Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.

中文翻译：

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SIBaR：一种从移动空气污染测量中量化和去除背景的新方法

移动监测在精细空间尺度上表征空气污染变得越来越流行。在确定本地源对测量污染物浓度的贡献时，背景的检测和量化是许多移动监测研究的关键步骤，但这样做的方法需要进一步发展以提高可重复性。在这里，我们讨论了一种在移动监测研究中量化和去除背景的新方法，即状态通知背景去除 (SIBaR)。该方法采用隐马尔可夫模型 (HMM)，这是一种流行的建模技术，可检测时间序列中的状态变化。我们讨论 SIBaR 的开发并评估其在外部数据集上的性能。我们发现 SIBaR 做出的预测与背景和非背景数据点的预定分配之间有 83% 的一致性。然后，我们通过映射指定为背景的点的分数并将源贡献与使用其他已发布的背景检测和去除技术得出的贡献进行比较来评估其在休斯顿收集的数据集的应用。所呈现的结果表明，SIBaR 建模的源贡献包含其他技术未检测到的源影响，但它们在外推时容易出现不切实际的源贡献估计。结果表明，SIBaR 可以作为一个框架，在未来的移动监测研究中改进背景量化和去除，同时确保适当处理外推案例。