Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle leve...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0313356 |
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| _version_ | 1841555360881573888 |
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| author | Martin Kostadinov Eftim Zdravevski Petre Lameski Paulo Jorge Coelho Biljana Stojkoska Michael A Herzog Vladimir Trajkovik |
| author_facet | Martin Kostadinov Eftim Zdravevski Petre Lameski Paulo Jorge Coelho Biljana Stojkoska Michael A Herzog Vladimir Trajkovik |
| author_sort | Martin Kostadinov |
| collection | DOAJ |
| description | Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution. |
| format | Article |
| id | doaj-art-0ed976f742614b8e9dd9d1d7774176e8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0ed976f742614b8e9dd9d1d7774176e82025-01-08T05:33:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031335610.1371/journal.pone.0313356Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.Martin KostadinovEftim ZdravevskiPetre LameskiPaulo Jorge CoelhoBiljana StojkoskaMichael A HerzogVladimir TrajkovikAir pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.https://doi.org/10.1371/journal.pone.0313356 |
| spellingShingle | Martin Kostadinov Eftim Zdravevski Petre Lameski Paulo Jorge Coelho Biljana Stojkoska Michael A Herzog Vladimir Trajkovik Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. PLoS ONE |
| title | Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. |
| title_full | Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. |
| title_fullStr | Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. |
| title_full_unstemmed | Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. |
| title_short | Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. |
| title_sort | forecasting air pollution with deep learning with a focus on impact of urban traffic on pm10 and noise pollution |
| url | https://doi.org/10.1371/journal.pone.0313356 |
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