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Grant: Alliance Grants

University of Alberta

$230,120.00

Predicting Methane Emissions from Alberta Oil Sands Territories Using a Holistic Model and Monitoring System

EF 2024-25

Résumé
Greenhouse gas (GHG) emissions from the oil sector in Alberta total about 70 Mt annually, with no limit in place. Much of these emissions derive from methane biogenesis, which is done by bacteria that live in oil sands tailings ponds and end-pit lakes. Because of the importance of methanogenic bacteria in this matter, accurate predictions of GHG emissions derived from oil sands tailings can aid in estimating specific emissions limits to reduce the effects of climate change. Also, methane produced by these bacteria can harm the animals living in end-pit lakes and the surrounding areas, foiling land reclamation strategies. We therefore intend to determine the holistic effects of methanogens on the areas they are found in. We will build mathematical models predicting GHG emissions by methanogens as a function of many different ecological and meteorological covariates. These models will feature both machine learning and mechanistic techniques, and hence combine high predictive power with analyzability and clear indications of causality. The models will be constructed by utilizing the results of lab experiments where methanogen activity will be observed in a controlled setting under different simulated environmental conditions. They will be fit using large amounts of field data from an array of air quality sensors, provided by our partners in the South African Consortium of Air Quality Monitoring, that we will deploy in and around tailings ponds and end-pit lakes. The data from these sensors will be analyzed in real time using AI methods. We will also perform lab experiments investigating the effects of methane on the health and behaviour of model organisms, as well as those of bitumen (which end-pit lake species encounter via indirect effects of methanogen activity). We will disseminate our findings by creating an app enabling stakeholders to estimate emissions by methanogens in any given location, and our partners in Alberta Environment and Protected Areas will use our results to shape policy on oil sands land reclamation and natural resource stewardship. Our project will facilitate improvements in Alberta's oil sands infrastructure and better air quality provincewide.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Alberta, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
577242-2022
Name nom
Wang, Hao H
Department département
Mathematical and Statistical Sciences
Organization id
9
Institution établissement
University of Alberta
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
230120
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
409
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Environmental impact of economic activities (including agriculture)
Area of application fr
Effet des activités économiques sur l'environnement (y compris agriculture)
Research subject code
1503
Research subject group en
Environmental engineering
Research subject group fr
Génie de l'environnement
Research subject en
Air and noise pollution
Research subject fr
Pollution de l'air et pollution par le bruit
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Predicting Methane Emissions from Alberta Oil Sands Territories Using a Holistic Model and Monitoring System
Keywords
air quality monitoring; bacteria; biodegradation; data mining; greenhouse gas emission; machine learning; methanogens; oil sands; stoichiometric modelling; tailings ponds
Application summary
Greenhouse gas (GHG) emissions from the oil sector in Alberta total about 70 Mt annually, with no limit in place. Much of these emissions derive from methane biogenesis, which is done by bacteria that live in oil sands tailings ponds and end-pit lakes. Because of the importance of methanogenic bacteria in this matter, accurate predictions of GHG emissions derived from oil sands tailings can aid in estimating specific emissions limits to reduce the effects of climate change. Also, methane produced by these bacteria can harm the animals living in end-pit lakes and the surrounding areas, foiling land reclamation strategies. We therefore intend to determine the holistic effects of methanogens on the areas they are found in. We will build mathematical models predicting GHG emissions by methanogens as a function of many different ecological and meteorological covariates. These models will feature both machine learning and mechanistic techniques, and hence combine high predictive power with analyzability and clear indications of causality. The models will be constructed by utilizing the results of lab experiments where methanogen activity will be observed in a controlled setting under different simulated environmental conditions. They will be fit using large amounts of field data from an array of air quality sensors, provided by our partners in the South African Consortium of Air Quality Monitoring, that we will deploy in and around tailings ponds and end-pit lakes. The data from these sensors will be analyzed in real time using AI methods. We will also perform lab experiments investigating the effects of methane on the health and behaviour of model organisms, as well as those of bitumen (which end-pit lake species encounter via indirect effects of methanogen activity). We will disseminate our findings by creating an app enabling stakeholders to estimate emissions by methanogens in any given location, and our partners in Alberta Environment and Protected Areas will use our results to shape policy on oil sands land reclamation and natural resource stewardship. Our project will facilitate improvements in Alberta's oil sands infrastructure and better air quality provincewide.