Abstract Measurements of airborne particles in buildings with low-cost optical particle counters (OPCs) are often inaccurate and subject to uncertainties. This study aims to provide a methodology to improve the performance of low-cost OPCs in measuring indoor particles through machine learning. A two-month field measurement campaign was conducted in an occupied net-zero energy house. The studied OPCs (OPC–N2, Alphasense Ltd.) report size fractionated concentrations from 0.38 to 17.5 μm. Co-located reference instrumentation included a scanning mobility particle sizer (SMPS: 0.01–0.30 μm) and an optical particle sizer (OPS: 0.30–10 μm). The machine learning field calibration method applies Gaussian Process Regression (GPR) and includes two components: (1.) correction of the size-resolved OPC counting efficiency from 0.38 to 10 μm and (2.) prediction of volume size distributions (mass proxy) below the 0.38 μm detection limit of the OPC. The field calibration method is applicable to OPCs that report size fractionated concentrations in different size bins. In (1.), a GPR function was used to correct the size-resolved counting efficiency of the OPCs between 0.38 and 10 μm using the OPS as reference. In (2.), a second GPR function was used to predict the volume size distribution below 0.38 μm using the SMPS/OPS as reference. This was done given the significant contribution of sub-0.38 μm particles to volume concentrations in the accumulation mode. The machine learning field calibration method resulted in a significant improvement in the accuracy of size-integrated volume concentrations (PV2.5, PV10) reported by the OPCs as compared to the SMPS/OPS. Improvements were seen in the Pearson coefficient (before correction: 0.59–0.83; after correction: 0.98–0.99); coefficient of determination (before correction: 0.35–0.69; after correction: 0.97–0.98); and mean absolute percentage error (before correction: 35–69%; after correction: 19–25%).

中文翻译：

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一种提高低成本粒子传感器性能的机器学习场标定方法

摘要 使用低成本光学粒子计数器 (OPC) 对建筑物中的空气传播粒子进行测量通常不准确且存在不确定性。本研究旨在提供一种方法来提高低成本 OPCs 通过机器学习测量室内颗粒的性能。在一座有人居住的净零能耗房屋中进行了为期两个月的实地测量活动。研究的 OPCs（OPC-N2，Alphasense Ltd.）报告了 0.38 到 17.5 μm 的大小分级浓度。并置参考仪器包括扫描迁移率粒度仪（SMPS：0.01–0.30 μm）和光学粒度仪（OPS：0.30–10 μm）。机器学习场校准方法应用高斯过程回归 (GPR) 并包括两个组成部分：(1.) 将尺寸分辨的 OPC 计数效率从 0.38 校正到 10 μm 和 (2. ) 预测体积尺寸分布（质量代理）低于 OPC 的 0.38 μm 检测极限。现场校准方法适用于报告不同尺寸箱中尺寸分馏浓度的 OPC。在 (1.) 中，使用 GPR 函数以 OPS 作为参考，在 0.38 和 10 μm 之间校正 OPCs 的尺寸分辨计数效率。在 (2.) 中，使用 SMPS/OPS 作为参考，使用第二个 GPR 函数来预测低于 0.38 μm 的体积尺寸分布。考虑到亚 0.38 μm 颗粒对累积模式中的体积浓度的显着贡献，这样做是可行的。与 SMPS/OPS 相比，机器学习现场校准方法显着提高了 OPCs 报告的尺寸积分体积浓度（PV2.5、PV10）的准确性。皮尔逊系数有所改善（校正前：0.59–0.83；校正后：0.98–0.99）；决定系数（修正前：0.35-0.69；修正后：0.97-0.98）；和平均绝对百分比误差（校正前：35-69%；校正后：19-25%）。