The Collaborator - Detail
A COSIA Newsletter
Issue 11 - November 2017
Adaptive monitoring framework signals meaningful changes
When environmental change occurs, it is necessary to understand whether the difference was expected or unexpected, whether it is stable or getting worse, and whether the change is site-specific, local or regional in nature. One of COSIA’s research efforts has been to design an adaptive monitoring framework for determining when a monitoring signal changes in a meaningful way.
A recent paper published by Korosi, et al. in the journal, Environmental Pollution, highlighted changes occurring near an in situ development in the Cold Lake area. We were interested in evaluating our adaptive monitoring tools against the data being reported. This case study provided an interpretation framework suitable for evaluating the broader relevance of statistical changes detected during environmental monitoring programs.
Since 2010, the intensity of monitoring efforts in the oil sands has expanded markedly. These efforts were augmented in 2012 by the requirement for industry to contribute $50M per year towards regional monitoring of cumulative effects from development, by the Joint Oil Sands Monitoring program (JOSM). JOSM has access to some of the top analytical laboratories in the world for chemical analyses, and through JOSM, monitoring has significantly increased in terms of the numbers of sites, the numbers of chemicals analyzed for, and the sensitivity of the equipment being used. Constant, continual improvements in our analytical capabilities, both through JOSM and as part of each site’s extensive monitoring activities, means our monitoring programs are becoming more and more sensitive, making it possible to identify smaller and smaller differences in the changes in chemical level of environments. However, the relevance of those small changes, and understanding of the range of factors contributing to the changes, needs to be put into context.
One of the challenges for monitoring in the oil sands has been the absence of pre-development baseline data to compare against. One of the ways researchers have dealt with the lack of historical data is by using techniques to look at chemical levels in sediment cores in lakes. Sediments are deposited over time – over hundreds of years – making it possible to go back in time and develop a picture of what was deposited in lakes over the years prior to development. This technique allowed researchers to develop a picture of what was “normal” before development and compare against recent changes.
There can be small fluctuations in measures over time that allow us to determine a statistical difference between sites or times relative to “normal” differences that occur that may not have biological meaning. By using different statistical approaches to estimate what “normal” represents, and using “normal ranges”, we can define triggers that signal when a monitoring result warrants closer inspection.
Our analyses, which have since been published in the journal, Environmental Pollution, (Using normal ranges for interpreting results of monitoring and tiering to guide future work: A case study of increasing polycyclic aromatic compounds in lake sediments from the Cold Lake oil sands (Alberta, Canada) described in Korosi et al. (2016), Munkittrick, Tim J. Arciszewski) provided insights that merit inclusion in the continued dialogue on environment management in the oil sands.
The ability to interpret data in terms of normal ranges at multiple scales provides the necessary context for understanding the importance of small statistical changes in monitoring programs. In terms of the recent study, the analyses showed that while changes appear real near in situ sites, they are much smaller than near mining sites, change is occurring slowly, and levels are within the noise of regional reference sites. While there is sufficient information to warrant more studies on the extent and magnitude of the issue near in situ sites, the changes seen so far are not large enough to warrant large scale studies.
The advantage of better tools and techniques is that you can detect issues before they become a threat to sustainability or acceptability, and once you identify an issue, it is possible to track it.