Characterizing variability and predictability for air pollutants with stochastic models

An ensemble multi-scale framework for long-term forecasting of air quality

The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.

Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.

Time reversal symmetry and the difference between relaxations and building-up periods

Autocorrelations in stationary systems are time-symmetric, irrespective of the signal's properties. Linear dynamics is usually associated with signals which are statistically time inversion invariant. This is known to be broken for non-Gaussian models. In this paper, we develop a theoretical framework of time reversibility of linear models based on noncontinuous driving. We identify the inverse decay exponent of the autocorrelation function as either a characteristic time for the building-up of extreme events in the time series or of relaxations after these extreme events. If the characteristic time is known, the dynamics can be inverted in both directions in time and the residuals can be compared, which gives a criterion for the type of time inversion asymmetry. The method is applied to two time series from atmospheric science with different behavior.