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Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia

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dc.citedby33
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorNapi N.N.L.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMansor W.N.W.en_US
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorAbdullah A.M.en_US
dc.contributor.authorRamly Z.T.A.en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57224902975en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56896999100en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57193067284en_US
dc.contributor.authorid57196459394en_US
dc.date.accessioned2023-05-29T08:10:51Z
dc.date.available2023-05-29T08:10:51Z
dc.date.issued2020
dc.descriptionAir quality; Carbon monoxide; Errors; Forecasting; Linear regression; Nitrogen oxides; Sulfur dioxide; Wind; Accuracy; Forecasting modeling; Malaysia; Multiple linear regressions; Particulate Matter; Precautionary measures; Stepwise multiple linear regression; Trans-boundary; Particles (particulate matter); accuracy assessment; error analysis; forecasting method; haze; multiple regression; particulate matter; prediction; Malaysiaen_US
dc.description.abstractMalaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to develop a PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. Therefore, this study aimed to develop and compare the best-fitted model for PM10 prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007-2009), which included particulate matter (PM10), ozone (O3), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO2), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM10, t+1 model was the best MLR model to predict PM10 during transboundary haze events compared to PM10,. t+2 and PM10, t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM10 concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo289
dc.identifier.doi10.3390/atmos11030289
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85082306221
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082306221&doi=10.3390%2fatmos11030289&partnerID=40&md5=ec07e9d096ed4bf68505f2273d2a695d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25556
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleAtmosphere
dc.titleDevelopment of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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