Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK.
Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK.
Sci Rep. 2019 May 16;9(1):7497. doi: 10.1038/s41598-019-43716-3.
Exposure to ambient particulate matter (PM) air pollution is a leading risk factor for morbidity and mortality, associated with up to 8.9 million deaths/year worldwide. Measurement of personal exposure to PM is hindered by poor spatial resolution of monitoring networks. Low-cost PM sensors may improve monitoring resolution in a cost-effective manner but there are doubts regarding data reliability. PM sensor boxes were constructed using four low-cost PM micro-sensor models. Three boxes were deployed at each of two schools in Southampton, UK, for around one year and sensor performance was analysed. Comparison of sensor readings with a nearby background station showed moderate to good correlation (0.61 < r < 0.88, p < 0.0001), but indicated that low-cost sensor performance varies with different PM sources and background concentrations, and to a lesser extent relative humidity and temperature. This may have implications for their potential use in different locations. Data also indicates that these sensors can track short-lived events of pollution, especially in conjunction with wind data. We conclude that, with appropriate consideration of potential confounding factors, low-cost PM sensors may be suitable for PM monitoring where reference-standard equipment is not available or feasible, and that they may be useful in studying spatially localised airborne PM concentrations.
暴露于环境颗粒物(PM)空气污染是发病率和死亡率的主要危险因素,与全球每年多达 890 万人的死亡有关。由于监测网络的空间分辨率差,对个人 PM 暴露的测量受到阻碍。低成本 PM 传感器可以以具有成本效益的方式提高监测分辨率,但数据可靠性存在疑问。PM 传感器盒使用四种低成本 PM 微传感器模型构建。在英国南安普顿的两所学校中的每所学校都部署了三个盒子,大约一年时间,并对传感器性能进行了分析。传感器读数与附近背景站的比较表明中度至良好的相关性(0.61 < r < 0.88,p < 0.0001),但表明低成本传感器的性能随不同的 PM 源和背景浓度而变化,受相对湿度和温度的影响较小。这可能对它们在不同地点的潜在用途产生影响。数据还表明,这些传感器可以跟踪污染的短暂事件,尤其是与风数据结合使用时。我们的结论是,在适当考虑潜在混杂因素的情况下,低成本 PM 传感器可能适用于没有或无法使用参考标准设备的 PM 监测,并且它们可能有助于研究空间局部化的空气中 PM 浓度。