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Optimisation of monitoring network design and advanced plume modelling for air pollution in complex built environments

  • Full or part time
  • Application Deadline
    Friday, January 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

A key task for environmental regulators is to protect human and environmental health by ensuring that exposure to harmful pollutants is controlled. Air pollution is a particular challenge at present: estimates of deaths due to exposure to air pollution in the UK are in tens of thousands, and WHO estimated that in 2012, nearly seven million deaths (1 in 8 of deaths globally) were attributable to air pollution.
In relatively simple situations, current “routine” assessment tools based around Gaussian dispersion models provide valuable insights into what exposures are likely to be received. Many current regulatory situations, however, involve complex flow fields, e.g. in built environments. Sources can be at ground or elevated levels and buildings have a strong impact on airflow and turbulence characteristics, and hence on pollution transport and dispersion. The use of models (e.g. CFD) which are able to resolve flows in complex topography is time and labour-intensive, and beyond the present resources of regulators in any but the highest risk situations.

Complex flow fields yield two related challenges:
1. How can we better estimate probable peak and time-averaged pollution concentrations across an area of complex topography in order to apply appropriate limits to emissions at source?
2. How can we interpret measurements taken within complex flow fields to get the best estimate of emission rate (and in some situations, such as those involving diffuse or multiple sources, source locations)? Conversely, where should we place monitors in order to get the best possible information on pollutant concentration and how long must we monitor for to get representative concentration data?

In this project, we aim to address the above two challenges using CFD-based sensitivity analyses combined with multiscale modelling to derive heuristics and a tool which may be applied in real-world situations to optimise sensor placement, and to gain the best understanding of pollutant concentrations to support regulatory decision-making. The outcome will be that regulators will be better equipped to estimate likely exposures resulting from a polluting activity. Designers will also benefit, gaining insights into “problem” emissions which might be avoided by alternative plant design.

Funding Notes

CENTA studentships are for 3.5 years and are funded by the Natural Environment Research Council (NERC). In addition to the full payment of their tuition fees, successful candidates will receive the following financial support.
• Annual stipend, set at £15,009 for 2019/20
• Research training support grant (RTSG) of £8,000

References

Ainslie B, et al., 2009: Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants, J. Environ. Manage., 90, 2715–2729. Doi: 10.1016/j.jenvman.2009.02.01
Fallah-Shorshani, M. et al., 2017: Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near-road air pollution, Atmos. Env., 153, 21-31. Doi: 10.1016/j.atmosenv.2017.01.006
O'Neill, J., X.-M. Cai and R. Kinnersley, 2016: Stochastic backscatter modelling for the prediction of pollutant removal from an urban street canyon: a large-eddy simulation, Atmos. Env., 142, 9-18. Doi: 10.1016/j.atmosenv.2016.07.024
Soulhac, L. et al, 2011: The model SIRANE for atmospheric urban pollutant dispersion; part I, presentation of the model, Atmos. Env., 45, 7379-7395. Doi: 10.1016/j.atmosenv.2011.07.008
Stocker J., et al, 2012: ADMS-Urban: developments in modelling dispersion from the city scale to the local scale, Int. J. Env. Poll., 50, 308-316. Doi: 10.1504/ijep.2012.051202.
Tzella, A. and J. Vanneste, 2016: Dispersion in Rectangular Networks: Effective Diffusivity and Large-Deviation Rate Function, Phys. Rev. Lett., 117, 114501. Doi: 10.1103/PhysRevLett.117.114501
Zhong, J., X.-M. Cai and W. J. Bloss, 2016: Modelling photochemical pollutants in a deep urban street canyon: Application of a coupled two-box model approximation, Atmos. Env., 143, 86-107. Doi: 10.1016/j.atmosenv.2016.08.027

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