Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America.
Department of Mechanical Engineering, College of Engineering, University of Washington, Seattle, Washington, United States of America.
PLoS One. 2021 Nov 11;16(11):e0259745. doi: 10.1371/journal.pone.0259745. eCollection 2021.
Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.
低成本的光学散射颗粒物 (PM) 传感器报告总颗粒物或特定粒径颗粒物的数量和质量浓度。PM 浓度和粒径由原始设备制造商 (OEM) 的专有算法估算,由于颗粒物散射取决于颗粒物的特性,如大小、形状和复折射率 (CRI),以及环境参数,如温度和相对湿度 (RH),因此这些算法存在固有局限性。由于低成本 PM 传感器无法分辨单个颗粒物,因此需要在受控环境下对传感器性能进行表征和校准。在这里,我们提出了改进的 Plantower PMS A003 传感器质量指数和粒径分辨数浓度的校准算法。采用气溶胶室实验方案来评估传感器间数据的重现性。使用四种多分散测试气溶胶进行校准。PMS A003 传感器的颗粒尺寸分布 OEM 校准与参考单颗粒粒径仪测量结果不一致。对于数浓度校准,不考虑气溶胶特性和环境条件的线性模型与参考仪器相比产生约 4.0%的绝对误差 (NMAE)。针对 OEM 质量浓度估计中的非线性,校准模型中调整了颗粒物的 CRI 和密度,使得 NMAE 在 5.0%以内。本研究中开发的校准算法可用于室内空气质量监测、职业/工业暴露评估或现场校准可能具有挑战性的近源监测场景。