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.

Two air pollutants, oxides of nitrogen (NOx) and particulate matter (PM10), are monitored and modelled employing Airviro air quality dispersion modelling system in Sheffield, United Kingdom. The aim is to determine the most significant emission sources and their spatial variability. NOx emissions (ton/year) from road traffic, point and area sources for the year 2017 were 5370, 6774, and 2425, whereas those of PM10 (ton/year) were 345, 1449, and 281, respectively, which are part of the emission database. The results showed three hotspots of NOx, namely the Sheffield City Centre, Darnall and Tinsley Roundabout (M1 J34S). High PM10 concentrations were shown mainly between Sheffield Forgemasters International (a heavy engineering steel company) and Meadowhall Shopping Centre. Several emission scenarios were tested, which showed that NOx concentrations were mainly controlled by road traffic, whereas PM10 concentrations were controlled by point sources. Spatiotemporal variability and public exposure to air pollution were analysed. NOx concentration was greater than 52 µg/m3 in about 8 km2 area, where more than 66 thousand people lived. Models validated by observations can be used to fill in spatiotemporal gaps in measured data. The approach used presents spatiotemporal situation awareness maps that could be used for decision making and improving the urban infrastructure.

Airborne particulate matter contains a mixture of pollutants.  Identifying the source of these particles, their composition and physical/chemical properties would help to provide a clear connection between their impacts on the environment and the human health.  Individual particles have a different chemical morphology and this data could provide information on the formation and reaction mechanism of these particles. It also helps to identify the source they originate from as well as their atmospheric history.  Over the years, numerous studies have been conducted to characterise PM_10­ and little work has been carried out on PM_2.5.  However, there is an emerging interest in identifying the effects of very fine particles such as nano-particles. 

The main objective of this research project was to carry out a comprehensive characterisation study of nano-particles collected from a city environment.  Environmental monitoring samples from a local authority monitoring site were collected over a period of 7 months using a tapered element oscillating microbalance technique (TEOM). The sample filters were then analysed for their morphology and elemental compositions using SEM/EDS and LA-ICP-MS.    SEM/EDS analysis was able to detect several heavy metal particulate matter while the LA-ICP-MS showed that there were more heavy metals present in the filter samples especially the heavier metals.  Some of these heavier elements could have been inhibited by organic or higher amounts of the more common metals found in the EDS such as Fe, Zn, Si and Al.  Nano-particles originated from high temperature sources, biological, carbonaceous and road transport were also detected in the samples.  It was also found that particles containing more metallic elements tended to have a more defined shape while carbonaceous materials typically had amorphous structures. Tests showed that particles with environmental dust compositions of Ca, Al and Si were abundant. ...

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