An EPSRC funded Ph.D. position in Statistics is available at the University of Durham under the supervision of Peter Craig to work on the project described below. Please bring this studentship to the attention of anyone who might be interested in starting a Ph.D. in the near future.

All enquiries should be directed to Peter Craig:
  E-mail:   [log in to unmask]
  Telephone: (0191) 3742376
  Snail:    Peter Craig, Dept. of Math. Sciences, Univ. of Durham,
            South Road, Durham DH1 3LE, England

DECONSTRUCTING ADSORPTION VARIABILITY: the prediction of spatial uncertainty in pollutant movement from contaminated land.

The re-use of land contaminated by pollutants from manufacturing or other industrial processes is currently high on the political agenda. Consequently, there is a growing need for good quality assessment of the risk (both to groundwater and directly to humans residing/working on such land) from historical/current pollution. This multi-disciplinary project seeks to understand and quantify the influence of factors controlling adsorption of pollutants into soils; adsorption (not absorption) means that the pollutant gets stuck in the soil and ceases to move and so no longer poses a threat to groundwater although it may still be a threat to people/animals in contact with the soil.

The project is a collaboration between the departments of Geology and Mathematical Sciences at the University of Durham and the department of Civil and Structural Engineering at the University of Sheffield.  Soil sampling and chemical and other analyses to build models of how adsorption depends on pollutant and soil properties will be carried out by Durham Geology and Sheffield Engineering. The statistical part of the project will be carried out jointly by the research student and Peter Craig who is a statistician working in Durham Mathematical Sciences.

The statistical component involves (i) design of suitable spatial sampling methods for site sampling; (ii) quantifying spatial variability in soil properties within and between sites; (iii) quantifying uncertainty in the adsorption relationship developed in the rest of the project and (iv) pooling these sources of uncertainty for the purposes of site-specific risk assessment. It is anticipated that Bayesian methods will be used throughout. There will be a substantial computational component to the work and suitable computing resources will be provided. It seems likely that the project would open up substantial academic and/or commercial avenues for a successful student.