Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts 02215, USA.
Environ Health Perspect. 2011 Jun;119(6):852-9. doi: 10.1289/ehp.1002519. Epub 2011 Jan 24.
The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion.
We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time.
In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources.
We observed spatial heterogeneity of both UFP and PM2.5 concentrations. A variety of statistical methods consistently found a 15-20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For PM2.5, temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways.
The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.
交通排放与移动源空气污染物浓度之间的关系在空间和时间上具有高度可变性，因此难以准确建模，尤其是在地形复杂的城市环境中。基于回归的方法使用连续的实时移动测量数据，可能能够描述与交通相关的污染物浓度的时空变异性，但需要以物理可解释的方式将时间变化的气象和源强纳入其中的方法。
我们开发了一种统计模型，以评估气象和交通对移动源空气污染物在空间和时间上的测量浓度的联合影响。
在这项研究中，我们在纽约州布鲁克林的威廉斯堡社区（美国）连续测量了与交通有关的空气污染物，该社区受到一座大型桥梁和主要高速公路上的交通影响。使用移动监测协议，以一分钟的平均浓度测量了超细颗粒物（UFP）、细颗粒物（≤2.5μm 空气动力学直径（PM2.5））和颗粒结合多环芳烃。使用回归建模方法来量化气象、交通量和靠近主要道路对污染物浓度的影响。这些模型采用了捕获空间变异性、长期和短期趋势以及多个源的技术。
我们观察到 UFP 和 PM2.5 浓度的空间异质性。各种统计方法一致发现，在两条主要道路的每一条道路的前 100 米范围内，UFP 浓度降低了 15-20%。对于 PM2.5，时间变异性主导了空间变异性，但我们观察到浓度从道路上呈一致的线性下降。
移动监测和回归分析的结合能够量化相对于背景的本地源贡献，同时考虑到物理可解释的参数。我们的结果提供了对城市暴露梯度的深入了解。
