Vehicular emissions on main roads in Makkah, Saudi Arabia—a dispersion modelling study
Vehicular emissions on main roads in Makkah, Saudi Arabia—a dispersion modelling study
复制标题
沙特阿拉伯麦加主要道路上的车辆排放——扩散模型研究
DOI：
--
复制
发表时间：
2018
影响因子：
--
通讯作者：
T. Habeebullah
中科院分区：
文献类型：
--
作者：
S. Munir;T. Habeebullah
颗粒物 (PM) 是麦加主要关注的大气污染物；因此，需要对其进行有效的监测、建模和管理。本研究采用大气扩散建模系统（ADMS）-城市模型，这是一种著名的大气扩散建模系统。多年来（2007 年至 2012 年）斋戒月和朝觐期间在麦加的 6 条主要道路上收集的交通数据。数据分析显示，麦加道路上平均有83%的轻型车辆和17%的重型车辆；然而，这一百分比在空间和时间上略有变化。 2007年至2012年，6条道路的车辆数量呈现增加趋势。利用车速、车辆类型和数量等交通特征来计算PM10和PM2.5的排放量。除了污染物排放外，ADMS-Urban 还需要风速和风向、温度、相对湿度、云量和边界层高度等气象参数。以三种不同的形式预测浓度：(a) 六种受体，(b) 昼夜周期，(c) 整个麦加市的等高线图。将模型浓度与马斯法拉赫和气象与环境总统府 (PME) 监测站观测到的浓度进行了比较。 ADMS-Urban 低估了 PM10 和 PM2.5 浓度；然而，PME 站（约 73%）的差异比 Masfalah 站（约 24%）大得多。讨论了差异的原因，并计算了各种统计指标来评估模型性能。需要更多的排放数据来提高模型的性能并最大限度地减少观测浓度和预测浓度之间的差距。
Particulate matter (PM) is the atmospheric pollutant of main concern in Makkah; therefore, there is a need for its effective monitoring, modelling and management. In this study, Atmospheric Dispersion Modelling System (ADMS)-Urban model is employed, which is a well-known atmospheric dispersion modelling system. Traffic data were collected for several years (2007–2012) on six main roads in Makkah during the months of Ramadhan and Hajj. Data analysis showed that on average, there were 83% light-duty vehicles and 17% heavy-duty vehicles on Makkah roads; however, this percentage slightly varied both spatially and temporally. The number of vehicles demonstrated increasing trend from 2007 to 2012 on the six roads. Traffic characteristics, such as vehicle speed, vehicle type and number, were used to calculate the emissions of PM10 and PM2.5. Along with pollutant emissions, ADMS-Urban requires meteorological parameters such as wind speed and direction, temperature, relative humidity, cloud cover and boundary layer height. Concentrations were predicted in three different forms: (a) for six receptors, (b) as diurnal cycles and (c) as contour maps for the whole Makkah City. Modelled concentrations were compared with the observed concentrations at Masfalah and Presidency of Meteorology and Environment (PME) monitoring stations. ADMS-Urban underestimated both PM10 and PM2.5 concentrations; however, the difference was much greater at the PME (about 73%) than at the Masfalah station (about 24%). Reasons for the discrepancies are discussed, and various statistical metrics are calculated to assess the model performance. More emission data are required to improve the performance of the model and minimise the gap between observed and predicted concentrations.