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Modeling Ground Level Ozone (O3) of Air Pollution Using Regression Technique

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dc.contributor.authorAhmad A.N.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorDom N.C.en_US
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorYusof K.M.K.K.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorPrabamroong T.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorid57810266500en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57217286875en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid57217119888en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55520774800en_US
dc.contributor.authorid57210403363en_US
dc.date.accessioned2023-05-29T09:37:13Z
dc.date.available2023-05-29T09:37:13Z
dc.date.issued2022
dc.description.abstractIntroduction: Ground-level ozone (O3) was a secondary pollutant involving several types of reactions arising from complicated atmospheric chemistry. This research utilized statistical equations to discern the complex influence of meteorological parameters and precursor contaminants influencing O3 chemistry and concentrations. The goal of this study was to predict ozone (O3) concentrations in Nilai, Negeri Sembilan. Methods: Data were collected from 1 January 2016 until 31 December 2018 that including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), temperature, and relative humidity (RH). The data were analyzed by using Multiple Linear Regression (MLR) in predicting the next hours of O3 concentration. Results: O3 concentration reached its peak during 15:00 hours and lower at night time (20:00 hours) due to the absence of sunlight and redox reactions. There exists strong significant correlation between O3 and temperature (r= 0.729, p<0.01), relative humidity (r= -0.732, p<0.01), NOx (r= -0.654, p<0.01), NO (r= -0.630, p<0.01) and NO2 (r= -0.535, p<0.01). Meanwhile, MLR models executed high accuracy for O3,t+1 (R2= 0.5565), O3,t+2 (R2= 0.5326) and O3,t+3 (R2= 0.5197). Conclusion: In conclusion, the MLR model is suitable for the next hours O concentration prediction. � 2022 UPM Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.47836/mjmhs18.8.14
dc.identifier.epage103
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85134487625
dc.identifier.spage97
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85134487625&doi=10.47836%2fmjmhs18.8.14&partnerID=40&md5=1773f5a5e8c7031bbba77f150b8f6273
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26850
dc.identifier.volume18
dc.publisherUniversiti Putra Malaysia Pressen_US
dc.sourceScopus
dc.sourcetitleMalaysian Journal of Medicine and Health Sciences
dc.titleModeling Ground Level Ozone (O3) of Air Pollution Using Regression Techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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