Spatial PM2.5 mobile source impacts using a calibrated indicator method.
Spatial PM2.5 mobile source impacts using a calibrated indicator method.
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使用校准指标方法进行空间 PM2.5 移动源影响。
DOI：
10.1080/10962247.2018.1532468
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Hu,Yongtao
中科院分区：
文献类型：
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作者：
Zhai,Xinxin;Mulholland,JamesA;Friberg,MarielD;Holmes,HeatherA;Russell,ArmisteadG;Hu,Yongtao
机动车辆是细颗粒物 (PM2.5) 的主要来源，而移动车辆产生的 PM2.5 会对健康产生不利影响。采用受体模型来估计源影响的传统方法受到观测数据可用性的限制。为了更好地估计时间和空间上解析的移动源对 PM2.5 的影响，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 (NOx) 测量作为移动源影响指标的方法。我们在三个方面对原有的综合移动源指示符（IMSI）方法进行了扩展。首先，我们通过应用一种融合化学物质迁移模型（社区多尺度空气质量模型 [CMAQ]）模拟和观测的方法，使用 24 小时 EC、CO 和 NOx 平均浓度（以 4 公里分辨率估算）生成空间分辨指标。其次，我们在 IMSI 公式中使用空间分辨排放而不是县级排放。第三，我们根据受体模型化学质量平衡（CMB）估计的年平均移动源影响对无单位指标进行了空间校准。乔治亚州 2002 年至 2008 年期间移动源对 PM2.5 的每日总影响以及单独的汽油和柴油车辆影响估计为 12 公里分辨率，2008 年至 2010 年为 4 公里分辨率。总移动源和单独车辆源影响与每日 CMB 结果相比较，具有较高的时间相关性（例如，对于 9 个地点分辨率为 4 公里的总移动源，R 范围为 0.59 至 0.88）。与基于观测的 CMB 估计相比，移动源总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。总体而言，增强方法提供了与基于观测的估计类似的空间解析移动源影响，可用于改进对健康影响的评估。启示：基于集成移动源指示符方法开发了一种方法来估计时空 PM2.5 移动源影响。该方法采用了三个空气污染物浓度场，可以在 4 公里和 12 公里分辨率下轻松模拟，并使用 PM2.5 源分配建模结果进行校准，以生成佐治亚州的每日移动源影响。估计的源影响可用于交通污染和健康调查。
Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5from 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 PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) 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 NOxestimated 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 PM2.5, 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.,Rranges 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 PM2.5mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5source 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.