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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Thursday 02 July 2020

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DATA MINING INDUSTRIAL AIR POLLUTION DATA FOR TREND ANALYSIS AND AIR QUALITY INDEX ASSESSMENT USING A NOVEL BACK-END AQMS APPLICATION SOFTWARE


Volume 11, Issue 2, November 2014, Pages 237–247

 DATA MINING INDUSTRIAL AIR POLLUTION DATA FOR TREND ANALYSIS  AND AIR QUALITY INDEX ASSESSMENT USING A NOVEL BACK-END AQMS APPLICATION SOFTWARE

E. O. Ofoegbu, M. A. Fayemiwo, and M. O. Omisore

Original language: English

Received 20 August 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


An air pollution monitoring application system for analyzing and forecasting air pollutant data was developed in order to provide information about the quality of air we breathe. Two industrial environments were used as case studies namely Ife steel plant and Ibadan Asphalt Company. The application was developed using Microsoft visual studio 2012 for the client side and user interface while MYSQL was used for the database. System flowchart was used to design the application modules. Relevant data were collated from the data acquisition systems in Ile-Ife and Ibadan to develop the application. The application when implemented will enable users living and working in the area of study to know the extent at which the air is polluted, forecast the air data and store the collated data in a relational database which will be updated periodically for analysis. This study will attempt to help individuals to know the quality of air they breathe in any particular environment.

Author Keywords: Pollution, AQI, Air, Model.


How to Cite this Article


E. O. Ofoegbu, M. A. Fayemiwo, and M. O. Omisore, “DATA MINING INDUSTRIAL AIR POLLUTION DATA FOR TREND ANALYSIS AND AIR QUALITY INDEX ASSESSMENT USING A NOVEL BACK-END AQMS APPLICATION SOFTWARE,” International Journal of Innovation and Scientific Research, vol. 11, no. 2, pp. 237–247, November 2014.