Atmospheric Methane Research problem statements are shared to build community and knowledge around key challenges to accelerate progress.
Submit a problem statementView all problem statementsPaul Reginato, Mary Lidstrom, Jeremy Semrau, Jessica Swanson, Lisa Stein, Wenyu Gu, Mark Hansen, Paige Brocidiacono, Ariana Caiati, and Erin Wilson
The Context for this problem statement was originally shared by Wenyu Gu at a Homeworld Collective workshop on protein engineering and climate tech. It was further explored as part of a collaborative effort between Homeworld Collective and Spark Climate Solutions to identify and share priority problems at the intersection of biotech and atmospheric methane removal. This problem statement is also hosted on Homeworld's problem statement repository here.
This problem statement was created as part of a collaborative effort between Homeworld Collective and Spark Climate Solutions to identify and share priority problems at the intersection of biotech and atmospheric methane removal.
CH4 emissions have contributed ~30% of global warming to date (IEA 2022), and natural sources may increase via feedback to warming (Zheng 2023). Technologies for oxidizing atmospheric CH4, area CH4 emissions, and unavoidable point sources could substantially mitigate climate change. While CH4 above ~44,000 ppm can be flared, ~75% of CH4 pollution is atmospheric (2 ppm) or area emissions below 1000 ppm that are too dilute to be oxidized at scale using existing technologies (Abernethy 2023).
Methane monooxygenase (MMO) enzymes, found in methanotrophic bacteria, naturally catalyze oxidation of CH4 to methanol in a one-step reaction at ambient conditions (Tucci 2024). Oxidation of dilute CH4 at scale may be possible using methanotrophs or cell-free MMO in flow-through reactors; however, substantial efficiency improvements over the state of the art are needed for such reactors to be economical (Lidstrom 2024, Yoon 2009). For example, modeling of a reactor system using Methylosinus trichosporium OB3b estimated up to 10-fold cost reduction is necessary to oxidize 500 ppm CH4 for $100/t CO2e (Yoon 2009).
CH4 oxidation efficiency improvements may be achieved by discovering or engineering methanotrophs or MMO variants that operate efficiently at low CH4 concentrations. Such work is underway (He 2023) and there is much more to be done (see related problem statements and Reginato 2024). However, researchers lack a clear performance target for biological CH4 oxidation agents, because there is no available analysis describing the relationship between biological oxidation performance and cost across a range of dilute CH4 concentrations. Further, methanotroph-based bioreactor systems thus far have generally not been designed to handle CH4 concentrations below 1000 ppm (La 2018). Development of reactor designs with associated techno-economic and life-cycle analyses would illuminate the potential for economic feasibility at scale and orient bioengineering of methane oxidation toward scalable targets.
Designs should be developed for CH4 bioreactors that are optimized for low (2-1000 ppm) CH4 concentration using methanotrophs, mixed microbial cultures, or cell-free MMO. High-level techno-economic and life-cycle analysis should be performed to assess the costs of biological methane oxidation efficiency at scale. For cell-free MMO, the need for reducants to activate O2 should be addressed by assuming reductants are regenerated electrochemically or that an enzyme system is used that generates reactive oxygen through methane oxidation reactions without other inputs (Reginato 2024).
Rather than treating oxidation efficiency as a constant, modeling should assess the impact of a range of oxidation efficiencies on cost, to provide targets for organism discovery and engineering. The impact of oxidation efficiency on optimal reactor design should also be considered, as well as methods to enhance CH4 mass transfer. CH4 oxidation performance should be discussed in terms of specific affinity (a˚S), defined as Vmax app/kM, since a˚S is known to be a more relevant parameter for enzyme performance at substrate concentrations well below kM (Lidstrom 2024).
Submitted by
Paul Reginato, Lisa Stein, Mary Lidstrom, Jeremy Semrau, Jessica Swanson, Wenyu Gu, Noah Helman, James Weltz, Mark Hansen, Paige Brocidiacono, Ariana Caiati, Erin Wilson
Submitted by
Paul Reginato, Lisa Stein, Mary Lidstrom, Jeremy Semrau, Jessica Swanson, Wenyu Gu, Noah Helman, James Weltz, Mark Hansen, Paige Brocidiacono, Ariana Caiati, Erin Wilson
Submitted by
Paul Reginato, Chris Eiben, James Weltz, Paige Brocidiacono
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