Abstract
A wide range of pollutants cannot be perceived with human senses, which is why the use of gas sensors is indispensable for an objective assessment of air quality. Since many pollutants are both odorless and colorless, there is a lack of awareness, in particular among students. The project SUSmobil (funded by DBU – Deutsche Bundesstiftung Umwelt) aims to change this. In three modules on the topic of gas sensors and air quality, the students (a) learn the functionality of a metal oxide semiconductor (MOS) gas sensor, (b) perform a calibration process and (c) carry out environmental measurements with calibrated sensors. Based on these introductory experiments, the students are encouraged to develop their own environmental questions. In this paper, the student experiment for the calibration of a MOS gas sensor for ethanol is discussed. The experiment, designed as an HTML-based learning, addresses both theoretical and practical aspects of a typical sensor calibration process, consisting of data acquisition, feature extraction and model generation. In this example, machine learning is used for generating the evaluation model as existing physical models are not sufficiently exact.
Highlights
Introduction and motivationAir pollution is the single largest environmental health risk in Europe with over 400.000 deaths per year in 2018 [1]
In three learning modules the students learn about the function principle of metal oxide semiconductor (MOS) gas sensors, the required calibration process for quantification of target gas concentrations and perform practical measurements of indoor air quality (IAQ, module 3)
This paper focuses on module 2 - quantitative calibration of a MOS gas sensor, here for different ethanol concentrations
Summary
Air pollution is the single largest environmental health risk in Europe with over 400.000 deaths per year in 2018 [1]. In three learning modules the students learn about the function principle of metal oxide semiconductor (MOS) gas sensors (module 1), the required calibration process for quantification of target gas concentrations (module 2) and perform practical measurements of indoor air quality (IAQ, module 3). These modules form the theoretical basis for students to develop their own environmental studies in the form of citizen science projects [10], [11]. Starting with a conceptualization of the term "calibration", aspects such as the recording of training data, feature extraction and model formation using an Artificial Neural Network (ANN) are dealt with in a HTML-based learning course
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