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Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield
- Munir, Said, Mayfield, Martin, Coca, Daniel, Jubb, Stephen A., Osammor, Ogo
- Environmental monitoring and assessment 2019 v.191 no.2 pp. 94
- air pollutants, air pollution, air quality, carbon monoxide, case studies, linear models, meteorological parameters, monitoring, nitric oxide, nitrogen dioxide, regression analysis, temporal variation
- 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, NO₂, 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. NO₂ 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 NO₂ 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 NO₂ 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 (R²), 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.