MATEC Web Conf.
Volume 252, 2019III International Conference of Computational Methods in Engineering Science (CMES’18)
|Number of page(s)||7|
|Section||Computational Artificial Intelligence|
|Published online||14 January 2019|
A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers
Lublin University of Technology, Faculty of Management, Nadbystrzycka 38A, 20-618 Lublin, Poland
2 Research and Development Center, Netrix S.A., Związkowa 26, 20-001 Lublin, Poland
3 University of Economics and Innovation in Lublin, Projektowa 4, 20-209 Lublin, Poland
4 Pope John Paul II State School of Higher Education in Biala Podlaska, Sidorska 95/97, 21-500 Biała Podlaska, Poland
* Corresponding author: firstname.lastname@example.org
This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such as e.g. air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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