Improving desk-based methods for quantifying and enhancing air pollution mitigation by green infrastructure at different spatial scales

Air pollution is the greatest environmental hazard to human health, and of particular concern in urban areas, where elevated pollutant concentrations and potential sufferers converge. Green infrastructure (GI) is known to be an effective method of carbon sequestration, thereby helping to tackle the intertwined issue of climate change. Pollutants may also be directly mitigated through various physical, biological and chemical interactions between GI and the surrounding atmosphere.
A mounting body of evidence supports the use of GI as a passive control system for urban air pollution. Targeted design and management of GI can maximise its air pollution mitigation potential. However, the relationship between GI and air quality is complex, and the literature regarding vegetation species selection for enhanced air pollution abatement under specific urban contexts is not clear-cut. At local scale, GI can act as a barrier between air pollutants and pedestrians as well as a means to capture or disperse ambient pollutants. Efficient GI design at local scale, including appropriate species selection, requires an appreciation of site-specific physical and environmental conditions. Resource-effective GI design and implementation at city scale and above, however, requires efficient assessment methodologies and broadly applicable principles.
Research findings to date illustrate that there is an urgent need for a pragmatic framework that facilitates resource-effective GI management for improved urban air quality. The founding objective of this PhD project is to build upon results from the student's Masters project, in which it was found that a compromise may exist between high-resolution, on-the-ground assessments of air pollution mitigation by existing GI and lower-resolution, desk-based methodologies that utilise remote sensing. We intend to extend this novel, desk-based approach to include design recommendations that are generated according to context-specific input criteria.
The aim of this PhD project is to develop a GI design framework for enhanced urban air pollution mitigation, which will involve a novel methodology for the generation of site-specific recommendations, incorporating results from field experimentation, remote sensing, and citizen science work.