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Multivariate Analysis For Air Contamination And Meteorological Parameters In Zonguldak Turkey

Submitted2022-04-15
Last Update2022-10-01
TitleMultivariate Analysis For Air Contamination And Meteorological Parameters In Zonguldak Turkey
Author(s)Author #1
Author title:Doctor
Name: Kadir Ulutaş
Org: 1Karabuk University, Faculty of Engineering, Department of Environmental Engineering
Country: Turkey
Email: kadirulutas@karabuk.edu.tr

Author #2
Author title:Professor
Name: Abbas Alkarkhi
Org: Universiti Kuala Lumpur
Country: Malaysia
Email: alkarkhii@gmail.com

Author #3
Author title:Dr.
Name: Sohaib Abujayyab
Org: International college of Engineering and management
Country: Oman
Email: s.jayyab@hotmail.com

Author #4
Author title:Dr.
Name: Salem Abu Amr
Org: International college of Engineering and management
Country: Oman
Email: sabuamr@hotmail.com

Other Author(s)
Contact AuthorAuthor #2
Alt Email: alkarkhii@gmail.com
Telephone: 60124351833
KeywordsAir Quality, cluster analysis, factor analysis, particulate matter
AbstractThis study evaluates the concentration of PM10, PM2.5, NOX, NO2, CO, and SO2 parameters and the four climatological parameters (Temperature, wind speed, humidity, and net radiation flux) during the four seasons. Various statistical techniques were utilized to study the behavior of the selected parameters during seasons. Descriptive statistics exhibited that most parameters have a high concentration in winter except NO2 (which has a high concentration in autumn). While the concentration of those parameters was the lowest in summer except NO2 and NOX (have a high concentration in spring). Factor analysis (FA) showed that more than 80% of the total variation belongs to two factors. Where,19.47% of the variation due to wind speed and humidity while other parameters responsible for 62.90% of the total variance Cluster analysis (CA) evaluated the similarity and dissimilarity between various elements through identifying; four clusters representing the seasons, cluster 1: autumn, cluster 2: winter, cluster 3: spring, and cluster 4: summer. This clustering indicates that the four seasons are entirely different. The highest dissimilarity was reported between summer and the other seasons. CA also classified all parameters into five statistically different clusters; cluster 1: PM10, PM 2.5, and CO; cluster 2: SO2, NOX, and NO2; cluster 3: humidity; cluster 4: Temperature and radiation and the last is cluster 5: wind speed. This study illustrates the benefits of using multivariate techniques for evaluation and interpretation of total variation to get a better picture of the pollution sources/factors and understand the behavior of the parameters in the air.
Paperview paper 6687.pdf (454KB)

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