Identifying times when water is unsafe for recreation, for drinking, or for aquatic life is a major challenge. Traditionally, sampling has been the preferred means of determining whether water is safe. Predictive modeling based on artificial intelligence (AI) is an approach that is becoming more and more popular.
In this article, we will instead focus on the legislation in place in Quebec to monitor overflows, as well as on the actions that can be taken to mitigate their impacts, taking into account the effects of climate change.
In October 2015, during the now famous Flushgate, the city of Montreal discharged eight billion liters of wastewater into the St. Lawrence River in order to carry out repairs on the sewer system. Through this article, I intend to provide answers to several questions concerning overflows, such as: “What are overflows?” and “What is their real impact on waterways in Quebec?”
What factors can influence the concentration of fecal indicator bacteria (FIB) at a given location and time? The following is intended to provide an overview of the factors affecting the transport, survival and redistribution of FIB in surface waters.
Water management is a complicated challenge, and recent progress in artificial intelligence could clearly help with part of this. This article presents some food for thought for us to be able to fully take advantage of the data revolution to optimize one of our most precious resources: blue gold.
We often tend to forget that the idea of “good” water is a matter of perspective. Lakes and rivers are used for a variety of functions such as drinking water, swimming, irrigation and therefore each person’s perspective of “good” water quality comes from how they use it. When assessing the health of a waterway, we need to be able to evaluate these different perspectives as a whole, but not much is known about how these perspectives overlap in terms of safety standards.