Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017

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To Cite : Sarvi F, Nadali A, Khodadost M, Kharghani Moghaddam M, Sadeghifar M. et al. Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017, Avicenna J Environ Health Eng. 2017 ;4(1):e58031. doi: 10.5812/ajehe.58031.
Copyright: Copyright © 2017, Hamadan University of Medical Sciences. .
Abstract
1. Background
2. Methods
3. Results and Discussion
4. Conclusions
Footnote
References
  • 1. pollution. 2015;
  • 2. Fattore E, Paiano V, Borgini A, Tittarelli A, Bertoldi M, Crosignani P, et al. Human health risk in relation to air quality in two municipalities in an industrialized area of Northern Italy. Environ Res. 2011; 111(8): 1321-7[DOI][PubMed]
  • 3. Matus K, Nam KM, Selin NE, Lamsal LN, Reilly JM, Paltsev S. Health damages from air pollution in China. Glob Environ Change. 2012; 22(1): 55-66[DOI]
  • 4. Chafe ZA, Brauer M, Klimont Z, Van Dingenen R, Mehta S, Rao S, et al. Household cooking with solid fuels contributes to ambient PM2. 5 air pollution and the burden of disease. Environ Health Perspect. 2014; 122(12): 1314
  • 5. Zanobetti A, Dominici F, Wang Y, Schwartz JD. A national case-crossover analysis of the short-term effect of PM2.5 on hospitalizations and mortality in subjects with diabetes and neurological disorders. Environ Health. 2014; 13(1): 38[DOI][PubMed]
  • 6. Dong M, Yang D, Kuang Y, He D, Erdal S, Kenski D. PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining. Expert Systems Appl. 2009; 36(5): 9046-55[DOI]
  • 7. Buczynska AJ, Krata A, Van Grieken R, Brown A, Polezer G, De Wael K, et al. Composition of PM2.5 and PM1 on high and low pollution event days and its relation to indoor air quality in a home for the elderly. Sci Total Environ. 2014; 490: 134-43[DOI][PubMed]
  • 8. Hoek G, Krishnan RM, Beelen R, Peters A, Ostro B, Brunekreef B, et al. Long-term air pollution exposure and cardio- respiratory mortality: a review. Environ Health. 2013; 12(1): 43[DOI][PubMed]
  • 9. Atash F. The deterioration of urban environments in developing countries: Mitigating the air pollution crisis in Tehran, Iran. Cities. 2007; 24(6): 399-409[DOI]
  • 10. Mawer C. Air pollution in Iran. BMJ. 2014; 348[DOI][PubMed]
  • 11. Sun W, Zhang H, Palazoglu A, Singh A, Zhang W, Liu S. Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Sci Total Environ. 2013; 443: 93-103[DOI]
  • 12. Reich SL, Gomez DR, Dawidowski LE. Artificial neural network for the identification of unknown air pollution sources. Atmospher Environ. 1999; 33(18): 3045-52[DOI]
  • 13. McKendry IG. Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM10and PM2.5) Forecasting. J Air Waste Manag Assoc. 2002; 52(9): 1096-101[DOI]
  • 14. Domanska D, Wojtylak M. Application of fuzzy time series models for forecasting pollution concentrations. Expert Systems Appl. 2012; 39(9): 7673-9[DOI]
  • 15. Zucchini W, MacDonald I. Hidden Markov models for time series: an introduction using R. 2009; [DOI]
  • 16. NAAQS Table. 2015;
  • 17. Berhane K, Gauderman WJ, Stram DO, Thomas DC. Statistical Issues in Studies of the Long-Term Effects of Air Pollution: The Southern California Children?s Health Study. Statistic Sci. 2004; 19(3): 414-49[DOI]
  • 18. Zhang H, Zhang W, Palazoglu A, Sun W. Prediction of ozone levels using a Hidden Markov Model (HMM) with Gamma distribution. Atmospher Environ. 2012; 62: 64-73[DOI]
  • 19. Álvarez LJ, Rodrigues ER. Trans-dimensional MCMC algorithm to estimate the order of a Markov chain: an application to ozone peaks in Mexico City. Int J Pure Appl Math. 2008; 48: 315-31
  • 20. Sadeghifar M, Seyed-Tabib M, Haji-Maghsoudi S, Noemani K, Aalipur-Byrgany F. The application of Poisson hidden Markov model to forecasting new cases of congenital hypothyroidism in Khuzestan province Journal of Biostatistics and Epidemiology. 2016; 2(1): 14-9
  • 21. Agresti A, Kateri M. Categorical data analysis. 2011;
  • 22. Halek F, Kianpour-Rad M, Kavousirahim A. Seasonal variation in ambient PM mass and number concentrations (case study: Tehran, Iran). Environ Monitor Assess. 2010; 169(1): 501-7
  • 23. Kulshrestha A, Satsangi PG, Masih J, Taneja A. Metal concentration of PM(2.5) and PM(10) particles and seasonal variations in urban and rural environment of Agra, India. Sci Total Environ. 2009; 407(24): 6196-204[DOI][PubMed]
  • 24. Zhao X, Zhang X, Xu X, Xu J, Meng W, Pu W. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing. Atmospher Environ. 2009; 43(18): 2893-900[DOI]
  • 25. Murakami J. Bayesian posterior mean estimates for Poisson hidden Markov models. Comput Statistics Data Analysis. 2009; 53(4): 941-55[DOI]
  • 26. Perez P, Gramsch E. Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes. Atmospher Environ. 2016; 124: 22-7[DOI]
  • 27. Sun W, Zhang H, Palazoglu A. Prediction of 8 h-average ozone concentration using a supervised hidden Markov model combined with generalized linear models. Atmospher Environ. 2013; 81: 199-208[DOI]
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