a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA.
b Atmospheric Sciences Program, Department of Physics , University of Nevada , Reno, Reno, NV, USA.
J Air Waste Manag Assoc. 2019 Apr;69(4):402-414. doi: 10.1080/10962247.2018.1532468. Epub 2019 Mar 1.
Motor vehicles are major sources of fine particulate matter (PM), and the PM from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.
机动车是细颗粒物（PM）的主要来源，移动车辆产生的 PM 与不良健康影响有关。传统的基于受体模型的源影响估计方法受到观测数据的可用性限制。为了更好地估计 PM 的时间和空间分辨率的移动源影响，我们开发了一种基于使用元素碳（EC）、一氧化碳（CO）和氮氧化物（NO）测量作为移动源影响指标的方法。我们从三个方面扩展了原始的综合移动源指标（IMSI）方法。首先，我们通过应用一种融合化学输送模型（社区多尺度空气质量模型[CMAQ]）模拟和观测的方法，生成了空间分辨率的指标，该方法可用于估计 4 公里分辨率下的 24 小时平均 EC、CO 和 NO 浓度。其次，我们在 IMSI 公式中使用了空间分辨率的排放量而不是县一级的排放量。第三，我们对无量纲指标进行了空间校准，以校准由受体模型化学质量平衡（CMB）估计的每年平均移动源影响。2002 年至 2008 年，我们以 12 公里的分辨率和 2008 年至 2010 年的 4 公里分辨率，对格鲁吉亚的 PM 进行了每日总移动源影响以及单独的汽油和柴油车辆影响的估计。总移动源和单独车辆源的影响与每日 CMB 结果非常吻合，具有较高的时间相关性（例如，在九个地点的 4 公里分辨率下，总移动源的相关系数 R 范围从 0.59 到 0.88）。与基于观测的 CMB 估计相比，总移动源的影响比单独的汽油和柴油源具有更高的相关性和更低的误差。总的来说，改进后的方法提供了类似于基于观测的估计的空间分辨率移动源影响，可用于改善健康影响评估。意义：基于综合移动源指标方法开发了一种方法，用于估计时空 PM 移动源影响。该方法采用三种空气污染物浓度场，可在 4 公里和 12 公里分辨率下进行模拟，并使用 PM 源分配建模结果进行校准，以生成格鲁吉亚的每日移动源影响。估计的源影响可用于交通污染和健康调查。
