Abstract
This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models’ performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
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References
Afroz, R., Hassan, M. N., Ibrahim, N. A., et al. (2003). Review of air pollution and health impacts in Malaysia. Environmental Research, 92, 71–77.
Anderson, J. O., Thundiyil, J. G., Stolbach, A., et al. (2012). Clearing the air: a review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8, 166–175.
Bhattacharjee, H., Drescher, M., Good, T., Hartley, Z., Leza, J.-D., Lin, B., Moss, J., Massey, R., Nishino, T., Ryder, S., Sachs, N., Tozan, Y., Taylor, C., & Wu, D. (1999). Section 1: Introduction. In Particulate Matter in New Jersey. Princeton: Princeton University.
Brockwell, P. J., & Davis, R. A. (2002). Introduction to time series and forecasting (Second ed.). New York: Springer.
Chelani, A. B., & Devotta, S. (2006). Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air. Journal of the Air & Waste Management Association, 56(1), 78–84.
van der Wal, J. T., & Janssen, L. H. J. M. (2000). Analysis of spatial and temporal variations of PM10 concentrations in the Netherlands using Kalman filtering. Atmospheric Environment, 34, 3675–3687.
Diaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., Moncada-Herrera, J. A., et al. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmospheric Environment, 42, 8331–8340.
DOE Department of Environment. (2000). A guide to Air Pollution Index (API) in Malaysia. Kuala Lumpur: Ministry of Science, Technology and the Environment Malaysia.
DOE Department of Environment. (2015). Malaysia Environmental Quality Report 2014. Kuala Lumpur: Ministry of Natural Resources and Environment Malaysia.
Joo, T. W., & Kim, S. B. (2015). Time series forecasting based on wavelet filtering. Expert Systems with Applications, 42, 3868–3874.
Liew, J., Latif, M. T., Tangang, F. T., Mansor, H., et al. (2009). Spatio-temporal characteristics of PM10 concentration across Malaysia. Atmospheric Environment, 43, 4584–4594.
Liew, J., Latif, M. T., Tangang, F. T., et al. (2011). Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer. Atmospheric Environment, 45, 4370–4378.
Liu, P.-W. G. (2009). Simulation of the daily average PM10 concentrations at Ta-Liao with Box- Jenkins time series models and multivariate analysis. Atmospheric Environment, 43, 2104–2113.
Peng, R. D., Chang, H. H., Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., Dominici, F., et al. (2008). Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. American Medical Association, 299(18), 2172–2179.
Percival, D. B., & Walden, A. T. (2000). Wavelet methods for time series. Cambridge: Cambridge University Press.
Reisen, V. A., Sarnaglia, A. J. Q., Reis, N. C., Jr., Levy-Leduc, C., Santos, J. M., et al. (2014). Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility. Environmental Modelling & Software, 51, 286–295.
Sahani, M., Zainon, N. A., Wan Mahiyuddin, W. R., Latif, M. T., Hod, R., Khan, M. F., Mohd Tahir, N., Chan, C. C., et al. (2014). A case-crossover analysis of forest fire haze events and mortality in Malaysia. Atmospheric Environment, 96, 257–265.
Shaharuddin, M., Zaharim, A., Mohd. Nor, M. J., Karim, O. A., Sopian, K., et al. (2008). Application of wavelet transform on airborne suspended particulate matter and meteorological temporal variations. WSEAS Transactions on Environment and Development, 4(2), 89–98.
Siwek, K., & Osowski, S. (2012). Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Engineering Applications of Artificial Intelligence, 25, 1246–1258.
Vlachogianni, A., Kassomenos, P., Karppinen, A., Karakitsios, S., Kukkonen, J., et al. (2011). Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Science of the Total Environment, 409, 1559–1571.
WHO World Health Organization. (2013). Health effects of particulate matter: policy implications for countries in Eastern Europe, Caucasus and Central Asia. Copenhagen: WHO Regional Office for Europe.
Acknowledgements
The authors would like to acknowledge the Malaysia Department of Environment for providing the data for this study. The first author also thanks Universiti Sains Malaysia and the Ministry of Higher Education for their financial support through the Fellowship Scheme and MyMaster.
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Yong, N.K., Awang, N. Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia. Environ Monit Assess 191, 64 (2019). https://doi.org/10.1007/s10661-019-7209-6
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DOI: https://doi.org/10.1007/s10661-019-7209-6