Ediclê de Souza Fernandes Duarte, Vanda Salgueiro, Maria João Costa, Paulo Sérgio Lucio, Miguel Potes, Daniele Bortoli, Rui Salgado
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Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O<sub>3</sub>. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO<sub>2</sub> concentration. Cluster 2 included warmer and dried months, and higher PM<sub>10</sub>, PM<sub>2.5</sub>, CO, and O<sub>3</sub> concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (<i>p</i>-value < 0.05) correlation (<i>r</i>) between RSDxPBI (<i>r</i><sub>RSD</sub> = 0.58) and PNEUxPBI (<i>r</i><sub>PNEU</sub> = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (<i>r</i><sub>RSD</sub> = 0.48), PNEUxPBI (<i>r</i><sub>PNEU</sub> = 0.47), COPDxPBI (<i>r</i><sub>COPD</sub> = 0.45), CSDxAPI (<i>r</i><sub>CSD</sub> = 0.70), RSDxAPI (<i>r</i><sub>CSD</sub> = 0.71), PNEUxAPI (<i>r</i><sub>PNEU</sub> = 0.49), and COPDxAPI (<i>r</i><sub>PNEU</sub> = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"7 10","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2023GH000802","citationCount":"1","resultStr":"{\"title\":\"Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons\",\"authors\":\"Ediclê de Souza Fernandes Duarte, Vanda Salgueiro, Maria João Costa, Paulo Sérgio Lucio, Miguel Potes, Daniele Bortoli, Rui Salgado\",\"doi\":\"10.1029/2023GH000802\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM<sub>10</sub>, PM<sub>2.5</sub>), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), ozone (O<sub>3</sub>), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O<sub>3</sub>. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO<sub>2</sub> concentration. Cluster 2 included warmer and dried months, and higher PM<sub>10</sub>, PM<sub>2.5</sub>, CO, and O<sub>3</sub> concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (<i>p</i>-value < 0.05) correlation (<i>r</i>) between RSDxPBI (<i>r</i><sub>RSD</sub> = 0.58) and PNEUxPBI (<i>r</i><sub>PNEU</sub> = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (<i>r</i><sub>RSD</sub> = 0.48), PNEUxPBI (<i>r</i><sub>PNEU</sub> = 0.47), COPDxPBI (<i>r</i><sub>COPD</sub> = 0.45), CSDxAPI (<i>r</i><sub>CSD</sub> = 0.70), RSDxAPI (<i>r</i><sub>CSD</sub> = 0.71), PNEUxAPI (<i>r</i><sub>PNEU</sub> = 0.49), and COPDxAPI (<i>r</i><sub>PNEU</sub> = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.</p>\",\"PeriodicalId\":48618,\"journal\":{\"name\":\"Geohealth\",\"volume\":\"7 10\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2023GH000802\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geohealth\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2023GH000802\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geohealth","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2023GH000802","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons
This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value < 0.05) correlation (r) between RSDxPBI (rRSD = 0.58) and PNEUxPBI (rPNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.48), PNEUxPBI (rPNEU = 0.47), COPDxPBI (rCOPD = 0.45), CSDxAPI (rCSD = 0.70), RSDxAPI (rCSD = 0.71), PNEUxAPI (rPNEU = 0.49), and COPDxAPI (rPNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.
期刊介绍：
GeoHealth will publish original research, reviews, policy discussions, and commentaries that cover the growing science on the interface among the Earth, atmospheric, oceans and environmental sciences, ecology, and the agricultural and health sciences. The journal will cover a wide variety of global and local issues including the impacts of climate change on human, agricultural, and ecosystem health, air and water pollution, environmental persistence of herbicides and pesticides, radiation and health, geomedicine, and the health effects of disasters. Many of these topics and others are of critical importance in the developing world and all require bringing together leading research across multiple disciplines.
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