Air Quality Index Time Series Research Articles

Forecasting air quality Index in yan’an using temporal encoded Informer

Predictions of the Air Quality Index (AQI) can provide information on air quality, aiding individuals in personal protection and environmental conservation, and facilitating policymakers in implementing measures to control air pollution. In the prediction of AQI time series, single-step forecasting tasks are less challenging. However, as the length of the forecast sequence increases, the prediction error tends to grow remarkably. Furthermore, tacking only univariate features as model inputs can lead to biases in forecasting outputs, as the models fail to capture the overall trends of the series based on a single feature. To address this issue, we developed a novel time series prediction model named Temporal feature Encoded Informer (TE-Informer) using the historical air pollution dataset from Yan’an City. This model incorporates attention mechanisms and periodic encoding, utilizing multiple pollutant series as feature inputs. It enriches the Informer architecture with more comprehensive temporal and global features to predict AQI series. The experimental results demonstrated that the Informer model, enhanced with periodic time encoding, has a significant advantage in extracting features from the input information. In the context of multi-step AQI time series forecasting, the model achieved an MSE (Mean Squared Error) of 24.8692 and an R2-score of 0.9793, outperforming other comparative models in all comprehensive metrics. Consequently, this model can effectively improve prediction accuracy and better capture important characteristic information within the AQI time series. The source code is available at https://github.com/msskx/TE-Informer.

A hybrid multi-scale fusion paradigm for AQI prediction based on the secondary decomposition.

With rapid industrialization and urbanization, air pollution has become an increasingly severe problem. As a key indicator of air quality, accurate prediction of the air quality index (AQI) is essential for policymakers to establish effective early warning management mechanisms and adjust living plans. In this work, a hybrid multi-scale fusion prediction paradigm is proposed for the complex AQI time series prediction. First, an initial decomposition and integration of the original data is performed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE). Then, the subsequences, divided into high-frequency and low-frequency groups, are applied to different processing methods. Among them, the variational mode decomposition (VMD) is chosen to perform a secondary decomposition of the high-frequency sequence groups and integrated by using K-means clustering with sample entropy. Finally, multi-scale fusion training of sequence prediction results with different frequencies by using long short-term memory (LSTM) yields more accurate results with R2 of 0.9715, RMSE of 2.0327, MAE of 0.0154, and MAPE of 0.0488. Furthermore, validation of the AQI datasets acquired from four different cities demonstrates that the new paradigm is more robust and generalizable as compared to other baseline methods. Therefore, this model not only holds potential value in developing AQI prediction models but also serves as a valuable reference for future research on AQI control strategies.

A novel encoder-decoder model based on Autoformer for air quality index prediction.

Rapid economic development has led to increasingly serious air quality problems. Accurate air quality prediction can provide technical support for air pollution prevention and treatment. In this paper, we proposed a novel encoder-decoder model named as Enhanced Autoformer (EnAutoformer) to improve the air quality index (AQI) prediction. In this model, (a) The enhanced cross-correlation (ECC) is proposed for extracting the temporal dependencies in AQI time series; (b) Combining the ECC with the cross-stage feature fusion mechanism of CSPDenseNet, the core module CSP_ECC is proposed for improving the computational efficiency of the EnAutoformer. (c) The time series decomposition and dilated causal convolution added in the decoder module are exploited to extract the finer-grained features from the original AQI data and improve the performance of the proposed model for long-term prediction. The real-world air quality datasets collected from Lanzhou are used to validate the performance of our prediction model. The experimental results show that our EnAutoformer model can greatly improve the prediction accuracy compared to the baselines and can be used as a promising alternative for complex air quality prediction.

A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm

Accurate forecasting of the air quality index (AQI) plays a crucial role in taking precautions against upcoming air pollution risks. However, air quality may fluctuate greatly in a certain period. Existing forecasting approaches always face the problem of losing valuable information on air quality status, even in the interval models of recent research. To address this issue, this paper suggests a new AQI forecasting perspective and paradigm built upon ternary interval-valued time series (TITS), multivariate variational mode decomposition (MVMD), multivariate relevance vector machine (MVRVM), mixed coding particle swarm optimization (MCPSO), and meteorological factors, which is able to capture the trend and volatility changes of AQI concurrently. The proposed paradigm involves four procedures: TITS construction in terms of the daily minimum, daily mean, and daily maximum AQI, multi-scale decomposition via MVMD, individual forecasting by MCPSO-optimized MVRVM, and ensemble learning forecasting using a simple addition approach. Experiments based on datasets collected from four municipalities in China demonstrated that the presented paradigm can hit higher accuracy than other comparable models, and the application analysis also shows that it has application potential in the AQI online forecasting system. To conclude, the proposed paradigm provides a promising alternative for AQI time series forecasting.

A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities.

Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

This paper formalizes the model of choosing a strategy for reducing air pollution in an urban environment. The model involves determining the optimal location of biotechnological systems – biotechnological filter systems or smart air purification devices based on solving the problem of discrete optimization, taking into consideration the forecast of the air quality index. Two subtasks have been formalized, which make it possible to form a strategy for reducing air pollution. To solve one of the subtasks, a combined selective model for predicting the time series of the Air Quality Index (CSM) was built. The combined model software suite consists of the EMD-ESM hybrid model (Empirical Mode Decomposition-Exponential Smoothing Model), the HWM additive model (Holt-Winters Model), and the adaptive TLM (Trigg-Lich Model). To verify the proposed combined selective model, the time series of air quality indices (AQI) for the city of Nur-Sultan (data from 2010‒2021, period 6 hours) were selected. As a result of verification, it was established that in the case of short-term forecasting of the air quality index time series, the EMD-ESM model has an advantage according to the criterion of a minimum root mean square error (RMSE), δ=0.11. For the case of medium-term forecasting of 3<τ≤5, the combined selective model (CSM) has the advantage. The results reported here are input data for the task of choosing strategies for reducing the volume of air pollution in the urban environment. The study’s results make it possible to increase the flexibility of the formation of strategies for reducing air pollution since they avoid restrictions on the location of cleaners in specific urban areas. The consequence is the improvement of the environmental situation in the city and the development of the region in general

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

AbstractAccurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution.

Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system (ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid optimization method to improve ANFIS performance, called PSOSMA, using a new modified meta-heuristics (MH) algorithm, Slime mould algorithm (SMA), which is improved by using the particle swarm optimizer (PSO). The proposed PSOSMA-ANFIS has been trained with air quality index time series data of three years and has been applied to forecast the fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), and nitrogen dioxide (NO2) for one year. We also compared the proposed PSOSMA to other MH algorithms used to train ANFIS. We found that the modified ANFIS using PSOSMA achieved better performance than compared algorithms. Moreover, we analyzed the impacts of the lockdown of Wuhan City on the concentrations of PM2.5, NO2, CO2, and SO2. We compared the correspondence period with previous years, and we concluded that there are significant decreases in the concentrations of PM2.5, CO2, SO2, and NO2.

On the multifractal analysis of air quality index time series before and during COVID-19 partial lockdown: A case study of Shanghai, China

Due to the COVID-19 pandemic, human activities are largely restricted in Shanghai, China and it is a valuable experiment to testify the correlation of air quality and human activities. In consideration of the complexity of air pollution, this study aims to compare the multifractal characteristics of air quality index (AQI) time series before and during COVID-19 partial lockdown, and analyze the correlations between multifractal parameters of AQI time series and human activities in Shanghai, China. The hourly AQI series in Shanghai from November 27, 2019 to March 23, 2020 is used for this study. Firstly, using the MF-DFA method, the multifractal characteristics of the AQI series are explored. Secondly, the causes of the multifractality of the AQI series are determined. Finally, the correlations between multifractal parameters of AQI time series and human activities are investigated. The multifractal analysis results reveal that the AQI series during COVID-19 partial lockdown also has multifractal characteristics, and the slightly weaker multifractal characteristics and marginally smaller multifractal degree are obtained in comparison with the pre-lockdown phase. However, the contribution of the effective or intrinsic multifractality before and during COVID-19 partial lockdown are very close. The results via the sliding window procedure indicate that the multifractal parameters (ΔH,Δα,Δf) show the similar fluctuations along with the fluctuations of passenger volume in Shanghai Metro. Furthermore, it is found that ΔH,Δα and Δf and adjusted passenger volume in Shanghai Metro are positively correlated. The possible trend is that the higher adjusted passenger volume is, the larger the value of ΔH,Δα, Δf becomes, which means the stronger multifractal characteristics and larger multifractal degree of air quality system.

Double decomposition and optimal combination ensemble learning approach for interval-valued AQI forecasting using streaming data.

To forecast possible future environmental risks, numerous models are developed to predict the hourly values or daily averages of air pollutant concentrations using streaming data (a kind of big data collected from the Internet). On the one hand, real-time hourly data is massive and redundant, making it difficult to process. On the other hand, daily averages cannot reflect the fluctuations of air pollutant concentrations throughout the day. Therefore, a double decomposition and optimal combination ensemble learning approach is proposed for interval-valued AQI (air quality index) forecasting in this paper. In the first decomposition, considering the strong seasonal representation of AQI, the original data of each year is decomposed into four seasonal subseries on the basis of the Chinese calendar. Subsequently, we reconstruct the data of the same season in different years to get a new seasonal series to reduce the interference of seasonal changes on AQI forecasting. In the second decomposition, due to the nonlinearity and irregularity of interval-valued AQI time series, BEMD (bivariate empirical mode decomposition) is employed to decompose the interval-valued signals into a finite number of complex-valued IMF (intrinsic mode function) components and one complex-valued residue component with different frequencies to reduce the complexity of interval times series. Interval multilayer perceptron (iMLP) is utilized to model the lower bound and the upper bound simultaneously of the total components to obtain the corresponding forecasting results, which are merged to produce the final interval-valued output by an optimal combination ensemble method. Empirical study results show that the proposed model with different datasets and different forecasting horizons is significantly better than other considered models for its superior forecasting performances.

A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

Exploring the fluctuant transmission characteristics of Air Quality Index based on time series network model

Abstract Although many works have focused on the issues related to Air Quality Index (AQI) from the perspective of statistics or economics, the dynamic and temporal characteristics of AQI time series remain unclear. The main reason is that AQI time series has both non-linearity and complexity, which cannot be characterized by conventional ways. The inherent transmission laws of AQI volatility are of great importance for environment monitoring and management system. Thus, this paper has endeavored to address the issue from the interdisciplinary perspective of complex network theory. First, we collect the annual and the total AQI data of three cities (Shanghai, Wuhan and Guangzhou) in China from January 1, 2015 to December 31, 2017. Then the coarse graining method is employed in combination with the theory of phase space reconstruction to convert AQI time series into complex network. The coarse graining method is used to transform yearly and total AQI time series into the corresponding symbol sequences. In accordance with the theory of phase space reconstruction, we adopt C C method and false nearest neighbor algorithm to estimate the optimal time delay and embedding dimension of AQI time series, respectively. The optimal time delay is integrated with the embedding dimension to map symbol sequences into AQI complex network. Based on complex network theory, we investigate the transmission of AQI fluctuation by the temporal characteristics and dynamics of yearly AQI networks (YAN) and total AQI networks (TAN), which include node strength, betweenness centrality and community structure. The empirical results show: (1) the AQI networks of various periods display small-world effect; (2) network evolution tends to be more complex and the modality in YAN has less alternative transformation paths than that in TAN; (3) the node strength and betweenness centrality in measuring the importance of nodes achieve a relative consistency; (4) the three cities have the same dominant fluctuation modalities both in YAN and in TAN; (5) the uneven distribution of transmission ability between clusters implies a preferential transmission whether in YAN or TAN. These findings not only help us to interpret the frequent volatility of AQI and control some extreme short-term AQI fluctuation, but also contribute to the reliability assessment of AQI and the prediction of AQI fluctuation. In summary, this paper not only reveals the temporal dynamic properties of AQI time series, but also provides some policy recommendations for environmental monitoring and management.

Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk.

The impact of air pollution on people's health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi'an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model's dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.

Multifractal characterization of air polluted time series in China

This paper adopts multifractal methods to analyze the multifractal characteristics of polluted time series in Beijing, Zhengzhou, and Jinan. The results of the multifractal detrended fluctuation analysis (MF-DFA) show that each of three air quality index (AQI) time series has multifractal characteristics, which provides evidence that the multifractal method but the traditional linear or monofractal method is feasible. The sources of multifractality in three AQI time series are all from long-range correlations and fat tail distributions, although the highlighted sources are different in three cities. The results of the coupling detrended fluctuation analysis (CDFA) show that SO2 has a vital impact on air quality in each of three cities. O3 has an important impact on air quality in Beijing and Jinan city and NO2 has an important impact on air quality in Zhengzhou city. It is noted that PM2.5 has not as large an impact as we imagine in three cities. It has a relatively large impact on air quality of Zhengzhou city, while it has a small impact on air quality of Beijing and Jinan city. The chi square χ2 test further confirms the above results.

Long-Range Correlations in Air Quality Time Series: Effect of Differencing and Shuffling

ABSTRACTLong-range correlations in the air quality index (AQI) are analysed using rescaled range analysis (R/S), detrended fluctuation analysis (DFA) and power spectral density analysis. Air quality index in five cities of India is considered for this purpose. Statistical transformations such as differencing and shuffling have been carried out to examine the effect of temporal correlations on long-range correlation property of the time series. All three methods indicated the presence of persistence in original AQI time series. After differencing, long-range correlation property is, however, observed to be distorted. R/S analysis did not show the similar results as DFA and power spectral density analysis. Shuffled time series is shown to possess persistence as in the original one by using R/S analysis, whereas other two methods showed random behaviour at most of the locations. This suggests that the persistence property is largely influenced by short-range correlations in the AQI time series. The incorporation of this information can enhance the performance of the models to forecast the air quality. The similarity in the results of DFA and power spectral density analysis suggests that both methods can be relied more than R/S analysis in studying the persistence property of the time series.