Department of Information Engineering (DINFO), University of Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.
National Research Council-Institute of Biometeorology (CNR-IBIMET), Via Caproni 8, 50145 Firenze, Italy.
Sensors (Basel). 2018 Aug 28;18(9):2843. doi: 10.3390/s18092843.
A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO₂, NO₂, O₃, VOC, PM and PM were integrated on an Arduino Shield compatible board. As concerns PM and PM sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full "heating season" (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM and PM mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.
已开发出一种低成本空气质量监测站,用于实时监测主要大气污染物。CO、CO₂、NO₂、O₃、VOC、PM 和 PM 传感器集成在一个与 Arduino Shield 兼容的电路板上。就 PM 和 PM 传感器而言,该站经过了实验室校准和随后的现场验证。实验室校准在意大利佛罗伦萨的 CNR-IBIMET 总部进行,使用 TSI DustTrak 参考仪器进行。开发并实施了一个 MATLAB 程序,该程序采用先进的数学技术来检测传感器信号与参考数据之间可能存在的复杂非线性关系,以完成实验室校准。现场验证在整个“供暖季”(2016 年 11 月 1 日至 2017 年 4 月 15 日)进行,将该站与佛罗伦萨的一个道路站点并置,该站点设有一个官方固定空气质量监测站在运行。在大多数情况下,校准和验证过程的得分都很好,优于文献中类似系统的得分。特别是在现场验证期间,对于 PM 和 PM 的平均偏差分别为 0.036 和 0.598 µg/m³,RMSE 分别为 4.056 和 6.084 µg/m³,R² 分别为 0.909 和 0.957。该开发的监测站的稳健性是另一个关键点,该监测站在长达五个半月的户外测试中无缝部署,没有记录到传感器故障或漂移。