耦合交通流-机动车排放-PM2.5浓度的非线性响应数值模型研究

As the pollution patterns in many China's mega cities obviously present a mix of coal-and vehicle-based air pollution, vehicle emission control has become one of the most important tasks to improve urban and regional air quality. Increasingly frequent traffic congestion would further worsen vehicle emissions and urban air quality. However, there are rare studies on comprehensive relationships among traffic flow characteristics, vehicle emissions and air quality on a city-level scale. Considerable uncertainties still remain in this issue as we are lacking of a quantitative and efficient method to evaluate environmental benefits from traffic-related policies and measures. This study first collects typical real-world traffic flow data in representative mega cities in China, and establishes a traffic-flow database providing high-resolution spatial and temporal characteristics. On-road instantaneous emission rates and driving activity profiles will be collected under typical driving conditions, which will be analyzed to build a vehicle emission model integrated with dynamic traffic characteristics. A high-resolution vehicle emission inventory will be established on the basis of traffic flow data of road net. We will further apply a multi-scale air quality simulation system to analyze the contribution of PM2.5 concentrations from traffic emissions. In addition, this study will establish a numerical model for the multilevel nonlinear responses coupling traffic flow, vehicle emissions and PM2.5 concentration, which is able to quantitative evaluate benefits of vehicle emission mitigation and air quality improvement from traffic-related policies and measures. This study aims for a better understanding of relationships between traffic flow characteristics and PM2.5 concentration and to provide scientific support for further policy-making of vehicle emission control and air quality improvement in China's mega cities.

中国诸多大城市空气污染特征已表现为明显的煤烟-机动车复合型污染，减少机动车排放成为改善城市空气质量的重点。日益拥堵的交通会导致机动车排放和城市空气质量恶化，但有关路网交通流、机动车排放和大气中PM2.5浓度变化的综合研究比较缺乏。本研究通过大量交通流数据采集与调研，研究中国典型城市路网交通流的时空变化规律；完善和扩充典型车辆的不同工况特征的排放测试和行驶数据采样，建立耦合动态交通流特征和机动车排放的数学模型；编制基于路网动态交通流的高时空分辨率的城市机动车排放清单，定量解析机动车排放对城市空气中PM2.5的浓度贡献，并建立排放-浓度的快速响应模型。在此基础上，建立耦合交通流-机动车排放-PM2.5浓度的多重非线性响应数值模型，分析交通流特征与机动车排放和PM2.5浓度的定量响应规律，为定量评估交通流措施的减排和环境效益提供科学依据，并为今后机动车排放污染控制和空气质量改善决策提供科学支持。

中国诸多大城市空气污染特征已表现为明显的煤烟-机动车复合型污染，减少机动车排放成为改善城市空气质量的重点。本研究选择北京为典型城市，通过建立关键污染物交通排放耦合模型、开发城市动态高时空分辨率清单、研究多层嵌套的PM2.5浓度模拟技术，开发了“交通-排放-浓度”多级非线性数值模拟方法，为我国典型大城市的机动车排放控制和空气质量改善提供科学依据。.研究综合了路网高分辨率实时拥堵信息动态采集、关键道路车流加密观测和排放控制措施调研等方式，建立了北京高时空分辨率的路网交通特征数据库。并且系统分析了小客车限购、尾号限行和推广新能源车等交通措施对实际路网交通流影响的规律。.研究选取典型车辆开展各类交通流特征下的实际道路车载排放测试，分析获取典型车辆PM2.5、关键组分和重要前体物的排放和实际行驶工况特征的关系，建立了动态耦合交通流特征和污染物排放因子的模型。.通过开发耦合动态交通大数据和全路网交通流模拟的典型城市路网交通流模型，集成机动车排放因子模型，建立了典型城市高时空分辨路网排放清单。以2013年工作日为例，北京市全路网CO、THC、NOX和PM2.5四种主要污染物的排放量分别为：823.3吨、201.8吨、325.9吨和10.65吨，外地过境货车对NOX和PM2.5排放贡献突出。.研究实现了典型城市多尺度空气质量模型的嵌套集成：在“区域-城市”大尺度，应用改善的CMAQ模型评估了交通排放对PM2.5浓度贡献的季节、组分和时空特征。在“城市-街区”中小尺度，应用AERMOD模型评估了交通污染物扩散浓度的时空变化规律；并进一步识别了热点排放地区，通过加密观测验证了模型系统的准确性。.研究构建了未来年（2030）典型城市（北京）机动车交通发展和排放控制综合控制情景，定量评估城市各种交通控制对策的排放削减和空气质量改善效益。在此基础上，提出了中国典型城市交通排放控制的政策建议。