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Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons
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  • Ediclê de Souza Fernandes Duarte,
  • Maria Joao Costa,
  • Vanda Salgueiro,
  • Paulo Sérgio Lucio,
  • Miguel Potes,
  • Daniele Bortoli,
  • Rui Salgado
Ediclê de Souza Fernandes Duarte
University of Évora

Corresponding Author:[email protected]

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Maria Joao Costa
University of Evora
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Vanda Salgueiro
University of Évora
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Paulo Sérgio Lucio
Universidade Federal do Rio Grande do Norte
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Miguel Potes
Institute of Earth Sciences
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Daniele Bortoli
University of Evora
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Rui Salgado
Instituto de Ciências da Terra, Polo de Evora
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Abstract

Wildfires expose populations to increased morbidity and mortality due to increased air pollutant concentrations. Data included burned area, particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind-speed, aerosol optical depth (AOD) and mortality rates due to Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD), and Asthma (ASMA). Only the months of the 2011-2020 wildfire season (June-July-August-September-October) with burned area greater than 1000 ha were considered. Multivariate statistical methods were used to reduce the dimensionality of the data to create two fire-pollution-meteorology indices (PBI, API), which allow us to understand how the combination of these variables affect cardio-respiratory mortality. Cluster analysis applied to PBI-API-Mortality divided the data into two Clusters. Cluster 1 included the months with lower temperatures, higher relative humidity, and high PM10, PM2.5, and NO2 concentrations. Cluster 2 included the months with more extreme weather conditions such as higher temperatures, lower relative humidity, larger forest fires, high PM10, PM2.5, O3, and CO concentrations, and high AOD. The two clusters were subjected to linear regression analysis to better understand the relationship between mortality and the PBI and API indices. The results showed statistically significant (p-value < 0.05) correlation (r) in Cluster 1 between RSDxPBI (rRSD = 0.539), PNEUxPBI (rPNEU = 0.644). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.464), PNEUxPBI (rPNEU = 0.442), COPDxPBI (rCOPD = 0.456), CSDxAPI (rCSD = 0.705), RSDxAPI (rCSD = 0.716), PNEUxAPI (rPNEU = 0.493), COPDxAPI (rPNEU = 0.619).