A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru
The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in...
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| Lenguaje: | inglés |
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2022
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| author | Carbo-Bustinza, Natalí Belmonte, Marisol Jimenez, Vasti Montalban, Paula Rivera, Magiory Gutiérrez Martínez, Fredi Mohamed, Mohamed Mehdi Hadi Huaman de la Cruz, Alex Ruben da Costa, Kleyton López Gonzales, Javier Linkolk |
| author_browse | Belmonte, Marisol Carbo-Bustinza, Natalí Gutiérrez Martínez, Fredi Huaman de la Cruz, Alex Ruben Jimenez, Vasti López Gonzales, Javier Linkolk Mohamed, Mohamed Mehdi Hadi Montalban, Paula Rivera, Magiory da Costa, Kleyton |
| author_facet | Carbo-Bustinza, Natalí Belmonte, Marisol Jimenez, Vasti Montalban, Paula Rivera, Magiory Gutiérrez Martínez, Fredi Mohamed, Mohamed Mehdi Hadi Huaman de la Cruz, Alex Ruben da Costa, Kleyton López Gonzales, Javier Linkolk |
| author_sort | Carbo-Bustinza, Natalí |
| collection | Producción científica |
| description | The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/432 |
| institution | UPeU |
| language | English |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4322026-10-30T17:00:26ZA machine learning approach to analyse ozone concentration in metropolitan area of Lima, PeruCarbo-Bustinza, NatalíBelmonte, MarisolJimenez, VastiMontalban, PaulaRivera, MagioryGutiérrez Martínez, FrediMohamed, Mohamed Mehdi HadiHuaman de la Cruz, Alex Rubenda Costa, KleytonLópez Gonzales, Javier LinkolkEnvironmental sciencesEnvironmental impactThe main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.Articlehttps://pubmed.ncbi.nlm.nih.gov/36543811https://www.nature.com/articles/s41598-022-26575-3.pdfhttps://doi.org/10.1038/s41598-022-26575-3PMC976948610.1038/s41598-022-26575-3https://hdl.handle.net/20.500.15037/43236543811W4312115281enScientific Reports2045-23222022-12-212026-06-13T08:13:28Z2026-06-13T08:13:28Z |
| spellingShingle | Environmental sciences Environmental impact Carbo-Bustinza, Natalí Belmonte, Marisol Jimenez, Vasti Montalban, Paula Rivera, Magiory Gutiérrez Martínez, Fredi Mohamed, Mohamed Mehdi Hadi Huaman de la Cruz, Alex Ruben da Costa, Kleyton López Gonzales, Javier Linkolk A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title | A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title_full | A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title_fullStr | A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title_full_unstemmed | A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title_short | A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru |
| title_sort | machine learning approach to analyse ozone concentration in metropolitan area of lima peru |
| topic | Environmental sciences Environmental impact |
| topic_facet | Environmental sciences Environmental impact |
| url | https://pubmed.ncbi.nlm.nih.gov/36543811 https://www.nature.com/articles/s41598-022-26575-3.pdf https://doi.org/10.1038/s41598-022-26575-3 https://hdl.handle.net/20.500.15037/432 |
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