A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment
Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these lim...
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2025-06-01
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| author | Ioannis Stergiou Nektaria Traka Dimitrios Melas Efthimios Tagaris Rafaella-Eleni P. Sotiropoulou |
| author_facet | Ioannis Stergiou Nektaria Traka Dimitrios Melas Efthimios Tagaris Rafaella-Eleni P. Sotiropoulou |
| author_sort | Ioannis Stergiou |
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| description | Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R<sup>2</sup> improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands. |
| format | Article |
| id | doaj-art-1e33115399a64f3ea25a8949ac8b2bb1 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-1e33115399a64f3ea25a8949ac8b2bb12025-08-20T03:32:27ZengMDPI AGAtmosphere2073-44332025-06-0116673910.3390/atmos16060739A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern AlignmentIoannis Stergiou0Nektaria Traka1Dimitrios Melas2Efthimios Tagaris3Rafaella-Eleni P. Sotiropoulou4Air & Waste Management Lab, Polytechnic School, University of Western Macedonia, 50132 Kozani, GreeceAir & Waste Management Lab, Polytechnic School, University of Western Macedonia, 50132 Kozani, GreeceLaboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceAir & Waste Management Lab, Polytechnic School, University of Western Macedonia, 50132 Kozani, GreeceAir & Waste Management Lab, Polytechnic School, University of Western Macedonia, 50132 Kozani, GreeceAccurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R<sup>2</sup> improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands.https://www.mdpi.com/2073-4433/16/6/739deep learningconvolutional neural networks—CNNsLong Short-Term Memory—LSTMCommunity Multiscale Air Quality—CMAQbias correction |
| spellingShingle | Ioannis Stergiou Nektaria Traka Dimitrios Melas Efthimios Tagaris Rafaella-Eleni P. Sotiropoulou A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment Atmosphere deep learning convolutional neural networks—CNNs Long Short-Term Memory—LSTM Community Multiscale Air Quality—CMAQ bias correction |
| title | A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment |
| title_full | A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment |
| title_fullStr | A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment |
| title_full_unstemmed | A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment |
| title_short | A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment |
| title_sort | deep learning method for improving community multiscale air quality forecast bias correction event detection and temporal pattern alignment |
| topic | deep learning convolutional neural networks—CNNs Long Short-Term Memory—LSTM Community Multiscale Air Quality—CMAQ bias correction |
| url | https://www.mdpi.com/2073-4433/16/6/739 |
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