<b>Methane-</b><b>d</b><b>riven</b><b> </b><b>w</b><b>arming</b><b> </b><b>f</b><b>eedback of</b><b> </b><b>a</b><b>lgal</b><b> </b><b>b</b><b>looms:</b><b> </b><b>CO</b><sub><strong>2</strong></sub><b>-</b><b>e</b><b>quivalent</b><b> </b><b>e</b><b>vidence from eutrophic Lake Taihu, China</b>
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In eutrophic lakes, algae sequester atmospheric carbon dioxide (CO<sub>2</sub>) and convert it into organic carbon (OC). However, most of this highly biodegradable endogenous OC is eventually decomposed into CO<sub>2</sub> and the potent greenhouse gas methane (CH<sub>4</sub>), potentially offsetting the initial carbon sink effect. In this study, CO<sub>2</sub>-equivalent (CO<sub>2</sub>-eq) was used as a proxy to quantify the net change from initial CO<sub>2</sub> sequestration to subsequent CO<sub>2</sub> and CH<sub>4</sub> release, and a dataset of 3,223 records was used to train machine learning models predicting the impacts of future climate warming and lake management strategies on algal net CO<sub>2</sub>-eq in Lake Taihu, a highly eutrophic lake in China.
在富营养化湖泊中，藻类可固存大气中的二氧化碳（CO₂）并将其转化为有机碳（OC）。然而，这类高生物可降解的内源有机碳大多最终会被分解为二氧化碳与强效温室气体甲烷（CH₄），进而可能抵消最初的碳汇效应。本研究采用二氧化碳当量（CO₂-eq）作为替代指标，量化从初始二氧化碳固存到后续二氧化碳与甲烷释放的净变化，并利用包含3223条记录的数据集训练机器学习模型，以预测未来气候变暖与湖泊管理策略对中国大型富营养化湖泊太湖的藻类净二氧化碳当量的影响。
