Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities-a case study in Sheffield.

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.

Characterisation of particulate matter on the receptor level in a city environment.

Airborne particulate matter (PM) has become one of the dominant pollutants with the increasing material and energy demand due to global economic growth. The main objective of this research is to provide a comprehensive receptor level characterisation of the particulate matter collected in a city environment. Particulate matter samples were collected on Tapered Element Oscillating Microbalance (TEOM) filters from five monitoring sites over a period of 1 year. An Andersen eight-stage cascade impactor was also used to collect airborne PM samples from three other locations to compare with the samples collected by TEOM. All the samples were then subjected to individual particle morphology and chemical composition analysis by SEM/EDS. Bulk chemical composition of the samples were also analysed through ICP-OES. Based on these analyses, possible sources of the PM samples were identified. The results showed that the monitoring sites in residential environments were dominated by transportation-derived particles and other migratory particulates. Monitoring sites near the city centre were dominant by particles from transportation, with biological particles abundant for the site closer to a river. The monitoring station located close to the industrial area, despite only 200 m away from a motorway, has low contribution of non-exhaust particulates from vehicles. Instead, the particulates collected from this site were dominated by industrial sources. An air dispersion modelling package was also used to model the particulate matter dispersion in the city area for the period of sampling. The results from the model showed that the points of high emissions were around industrial areas.