Road traffic air and noise pollution exposure assessment - A review of tools and techniques

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures. (C) 2018 Elsevier B.V. All rights reserved.