The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial–temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM_2.5, PM₁₀ and O₃ at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.

Accurate and stable prediction of atmospheric PM_2.5 concentrations is crucial for air pollution prevention and control. Existing studies usually rely on a single model or use a single evaluation criterion in multi-model ensemble weighted forecasts, neglecting the dual needs for accuracy and stability in PM_2.5 forecast. In this study, a novel ensemble forecast model is proposed that overcomes these drawbacks by simultaneously taking into account both forecast accuracy and stability. Specifically, four advanced deep learning models—Long Short-Term Memory Network (LSTM), Graph Convolutional Network (GCN), Transformer, and Graph Sample and Aggregation Network (GraphSAGE)—are firstly introduced. And then, two combined models are constructed as predictors, namely LSTM–GCN and Transformer–GraphSAGE. Finally, a combined weighting strategy is adopted to assign weights to these two combined models using a multi-objective optimization algorithm (MOO), so as to carry out more accurate and stable predictions. The experiments are conducted on the dataset from 36 air quality monitoring stations in Beijing, and results show that the proposed model achieves more accurate and stable predictions than other benchmark models. It is hoped that this proposed ensemble forecast model will provide effective support for PM_2.5 pollution forecast and early warning in the future.

Dust events not only cause local ecosystem degradation and desertification, but also have profound impacts on regional and global climate system, as well as air quality and human health. Dust events in Xinjiang Basin, as the important dust source of Eastern Asia, have undergone a significant change under the global warming background and may be in a new active period after 2000, which is worthy of study. This study provides the temporal and spatial variations of dust events in the Xinjiang Basin based on surface meteorological station observation data during 1960–2015. The results show that Southern Xinjiang is the main dust occurrence region where dust events are significantly more than that in the Northern Xinjiang, and each year more than 73% of dust events occurred in spring and summer. The dust index (DI), which is defined to represent the large-scale variation of dust event, shows a significant downward trend during the past 56 years with a linear decreasing rate −8.2 years⁻¹in Southern Xinjiang. The DI is positively correlated to surface wind speed with a mean correlation coefficient of 0.79. The declining trend of surface wind speed could explain dust events variation during 1960–2000. But in the new active period after 2000, the increase of DI is not consistent with the rising wind speed with the correlation coefficient decreasing to 0.34. It is found that, compared with 1960–1999, the average annual precipitation and frequency increased by 17.4 and 13% during 2000–2015, respectively, and the NDVI also increased at the same time, which indicates that the surface condition changes induced by the increase of precipitation might suppress the occurrence of dust. Moreover, the analysis of high-altitude wind field shows that the variation of the East Asian general circulation’s intensity, dominating the upper-level wind fields in the Xinjiang basin, will change the surface wind speed and precipitation, and further affect the occurrence of dust events.