On-road vehicular emissions contribute to the formation of fine particulate matter and ozone which can lead to increased adverse health outcomes near the emission source and downwind. In this study, we present a transportation-specific modeling platform utilizing the community multiscale air quality model (CMAQ) with the decoupled direct method (DDM) to estimate the air quality and health impacts of on-road vehicular emissions from five vehicles classes; light-duty autos, light-duty trucks (LDT), medium-duty trucks, heavy-duty trucks (HDT), and buses (BUS), on PM_2.5 and O₃ concentrations at a 12 12 kilometer scale for 12 states and Washington D.C. as well as four large metropolitan statistical areas in the Northeast and Mid-Atlantic U.S. in 2016. CMAQ-DDM allows for the quantification of sensitivities from individual precursor emissions (NO, SO₂, NH₃, volatile organic compounds, and PM_2.5) in each state to pollution levels and health effects in downwind states. In the region we considered, LDT are responsible for the most PM_2.5-attributable premature mortalities at 1234 with 46% and 26% of those mortalities from directly emitted primary particulate matter and NH₃, respectively; and O₃-attributable premature mortalities at 1129 with 80% of those mortalities from NO emissions. Based on a detailed source-receptor matrix of sensitivities with subsequent monetization of damages that we computed, we find that the largest damages-per-ton estimate is approximately $4 million per ton of directly emitted primary particulate matter from BUS in the New York-Newark-Jersey City metropolitan statistical area. We find that on-road vehicular NH₃ emissions are the second largest contributor to PM_2.5 concentrations and health impacts in the study region, and that reducing 1 ton of NH₃ emissions from LDT is ∼75 times and from HDT is ∼90 times greater in terms of damages reductions than a 1 ton reduction of NO. By quantifying the impacts by each combination of source region, vehicle class, and emissions precursor this study allows for a comprehensive understanding of the largest vehicular sources of air quality-related premature mortalities in a heavily populated part of the U.S. and can inform future policies aimed at reducing those impacts.

道路车辆排放有助于形成细颗粒物和臭氧，这可能导致排放源附近和下风向的不良健康后果增加。在这项研究中，我们提出了一个交通特定的建模平台，利用社区多尺度空气质量模型 (CMAQ) 和解耦直接方法 (DDM) 来估计五种车辆类别的道路车辆排放对空气质量和健康的影响；轻型汽车、轻型卡车 (LDT)、中型卡车、重型卡车 (HDT) 和公共汽车 (BUS)，PM _2.5和 O ₃浓度在2016年CMAQ-DDM 1212公里规模为12个州和华盛顿特区以及东北部和中大西洋美国四个大大都市统计区允许从单个初级排放灵敏度的定量（NO ，SO ₂、NH ₃、挥发性有机化合物和 PM _2.5）对顺风州的污染水平和健康影响。在我们考虑的区域中，LDT 是造成 PM _2.5归因于 1234 人过早死亡人数最多的原因，其中 46% 和 26% 的死亡人数分别来自直接排放的初级颗粒物和 NH ₃；和O ₃- 归因于 1129 的过早死亡，其中 80% 的死亡来自 NO排放。根据我们计算的详细的源-受体敏感性矩阵以及随后的损害货币化，我们发现最大的每吨损害估计约为 400 万美元/吨从纽约-纽瓦克的 BUS 直接排放的初级颗粒物-泽西市大都会统计区。我们发现，道路车辆的 NH ₃排放是研究区域PM _2.5浓度和健康影响的第二大贡献者，从 LDT减少 1 吨 NH ₃排放约 75 倍，从 HDT 减少约 90 倍在损害减少方面比减少 1 吨 NO. 通过量化源区域、车辆类别和排放前兆的每种组合的影响，这项研究可以全面了解美国人口稠密地区与空气质量相关的过早死亡的最大车辆来源，并可以为未来的政策提供信息减少这些影响。