---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '21344'
abstract:
- lang: eng
text: Tropospheric ozone has the potential to become an increasingly pressing public
health issue in Bogotá, Colombia, due to rising concentrations across the city
driven by complex interactions among emissions, meteorology, and urban structure.
This study presents a comprehensive spatiotemporal analysis of ozone levels from
2013 to 2023 and assesses the associated health burden using mortality data from
the same period. Results reveal a consistent upward trend in ozone concentrations,
particularly in northern, western, and southern localities, with seasonal peaks
linked to biomass burning and photochemical conditions. Mortality analysis, based
on the Global Exposure Mortality Model, estimates that 18.3% of all deaths among
individuals aged 25 and older are attributable to long-term ozone exposure. The
highest burdens are found in densely populated and socioeconomically vulnerable
areas such as Kennedy, Suba, and Ciudad Bolívar, with the elderly being the most
affected. Building on these findings, we developed a machine learning prediction
model for ozone using a convolutional merge with a long-short term memory network
architecture trained on air quality and meteorological variables. The model demonstrated
strong predictive performance (mean Rho=0.86, RMSE=3.5 μg/m3) across monitoring
stations (17 with at least 35000 data points), supporting its potential application
in real-time early warning systems across Bogotá. This integrated approach highlights
the importance of localized air quality management, combining epidemiological
assessment with predictive modeling. The findings underscore the urgency of implementing
region-specific mitigation strategies and improving monitoring infrastructure
to reduce health risks from ozone exposure in Bogotá’s rapidly growing urban environment.
acknowledgement: EAL-B and CP-R received support from Sergio Arboleda University through
project No. IN.BG.086.24.014. AC acknowledges support by the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement
No 101034413. We thank two anonymous reviewers for thein insightful comments that
largely improve the manuscript. Open access funding provided by Institute of Science
and Technology (IST Austria). This work was funded by the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement
No 101034413. The work also received funding from Sergio Arboleda University through
project No. IN.BG.086.24.014.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Daniela
full_name: Bustos, Daniela
last_name: Bustos
- first_name: Diana
full_name: Garcia, Diana
last_name: Garcia
- first_name: Nestor Y.
full_name: Rojas, Nestor Y.
last_name: Rojas
- first_name: Ellie A.
full_name: Lopez-Barrera, Ellie A.
last_name: Lopez-Barrera
- first_name: Carlos
full_name: Peña-Rincon, Carlos
last_name: Peña-Rincon
- first_name: Alejandro
full_name: Casallas Garcia, Alejandro
id: 92081129-2d75-11ef-a48d-b04dd7a2385a
last_name: Casallas Garcia
orcid: 0000-0002-1988-5035
citation:
ama: 'Bustos D, Garcia D, Rojas NY, Lopez-Barrera EA, Peña-Rincon C, Casallas Garcia
A. Ozone trends and mortality risk: The growing need for machine learning predictions
in Bogotá, Colombia. Earth Systems and Environment. 2026. doi:10.1007/s41748-026-01052-3'
apa: 'Bustos, D., Garcia, D., Rojas, N. Y., Lopez-Barrera, E. A., Peña-Rincon, C.,
& Casallas Garcia, A. (2026). Ozone trends and mortality risk: The growing
need for machine learning predictions in Bogotá, Colombia. Earth Systems and
Environment. Springer Nature. https://doi.org/10.1007/s41748-026-01052-3'
chicago: 'Bustos, Daniela, Diana Garcia, Nestor Y. Rojas, Ellie A. Lopez-Barrera,
Carlos Peña-Rincon, and Alejandro Casallas Garcia. “Ozone Trends and Mortality
Risk: The Growing Need for Machine Learning Predictions in Bogotá, Colombia.”
Earth Systems and Environment. Springer Nature, 2026. https://doi.org/10.1007/s41748-026-01052-3.'
ieee: 'D. Bustos, D. Garcia, N. Y. Rojas, E. A. Lopez-Barrera, C. Peña-Rincon, and
A. Casallas Garcia, “Ozone trends and mortality risk: The growing need for machine
learning predictions in Bogotá, Colombia,” Earth Systems and Environment.
Springer Nature, 2026.'
ista: 'Bustos D, Garcia D, Rojas NY, Lopez-Barrera EA, Peña-Rincon C, Casallas Garcia
A. 2026. Ozone trends and mortality risk: The growing need for machine learning
predictions in Bogotá, Colombia. Earth Systems and Environment.'
mla: 'Bustos, Daniela, et al. “Ozone Trends and Mortality Risk: The Growing Need
for Machine Learning Predictions in Bogotá, Colombia.” Earth Systems and Environment,
Springer Nature, 2026, doi:10.1007/s41748-026-01052-3.'
short: D. Bustos, D. Garcia, N.Y. Rojas, E.A. Lopez-Barrera, C. Peña-Rincon, A.
Casallas Garcia, Earth Systems and Environment (2026).
corr_author: '1'
date_created: 2026-02-23T08:26:51Z
date_published: 2026-02-20T00:00:00Z
date_updated: 2026-02-24T08:02:58Z
day: '20'
ddc:
- '550'
department:
- _id: CaMu
doi: 10.1007/s41748-026-01052-3
ec_funded: 1
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s41748-026-01052-3
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Earth Systems and Environment
publication_identifier:
eissn:
- 2509-9434
issn:
- 2509-9426
publication_status: epub_ahead
publisher: Springer Nature
quality_controlled: '1'
status: public
title: 'Ozone trends and mortality risk: The growing need for machine learning predictions
in Bogotá, Colombia'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...