A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

Natalí Carbo-Bustinza; Marisol Belmonte; Vasti Jimenez; Paula Montalban; Magiory Rivera; Fredi Gutiérrez Martínez; Mohamed Mehdi Hadi Mohamed; Alex Rubén Huamán De La Cruz; Kleyton da Costa; Javier Linkolk López-Gonzales

doi:10.1038/s41598-022-26575-3

A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

Natalí Carbo-Bustinza, Marisol Belmonte, Vasti Jimenez, Paula Montalban, Magiory Rivera, Fredi Gutiérrez Martínez, Mohamed Mehdi Hadi Mohamed, Alex Rubén Huamán De La Cruz, Kleyton da Costa, Javier Linkolk López-Gonzales

Research Group Mathematical models in science and technology

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

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.

Original language	English
Article number	22084
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-26575-3
State	Published - Dec 2022

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title = "A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru",

abstract = "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.",

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doi = "10.1038/s41598-022-26575-3",

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AU - Carbo-Bustinza, Natalí

AU - Belmonte, Marisol

AU - Jimenez, Vasti

AU - Montalban, Paula

AU - Rivera, Magiory

AU - Martínez, Fredi Gutiérrez

AU - Mohamed, Mohamed Mehdi Hadi

AU - De La Cruz, Alex Rubén Huamán

AU - da Costa, Kleyton

AU - López-Gonzales, Javier Linkolk

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AB - 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.

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A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

Abstract

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