Real-time integrated modelling of transport-related air pollution in urban street networks - risk assessment and policy evaluation

项目说明：随着城市交通日益拥挤，与交通有关的排放物的增加引起了人们对人类健康和城市环境风险的关注。街道峡谷是交通相关空气污染的热点，因为在这个空间水平上，交通量的显著变化和污染物向受体（即暴露人群）的运输都是在分钟的时间尺度上。许多排放污染物具有化学反应性，在几秒到几分钟的时间尺度上可以发生快速反应，产生二次污染物。因此，城市空气质量的变化只能通过考虑车队组成、交通产生的湍流、街道和建筑几何形状以及实时气象条件（即以分钟为时间尺度）的综合影响来理解。基本的科学问题仍然存在，例如，如何模拟从交通活动到街道峡谷内空气污染分布的因果过程的实时动态，以及如何解释城市街道网络中空气污染的异质性分布及其随时间的丰富变化。这些问题的答案对于更好地评估个人或人群接触和对人类健康的潜在风险至关重要。为了对这一因果链进行建模，综合建模方法建立在对三个复杂系统的桥接研究之上：(i)旅行需求的动态变化，（ii）车辆排放，以及（iii）街道峡谷中空气污染物的扩散。因此，这种方法不仅能够提供街道峡谷空气质量的详细指示，而且还可以识别出行人和骑自行车者等易受伤害的行人较多的街道路段。这些综合起来有助于更好地量化可能对有关人口造成的潜在健康风险。此外，出行需求的变化对不同的政策有响应，如清洁空气区的速度限制和道路收费；这种影响从不同的交通传播到排放，并导致污染浓度的变化。正是通过这一模式链所反映的因果关系，才能评价旨在减轻风险的运输政策在减少对人类健康和环境的不利影响方面的有效性。本项目本质上涉及综合建模方法中的大数据、风险和缓解问题。博士生将从三个方面接受相关培训：1)城市交通和空气质量建模与应用；2)编程、计算和数据处理；3)不确定性和风险相关分析。

Project description: As cities become increasingly congested with growing traffic, the increase in transport-related emissions has raised concerns over the risk on human health and urban environment. Street canyons are hotspots of traffic-related air pollution, because at this spatial level significant variation of traffic volumes and the pollutant transport to receptors (i.e. exposed population) are both at a time scale of minutes. Many emission pollutants are chemically reactive, and fast reactions can take place at second-to-minute timescales to generate secondary pollutants. As a result, the variation of urban air quality can only be understood by considering the combined effects of fleet composition, traffic-produced turbulence, street and building geometries, and meteorological conditions in real time (i.e., a time scale of minutes). Fundamental scientific questions remain, for example, how to model real-time dynamics of the cause-and-effect process from transport activity to distribution of air pollution within street canyons, and how to account for both the heterogeneous distribution of air pollution across an urban street network and its variation in abundance with time. Answers to these questions are of utmost importance to better assess individual or population exposure and the potential risks on human health.To model this cause-and-effect chain, an integrated modelling approach is built upon bridging research on three complex systems: (i) dynamical changes in travel demand, (ii) vehicle emissions, and (iii) dispersion of air pollutants in street canyons. This approach is thus, not only able to provide detailed indication of air quality in street canyons, but also can identify street sections with high volumes of vulnerable travellers, such as pedestrians and cyclists. These together contribute to a better quantification of potential health risks that could impose on a population in question. Furthermore, travel demand changes respond to different policies, such as speed limit and road pricing in Clean Air Zones; the effects propagate from varying traffic to emissions and cause the change in pollution concentrations. It is through the causes and effects reflected by this model chain that transport policies aiming at risk mitigation can be evaluated in regards to their effectiveness in reducing adverse impacts on human health and environment.This project inherently deals with issues of Big Data, risk and mitigation in the integrated modelling approach. PhD students will receive related training from three aspects: 1) urban transport and air quality modelling and applications, 2) programming, computing and data handling, and 3) uncertainty and risk related analysis.