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Indigenous tracers for assessing karstic flow and risks to water quality in Chalk catchments


   Faculty of Environment

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  Dr Jared West  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Highlights and Novelty 

  • Opportunity for investigating the impacts of contaminants on water quality and quantity.  
  • Investigation of one of the world’s most important aquifers, the Cretaceous Chalk. 
  • Determination of karst extent in chalk, to better understand its development, and identify the impact of rapid karstic flow processes on water quality. 
  • Working with a very experienced team including British Geological Survey and Affinity Water staff to conduct fieldwork using e.g., novel tracer technology and analysis to identify karst pathways. 
  • Identification of preventive actions to be taken by Water Company partner to facilitate improved future groundwater and surface water resource management and protection.  

Background 

Cretaceous Chalk catchments represent the most important groundwater resource in the UK and are important ecologically because of their unique chalk stream ecosystems. Similar aquifers exist in France, Belgium, Denmark, Netherlands and Sweden.  Cryptosporidium and other pathogens threaten public water supplies and inland recreational water quality in groundwater-fed streams; particulates such as silt, micro-plastics and associated bound P and N threaten river habitats; pesticides, pharmaceuticals, PFOS (perfluorooctanesulfonate, which are bioaccumulative organic compounds), hydrocarbons, heavy metals and dissolved nutrients (N and P) threaten both public water abstractions and river habitats.  The relative importance of these threats to water quality depends on chalk catchment functioning, i.e., extent of development of karstic features (stream sinks, widened fractures and conduit development in chalk due to dissolution by groundwater flow) which facilitate transport of contaminants from sources such as agricultural land, discharges from factory farms, industrial activities and road runoff. The extent of karstic features is of interest because where these features connect sources of contaminants directly to borehole abstractions or streams, water quality may be poor. Where flow is more distributed because karst features are less developed, water quality is consistently better.  

 We will obtain improved understanding of catchment function by analysis of the temporal signals from indigenous tracers such as turbidity (sediment load and composition), electrical conductivity, and natural organic matter fluorescence.  Indigenous tracers have several advantages including availability of long-time data series at widespread locations because of statutory requirements on Water Companies for monitoring groundwater abstractions.  By considering the catchment as a system that transforms the input (rainfall) signal into the output signals (turbidity, conductivity, natural dissolved organic matter fluorescence, see Figure 1), analysis of the time-series data can identify contaminant pathways and lag times.  These pathways and lag times can be verified for specific control points within catchments (sinking streams, soakaway drains, monitoring wells) by injection/recovery of novel artificial tracers such as bacteriophage (non-harmful virus particles small enough to pass through fractured aquifer systems) which can be detected in very low concentrations (see Figure 2).   Using these approaches together we aim to identify key factors controlling development of fast pathways in Cretaceous Chalk catchments and the impact of these on contaminant transport and therefore water quality. Fast pathways include widened fractures, conduits etc, but also groundwater ingress to leaky sewer/drain network producing overloading of waste-water treatment systems and hence direct stream discharge of untreated water.  

Objectives 

1. Identify specific catchments with sufficiently long timeseries of water quality indicator datasets; instrument catchments for additional quality indicators e.g., fluorescence, sediment composition.  

2. Analyse samples i.e., to determine the origin of signals such as turbidity (e.g. mineralogical analysis of suspended sediment responsible for turbidity using XRD and SEM imaging) 

3.  Undertake correlation and spectral analysis for input (rainfall) and output (turbidity, specific conductance, and fluorescence) signals – i.e.  Fourier transformation of time series, generation of Impulse Response Functions for cross-correlation and wavelet analysis to quantify memory effects and lag times.  

4. Interpret results of correlation and spectral analysis with respect to geological, structural and hydrological factors, including catchment area, presence/absence of sinking streams or other surface karst features, unsaturated zone thicknesses, superficial deposits (see Figure 2) and specific contaminant sources. 

5. Validate connections and lag times inferred from indigenous tracer analysis with bacteriophage and/or fluorescent dye tracing from selected injection points (sinking streams, soakaways, monitoring wells) to abstraction wells and streams; identify the extent to which indigenous tracers can identify fast pathways and contaminant risks at the catchment scale. 

 Full project description available on the NERC Panaroma DTP website: https://panorama-dtp.ac.uk/research/


References

Key Supervisor Publications
Agbotui, P. Y., West, L. J., & Bottrell, S. H. (2020). Characterisation of fractured carbonate aquifers using ambient borehole dilution tests. Journal of Hydrology, 589, 125191.
Allshorn SL; Bottrell SH; West LJ; Odling NE (2007) Rapid karstic bypass flow in the unsaturated zone of the Yorkshire chalk aquifer and implications for contaminant transport, In: Parise M; Gunn J (Ed) Natural and Anthropogenic Hazards in Karst Areas: Recognition, Analysis and Mitigation, Geological Society Special Publications, Geological Society of London, pp.111-122.
Maurice, L D, Atkinson, T A, Barker, J A, Bloomfield, J P, Farrant, A R, and Williams, A T. 2006. Karstic behaviour of groundwater in the English Chalk. Journal of Hydrology 330 53–62. 10.1016/j.jhydrol.2006.04.012
Maurice, L D, Atkinson, T C, Williams, A T, Barker, J, and Farrant, A R. 2010. Catchment scale tracer testing from karstic features in a porous limestone. Journal of Hydrology. 389 (1–2) 31–4. 10.1016/j.jhydrol.2010.05.019
Maurice, L D, Atkinson, T C, Barker, J, Williams, A T, and Gallagher, A. 2012. The nature and distribution of flowing features in a weakly karstified porous limestone aquifer. Journal of Hydrology. 438–439, 3–15. 10.1016/j.jhydrol.2011.11.050
Medici G, West LJ, Chapman PJ, Banwart SA. 2019. Prediction of contaminant transport in fractured carbonate aquifer-types; case study of the Permian Magnesian Limestone Group (NE England, UK). Environmental Science and Pollution Research. 26(24), pp. 24863-24884
Medici, G., & West, L. J. 2021. Groundwater flow velocities in karst aquifers; importance of spatial observation scale and hydraulic testing for contaminant transport prediction. Environmental Science and Pollution Research, 28(32), 43050-43063.
Other key references
Cook, S J, Fitzpatrick, C M, Burgess, W G, Lytton, L, Bishop, P, and Sage, R. 2012. Modelling the influence of solution-enhanced conduits on catchment-scale contaminant transport in the Hertfordshire Chalk Aquifer. In: Groundwater resources modelling a case study from the UK, Geological Society Special Publication 364, p. 205- 225
El Janyani, S., Dupont, J.P., Massei, N., Slimani, S. and Dörfliger, N., 2014. Hydrological role of karst in the Chalk aquifer of Upper Normandy, France. Hydrogeology Journal, 22(3), pp.663-677.
Massei, N., Lacroix, M., Wang, H. Q., & Dupont, J. P. (2002). Transport of particulate material and dissolved tracer in a highly permeable porous medium: comparison of the transfer parameters. Journal of Contaminant Hydrology, 57(1-2), 21-39.
Massei, N., Dupont, J. P., Mahler, B. J., Laignel, B., Fournier, M., Valdes, D., & Ogier, S. (2006). Investigating transport properties and turbidity dynamics of a karst aquifer using correlation, spectral, and wavelet analyses. Journal of hydrology, 329(1-2), 244-257.
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