Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality

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.

Keywords: air pollution; in situ field calibration; micro sensing units; multi-sensor nodes; spatiotemporal variability; wireless distributed environmental sensor networks.

Figure 1

Daily patterns of 30 min. average O₃ concentrations during weekdays (Sunday to Thursday; upper row) and Saturdays (lower row) (a,d) before calibration; (b,e) after calibration against a nearby AQM measurements from 1:00 to 4:00 am; (c,f) and after calibration against the mean half-hourly reading between 1:00 and 4:00 am of all the WDESN nodes. (Reproduced with permission from [19]).

Figure 2

Daily patterns of 30 min average NO₂ (black) and total volatile organic compounds (TVOC) (red) concentrations in (a) site C during weekdays (Sunday to Thursday); (b) site A during weekdays (note the adaptation of node 424 to its microenvironments upon relocation); (c) site B during weekdays; and (d) site B during Saturdays. The MSUs were deployed in a residential neighborhood after they were calibrated while collocated at an AQM station within the neighborhood. (Reproduced with permission from [19]).