Richmond-Bryant Jennifer, Owen R Chris, Graham Stephen, Snyder Michelle, McDow Stephen, Oakes Michelle, Kimbrough Sue
National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
Air Qual Atmos Health. 2017 Jun;10(5):611-625. doi: 10.1007/s11869-016-0455-7.
This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO) and oxides of nitrogen (NO) 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 NO and NO 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 (R = 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 NO 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 NO 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 NO/NO 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 NO/NO ratios were a driving factor in the on-road and downwind NO/NO ratios.
本文介绍了一种新的回归建模方法,该方法利用近道路监测网络的数据来估算道路上二氧化氮(NO₂)和氮氧化物(NOₓ)的浓度以及近道路空间梯度。2008年12月至2010年1月期间,在内华达州拉斯维加斯市,于距离15号州际公路20米、100米和300米处设置的三个监测器收集了现场数据。NO₂和NOₓ的测量值按1小时间隔进行积分,并与气象数据匹配。对几种数学变换进行了测试,以将污染物浓度与距道路的距离进行回归分析。发现logit-ln模型拟合效果最佳(R = 94.7%),并且还提供了符合实际物理情况的分布。该数学模型使用近道路监测器的数据来估算道路上的浓度以及近道路梯度,在该梯度上移动源污染物的浓度高于背景水平。道路上NO₂浓度的平均估计值和最大值分别为33 ppb和105 ppb。与夜间相比,早晨和傍晚的浓度梯度更陡,因为稳定条件会阻止混合。估计的道路上浓度在傍晚也最高。与冬季相比,夏季道路上和梯度NO₂浓度的中位数估计值较低,且夏季梯度更陡,因为夏季一天中对流混合发生的时间更长。与平行风相比,垂直于道路的风的道路上浓度估计值更高,并且在中性到不稳定的大气条件下的大气稳定性情况下也是如此。与稳定条件相比,中性到不稳定条件下以及与垂直风相比平行风条件下,随着距道路距离增加的浓度梯度估计更陡。NO₂/NOₓ比率的回归分析得出道路上的比率范围为0.25至0.35,大大高于预期的尾气排放比率。比率结果还表明,背景NO₂/NOₓ比率的昼夜循环是道路上和下风向NO₂/NOₓ比率的驱动因素。