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Analysis of local traffic-air quality in northwest Indiana adjacent to the Borman Expressway.

机译:印第安纳州西北部毗邻博曼高速公路的当地交通空气质量分析。

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The purpose of the study was to quantify the impact of traffic conditions such as free flow and congestions on local air quality. The Borman Expressway in Northwest Indiana is considered a test-bed for this research due to the high volume of class-9-truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve the traffic management along the highway stretch.; An empirical Traffic-Air-Quality model (TAQ model) was developed to estimate the PM2.5 emission factors (g/mi) based solely on the measured traffic parameters such as average speed, average acceleration and truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the EPA-PART5 model. During congestions (speeds 30 mi/h), the TAQ model, on average, over predicted the measured values by 1.2 folds, in comparison to the 4.0 folds under predictions of the EPA-PART5 model. On the other hand, during free flow (speeds > 50 mi/h), the TAQ model, on average, over predicted the measured values by 1.5 folds.; The measured values as well as the TAQ model have shown that the PM 2.5 emission factors change more aggressively with respect to the average truck speeds on the Borman Expressway more than the EPA-PART5 model predictions which assume constant emission values with respect to speed. On average a 74% improvement in air quality is expected when the average Borman speed range is improved from 30 mi/h to >50 mi/h (based on reduction of mass emitted per mile [g/mi]).; An autoregressive (AR) model was also developed to forecast hourly averaged emission factors using the TAQ model. The AR-TAQ model has shown the ability to predict PM2.5 emission factors based on traffic parameters.
机译:这项研究的目的是量化交通状况(如自由流和拥堵)对当地空气质量的影响。印第安纳州西北部的Borman高速公路被认为是该研究的试验台,因为其上行驶着大量的9类卡车交通,并且现有并持续安装了智能交通系统(ITS)以改善交通状况。高速公路沿线的交通管理。开发了一个经验性的交通空气质量模型(TAQ模型)来仅基于测得的交通参数(例如平均速度,平均加速度和卡车密度)来估计PM2.5排放因子(g / mi)。与EPA-PART5模型相比,TAQ模型显示出更好的预测,与排放因子测量值更匹配。在拥堵期间(速度<30 mi / h),平均而言,TAQ模型比EPA-PART5模型预测的4.0倍高出预测值1.2倍。另一方面,在自由流动期间(速度> 50 mi / h),TAQ模型平均将测得的值高出预测的1.5倍。测量值以及TAQ模型表明,相对于假设速度恒定排放值的EPA-PART5模型预测,PM 2.5排放因子相对于Borman Expressway上卡车的平均速度具有更大的变化。当平均Borman速度范围从<30 mi / h提高到> 50 mi / h(基于每英里排放质量的减少[g / mi])时,平均空气质量有望提高74%。还开发了自回归(AR)模型,以使用TAQ模型预测每小时平均排放因子。 AR-TAQ模型显示了根据交通参数预测PM2.5排放因子的能力。

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