Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach
Particulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investig...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2025
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| _version_ | 1881508987487125504 |
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| author | Solis Teran, Miguel Angel Leite Coelho da Silva, Felipe Torres Armas, Elías A. Carbo‐Bustinza, Natalí López Gonzales, Javier Linkolk |
| author_browse | Carbo‐Bustinza, Natalí Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk Solis Teran, Miguel Angel Torres Armas, Elías A. |
| author_facet | Solis Teran, Miguel Angel Leite Coelho da Silva, Felipe Torres Armas, Elías A. Carbo‐Bustinza, Natalí López Gonzales, Javier Linkolk |
| author_sort | Solis Teran, Miguel Angel |
| collection | Producción científica |
| description | Particulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM10 data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the available PM10 data and evaluate the quality of time series classical models and neural networks for short-term forecasting. This study demonstrates that classical time series models, particularly ARIMA and SSA, achieve lower average forecast errors than LSTM across stations SMP, CRB, and ATE. This finding suggests that for data with seasonal patterns and relatively short time series, traditional models may be more efficient and robust. Although neural networks have the potential to capture more complex relationships and long-term dependencies, their performance may be limited by hyperparameter settings and intrinsic data characteristics. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/400 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4002026-11-02T22:00:04ZModeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network ApproachSolis Teran, Miguel AngelLeite Coelho da Silva, FelipeTorres Armas, Elías A.Carbo‐Bustinza, NatalíLópez Gonzales, Javier LinkolkParticulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM10 data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the available PM10 data and evaluate the quality of time series classical models and neural networks for short-term forecasting. This study demonstrates that classical time series models, particularly ARIMA and SSA, achieve lower average forecast errors than LSTM across stations SMP, CRB, and ATE. This finding suggests that for data with seasonal patterns and relatively short time series, traditional models may be more efficient and robust. Although neural networks have the potential to capture more complex relationships and long-term dependencies, their performance may be limited by hyperparameter settings and intrinsic data characteristics.Articlehttps://doi.org/10.3390/environments1206019610.3390/environments12060196https://hdl.handle.net/20.500.15037/400W4411185194enEnvironments2076-32982025-06-102026-06-13T08:12:44Z2026-06-13T08:12:44Z |
| spellingShingle | Solis Teran, Miguel Angel Leite Coelho da Silva, Felipe Torres Armas, Elías A. Carbo‐Bustinza, Natalí López Gonzales, Javier Linkolk Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title | Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title_full | Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title_fullStr | Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title_full_unstemmed | Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title_short | Modeling Air Pollution in Metropolitan Lima: A Statistical and Artificial Neural Network Approach |
| title_sort | modeling air pollution in metropolitan lima a statistical and artificial neural network approach |
| url | https://doi.org/10.3390/environments12060196 https://hdl.handle.net/20.500.15037/400 |
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