Numerous low-cost air quality sensors have become available in the market in the last decade. If their limitations regarding data accuracy are understood and proper calibration is performed, these low-cost devices become a powerful tool for air quality monitoring in urban environments. Large networks of these sensors can be deployed to supplement existing official monitoring stations, filling spatial and temporal gaps, and their data can be uploaded to global platforms such as the UNEP AirVisual platform and OpenAQ. Moreover, they have shown the potential to obtain air pollution data with a spatial and temporal resolution un-achievable by reference stations. The data from such approaches provide a rich depth of information, which could be used to drive urban planning and policy towards creating a healthier environment.
|Reference station||Citizen science
|+ High accuracy||+ Enables identification of pollution hotspots
|- Expensive||+ Low cost|
|- Stationary||+ High spatial/temporal resolution|
|+ Engaging and educational|
Combining the use of low-cost sensors with a citizen science approach makes them even more powerful by enabling the monitoring of personal exposure and the identification of hotspots of air pollution. Engaging citizens allows raising their awareness about the importance of taking action to reduce air pollution and to empower them as active stakeholders in the search for solutions. A society that is aware can have a huge impact in reducing air pollution: by reducing their own individual emissions, by minimising their personal exposure, and by complying to measures to reduce emissions implemented in cities by local governments.
The EPA Air sensors guideline1 classifies the performance of air quality sensors in five types (Tiers I-V). The suggested performance goals are summarised in the table below
|I||Education and information||±50%|
|II||Hotspot identification and characterisation||±30%|
Open-seneca monitoring devices use the Sensirion SPS30 Particulate Matter Sensor. This sensor has received the MCERTS2 Performance Standards for Indicative Ambient Particulate Monitors for PM2.5 in the range of 0 - 75µg/m3. The uncertainty of this sensor is ±8.9%, complying with the data quality objective for indicative measurements.
According to this classification, the SPS30 sensor used in the open-seneca air quality sensor would be suitable for any of the applications of Tier I-V. The aim of the open-seneca initiative is to empower citizens with data about their personal exposure to particulate pollution (Tier IV), to raise awareness and drive behaviour change (Tier I), and to provide high spatial and temporal resolution pollution maps that highlight hotspots of particulate pollution (Tier II and III) to inform policy. However, in a citizen science setting where the sensors are mobile, the uncertainty required for Tier V might be compromised, and the initiative does not aim to ensure regulatory compliance of cities to current air quality regulations.
We currently use off-the-shelf components interfaced with a 'plug & play' style PCB. They aim to be simple to build and versatile enough to be applied to any setting, and to be modular and accesible to everyone. Our designs are open-source and available from our public GitHub.
The device requests data every second from each respective sensor, and stores it on the microSD card. The requested data is similarly transmitted via BLE or GSM. Additional sensors and other common IoT communication protocols (e.g. WiFi, LoRa, Sigfox, NBIoT) can be added upon request. We currently have a basic Android app to interface with our sensors via BLE, which can be found here.