Targeted green infrastructure (GI) is a promising passive control system for air pollution. However, there is a lack of clear guidance regarding the optimal configuration and plant composition of GI under divergent contexts and considering different spatial scales. The core objectives of this PhD project were: (i) to critically evaluate the literature and identify effective GI design principles for ambient air quality; (ii) to develop a prototype GI design framework and user-directed decision support tool (DST); (iii) to investigate gaps in the literature regarding the efficacy of vegetation barriers; (iv) to investigate potentially viable plant species for air pollution abatement; and (v) to revise the GI design framework and DST to reflect research findings. This iterative process began with a literature review, outcomes of which included a trait-based matrix of 61 potentially advantageous tree species and a supplementary plant selection system. These outcomes supported the development of a GI design framework, which was computerised as a user-friendly DST (HedgeDATE: Hedge Design for Abatement of Traffic Emissions) that uses input data (e.g. planting space) to generate output recommendations (e.g. plant species). A public workshop provided user feedback on the HedgeDATE prototype, to support its development. An extensive field campaign was then undertaken to validate assumptions in the model, including the impacts of foliage longevity and barrier porosity across seasons. This was followed by another extensive study, which investigated the influences of evergreen leaf types on net pollutant deposition and wash-off across rainfall events. Finally, the design framework was revised in accordance with findings, and HedgeDATE was updated to reflect the revised design framework as well as further user feedback. This project has thereby produced a novel GI design tool, for public engagement and practitioner guidance, for the benefit of improved urban air quality and reduced human exposure to air pollution.