Wong Yee Ka, Huang X H Hilda, Cheng Yuk Ying, Louie Peter K K, Yu Alfred L C, Tang Alice W Y, Chan Damgy H L, Yu Jian Zhen
Division of Environment & Sustainability, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong.
Department of Chemistry, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong.
Sci Total Environ. 2019 Jul 1;672:776-788. doi: 10.1016/j.scitotenv.2019.03.463. Epub 2019 Apr 1.
Vehicular emissions (VE) are among the major sources of airborne fine particulate matter (PM) in urban atmospheres, which adversely impact the environment and public health. Receptor models are widely used for estimating PM source contributions from VE (PM), but often give inconsistent results due to different modelling principles and assumptions. During December 2015-May 2017, we collected nine-months of hourly organic carbon (OC) and elemental carbon (EC) data, as well as 24-h PM speciation data including major species and organic tracers on select days from an ad hoc roadside site in Hong Kong. The weekday vs. holiday and diurnal variations of EC tracked closely with those of traffic flow volume, indicating EC as a reliable tracer for PM in this area. We applied multiple approaches to estimate the PM, including the EC-tracer method with the hourly OC-EC data, and chemical mass balance (CMB) and positive matrix factorization (PMF) analyses with the filter-based speciation data. Considering source profile variability, CMB gave the lowest PM estimate among the three approaches, possibly due to the degradation of organic markers (i.e., hopanes). The PM derived from the EC-tracer method and PMF were comparable, accounting for ~12% (3.4-4.0 μg/m) of PM averaged across 20 samples in both approaches, but a larger sample size is needed for a more robust PMF solution. The monthly PM derived from the EC-tracer method was in the range of 3.2-6.6 μg/m. The continuous measurement reveals a decreasing trend in PM throughout the entire sampling period, indicating the effectiveness of a recent vehicle control measures implemented by the Government in phasing out pre-Euro IV diesel commercial vehicles. This work implies that hourly OC-EC monitoring at strategically located spots is an effective way of monitoring vehicle control measures. It provides reasonable estimate of PM through comparing with other more sophisticated receptor models.
车辆排放是城市大气中空气细颗粒物（PM）的主要来源之一，对环境和公众健康产生不利影响。受体模型被广泛用于估算车辆排放对PM的源贡献，但由于建模原理和假设不同，结果往往不一致。在2015年12月至2017年5月期间，我们在香港一个临时路边站点收集了9个月的每小时有机碳（OC）和元素碳（EC）数据，以及特定日期的24小时PM形态数据，包括主要成分和有机示踪剂。工作日与节假日以及EC的日变化与交通流量密切相关，表明EC是该地区PM的可靠示踪剂。我们采用多种方法估算PM，包括利用每小时OC-EC数据的EC示踪法，以及利用基于滤膜的形态数据的化学质量平衡（CMB）和正定矩阵因子分解（PMF）分析。考虑到源谱的变异性，CMB在三种方法中给出的PM估算值最低，这可能是由于有机标志物（即藿烷）的降解。EC示踪法和PMF得出的PM相当，在两种方法中，20个样本的平均PM中分别占~12%（3.4-4.0μg/m），但需要更大的样本量才能得到更可靠的PMF结果。EC示踪法得出的每月PM范围为3.2-6.6μg/m。连续测量显示整个采样期间PM呈下降趋势，表明政府最近实施的淘汰欧IV前柴油商用车的车辆控制措施是有效的。这项工作表明，在战略位置进行每小时OC-EC监测是监测车辆控制措施的有效方法。通过与其他更复杂的受体模型比较，它能提供合理的PM估算值。
