Abstract
Respirable particulate matter may cause serious health problems, hence as a precaution, the authorities should take appropriate action to control pollution. An accurate forecast of future particulate matter concentration is important as it can assist the authorities to implement appropriate laws and regulations to address the impact of air pollution. The aim of this study is to investigate the best statistical method for analysing and forecasting particulate matter concentration in Pasir Gudang, Johor, Malaysia. Three statistical approaches are considered, namely Multiple Linear Regression, Principal Component Regression and Time Series Analysis to model and forecast the daily maximum particulate matter concentration levels. The performance of these models is evaluated based on the root mean square error, mean absolute error and mean absolute percentage error. The findings show that Multiple Linear Regression is the best fitted model in forecasting future concentrations, followed by Principal Component Regression, while the time series model produces the least accurate forecasts.
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References
De-Souza, A., Aristones, F., Pavão, H.G., Fernandes, W.A.: Development of a short-term ozone prediction tool in campo grande-MS-Brazil area based on meteorological variables. Open J. Air Pollut 3(02), 42–51 (2014)
DOE Department of Environment: A guide to Air Pollution Index (API) in Malaysia. Ministry of Science, Technology and the Environment (2000)
Liew, J., Latif, M.T., Tangang, F.: Factors influencing the variations of PM10 aerosol dust in Klang Valley Malaysia during the summer. Atmos Environ 45, 4370–4378 (2011)
Ng, K.Y., Awang, N.: Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia. Environ. Monit. Assess. 190(63) (2018)
Ng, K.Y., Awang, N.: Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia. Environ. Monit. Assess. 191(64) (2019)
Manga, E., Awang, N.: Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia. Stoch. Env. Res. Risk Assess. 32(12), 3409–3419 (2018)
Singh, H.P., Karpe, N.: Estimation of mean, ratio and product using auxiliary information in the presence of measurement errors in sample surveys. J. Stat. Theory Practice 4(1), 111–136 (2010)
Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M.: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 4th edn. John Wiley & Sons, New Jersey (2016)
Cook, E.R., Jacoby, G.C.Jr.: Tree-ring-drought relationships in the Hudson Valley, New York. Science 198, 399–401 (1977)
Fritts, H.C., Blasing, T.J., Hayden, B.P., Kutzbach, J.E.: Multivariate techniques for specifying tree-growth and climate relationships and for reconstructing anomalies in paleoclimate. J. Appl. Meteorol. 10, 845–864 (1971)
Todoko, C.A.K.: Time series analysis of water consumption in the hohoe municipality of the Volta Region of Ghana. Master Thesis, Kwame Nkrumah University of Science and Technology (2013)
DOE Department of Environment: Malaysia Environmental Quality Report, Ministry of Natural Resources and Environment Malaysia (2014)
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The authors would like to thank the Department of Environment, Malaysia for providing the data for this study.
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Zulkifli, N.M., Ezzadin, N.W.S., Awang, N. (2022). Performance of Several Statistical Methods in Forecasting Particulate Matter Concentrations in Pasir Gudang, Johor. In: Abdul Karim, S.A. (eds) Intelligent Systems Modeling and Simulation II. Studies in Systems, Decision and Control, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04028-3_31
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DOI: https://doi.org/10.1007/978-3-031-04028-3_31
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