Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia

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dc.contributor.authorKar Yong Ng
dc.contributor.authorNorhashidah Awang
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:24Z
dc.date.available2024-05-10T03:25:24Z
dc.date.issued2018
dc.description.abstractFrequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants.
dc.identifier.citationNg, K. Y., & Awang, N. (2018). Multiple linear regression and regression with time series error models in forecasting PM 10 concentrations in Peninsular Malaysia. Environmental monitoring and assessment, 190, 1-11.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/557
dc.language.isoen
dc.publisherElsevier
dc.publisher.alternativeScience of The Total Environment
dc.subjectMultiple linear regression
dc.subjectRegression with time series error
dc.subjectPM10
dc.subjectForecast
dc.titleMultiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia
dc.typeJournal

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