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This paper illustrates the early results of ongoing research developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope is to integrate the new models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas.This first stage concerns the relationship between the hourly mean concentration of nitrogen dioxide (NO2) and explanatory factors reflecting the NO2 mean level one hour back, along with traffic and weather conditions. Particular attention is given to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two model frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated.The analysis of findings points out that the prediction of extreme concentrations is best performed by integrating the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the concentration's dependency upon wind characteristics. So, the Neural Network can be exploited to highlight the involved functional forms and improve the ARIMAX model specification. In the end, the study shows that the ability to forecast exceedances of legal pollution limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a lower threshold than the normative one

Hydraulic fracturing (fracking) has been used extensively in the US and Canada since the 1950s and offers the potential for significant new sources of oil and gas supply. Numerous other countries around the world (including the UK, Germany, China, South Africa, Australia and Argentina) are now giving serious consideration to sanctioning the technique to provide additional security over the future supply of domestic energy. However, relatively high population densities in many countries and the potential negative environmental impacts that may be associated with fracking operations has stimulated controversy and significant public debate regarding if and where fracking should be permitted. Road traffic generated by fracking operations is one possible source of environmental impact whose significance has, until now, been largely neglected in the available literature. This paper therefore presents a scoping-level environmental assessment for individual and groups of fracking sites using a newly-created Traffic Impacts Model (TIM). The model produces estimates of the traffic-related impacts of fracking on greenhouse gas emissions, local air quality emissions, noise and road pavement wear, using a range of hypothetical fracking scenarios to quantify changes in impacts against baseline levels. Results suggest that the local impacts of a single well pad may be short duration but large magnitude. That is, whilst single digit percentile increases in emissions of CO2, NOx and PM are estimated for the period from start of construction to pad completion (potentially several months or years), excess emissions of NOx on individual days of peak activity can reach 30% over baseline. Likewise, excess noise emissions appear negligible (<1dBA) when normalised over the completion period, but may be considerable (+3.4dBA) in particular hours, especially in night-time periods. Larger, regional scale modelling of pad development scenarios over a multi-decade time horizon give modest CO2 emissions that vary between 2.5 and 160.4kT, dependent on the number of wells, and individual well fracking water and flowback waste requirements. The TIM model is designed to be adaptable to any geographic area where the required input data are available (such as fleet characteristics, road type and quality), and we suggest could be deployed as a tool to help reach more informed decisions regarding where and how fracking might take place taking into account the likely scale of traffic-related environmental impacts.

This paper presents results of comprehensive analyses of data from the first 122 commercially available wireless environmental pervasive sensors (motes), developed by Newcastle University and deployed in England. Measurements of pollution, meteorology and traffic are used to investigate the complexity of the physical and chemical processes governing the levels of traffic-related pollution in urban areas. Following a brief introduction on health impacts associated with air quality, description of the mote technology is given. Cluster analysis statistics to investigate the relationship between different pollutant types and traffic data demonstrated that traffic flow regimes alone cannot be used to estimate diurnal kerbside pollutant concentrations. Also, the absolute levels, whilst dependent on meteorological conditions and static parameters are only partially governed by the pollutant dispersion. The research clearly illustrates the benefits and added value of pervasive concentration measurement in urban micro environments with potential to effectively evaluate human exposure to transport-related emissions.

Due to its geometric design, turbo-roundabouts impose greatest constraints to the vehicular trajectories; by consequence, one can expect a more unfavourable impact of heavy vehicles on the traffic conditions than on other types of roundabouts. The present paper addresses the question of how to estimate Passenger Car Equivalents (PCEs) for heavy vehicles driving turbo-roundabouts. The microsimulation approach used revealed as a useful tool for evaluating the variation of quality of traffic in presence of mixed fleets (different percentages of heavy vehicles). Based on the output of multiple runs of several scenarios simulation, capacity functions for each entry lane of the turbo-roundabout were developed and variability of the PCEs for heavy vehicles were calculated by comparing results for a fleet of passenger cars only with those of the mixed fleet scenarios. Results show a dependence of PCEs for heavy vehicles on operational conditions, which characterise the turbo-roundabout. Assuming the values of PCEs for roundabouts provided by the 2010 Highway Capacity Manual (HCM), depending on entering manoeuvring underestimation and overestimation of the effect of heavy vehicles on the quality of traffic conditions have been found.