Recent extreme wildfire events (EWE) in Australia, the United States of America (USA), Greece and Portugal highlighted the seriousness of wildfire smoke impacts on society. Nowadays, about 2000 premature deaths occur annually in the USA due to chronic wildfire smoke exposure, and this value is expected to be doubled by the end of the century as tens of million people will get exposed to massive “smoke waves” under the climate change framework. In Portugal, the destructive 2017 wildfires seriously changed the way the population think about wildfires safety, with 117 persons dying and many others needing medical assistance due to smoke intoxications.
The main purpose of this study was to quantify the atmospheric pollution caused by the EWE that occurred between 15 and 16 October 2017, in Portugal, and their impacts on population and firefighters health. For that, the Weather Research and Forecasting Model, combined with a semi-empirical fire-spread algorithm (WRF-SFIRE), was used to simulate the dispersion of emitted pollutants during the EWE, while the human health effects were estimated using concentration–response functions, based on relative risk models that convert concentration changes into human health impacts.
The results show that high particulate matter and nitrogen dioxide concentrations (>1000 µg/m3) were simulated near the wildfires, as well as in neighbouring cities. These results were compared to available air quality data with a good agreement. Furthermore, population and firefighters were exposed to dangerous levels of air pollution with several estimated cases of morbidity (hospital admissions by respiratory diseases) and mortality caused by the EWE.
This work highlights the potential of numerical models to predict the population potentially exposed to critical levels of air pollution due to active wildfires, as well as to provide useful information to the citizens, civil protection and health entities to drastically reduce the impact of wildfires on society.
Conceptualization, D.L., I.M. and C.G.; methodology, D.L. and I.M.; software, I.M.; validation, D.L.; formal analysis, D.L.; investigation, D.L.; resources, C.B. and A.I.M.; data curation, A.P.F., S.S. and J.R.; writing—original draft preparation, D.L.; writing—review and editing, A.M. and A.I.M.; visualization, D.L.; supervision, C.B.; project administration, D.L. and A.I.M.; funding acquisition, C.B. and A.I.M. All authors have read and agreed to the published version of the manuscript.
The authors acknowledge the financial support of FEDER through the COMPETE Programme and the national funds from FCT – Science and Technology Portuguese Foundation within the projects FIRESTORM (PCIF/GFC/0109/2017), SmokeStorm (PCIF/MPG/0147/2019), Firesmoke (PTDC/CTA-MET/3392/2020) and BigAir (PTDC/EAM-AMB/2606/2020). The authors also acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890. Thanks are due for the financial support to CESAM (UIDB/50017/2020 + UIDP/50017/2020), to FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, Diogo Lopes, upon reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
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