The co-development of HedgeDATE, a public engagement and decision support tool for air pollution exposure mitigation by green infrastructure. (December 2021) - Barwise, Yendle; Kumar, Prashant; Tiwari, Arvind; Rafi-Butt, Fahad; McNabola, Aonghus; Cole, Stuart; Field, Benjamin C.T.; Fuller, Justine; Mendis, Jeewaka; Wyles, Kayleigh J.
Highlights: A decision support tool for GI design for improved air quality is developed. Data from the literature is synthesised to develop the underlying model. Pollutant exposure and impacts of GI can be predicted for open road environments. End-user feedback via a public workshop validates the concept and prototype. Development of the prototype sets the foundation for more advanced tools. Abstract: There is a lack of clear guidance regarding the optimal configuration and plant composition of green infrastructure (GI) for improved air quality at local scale. This study aimed to co-develop (i.e. with feedback from end-users) a public engagement and decision support tool, to facilitate effective GI design and management for air pollution abatement. The underlying model uses user-directed input data (e.g. road type) to generate output recommendations (e.g. plant species) and pollution reduction projections. This model was computerised as a user-friendly tool named HedgeDATE (Hedge Design for Abatement of Traffic Emissions). A workshop generated feedback on HedgeDATE, which we also discuss. We found that data from the literature can be synthesised to predict air pollutant exposure and abatement in open road environments. However, further research is required to describe pollutant decay profiles under more diverse roadside scenarios (e.g. split-level terrain) and to strengthen projections. Workshop findings validated the HedgeDATE concept and indicated scope for uptake. End-user feedback was generally positive, although potential improvements were identified. For HedgeDATE to be made relevant for practitioners and decision-makers, future iterations will require enhanced applicability and functionality. This work sets the foundation for the development of advanced GI design tools for reduced pollution exposure. Graphical abstract: Image, graphical abstract