公交出行车内可吸入颗粒物浓度时空演化机理研究

The share and length of trips using public transport is larger than other modes in many Chinese cities, and the particle pollution in urban public transit plays an important role in human health. The public can get the information of air quality that is measured by fixed-site urban monitoring stations, but have little information about the air quality in urban public transit. In regard to air pollution and its health effects, this study aims to investigate the mechanism of spatial-temporal variations in concentrations of in-vehicle inhalable particles during public transit. This study consists of three parts: (1) multisource data collection and spatial-temporal fusion; (2) explore the spatial-temporal patterns of in-vehicle particle concentration and its driven factors; and (3) simulate the dispersion of particle concentration inside public bus. This research develops a mobile-based approach to monitoring the travel environment, and implements GIS-based techniques to achieve the spatial-temporal fusion of multi-source data of public transit environment. Moreover, spatial-temporal data model and statistical methods are employed to explore the spatial-temporal patterns of in-vehicle particle concentration and its driven factors at in-cabin and urban scales. In addition, CFD technique is carried out to simulate the dispersion of particle concentration inside public bus. The findings of this research can contribute to decision-making support in improving the air quality in urban public transit，as well as the technical supports in assessment of human exposure to particle pollution.

我国城市公共交通出行分担率较高，公交车内环境对健康出行具有重要意义。城市固定监测站提供了开敞环境中的空气质量信息，但对公交出行过程中车内空气质量的关注不足。针对公交出行车内可吸入颗粒物污染的问题，本项目旨在探索公交出行车内可吸入颗粒物浓度的时空演化机理。研究内容主要包含三个方面：（1）面向公交出行环境的多源数据采集与时空关联融合；（2）（车内）颗粒物浓度时空演化特征及其驱动因素解析；（3）车厢三维空间颗粒物浓度扩散模拟。本研究提出基于移动观测的公交出行环境多源数据采集方法，并基于GIS平台实现多源数据时空关联融合；在此基础上，运用GIS时空数据模型与统计分析技术探索颗粒物浓度在多尺度的时空演化特征及其驱动因素，并利用CFD技术实现车厢三维空间内颗粒物浓度运动与分布的数值模拟。研究成果一方面可为相关机构营造公交健康出行环境提供决策依据，另一方面可为公交出行暴露风险评估等问题分析提供支持。

在城市居民通勤中，公共交通出行扮演着重要的角色，公交车内环境对健康出行具有重要意义。暴露于交通微环境中的可吸入颗粒物污染，对人体的健康有害。针对公交出行车内可吸入颗粒物污染的问题，本研究从三个方面探索了公交出行车内可吸入颗粒物浓度的时空演化机理，即面向公交出行环境的多源数据采集与时空关联融合、（车内）颗粒物浓度时空演化特征及其驱动因素解析、车厢三维空间颗粒物浓度扩散模拟。为此，本研究提出基于移动观测的公交出行环境多源数据采集方法，并基于GIS平台实现多源数据时空关联融合；在此基础上，运用数理统计与时空分析方法研究了公交出行过程中（车内）颗粒物浓度在车厢内、公交线路段两个空间尺度下的时空演化特征及其影响因素，并利用CFD (Computational Fluid Dynamics) 技术实现车厢三维空间内颗粒物浓度运动与分布的数值模拟。研究成果一方面可为相关机构营造公交健康出行环境提供决策依据，另一方面可为公交出行暴露风险评估等问题分析提供支持。