What can we learn from nested IoT low-cost sensor networks for air quality? A case study of PM_2.5 in Birmingham, UK

Low-cost sensing and the Internet of Things (IoT), present new possibilities for unconventional monitoring of environmental parameters. This paper describes a series of intersecting networks of particulate matter sensors that were deployed across the Birmingham conurbation for a 12-month period. The networks consisted of a combination of commercially available sensors and University developed sensors. Data from these networks were assimilated with data from a third-party Zephyr deployment, along with the DEFRA AURN network, which was hosted on an open-source online platform. This nesting of sensor networks allowed for new insights into sensor performance, including the accuracy of a large network to detect regional concentrations and the number of sensors needed for effective monitoring beyond indicative measurements. After comprehensive data validation steps, the sensors were shown to perform well during co-location with reference instrumentation (exhibiting slopes of 0.74–1.3). The sensors demonstrated good capability of detecting temporal patterns of regional PM2.5 with the mean of the entire sensor network recording an annual mean PM2.5 concentration within 0.2 μgm−3 of the regulatory network annual mean observation. Network-derived statistics for estimating urban background concentrations compared to a reference site increase in-line with the number of sensors available, however when assessing this for near-source concentrations the importance of sensor location rather than the number of sensors is highlighted. Overall, the network provided novel insights into local concentrations, detecting similar hotspots to those identified by a high-resolution model. The increased spatial coverage afforded by the sensor network has the potential to support higher resolution evaluation of models and provide unprecedented spatial evidence for air pollution management interventions

In-vehicle exposure to NO2 and PM2.5:A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter

Implications of regional surface ozone increases on visibility degradation in southeast China

Long-term visibility (1968–2010) and air pollutant (1984–2010) data records in Hong Kong reveal that the occurrence of reduced visibility (RV, defined as the percentage of hours per month with visibility below 8 km in the absence of rain, fog, mist or relative humidity above 95%) in southeast China has increased significantly in the last four decades. The most pronounced rate of increase was observed after 1990 (nine times higher than that before 1990), when notable increases in surface ozone (O3) levels were simultaneously observed (1.06 µg m−3 per yr). The greatest increases in RV, and in O3, NO2 and SO2 concentrations are coincident in the autumn (1.47, 0.20 and 0.45 µg m−3 per yr respectively), when southeast China is strongly influenced by regional O3 formation and accumulation due to continental outflow of pollution from the east China coast under favourable meteorological conditions. Multiple regression revealed that the RV percentage correlated well (p<0.05) with NO2 and NO x in the 1980s, and with NO2, SO2 and O3 after the 1990s, suggesting that there have been changes in the predominant factors causing visibility degradation. In order to elucidate the reasons for these changes, the results were integrated with data from previous research. Possible impacts of elevated O3 on secondary particle formation and their effects on visibility degradation and aerosol radiative forcing in an oxidant-enhanced southeast China are highlighted. Other factors potentially leading to visibility degradation, such as ship emissions and biomass burning, are also discussed

Street-scale air quality modelling over the West Midlands, United Kingdom:Effect of idealised traffic reduction scenarios

Air pollution is the major environmental risk to human health. Road transport is one of the major sources for air pollution, particularly nitrogen dioxide, in urban areas, and hence traffic control is an important measure in air quality management. A street-scale air quality model, ADMS-Urban, was configured for a case study of the West Midlands, UK to represent a baseline year (2019). Model outputs were evaluated using hourly air pollutant measurement data, and the model demonstrates good performance overall. This modelling tool was then used to explore the effect of five hypothetical traffic reduction scenarios, ranging from 10% to 90% reduction in traffic activity; scenario impacts were analysed over a range of spatial resolutions. The impacts of traffic reduction are highly dependent on spatial resolution (i.e. street scale, electoral ward level and local authority level), which has to be taken into account when formulating policies for managing air quality on local and city-wide scales. There was an almost linear relationship between the predicted annual concentration and traffic reduction for both NO2 and PM2.5. Traffic reduction would principally reduce NO2 concentrations, with even very substantial changes in traffic having more limited effects on reducing PM2.5 concentrations reflecting the importance of regional and non-traffic PM2.5 sources.</p