Olvera Hector A, Jimenez Omar, Provencio-Vasquez Elias
Center for Environmental Resource Management, University of Texas at El Paso, 500 W. University Ave., El Paso TX 79968, USA ; School of Nursing, University of Texas at El Paso, 500 W. University Ave., EL Paso TX 79968, USA ; Hispanic Health Disparities Research Center, University of Texas at El Paso, 500 W. University Ave., EL Paso TX 79968, USA.
Department of Civil Engineering, University of Texas at El Paso, 500 W. University Ave., El Paso TX 79968, USA.
Atmos Environ (1994). 2014 Dec 1;98:581-590. doi: 10.1016/j.atmosenv.2014.09.030.
Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 10 particles/cc and 1.3 × 10 particles/cc, and averaged 2.5 × 10 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 10 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available.
利用大气扩散模型和土地利用回归模型估算了高速公路周边的年平均日颗粒物数量浓度。扩散模型用于估算得克萨斯州埃尔帕索市98个位于10号州际公路沿线地点的颗粒物浓度。该模型采用年平均风速和年平均日交通流量作为输入参数。以车辆行驶公里数为预测变量的土地利用回归模型用于估算远离高速公路的本地背景浓度，以调整高速公路附近的浓度估算值。估算的颗粒物数量浓度在9.8×10个/立方厘米至1.3×10个/立方厘米之间，平均为2.5×10个/立方厘米（标准误差421.0）。将估算值与该地区沿10号州际公路七个地点测量的值进行了比较。平均相对误差为6%，各地点的误差在-1%至-13%之间。在交通流量最低的半农村地点观察到最大偏差为-13%。城市地点的平均偏差为5%。估算的准确性主要取决于排放因子和对本地背景条件的调整。基于文献中提出的值并结合本地测量进行调整，得出排放因子为1.63×10个/车辆公里。两种建模技术的结合确保了颗粒物数量浓度估算能够反映高速公路和干道交通的影响。本研究中使用的两种建模技术的性能和经济方面表明，在交通和气象数据容易获取的城市地区，沿主要道路生成颗粒物浓度面是可行的。
