MESPOM is an Erasmus Mundus Masters course in Environmental Sciences, Policy and Management operated by four leading European and two North American Universities and supported by the European Commission. MESPOM prepares students for identifying and implementing solutions to complex environmental challenges, especially in an international context. USask is a partner institution and MESPOM students may study with faculty here at the School of Environment and Sustainability (SENS). 

Project Offerings

The following School of Environment and Sustainability (SENS) faculty are willing to work with MESPOM students interested in the projects listed below. Please contact the faculty associated with your project of choice directly for more information.

Chemical contamination of our natural ecosystems is regarded as one of the planet’s greatest threats (The Lancet, 2017). In particular, human activities result in the discharge of many chemicals into aquatic ecosystems. Regulatory agencies and businesses are tasked with managing these chemicals but face significant challenges due to the sheer number of compounds for which toxicity data are required as well as the plethora of different species requiring protection, including humans. In fact, over 100,000 chemicals require evaluation worldwide. It becomes increasingly apparent that current risk assessment strategies that rely heavily on animal testing and are prohibitively time-consuming and expensive are not able to address these testing mandates.

The research conducted in my laboratory focuses on addressing these challenges by 1) developing high-throughput molecular early life-stage toxicity assays to facilitate rapid and more ethical testing of chemicals while significantly reducing the number of live animals; and 2) developing modeling approaches that enable predicting the sensitivity of native fish species to environmental contaminants of concern.

Roles and responsibilities:

  • Plan and conduct short-term exposure studies with early life-stages of fishes or amphibians.
  • Routine water quality assessment and maintenance of exposure experiments.
  • Bio-analytical investigations (determine growth, deformities and mortality of fish; collect tissues and analyze sub-lethal biological effects such as changes in gene expression, biochemical homeostasis, develop toxicity models linking mechanistic toxicity data with apical outcome of regulatory relevance).
  • Interact with stakeholders from industry and government.
  • Statistical data evaluation.
  • Write a summary report, and - if permitted by the data - contribute to writing a peer-reviewed publication (student will be listed as a co-author).
  • The student will need to complete the following safety course at the U of S before any work can commence (all courses are offered online):
    • Animal Ethics Training,
    • Biosafety,
    • Laboratory Safety.

Skills required:

  • Very good English reading and writing skills.
  • Natural science (biology) background with some laboratory experience (pipetting, etc.).
  • Experience with working with aquatic vertebrates will be beneficial but is not critical.
  • Basic knowledge in statistical evaluation of data sets is expected.

Lead: Markus Hecker
Professor, SENS; Canada Research Chair in Predictive Aquatic Ecotoxicology

This project will be imbedded within a larger project focussed on ‘Omics’ approaches to Water Quality funded by the Global Water Futures Program. The specific goals of the overall project are to use detailed non-targeted chemical analysis to assess the nature of the dissolved organic matter present in all aquatic ecosystems. We use OrbiTrap mass spectrometry to measure all the chemicals present in water samples. Interpretation and assimilation of the raw mass spectrometry data into usable chemoinformatic information requires extensive data handling. The best available platform for this data handling in the R platform however the data analysis still requires advanced skills in R programming. This project will focus on the development of an R application which will permit the required chemoinformatic analysis based on already available sample sets. In addition, depending on progress, we may develop new mass spectrometry data approaches for data acquisition.

Roles and responsibilities:

  • Collect (depending on weather) and prepare water samples for chemical analysis.
  • Write a summary report, and - if permitted by the data - contribute to writing a peer-reviewed publication (student will be listed as a co-author).
  • All R code generated as part of the project will need to be fully annotated and will be considered to be the joint intellectual property of the PI and the student.
  • The student will need to complete the following safety course at the University of Saskatchewan before any work can commence (courses are between 1/2 and 1 day long): 
    • Laboratory Safety

Skills required:

  • Very good English reading and writing skills.
  • Strong experience with R programming language is required.
  • Natural science background with some laboratory (pipetting, etc.) experience.
  • The student should be willing to participate in possible field excursions to collect samples.
  • Basic knowledge in statistical evaluation of data sets is expected.

Lead: Paul Jones
Professor, SENS/Toxicology Program

Sensor networks have been widely used for years in smart factories and smart cities, and in marine science to monitor the integrity of underwater infrastructure and track routine water quality.  However, the use of sensors to monitor water quality in freshwater environments has been limited, especially in Canada.  The use of autonomous sensor networks comprised of multiple sensors deployed at different sites autonomously transmitting data via a cellular network or the Internet of Things is even more limited.  Very little use of this technology (smart watersheds) which will dominate environmental monitoring within 20 years has occurred in Canada.

An autonomous sensor system was deployed and calibrated in 2018, and used in an initial survey of water quality at a Canadian mining operation.  The system was subsequently deployed in 2019 to thoroughly track water quality information in real-time, to incorporate information into estimates of transport and flux of contaminants, and to estimate the risk of those contaminants to aquatic organisms living downstream of the mining operation.  In 2020, a new, more advanced system with multiple nodes will be deployed at a Canadian oil sands mining operation to track and model changes in water quality over time (within a reclamation context).

This research will use an advanced sensor system that autonomously relays information on multiple water quality variables in real-time from multiple sites in a demonstration lake (containing mine tailings and process water).  Data from sensors deployed at different locations with probes at different depths will provide improved delineation of contaminant exposure profiles and thus enhance the ability to predict changes over time.  The study will also correlate concentrations of contaminants that the sensors cannot detect with measurable parameters so that those parameters can be used as surrogates for key contaminants of concern.  Changes in toxicity of the surface water over time will also be assessed.

Lead: Karsten Liber
Professor, School of Environment and Sustainability; Director, Toxicology Centre


Please contact the faculty involved with each project for more information.

The MESPOM website has more information about the Study Programme and the admission process.