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Policies, goals and objectives


Case study Box 3.3.3: Indigenous rights and bituminous sands development


The northeast section of Alberta province is the home to the vast majority of Canada’s bituminous sands deposits. This petroleum resource is large in energy content, but the reserves are in low concentrations, shallow in depth, and cover a large geographic area relative to conventional oil reserves. As a result, the resource is extracted using strip mining, and processing it requires large amounts of water, some of which is released into groundwater systems, significantly impacting the environment and climate.


This area of Alberta is home to multiple aboriginal communities including five First Nations and seven Métis Locals. Their relationship with the industrial projects is complex. Jim Boucher, chief of the Fort McKay First Nation located in the region, has said, “we have to be realistic ... about what is going on in the oil sands developments here. They are massive, and doing a lot in terms of destruction of the land, we are losing our land. On the other hand there is no more opportunity for our people to be employed or have some benefits except the oil sands.” This dichotomy between economic development and environmental/human health is difficult for all scales of governments (Sterritt 2014).


science data and information from several sources to minimize environmental damage.


The new data landscape is permitting much more rapid assessment and detection of environmental risks, driven by falling costs, more powerful and flexible sensors, and an approximation of plug-and-play functionality.


The


unprecedented pace and magnitude of monitoring and assessment capabilities that were deployed in the aftermath of the 2010 Deepwater Horizon oil spill in the Gulf of Mexico constitute a case in point (Lubchenco et al. 2012).


The Deepwater Horizon response relied on big data integration and interoperability efforts to contain the contaminants stop the oil spill and respond to the damage. Both the public and private sector coordinated efforts to integrate engineering data, information and advice within weeks. This accelerated effort mobilized teams of scientists to analyze ocean, weather and plant data and to predict the areas that would be affected in order to rapidly dispatch personnel and equipment for the cleanup. There has been rapid acceleration in the science of deep spill containment and mitigation since responding to this event, although scientists still emphasize the importance of obtaining more


On the other hand, the limitations with remote leak detection systems cannot be underestimated. Remote sensors may be better at detecting large spills and ruptures, but not necessarily good at detecting smaller spills, according to the data collected by the Pipeline and Hazardous Materials Safety Administration (PHMSA). There is no perfect solution to spotting oil spills suggesting that companies combine the best leak detection technology with experienced operators (O’Connor 2014; Song 2012).


Another example of how technological advances help reduce the time required to react to environmental risks is the detection of harmful algal blooms (HABs). These occur naturally, yet in recent years an intensification of HAB development is appearing across the world. This intensification is caused by such factors as climate change, nutrient-rich agricultural runoff, coastal aquaculture farms


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baseline data to quickly and clearly understand pre-disaster ecosystems, toxicity levels, human health and the movement of oil. The lessons learned are applicable to future events and to educating government and industry on the best ways of preparing and responding to disasters (Alexander-Bloch 2014; Lubchenco et al. 2012).


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