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

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...

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Bibliographic Details
Published in:Scientific reports Vol. 12; no. 1; pp. 22084 - 12
Main Authors: Carbo-Bustinza, Natalí, Belmonte, Marisol, Jimenez, Vasti, Montalban, Paula, Rivera, Magiory, Martínez, Fredi Gutiérrez, Mohamed, Mohamed Mehdi Hadi, De La Cruz, Alex Rubén Huamán, da Costa, Kleyton, López-Gonzales, Javier Linkolk
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21-12-2022
Nature Publishing Group
Nature Portfolio
Subjects:
704/172
704/172/4081
Air Pollutants - analysis
Air pollution
Air Pollution - analysis
Air quality
Algorithms
Climate change
Environmental impact
Environmental Monitoring - methods
Humanities and Social Sciences
Learning algorithms
Machine Learning
Metropolitan areas
multidisciplinary
Nitrogen dioxide
Outdoor air quality
Ozone
Ozone - analysis
Peru
Pollutants
Pollution
Population studies
Science
Science (multidisciplinary)
Seasons
VOCs
Volatile organic compounds
Peru
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-26575-3