一种动态空间滤波方法来减轻现场校准的低成本传感器空气污染数据中的低估偏差
A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air-pollution data
摘要：
低成本的空气污染传感器，提供污染物浓度的超局部表征，在环境和公共卫生研究中变得越来越普遍。然而，低成本的空气污染数据可能存在噪声，受环境条件的影响，通常需要通过将低成本传感器与参考级仪器搭配进行现场校准。我们从理论和经验上表明，使用配置数据的回归校准的常见程序系统地低估了高空气污染浓度，这是从健康角度诊断的关键。目前的校准实践也往往未能利用污染物浓度的空间相关性。我们提出了一种新的空间滤波方法，用于基于配置的低成本网络校准，通过使用逆回归来缓解低估问题。逆回归还允许通过使用条件高斯过程的真实污染物浓度的第二阶段模型来并入空间相关性。我们的方法适用于网络中的一个或多个并置站点，并且是动态的，利用与最新可用参考数据的空间相关性。通过大量的模拟，我们展示了空间滤波如何大大提高污染物浓度的估计，并以更高的精度测量峰值浓度。我们应用的方法校准的低成本PM2.5网络在马里兰州巴尔的摩市，诊断空气污染的回归校准错过的峰值。
英文摘要：
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically, that the common procedure of regression-based calibration using collocated data systematically underestimates high air pollution concentrations, which are critical to diagnose from a health perspective. Current calibration practices also often fail to utilize the spatial correlation in pollutant concentrations. We propose a novel spatial filtering approach to collocation-based calibration of low-cost networks that mitigates the underestimation issue by using an inverse regression. The inverse-regression also allows for incorporating spatial correlations by a second-stage model for the true pollutant concentrations using a conditional Gaussian Process. Our approach works with one or more collocated sites in the network and is dynamic, leveraging spatial correlation with the latest available reference data. Through extensive simulations, we demonstrate how the spatial filtering substantially improves estimation of pollutant concentrations, and measures peak concentrations with greater accuracy. We apply the methodology for calibration of a low-cost PM2.5 network in Baltimore, Maryland, and diagnose air pollution peaks that are missed by the regression-calibration.
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