Abstract
Sustainable development globally is highly impacted by increased air pollution which is attributed to increasing population, commercial and industrial activities. Combustion gas emissions attributed to transportation, social and other activities are a major cause of air pollution. To mitigate adverse impact of air pollution on human health, forecasting PM10 and PM2.5 is a necessity. This study employs Fourier series decomposition approach in combination with support vector machine and long short-term memory machine learning algorithms to predict PM10 and PM2.5. Hourly data was obtained from December 2020 to November 2021 for Mohammedia city in Morocco. The model’s performance was evaluated using RMSE, MAE and R2. LSTMF and SVMF models in combination with Fourier series decomposition performed better than the SMV and LSTM standalone models. Hourly prediction of PM10, LSTMF model performed better than other models during Autumn season with closely followed by the model in winter seasons. For PM2.5 prediction the model during autumn season was observed to be outperforming other models in all other seasons. These results were based on hourly prediction based on season. Then this study also, forecasted seven ahead for PM10 and PM2.5. LSTMF model performed best with R2 value of 0.95 (winter), 0.93 (spring), 0.85 (summer) and 0.96 (autumn) for PM2.5. For PM10 the LSTMF performance was also good with R2 value of 0.84 (winter), 0.92 (spring), 0.84 (summer), and 0.92 (autumn). This study highlights how hourly prediction can be achieved to identify in advance the patterns and trends for particulate matter concentration. This will aid the decision and policy makers to adopt mitigation measures and policy in advance to address air pollution issues during peak hours.
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Data availability
The datasets analysed during the current study were obtained from the “Direction de la Météorologie Nationale” (Morocco) and are not publicly available due to data sharing restrictions.However, they are available from the corresponding author (Bennis Mohamed) upon reasonable request.
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Acknowledgements
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/67/46. The database used in this article was funded under project scheme, programme ibn khaldoun d appui à la recherche dans le domaine des sciences humaines et sociales, CNRST, Morocco, for project entitled “Health security in Casablanca” project number IK/2018/23.
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M.B. contributed to methodology, data curation, and writing – original draft.M.Y. was responsible for conceptualization, validation, supervision, and resources.R.E.M. carried out formal analysis, investigation, software development, and visualization.M.A. contributed to methodology and review & editing.R.A.K. handled project administration and contributed to writing.All authors reviewed and approved the final manuscript.
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Bennis, M., Youssfi, M., Morabet, R.E. et al. Particulate matter (PM2.5 and PM10) prediction using fourier series decomposition in combination with LSTM and SVM. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38436-4
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DOI: https://doi.org/10.1038/s41598-026-38436-4
