建立基于排放-交通二维动态大数据的重点区域高分辨率机动车排放清单

Rapidly soaring vehicle stock, high vehicle-use intensity and spatially concentrated traffic density have triggered substantial challenges to regional air quality in East China. Controlling on-road vehicle emissions has become an imperative task to improve regional and urban air quality, reduce the risk to public health and alleviate climate forcing. However, a single measurement technique cannot achieve a large-sized and precise sampling of all major air pollutants (including black carbon [BC], volatile organic carbons [VOCs], semi volatile organic compounds [SVOCs], and polycyclic aromatic hydrocarbons [PAHs], and their detailed speciation). Furthermore, traditional vehicle emission inventory techniques in China usually do not represent real-world traffic flow characteristics and in particular ignore the intercity traffic due to an increasingly unified regional transportation. This leads to a mismatch between a demand for more precise and dynamic source apportionment and higher resolution for a region. In this study, we select the Beijing-Tianjin-Hebei (i.e., Jing-Jin-Ji) region and the Yangtze River Delta region as two domains. First, we will develop an integrated on-road vehicle emission measurement system by combining the portable emissions measurement system (PEMS), chasing and remote sensing (RS) techniques, to measure and analyze large-sized samples of real-world vehicular emissions. The impacts of micro traffic conditions and technology configurations on each major air pollutant and its speciation will be carefully evaluated. Second, to fully understand the traffic characteristics of the overall regional (including intracity and intercity) road network, an intelligent traffic monitoring and big-data simulation system will be developed by integrating radio frequency identification (RFID), global position system (GPS) and the intercity traffic monitoring platforms. By combining the dynamic big-data of on-road emissions and traffic flows mentioned above, the high-resolution vehicle emission inventories for the two major regions will be developed, and the spatial and temporal characteristics of regional vehicle emissions will be fully analyzed. The outcomes will provide a solid basis for a better understanding of sources and control of regional air pollution complex, as well as fundamental methodology and tools/models for the development of national emission inventories with high-resolution.

中国机动车高速增长、高频使用和高度集中等特征，给东部区域的空气质量带来严重挑战。机动车排放控制已成为改善空气质量、降低健康风险和缓解气候效应的关键工作。但是，单一测试技术无法实现对机动车BC、VOCs/SVOCs和PAHs等污染物全组分大样本的准确测量；并且，传统清单与实际交通流脱节，忽略区域交通一体化导致的城际运输，已无法适应区域高分辨率动态源解析的需求。本研究选择京津冀和长三角作为研究区域，建立综合车载-跟车-遥感多种技术的道路排放测试系统，实现对机动车大样本排放数据的采集，深入分析微观工况和车辆技术对关键组分的影响规律；集成射频识别-全球定位系统-公路监测网等最先进的智能交通大数据系统，实现对区域全路网交通流的精细解析；基于此，耦合“排放-交通”动态大数据，建立两大区域高分辨率机动车排放清单。成果将为今后建立全国高分辨率排放清单提供方法学，并为深入理解区域复合污染成因提供重要支持。

中国机动车高速增长、高频使用和高度集中等特征，给东部区域的空气质量带来严重挑战。机动车排放控制已成为改善空气质量、降低健康风险和缓解气候效应的关键工作。但是，单一测试技术无法实现对机动车BC、VOCs和PAHs等污染物多组分大样本的准确测量；并且，传统清单与实际交通流脱节，忽略区域交通一体化导致的城际运输，已无法适应区域高分辨率动态源解析的需求。本研究选择京津冀、长三角及区域内典型城市为研究对象，建立车载-跟车-遥感多种技术的道路排放测试系统，建立大样本排放因子获取方法和优化组分离线分析方法，对车载-跟车-遥感的测试数据进行融合，深入分析微观工况和车辆技术等因素对关键组分的影响规律，相关测试成果为国五/六重型车标准及实际道路监管提供重要参数和技术方法；集成射频识别-全球定位系统-公路监测网等最先进的智能交通大数据系统，解析了典型车队出行特征，建立了典型城市高分辨率交通流数据库。基于随机森林等机器学习方法，融合“区域-城市”交通流特征，建立了融合区域交通流、道路信息和土地利用信息的宏观统计模型，刻画了区域交通流时空排放特征。完善基于出行特征的启动分配模块、外地车识别模块等“交通-排放”耦合模型，建立区域高分辨率排放清单，并通过“区域-城市-热点”等多尺度空气质量模型验证清单准确性。利用清单结果，评估了其对传统清单分配方式的改进，分析了外地车等典型车队排放浓度影响，评估了典型管控措施的交通排放改善效果。