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首页> 外文会议>Transportation Research Board Annual meeting >Modeling Roadway Link PM2.5 Emissions with Accurate Truck Activity Estimate for Regional-Level Transportation Conformity Analysis
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Modeling Roadway Link PM2.5 Emissions with Accurate Truck Activity Estimate for Regional-Level Transportation Conformity Analysis

机译：使用准确的卡车活动估计量建模道路链接PM2.5排放量，以进行区域级运输符合性分析

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The impact of fine particulate matter on public health has been a long concernedproblem. It has been proved that the primary mobile sources of fine particulate matter (PM2.5) arediesel trucks. In practice accurate roadway link-based modeling of the truck emissions remains abig challenge due to aggregated and unreliable truck activity data. The advanced emissionestimation model MOVES has been recommended by US Environmental Protection Agency forestimating the emission factors, but, supplying accurate and very detailed truck activity relatedinputs becomes another challenge. The daily truck traffic activity is usually not estimatedaccurately and cannot be disaggregated to hourly activity using the traditional methods. Toaddress this problem, two innovative econometric methods have been successfully enhanced inthis study to enable accurate truck activity based inputs for the emission estimation. The truckfactor spatial panel model (TFSP) and multinomial probit hourly VMT (MNP-HVMT) modelshave been improved and tested using the Greater Cincinnati area’s regional traffic data. Theapplication of those models indicates that using MOVES default input data underestimates theregional PM2.5 inventory. The proposed methodology also enables plotting the spatiotemporaldistribution of PM2.5 emissions in a subarea. Such an integrated method provides a very usefuldecision support tool for practitioners since they can also model PM2.5 emissions at a detailedlevel as required by project-level conformity analysis. The presented methodology is scalableand transferable and holds technical promise in its application for different regions and fordifferent pollutants.
机译：长期以来，细颗粒物对公共卫生的影响一直备受关注 问题。事实证明，细小颗粒物（PM2.5）的主要移动来源是 柴油卡车。在实践中，基于卡车排放的基于精确道路链接的建模仍然是 由于卡车活动数据的汇总和不可靠，这是一个巨大的挑战。先进的排放 估计模型MOVES已被美国环境保护署推荐用于 估算排放因子，但是提供与卡车活动相关的准确和非常详细的信息 输入成为另一个挑战。通常不估计卡车的日常交通活动 使用传统方法无法准确地将其分解为每小时活动。到 为了解决这个问题，在以下方面成功地增强了两种创新的计量经济学方法： 这项研究可以为排放估算提供基于卡车活动的准确输入。卡车 因子空间面板模型（TFSP）和每小时多项式概率VMT（MNP-HVMT）模型 使用大辛辛那提地区的区域交通数据进行了改进和测试。这 这些模型的应用表明使用MOVES的默认输入数据会低估 区域PM2.5库存。所提出的方法还可以绘制时空图 分区中PM2.5排放的分布。这样的集成方法提供了非常有用的 执业者的决策支持工具，因为他们还可以对PM2.5排放进行详细建模 项目级别的合格性分析要求的级别。提出的方法是可扩展的 并具有可转让性，并且在不同地区和地区的应用中均具有技术前景 不同的污染物。

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