Skip to main content

Advertisement

SpringerLink
Log in

Modeling the transition behaviors of PM10 pollution index

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Afroz, A., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impact in Malaysia. Environmental Research, 92, 71–77.

    CAS  Google Scholar 

  • Al-Dhurafi, N. A., Masseran, N., & Zamzuri, Z. H. (2018a). Compositional time series analysis for air pollution index data. Stochastic Environmental Research and Risk Assessment., 32(10), 2903–2911.

    Google Scholar 

  • Al-Dhurafi, N. A., Masseran, N., Zamzuri, Z. H., & Razali, A. M. (2018b). Modeling unhealthy air pollution index using a peaks-over-threshold method. Environmental Engineering Science., 35(2), 101–110.

    CAS  Google Scholar 

  • Al-Dhurafi, N. A., Masseran, N., Zamzuri, Z. H., & Safari, M. A. M. (2018c). Modeling the Air Pollution Index based on its structure and descriptive status. Air Quality, Atmosphere and Health, 11(2), 171–179.

  • Alyousifi, Y., Masseran, N., & Ibrahim, K. (2018). Modeling the stochastic dependence of air pollution index data. Stochastic Environmental Research and Risk Assessment., 32(6), 1603–1611.

    Google Scholar 

  • Anderson, T. W., & Goodman, L. A. (1957). Statistical inference about Markov chain. The Annals of Mathematical Statistics., 28, 89–110.

    Google Scholar 

  • Bartoletti, S., & Loperfido, N. (2010). Modelling air pollution data by the skew-normal distribution. Stochastic Environmental Research and Risk Assessment., 24, 513–517.

    Google Scholar 

  • Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., & Di Carlo, P. (2017). Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research., 8, 652–659.

    Google Scholar 

  • Bowerman, B. L., O’Connell, R. T., & Koehler, A. B. (2005). Forecasting, time series and regression, an applied approach (4th ed.). Belmont: Thomson Brooks.

    Google Scholar 

  • Brunelli, U., Piazza, V., Pignato, L., Sorbello, F., & Vitabile, S. (2007). Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo. Italy. Atmospheric Environment., 41, 2967–2995.

    CAS  Google Scholar 

  • Chaloulakou, G. G. A. (2006). Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens. Greece. Atmospheric Environment., 40, 1216–1229.

    Google Scholar 

  • Chelani, A. B., Gajghate, D. G., & Hasan, M. Z. (2002). Prediction of ambient PM10 and toxic metals using artificial neural networks. Journal of the Air & Waste Management Association., 52, 805–810.

    CAS  Google Scholar 

  • Ching, W.-K., Huang, X., Ng, M. K., & Siu, T.-K. (2013). Markov chain: models, algorithms and applications. International series in operation research & management science (2nd ed.). New York: Springer.

    Google Scholar 

  • Daryanoosh, M., Goudarzi, G., Rashidi, R., Keishams, F., Hopke, P. K., Mohammadi, M. J., Nourmoradi, H., Sicard, P., Takdastan, A., Vosoughi, M., Veysi, M., Kianizadeh, M., & Omidi Khaniabadi, Y. (2018). Risk of morbidity attributed to ambient PM10 in the western cities of Iran. Toxin Reviews., 37(4), 313–318.

    CAS  Google Scholar 

  • Department of Environment. (2000). A guide to air pollutant index in Malaysia (API). Kuala Lumpur: Ministry of Science, Technology and the Environment.

    Google Scholar 

  • Di Menno, A., Bartoletti, S., Gaeta, A., Gandolfo, G., Caricchia, A., and Cirillo, M. (2007). Qualita` dell’aria in Italia il particolato sospeso PM10 anno 2005. http://www.apat.gov.it/site/it-IT/Temi/Aria/Documenti_tecnici/.

  • Ercelebi, S.G., & Toros, H. (2009). Extreme value analysis of Istanbul air pollution data. Clean, 37, 122–131.

  • Gin, O.K. (2009). Historical dictionary of Malaysia. Scarecrow Press, 157–158.

  • Gocheva-Ilieva, S. G., Ivanov, A. V., Voynikova, D. S., & Boyadzhiev, D. T. (2014). Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach. Stochastic Environmental Research and Risk Assessment., 28(4), 1045–1060.

    Google Scholar 

  • Google. (2019). Source : https://maps.googleapis.com/maps/api/geocode/json?address=Klang%2CSelangor&key=xxx.

  • Grinstead, C., & Snell, J. (2012). Introduction to probability. New York: American Mathematical Society.

    Google Scholar 

  • Hamm, N. A. S., Finley, A. O., Schaap, M., & Stein, A. (2015). A spatially varying coefficient model for mapping PM10 air quality at the European scale. Atmospheric Environment., 102, 393–405.

    CAS  Google Scholar 

  • Ibe, O. C. (2013). Markov processes for stochastic modeling (2nd ed.). Waltham: Elsevier.

    Google Scholar 

  • Jeong, S. J. (2013). The impact of air pollution on human health in Suwon City. Asian Journal of Atmospheric Environment., 7, 227–233.

    CAS  Google Scholar 

  • Kao, E. P. C. (1997). An introduction to stochastic processes. Belmont: Wadsworth Publishing Company.

    Google Scholar 

  • Khaniabadi, Y. O., Goudarzi, G., Daryanoosh, S. M., Borgini, A., Tittarelli, A., & De Marco, A. (2017). Exposure to PM10, NO2, and O3 and impacts on human health. Environmental Science and Pollution Research., 24(3), 2781–2789.

    CAS  Google Scholar 

  • Kisi, O., Parmar, K. S., Soni, K., & Demir, V. (2017). Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality, Atmosphere & Health., 10(7), 873–883.

    CAS  Google Scholar 

  • Korhonen, A., Lehtomäki, H., Rumrich, I., Karvosenoja, N., Paunu, V.-K., Kupiainen, K., Sofiev, M., Palamarchuk, Y., Kukkonen, J., Kangas, L., Karppinen, A., & Hänninen, O. (2019). Influence of spatial resolution on population PM2.5 exposure and health impacts. Air Quality, Atmosphere & Health., 12(6), 705–718.

    CAS  Google Scholar 

  • Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health., 12(8), 899–908.

    CAS  Google Scholar 

  • Lin, K.-P., Pai, P.-F., & Yang, S.-L. (2011). Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms. Applied Mathematics and Computation., 17(12), 5318–5327.

    Google Scholar 

  • Manga, E., & Awang, N. (2018). Bayesian autoregressive spatiotemporal model of PM concentrations across Peninsular Malaysia. Stochastic Environmental Research and Risk Assessment, 32(12), 3409–3419.

  • Martins, L. D., Wikuats, C. F. H., Capucim, M. N., de Almeida, D. S., da Costa, S. C., Albuquerque, T., Barreto Carvalho, V. S., de Freitas, E. D., de Fátima Andrade, M., & Martins, J. A. (2017). Extreme value analysis of air pollution data and their comparison between two large urban regions of South America. Weather and Climate Extremes., 18, 44–54.

    Google Scholar 

  • Masseran, N. (2017). Modeling fluctuation of PM10 data with existence of volatility effect. Environmental Engineering Science., 34(11), 816–827.

    CAS  Google Scholar 

  • Masseran, N. (2018). Integrated approach for the determination of an accurate wind-speed distribution model. Energy Conversion and Management., 173, 56–64.

    Google Scholar 

  • Masseran, N., & Safari, M. A. M. (2020a). Risk assessment of extreme air pollution based on partial duration series: IDF approach. Stochastic Environmental Research and Risk Assessment., 34, 545–559.

    Google Scholar 

  • Masseran, N., & Safari, M. A. M. (2020b). Intensity-duration-frequency approach for risk assessment of air pollution events. Journal of Environmental Management, 264, 110429.

    Google Scholar 

  • Masseran, N., Razali, A. M., Ibrahim, K., Zaharim, A., & Sopian, K. (2013). Application of the single imputation method to estimate missing wind speed data in Malaysia. Research Journal of Applied Sciences, Engineering and Technology., 6, 1780–1784.

    Google Scholar 

  • Masseran, N., Razali, A. M., Ibrahim, K., & Latif, M. T. (2016). Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model. Environmental Monitoring and Assessment 188(1). Article number, 65, 1–12.

    Google Scholar 

  • Mehdipour, V., Stevenson, D. S., Memarianfard, M., & Sihag, P. (2018). Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Quality, Atmosphere & Health., 11(10), 1155–1165.

    CAS  Google Scholar 

  • Muñoz, E., Martín, M. L., Turias, I. J., Jimenez-Come, M. J., & Trujillo, F. J. (2014). Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain. Stochastic Environmental Research and Risk Assessment., 28(6), 1409–1420.

    Google Scholar 

  • Nicolantonio, W. D., Cacciari, A., Bolzacchini, E., Ferrero, L., Volta, M., and Pisoni, E. (2007). Modis aerosol optical properties over north Italy for estimating surface-level PM2.5. Paper presented at the European Space Agency, (Special Publication) ESA SP, (SP-636).

  • Nieto, P. J. G., Combarro, E. F., del Coz Díaz, J. J., & Montañés, E. (2013). A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Applied Mathematics and Computation., 219(17), 8923–8937.

    Google Scholar 

  • Park, S., Kim, M., Kim, M., Namgung, H.-G., Kim, K.-T., Choc, K. H., & Kwon, S.-B. (2018). Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN). Journal of Hazardous Materials., 341, 75–82.

    Google Scholar 

  • Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Association., 287(9), 1132–1141.

    CAS  Google Scholar 

  • Reisen, V. A., Sarnaglia, A. J. Q., Reis Jr., N. C., Lévy-Leduc, C., & Santos, J. M. (2014). Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility. Environmental Modelling & Software., 51, 286–295.

    Google Scholar 

  • Ross, S. M. (2014). Introduction to probability models (12th ed.). Burlington: Academic Press.

    Google Scholar 

  • Safari, M. A. M., & Wan Zin, W. Z. (2017). Modelling of probability distributions of extreme particulate matter in Klang Valley. Sains Malaysiana., 46(6), 989–999.

    Google Scholar 

  • Scholz, M. (2014). R Package clickstream: analyzing clickstream data with Markov chains. R package version, 1(1), 2.

    Google Scholar 

  • Shahraiyni, H. T., & Sodoudi, S. (2016). Statistical modeling approaches for PM10 prediction in urban areas; a review of 21st-century studies. Atmosphere., 7(15), 1–24.

    Google Scholar 

  • Thunis, P., Clappier, A., Pisoni, E., & Degraeuwe, B. (2015). Quantification of non-linearities as a function of time averaging in regional air quality modeling applications. Atmospheric Environment, 103, 263–275.

    CAS  Google Scholar 

  • Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., & Hamid, H. A. (2013). Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principal component analysis (PCA). Atmospheric Environment., 77, 621–630.

    CAS  Google Scholar 

  • van de Kassteele, J., Koelemeijer, R. B. A., Dekkers, A. L. M., Schaap, M., Homan, C. D., & Stein, A. (2006). Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations. Stochastic Environmental Research and Risk Assessment., 21(2), 183–194.

    Google Scholar 

  • Ventura, L. M. B., Pinto, F.d. O., Soares, L. M., Luna, A. S., & Gioda, A. (2019). Forecast of daily PM2.5 concentrations applying artificial neural networks and Holt–Winters models. Air Quality, Atmosphere & Health., 12(3), 317–325.

  • Vlachogiannia, A., Kassomenos, P., Karppinen, A., Karakitsios, S., & Kukkonen, J. (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(8), 1559–1571.

    Google Scholar 

  • Zhang, H., Zhang, S., Wang, P., Qin, Y., & Wang, H. (2017). Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. Journal of the Air & Waste Management Association., 67(7), 776–788.

    CAS  Google Scholar 

Download references

Acknowledgments

The authors are indebted to the staff of Malaysian Department of Environment for providing PM10 index data. This research would not have been possible without the sponsorship of Universiti Kebangsaan Malaysia (grant no. DIP-2018-038).

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Nurulkamal Masseran

  2. Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Muhammad Aslam Mohd Safari

Authors
  1. Nurulkamal Masseran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Aslam Mohd Safari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nurulkamal Masseran.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masseran, N., Safari, M.A.M. Modeling the transition behaviors of PM10 pollution index. Environ Monit Assess 192, 441 (2020). https://doi.org/10.1007/s10661-020-08376-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-020-08376-1

Keywords

Search

Navigation