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Semi-markovian approach to modelling air pollution

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Identyfikatory
Warianty tytułu
Konferencja
15th Summer Safety & Reliability Seminars - SSARS 2021, 5-12 September 2021, Ciechocinek, Poland
Języki publikacji
EN
Abstrakty
EN
The air pollution assessment based on concentration’s changes of sulphur dioxide, carbon monoxide, nitrogen dioxide, ozone, benzene, and particulate matter is discussed in the chapter. The semi-Markov model of the environmental pollution process is introduced and its characteristics are determined. Next the proposed model is practically applied to examine and characterized air pollution in Gdańsk (Poland) as the exemplary industrial agglomeration. The main parameters and characteristics of the air pollution process are determined, such as concentration states of particular kinds of air pollutants, the limit values of transient probabilities and the mean total sojourn times staying at the air pollutants’ concentration states, for the fixed time interval.
Twórcy
Bibliografia
  • Bai, L., Wang, J., Ma, X. & Lu, H. 2018. Air pollutants forecasts: an overview. International Journal of Environmental Research and Public Health 15(4), 780.
  • Bai, Y., Li, Y., Wang, X., Xie, J. & Li, C. 2016.Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmospheric Pollution Research 7, 557–566.
  • Bogalecka, M. 2020. Consequences of Maritime Critical Infrastructure Accidents. Environmental Impacts. Modeling - Identification - Prediction - Optimization - Mitigation. Elsevier, Amsterdam - Oxford - Cambridge.
  • Bogalecka, M. 2021. Probabilistic approach to modelling, identification and prediction of environmental pollution (unpublished).
  • Bouharati, S., Benzidane, C., Braham-Chaouch, W.& Boumaïza, S. 2014. Air quality index and public health: Modelling using fuzzy inference system. American Journal of Environmental Engineering and Science 1(4), 85-89.
  • Chen, D., Xu, T., Li, Y., Zhou, Y., Lang, J., Liu, X. & Shi, H. 2015. A hybrid approach to forecast air quality during high-PM concentration pollution period. Aerosol and Air Quality Research 15, 1325-1337.
  • Dalal, P. 2015. Modeling of air quality index. International Journal of Advanced Research in Engineering and Applied Sciences 4(9), 1-11.
  • Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L. & Wang, J. 2015. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment 107, 118-128.
  • Fu, M., Wang, W., Le, Z. & Khorram, M.S. 2015. Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model. Neural Computing and Applications 26, 1789-1797.
  • Grabski, F. 2015. Semi-Markov Processes: Applications in System Reliability and Maintenance. Elsevier, Amsterdam - Boston - Heidelberg - London - New York - Oxford - Paris - San Diego - San Francisco - Sydney - Tokyo.
  • Iosifescu, M. 1980. Finite Markov Processes and Their Applications. John Wiley & Sons, Ltd, New York.
  • Huebnerova, Z. & Michalek, J. 2014. Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic. Atmospheric Pollution Research 5, 471-476.
  • Kaboodvandpour, S., Amanollahi, J., Qhavami, S. & Mohammadi, B. 2015. Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran. Natural Hazards 78, 879-893.
  • Kołowrocki, K. 2004. Reliability of Large Systems. Elsevier, Amsterdam - Boston - Heidelberg - London - New York - Oxford - Paris - San Diego - San Francisco - Singapore - Sydney - Tokyo.
  • Kołowrocki, K. 2014. Reliability of Large and Complex Systems. Elsevier, Amsterdam - Boston - Heidelberg - London - New York - Oxford - Paris - San Diego - San Francisco - Singapore - Sydney - Tokyo.
  • Kołowrocki, K.& Soszyńska-Budny, J. 2011. Reliability and Safety of Complex Technical Systems and Processes: Modeling - Identification - Prediction - Optimization. Springer, London - Dordrecht - Heidelberg - New York.
  • Korolyuk, V.S., Brodi, S.M.& Turbin, A.F. 1975. Semi-Markov processes and their applications. Journal of Soviet Mathematics 4(3), 244-280.
  • Lev’y, P. 1954. Proceesus semi-markoviens. Proceedings of International Congress of Mathematicians, Amsterdam, 416-426.
  • Limnios, N. & Oprisan, G. 2001. Semi-Markov Processes and Reliability. Birkhauser, Boston.
  • Olvera-Garcia, M.A., Carbajal-Hernandez, J.J. & Sanchez-Fernandez, L.P., Hernandez-Bautista, L. 2016. Air quality assessment using a weighted fuzzy inference system. Ecological Informatics 33, 57-74.
  • Park, Y., Kwon, B., Heo, J., Hu, X., Liu, Y. & Moon, T. 2020. Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks. Environmental Pollution 256, 113395.
  • PriyaDarshini, S., Sharma, M.& Singh, D. 2016. Synergy of receptor and dispersion modelling: quantification of PM10 emissions from road and soil dust not included in the inventory. Atmospheric Pollution Research 7(3), 403-411.
  • Qin, S., Liu, F., Wang, J. & Sun, B. 2014. Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmospheric Environment 98, 665-675.
  • Rahman, N.H.A., Lee, M.H., Suhartono, & Latif, M.T. 2015. Artificial neural networks and fuzzy time series forecasting: an application to air quality. Quality and Quantity 49, 2633-2647.
  • Sarwat, E. & El-Shanshoury, G.I. 2018. Estimation of air quality index by merging neural network with principal component analysis. International Journal of Computer Application 1(8), 2250-1797.
  • Shadab, A., Farhan, A.K. & Kafeel, A. 2019. Evaluating traffic-related near-road CO dispersions on an urban road during summer season: A model inter-comparison. Asian Journal of Water, Environment and Pollution 16(1), 69-79.
  • Shafabakhsh, G.A., Taghizadeh, S.A. & Kooshki, S.M. 2018. Investigation and sensitivity analysis of air pollution caused by road transportation at signalized intersections using IVE model in Iran. European Transport Research Review 10:7.
  • Sivacoumar, R., Bhanarkar, A.D., Goyal, S.K., Gadkarib, S.K.& Aggarwal, A.L. 2001. Air pollution modeling for an industrial complex and model performance evaluation. Environmental Pollution 111(3), 471-477.
  • Smith, W.L. 1955. Regenerative stochastic processes. Proceedings of the Royal Society, Ser. A 232, 631.
  • Wang, P., Liu, Y., Qin, Z. & Zhang, G. 2015. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Science of the Total Environment 505, 1202-1212.
  • WHO. 2014. 7 million premature deaths annually linked to air pollution, Air Quality & Climate Change 22(1), 53-59.
  • Wongsathan, R. & Seedadan, I. 2016. A hybrid ARIMA and neural networks model for PM-10 pollution estimation: the case of Chiang Mai City Moat Area 9. Procedia Computer Science 86, 273-276.
  • Wu, Q.& Lin, H. 2019. A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors. Science of the Total Environment 683, 808-821.
  • Xu, Q & Xu, K. 2018. Assessment of air quality using a cloud model method. Royal Society Open Science 5(9), 171580.
  • Yadav, J., Kharat, V. & Deshpande, A. 2015. Fuzzy-GA modeling in air quality assessment. Environmental Monitoring and Assessment 187, 1-14.
  • Yan, L., Wu, Y., Yan, L.& Zhou, M. 2018. Encoder-decoder model for forecast of PM2.5 concentration per hour. Proceedings of 1st International Cognitive Cities Conference (IC3), 45-50.
  • Yang, H., Jiang, Z. & Lu, H. 2017. A hybrid wind speed forecasting system based on a “decomposition and ensemble” strategy and fuzzy time series. Energies 10(9), 1422.
  • Yang, H., Zhu, Z., Li, Ch.& Li, R. 2020. A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight. Applied Soft Computing 87, 105972.
  • Zhou, Q., Jiang, H., Wang, J. & Zhou J. 2014.A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Science of the Total Environment 496, 264-274.
  • Zhu, S., Yang, L., Wang, W., Liu, X., Lu, M.& Shena, X. 2018. Optimal-combined model for air quality index forecasting: 5 cities in North China. Environmental Pollution 243B, 842-850.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5ef9b88f-9731-4ca7-b78d-64accbb7529c
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