Department of Civil and Structural Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK.
Environ Monit Assess. 2019 Jan 22;191(2):94. doi: 10.1007/s10661-019-7231-8.
Traditional real-time air quality monitoring instruments are expensive to install and maintain; therefore, such existing air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant maps. More recently, low-cost sensors have been used to collect high-resolution spatial and temporal air pollution data in real-time. In this paper, for the first time, Envirowatch E-MOTEs are employed for air quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a year (October 2016 to September 2017) monitoring several air pollutants (NO, NO, CO) and meteorological parameters. Their performance was compared to each other and to a reference instrument installed nearby. E-MOTEs were able to successfully capture the temporal variability such as diurnal, weekly and annual cycles in air pollutant concentrations and demonstrated significant similarity with reference instruments. NO concentrations showed very strong positive correlation between various sensors. Mostly, correlation coefficients (r values) were greater than 0.92. CO from different sensors also had r values mostly greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several multiple linear regression models (MLRM) and generalised additive models (GAM) were developed to calibrate the E-MOTE data and reproduce NO and NO concentrations measured by the reference instruments. GAMs demonstrated significantly better performance than linear models by capturing the non-linear association between the response and explanatory variables. The best GAM developed for reproducing NO concentrations returned values of 0.95, 3.91, 0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), coefficient of determination (R), normalised mean biased (NMB) and coefficient of efficiency (COE), respectively. The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.
传统的实时空气质量监测仪器安装和维护成本高昂；因此，现有的空气质量监测网络部署稀疏，缺乏开发高分辨率时空空气污染地图的测量密度。最近，低成本传感器已被用于实时收集高分辨率的时空空气污染数据。在本文中，首次将 Envirowatch E-MOTEs 用作谢菲尔德的空气质量监测案例研究。十个 E-MOTEs 部署了一年（2016 年 10 月至 2017 年 9 月），监测了几种空气污染物（NO、NO、CO）和气象参数。将它们的性能相互进行了比较，并与附近安装的参考仪器进行了比较。E-MOTEs 成功地捕捉到了污染物浓度的时间变化，如昼夜、每周和年度周期，并与参考仪器表现出显著的相似性。NO 浓度与各种传感器之间显示出非常强的正相关性。大多数情况下，相关系数（r 值）大于 0.92。不同传感器的 CO 也有大多数大于 0.92 的 r 值；然而，NO 的 r 值小于 0.5。此外，还开发了几个多元线性回归模型（MLRM）和广义加性模型（GAM）来校准 E-MOTE 数据并再现参考仪器测量的 NO 和 NO 浓度。GAMs 通过捕捉响应和解释变量之间的非线性关系，表现出明显优于线性模型的性能。为再现 NO 浓度而开发的最佳 GAM 返回的 FAC2、RMSE、R、NMB 和 COE 值分别为 0.95、3.91、0.81、0.005 和 0.61。低成本传感器提供了一种更经济实惠的替代方案，可提供具有可接受性能的实时高分辨率时空空气质量和气象参数数据。
