From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors
From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors
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DOI：
10.1016/j.jaerosci.2021.105833
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发表时间：
2021-07-15
影响因子：
4.5
通讯作者：
Subramanian, R.
中科院分区：
文献类型：
--
作者：
Giordano, Michael R.;Malings, Carl;Subramanian, R.
颗粒物质质量（PM）的低成本传感器可实现传统参考监测无法进行空间密集的高时间分解测量。低成本的PM传感器在低收入和中等收入国家中特别有益，那里的参考年级测量很少，并且在空气污染物的浓度领域具有显着的空间梯度的地区。不幸的是，低成本PM传感器还带来了许多挑战，如果将其数据产品用于空气质量的定性表征以外的任何东西，则必须解决这些挑战。低成本监测器中使用的各种PM传感器都受到偏差和校准依赖性的影响，校正的校正范围从相对直接（例如气象，传感器年龄）到复合物（例如气溶胶源，组成，折射率）。校正和校准文献中使用的这些偏见和依赖关系的方法，从简单的线性和二次模型到复杂的机器学习算法。在这里，我们在尝试从低成本传感器中获取高质量数据时会审查需求和挑战。我们还提供了一组最佳实践，以遵循这些低成本传感器的高质量数据。
Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolu-tion measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.