Dipartimento di Scienze Agrarie, Forestali e Alimentari (DISAFA), Università Degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, TO, Italy; Institute of Environmental Assessment and Water Research (IDÆA), Spanish National Research Council (CSIC), C/ Jordi Girona 18-26, 08034 Barcelona, Spain.
Dipartimento di Scienze Agrarie, Forestali e Alimentari (DISAFA), Università Degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, TO, Italy.
Environ Pollut. 2018 Jun;237:713-720. doi: 10.1016/j.envpol.2017.10.115. Epub 2017 Nov 9.
The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables. We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.
非尾气排放源（即道路灰尘、轮胎磨损、道路磨损和刹车片磨损颗粒）对城市空气质量的影响越来越大。其中，道路灰尘再悬浮通常对 PM 浓度的影响最大，但对其时空可变性的研究和建模却很少。一些最近的研究试图观察和描述时间变化，但由于它受到交通和气象的驱动，排放的季节性仍然存在不确定性。对空间可变性的认识差距要大得多，因为有几个因素被认为是导致道路灰尘积聚的原因：路面特性、交通强度和速度、车队组成、靠近交通信号灯，以及外部来源的存在。然而，作为这些变量的函数，还没有可用的参数化。我们在两个气候和交通条件不同的城市（巴塞罗那和都灵）研究了小于 10μm 的移动道路灰尘（MF10），以探索 MF10 的季节性变化以及 MF10 与站点特征（路面宏观纹理、交通强度和靠近制动区）之间的关系。此外，我们还提供了波河谷在夏季和冬季条件下的首次排放因子估计值。我们的研究结果表明，MF10 与宏观纹理、交通强度和距最近制动区的距离之间存在良好的反比关系。我们还发现道路灰尘排放具有明显的季节性效应，夏季排放较高，可能是由于路面水分较少。这些结果使得可以基于上述数据建立一个简单的经验模式，预测最大灰尘负荷，从而预测排放潜力。为了考虑天气和路面水分的影响，需要使用考虑天气和路面水分的方法对该模型进行扩展。这可以大大改进基于活动的排放清单，以用于排放的空间分配和空气质量管理，从而选择那些排放较高的道路采取缓解措施。
