Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru
The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of th...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2024
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| _version_ | 1881508905085829120 |
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| author | Quispe, Flor Salcedo, Eddy Iftikhar, Hasnain Zafar, Aimel Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_browse | Iftikhar, Hasnain Khan, Murad López Gonzales, Javier Linkolk Quispe, Flor Rodrigues, Paulo Canas Salcedo, Eddy Turpo Chaparro, Josue Edison Zafar, Aimel |
| author_facet | Quispe, Flor Salcedo, Eddy Iftikhar, Hasnain Zafar, Aimel Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_sort | Quispe, Flor |
| collection | Producción científica |
| description | The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O$ _{3} $ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O$ _{3} $ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/421 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4212026-10-30T16:14:37ZMulti-step ahead ozone level forecasting using a component-based technique: A case study in Lima, PeruQuispe, FlorSalcedo, EddyIftikhar, HasnainZafar, AimelKhan, MuradTurpo Chaparro, Josue EdisonRodrigues, Paulo CanasLópez Gonzales, Javier LinkolkResearch articlemulti-step ahead ozone forecastingglobal healthmultiple linear regression modeltime series modelscomponent-based forecasting techniqueThe rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O$ _{3} $ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O$ _{3} $ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner.Articlehttps://doi.org/10.3934/environsci.202402010.3934/environsci.2024020https://hdl.handle.net/20.500.15037/421W4399264725enAIMS environmental science2372-03442372-03522024-01-012026-06-13T08:13:13Z2026-06-13T08:13:13Z |
| spellingShingle | Research article multi-step ahead ozone forecasting global health multiple linear regression model time series models component-based forecasting technique Quispe, Flor Salcedo, Eddy Iftikhar, Hasnain Zafar, Aimel Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title | Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title_full | Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title_fullStr | Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title_full_unstemmed | Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title_short | Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru |
| title_sort | multi step ahead ozone level forecasting using a component based technique a case study in lima peru |
| topic | Research article multi-step ahead ozone forecasting global health multiple linear regression model time series models component-based forecasting technique |
| topic_facet | Research article multi-step ahead ozone forecasting global health multiple linear regression model time series models component-based forecasting technique |
| url | https://doi.org/10.3934/environsci.2024020 https://hdl.handle.net/20.500.15037/421 |
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