Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Identifying spatial relations of industrial carbon emissions among provinces of China: evidence from unsupervised clustering algorithms

Reducing the total carbon emissions of modern industry is of great significance for China to achieve the carbon peak mission. The MD-SNA spatial correlation measure methodology was innovatively proposed in this paper, which was based on the clustering algorithm of similarity measure. Furthermore, the social network analysis (SNA) method was incorporated to explore the spatial relationship of provincial industrial carbon emissions. The GINI coefficient, Theil index (GE₀), and mean of logarithmic deviation (GE₁) were used to measure the regional differences of China's industrial carbon emissions. More specifically, we adopted a combined tactic of spatial difference and spatial correlation frameworks. The primary objective of the proposed methodology is to empirically investigate the structural characteristics and spatial relations of different provinces. The results of the case study are as follows. First, the regional industrial carbon emission intensity was unbalanced, among which energy-rich provinces and eastern developed provinces were relatively strong. Second, Beijing, Shandong, Shaanxi, Henan, Sichuan, and Xinjiang were located at the center of the spatial network of industrial carbon emissions. Third, our work clarified the node attributes and different functions of provinces. More than half of the core provinces belonged to the primary beneficial block, which was in the central position of spatial correlation network. The conclusion can help policymakers clarify the overall industrial sector spatial pattern and provinces' roles and functions.

A hybrid method of time series forecasting based on information granulation and dynamic selection strategy1

Forecasting trend and variation ranges for time series has been challenging but crucial in real-world modeling. This study designs a hybrid time series forecasting (FIGDS) model based on granular computing and dynamic selection strategy. Firstly, with the guidance of the principle of justifiable granularity, a collection of interval-based information granules is formed to characterize variation ranges for time series on a specific time domain. After that, the original time series is transformed into granular time series, contributing to dealing with time series at a higher level of abstraction. Secondly, the L 1 trend filtering method is applied to extract trend series and residual series. Furthermore, this study develops hybrid predictors of the trend series and residual series for forecasting the variation range of time series. The ARIMA model is utilized in the forecasting task of the residual series. The dynamic selection strategy is employed to identify the ideal forecasting models from the pre-trained multiple predictor system for forecasting the test pattern of the trend series. Eventually, the empirical experiments are carried out on ten time series datasets with a detailed comparison for validating the effectiveness and practicability of the established hybrid time series forecasting method.

Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments.

Time Series Forecasting of Air Quality: A Case Study of Sofia City

Air pollution has a significant impact on human health and the environment, causing cardiovascular disease, respiratory infections, lung cancer and other diseases. Understanding the behavior of air pollutants is essential for adequate decisions that can lead to a better quality of life for citizens. Air quality forecasting is a reliable method for taking preventive and regulatory actions. Time series analysis produces forecasting models, which study the characteristics of the data points over time to extrapolate them in the future. This study explores the trends of air pollution at five air quality stations in Sofia, Bulgaria. The data collected between 2015 and 2019 is analyzed applying time series forecasting. Since the time series analysis works on complete data, imputation techniques are used to deal with missing values of pollutants. The data is aggregated by granularity periods of 3 h, 6 h, 12 h, 24 h (1 day). The AutoRegressive Integrated Moving Average (ARIMA) method is employed to create statistical analysis models for the prediction of pollutants’ levels at each air quality station and for each granularity, including carbon oxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine particles (PM2.5). In addition, the method allows us to find out whether the pollutants’ levels exceed the limits prescribed by the World Health Organization (WHO), as well as to investigate the correlation between levels of a given pollutant measured in different air quality stations.

Analysis and Forecast of Beijing’s Air Quality Index Based on ARIMA Model and Neural Network Model

Based on Beijing’s Air Quality Index (AQI) and concentration changes of the six major pollutants from 2019 to 2021, the results are visualized through descriptive statistics, and the air pollution status and influencing factors of Beijing’s AQI are analyzed using the ARIMA model and neural network. A forecast system is built and the fitting effects of the two models are compared. The results show that PM2.5, PM10, and O3 of the six major pollutants have the greatest impact on AQI. Beijing’s air quality now shows a trend of improvement in recent years; however, there is obvious seasonal evidence that the summer pollution index has been high. Therefore, special attention should be paid to the treatment of ozone pollution in summer. Both models are useful for the forecast of AQI, but the forecast effect of the neural network model is better than that of the ARIMA model. Moreover, when using the additive seasonal model for the long-term forecast of monthly data, it is found that the Beijing AQI still shows seasonal cyclicality and has a slightly decreasing trend in the next two years. This research provides a basis for the forecast of air quality and policy enlightenment for environmental protection departments to deal with air pollution.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.

Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm

Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems.