A new approach to link transport emissions and air quality: An intelligent transport system based on the control of traffic air pollution

Abstract

Road transport has become by far the major source of environmental pollution and traffic congestion in urban areas. Though a lot of research has been done to investigate the functional relationship linking air quality and air pollution from transport, a further improvement in the knowing of this relationship is needed. The aim of this study was to analyze this relationship and to develop a more flexible framework to allow communication between transport emissions and air quality concentrations. This paper describes the development of this framework, suggests methodological tools to mitigate its problems and shows its application to the mega-city of Beijing, in P.R. China. The result of implementing this methodology would be a system providing high time/space resolution measurements of both air pollutant concentrations and traffic emissions data, as well as real-time transportation and dispersion modelling of those data. The key advantage of the system proposed would be the runtime integration of modelling, to interpret the data measured, with measurements, to validate the data modelled. The findings from the case-study of Beijing show that the integrated system can link traffic air pollution measurements through various modelling modules in order to automate transport-related air pollution assessment.

Introduction

Road transport has become by far the major source of environmental pollution and traffic congestion in urban areas. The continual traffic growth has raised concerns over the impact of traffic emissions on human health and urban environmental quality, and has fuelled the demand for a coherent regulatory framework for the management of traffic, air quality and emission at urban level, as well as at regional and national scales. Particularly, urban congestion and air pollution are seen as a high priority problem in China (UNEP, 1997). In Beijing air quality is in transition from coal burning caused problems to traffic exhaust related pollution (Zhang et al., 1997) since the number of private passenger cars is increasing dramatically recently and in the near future. Vehicle emissions are, therefore, projected to double within the next two decades unless drastic strategies to lower actual emissions are employed.

In the design of cost effective abatement strategies it must be realized that the relations between emissions and resulting concentrations are by no means simple. A lot of research has been done to investigate this issue. Most of the emission factors used by the emission inventories to quantify traffic contribution upon total emissions originate from laboratory measurements carried out according to specific measure and driving protocols (e.g. Hausberger et al., 2003). However, for real world application the model calculations must be based on true emission data and their estimation is not trivial. In a recent work conducted by analyzing the relationships between the Danish Operational Street Pollution Model (OSPM) predictions and concentrations measurements, Berkowicz et al. (2006) found that the application of COPERT emission factors can lead to a significant underestimation of street level pollution concentrations. On the opposite, pollutant concentrations have been measured on the road to infer emission profiles and rates (Querol et al., 2002, Shi et al., 1999): besides temperature and humidity, these results strongly depend on wind speed and direction, and, therefore, on the temporal variation of dilution rate; they account for a mix of vehicles indeed. To remove the effect of wind, measurement stations inside tunnels have been used. However, distribution profiles of not all the pollutants measured in such a way can represent ambient air conditions (Sturm et al., 2003). In order to solve these issues and also obtain results that can discriminate emitting vehicle type, whether chasing experiments on the road (e.g. Kittelson et al., 2002, Vogt et al., 2003) or simulation of exhaust dilution in ambient air (e.g. Sasaki and Nakajima, 2002, Maricq et al., 2002) have been carried out.

However, a further improvement in the knowing of this relationship is needed. Measurements are still the foundation of our understanding, but application of mathematical and physical modelling is of increasing importance in urban air pollution management (Fenger, 1999). In a recent study (Mediavilla-Sahagun and ApSimon, 2006) an Urban Scale Integrated Assessment Model (USIAM) was used to assess potential air pollution abatement strategies; source–receptor relationships were defined by means of pre-calculated dispersion matrices for investigation in terms of achieving environmental target and cost effectiveness. In another recent study (Calori et al., 2006) an integrated modelling system was used to reproduce concentration behaviours; the reproducibility was higher in “urban background” or suburban stations, and lower in sites heavily affected by nearby traffic. This was attributed to spatial resolution effects (local traffic emissions' modulation and urban boundary layer modelling) whose importance on urban sustainability and air quality have been assessed in another recent work (Borrego et al., 2006). Moreover, statistical models have recently been applied to provide with an operational air quality forecasting module (e.g. for PM₁₀ Slini et al., 2006) and to evaluate the impact of traffic emissions on the statistical distributional form of air pollutant concentrations (e.g. Gokhale and Khare, 2007, Costabile et al., 2006b). They demonstrate promising operational forecasting capabilities. All those models can be further developed to form full decision support systems (e.g. Dennis et al., 1996) to be integrated with measurements (e.g. Carras et al., 2002, Schmidt and Schafer, 1998). Recent developments show a continuum between integrated assessment modelling and environmental decision support systems (EDSS) with varying levels of stakeholder participation in both EDSS development and application (Matthies et al., 2007).

This paper reports some of the results of a big project on-going in Beijing (China) aimed at analyzing the relationship between urban traffic and air quality. The objective of this paper is to assess a new framework which allows the communication between transport emissions and air pollutant concentrations, as well as address a need of continuous research. It would also provide policy-makers with valuable lessons learned regarding transportation programs which promote economic development while reducing pollution impacts. The design of the system, the methodologies and the requirements of measurements and modelling, and the results from its feasibility study for the mega-city of Beijing are discussed; the test run still in progress should be addressed in future works.