Can extreme rainfall associated with landslides affect air quality? A case study in a coastal city (São Sebastião, São Paulo State, Brazil).
- 1Faculdade de Tecnologia, Universidade Estadual de Campinas (UNICAMP), Limeira, São Paulo, Brazil.
- 2Faculdade de Tecnologia, Universidade Estadual de Campinas (UNICAMP), Limeira, São Paulo, Brazil. [email protected].
Abstract
This study investigated the impact of an extreme precipitation event and the subsequent landslides that occurred in São Sebastião municipality (São Paulo State, Brazil) in February 2023 on particulate matter (PM₁₀ and PM2.5) concentration. Hourly air quality and meteorological data from December 2021 to February 2025 were analyzed. The methodology combined cluster analysis (CA) using the k-means algorithm to classify meteorological patterns and the Openair package for spatio-temporal assessment. CA identified a specific group of days associated with anomalous behavior during the extreme event. Analyses using bivariate polar plots and variation in time plots revealed that, while intense rainfall initially promoted wet scavenging of pollutants, the post-landslide period was characterized by a 124% increase in PM₁₀ concentrations and a 93.3% increase in PM2.5 concentrations. Two additional high precipitation events were examined for comparison, showing that PM also decreased during intense rainfall, while the post-rainfall recovery differed among events. These results indicate that the February 2023 increase cannot be explained by landslide occurrence alone and may have reflected the combined influence of meteorological conditions, changes in local activities, and potential particle resuspension. Overall, the findings highlight the importance of adequate monitoring networks in place for understanding the secondary environmental effects of extreme rainfall and disaster events in vulnerable coastal areas.
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BibTeX
@article{magalhaes2026extreme,
title = {{Can extreme rainfall associated with landslides affect air quality? A case study in a coastal city (São Sebastião, São Paulo State, Brazil).}},
author = {Leticia Rocha Magalhães and Simone Andréa Pozza and Danilo Covaes Nogarotto},
journal = {Environmental monitoring and assessment},
year = {2026},
month = oct,
doi = {10.1007/s10661-026-16071-w},
url = {https://europepmc.org/article/MED/42907699},
note = {CC BY},
}
RIS
TY - JOUR
TI - Can extreme rainfall associated with landslides affect air quality? A case study in a coastal city (São Sebastião, São Paulo State, Brazil).
AU - Leticia Rocha Magalhães
AU - Simone Andréa Pozza
AU - Danilo Covaes Nogarotto
PY - 2026
DA - 2026/10/22
JO - Environmental monitoring and assessment
DO - 10.1007/s10661-026-16071-w
UR - https://europepmc.org/article/MED/42907699
AB - This study investigated the impact of an extreme precipitation event and the subsequent landslides that occurred in São Sebastião municipality (São Paulo State, Brazil) in February 2023 on particulate matter (PM₁₀ and PM2.5) concentration. Hourly air quality and meteorological data from December 2021 to February 2025 were analyzed. The methodology combined cluster analysis (CA) using the k-means algorithm to classify meteorological patterns and the Openair package for spatio-temporal assessment. CA identified a specific group of days associated with anomalous behavior during the extreme event. Analyses using bivariate polar plots and variation in time plots revealed that, while intense rainfall initially promoted wet scavenging of pollutants, the post-landslide period was characterized by a 124% increase in PM₁₀ concentrations and a 93.3% increase in PM2.5 concentrations. Two additional high precipitation events were examined for comparison, showing that PM also decreased during intense rainfall, while the post-rainfall recovery differed among events. These results indicate that the February 2023 increase cannot be explained by landslide occurrence alone and may have reflected the combined influence of meteorological conditions, changes in local activities, and potential particle resuspension. Overall, the findings highlight the importance of adequate monitoring networks in place for understanding the secondary environmental effects of extreme rainfall and disaster events in vulnerable coastal areas.
SN - 0167-6369
SN - 1573-2959
ER -