Enhancing air pollution mitigation by urban green infrastructure

Appropriate green infrastructure (GI) is one of several promising passive control systems for air pollution. However, there is a lack of clear guidance regarding the optimal configuration and plant composition of GI for improved air quality under divergent physical and environmental contexts and considering different spatial scales. This PhD project aims to address this gap. A comprehensive literature review was conducted, 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 tool (HedgeDATE: Hedge Design for the Abatement of Traffic Emissions) that uses input data (e.g. distance from road) to generate output recommendations (e.g. plant species). A public workshop generated user feedback on the utility, functionality, and interface of HedgeDATE. An extensive field campaign was undertaken to validate assumptions in the model concerning barrier efficacy and clarify the impact of foliage longevity, leaf phenology, and barrier porosity across seasons. The next step is to investigate individual plant species, including analysis of leaf traits for ultrafine particle deposition. Finally, the GI design framework will be revised in accordance with findings, and HedgeDATE will be updated to reflect feedback collected during the intervening year. This project thereby intends to create a novel, robust, and pragmatic decision-support tool for GI practitioners and decision-makers, for the benefit of improved urban air quality and reduced human exposure to air pollution.