Abstract
We address the problem of improving the performance of an environmental sensor for air pollution estimations, by employing computational intelligence methods to analyze and model the sensor readings. Such sensors may complement the official air quality monitoring stations established by authorities and may support the development of a dense network especially useful in urban areas. Our study demonstrates that it is possible to improve the accuracy of a low-cost air quality monitoring system (LCAQMS) based on an optical particulate matter sensor, making use of available atmospheric environment measurements. We also investigate whether such an improvement is possible by employing meteorological data only, to achieve better cost efficiency. On this basis, such LCAQMS can be used to access the quality of the atmospheric environment in an area of interest and to support relevant predictions.
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Nikolis, D., Karatzas, K., Kuula, J., Timonen, H. (2023). Analysis and Modelling of an Optical Particulate Matter Sensor Data Towards Its Performance Improvement. In: De Vito, S., Karatzas, K., Bartonova, A., Fattoruso, G. (eds) Air Quality Networks. Environmental Informatics and Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-08476-8_8
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