Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA.
Department of Environmental Health Sciences, University of Alabama at Birmingham School of Public Health, Birmingham, AL, 205-934-8927, USA.
Sci Rep. 2022 Aug 26;12(1):14571. doi: 10.1038/s41598-022-18200-0.
Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.
先前的研究已经探索了使用经过校准的低成本颗粒物（PM）传感器，但长期性能和可靠性方面仍存在重要的研究空白。通过测量使用 2 年期间传感器性能的变化，评估低成本粒子传感器的纵向性能。2019 年至 2021 年期间，51 个低成本粒子传感器（Airbeam 1 N=29；Airbeam 2 N=22）在 2 年的时间框架内进行了四次校准。通过直接比较 Thermo Scientific Personal DataRAM PDR-1500 单元与 2.5 µm 入口的 1 分钟同步读数，为 Airbeam 1 和 2 PM 传感器创建了特定于香烟烟雾的校准曲线。传感器校准系数的传感器间变异性很高，特别是在研究开始时的 Airbeam 1 传感器中。两种传感器类型的校准系数随时间呈下降趋势，最终校准时间点均<1 [Airbeam 1 平均值（SD）=0.87（0.20）；Airbeam 2 平均值（SD）=0.96（0.27）]。由于电子设备、电池或数据输出问题，我们失去了更多的 Airbeam 1 传感器（56 个中的 27 个，失效率 48.2%），而不是 Airbeam 2（24 个中的 2 个，失效率 8.3%）。证据表明，随时间推移的降解可能更多地取决于粒子传感器类型，而不是个人使用情况。对低成本粒子传感器进行重复校准可能会增加对纵向室内空气污染研究中报告的 PM 水平的信心。
