Decoding methane concentration in Alberta oil sands: A machine learning exploration
Most activities associated with Alberta’s oil sands industry are widely recognized as a serious threat to the environment, particularly the emission of greenhouse gases; the industrial residue that accumulates in oil sands tailings ponds (OSTPs) has the potential to emit large quantities of methane....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24012925 |
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| author | Liubov Sysoeva Ilhem Bouderbala Miles H. Kent Esha Saha B.A. Zambrano-Luna Russell Milne Hao Wang |
| author_facet | Liubov Sysoeva Ilhem Bouderbala Miles H. Kent Esha Saha B.A. Zambrano-Luna Russell Milne Hao Wang |
| author_sort | Liubov Sysoeva |
| collection | DOAJ |
| description | Most activities associated with Alberta’s oil sands industry are widely recognized as a serious threat to the environment, particularly the emission of greenhouse gases; the industrial residue that accumulates in oil sands tailings ponds (OSTPs) has the potential to emit large quantities of methane. Mathematical modeling of these emissions, and hence deducing where and why high methane concentrations can be found, is often infeasible due to complex interactions between different sources of methane and lack of availability of appropriate data. Additionally, stationing advanced monitoring devices either inside or in the vicinity of methane emitting sources can be expensive, and may require permits that are hard to obtain. Interpretable machine learning techniques, coupled with existing data from weather monitoring stations, offer a cost-effective alternative approach for modeling and understanding methane emissions sources. We introduce a multi-step framework for finding the primary factors associated with higher methane concentrations, powered by machine learning models (such as random forest) trained on high dimensional datasets sourced from multiple weather monitoring stations located in the Lower Athabasca region. The proposed framework can predict methane concentration levels, illustrate the dependence between the important features and their impact on these levels, and (via the incorporation of wind data) uncover locations of methane sources. We use it to locate such sources in northeastern Alberta. We additionally use Shapley values to find that O3’s relationship with methane concentration is consistently concave, while that of NOX changes from linear increase to a saturation function with increasing distance from OSTPs. This paper serves as a guide for building machine learning-driven models to estimate methane concentration in Alberta’s oil sands, or similar regions with methane-producing extractive industries. |
| format | Article |
| id | doaj-art-0543c880ccdd4d85bd72c9ac933b50cf |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-0543c880ccdd4d85bd72c9ac933b50cf2025-01-31T05:10:16ZengElsevierEcological Indicators1470-160X2025-01-01170112835Decoding methane concentration in Alberta oil sands: A machine learning explorationLiubov Sysoeva0Ilhem Bouderbala1Miles H. Kent2Esha Saha3B.A. Zambrano-Luna4Russell Milne5Hao Wang6Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, CanadaInterdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, Canada; Département de Biologie, Université Laval, Québec (QC), G1V 0A6, CanadaInterdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, CanadaInterdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, CanadaInterdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, CanadaInterdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, Canada; Corresponding authors.Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE), University of Alberta, Edmonton (AB), T6G 2J5, Canada; The Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton (AB), T6G 2J5, Canada; Corresponding authors.Most activities associated with Alberta’s oil sands industry are widely recognized as a serious threat to the environment, particularly the emission of greenhouse gases; the industrial residue that accumulates in oil sands tailings ponds (OSTPs) has the potential to emit large quantities of methane. Mathematical modeling of these emissions, and hence deducing where and why high methane concentrations can be found, is often infeasible due to complex interactions between different sources of methane and lack of availability of appropriate data. Additionally, stationing advanced monitoring devices either inside or in the vicinity of methane emitting sources can be expensive, and may require permits that are hard to obtain. Interpretable machine learning techniques, coupled with existing data from weather monitoring stations, offer a cost-effective alternative approach for modeling and understanding methane emissions sources. We introduce a multi-step framework for finding the primary factors associated with higher methane concentrations, powered by machine learning models (such as random forest) trained on high dimensional datasets sourced from multiple weather monitoring stations located in the Lower Athabasca region. The proposed framework can predict methane concentration levels, illustrate the dependence between the important features and their impact on these levels, and (via the incorporation of wind data) uncover locations of methane sources. We use it to locate such sources in northeastern Alberta. We additionally use Shapley values to find that O3’s relationship with methane concentration is consistently concave, while that of NOX changes from linear increase to a saturation function with increasing distance from OSTPs. This paper serves as a guide for building machine learning-driven models to estimate methane concentration in Alberta’s oil sands, or similar regions with methane-producing extractive industries.http://www.sciencedirect.com/science/article/pii/S1470160X24012925Greenhouse gas emissionsFeature extractionTailings pondsMethane source detection |
| spellingShingle | Liubov Sysoeva Ilhem Bouderbala Miles H. Kent Esha Saha B.A. Zambrano-Luna Russell Milne Hao Wang Decoding methane concentration in Alberta oil sands: A machine learning exploration Ecological Indicators Greenhouse gas emissions Feature extraction Tailings ponds Methane source detection |
| title | Decoding methane concentration in Alberta oil sands: A machine learning exploration |
| title_full | Decoding methane concentration in Alberta oil sands: A machine learning exploration |
| title_fullStr | Decoding methane concentration in Alberta oil sands: A machine learning exploration |
| title_full_unstemmed | Decoding methane concentration in Alberta oil sands: A machine learning exploration |
| title_short | Decoding methane concentration in Alberta oil sands: A machine learning exploration |
| title_sort | decoding methane concentration in alberta oil sands a machine learning exploration |
| topic | Greenhouse gas emissions Feature extraction Tailings ponds Methane source detection |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24012925 |
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