Accurate Air Quality Prediction Research Articles

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model—the Gaussian Hidden Markov Model (GHMM)—using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM’s strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

An evolutionary deep learning model based on XGBoost feature selection and Gaussian data augmentation for AQI prediction

Accurate prediction of air quality is crucial for ensuring the scientific validity and effectiveness of air pollution control measures. This study proposes a combined deep learning (DL) model (XGBoost-GDA-TCN-IMRFO-GRU) for predicting hourly air quality index (AQI) data in four cities. The model integrates Extreme gradient boosting (XGBoost) for feature selection, Gaussian data augmentation (GDA), improved manta ray foraging optimization (IMRFO) algorithm, temporal convolutional network (TCN), and gated recurrent unit (GRU). XGBoost calculates the scores of pollutant gases affecting AQI, selecting the top four important pollutants (PM2.5, PM10, NO2, O3) based on their importance rankings. GDA enhances the robustness of the DL models and addresses the limitations of insufficient and overfitting training datasets. Additionally, the IMRFO algorithm, with two improved strategies, is applied to enhance the GRU model. TCN extracts spatiotemporal features of AQI, while GRU constructs a temporal model for efficient computations. Compared to eleven benchmark models, the proposed model demonstrates superior performance in terms of MAE, RMSE, MAPE, and NSE, achieving high accuracy and optimal prediction performance. Specifically, the XGBoost-GDA-TCN-IMRFO-GRU model reduces RMSE, MAE, and MAPE by 33–60 %, 39–68 %, and 39–66 %, respectively, compared to the TCN model. Therefore, the XGBoost-GDA-TCN-IMRFO-GRU model can provide reliable early warnings for air quality, which is of great significance for air pollution prevention and the sustainable development of society.

Air quality prediction based on improved quantum long short-term memory neural networks ★ ★ This work was supported by National Natural Science Foundation of China (No.61772295), Key Projects of Chongqing Natural Science Foundation Innovation Development Joint Fund (CSTB2023NSCQ-LZX0139), Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208) and Technology Research Program of Chongqing

Accurate prediction of urban air quality is of vital importance in preventing urban air pollution and improving the quality of life of urban residents. In order to achieve more accurate prediction of air quality, this study proposes a novel hybrid quantum neural network prediction model that combines an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a quantum long short-term memory network (QLSTM) optimized by the particle swarm optimization (PSO) algorithm.This study focuses on using the QLSTM model to mine the time-real fluctuations and historical dependence of air quality data and applying the PSO algorithm to optimize the hyper-parameters of the quantum model to improve the prediction accuracy; then, ICEEMDAN is introduced to disassemble the original air quality data series into multiple pattern components containing different information, which effectively reduces the complexity of the data; the feasibility and validity of the proposed methodology are verified through comparison experiments with other prediction modeling methods. The results show that the proposed QLSTM prediction method incorporating ICEEMDAN and PSO optimization has the highest prediction accuracy in terms of prediction accuracy, which contributes a novel and quantum-specific technical approach to the field of air quality prediction.

Predicting air quality using a multi-scale spatiotemporal graph attention network

As urbanization accelerates, air quality has become a pressing concern. Accurate air quality prediction is essential for informed governmental decision-making and for protecting public health. Variations in air quality are influenced by complex multi-scale spatiotemporal processes. Existing research primarily relies on capturing single spatiotemporal features of air quality to predict changes. Meanwhile, when constructing spatiotemporal dynamic graphs, the inherent characteristics of the input data and the comprehensive effects of both global and local influences are not fully considered. To address these problems, we propose a graph-attention-based approach, named Multi-scale Spatiotemporal Graph Attention Network (MSTGAN). MSTGAN addresses the intricate spatiotemporal patterns of air quality across various scales through three key components: (1) a multistation transformer to model the temporal patterns of air quality at individual monitoring stations; (2) a bilinear spatiotemporal attention mechanism to capture the spatiotemporal dynamic global dependencies among all stations in a region; and (3) a set of spatiotemporal dependence graph-coupled Chebyshev graph convolution gate recurrent units to extract and aggregate the local spatiotemporal features of interrelated stations. Experiments conducted on three real-world datasets demonstrated that MSTGAN achieved significant improvements of 4.2%, 3.9%, and 7.8% in the mean absolute error, root mean square error, and R2 evaluation metrics, respectively, compared to seven state-of-the-art time-series forecasting methods. This code is publicly available at https://github.com/HPSCIL/MSTGAN-airquality-prediction.

Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images

ABSTRACTOur research addresses the imperative need for an efficient air quality monitoring and forecasting system to mitigate the significant health risks of air pollution. Departing from conventional binary data collection methods, we employ image‐based techniques to overcome inherent limitations. A pioneering aspect of our work involves the development of a novel model capable of predicting six air pollutants (PM2.5, PM10, O3, CO, SO2, NO2) along with Air Quality Index for Bengaluru, Delhi, and Tamil Nadu, achieving a commendable mean absolute error of 0.1432 on the test set. The efficacy of our approach is validated through a meticulously curated dataset comprising approximately 5455 images. We emphasize the significance of normalization by presenting outputs before and after, shedding light on the impact of parameters with varying ranges and strategies employed to mitigate such discrepancies. A detailed analysis of our model's best and worst outputs provides valuable insights into its strengths and limitations. To enhance user accessibility, we introduce an innovative image‐based, real‐time, user‐friendly dashboard that allows users to conveniently assess a location's air pollution levels by uploading an image. This holistic approach offers a promising avenue for accurate air quality prediction and real‐time monitoring.

Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm.

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction.

Fine particulate matter concentration prediction based on hybrid convolutional network with aggregated local and global spatiotemporal information: A case study in Beijing and Chongqing

Air pollution is a highly concerned environmental issue that have serious impacts on human health and the ecological environment. Accurate air quality prediction can help people effectively deal with the threats posed by air pollution. Most previous work mainly focused on temporal modeling of air quality data at monitoring stations. In recent years, a number of works have used graph convolution to model the spatial dependencies between neighboring sites to extract spatial features and combined temporal model to extract temporal features for predicting PM2.5. However, these models only considered the local spatial relationships of adjacent sites and ignored sites with longer geographic distance. Therefore, this study proposes a spatio-temporal hybrid model based on convolution and attention (named Attentive Graph Convolution and 1D Convolution Network, AGCC) to predict multi-site and multi-step PM2.5 concentration. This method not only models the local spatial relationships of adjacent sites, but also combines spatial attention and graph convolutional network (GCN) to model the global spatial relationships of sites. The local and global spatial features are obtained respectively and are fused to obtain spatial features with richer semantic information. Meanwhile, the temporal dependency relationship is modeled through a temporal module composed of temporal attention and one-dimensional convolutional neural network (Conv 1D). AGCC was compared to six baseline models at different prediction horizon based on the air quality datasets of Beijing and Chongqing. The experimental results demonstrate that the model proposed by our achieves the best performance and verifies the feasibility of the model.

Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley’s analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning.

A novel ensemble machine learning method for accurate air quality prediction

A novel ensemble machine learning method for accurate air quality prediction

Hyperparameter optimization for hourly PM2.5 pollutant prediction

Air pollution, particularly the presence of Particulate Matter (PM) 2.5, poses significant health risks to humans, with industrial growth and urban vehicle emissions being major contributors. This study utilizes machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, to predict PM2.5 levels. A dataset from Kaggle consisting of PM2.5 and other pollutant parameters is preprocessed and split into training and testing sets. The models are trained, evaluated, and compared using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Additionally, hyperparameters are applied to optimize the models. Results show that SVM with hyperparameters performs better, indicating its potential for accurate air quality prediction. These findings can aid policymakers in implementing effective pollution control strategies.

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction

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 PM2.5, 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.

Spatiotemporal hierarchical transmit neural network for regional-level air-quality prediction

Air pollution has become a growing problem owing to its harmful effects on human health, making accurate air quality prediction an important task to avoid serious environmental impacts. Recently, graph-neural-network-based methods have emerged as promising approaches to modeling the spatial dependencies between adjacent stations. However, they have limitations in capturing the hierarchical temporal features of the time series, which include trends, and periodic components. They also overlook dynamic correlations between stations and multiple types of connections, resulting in generated graphs that lack dynamics. In this paper, we proposed a spatiotemporal hierarchical transmit neural network for the prediction of air quality by extracting long-term periodic features and short-term spatiotemporal dependencies. It incorporates a periodic feature extraction component (PFEC), a scene dynamic graph module (SDGM), a spatiotemporal extraction component (STEC), and a transmit attention (TransATT) component. The PFEC applies discrete Fourier transform and trend decomposition techniques to extract long-term periodic features from the spatiotemporal graph. The SDGM generates dynamic graphs by combining node features of time series with predefined graphs to encode diverse station relationships. The STEC comprises of two convolutional operations and attention mechanisms, enabling the model to capture the short-term spatiotemporal dependencies. TransATT integrates the extracted short-term spatiotemporal dependencies and long-term periodic features, allowing the model to transmit with short- and long-term features. To demonstrate the effectiveness of the proposed model, we conducted experiments on three real-world datasets and found that our approach outperforms state-of-the-art methods.

Large differences of highly oxygenated organic molecules (HOMs) and low-volatile species in secondary organic aerosols (SOAs) formed from ozonolysis of β-pinene and limonene

Abstract. Secondary organic aerosols (SOAs) play a key role in climate change and public health. However, the oxidation state and volatility of SOAs are still not well understood. Here, we investigated the highly oxygenated organic molecules (HOMs) in SOAs formed from ozonolysis of β-pinene and limonene. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize HOMs in aerosol filter samples, and a scanning mobility particle sizer (SMPS) was used to measure the concentration and size distribution of SOA particles. The relative abundance of HOMs (i.e., ratio of summed mass spectrometry peak intensity of HOMs to totally identified organic compounds) in limonene SOA was 14 %–20 %, higher than in β-pinene SOA (3 %–13 %), exhibiting different trends with increasing ozone concentrations. β-pinene oxidation-derived HOMs exhibit higher yield at high ozone concentration, accompanied by substantial formation of ultra-low-volatile organic compounds (ULVOCs). Limonene oxidation-derived HOMs exhibit higher yield at moderate ozone concentrations, with semi-, low-, and extremely low-volatile organic compounds (SVOCs, LOVCs, and ELVOCs) play a major role. Combined experimental evidence and theoretical analysis indicate that oxygen-increasing-based peroxy radical chemistry is a plausible mechanism for the formation of oxygenated organic compounds with 10 carbon atoms. Our findings show that HOMs and low-volatile species in β-pinene and limonene SOA are largely different. The ozone concentration-driven SOA formation and evolution mechanism for monoterpenes is suggested to be considered in future climate or exposure risk models, which may enable more accurate air quality prediction and management.

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively.

Estimation of PM2.5 Hourly Concentration in Sichuan Province Based on GTWR-XGBoost Model

Aerosol optical depths of satellites and meteorological factors have been widely used to estimate concentrations of surface particulate matter with an aerodynamic diameter ≤ 2.5 μm. Research on a high time resolution and high-precision PM2.5 concentration estimation method is of great significance for timely and accurate air quality prediction and air pollution prevention and mitigation. Himawari-8 AOD hour product and ERA5 meteorological reanalysis data were used as estimation variables, and a GTWR-XGBoost combined model was proposed to estimate hourly PM2.5 concentration in Sichuan Province. The results showed that:① the performance of the proposed combination model was better than that of the KNN, RF, AdaBoost, GTWR, GTWR-KNN, GTWR-RF, and GTWR-AdaBoost models in the full dataset; the fitting accuracy indexes R2, MAE, and RMSE were 0.96, 3.43 μg·m-3, and 5.52 μg·m-3, respectively; and the verification accuracy indexes R2, MAE, and RMSE were 0.9, 4.98 μg·m-3, and 7.92 μg·m-3, respectively. ② The model had a high goodness of fit (R2 of the whole dataset was 0.96, and R2 of different times ranged from 0.91 to 0.98) when applied to the estimation of PM2.5 concentration hour. It showed that the model had good time stability for hourly estimation and could provide accurate estimation information for regional air quality assessment. ③ In terms of time, the annual average PM2.5hourly concentration estimation showed an inverted U-shaped trend. It began to increase gradually at 09:00 am to a peak of 44.56 μg·m-3 at 11:00 and then gradually decreased. Moreover, the seasonal variation was very obvious, with winter>spring>autumn>summer. ④ In terms of spatial distribution, it showed the characteristics of high in the east and low in the west and a high degree of local pollution.

Multi-view multi-task spatiotemporal graph convolutional network for air quality prediction

Accurate air quality prediction is a crucial but arduous task for intelligent cities. Predictable air quality can advise governments on environmental governance and residents on travel. However, complex correlations (i.e., intra-sensor correlation and inter-sensor correlation) make prediction challenging. Previous work considered the spatial, temporal, or combination of the two to model. However, we observe that there are also logical semantic and temporal, and spatial relations. Therefore, we propose a multi-view multi-task spatiotemporal graph convolutional network (M2) for air quality prediction. We encode three views, including spatial view (using GCN to model the correlation between adjacent stations in geographic space), logical view (using GCN to model the correlation between stations in logical space), and temporal view (using GRU to model the correlation among historical data). Meanwhile, M2 chooses a multi-task learning paradigm that includes a classification task (auxiliary task, coarse granularity prediction of air quality level) and a regression task (main task, fine granularity prediction of air quality value) to predict jointly. And the experimental results on two real-world air quality datasets demonstrate our model performances over the state-of-art methods.

A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.

A hybrid prediction model of air quality for sparse station based on spatio-temporal feature extraction

Accurate prediction of air quality is helpful for effective prevention and control of air pollution. However, there are relatively few studies on the air-quality prediction for sparse station. Through the extraction of spatio-temporal features, this research aims to achieve the hybrid prediction of air quality for sparse station. The proposed method is comprised of three parts. The first part is the extraction of multi-scale temporal features. Specifically, the multi-scale time lags of meteorological factors are extracted to cope with sudden changes, and the key features from multi-source data are extracted to avoid the interference of redundant features. The second part is to extract spatial features of related stations based on spatial hierarchy division. The third part is the hybrid model prediction based on spatio-temporal feature groups. The following results are obtained. (1) For sudden-change prediction, the processing method proposed in this paper is more effective. (2) Compared with the baselines and models popular in dense station, the proposed model has superior performance in the air-quality prediction of sparse stations. (3) Compared with the common spatial-related station selection method, the proposed method is more suitable for sparse stations. (4) For multi-step prediction, the proposed model has significant advantages in long-term prediction.