Department of Biotechnology and Chemical Engineering, Faculty of Technical Sciences, Aarhus University, Aarhus, Denmark.
Hafner Consulting LLC, Reston, Virginia, United States of America.
PLoS One. 2021 Jun 10;16(6):e0252881. doi: 10.1371/journal.pone.0252881. eCollection 2021.
Liquid manure (slurry) from livestock releases methane (CH4) that contributes significantly to global warming. Existing models for slurry CH4 production-used for mitigation and inventories-include effects of organic matter loading, temperature, and retention time but cannot predict important effects of management, or adequately capture essential temperature-driven dynamics. Here we present a new model that includes multiple methanogenic groups whose relative abundance shifts in response to changes in temperature or other environmental conditions. By default, the temperature responses of five groups correspond to those of four methanogenic species and one uncultured methanogen, although any number of groups could be defined. We argue that this simple mechanistic approach is able to describe both short- and long-term responses to temperature where other existing approaches fall short. The model is available in the open-source R package ABM (https://github.com/sashahafner/ABM) as a single flexible function that can include effects of slurry management (e.g., removal frequency and treatment methods) and changes in environmental conditions over time. Model simulations suggest that the reduction of CH4 emission by frequent emptying of slurry pits is due to washout of active methanogens. Application of the model to represent a full-scale slurry storage tank showed it can reproduce important trends, including a delayed response to temperature changes. However, the magnitude of predicted emission is uncertain, primarily as a result of sensitivity to the hydrolysis rate constant, due to a wide range in reported values. Results indicated that with additional work-particularly on the magnitude of hydrolysis rate-the model could be a tool for estimation of CH4 emissions for inventories.
牲畜的液态粪肥（粪浆）会释放出甲烷（CH4），这对全球变暖有重大影响。现有的粪浆 CH4 产生模型——用于缓解和清查——包括有机质负荷、温度和停留时间的影响，但无法预测管理的重要影响，也无法充分捕捉基本的温度驱动动态。在这里，我们提出了一个新模型，其中包括多个产甲烷菌群体，其丰度会根据温度或其他环境条件的变化而变化。默认情况下，五个群体的温度响应与四个产甲烷物种和一个未培养的产甲烷菌的温度响应相对应，尽管可以定义任意数量的群体。我们认为，这种简单的机械方法能够描述其他现有方法无法描述的短期和长期对温度的响应。该模型可在开源 R 包 ABM（https://github.com/sashahafner/ABM）中找到，作为一个单一的灵活功能，可以包括粪浆管理（例如，排空频率和处理方法）以及随时间变化的环境条件的影响。模型模拟表明，通过频繁排空粪坑来减少 CH4 排放是由于活性产甲烷菌的冲洗。该模型在代表全规模粪浆储存罐中的应用表明，它可以再现重要趋势，包括对温度变化的延迟响应。然而，预测排放量的大小不确定，主要是由于水解速率常数的敏感性，因为报道的值范围很广。结果表明，通过进一步的工作——特别是水解速率的大小——该模型可以成为清查中 CH4 排放量估算的工具。
