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A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru
Author(s)
Carbo-Bustinza, Natalí
Belmonte, Marisol
Universidad de Playa Ancha de Ciencias de la Educación
Jimenez, Vasti
Montalban, Paula
Rivera, Magiory
Gutiérrez Martínez, Fredi
Los Andes Peruvian University
Mohamed, Mohamed Mehdi Hadi
Los Andes Peruvian University
Universidad Nacional Intercultural de la Selva Central Juan Santos Atahualpa
da Costa, Kleyton
Date Issued
21 de diciembre de 2022
Type
Article
Volume
12
Issue
1
Series/Report No.
Scientific Reports
Start Page
22084
End Page
22084
ISSN
2045-2322
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.