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Estimation of on-road NO2 concentrations NO2/NOX ratios and related roadway gradients from near-road monitoring data

机译:从近路监测数据估算道路上的NO2浓度NO2 / NOX比率以及相关的道路坡度

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摘要

This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO2) and oxides of nitrogen (NOX) concentrations and near-road spatial gradients using data from a near-road monitoring network. Field data were collected in Las Vegas, NV at three monitors sited 20, 100, and 300 m from Interstate-15 between December, 2008 and January, 2010. Measurements of NO2 and NOX were integrated over 1-hour intervals and matched with meteorological data. Several mathematical transformations were tested for regressing pollutant concentrations against distance from the roadway. A logit-ln model was found to have the best fit (R2 = 94.7%) and also provided a physically realistic profile. The mathematical model used data from the near-road monitors to estimate on-road concentrations and the near-road gradient over which mobile source pollutants have concentrations elevated above background levels. Average and maximum on-road NO2 concentration estimates were 33 ppb and 105 ppb, respectively. Concentration gradients were steeper in the morning and late afternoon compared with overnight when stable conditions preclude mixing. Estimated on-road concentrations were also highest in the late afternoon. Median estimated on-road and gradient NO2 concentrations were lower during summer compared with winter, with a steeper gradient during the summer, when convective mixing occurs during a longer portion of the day On-road concentration estimates were higher for winds perpendicular to the road compared with parallel winds and for atmospheric stability with neutral-to-unstable atmospheric conditions. The concentration gradient with increasing distance from the road was estimated to be sharper for neutral-to-unstable conditions when compared with stable conditions and for parallel wind conditions compared with perpendicular winds. A regression of the NO2/NOX ratios yielded on-road ratios ranging from 0.25 to 0.35, substantially higher than the anticipated tail-pipe emissions ratios. The results from the ratios also showed that the diurnal cycle of the background NO2/NOX ratios were a driving factor in the on-road and downwind NO2/NOX ratios.
机译:本文介绍了一种新的回归建模方法,可使用来自近路监控网络的数据估算出道路上的二氧化氮(NO2)和氮氧化物(NOX)的浓度以及近距离的空间梯度。在2008年12月至2010年1月之间,从内华达州拉斯维加斯的15号州际公路上20、100和300 m处的三台监测仪收集了现场数据。NO2和NOX的测量值在1小时的间隔内进行了积分,并与气象数据相匹配。测试了一些数学转换,以相对于距道路的距离回归污染物浓度。发现logit-ln模型具有最佳拟合度(R 2 = 94.7%),并且还提供了物理上逼真的轮廓。该数学模型使用了来自附近道路监控器的数据来估算道路上的浓度和道路上的移动污染物浓度高于背景水平的道路上的梯度。道路上平均NO2浓度最高和最高浓度分别为33 ppb和105 ppb。与之相比,在稳定条件下无法进行混合的情况下,与夜间相比,早晨和下午的浓度梯度要陡峭。估计下午晚些时候的道路上浓度也最高。与冬季相比,夏季估计的道路和梯度NO2浓度中值比冬季要低,夏季则是在一天的较长时间内发生对流混合的情况下,梯度较陡,与垂直于道路的风相比,道路上的浓度估计要高具有平行风,并在中性到不稳定的大气条件下保持大气稳定。与中风到不稳定的条件相比,与中风到不稳定条件的比较,与垂直风相比,在平行风的条件下,随着距道路距离的增加,浓度梯度估计会更陡峭。 NO2 / NOX比率的回归得出道路行驶比率在0.25到0.35之间,大大高于预期的尾管排放比率。这些比率的结果还表明,本底NO2 / NOX比率的昼夜循环是公路和顺风NO2 / NOX比率的驱动因素。

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