Assessment of the global budget of the greenhouse gas nitrous oxide ( O) is limited by poor knowledge of the oceanic O flux to the atmosphere, of which the magnitude, spatial distribution, and temporal variability remain highly uncertain. Here, we reconstruct climatological O emissions from the ocean by training a supervised learning algorithm with over 158,000 O measurements from the surface ocean—the largest synthesis to date. The reconstruction captures observed latitudinal gradients and coastal hot spots of O flux and reveals a vigorous global seasonal cycle. We estimate an annual mean O flux of 4.2 ± 1.0 Tg N , 64% of which occurs in the tropics, and 20% in coastal upwelling systems that occupy less than 3% of the ocean area. This O flux ranges from a low of 3.3 ± 1.3 Tg N in the boreal spring to a high of 5.5 ± 2.0 Tg N in the boreal summer. Much of the seasonal variations in global O emissions can be traced to seasonal upwelling in the tropical ocean and winter mixing in the Southern Ocean. The dominant contribution to seasonality by productive, low-oxygen tropical upwelling systems (>75%) suggests a sensitivity of the global O flux to El Niño–Southern Oscillation and anthropogenic stratification of the low latitude ocean. This ocean flux estimate is consistent with the range adopted by the Intergovernmental Panel on Climate Change, but reduces its uncertainty by more than fivefold, enabling more precise determination of other terms in the atmospheric O budget.
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This content will become publicly available on July 19, 2026
A rollout approach for condition-based maintenance of large multi-unit systems with economic dependence
With advancements in sensor technology, real-time monitoring of machine health conditions allows us to perform condition-based maintenance (CBM) for multi-unit systems. The maintenance decision of a unit is usually dependent on other units in a multi-unit system, inducing an exponentially large state space, which makes CBM of large multi-unit systems a very challenging engineering problem. In this work, we first propose two heuristic decision policies for multi-unit systems, namely the binary action policy and the -policy. Then we propose a multi-step lookahead rollout approach using the two heuristic policies to solve the challenging CBM problem. By applying the binary action policy, we can effectively reduce the action space and thus reduce the computational load in the rollout, while the -policy can be an excellent base policy for the rollout to improve upon. The theoretical gap between the proposed rollout approach and the optimal policy is also derived. The study further shows extensive experimentation to demonstrate the effectiveness of the proposed lookahead rollout approach for solving the CBM problem for small (3 and 5 units), medium (10 and 15 units), and large (20, 30, 40, and 50 units) scale systems.
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- Award ID(s):
- 2323082
- PAR ID:
- 10653315
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
- ISSN:
- 1748-006X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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