Abstract
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 ase.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.
Highlights
With the development of urban conurbations and industry, air pollution has become a serious problem
In Poland, according to data published by the World Health Organization (WHO), in 2012 more than 26 thousand deaths were recorded that were caused by toxic chemicals contained in the inhaled air [2]
The method relies on compression of data points into micro-clusters using the clustering features (CFs) of balanced iterative reducing and clustering using hierarchies (BIRCH) [6], upper and lower bounds of the reachability distances are derived along with, lrd-values, and local outlier factor (LOF)-values for points within micro-clusters
Summary
With the development of urban conurbations and industry, air pollution has become a serious problem. Residents of a given area knowing the exact location of air quality sensors will be able to protect themselves against harmful conditions or to intervene in cases when a source of pollution is found. Such actions can only be taken in cases where quality monitors are available in large quantities and work in a connected sensor network. Atmospheric conditions, in the future such systems will enable collection of a large amount of historical data, enabling the creation of models describing environmental changes, air quality, and atmospheric conditions Such systems in the future will form a part of smart city systems. The authors describe the assumptions of the system collecting measurement data from basic sensors measuring selected gas compounds and present practical methods for capturing outliers in time series data
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