Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models
Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predic...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | AppliedMath |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9909/4/4/76 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846106112655360000 |
|---|---|
| author | Jesús Cáceres-Tello José Javier Galán-Hernández |
| author_facet | Jesús Cáceres-Tello José Javier Galán-Hernández |
| author_sort | Jesús Cáceres-Tello |
| collection | DOAJ |
| description | Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predict future PM2.5 levels. The results reveal a decreasing trend in PM2.5 levels from 2019 to mid-2024, suggesting the effectiveness of policies implemented by the Madrid City Council. However, the observed interannual fluctuations and peaks indicate the need for continuous policy adjustments to address specific events and seasonal variations. The comparison of local policies and those of the European Union underscores the importance of greater coherence and alignment to optimize the outcomes. Predictions made with the Prophet–LSTM model provide a solid foundation for planning and decision making, enabling urban managers to design more effective strategies. This study not only provides a detailed understanding of pollution patterns, but also emphasizes the need for adaptive environmental policies and citizen participation to improve air quality. The findings of this work can be of great assistance to environmental policymakers, providing a basis for future research and actions to improve air quality in Madrid. The hybrid Prophet–LSTM model effectively captured both seasonal trends and pollution spikes in PM2.5 levels. The predictions indicated a general downward trend in PM2.5 concentrations across most districts in Madrid, with significant reductions observed in areas such as Chamartín and Arganzuela. This hybrid approach improves the accuracy of long-term PM2.5 predictions by effectively capturing both short-term and long-term dependencies, making it a robust solution for air quality management in complex urban environments, like Madrid. The results suggest that the environmental policies implemented by the Madrid City Council are having a positive impact on air quality. |
| format | Article |
| id | doaj-art-1d76678f4aad44f381f8eb843237df47 |
| institution | Kabale University |
| issn | 2673-9909 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AppliedMath |
| spelling | doaj-art-1d76678f4aad44f381f8eb843237df472024-12-27T14:07:09ZengMDPI AGAppliedMath2673-99092024-11-01441428145210.3390/appliedmath4040076Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid ModelsJesús Cáceres-Tello0José Javier Galán-Hernández1Faculty of Statistical Studies, Complutense University of Madrid, 28040 Madrid, SpainFaculty of Statistical Studies, Complutense University of Madrid, 28040 Madrid, SpainParticulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predict future PM2.5 levels. The results reveal a decreasing trend in PM2.5 levels from 2019 to mid-2024, suggesting the effectiveness of policies implemented by the Madrid City Council. However, the observed interannual fluctuations and peaks indicate the need for continuous policy adjustments to address specific events and seasonal variations. The comparison of local policies and those of the European Union underscores the importance of greater coherence and alignment to optimize the outcomes. Predictions made with the Prophet–LSTM model provide a solid foundation for planning and decision making, enabling urban managers to design more effective strategies. This study not only provides a detailed understanding of pollution patterns, but also emphasizes the need for adaptive environmental policies and citizen participation to improve air quality. The findings of this work can be of great assistance to environmental policymakers, providing a basis for future research and actions to improve air quality in Madrid. The hybrid Prophet–LSTM model effectively captured both seasonal trends and pollution spikes in PM2.5 levels. The predictions indicated a general downward trend in PM2.5 concentrations across most districts in Madrid, with significant reductions observed in areas such as Chamartín and Arganzuela. This hybrid approach improves the accuracy of long-term PM2.5 predictions by effectively capturing both short-term and long-term dependencies, making it a robust solution for air quality management in complex urban environments, like Madrid. The results suggest that the environmental policies implemented by the Madrid City Council are having a positive impact on air quality.https://www.mdpi.com/2673-9909/4/4/76particulate matter 2.5 μmpollution predictiondata scienceenvironmental policiesCRISP-DMProphet–long short-term memory (LSTM) |
| spellingShingle | Jesús Cáceres-Tello José Javier Galán-Hernández Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models AppliedMath particulate matter 2.5 μm pollution prediction data science environmental policies CRISP-DM Prophet–long short-term memory (LSTM) |
| title | Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models |
| title_full | Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models |
| title_fullStr | Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models |
| title_full_unstemmed | Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models |
| title_short | Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models |
| title_sort | analysis and prediction of pm2 5 pollution in madrid the use of prophet long short term memory hybrid models |
| topic | particulate matter 2.5 μm pollution prediction data science environmental policies CRISP-DM Prophet–long short-term memory (LSTM) |
| url | https://www.mdpi.com/2673-9909/4/4/76 |
| work_keys_str_mv | AT jesuscacerestello analysisandpredictionofpm25pollutioninmadridtheuseofprophetlongshorttermmemoryhybridmodels AT josejaviergalanhernandez analysisandpredictionofpm25pollutioninmadridtheuseofprophetlongshorttermmemoryhybridmodels |