To evaluate the performance of low-cost PM_2.5 sensors and develop calibration models for correcting for the PM_2.5 sensor data (PM_2.5,S), field comparison tests were conducted based on Met One BAM-1020 data at various locations using long-term (≥one year) data of Plantower PMS5003, Sensirion SPS30, and Honeywell HPMA115S0 PM_2.5 sensors. Both multivariate linear regression (MLR) and non-linear regression (NLR) models using hourly RHs and original sensor PM_2.5 data as parameters were able to obtain accurate calibrated hourly PM_2.5 values with MNBs (mean normalized biases) less than about ±10% and MNEs (mean normalized errors) less than about 30% for all three types of PM_2.5 sensors at all monitoring locations. On the other hand, the MNB and MNE of the calibrated 24-hr average PM_2.5 data for the two models were less than ±13% and 20%, respectively. Moreover, the slope, intercept, and R² of the linear regression line of the calibrated 24-hr average PM_2.5 and BAM-1020 data were as good as 1.0 ± 0.1, 0.0 ± 2.0 μg/m³, and ≥0.88, respectively. Therefore, these well-calibrated sensors can well be served for education and information (MNE<50%), hotspot identification and characterization (MNE<30%), and personal exposure study (MNE<30%) purposes, and even supplement the existing daily PM_2.5 data of the air quality monitoring stations (MNE<20%).

为了评估低成本 PM _2.5传感器的性能并开发用于校正 PM _2.5传感器数据 (PM _2.5,S ) 的校准模型，基于 Met One BAM-1020 数据在不同位置使用长期Plantower PMS5003、Sensirion SPS30 和 Honeywell HPMA115S0 PM _2.5传感器的（≥一年）数据。使用每小时 RH 和原始传感器 PM _2.5数据作为参数的多元线性回归 (MLR) 和非线性回归 (NLR) 模型都能够获得准确校准的每小时 PM _2.5值，MNB（平均归一化偏差）小于约 ±10%和 MNEs（平均归一化误差）对于所有三种类型的 PM 都小于约 30%_2.5在所有监测位置的传感器。另一方面，两种模型校准后的 24 小时平均 PM _2.5数据的 MNB 和 MNE分别小于 ±13% 和 20%。此外，校准的 24 小时平均 PM _2.5和 BAM-1020 数据的线性回归线的斜率、截距和 R ²分别为 1.0 ± 0.1、0.0 ± 2.0 μg/m ³和≥0.88 . 因此，这些经过良好校准的传感器可以很好地用于教育和信息（MNE<50%）、热点识别和表征（MNE<30%）以及个人暴露研究（MNE<30%）目的，甚至可以补充现有的空气质量监测站每日 PM _2.5数据（MNE<20%）。