PM2.5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time

Yang, Jie; Yan, Rui; Nong, Mingyue; Liao, Jiaqiang; Li, Feipeng; Sun, Wei

doi:10.1016/j.apr.2021.101168

Cited by 53 publications

(13 citation statements)

References 41 publications

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“…However, these extensions have yet addressed the spatial dependence of air pollutant data. By incorporating a CNN or GCN component into the RNN-based model, hybrid deep learning models such as CNN-LSTM [63], [64], GCN-LSTM [65], and GCN-GRU [66] were proposed for air pollution forecast, to take into account the spatial dependence of nearby observations including air pollution and auxiliary data such as meteorology and urban morphology. In addition to the RNN-based modelling, the one-dimensional CNN (1D-CNN) model was used to extract the temporal dependence of urban dynamics observed at a station [67] or nearby stations [68].…”

Section: ) Air Pollution Forecastmentioning

confidence: 99%

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Zhang

Han

et al. 2022

IEEE Access

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Air pollution presents a serious health challenge in urban metropolises. While accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models have yet fully captured the complex interactions between the temporal characteristics of air pollution and the spatial characteristics of urban dynamics. Our proposed Deep-AIR fills this gap to provide fine-grained citywide air pollution estimation and station-wide forecast, by exploiting domain-specific features (including Air Pollution, Weather, Urban Morphology, Transport, and Time-sensitive features), with a hybrid CNN-LSTM structure to capture the spatio-temporal features, and 1x1 convolution layers to enhance the learning of temporal and spatial interaction. Deep-AIR outperforms compatible baselines by a higher accuracy of 1.5%, 2.7%, and 2.3% for Hong Kong and 1.4%, 1.4% and 3.3% for Beijing in fine-grained 1-hr pollution estimation, and 1-hr and 24-hr forecasts, respectively. Saliency analysis reveals that for Hong Kong, spatial features, including street canyon and road density, are the best predictors for NO 2 , while temporal features, including historical air pollutants and weather, are the best predictors for PM 2.5 . For Beijing, historical air pollutant data, traffic congestion, wind direction and seasonal indicator are the best predictors for all pollutants. PM 10 in Hong Kong is achieving the best estimation and forecast accuracy, whilst CO in Beijing is achieving the best results.

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Section: ) Air Pollution Forecastmentioning

confidence: 99%

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Zhang

Han

et al. 2022

IEEE Access

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“…The well-known deep learning techniques contain Deep Belief Network (DBN) (Hinton and Salakhutdinov 2006), Convolutional Neural Network (CNN) (Krizhevsky et al 2012), Recurrent Neural Network (RNN) (Elman 1990) and its variant of Long Short-term Memory (LSTM) (Hochreiter and Schmidhuber 1997), and so on. At present, a variety of deep learning techniques have been successfully applied for air quality forecasting (Akbal and Ünlü 2022;Dhakal et al 2021;Wong et al 2021;Yang et al 2021;Zhang et al 2020aZhang et al , 2022Zhou et al 2022). For instance, a deep stacked autoencoder (AE) model (Li et al 2016), as a variant of DBN, was used to learn inherent air features for air quality prediction.…”

Section: Introductionmentioning

confidence: 99%

Modeling air quality PM2.5 forecasting using deep sparse attention-based transformer networks

Zhang

2023

Int. J. Environ. Sci. Technol.

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Air quality forecasting is of great importance in environmental protection, government decision-making, people's daily health, etc. Existing research methods have failed to effectively modeling long-term and complex relationships in time series PM2.5 data and exhibited low precision in long-term prediction. To address this issue, in this paper a new lightweight deep learning model using sparse attention-based Transformer networks (STN) consisting of encoder and decoder layers, in which a multi-head sparse attention mechanism is adopted to reduce the time complexity, is proposed to learn long-term dependencies and complex relationships from time series PM2.5 data for modeling air quality forecasting. Extensive experiments on two real-world datasets in China, i.e., Beijing PM2.5 dataset and Taizhou PM2.5 dataset, show that our proposed method not only has relatively small time complexity, but also outperforms state-of-the-art methods, demonstrating the effectiveness of the proposed STN method on both short-term and long-term air quality prediction tasks. In particular, on singe-step PM2.5 forecasting tasks our proposed method achieves R2 of 0.937 and reduces RMSE to 19.04 µg/m3 and MAE to 11.13 µg/m3 on Beijing PM2.5 dataset. Also, our proposed method obtains R2 of 0.924 and reduces RMSE to 5.79 µg/m3 and MAE to 3.76 µg/m3 on Taizhou PM2.5 dataset. For long-term time step prediction, our proposed method still performs best among all used methods on multi-step PM2.5 forecasting results for the next 6, 12, 24, and 48 h on two real-world datasets.

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“…It is found that they can successfully forecast the PM 2.5 for their capacity of nonlinear mapping. In addition, the deep learning technology has sparked a lot of interest (Pak et al, 2020;Menares et al, 2021;Yang et al, 2021) and proven its superiority in several fields. Deep learning technology is proposed for analyzing the characteristics of historical data.…”

Section: Introductionmentioning

confidence: 99%

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

Liu

Xiaoqin²,

Chen³

et al. 2023

Front. Earth Sci.

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Accurate long-term forecasts of PM2.5 pollution are essential to mitigating health risks and formulating pollutant control strategies for decision-makers in China. In this study, an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) is developed based on medium- and long-term ensemble forecasts of PM2.5 in Beijing-Tianjin-Hebei region and its surrounding areas. The results show that the observed PM2.5 pollution ratio increases with the aggravating PM2.5 pollution. For example, the ratio of meteorological stations with heavy pollution is 4.4 times that of light pollution and 3.9 times that of moderate pollution. In addition, the correlation coefficients between observations and forecasts are above 0.60 for all forecast leading times. Statistical results show that the average accuracy for forecasts with the leading times of 1–3 days, 4–7 days, and 8–15 days are 74.1%, 81.3%, and 72.9% respectively, indicating that the OIF-PM2.5 method has a high reliability in forecasts with the leading times of 1–15 days. The OIF-PM2.5 method is further applied in a severe PM2.5 pollution episode in the December of 2021, and the average forecast precision in forecasts with the leading times of 6–8 days reaches as high as 100%, showing a certain reference value for PM2.5 forecasts.

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PM2.5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time

Cited by 53 publications

References 41 publications

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Modeling air quality PM2.5 forecasting using deep sparse attention-based transformer networks

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

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