Introduction Citizen Science describes the collaboration between citizens and scientists on scientific issues and has gained in importance in recent years [1]. The development of the topic Citizen Science shows that citizens should no longer act as mere data collectors, but are more and more integrated into the scientific work. This includes the imparting of background knowledge, the introduction to scientific methods as well as joint data evaluation and interpretation. Through this approach, science is made accessible, and citizens often draw more conclusions regarding their own actions, behaviors and habits [2]. Especially young people are developing ever greater environmental awareness due to the already noticeable effects of climate change. Current topics in the media about Fridays for Future, but also exhaust emissions of cars (“Diesel scandal”) and increasing concentrations of particulate matter in inner cities show that air quality is becoming more and more important for them [3].In the project “SUSmobil” students aged between 12 and 18 receive fundamental information on gas sensing, consisting of functionality, calibration process and data evaluation, and then have the opportunity to develop their own environmental studies, such as an “air pollution map” or “the investigation of the air composition in beehives”. The project is divided into four modules, each of which focuses on one aspect of gas sensing and air quality. The concept is presented below. Fundamentals of gas sensing Beginning with the theoretical fundamentals on how semiconductor gas sensors work, the first module describes the functionality of metal oxide semiconductor (MOS) gas sensors. By examining the sensor reaction to the headspace of different substances, water, apple juice and non-alcoholic beer, the students learn about the relevant processes on the sensor surface. The second module offers an insight into modern methods of calibration of semiconductor gas sensors. In contrast to the first module, which was dealing with qualitative discrimination of substances, the focus here is laid on the quantification of one substance. The sensor model previously developed in module 1 is extended by the temperature cycled operation (TCO), which can be used to improve sensitivity, selectivity and stability of the sensor [4]. By addressing the topics of data analysis, feature extraction and mathematical modeling using neural networks, a comprehensive picture of the scientific work with air quality sensors is provided.The third module addresses environmental measurements and relevant parameters, e.g. particulate matter (PM), volatile organic compounds (VOCs) and CO2. Students learn about different sensor principles such as non-dispersive infrared absorption (NDIR) and laser scattering. Threshold limit values and possible sources of harmful VOCs are described and tested with model substances. Example of an environmental study: Air pollution map Motivated students are given the opportunity to create their own environmental studies using a citizen science approach supported by scientists. One example for an environmental study is the creation of an “air pollution map”, which two students (age 17 and 18) developed as part of the German youth research competition "Jugend forscht” (figure 1). The aim of this project was to perform air quality measurements inside and outside of the city and visualize the data using a freely accessible web page. The students designed a portable 3D-printed sensor system and integrated the air quality sensor “BME680” from Bosch (figure 2). The integrated gas sensor measures temperature, air pressure, humidity and a so called “air quality index” (AQI) derived from the raw signals of a MOS sensor and humidity. This index with a range between 0 (“Good”) and >300 (“Hazardous”) is not an official index, since the sensor was not calibrated based on an excepted test standard. Yet this value gives a quick indication of air quality. The system is controlled by the internet-enabled microcontroller “ESP32”. Via an internet hotspot, provided by a smartphone, the collected data, including GPS information of the smartphone, is sent to the online database of the free to use IoT platform “Blynk”. The corresponding smartphone app allows visualization of the data in real time (figure 3). Furthermore the students created a website, which collects the data from the online server and displays the values on a “Google Maps” based heatmap (figure 4). The color of the marked areas corresponds to the air quality index. Outlook The students were able to create a basic setup to measure air quality with a mobile sensor system. This can be adapted and further extended in subsequent studies by other students, for example using other sensors and including rigorous calibration and testing. Acknowledgement The project is funded by the environmental foundation DBU (“Deutsche Bundesstiftung Umwelt”).
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May 29, 2020
Anton V Ubaychin + 2
Open Access