摘要：Highlights•Urban areas are hot spots for exposure to traffic-related air pollution (TRAP).•A protocol is presented for our upcoming systematic evidence map (SEM).•We will identify urban-level policy interventions to reduce traffic emissions/TRAP.•We will explore and characterize the available evidence base.•An open access, interactive database will host extracted information.AbstractIntroductionCities are the world’s engines of economic growth, innovation, and social change, but they are also hot spots for human exposure to air pollution, mainly originating from road traffic. As the urban population continues to grow, a greater quantity of people risk exposure to traffic-related air pollution (TRAP), and therefore also risk adverse health effects. In many cities, there is scope for further improvement in air quality through targeted urban policy interventions. The objective of this protocol is to detail the methods that will be used for a systematic evidence map (SEM) which will identify and characterize the evidence on policy interventions that can be implemented at the urban-level to reduce traffic emissions and/or TRAP from on-road mobile sources, thus reducing human exposures and adverse health impacts.MethodsArticles will be searched for and selected based on a predetermined search strategy and eligibility criteria. A variety of databases will be searched for relevant articles published in English between January 1, 2000 and June 1, 2020 to encompass the interdisciplinary nature of this SEM, and articles will be stored and screened using Rayyan QCRI. Predetermined study characteristics will be extracted and coded from included studies in a Microsoft Excel sheet, which will serve as an open access, interactive database, and two authors will review the coded data for consistency. The database will be queryable, and various interactive charts, graphs, and maps will be created using Tableau Public for data visualization. The results of the evidence mapping will be detailed via narrative summary.ConclusionThis protocol serves to increase transparency of the SEM methods and provides an example for researchers pursuing future SEMs.
