增强岩石风化去除二氧化碳的质量平衡MRV：方法、模拟与推断
Mass-Balance MRV for Carbon Dioxide Removal by Enhanced Rock Weathering: Methods, Simulation, and Inference
AI总结：
本文针对增强岩石风化碳去除的量化难题，系统阐述质量平衡MRV方法、实验最佳实践与配套软件包，并提出贝叶斯推断模型，通过模拟数据验证其能准确恢复碳去除量并表征不确定性。
摘要：
为了避免未来显著的气候变暖和气候变化，很可能需要从大气中去除二氧化碳。目前正在开发能够从大气中去除大量碳的技术。增强岩石风化（Enhanced Rock Weathering, ERW）是将细粒硅酸盐矿物撒施到土壤上的一种有前景的碳去除方法，同时还能支持作物产量并维持整体土壤健康。量化ERW去除的碳量对于理解ERW在全球范围内的潜力以及建立对商业运营的信任至关重要。然而，在土壤这类复杂介质中进行可靠且可扩展的量化具有挑战性，目前对于最佳方法尚未形成共识。本文讨论了质量平衡方法，即通过随时间监测土壤中碱基阳离子的储量，来推断风化反应带入溶液的无机碳量。首先，我们回顾了质量平衡方法的基本概念，并解释了处理质量平衡问题的不同方式。然后，我们讨论了实验规划与数据收集，并提出了一些最佳实践建议。接下来，我们介绍了一个软件包，旨在促进ERW中的一系列任务，例如不确定性分析、规划田间试验以及验证统计方法。最后，我们简要回顾了利用质量平衡估算碳去除量的方法，随后讨论了贝叶斯推断在这一背景下的若干优势，并给出了一个贝叶斯模型示例。该模型拟合到模拟数据上，能够恢复出正确答案，并清晰地表征不确定性。
英文摘要：
Carbon dioxide will likely need to be removed from the atmosphere to avoid significant future warming and climate change. Technologies are being developed to remove large quantities of carbon from the atmosphere. Enhanced rock weathering (ERW), where fine-grained silicate minerals are spread on soil, is a promising carbon removal method that can also support crop yields and maintain overall soil health. Quantifying the amount of carbon removed by ERW is crucial for understanding the potential of ERW globally and for building trust in commercial operations. However, reliable and scalable quantification in complex media like soil is challenging and there is not yet a consensus on the best method of doing so. Here we discuss mass-balance methods, where stocks of base cations in soil are monitored over time to infer the amount of inorganic carbon brought into solution by weathering reactions. First, we review the fundamental concepts of mass-balance methods and explain different ways of approaching the mass-balance problem. Then we discuss experimental planning and data collection, suggesting some best practices. Next, we present a software package designed to facilitate a range of tasks in ERW like uncertainty analysis, planning field trials, and validating statistical methods. Finally, we briefly review ways of estimating carbon removal using mass balance before discussing some advantages of Bayesian inference in this context and presenting an example Bayesian model. The model is fit to simulated data and recovers the correct answer with a clear representation of uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
