Abstract
The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.
Highlights
Recent developments in sensory and communication technologies have made the deployment of small, portable, and relatively low-cost Monitoring Sensor Units (MSUs) possible [1]
In a previous study [19], we examined MSUs that contained metal oxide (MO) chemoresistive sensors for O3, NO2, and total volatile organic compounds (TVOC), testing their suitability for measuring ambient pollutant levels and for capturing their spatiotemporal variability
Reference ambient pollutant concentrations (30 min resolution) were obtained from the Neve Shaanan air quality monitoring (AQM) station, which is situated in the neighborhood that served as our study area
Summary
Recent developments in sensory and communication technologies have made the deployment of small, portable, and relatively low-cost Monitoring Sensor Units (MSUs) possible [1]. Depending on the sensor technology and algorithm, these sensors enable us to measure the particle number concentration (PNC) or the particulate mass concentration (particulate matter—PM) The emergence of these relatively low-cost sensor technologies opened new applications for air pollution data gathering beyond the regulatory and scientific uses. Such applications include the empowerment of citizens by providing them with quantitative information about pollutant levels in their vicinity, facilitating the measurement of indoor air quality at home and/or in public spaces, enabling measurement by mobile/personal/wearable sensors rather than only by stationary high-end instruments, etc.
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