## 2 Motivations and Contributions  
Road traffic makes a significant contribution to the following emissions of pollutants: benzene(C\({}_{6}\)H\({}_{6}\)), 1,3-butadiene, carbon monoxide(CO), lead, nitrogen dioxide(NO\({}_{2}\)), Ozone(O\({}_{3}\)), particulate matter(PM\({}_{10}\) and PM\({}_{2.5}\)) and sulphur dioxide(SO\({}_{2}\)). The impact of local air quality pollutants on the environment and health have been studied and well documented [6]. We summarize the interaction and cooperation chain of the population, traffic, air quality and health as Figure 1.  
The figure shows that, increased car ownership and use in urban areas (road traffic) generate some chemical emissions to the air to form the air pollution. With various weather conditions (effected by the temperature, wind, humidity, pressure, etc. ), these pollutants pose different air qualities. When human beings expose to the polluted air (especially in the urban areas), driving in heavy traffic, near the highways or at the 'downwind' locations, with the dose-response, people may suffer breathing problems and asthma attacks, which will contribute to risk of heart attacks among people with heart disease. Under the current Environment Act of UK [7], most local authorities have air quality monitoring stations to provide environmental information to public daily via internet. To date, the development of work in these areas has been hampered by critical data gaps and asymmetries in data coverage, as well as the lack of on-line data processing capability offered by the e-Science. Information on a number of key factors such as individual driver/vehicle activity, pollution concentration and individual human exposure has traditionally either simply not been available or only available at high levels of spatial and temporal aggregation, which average out scientifically critical local variations. For example, the conventional approach to assessing pollution concentration levels is based on data collected from a network of permanent air quality monitoring stations. However, permanent monitoring stations are frequently situated so as to measure ambient background concentrations or at potential 'hotspot' locations and are usually several kilometers apart. According to our earlier research of 'Discovery Net EPSRC e-Science Pilot Project' [8] (the data generated from statically located urban pollution monitoring sensors), we learnt that the pollution levels and the hot spots change with time as shown in Figure 2. However, those results are all computed offline and can't give a real-time track. As the result, it can't make a prompt feedback or supervision to individuals and the air pollution monitor systems. Figure 1: The adverse health impacts chain. Figure 2: Pollution levels change at East London during a day. Besides, while traffic monitoring systems provide information on aggregate traffic parameters, they do not inform on individual vehicle trajectories, in particular, key features (for emissions estimation) such as the incidence of acceleration, deceleration and idling episodes. Moreover, epidemiological studies typically base estimates of exposure on home post code, despite the fact that many people clearly spend large parts of the day in other locations. These data gaps have led to a number of critical barriers to the successful development of key research issues. These include:  
1. Model validation: Conventional environmental data sources do not provide a sufficient detail of temporal or spatial resolution to enable existing or emerging traffic, vehicle emissions and pollution dispersion models to be validated at the micro-scale, especially at street level. This is inhibiting the development of necessary understandings of how best to design local traffic management and urban design interventions to reduce pollution concentrations in critical areas, including pollution 'hot spots'. 2.