School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China; Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China.
Ecotoxicol Environ Saf. 2021 Mar 15;211:111958. doi: 10.1016/j.ecoenv.2021.111958. Epub 2021 Jan 25.
Accurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements.
To assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality.
We conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration.
Strong correlations (≥0.82) and relatively small relative standard deviations (16-21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56-0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent.
Our study validated low-cost optical PM sensors under a wide range of PM concentrations (8-167 μg/m). Our findings indicated potential applicability of PM sensors in PM exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.
准确的个体暴露评估对于评估颗粒物（PM）的健康影响至关重要。各种基于低成本光学传感器的便携式监测器已经出现。然而，它们应用的主要挑战是保证测量的准确性。
评估新开发的 PM 传感器的性能，并开发事后数据校准方法以优化其数据质量。
我们进行了一系列实验室实验和现场评估，以量化 Plantower PM 传感器 7003（PMS 7003）内的可重复性以及传感器与两种已建立的 PM 测量方法[锥形元素振荡微天平（TEOM）和重量法（GM）]之间的一致性。传感器的事后数据校准方法基于多元线性回归模型（MLRM）和随机森林模型（RFM）。计算原始读数与参考方法数据的校准读数之比，以检查校准后的改进。
在实验室和现场采样期间，发现传感器之间存在很强的相关性（≥0.82）和相对较小的相对标准偏差（16-21%）。与参考方法相比，观察到中等至强的决定系数（0.56-0.83）；然而，存在显著偏差。校准后，PMS 测量值与两种参考方法的比值均趋于收敛。
我们的研究在广泛的 PM 浓度（8-167μg/m）范围内验证了低成本光学 PM 传感器。我们的研究结果表明 PM 传感器在 PM 暴露评估中的潜在适用性，并证实了校准的必要性。线性校准方法可能足以用于使用 TEOM 作为参考的环境监测，而非线性校准方法可能更适合使用 GM 作为参考的室内监测。
