In addition to providing sensors, we aim to educate. We host workshops prior to each deployment. In each workshop we cover the relevant topics related to air pollution and how it affects our health and our environment. We also cover how one can monitor air quality and how each sensing method works. This then follows an interactive session where we show how one can build a sensor, using one of our open-source designs.
In order to ensure the quality of the data collected with open-seneca air quality sensors, a two-stage sensor calibration is performed. The first stage is done by the manufacturer (laboratory calibration under controlled conditions) and the second one is performed by open-seneca before and after a pilot period (co-localisation to ensure each sensor output is cross-comparable, recommended duration of 2 weeks). By integrating the data collected with the low-cost monitors with the existing regulatory stations, further calibration can be performed in a dynamic way during data collection periods.
After initial calibration, the mobile air quality sensors are handed over to volunteers to carry for the period of the pilot, to collect geo-tagged air quality data. We recommend a minimum of 1 air quality sensor for every 4 km2 desired covered per month of the pilot. Each volunteer, also called citizen scientists, are asked to follow a strict protocol to ensure data is collected in a consistent manner. The data they collect is protected under GDPR. However, they are welcome to share their routes publically should they choose to, much like on popular sports activity apps (e.g. Strava). Each collection session is viewable on our online interactive platform, and details information about their personal exposure to pollutants, e.g. PM2.5.
At the end of the pilot, the sensors should be colocated again. From both colocation sessions, each sensor has a final calibration curve applied and any anomalous sensor data removed. The data is anonymised and aggregated to produce maps of the regions covered by the citizen scientists. The aggregated maps enable the identification of hotspots in the covered region. Anonymised data products are made public and passed onto policy makers.
Summary of the key steps:
Evidence based policy