Summary
This project will be primarily focused on assessing the abatement of particulate matter and metal pollution by roadside hedgerows in Edinburgh.
Project background
Road vehicles and infrastructure are among the leading sources of hazardous pollutants, such as heavy metals and fine particulate matter (PM2.5) [1]. Road traffic contamination is of particular concern in urban areas, where high pollutant concentrations and population converge [2,3]. The latest reports show that roadside PM2.5 levels breached the World Health Organization guidelines in 62% of the monitored locations in the UK [4]. In this context, green infrastructure is receiving increasing attention for the broad array of ecosystem services it provides in urban settings, including air pollution alleviation [5]. Hedges are particularly important because they constitute a ground-level barrier where traffic-related emissions are greater and more detrimental to residents, pedestrians, and especially children [6]. However, there are several knowledge gaps that need to be addressed, specifically concerning real-life scenarios, PM-linked heavy metal contamination and remediation, and how the traits of different hedge species influence PM2.5 and metal sequestration. This doctoral project will be primarily centred on assessing the abatement of PM and metal pollution by roadside hedges in Edinburgh, where 3.7% of deaths in adults over the age of 25 are attributable to PM2.5 (ranked first in Scotland, ahead of Glasgow, Aberdeen, and Dundee) [4]. The work will gather comprehensive real-world data on PM (coarse, PM10; and fine, PM2.5) and metal concentrations in different hedge species, PM vertical and horizontal displacement, and the effect of roadside distance, traffic volume, speed limits, and meteorological parameters, amongst other factors.
Research questions
- Which hedge species are more effective in mitigating particulate matter and metal pollution?
- Which hedge traits are more desirable to ensure greater particulate matter capture?
- What is the effect of road distance, traffic volume, street canyons, meteorology, and seasonality on particulate matter and metal accumulation in urban hedges?
- Can urban hedgerows be used as reliable biomonitors for particulate matter and metal pollution?
Methodology
The project will be divided into several tasks appropriately designed to fit the studentship duration and achieve the proposed objectives. The doctoral candidate will be required to undertake significant field and laboratory work, relevant data acquisition, and statistical analysis. Publication of the results in peer-reviewed journals and presentation of the main findings in local and international scientific events are envisaged.
Year 1: Literature review; Experimental design consolidation; Training; Data acquisition; Fieldwork/sampling kickstart; Submission of review paper.
Year 2: Fieldwork/sampling; Sample analysis; Further data acquisition; Presentation of preliminary findings in conference.
Year 3: Sample analysis; Submission of research paper; Thesis write-up.
Training
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. The student will receive cross-disciplinary training (e.g., study of plant physiological and biochemical traits, soil and dusts physicochemical characterisation, elemental analysis via inductively coupled plasma mass spectrometry and X-ray fluorescence spectrometry). Pertinent training in statistical analysis, data management, scientific writing and research ethics and integrity is available at the Institute for Academic Development (IAD, University of Edinburgh).
Requirements
The ideal candidate will have an environmental science background (at least 2:1 honours degree or equivalent experience). Previous experience in plant, soil, and/or atmospheric science research is desirable. A valid UK driving licence would be advantageous, but not essential.
Application Process
Please apply through the E4 DTP website https://www.ed.ac.uk/e4-dtp/how-to-apply/application-process