Year: 2024
Type: preprint
Abstract: Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models.
Source: arXiv (Cornell University)
Authors: Cerritos, J. M. Sánchez, Martínez-Cadena, J. A., Marín-López, A., Delgado-Fernández, J.
Institution: Universidad Autónoma Metropolitana
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404405294