Abstract
A greater understanding of ozone damage to the environment and health led to an increased demand for accurate predictions. This study provides two new accurate hybrid models of ozone prediction. The first one (CHIMERE-NARX) is based on a NARX model as a post-processing of the CHIMERE model. In the second (CHIMERE-NARX-DWT), a discrete wavelet transform (DWT) has been added. Our models were built and validated using ozone measurements from the Mediouna station in Casablanca, Morocco, from February 1st to March 27th, 2021. The results highlighted the CHIMERE model limitations, such as wind speed overestimation and insufficient emission data. The first hybrid successfully increased the correlation coefficient from 88 to 93% and reduced RMSE from 23.99 μg/m3 to −3.54 μg/m3, overcoming CHIMERE limitations to some extent, especially during nighttime. A second hybrid addressed the first hybrid limitation, such as using ozone as a single input. This hybrid successfully balanced the weight of NARX at night against the day, increasing the correlation coefficient to 98% and decreasing RMSE to −0.02 μg/m3. This study presents a new generation of post-processing based on deterministic model processes, with the possibility of training them with minimum input data, which can be applied to other models using various pollutants.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- WHO:
-
World Health Organization
- NOx:
-
Nitrogen oxides
- ML:
-
Machine learning
- CNN:
-
Convolutional neural network
- DA:
-
Data assimilation
- LSTM:
-
Long-short-term memory
- CMAQ:
-
Community multiscale air quality
- ANN:
-
Artificial neural network
- DWT:
-
Discrete wavelet transform
- LM:
-
Levenberg-Marquardt
- RMSE:
-
Root means square error
- CAMS:
-
Copernicus atmosphere monitoring service
- EnKF:
-
Ensemble Kalman filter
- VOC:
-
Volatile organic compounds
- CTM:
-
Chemistry transport models
- NN:
-
Neural network
- MB:
-
Mean bias
- CT:
-
Chi-square test
- NARX:
-
Nonlinear auto-regressive network with exogenous inputs
- NWP:
-
Numerical weather prediction
- FFNNs:
-
Feed-forward neural networks
- WRF:
-
Weather research forecasting
- HPC:
-
High-performance calculator
- LMD:
-
Laboratory of meteorology dynamic
- AQF:
-
Air quality forecasting
- AI:
-
Artificial intelligence
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Acknowledgments
The Faculty of Science University IBN ZOHR supported this research in all its stages. We would like to thank warmly Ministry of Interior, especially the Department of Energy and Environment of the Souss Massa region, for their collaboration. We are grateful to the Dynamic Meteorology Laboratory (LMD) for its valuable assistance. The calculations for this simulation were done using the national HPC managed by the National Center of Scientific and Technological Research (CNRST) in Morocco. The authors are grateful to the staff of “HPC” in particular Ms. Bouchra RAHIM, Scientific Computing team leader, for her availability and assistance.
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Amine Ajdour, Anas Adnane, Brahim Ydir, Dris Ben hmamou: conceptualization, methodology, validation, writing, original draft, writing—review and editing. Kenza KHOMSI, Hassan AMGHAR, Youssef CHELHAOUI: data acquisition and validation, review and editing. Jamal CHAOUFI, Radouane LEGHRIB: direction, methodology, investigation—review and editing.
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Ajdour, A., Adnane, A., Ydir, B. et al. A new hybrid models based on the neural network and discrete wavelet transform to identify the CHIMERE model limitation. Environ Sci Pollut Res 30, 13141–13161 (2023). https://doi.org/10.1007/s11356-022-23084-8
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DOI: https://doi.org/10.1007/s11356-022-23084-8