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      Probabilistic Approach to Modelling, Identification and Prediction of Environmental Pollution

      Environmental Modeling & Assessment
      Springer Science and Business Media LLC

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          Abstract

          The probabilistic general model of environmental pollution process based on the semi-Markov one is developed and presented in the paper. The semi-Markov chain model approach is based on using prior information to predict the characteristic of some systems. Now, the semi-Markov process is used for the environmental pollution assessment. The methods and procedures to estimate the environmental pollution process’s basic parameters such as the vector of initial probabilities and the matrix of probabilities of transition between the process’s states as well as the methods and procedures to identify the process conditional sojourn times’ distributions at the particular environmental pollution states and their mean values are proposed and defined. Next, the formulae to predict the main characteristics of the environmental pollution process such as the limit values of transient probabilities and mean total sojourn times in the particular states in the fixed time interval are given. Finally, the application of the presented model and methods for modelling, identification and prediction of the air environmental pollution process generated by sulphur dioxide within the exemplary industrial agglomeration is proposed.

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          Artificial neural networks forecasting of PM 2.5 pollution using air mass trajectory based geographic model and wavelet transformation

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            Regenerative Stochastic Processes

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              A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

              Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Environmental Modeling & Assessment
                Environ Model Assess
                Springer Science and Business Media LLC
                1420-2026
                1573-2967
                February 2023
                September 16 2022
                February 2023
                : 28
                : 1
                : 1-14
                Article
                10.1007/s10666-022-09854-1
                6214c869-e042-4ea7-92c0-9abf3ab658ed
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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