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Air quality traffic-related measures have been implemented worldwide to control the pollution levels of urban areas. Although some of those measures are claiming environmental improvements, few studies have checked their real impact. In fact, quantitative estimates are often focused on reducing emissions, rather than on evaluating the actual measures' effect on air quality. Even when air quality studies are conducted, results are frequently unclear. In order to properly assess the real impact on air quality of traffic-related measures, a statistical method is proposed. The method compares the pollutant concentration levels observed after the implementation of a measure with the concentration values of the previous year. Short- and long-term impact is assessed considering not only their influence on the average pollutant concentration, but also on its maximum level. To control the effect of the main confounding factors, only the days with similar environmental conditions are analysed. The changeability of the key meteorological variables that affect the transport and dispersion of the pollutant studied are used to identify and group the days categorized as similar. Resemblance of the pollutants' concentration of the previous day is also taken into account. The impact of the road traffic measures on the air pollutants' concentration is then checked for those similar days using specific statistical functions. To evaluate the proposed method, the impact on PM2.5 concentrations of two air quality traffic-related measures (M1 and M2) implemented in the city of Beijing are taken into consideration: M1 was implemented in 2009, restricting the circulation of yellow-labelled vehicles, while M2 was implemented in 2014, restricting the circulation of heavy-duty vehicles. To compare the results of each measure, a time-period when these measures were not applied is used as case-control.

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NOx, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NOx and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.

The volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX) are emitted into the atmosphere at gas stations (GS) leading to chronic exposure of nearby residents, which raises public health concerns. This study aimes at determining the contribution of GS emissions to BTEX exposure in nearby residents. Three Control and Exposed areas to BTEX emissions from GS were defined in a medium-sized European city (Porto, Portugal). BTEX atmospheric levels were determined in Control and Exposed areas using passive samplers deployed outdoors (n = 48) and indoors (n = 36), and human exposure was estimated for 119 non-smoking residents using the first urine of the day. Results showed that median BTEX outdoor and indoor concentrations were significantly higher for Exposed than Control areas, with exception of ethylbenzene and xylene indoor concentrations, where no marked differences were found. Comparison of urinary concentrations between Exposed and Control residents demonstrated no significant differences for benzene and ethylbenzene, whereas levels of toluene and xylene were significantly higher in Exposed residents. No marked correlation was obtained between atmospheric BTEX concentrations and urinary concentrations. Data indicate the potential impact on air quality of BTEX emissions from GS, which confirms the importance of these findings in urban planning in order to minimize the impact on health and well-being of surrounding populations.

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The introduction of eco-routing systems has been suggested as a promising strategy to reduce carbon dioxide emissions and criteria pollutants. The objective of this study is to scrutinize the impacts of an eco-routing guidance system on emissions through the use of a case study in a commuting corridor. This research aims at assessing the potential environmental benefits in terms of different pollutant emissions. Simultaneously, it addresses the extent of variations in system travel time that each eco-routing strategy implies. The methodology consists of three distinct phases. The first phase corresponded to the adjustment of a micro simulation platform of traffic and emissions with empirical data previously collected. Secondly, volume-emission-functions (VEF) were developed based on the integrated modelling structure. Finally, different scenarios of traffic flow optimization were performed at the network level based on a simplified assignment procedure. The results show that if the traffic assignment is performed with the objective of minimize overall impacts, total system environmental damage costs can be reduced up to 9% with marginal oscillations in total system travel time. However, if drivers are advised based on their own emissions minimization, total system emissions may be higher than under the standard user equilibrium flow pattern. Specifically, environmentally friendly navigation algorithms focused on individual goals may tend to do divert traffic to roads with less capacity affecting the performance of the remaining traffic. This case study brings new insights about the difficulties and potentials of implementing such systems.

Eco-routing has been shown as a promising strategy to reduce emissions. However, during peak periods, with limited additional capacity, the eco-friendliness of various routes may change. We have explored this issue empirically by covering about 13,300 km, in three different areas, using GPS-equipped vehicles to record second-by-second vehicle dynamics. This study has confirmed the importance of the eco-routing concept given that the selection of eco-friendly routes can lead to significant emissions savings. Furthermore, these savings are expected to be practically unchanged during the peak period. However, some potential negative externalities may arise from purely dedicated eco-friendly navigation systems.

Urban traffic emissions have been increasing in recent years. To reverse that trend, restrictive traffic measures can be implemented to complement national policies. We have proposed a methodology to assess the impact of three restrictive traffic measures in an urban arterial by using a microsimulation model of traffic and emissions integrated platform. The analysis is extended to some alternative roads and to the overall network area. Traffic restriction measures provided average reductions of 45%, 47%, 35%, and 47% for CO2, CO, NOX, and HC, respectively, due to traffic being diverted to other roads. Nevertheless, increases of 91%, 99%, 55%, and 121% in CO2, CO, NOX, and HC, respectively, can be expected on alternative roads.

Diesel-fueled buses have been replaced by Compressed Natural Gas (CNG) to minimize the high level of emissions in urban areas. However, differences in indoor exposure levels to Benzene, Toluene, Ethylbenzene and Xylene (BTEX) in those vehicles have not been investigated so far. The primary aim of this study was to determine if passengers are exposed to different BTEX levels when using buses powered by CNG or by diesel, and further explore if indoor levels are influenced by external air quality. For this purpose, BTEX air concentrations were measured in bus cabins (CNG and diesel), parking stations and in a background urban area using passive air samplers. Results showed that BTEX concentrations inside vehicles were higher than outside, but no significant differences were found between buses powered by CNG or by diesel. In CNG vehicles, high and significant positive correlation was found between benzene and the number of journeys in the same route (rs = 0.786, p

Advanced Traveller Information Systems (ATIS) have been used worldwide to mitigate different road traffic impacts such as traffic congestion. However, the impact of these systems in fuel use and emissions is still an important research topic, due to their non-linear behaviour. Therefore, this paper aims to evaluate the impact of traffic incidents on a regional scale on energy and emissions levels. To achieve these objectives, several scenarios assessing different degrees of information (through the use of ATIS) were analysed. To model both traffic operations and emissions a mesoscopic traffic model (DTALite) and a road emissions methodology (EMEP/EEA) were used. The data related to the characterization of the road network was collected between Oporto and Aveiro, in Portugal. To ensure reliable results, the traffic model was calibrated and validated taking into account statistical methods that compare observed traffic flows and travel times with the values estimated by the model. The simulation platform was then used to assess the impact of traffic incident when it occurs in two different road types (a motorway and a highway), in order to evaluate the impact of usage and availability of information on emissions and fuel consumption. The results show that in a regional road network for each occurred incident, the use of ATIS can allow a maximum reduction of emissions and energy consumption on those routes where they occurred to up 2%. Nonetheless, a global analysis of the network indicates different results depending if the incident occur in a motorway or in a highway. In both cases, drivers tend to choose a route with similar costs to the previously selected. Furthermore, a non-linear trend between the availability of information and the impact on emissions and energy consumption was found.

The fast economic growth of China along the last two decades has created a strong impact on the environment. The occurrence of heavy haze pollution days is the most visible effect. Although many researchers have studied such problem, a high number of spatio-temporal limitations in the recent studies were identified. From our best knowledge the long trends of PM2.5 concentrations were not fully investigated in China, in particular the year-to-year trends and the seasonal and daily cycles. Therefore, in this work the PM2.5 concentrations collected from automatic monitors from five urban sites located in megacities with different climatic zones in China were analysed: Beijing (40°N), Chengdu (31°N), Guangzhou (23°N), Shanghai (31°N) and Shenyang (43°N). For an inter-comparison a meta-analysis was carried out. An evaluation conducted since 1999 demonstrates that PM2.5 concentrations have been reduced until 2008, period which match with the occurrence of the Olympic Games. However, a seasonal analysis highlight that such decrease occurs mostly during warmer seasons than cold seasons. During winter PM2.5 concentrations are typically 1.3 to 2.7 higher than in summer. The average daily cycle shows that the lowest and highest PM2.5 concentrations often occurs in the afternoon and evening hours respectively. Such daily variations are mostly driven by the daily variation of the boundary layer depth and emissions. Although the PM2.5 levels have showing signs of improvement, even during the warming season the values are still too high in comparison with the annual environmental standards of China (35 μg m−3). Moreover, during cold seasons the north regions have values twice higher than this limit. Thus, to fulfil these standards the governmental mitigation measures need to be strongly reinforced in order to optimize the daily living energy consumption, primarily in the north regions of China and during the winter periods.

The atmospheric dispersion of NO x and PM10 was simulated with a second generation Gaussian model over a medium-size south-European city. Microscopic traffic models calibrated with GPS data were used to derive typical driving cycles for each road link, while instantaneous emissions were estimated applying a combined Vehicle Specific Power/Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (VSP/EMEP) methodology. Site-specific background concentrations were estimated using time series analysis and a low-pass filter applied to local observations. Air quality modelling results are compared against measurements at two locations for a 1 week period. 78% of the results are within a factor of two of the observations for 1-h average concentrations, increasing to 94% for daily averages. Correlation significantly improves when background is added, with an average of 0.89 for the 24 h record. The results highlight the potential of detailed traffic and instantaneous exhaust emissions estimates, together with filtered urban background, to provide accurate input data to Gaussian models applied at the urban scale.

The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power — VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM 10 , NO x and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM 10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM 10 , CO and O 3 . For NO 2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM 10 , CO and O 3 .