Android application for monitoring and diagnosing air quality

Georgiana Vișan, Mădălina Cărbureanu, Sanda Mihalache

Abstract


Air pollution is a major factor in developing health problems and its monitoring is essential in preventing them. The proposed application (AirAI) aims to provide information to people about the environment, respectively about the quality of the air they breathe in daily life. Through it, the concentration of each air pollutant, the corresponding index and the evolution in time can be identified. The people may also receive warning messages if a pollutant is about to exceed the allowable threshold or alert messages if the threshold has been exceeded and human health may be affected. Once warned, the application also presents a series of instructions that people can follow so as not to endanger their health. The application also uses artificial intelligence techniques, as it also presents a module that contains an expert system (knowledge base and inference engine) for establishing the general quality index of an air monitoring station and for issuing the corresponding warning messages. The developed expert system calculates the general index of an area gradually: initially it takes over all the concentrations of the pollutants, allocates for each pollutant a particular index, depending on its concentration.

Full Text:

PDF

References


M. Oprea, C. Nichita, D. Dunea, AI applications in environmental protection (in Romanian), Ploiesti: Petroleum-Gas University, 2008.

I. Kok, M. Guzel and S. Ozdemir, “8 - Recent trends in air quality prediction: An artificial intelligence perspective”, Editor(s): S. Bhattacharyya, N. Kumar Mondal, J. Platos, V. Snášel, Pl Krömer, in Intelligent data-centric systems, intelligent environmental data monitoring for pollution management, Academic Press, 2021, pp. 195-221, https://doi.org/10.1016/B978-0-12-819671-7.00008-7.

D. Schürholz, S. Kubler and A. Zaslavsky, “Artificial intelligence-enabled context-aware air quality prediction for smart cities”, Journal of Cleaner Production, 271(3):121941, 2020, 121941, DOI:10.1016/j.jclepro.2020.121941.

National Air Quality Monitoring Network, https://www.calitateaer.ro/public/home-page/?local e=ro.

Law no. 104 of June 15, 2011 on ambient air quality, https://www.calitateaer.ro/export/sites/default/.galleries/Legislation/national/Lege-nr.-104_2011-calitatea-aerului-inconjura tor.pdf_2063068895.pdf.

Assessment report on xylenes for developing ambient air quality objectives, https://open.alberta.ca/dataset/2a18a23d-2136-48e8-8c62-bf3bbf9a3e62/resource/3b0fa971-b33e-491c -b1f4-bf006b1e61f6/download/2004-assessmentreport-xylene s-nov2004.pdf.

Assessment report on ethylbenzene for developing ambient air quality objectives, https://open.alberta.ca/dataset/ 9736fb a1-a699-4a69-8667-c22 a8b6c5e1b/resource/70f15981-8032-45d2-a175-f623769e69b a/download/2004-assessmentreport-ethylbenzene-nov2004. pdf.

Assessment report on toluene for developing ambient air quality objectives, https://open.alberta.ca/dataset/22a3b2b2-19ba-49cd-8437-94 4050b53a90/resource/c7e769e7-43d9-4c6b-8401-a2e4f9cb62 6f/download/2004-assessmentreport-toluene-nov2004.pdf.

Air quality in Europe-2019 Report, https://www.eea.euro pa.eu/publications/air-quality-in-europe-2019.

M. Oprea et al., "On the development of an intelligent system for particulate matter air pollution monitoring, analysis and forecasting in urban regions," 19th International Conference on System Theory, Control and Computing (ICSTCC), 2015, pp. 711-716, DOI:10.1109/ICSTCC.2015.7321377.

M. Oprea, D. Dunea and Hai-Ying Liu, “Development of a knowledge based system for analyzing particulate matter air pollution effects on human health”, Environmental engineering and management journal, vol. 16 (3), 2017, pp. 669-676, https://eemj.eu/index.php/EEMJ/article/view/3217.

H. Liu, D. Dunea., M. Oprea, T. Savu and S. Iordache, “Improving the protection of children against air pollution threats in Romania - The RokidAIR Project Approach and Future Perspectives”, Rev. Chim., vol. 68(4), 2017, pp. 841-846, https://doi.org/10.37358/RC.17.4.5563.

Copernicus. Europe Eyes on Earth, https://emergency.Coper nicus.eu/mapping/ems/what-copernic us.

Copernicus. Climate Change Service, https://climate.Cop ernicus.eu/c3s-and-cams-host-first-artificial-intelligence-cop ernicus-workshop.

Weather Channel,https://www.techrepublic.com/article/ibms-the-weather-chan%20nel-app-using-machine-learning-to-for ecast-allergy-hotspots/.

Weather Channel, https://weather.com/forecast/allergy /l/Iowa +City+IA?canonicalCityId=3c40c142d463d70b22673bdfc9b41b46d85c2f7bfaa4d4f3682c7320735db3b8.

M. Oprea, M. Cărbureanu and E. Dragomir, “AirQMAS : A collaborative multi-agent system for air quality analysis”, 2012, http://ace.ucv.ro/analele/2012vol1/04_Oprea_Mihaela .pdf.

MIT App Inventor, https://appinventor.mit.edu/.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Journal of Electrical Engineering, Electronics, Control and Computer Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.