· You will join a large cross-university multidisciplinary research network working on air quality observations and modelling, health impact assessment and economic models, as part of a NERC funded major research projects – West Midlands Air Quality Improvement Programme (£5m) and UK Air Quality Supersite Triplet (UK-AQST, £1.3m)
· The contribution of different emission sources to air pollutants and greenhouse gases at Birmingham will be quantified simultaneously
· Hand-on training will be provided to the candidate to develop skills in advanced receptor modelling, novel machine learning techniques, and health and economic modelling, which will benefit future career of candidate whether in academia and public or private sectors
Air pollution is the single largest risk to human health, contributing to more deaths (7 million) than all other environmental risks combined (Landrigan et al., 2017). In the UK, air pollution causes up to 36,000 deaths per year and costs the economy £20 billion per year.
Air quality in the UK improved substantially in the past 20 years but the highly ambitious WHO air quality guidelines (AQGs) pose a huge challenge: Existing clean air actions will almost certainly be insufficient for pollution levels in the UK to comply with the AQGs. A key opportunity arises from emerging Net Zero that aim to tackle the increasing impacts of climate change – as many Net Zero policies, especially those which relate to fossil fuel combustion, will deliver significant AQ and health co-benefits (Figure 1) in addition to reducing greenhouse gases (GHGs). There is clear evidence that past climate actions have delivered significant benefits to air quality (Wang et al., 2022).
However - different net zero measures will have different impacts (scale, pollutant species) on air quality, which presents significant uncertainty in quantifying the air quality mediated benefits to health (Figure 1). Furthermore, not all climate actions will bring benefits to air quality. For example, the transition to electric vehicles has the potential to worsen urban ozone exposure (at least in the short term) (see Shi and Song et al., 2021) and tighter building insulation has the potential to worsen indoor air quality (Royal Society, 2021).
Therefore, we must take a balanced approach to evaluate the impacts of climate actions, by incorporating the benefits / disbenefits on air quality and health, which will help policymakers to better design net zero options.
To do this, we first need to better apportion the sources of air pollutants and greenhouse gases, from which we can prioritize the control of pollution sources with maximum benefits to both carbon reduction and air quality.
(1) Observations: UK Air Quality Supersite Triplets will be used to determine the concentration of both greenhouse gases (CO2 and CH4) and a large range of air pollutants (PM, SO2, NOx, O3, volatile organic compounds, speciated PM2.5) at a roadside, urban background and rural sites
(2) Source apportionment: receptor models will be used to apportion the sources of air pollutants and greenhouse gases (Wang et al., 2022); data from triple sites will significantly enhance source apportionment
(3) Machine learning: machine learning algorithms will be used to understand factors controlling temporal and spatial variation of air pollutants and greenhouse gases (Song et al., 2022).
(4) Health and economic modelling: Health burdens and economic cost due to air pollution of from different sources will be estimated
(5) Systemic assessment: a coordinated strategy to maximize the benefits of clean air and carbon policies to air quality / health and climate will be proposed
Training and skills:
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
Student will be trained to use R programme, the commonly used data analysis software package such as OpenAir, machine learning techniques (Song et al., 2022), and health and economic models. Hand-on training by research staff and students will also be provided to the candidate to improve or develop codes based on machine learning techniques to analyse the air quality data across networks and from low cost sensors.
Specific training on literature reading and review and scientific writing will be provided on a regular basis during weekly supervisory meetings.
Respiratory and Contact Infection Resilience of the Project:
In case of any respiratory and contact infection pandemic, the field work may be slowed down. In such cases, we will reduce the air quality observations to two locations. This will not fundamentally change the science of the project. Under extreme conditions where fieldwork will be limited, we will use existing data from the urban supersite which has been run for the last few years
Year 1: Learn to operate a range of advanced online instruments for air quality super-laboratories; carry out air quality and greenhouse observations at roadside, rural and urban background sites; learn advanced data techniques including receptor modelling and machine learning
Year 2: Continue field observations; carry out receptor and machine learning modelling; publish relevant results
Year 3: Carry out health and economic modelling; to carry out ensemble source apportionment; publish relevant results
Year 4: To synthesize results to propose most optimal control strategies to reduce carbon emissions and improve air quality; write up for publication and for thesis
Applications should include:
• CENTA application form, downloadable from CENTA application
• CV with the names of at least two referees (preferably three and who can comment on your academic abilities)
• The application should please completed via: https://sits.bham.ac.uk/lpages/LES068.htm. Please select Apply Now in the PhD Geography and Environmental Science (CENTA) section. Please quote CENTA23_[B24] when completing the application form.
For further information please visit https://centa.ac.uk/apply/how-to-apply/.
Professor Zongbo SHI
School of Geography Earth and Environmental Sciences
University of Birmingham
Email: [Email Address Removed]