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Received 29.09.2022

Revised 26.02.2022

Accepted 26.03.2023

Retrieved from Iss. 113, P. 1, 2023

Pages 130 -142

  • 110 Views

Suggested citation

Yesypova, A., Kobzysta, O., Kolomiiets, S., & Mykhailova, M. (2023). ASSESSMENT OF ATMOSPHERIC AIR QUALITY BASED ON DATA FROM AN AUTOMATIC MONITORING STATION. Automobile Roads and Road Construction, (113.1), 130-142. https://doi.org/10.33744/0365-8171-2023-113.1-130-142

ASSESSMENT OF ATMOSPHERIC AIR QUALITY BASED ON DATA FROM AN AUTOMATIC MONITORING STATION

Anna Yesypova Oksana Kobzysta Serhii Kolomiiets Maryna Mykhailova

Abstract

The article discusses the technologies for creating a network of public monitoring of the state of atmospheric air pollution in the city, which involves independent monitoring based on resolutions and other guidelines on air quality. It is proposed to introduce public environmental monitoring of atmospheric air quality on the basis of indicative measurement of air quality indicators through sensor sensors. The possibilities of using the Oxygen air quality monitoring station, which allows real-time monitoring and recording of the air condition, namely temperature, humidity, PM2.5 and PM10 dust concentration, CO (carbon monoxide), NO2 (nitrogen oxides) and NH3 (ammonia), are analyzed. It has been shown that the functionality of the Oxygen automatic air quality monitoring station allows not only to obtain operational data on air quality, but also to accumulate big data for assessing and predicting pollution indices and risks to public health using a personal account on the EcoCity website. During the experimental studies, the air quality monitoring station measured the maximum concentrations of CO, NH3, PM10 and PM2.5 in the air that did not exceed the MPCs, while periodically exceeding the concentration of nitrogen dioxide NO2. The data obtained indicate a harmful impact, mainly of vehicles, on the environment and public health in the area. In further studies, it is planned to assess the risks to public health in the Pechersk district of Kyiv in accordance with the guidelines "Assessment of the risk to public health from air pollution", taking into account the indicators of other public monitoring stations located in the study area

Keywords:

air quality monitoring, public monitoring, automatic monitoring station, pollutants, air quality index, public awareness

References

  1. EcoCity. (n.d.). Retrieved from https://eco-city.org.ua/.
  2. Methodical recommendations MR 2.2.12-142-2007. (2007). Assessment of risk to public health from air pollution. Kyiv: Ministry of Health of Ukraine.
  3. Cabinet of Ministers of Ukraine Resolution No. 827 "Some issues of state monitoring in the field of atmospheric air protection". (2019, August). Kyiv.
  4. World Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Retrieved from https://apps.who.int/iris/handle/10665/345329.
  5. SaveEcoBot. (n.d.). Environmental chatbot that combines data on pollution, pollutants and environmental protection tools. Retrieved from https://www.saveecobot.com/.
  6. European Union. (2012). CAQI Air quality index: Comparing urban air quality across borders. Brussels: European Regional Development Fund Regional Initiative Project Cite Air II, INTERREG IVC Programme.
  7. European Environment Agency. (n.d.). The European air quality index: Menu about the European air quality index. Retrieved from https://airindex.eea.europa.eu/Map/AQI/Viewer/.
  8. Cabinet of Ministers of Ukraine Resolution No. 1598 "On approval of the list of the most common and dangerous pollutants, emissions of which into the atmosphere are subject to regulation". (2001, November). Kyiv.
  9. Soroka, M.L. (Ed.). (2022). Radiation and smog alarm: Guidelines and principles for notifying the public about air quality, radiation and chemical hazards. Prague: Arnika.
  10. Kolomiiets, S., & Kolomiiets, A. (2021). Integral criterion of environmental safety as an indicator of the effectiveness of a motor transport enterprise management. In Lecture notes in computational intelligence and decision making: Advances in intelligent systems and computing (Vol. 1246). Cham: Springer.
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https://doi.org/10.33744/0365-8171-2023-113.1-130-142

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