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Time-series forecasting has a wide range of application scenarios. Predicting particulate matter with a diameter of 2.5 μm or less (PM2.5) in the future is a vital type of time-series forecasting task where valid forecasting would provide an important reference for public decisions. The current state-of-the-art general time-series model, TimesNet, has achieved a level of performance well above the mainstream level on most benchmarks. Attributing this success to an ability to disentangle intraperiod and interperiod temporal variations, we propose TimesNet-PM2.5. To make this model more powerful for concrete PM2.5 prediction tasks, task-oriented improvements to its structure have been added to enhance its ability to predict specific time spots through better interpretability and meaningful visualizations. On the one hand, this paper rigorously investigates the impact of various meteorological indicators on PM2.5 levels, examining their primary influencing factors from both local and global perspectives. On the other hand, using visualization techniques, we validate the capability of representation learning in time-series forecasting and performance on the forecasting task of the TimesNet-PM2.5. Experimentally, TimesNet-PM2.5 demonstrates an improvement over the original TimesNet. Specifically, the Mean Squared Error (MSE) improved by 8.8% for 1-h forecasting and by 22.5% for 24-h forecasting. [ABSTRACT FROM AUTHOR]

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. [ABSTRACT FROM AUTHOR]

To overcome the limitations of long-term prediction of PM2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM2.5 sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM2.5 time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R2 values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction. [ABSTRACT FROM AUTHOR]

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The "CEEMDAN-FE" decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model's performance in predicting the real-time concentrations of PM2.5. [ABSTRACT FROM AUTHOR]

Air pollution poses a significant threat to human health and ecosystems. Forecasting the concentration of key pollutants like particulate matter can help support air quality planning and prevention measures. Deep learning methods are becoming increasingly popular for predicting air pollution and particulate matter concentration. Architectures like Convolutional Neural Networks can effectively account for the geographical features of the study domain. This work tests a Feed-Forward, a Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN) on a polluted geographical domain in northern Italy. The best convolutional architecture was then implemented in two other quite different regions. The results show that the same CNN architecture provides remarkably accurate forecasts in all applications and that a network trained on PM10 data can accurately forecast PM2.5 concentrations up to 10 days ahead. These results suggest that the proposed CNN has high generalization capabilities and can thus be reliably used as a forecasting model for different areas. [ABSTRACT FROM AUTHOR]

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ≼ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure. [ABSTRACT FROM AUTHOR]

In this paper, an operational forecasting and daily assessment system of air quality is presented. This new system is thought of as a Copernicus-CAMS downstream national service, able to develop and implement a service for air quality forecasting and monitoring in the Italian domain, running every day on the National territory. The system is being developed on behalf of a cooperation between Agenzia Spaziale Italiana (ASI) and Sistema Nazionale Protezione Ambiente (SNPA). SNPA is the network between Istituto Superiore per la Protezione e Ricerca Ambientale (ISPRA) and the Regional Environmental Agencies (ARPAs). The objective of the cooperation is to provide full operation service in terms of continuity, sustainability, and availability of the air quality forecast and evaluation services at the national level. The system forecasts and analyzes air quality throughout Italy, with a focus on Italian regions, for the principal pollutants: Particulate matter with diameter smaller than 10 μm (PM10), ozone (O3), and nitrogen dioxide (NO2). It includes a Chemical Transport Model (CTM) nested with the Copernicus Atmosphere Monitoring Service (CAMS) global model and data from the air quality monitoring stations in Italy. The system, under public control and based on open software, is now under testing. To date, it is able to deliver free open data, which is available to environmental agencies and citizens. The data are delivered both as maps and graphs, and as numerical data, useful for providing boundary conditions to local–high resolution-air quality models or for developing customized services. In this work, a downscaling application to a regional nested domain highlights how the new air quality forecasting system gains better results than the Copernicus-CAMS system. [ABSTRACT FROM AUTHOR]

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants. [ABSTRACT FROM AUTHOR]

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique. [ABSTRACT FROM AUTHOR]

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications. [ABSTRACT FROM AUTHOR]

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R2 from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM2.5 components could be estimated with good accuracy, especially when collocated PM2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses. [ABSTRACT FROM AUTHOR]

Pollution in urban areas has been one of the most relevant problems of the last decade since it represents a threat to public health. Specifically, particulate matter (PM2.5) is a pollutant that causes serious health complications, such as heart and lung diseases. Centers for monitoring contaminants and climatic variables have been established to adopt measures to control the consequences of high levels of air pollution. However, these monitoring centers sometimes make decisions when pollution levels are already harmful to health, which may be related to sensor miscalibration and failures. This study presents a PM2.5 prediction system based on a state-space model—developed with real data from 2019—plus a Kalman filter to improve the prediction. The system was subsequently validated using real data captured in 2018 in Valle de Aburrá. Therefore, this is an important first step towards a more robust PM diagnosis and prediction system in the presence of false and mismatched data in the measurement. [ABSTRACT FROM AUTHOR]

In this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli. [ABSTRACT FROM AUTHOR]

This paper presents a statistical comparison of parallel hourly measurements of particulate matter smaller than 10 μ m (PM 10 ) from two monitoring stations that are located 560 m from each other in the northern part of Brno City. One monitoring station is located in a park, the other in a built-up area. The authors' aim is to describe the influence of a built-up area geometry and nearby traffic intensity on modeling of PM 10 pollution levels in the respective part of Brno. Furthermore, the purpose of this study is also to examine the influence of meteorological factors on the pollution levels; above all, to assess the influence of wind speed and direction, temperature change, and humidity change. In order to evaluate the obtained data, the following methods of mathematical statistics were applied: descriptive statistics, regression analysis, analysis of variance, and robust statistical tests. According to the results of the Passing–Bablok test, it can be stated that the parallel measurements of PM 10 are significantly different. A regression model for PM 10 pollution prediction was created and tested in terms of applicability; subsequently, it was used in order to compare measurements from both stations. It shows that in addition to the monitored meteorological factors, pollution levels are influenced mainly by traffic intensity and the geometry of the monitored built-up area. [ABSTRACT FROM AUTHOR]

The concern about air pollution in urban areas has substantially increased worldwide. One of its main components, particulate matter (PM) with aerodynamic diameter of ≤2.5 µm (PM2.5), can be inhaled and deposited in deeper regions of the respiratory system, causing adverse effects on human health, which are even more harmful to children. In this sense, the use of deterministic and stochastic models has become a key tool for predicting atmospheric behavior and, thus, providing information for decision makers to adopt preventive actions to mitigate air pollution impacts. However, stochastic models present their own strengths and weaknesses. To overcome some of disadvantages of deterministic models, there has been an increasing interest in the use of deep learning, due to its simpler implementation and its success on multiple tasks, including time series and air quality forecasting. Thus, the objective of the present study is to develop and evaluate the use of four different topologies of deep artificial neural networks (DNNs), analyzing the impact of feature augmentation in the prediction of PM2.5 concentrations by using five levels of discrete wavelet transform (DWT). The following types of deep neural networks were trained and tested on data collected from two living lab stations next to high-traffic roads in Guildford, UK: multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolutional neural network (1D-CNN) and a hybrid neural network composed of LSTM and 1D-CNN. The performance of each model in making predictions up to twenty-four hours ahead was quantitatively assessed through statistical metrics. The results show that wavelets improved the forecasting results and that discrete wavelet transform is a relevant tool to enhance the performance of DNN topologies, with special emphasis on the hybrid topology that achieved the best results among the applied models. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 μg/m3), and root mean square error (6.6 μg/m3), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants' spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons' instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]