. The TIME-EACM middleware will be compatible with data on pollution from pervasive environmental sensors. All data will be time-stamped and location-stamped and correlated with TIME-EACM data on traffic flow.

The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of researchaimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work hassought to use a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect data on key aspects ofdriver and traffic behaviour, emissions, pollutant concentrations and exposure. The variety and pervasiveness of the sensor inputsavailable will increase significantly in the future as a result both of the increasingly widespread penetration of existingtechnologies (e.g., GPS based vehicle tracking, CANbus interfaces to on-board engine management system data) within thevehicle parc and the introduction of new technologies (such as e.g., UV sensing and nanotechnology based micro sensors). Aparticularly exciting direction for future development will be in the use of vehicles as platforms for outward facing environmentalsensor systems, allowing vehicles to operate as mobile environmental probes, providing radically improved capability for thedetection and monitoring of environmental pollutants and hazardous materials.However, these developments present new and formidable research challenges arising from the need to transmit,integrate, model and interpret vast quantities of highly diverse (spatially and temporally varying) sensor data. Our approach in thisproject is to address these challenges by novel combination and extension of state-of-the-art eScience, sensor, positioning andmodelling (data fusion, traffic, transport, emissions, dispersion) technologies. By so doing, we aim to develop the capability tomeasure, model and predict a wide range of environmental pollutants and hazards (both transport related and otherwise) using agrid of pervasive roadside and vehicle-mounted sensors. This work will be at the leading edge of eScience, stretching thecapabilities of the grid in a number of aspects of the processing of massive volumes of sensor data.

The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of research aimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work has begun to exploit the capability of a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect real time data on important aspects of driver and traffic behaviour, vehicle emissions, pollutant dispersion, concentration and human exposure.The variety, pervasiveness and scale of these sensor data will increase significantly in the future as a result of technological developments that will enable sensors to become cheaper, smaller and lower in power consumption. This will open up enormous opportunities to improve our understanding of urban air pollution and hence improve urban air quality. However, handing the vast quantities of real time data that will be generated by these sensors will be a formidable task and will require the application of advanced forms computing, communication and positioning technologies and the development of ways of combining and interpreting many different forms of data.Technologies developed in EPSRC's e-Science research programme offer many of the tools necessary to meet these challenges. The aim of the PMESG project is to take these tools and by extending them where necessary in appropriate ways develop and demonstrate practical applications of e-Science technologies to enable researchers and practitioners to coherently combine data from disparate environmental sensors and to develop models that could lead to improved urban air quality.The PMESG project is led by Imperial College London, and comprises a consortium of partners drawn from the Universities of Cambridge, Southampton, Newcastle and Leeds who will work closely with one another and with a number of major industrial partners and local authorities.Real applications will be carried out in London, Cambridge, Gateshead and Leicester which will build on the Universities' existing collaborative arrangements with the relevant local authorities in each site and will draw on substantial existing data resources, sensor networks and ongoing EPSRC and industrially funded research activities. These applications will address important problems that to date have been difficult or impossible for scientists and engineers working is this area of approach, due to a lack or relevant data. These problems are of three main types; (i) measuring human exposure to pollutants, (ii) the validation of various detailed models of traffic behaviour and pollutant emission and dispersion and (iii) the development of transport network management and control strategies that take account not just of traffic but also air quality impacts. The various case studies will look at different aspects of these questions and use a variety of different types of sensor systems to do so. In particular, the existing sensor networks in each city will be enhanced by the selective deployment of a number of new sensor types (both roadside and on-vehicle/person) to increase the diversity of sensor inputs.The e-Science technologies will be highly general in nature meaning that will have applications not only in transport and air quality management but also in many other fields that generate large volume of real time location-specific sensor data.

This project is part of a collaborative eScience pilot project.Please see the corresponding application from Imperial College for the summary of the project