Objective: Develop an algorithm to decompose streamflow electrical conductivity signal into its “harmonics”, i.e. into the specific contribution of different ion species.

Level: BS, Masters

Description: The electrical conductivity (EC) of an aqueous solution is the capacity to transmit electrical current through the movement of charged ions. Typical major ions in natural waters are: H+, Na+, Ca2+, Mg2+, K+, Cl−, SO2−, NO− and HCO−. For a number of environmental applications, EC can be expressed as a linear combination of the concentrations of the major solutes dissolved in water.
As EC can be measured continuously through simple sensors, there is great potential in using it for environmental monitoring. However, separating the contributions of individual solutes from the bulk measured signal can be challenging. One opportunity is that of investigating high-resolution datasets, where such contributions can be measured, and search for characteristic signatures that allow developing a predictive algorithm to estimate solute concentration from EC measurements.

Data Example: The figure shows an example of EC decomposition obtained for the UHF dataset, Plynlimon, Wales. Chloride (Cl−) and sodium (Na+) are responsible for most of the EC signal, while potas- sium (K+) and nitrate (NO−3) have almost negligible contributions. Almost all the elements have strong dependence on flow which causes either peaks (as for hydrogen (H+)) or sharp depressions.

Tasks:
1 - basic statistical analysis of existing water-quality datasets
2 - characterisation of elements contribution to EC, using additional information such as river flow and temperature
3 - development of algorithm(s) to invert equation 1 and estimate solute concentration from EC measurements

Type of Work: 50% design and 50% development.

LCAV1131050917