Gately Conor K, Hutyra Lucy R, Peterson Scott, Sue Wing Ian
Boston University, Department of Earth and Environment, 685 Commonwealth Avenue, Boston, MA, 02215, United States; Harvard University, Earth and Planetary Sciences Department, 20 Oxford Street, Cambridge, MA, 02138, United States.
Boston University, Department of Earth and Environment, 685 Commonwealth Avenue, Boston, MA, 02215, United States.
Environ Pollut. 2017 Oct;229:496-504. doi: 10.1016/j.envpol.2017.05.091. Epub 2017 Jun 30.
On-road emissions vary widely on time scales as short as minutes and length scales as short as tens of meters. Detailed data on emissions at these scales are a prerequisite to accurately quantifying ambient pollution concentrations and identifying hotspots of human exposure within urban areas. We construct a highly resolved inventory of hourly fluxes of CO, NO, NO, PM and CO from road vehicles on 280,000 road segments in eastern Massachusetts for the year 2012. Our inventory integrates a large database of hourly vehicle speeds derived from mobile phone and vehicle GPS data with multiple regional datasets of vehicle flows, fleet characteristics, and local meteorology. We quantify the 'excess' emissions from traffic congestion, finding modest congestion enhancement (3-6%) at regional scales, but hundreds of local hotspots with highly elevated annual emissions (up to 75% for individual roadways in key corridors). Congestion-driven reductions in vehicle fuel economy necessitated 'excess' consumption of 113 million gallons of motor fuel, worth ∼ $415M, but this accounted for only 3.5% of the total fuel consumed in Massachusetts, as over 80% of vehicle travel occurs in uncongested conditions. Across our study domain, emissions are highly spatially concentrated, with 70% of pollution originating from only 10% of the roads. The 2011 EPA National Emissions Inventory (NEI) understates our aggregate emissions of NO, PM, and CO by 46%, 38%, and 18%, respectively. However, CO emissions agree within 5% for the two inventories, suggesting that the large biases in NO and PM emissions arise from differences in estimates of diesel vehicle activity. By providing fine-scale information on local emission hotspots and regional emissions patterns, our inventory framework supports targeted traffic interventions, transparent benchmarking, and improvements in overall urban air quality.
道路排放物在短至几分钟的时间尺度和短至几十米的长度尺度上变化很大。这些尺度下的详细排放数据是准确量化环境污染浓度以及识别城市区域内人类暴露热点的前提条件。我们构建了2012年马萨诸塞州东部280,000条道路路段上路车辆每小时CO、NO、NO₂、PM₂.₅和CO₂通量的高分辨率清单。我们的清单将来自手机和车辆GPS数据的每小时车速大型数据库与车辆流量、车队特征和当地气象的多个区域数据集进行了整合。我们对交通拥堵产生的“额外”排放进行了量化，发现在区域尺度上拥堵增强幅度较小（3%-6%），但有数百个局部热点区域年排放量大幅升高（关键走廊中个别道路高达75%）。拥堵导致车辆燃油经济性下降，从而造成了1.13亿加仑汽车燃料的“额外”消耗，价值约4.15亿美元，但这仅占马萨诸塞州总燃料消耗的3.5%，因为超过80%的车辆行驶处于非拥堵状态。在我们的研究区域内，排放物在空间上高度集中，70%的污染仅来自10%的道路。2011年美国环境保护局国家排放清单（NEI）分别低估了我们的NO、PM₂.₅和CO₂总排放量46%、38%和18%。然而，两种清单的CO₂排放量相差在5%以内，这表明NO和PM₂.₅排放的巨大偏差源于柴油车活动估计的差异。通过提供有关局部排放热点和区域排放模式的精细尺度信息，我们的清单框架支持有针对性的交通干预、透明的基准测试以及整体城市空气质量的改善。
