The aim of the work is to demonstrate the possibility of building models to correct the results of measurements of particulate matter PM10 concentrations obtained using low-cost devices. Such devices apply the optical method to values comparable with those obtained using the reference gravimetric method. An additional goal is to show that the results corrected in this way can be used to carry out the procedure for testing equivalence of these methods. The study used generalized regression models (GRMs) to construct corrective functions. The constructed models were assessed using the coefficients of determination and the methodology of calculating the measurement uncertainty of the device. Measurement data from the two tested devices and the reference method were used to estimate model parameters. The measurement data were collected on a daily basis from 1 February to 30 June 2018 in Nowy Sącz. Regression allowed building multiple models with various functional forms and very promising statistical properties as well as good ability to describe the variability of reference measurements. These models also had very low values of measurement uncertainty. Of all the models constructed, a linear model using the original PM10 concentrations from the tested devices, air humidity, and wind speed was chosen as the most accurate and simplest model. Apart from the coefficient of determination, expanded relative uncertainty served as the measure of quality of the obtained model. Its small value, much lower than 25%, indicates that after correcting the results it is possible to carry out the equivalence testing procedure for the low-cost devices and confirm the equivalence of the tested method with the reference method.

中文翻译：

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使用选定的校正函数测量 PM10 浓度的参考方法评估低成本传感器的等效性

这项工作的目的是展示建立模型以校正使用低成本设备获得的颗粒物 PM10 浓度测量结果的可能性。此类设备将光学方法应用于与使用参考重量法获得的值相当的值。另一个目标是表明以这种方式校正的结果可用于执行这些方法的等效性测试程序。该研究使用广义回归模型 (GRM) 来构建校正函数。使用确定系数和计算设备测量不确定度的方法来评估构建的模型。来自两个测试设备的测量数据和参考方法用于估计模型参数。测量数据是从 2018 年 2 月 1 日至 6 月 30 日在 Nowy Sącz 每天收集的。回归允许构建具有各种功能形式和非常有前途的统计特性的多个模型，以及描述参考测量变化的良好能力。这些模型的测量不确定度值也非常低。在构建的所有模型中，使用来自测试设备的原始 PM10 浓度、空气湿度和风速的线性模型被选为最准确和最简单的模型。除了确定系数外，扩展的相对不确定性还作为所获得模型质量的衡量标准。它的价值很小，远低于 25%，