Air Pollution Monitoring System
Todor Minkov Rachovski1, Ivan Marianov Ivanov2, Emil Nikolov Hadzhikolev3, Stanka Ivanova Hadzhikoleva4

1Todor Minkov Rachovski, Ph.D., University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria.
2Ivan Marianov Ivanov, student, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria.
3Emil Nikolov Hadzhikolev, Assoc. Prof., Department of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria.
4Stanka Ivanova Hadzhikoleva, Assoc. Prof., Department of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria.

Manuscript received on 27 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 2275-2279 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20670981119/2019©BEIESP | DOI: 10.35940/ijitee.K2067.0981119
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Abstract: In recent years, the problem of low air quality has been discussed in mass media over and over, with increasing urgency. Air pollutants are many and varied – caused by industrial and domestic activities, natural disasters and accidents, and more. Continuous daily breathing of polluted air has a bad effect on human health. The availability and easy access to up-to-date air quality information is useful for citizens when they plan outdoor activities and for their health prevention. There are numerous software applications on the web that track different characteristics of air quality in various cities. Some of them collect data using their own measuring stations, while others collect data from specialized sensors that citizens purchase and install at their preferred location. The task of aggregating data from multiple sources and providing it to users in an appropriate format is a topical task. The paper presents a web application that reports real-time air quality in a user-selected city. The application visualizes information on air temperature and humidity, particulate matter levels, ozone, nitrogen dioxide, ozone and sulfur dioxide. The data is collected using web services from various sources – informational websites and specialized sensors. Future work is directed toward the use of artificial neural networks to predict air pollution, and to determine real-time air quality at points where no measuring stations exist.
Keywords: Air quality, Air pollution, Air quality index, Particulate matter air pollution.
Scope of the Article: Health Monitoring and Life Prediction of Structures