标题： 增强岩石风化去除二氧化碳的质量平衡MRV：方法、模拟和推断 显示英文标题

标题： Mass-Balance MRV for Carbon Dioxide Removal by Enhanced Rock Weathering: Methods, Simulation, and Inference

摘要： 二氧化碳很可能会从大气中被移除以避免显著的未来变暖和气候变化。正在开发技术来从大气中移除大量的碳。增强的岩石风化（ERW），即将细粒硅酸盐矿物散布在土壤上，是一种有前景的碳移除方法，同时也能支持作物产量并维持整体土壤健康。量化ERW所移除的碳对于理解ERW在全球的潜力以及建立商业运营的信任至关重要。然而，在复杂介质如土壤中进行可靠且可扩展的量化具有挑战性，并且尚未达成关于最佳方法的共识。 在这里我们讨论质量平衡方法，其中通过监测土壤中碱性阳离子的储量随时间变化来推断由风化反应带入溶液中的无机碳的数量。首先，我们回顾质量平衡方法的基本概念并解释解决质量平衡问题的不同方法。然后我们讨论实验规划和数据收集，提出一些最佳实践。接下来，我们介绍一个软件包，旨在促进ERW范围内的任务，如不确定性分析、现场试验规划和验证统计方法。最后，我们简要回顾使用质量平衡估算碳移除的方法，讨论在这种情况下贝叶斯推理的一些优点，并展示一个贝叶斯模型。该模型拟合模拟数据并以清晰的方式表示不确定性，恢复了正确的答案。 显示英文摘要

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.