Storing waste materials from oil sands extraction in tailing ponds could become harmful to the natural habitat and neighboring communities as they leach into groundwater and surface ecosystems.
UBCO researchers are using fluorescence spectroscopy to quickly detect key toxins in tailing ponds water. Image Credit: The University of British Columbia
So far, the challenge for the oil sands industry is that the right analysis of hazardous waste materials has been hard to achieve without complicated and prolonged testing. Also, there is a backlog.
For instance, in Alberta alone, there are approximately 1.4 billion cubic meters of fluid tailings, explained Nicolás Peleato, an Assistant Professor of Civil Engineering at UBC Okanagan.
Peleato’s group of researchers at UBCO’s School of Engineering has exposed a new, quicker and firmer technique of examining these samples. It is the first step, states Dr. Peleato, but the outcomes look hopeful.
Current methods require the use of expensive equipment and it can take days or weeks to get results. There is a need for a low-cost method to monitor these waters more frequently as a way to protect public and aquatic ecosystems.
Nicolás Peleato, Assistant Professor, Civil Engineering, University of British Columbia
Together with master’s student María Claudia Rincón Remolina, the scientists utilized fluorescence spectroscopy to rapidly detect the major toxins present in the water. Also, they ran the outcomes through a modeling program that precisely predicted the water’s composition.
Rincón explained that the composition could be utilized as a benchmark for additional testing of other samples. The scientists are making use of a convolutional neural network that processes data in a grid-like topology, like an image. It is similar to the kind of modeling utilized for categorizing hard-to-determine facial recognition, fingerprints and even self-driving cars.
The modeling takes into account variability in the background of the water quality and can separate hard to detect signals, and as a result, it can achieve highly accurate results.
María Claudia Rincón Remolina, Student, University of British Columbia
The study investigated a mixture of organic compounds that are hazardous, such as naphthenic acids — which can be discovered in several petroleum sources. By utilizing the high-dimensional fluorescence, the scientists were able to find most types of organic matter.
The modeling method searches for key materials and maps out the sample’s composition. The results of the initial sample analysis are then processed through powerful image processing models to accurately determine comprehensive results.
Nicolás Peleato, Assistant Professor, Civil Engineering, University of British Columbia
So far, the outcomes seem to be encouraging, both Rincón and Dr. Peleato warn that the method needs to be additionally assessed at a bigger scale — at which point there might be potential to incorporate screening of additional toxins.
Peleato described that this potential screening tool is the first step, but it does have few restrictions as not all naphthenic acids or toxins could be detected — only those that are fluorescent. Also, the technology will have to be intensified for future, more in-depth testing.
Dr. Peleato states while it will not replace present analytical techniques that are more precise, this method will enable the oil sands industry to perfectly screen and treat its waste materials. This is an essential step to continue to fulfill the Canadian Council of Ministers of the Environment standards and guidelines.
This work has been financially supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant program.
Journal Reference:
Remolina, M. C. R., et al. (2022) Application of machine learning methods for rapid fluorescence-based detection of naphthenic acids and phenol in natural surface waters. Journal of Hazardous Materials. doi.org/10.1016/j.jhazmat.2022.128491
Source: https://www.ubc.ca/