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  <front>
    <journal-meta><journal-id journal-id-type="publisher">NHESS</journal-id><journal-title-group>
    <journal-title>Natural Hazards and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">NHESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Nat. Hazards Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1684-9981</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/nhess-21-2867-2021</article-id><title-group><article-title>Impact of large wildfires on <inline-formula><mml:math id="M1" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> levels and <?xmltex \hack{\break}?>human mortality in Portugal</article-title><alt-title>Impact of wildfires on human mortality</alt-title>
      </title-group><?xmltex \runningtitle{Impact of wildfires on human mortality}?><?xmltex \runningauthor{P.~Tar\'{i}n-Carrasco~et~al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tarín-Carrasco</surname><given-names>Patricia</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-7101-0554</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff3">
          <name><surname>Augusto</surname><given-names>Sofia</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff4">
          <name><surname>Palacios-Peña</surname><given-names>Laura</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-5577-6840</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Ratola</surname><given-names>Nuno</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff6">
          <name><surname>Jiménez-Guerrero</surname><given-names>Pedro</given-names></name>
          <email>pedro.jimenezguerrero@um.es</email>
        <ext-link>https://orcid.org/0000-0002-3156-0671</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, <?xmltex \hack{\break}?>University of Murcia, Murcia, Spain</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>EPIUnit – Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciencias, Universidade de Lisboa (CE3C-FC-ULisboa), Lisbon, Portugal</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Dept. of Meteorology, Meteored, Almendricos, Spain</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>LEPABE-Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Biomedical Research Institute of Murcia (IMIB-Arrixaca), Murcia, Spain</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Pedro Jiménez-Guerrero (pedro.jimenezguerrero@um.es)</corresp></author-notes><pub-date><day>22</day><month>September</month><year>2021</year></pub-date>
      
      <volume>21</volume>
      <issue>9</issue>
      <fpage>2867</fpage><lpage>2880</lpage>
      <history>
        <date date-type="received"><day>31</day><month>January</month><year>2021</year></date>
           <date date-type="accepted"><day>1</day><month>September</month><year>2021</year></date>
           <date date-type="rev-recd"><day>30</day><month>August</month><year>2021</year></date>
           <date date-type="rev-request"><day>12</day><month>February</month><year>2021</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2021 </copyright-statement>
        <copyright-year>2021</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://nhess.copernicus.org/articles/.html">This article is available from https://nhess.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e167">Uncontrolled wildfires have a substantial impact on the environment, the economy and local populations. According to the European Forest Fire
Information System (EFFIS), between 2000 and 2013 wildfires burned up to 740 000 <inline-formula><mml:math id="M2" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of land annually in the south of Europe, Portugal
being the country with the highest percentage of burned area per square kilometre. However, there is still a lack of knowledge regarding the impacts of the
wildfire-related pollutants on the mortality of the country's population. All wildfires occurring during the fire season
(June–July–August–September) from 2001 and 2016 were identified, and those with a burned area above 1000 <inline-formula><mml:math id="M3" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> (large fires) were considered
for the study. During the studied period (2001–2016), more than 2 <inline-formula><mml:math id="M4" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">million</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of forest (929 766 <inline-formula><mml:math id="M5" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> from June to September alone)
were burned in mainland Portugal. Although large fires only represent less than 1 % of the number of total fires, in terms of burned area their
contribution is 46 % (53 % from June to September). To assess the spatial impact of the wildfires, burned areas in each region of
Portugal were correlated with <inline-formula><mml:math id="M6" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations measured at nearby background air quality monitoring stations. Associations between
<inline-formula><mml:math id="M7" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and all-cause (excluding injuries, poisoning and external causes) and cause-specific mortality (circulatory and respiratory) were
studied for the affected populations using Poisson regression models. A significant positive correlation between burned area and <inline-formula><mml:math id="M8" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> was
found in some regions of Portugal, as well as a significant association between <inline-formula><mml:math id="M9" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations and mortality, these being apparently
related to large wildfires in some of the regions. The north, centre and inland of Portugal are the most affected areas. The high temperatures and
long episodes of drought expected in the future will increase the probabilities of extreme events and therefore the occurrence of wildfires.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e259">Wildfires have a considerable impact on the environment and humans worldwide. Climate change has lately been identified as a very important variable
in this matter <xref ref-type="bibr" rid="bib1.bibx16" id="paren.1"/> since the future projections suggest an increase in the number of droughts, heat waves and dry spells
<xref ref-type="bibr" rid="bib1.bibx60" id="paren.2"/>. Global warming will produce changes in temperature and precipitation patterns leading to a higher prevalence and severity of
wildfires <xref ref-type="bibr" rid="bib1.bibx57 bib1.bibx6" id="paren.3"/> and consequently impacting future air quality <xref ref-type="bibr" rid="bib1.bibx56" id="paren.4"/>. In fact, this could not only
extend the burned area in chronically impacted areas <xref ref-type="bibr" rid="bib1.bibx10" id="paren.5"/> but also affect new ones, like Sweden in the summer of 2018
<xref ref-type="bibr" rid="bib1.bibx31" id="paren.6"/>. According to the<?pagebreak page2868?> 2016 European Forest Fire Information System (EFFIS) report <xref ref-type="bibr" rid="bib1.bibx55" id="paren.7"/>, the south of Europe
(Portugal, Spain, France, Italy and Greece) is the area most affected by wildfires from 1980 until today, considering Europe, the Middle East and North
Africa. In the last decades, Portugal was by far the country with the largest burned area, almost 50 %, between the southern European countries
<xref ref-type="bibr" rid="bib1.bibx44" id="paren.8"/>. Although there has been a slight decreasing trend in the burned area in this region since 2000 after an increasing period in
the previous 20 years (European Environment Agency,
<uri>https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-3/assessment</uri>, last access: 30 June 2020), recent
extreme events like the 2017 fires in Portugal and the 2018 fires in Greece which resulted in a severe loss of human lives are confirming the worst-case
projections.</p>
      <p id="d1e290">Uncontrolled wildfires emit numerous pollutants derived from the incomplete combustion of biomass fuel which cause damage to human health,
particularly the cardiovascular and respiratory systems <xref ref-type="bibr" rid="bib1.bibx61" id="paren.9"/>. Examples include particulate matter (PM), carbon monoxide,
methane, nitrous oxide, nitrogen oxides, volatile organic compounds (VOCs) and other secondary pollutants <xref ref-type="bibr" rid="bib1.bibx8" id="paren.10"/> that are released
mainly into the atmosphere but can be transported to many other environmental compartments. Moreover, they can affect the physicochemical properties
of the atmosphere, like, for instance, the interaction of PM with solar radiation which can prompt a modification of the temperature depending on the
characteristics of the aerosol <xref ref-type="bibr" rid="bib1.bibx59" id="paren.11"/>. Consequently, some of these chemicals are regulated by the European Directive 2008/50/EC of
21 May 2008 of the European Parliament and of the Council on Ambient Air Quality and Cleaner Air for Europe, which establishes threshold values for a
safe air quality. But although wildfire emissions are a crucial parameter for the local air quality <xref ref-type="bibr" rid="bib1.bibx27" id="paren.12"/>, where in some cases there are
already chronically exposed populations due to the frequency and dimension of the events, they are not contained by political borders and can also
affect areas far from the ignition points due to the atmospheric transport of the pollutant plumes. A number of studies
(e.g. <xref ref-type="bibr" rid="bib1.bibx32 bib1.bibx20 bib1.bibx30 bib1.bibx3" id="altparen.13"/>, among others) report the influence of natural and anthropogenic emissions on air quality
composition across different countries, especially PM and tropospheric <inline-formula><mml:math id="M10" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>. For wildfires it is also important to take into account some
factors which influence the plume dispersion, such as the duration and space evolution of the fire event and the meteorological conditions associated with it <xref ref-type="bibr" rid="bib1.bibx29" id="paren.14"/>. An increase in cardiovascular and respiratory morbidity and mortality are some of the impacts these contaminants can have on
humans <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx58" id="paren.15"/>. For instance, there is strong evidence for the relationship between PM in general and mortality,
especially from cardiovascular diseases, for both long-term and short-term exposure <xref ref-type="bibr" rid="bib1.bibx2" id="paren.16"/>. Although some studies corroborate the
existence of a link between the exposure to wildfire-related air pollutants and hospital admissions, visits to emergency clinics or even respiratory
morbidity <xref ref-type="bibr" rid="bib1.bibx33 bib1.bibx52" id="paren.17"/>, the impacts on human health are difficult to quantify and the real effects still poorly known.</p>
      <p id="d1e332">Concerning PM, a recent study focusing on 10 southern European cities revealed that cardiovascular and respiratory mortality associated with
<inline-formula><mml:math id="M11" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (particles with an aerodynamic diameter below 10 <inline-formula><mml:math id="M12" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) was higher on days affected by wildfires' smoke than in smoke-free days
<xref ref-type="bibr" rid="bib1.bibx14" id="paren.18"/>. The authors also found that <inline-formula><mml:math id="M13" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> from forest fires increased mortality more than <inline-formula><mml:math id="M14" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> from other
sources. So, the estimation of mortality due to exposure to wildfire-generated pollutants is key to manage health resources and the necessary public
funds towards prevention and remediation in setting up appropriate policies and protocols <xref ref-type="bibr" rid="bib1.bibx50" id="paren.19"/>.</p>
      <p id="d1e385">The two main factors to take into account for the wildfire's effects are the location and, most importantly, the size of the fire event (characterised
by the respective burned area). When the wildfire occurs close to a large conurbation, the population exposed is higher. But as <xref ref-type="bibr" rid="bib1.bibx1" id="text.20"/>
showed in their study, small fires do not seem to have an effect on mortality, whereas medium and large episodes (with burned
areas <inline-formula><mml:math id="M15" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M16" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>) have a significant impact on human health, which increases with the size of the fire. Aiming to enhance the knowledge on
the effects of wildfires on human health, this study describes the pattern of wildfires in Portugal for 16 years (2001–2016) and assesses the impact
of those events on the country's population mortality during the fire season (June, July, August and September). In this work, the focus is placed on
indirect effects of pollutants emitted by wildfires, namely assessing the influence of wildfire-generated <inline-formula><mml:math id="M17" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> on the Portuguese population
mortality. The relationship between the burned area of large wildfires and <inline-formula><mml:math id="M18" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and this same pollutant and mortality was studied. The
Nomenclature of Territorial Units for Statistics (NUTS) level 3 (NUTS III) geographical division has been used to be able to compare the effects of
the fires in different parts of the country. Finally, monthly deaths due to all-cause (excluding injuries, poisoning and external causes) and
cause-specific mortality (cardiovascular and respiratory) for all ages for each NUTS III region have been studied. These causes have been selected due to
their well-known connection with air pollution.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e431"><bold>(a)</bold> Land cover in mainland Portugal in 2015 (Global Forest Watch, <uri>https://www.globalforestwatch.org</uri>, last access: 10 October 2020; <bold>(b)</bold> mainland Portugal NUTS III regions.</p></caption>
        <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f01.png"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Methodology and data</title>
      <p id="d1e456">In this study, the effects of short-term exposure to pollutants due to wildfires on human mortality were quantified. The forest fire pollutant
emissions were estimated for the period 2001–2016 during the summer months (June–July–August–September) in mainland Portugal (23 NUTS III regions and<?pagebreak page2869?> more
than 10 million people). In Portugal, large forest fires usually occur during the months of June, July, August and September, which correspond to the
time of the year with the highest temperatures and driest conditions. By focusing our study only on these 4 months, we can have enough data to perform
a valid statistical treatment, while avoiding a strong influence of <inline-formula><mml:math id="M19" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> from other sources in colder months (such as home heating or
traffic). At the same time, we do not include in the analysis the deaths due to, for instance, cold and flu that could become confounding factors.</p>
      <p id="d1e470">For the quantification, two steps were followed. First, an assessment of the incidence, patterns and variations of burned area on a large time frame
and spatially integrated by NUTS III was done on the levels of air pollutants. <inline-formula><mml:math id="M20" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and burned area were correlated through linear
regression, while the mortality data and <inline-formula><mml:math id="M21" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> were correlated with Poisson regression. Data were processed and ordered by NUTS region and by month
and year. Finally, the correlation between the pollutants emitted by forest fires, the wildfire-burned area and the different causes of mortality
during the period 2001–2016 for the summer months were studied. The study is focused on PM since it is one of the main pollutants emitted by
wildfires, which can increase PM concentrations by up to 50 % and more <xref ref-type="bibr" rid="bib1.bibx29" id="paren.21"/>. Moreover, there is a clear relation with several
effects on human health (including mortality), in particular with respiratory and circulatory diseases <xref ref-type="bibr" rid="bib1.bibx28 bib1.bibx52 bib1.bibx30" id="paren.22"/>. There
was not enough <inline-formula><mml:math id="M22" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> data collected from the Portuguese air quality management network to establish a correlation (only 20 stations measure
<inline-formula><mml:math id="M23" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> on the mainland). For all these reasons this study focuses on <inline-formula><mml:math id="M24" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>.</p>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Target area</title>
      <p id="d1e542">At 89 015 <inline-formula><mml:math id="M25" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">km</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> (9.11 <inline-formula><mml:math id="M26" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">Mha</mml:mi></mml:mrow></mml:math></inline-formula>) mainland Portugal accounts for over 96 % of the country's area and hosts over 10 million
inhabitants in the west Iberian Peninsula (southwestern Europe). With the largest urban areas along the west Atlantic coast, particularly around the
capital (Lisbon) – more to the south – and the second largest city (Porto) in the north (see Fig. <xref ref-type="fig" rid="Ch1.F1"/>a), the country has most of its mountain
ranges in the north, reaching 1993 <inline-formula><mml:math id="M27" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">m</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">a</mml:mi><mml:mo>.</mml:mo><mml:mi mathvariant="normal">s</mml:mi><mml:mo>.</mml:mo><mml:mi mathvariant="normal">l</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:math></inline-formula> in Serra da Estrela. Although showing a Mediterranean climate, this topographic display leads to
various climate patterns throughout the country, with increasing temperature and decreasing rainfall from northwest to southeast
<xref ref-type="bibr" rid="bib1.bibx35 bib1.bibx42" id="paren.23"/>. In terms of land cover, Fig. <xref ref-type="fig" rid="Ch1.F1"/>a shows a predominance of agriculture in 2015 (over 50 % and
mainly in the south), followed by forests and shrublands, which comprise 43 % of the territory (mainly in the north and southwest). This, combined
with high temperatures in the summer months, represents a potential fire hazard, which unfortunately has been often proved true almost every summer for
many years.</p>
</sec>
<?pagebreak page2870?><sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Datasets</title>
<sec id="Ch1.S2.SS2.SSS1">
  <label>2.2.1</label><title>NUTS III boundary data</title>
      <p id="d1e608">The target domain was divided by NUTS (Nomenclature of Territorial Units for Statistics) level 3 (Fig. <xref ref-type="fig" rid="Ch1.F1"/>a) for Portugal's mainland. NUTS is
a geocode standard developed by the European Union for referencing the subdivisions of countries for statistical purposes. The geocode is divided in
three levels (I, II, III) which are established by each EU member country. NUTS III regions from mainland Portugal (in total, 23) at a <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">60</mml:mn></mml:mrow></mml:math></inline-formula> million scale
were retrieved from the Eurostat web page <xref ref-type="bibr" rid="bib1.bibx12" id="paren.24"/> and treated with QGIS3 software.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e631">Number of wildfires and burned area (BA) by year for the period 2001–2016 in mainland Portugal. From left to right: number of occurrences (occ.) when the burned area is larger than 1000 <inline-formula><mml:math id="M29" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>; sum of the burned area for fires larger than 1000 <inline-formula><mml:math id="M30" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>; total burned area caused by all fires; percentage of burned area caused by large fires; and index between burned area and the number of occurrences.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="6">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Year</oasis:entry>
         <oasis:entry colname="col2">Occ. (<inline-formula><mml:math id="M31" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>) with BA <inline-formula><mml:math id="M32" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M33" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3">BA <inline-formula><mml:math id="M34" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M35" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">Total BA</oasis:entry>
         <oasis:entry colname="col5">%BA <inline-formula><mml:math id="M36" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M37" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col6">BA/occ. (<inline-formula><mml:math id="M38" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">N</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">2016</oasis:entry>
         <oasis:entry colname="col2">22</oasis:entry>
         <oasis:entry colname="col3">85 166</oasis:entry>
         <oasis:entry colname="col4">138 884</oasis:entry>
         <oasis:entry colname="col5">61</oasis:entry>
         <oasis:entry colname="col6">3871</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2015</oasis:entry>
         <oasis:entry colname="col2">8</oasis:entry>
         <oasis:entry colname="col3">16 629</oasis:entry>
         <oasis:entry colname="col4">63 227</oasis:entry>
         <oasis:entry colname="col5">26</oasis:entry>
         <oasis:entry colname="col6">2078</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2014</oasis:entry>
         <oasis:entry colname="col2">3</oasis:entry>
         <oasis:entry colname="col3">5560</oasis:entry>
         <oasis:entry colname="col4">19 771</oasis:entry>
         <oasis:entry colname="col5">28</oasis:entry>
         <oasis:entry colname="col6">1853</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2013</oasis:entry>
         <oasis:entry colname="col2">26</oasis:entry>
         <oasis:entry colname="col3">66 633</oasis:entry>
         <oasis:entry colname="col4">152 181</oasis:entry>
         <oasis:entry colname="col5">44</oasis:entry>
         <oasis:entry colname="col6">2563</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2012</oasis:entry>
         <oasis:entry colname="col2">11</oasis:entry>
         <oasis:entry colname="col3">41 884</oasis:entry>
         <oasis:entry colname="col4">108 965</oasis:entry>
         <oasis:entry colname="col5">38</oasis:entry>
         <oasis:entry colname="col6">3808</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2011</oasis:entry>
         <oasis:entry colname="col2">6</oasis:entry>
         <oasis:entry colname="col3">8694</oasis:entry>
         <oasis:entry colname="col4">72 006</oasis:entry>
         <oasis:entry colname="col5">12</oasis:entry>
         <oasis:entry colname="col6">1449</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2010</oasis:entry>
         <oasis:entry colname="col2">25</oasis:entry>
         <oasis:entry colname="col3">54 901</oasis:entry>
         <oasis:entry colname="col4">131 753</oasis:entry>
         <oasis:entry colname="col5">42</oasis:entry>
         <oasis:entry colname="col6">2196</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2009</oasis:entry>
         <oasis:entry colname="col2">9</oasis:entry>
         <oasis:entry colname="col3">18 018</oasis:entry>
         <oasis:entry colname="col4">86 478</oasis:entry>
         <oasis:entry colname="col5">21</oasis:entry>
         <oasis:entry colname="col6">2002</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2008</oasis:entry>
         <oasis:entry colname="col2">0</oasis:entry>
         <oasis:entry colname="col3">0</oasis:entry>
         <oasis:entry colname="col4">16 662</oasis:entry>
         <oasis:entry colname="col5">0</oasis:entry>
         <oasis:entry colname="col6">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2007</oasis:entry>
         <oasis:entry colname="col2">2</oasis:entry>
         <oasis:entry colname="col3">4623</oasis:entry>
         <oasis:entry colname="col4">32 595</oasis:entry>
         <oasis:entry colname="col5">14</oasis:entry>
         <oasis:entry colname="col6">2312</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2006</oasis:entry>
         <oasis:entry colname="col2">7</oasis:entry>
         <oasis:entry colname="col3">19 008</oasis:entry>
         <oasis:entry colname="col4">75 513</oasis:entry>
         <oasis:entry colname="col5">25</oasis:entry>
         <oasis:entry colname="col6">2715</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2005</oasis:entry>
         <oasis:entry colname="col2">61</oasis:entry>
         <oasis:entry colname="col3">167 133</oasis:entry>
         <oasis:entry colname="col4">338 262</oasis:entry>
         <oasis:entry colname="col5">49</oasis:entry>
         <oasis:entry colname="col6">2740</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2004</oasis:entry>
         <oasis:entry colname="col2">24</oasis:entry>
         <oasis:entry colname="col3">56 088</oasis:entry>
         <oasis:entry colname="col4">129 715</oasis:entry>
         <oasis:entry colname="col5">43</oasis:entry>
         <oasis:entry colname="col6">2337</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2003</oasis:entry>
         <oasis:entry colname="col2">81</oasis:entry>
         <oasis:entry colname="col3">338 486</oasis:entry>
         <oasis:entry colname="col4">425 742</oasis:entry>
         <oasis:entry colname="col5">80</oasis:entry>
         <oasis:entry colname="col6">4179</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2002</oasis:entry>
         <oasis:entry colname="col2">17</oasis:entry>
         <oasis:entry colname="col3">29 414</oasis:entry>
         <oasis:entry colname="col4">124 408</oasis:entry>
         <oasis:entry colname="col5">24</oasis:entry>
         <oasis:entry colname="col6">1730</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2001</oasis:entry>
         <oasis:entry colname="col2">21</oasis:entry>
         <oasis:entry colname="col3">32 509</oasis:entry>
         <oasis:entry colname="col4">112 166</oasis:entry>
         <oasis:entry colname="col5">29</oasis:entry>
         <oasis:entry colname="col6">1548</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S2.SS2.SSS2">
  <label>2.2.2</label><title>Wildfire data</title>
      <p id="d1e1124">The wildfire data, collected for the period from 2001 to 2016, were obtained from the Portuguese Institute for Nature Conservation and Forests
(<uri>https://www.icnf.pt/</uri>, last access: 1 September 2019). For this study only forest fires were considered, and from them, only
those with more than 1000 <inline-formula><mml:math id="M39" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of total burned area (which we denominated <italic>large fires</italic>) were selected. In total, there were 323 events
under that category (less than 1 % of the number of total fires), which were responsible for 46 % of the total burned
area. Table <xref ref-type="table" rid="Ch1.T1"/> shows the yearly variability during the studied period in the number of occurrences, total burned area or the
contribution of large fires to the burned area in mainland Portugal. Although other studies have shown a relationship with high temperatures and
drought periods <xref ref-type="bibr" rid="bib1.bibx60" id="paren.25"/>, the data in Table <xref ref-type="table" rid="Ch1.T1"/> suggest that it is not possible to perceive a yearly pattern of wildfires in the
country. For instance, in 2008 no large fires occurred, whereas 2003 accounted for the highest number of occurrences (81), which were responsible for
80 % of the total burned area in that year. But the latter contribution was as low as 12 % in 2011 and had a mean percentage for the whole
period of 34 %.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e1151">Total burned area per NUTS III region from June to September in the period 2001–2016. The numbers inside each NUTS III region represent the respective number of large fires (<inline-formula><mml:math id="M40" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M41" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>) in the same period.</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f02.png"/>

          </fig>

      <p id="d1e1175">Considering only June, July, August and September 2001–2016 (the months with the highest temperatures and drier conditions when more than 86 % of the
total fires and 311 of the 323 large fires – 96 % – occurred), these data were divided by month and year, and the respective monthly and yearly
sums were considered for each NUTS III level region. All NUTS III regions had at least one large fire during the study period. In terms of burned area
929 766 <inline-formula><mml:math id="M42" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of forest were lost in mainland Portugal from June to September (2001 to 2016), with about 53 % due to large fires
(<inline-formula><mml:math id="M43" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M44" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>). Figure <xref ref-type="fig" rid="Ch1.F2"/> represents the number of large fires and the burned area they were responsible for by NUTS III region.</p>
      <p id="d1e1204">The north and centre of Portugal present the most extensive forest cover in the country <xref ref-type="bibr" rid="bib1.bibx41" id="paren.26"/>, particularly abundant in pine and
eucalyptus trees, two highly combustible species that have been associated with extreme wildfire events <xref ref-type="bibr" rid="bib1.bibx34" id="paren.27"/>. Consequently, both areas
show the highest number of large fires and respective burned area (Beiras e Serra da Estrela and Médio Tejo being the most affected NUTS III regions)  but
also with Alto Alentejo and Algarve (more to the south; see Fig. <xref ref-type="fig" rid="Ch1.F1"/>) among the NUTS III regions with a higher incidence. Additionally, dense
Mediterranean forests over hard-to-reach mountains can also be found in these areas, which when combined enhance the difficulty of the firefighting
efforts. Algarve, despite being located on the south coast, also has some mountains with forests surrounded by a considerably dry and arid terrain,
especially in the summer <xref ref-type="bibr" rid="bib1.bibx41" id="paren.28"/>, leading to a burned area of 112 764 <inline-formula><mml:math id="M45" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>, the second highest at the NUTS III level. Beiras e Serra
da Estrela is the region which presented the largest burned area in the summer months from 2001 to 2016 (almost 117 000 <inline-formula><mml:math id="M46" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>) and the highest
number of large fires (50). On the other hand, Oeste and A.M. Lisbon (A.M. signifies metropolitan area; mainly non-forested areas) are the NUTS III regions with a smaller number of large
fires – only one during the target time frame. More detailed information can be found in Table SM1 in the Supplement.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e1237">Population of each NUTS III region  according to the 2011 Census (<uri>https://www.ine.pt</uri>, last access: 16 October 2019) and respective number of monitoring stations for <inline-formula><mml:math id="M47" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>. Red dots indicate the location of the air quality monitoring stations used in this study.</p></caption>
            <?xmltex \igopts{width=227.622047pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f03.png"/>

          </fig>

</sec>
<sec id="Ch1.S2.SS2.SSS3">
  <label>2.2.3</label><title>Pollution data</title>
      <p id="d1e1268">The information available on the levels of pollutants was obtained from the Portuguese Environment Agency air quality network
(<uri>https://qualar.apambiente.pt/qualar/index.php</uri>, last access: 10 October 2020), established to monitor the concentrations of
pollutants according to the European legislation<?pagebreak page2871?> requirements (European Directive 2008/50/EC of 21 May 2008). The locations of the air quality stations
are irregularly scattered throughout the country, with a stronger presence in the most populated areas (Fig. <xref ref-type="fig" rid="Ch1.F3"/>). The isolation of
pollutant emissions due to burned biomass is quite complicated as it depends on parameters such as vegetation type, the weather conditions at the
moment the fire is taking place and the contribution of other sources, among others.</p>
      <p id="d1e1276">Considering all the pollutants measured at the background stations, <inline-formula><mml:math id="M48" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> was the one with a potentially higher link to forest
fires. Although some stations also measured <inline-formula><mml:math id="M49" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, the coverage in this case was insufficient to draw any significant correlations. The main
anthropogenic sources of <inline-formula><mml:math id="M50" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> include road traffic, industrial activities and home heating. In this study, to minimise the influence of
non-wildfire causes for the <inline-formula><mml:math id="M51" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations, we selected only background stations (encompassing urban and semi-urban ones, which are
located within urban areas but with minimum influence of road traffic, and rural stations). Therefore, urban stations with road traffic influence and
stations close to industrial complexes were not selected. The influence of home heating was already minimised by selecting the summer period as our
target time frame.</p>
      <p id="d1e1323">As done before for the wildfire data, here the time range considered was also from 2001 to 2016 and only the months of June to September, with
monthly means used for the correlations. Concentrations of <inline-formula><mml:math id="M52" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> were obtained for mainland Portugal at all types of background stations (a
total of 91 which cover 17 NUTS III regions, as shown in Fig. <xref ref-type="fig" rid="Ch1.F3"/>). Given the uneven coverage of the target domain, most stations are located in
the metropolitan areas of Oporto (14 stations) and Lisbon (24 stations) and in the rest of the coastal areas, where the higher population (NUTS III regions commonly above 250 000 inhabitants; Fig. <xref ref-type="fig" rid="Ch1.F3"/>) demands a tighter control of the air quality, but where, in turn, not a lot of large
wildfires occur due to the urbanised land use. For the NUTS III regions with more than one station a mean between all the <inline-formula><mml:math id="M53" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations in
each NUTS region was calculated.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1355">Mean concentration of <inline-formula><mml:math id="M54" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> by NUTS III region from June to September in the period 2001–2016 in mainland Portugal.</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f04.png"/>

          </fig>

      <?pagebreak page2872?><p id="d1e1375">Figure <xref ref-type="fig" rid="Ch1.F4"/> shows the mean concentration of <inline-formula><mml:math id="M55" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> by NUTS III region from June to September in the period 2001–2016 at the stations
available in mainland Portugal. The highest mean concentrations of <inline-formula><mml:math id="M56" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> during the period 2001–2016 (June to September only) were observed
in Oporto, with 31 <inline-formula><mml:math id="M57" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>; followed by Lisbon, Alentejo Central and Ave, with levels ranging from 26 to 29 <inline-formula><mml:math id="M58" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>
(Fig. <xref ref-type="fig" rid="Ch1.F4"/>). Conversely, the NUTS III regions which present the lowest mean values, between 14 and 17 <inline-formula><mml:math id="M59" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>, are Oeste, Alto-Minho and
Viseu Dão-Lafões. Despite these values, no NUTS region in Portugal exceeds the threshold value of <inline-formula><mml:math id="M60" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
(40 <inline-formula><mml:math id="M61" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>)  established by the European Directive 2008/50/EC.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS4">
  <label>2.2.4</label><title>Mortality data</title>
      <p id="d1e1509">Mortality data covering the period from 2001 to 2016 were obtained from Statistics Portugal (<uri>https://www.ine.pt</uri>, last access: 16 October 2019). Monthly death counts due to all-cause (International Classification of Diseases ICD-10, codes A00–R99), excluding injuries, poisoning
and external causes, and cause-specific mortality – cardiovascular (codes I00-I99) and respiratory (J00–J99) – were collected for each NUTS III region of
Portugal, comprising residents of all ages. These mortality causes were selected since they have been reported previously in the literature as being important in
their connection with air pollution <xref ref-type="bibr" rid="bib1.bibx19 bib1.bibx33 bib1.bibx28 bib1.bibx37" id="paren.29"/>, in particular with particulate matter (PM). Other relevant
mortality causes, such as chronic obstructive pulmonary disease (COPD, codes J40–J45) and asthma (ICD-10, code J47), were also considered. However,
since in many months and in the NUTS III target time period there were no deaths for COPD and asthma, it was not possible to obtain a data series
large enough to correlate with <inline-formula><mml:math id="M62" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and wildfire series.</p>
</sec>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Statistical analysis</title>
<sec id="Ch1.S2.SS3.SSS1">
  <label>2.3.1</label><?xmltex \opttitle{Correlations between {$\protect\chem{PM_{{10}}}$} and burned area}?><title>Correlations between <inline-formula><mml:math id="M63" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and burned area</title>
      <p id="d1e1558">The correlations between <inline-formula><mml:math id="M64" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and the total burned area by large fires per month for each NUTS III region were estimated using Pearson correlations
coefficients <xref ref-type="bibr" rid="bib1.bibx45" id="paren.30"/>. The Pearson approach, used to correlate two continuous variables having a normal distribution, is widely found in
studies of air pollution (e.g. <xref ref-type="bibr" rid="bib1.bibx43 bib1.bibx49 bib1.bibx53" id="altparen.31"/>, among many others). Results were considered statistically
significant if the <inline-formula><mml:math id="M65" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> value was <inline-formula><mml:math id="M66" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>. Correlations were performed using the detrended data series of burned area and <inline-formula><mml:math id="M67" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> in order to
remove the strong seasonal cycle of these variables and avoid spurious correlations. The detrending method follows <xref ref-type="bibr" rid="bib1.bibx58" id="text.32"/>, using
the first-time difference time series.</p>
</sec>
<sec id="Ch1.S2.SS3.SSS2">
  <label>2.3.2</label><?xmltex \opttitle{Associations between burned area, {$\protect\chem{PM_{{10}}}$} and mortality}?><title>Associations between burned area, <inline-formula><mml:math id="M68" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and mortality</title>
      <p id="d1e1632">The associations of monthly average <inline-formula><mml:math id="M69" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> levels, and the occurrence of large wildfires (burned area <inline-formula><mml:math id="M70" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 1000 <inline-formula><mml:math id="M71" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>), with mean monthly
mortalities (all-cause and respiratory and cardiovascular causes) were studied for the months of June, July, August and September for the period between
2001 and 2016. The estimates of the effects were obtained for each NUTS III region using Poisson regression models
<xref ref-type="bibr" rid="bib1.bibx14 bib1.bibx21" id="paren.33"/>. Poisson coefficients can correlate a count variable (such as the number of deaths) with a continuous variable. The
results were expressed as the relative risk (RR) of all-cause, cardiovascular and respiratory mortalities with a 95 % confidence interval
(95 % CI). All regression models were performed using IBM SPSS Statistics 25.0 software.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e1666">Significance of Pearson correlations between burned area and <inline-formula><mml:math id="M72" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> for each NUTS III region in the period 2001–2016 (from June to September; dots represent significant correlations at 95 % confidence).</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f05.png"/>

          </fig>

</sec>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Relationship between burned area and particulate matter</title>
      <p id="d1e1703">For the correlation between the burned area from large fires and <inline-formula><mml:math id="M73" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, a significant positive correlation was found for 7 (out of 13 with
available data) of the NUTS III regions studied, represented by the dotted areas in the map of Fig. <xref ref-type="fig" rid="Ch1.F5"/>. For Oeste, Região de Leiria, Beira
Baixa, Médio Tejo, Cávado, Ave, Terras de Trás-os-Montes, Alto Tâmega and the four<?pagebreak page2873?> Alentejo NUTS (Alto Alentejo, Alentejo Central, Alentejo
Litoral and Baixo Alentejo), there were not enough pollutant and/or burned area data to establish statistical relationships. The correlations
are strongest for Cávado, Ave, Tâmega e Sousa, Região de Aveiro and Viseu Dão-Lafões, with correlation coefficients above 0.75 at a
confidence level of 0.95, followed by Alto Tâmega and Beiras e Serra da Estrela between 0.5 and 0.74. As expected, all these areas are in the north
and centre of mainland Portugal, in line with the denser forest cover.</p>
      <p id="d1e1719">Finally, in Alto Minho, A.M. Porto, Douro, Região de Coimbra, Região de Leiria and Algarve no significant correlations were found. The limited
number of stations in those areas (which means less data to correlate), their location (closer or farther from the large wildfire spots) and uneven
distribution, as well as the contribution of other sources to the <inline-formula><mml:math id="M74" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> levels, can be some of the explanations.  The location of the air monitoring
stations may play a key role in these correlations, especially when they are scarcer, but for these NUTS regions this is a good indication of where the influence
of wildfires on the emissions of <inline-formula><mml:math id="M75" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> is likely to be stronger. In fact, some authors have reported a contribution of wood burning to the
<inline-formula><mml:math id="M76" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> load even in urban environments, where the presence of other PM sources tends to be higher <xref ref-type="bibr" rid="bib1.bibx15 bib1.bibx46" id="paren.34"/>.</p><?xmltex \hack{\newpage}?><?xmltex \floatpos{t}?><table-wrap id="Ch1.T2" specific-use="star"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e1761">Mean number of deaths occurring in months affected by large fires (LFs) from June to September in the period 2001–2016 (total of 64 target summer months) in the 23 NUTS III sub-regions of mainland Portugal.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.82}[.82]?><oasis:tgroup cols="9">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right" colsep="1"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right" colsep="1"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:colspec colnum="9" colname="col9" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry rowsep="1" namest="col3" nameend="col4" align="center" colsep="1">Natural deaths (<inline-formula><mml:math id="M77" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>) </oasis:entry>
         <oasis:entry rowsep="1" namest="col5" nameend="col6" align="center" colsep="1">Cardiovasc. deaths (<inline-formula><mml:math id="M78" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>) </oasis:entry>
         <oasis:entry rowsep="1" namest="col7" nameend="col8" align="center">Respiratory deaths (<inline-formula><mml:math id="M79" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula>) </oasis:entry>
         <oasis:entry colname="col9"/>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">NUTS III</oasis:entry>
         <oasis:entry colname="col3">All months</oasis:entry>
         <oasis:entry colname="col4">Months with LFs</oasis:entry>
         <oasis:entry colname="col5">All months</oasis:entry>
         <oasis:entry colname="col6">Months with LF</oasis:entry>
         <oasis:entry colname="col7">All months</oasis:entry>
         <oasis:entry colname="col8">Months with LF</oasis:entry>
         <oasis:entry colname="col9">Inhabitants (2011)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">North</oasis:entry>
         <oasis:entry colname="col2">Alto Minho</oasis:entry>
         <oasis:entry colname="col3">44 434</oasis:entry>
         <oasis:entry colname="col4">13 213</oasis:entry>
         <oasis:entry colname="col5">16 586</oasis:entry>
         <oasis:entry colname="col6">4731</oasis:entry>
         <oasis:entry colname="col7">4975</oasis:entry>
         <oasis:entry colname="col8">1293</oasis:entry>
         <oasis:entry colname="col9">244 149</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Cávado</oasis:entry>
         <oasis:entry colname="col3">44 307</oasis:entry>
         <oasis:entry colname="col4">12 832</oasis:entry>
         <oasis:entry colname="col5">14 136</oasis:entry>
         <oasis:entry colname="col6">3931</oasis:entry>
         <oasis:entry colname="col7">5964</oasis:entry>
         <oasis:entry colname="col8">1471</oasis:entry>
         <oasis:entry colname="col9">411 028</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Ave</oasis:entry>
         <oasis:entry colname="col3">49 068</oasis:entry>
         <oasis:entry colname="col4">14 153</oasis:entry>
         <oasis:entry colname="col5">15 736</oasis:entry>
         <oasis:entry colname="col6">4421</oasis:entry>
         <oasis:entry colname="col7">5754</oasis:entry>
         <oasis:entry colname="col8">1363</oasis:entry>
         <oasis:entry colname="col9">425 661</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Alto Tâmega</oasis:entry>
         <oasis:entry colname="col3">20 527</oasis:entry>
         <oasis:entry colname="col4">5324</oasis:entry>
         <oasis:entry colname="col5">6687</oasis:entry>
         <oasis:entry colname="col6">1864</oasis:entry>
         <oasis:entry colname="col7">2298</oasis:entry>
         <oasis:entry colname="col8">576</oasis:entry>
         <oasis:entry colname="col9">93 615</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Terras de Trás-os-Montes</oasis:entry>
         <oasis:entry colname="col3">24 664</oasis:entry>
         <oasis:entry colname="col4">7190</oasis:entry>
         <oasis:entry colname="col5">8083</oasis:entry>
         <oasis:entry colname="col6">2312</oasis:entry>
         <oasis:entry colname="col7">2623</oasis:entry>
         <oasis:entry colname="col8">633</oasis:entry>
         <oasis:entry colname="col9">116 713</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">A.M. Porto</oasis:entry>
         <oasis:entry colname="col3">221 105</oasis:entry>
         <oasis:entry colname="col4">64 366</oasis:entry>
         <oasis:entry colname="col5">67 239</oasis:entry>
         <oasis:entry colname="col6">18 660</oasis:entry>
         <oasis:entry colname="col7">24 914</oasis:entry>
         <oasis:entry colname="col8">6252</oasis:entry>
         <oasis:entry colname="col9">175 8991</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Tâmega e Sousa</oasis:entry>
         <oasis:entry colname="col3">50 971</oasis:entry>
         <oasis:entry colname="col4">14 623</oasis:entry>
         <oasis:entry colname="col5">18 038</oasis:entry>
         <oasis:entry colname="col6">4847</oasis:entry>
         <oasis:entry colname="col7">6504</oasis:entry>
         <oasis:entry colname="col8">1574</oasis:entry>
         <oasis:entry colname="col9">432 946</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Douro</oasis:entry>
         <oasis:entry colname="col3">39 670</oasis:entry>
         <oasis:entry colname="col4">11 648</oasis:entry>
         <oasis:entry colname="col5">13 162</oasis:entry>
         <oasis:entry colname="col6">3642</oasis:entry>
         <oasis:entry colname="col7">4511</oasis:entry>
         <oasis:entry colname="col8">1143</oasis:entry>
         <oasis:entry colname="col9">204 121</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Centre</oasis:entry>
         <oasis:entry colname="col2">Região de Aveiro</oasis:entry>
         <oasis:entry colname="col3">53 380</oasis:entry>
         <oasis:entry colname="col4">15 440</oasis:entry>
         <oasis:entry colname="col5">17 705</oasis:entry>
         <oasis:entry colname="col6">4945</oasis:entry>
         <oasis:entry colname="col7">6664</oasis:entry>
         <oasis:entry colname="col8">1647</oasis:entry>
         <oasis:entry colname="col9">369 287</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Viseu Dão-Lafões</oasis:entry>
         <oasis:entry colname="col3">48 379</oasis:entry>
         <oasis:entry colname="col4">14 170</oasis:entry>
         <oasis:entry colname="col5">17 700</oasis:entry>
         <oasis:entry colname="col6">4918</oasis:entry>
         <oasis:entry colname="col7">6474</oasis:entry>
         <oasis:entry colname="col8">1679</oasis:entry>
         <oasis:entry colname="col9">266 207</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Região de Coimbra</oasis:entry>
         <oasis:entry colname="col3">81 397</oasis:entry>
         <oasis:entry colname="col4">23 648</oasis:entry>
         <oasis:entry colname="col5">28 146</oasis:entry>
         <oasis:entry colname="col6">7718</oasis:entry>
         <oasis:entry colname="col7">10 898</oasis:entry>
         <oasis:entry colname="col8">2807</oasis:entry>
         <oasis:entry colname="col9">456 871</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Beiras e Serra da Estrela</oasis:entry>
         <oasis:entry colname="col3">53 414</oasis:entry>
         <oasis:entry colname="col4">15 699</oasis:entry>
         <oasis:entry colname="col5">17 816</oasis:entry>
         <oasis:entry colname="col6">5036</oasis:entry>
         <oasis:entry colname="col7">6108</oasis:entry>
         <oasis:entry colname="col8">1558</oasis:entry>
         <oasis:entry colname="col9">233 478</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Região de Leiria</oasis:entry>
         <oasis:entry colname="col3">45 190</oasis:entry>
         <oasis:entry colname="col4">13 259</oasis:entry>
         <oasis:entry colname="col5">14 294</oasis:entry>
         <oasis:entry colname="col6">4015</oasis:entry>
         <oasis:entry colname="col7">5507</oasis:entry>
         <oasis:entry colname="col8">1426</oasis:entry>
         <oasis:entry colname="col9">293 941</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Médio Tejo</oasis:entry>
         <oasis:entry colname="col3">49 769</oasis:entry>
         <oasis:entry colname="col4">13 908</oasis:entry>
         <oasis:entry colname="col5">16 666</oasis:entry>
         <oasis:entry colname="col6">4595</oasis:entry>
         <oasis:entry colname="col7">5417</oasis:entry>
         <oasis:entry colname="col8">1444</oasis:entry>
         <oasis:entry colname="col9">245 940</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Beira Baixa</oasis:entry>
         <oasis:entry colname="col3">22 545</oasis:entry>
         <oasis:entry colname="col4">6624</oasis:entry>
         <oasis:entry colname="col5">8063</oasis:entry>
         <oasis:entry colname="col6">2306</oasis:entry>
         <oasis:entry colname="col7">2267</oasis:entry>
         <oasis:entry colname="col8">562</oasis:entry>
         <oasis:entry colname="col9">88 134</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Oeste</oasis:entry>
         <oasis:entry colname="col3">60 896</oasis:entry>
         <oasis:entry colname="col4">17 819</oasis:entry>
         <oasis:entry colname="col5">22 467</oasis:entry>
         <oasis:entry colname="col6">6285</oasis:entry>
         <oasis:entry colname="col7">6539</oasis:entry>
         <oasis:entry colname="col8">1660</oasis:entry>
         <oasis:entry colname="col9">362 311</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">A.M. Lisbon</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">399 704</oasis:entry>
         <oasis:entry colname="col4">118 206</oasis:entry>
         <oasis:entry colname="col5">147 172</oasis:entry>
         <oasis:entry colname="col6">41 599</oasis:entry>
         <oasis:entry colname="col7">38 931</oasis:entry>
         <oasis:entry colname="col8">10 117</oasis:entry>
         <oasis:entry colname="col9">282 7050</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Alentejo</oasis:entry>
         <oasis:entry colname="col2">Lezíria do Tejo</oasis:entry>
         <oasis:entry colname="col3">45 762</oasis:entry>
         <oasis:entry colname="col4">13 505</oasis:entry>
         <oasis:entry colname="col5">16 241</oasis:entry>
         <oasis:entry colname="col6">4491</oasis:entry>
         <oasis:entry colname="col7">5025</oasis:entry>
         <oasis:entry colname="col8">1397</oasis:entry>
         <oasis:entry colname="col9">247 857</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Alto Alentejo</oasis:entry>
         <oasis:entry colname="col3">29 145</oasis:entry>
         <oasis:entry colname="col4">8622</oasis:entry>
         <oasis:entry colname="col5">10 391</oasis:entry>
         <oasis:entry colname="col6">2957</oasis:entry>
         <oasis:entry colname="col7">3690</oasis:entry>
         <oasis:entry colname="col8">923</oasis:entry>
         <oasis:entry colname="col9">117 357</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Alentejo Central</oasis:entry>
         <oasis:entry colname="col3">33 134</oasis:entry>
         <oasis:entry colname="col4">9602</oasis:entry>
         <oasis:entry colname="col5">12 171</oasis:entry>
         <oasis:entry colname="col6">3252</oasis:entry>
         <oasis:entry colname="col7">2979</oasis:entry>
         <oasis:entry colname="col8">765</oasis:entry>
         <oasis:entry colname="col9">165 688</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Alentejo Litoral</oasis:entry>
         <oasis:entry colname="col3">19 241</oasis:entry>
         <oasis:entry colname="col4">5681</oasis:entry>
         <oasis:entry colname="col5">6916</oasis:entry>
         <oasis:entry colname="col6">1972</oasis:entry>
         <oasis:entry colname="col7">2294</oasis:entry>
         <oasis:entry colname="col8">635</oasis:entry>
         <oasis:entry colname="col9">97 878</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Baixo Alentejo</oasis:entry>
         <oasis:entry colname="col3">30 823</oasis:entry>
         <oasis:entry colname="col4">8988</oasis:entry>
         <oasis:entry colname="col5">11 955</oasis:entry>
         <oasis:entry colname="col6">3317</oasis:entry>
         <oasis:entry colname="col7">3190</oasis:entry>
         <oasis:entry colname="col8">875</oasis:entry>
         <oasis:entry colname="col9">125 875</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Algarve</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">71 445</oasis:entry>
         <oasis:entry colname="col4">21 715</oasis:entry>
         <oasis:entry colname="col5">22 591</oasis:entry>
         <oasis:entry colname="col6">6482</oasis:entry>
         <oasis:entry colname="col7">7926</oasis:entry>
         <oasis:entry colname="col8">2307</oasis:entry>
         <oasis:entry colname="col9">446 140</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Impact of wildfires on mortality</title>
<sec id="Ch1.S3.SS2.SSS1">
  <label>3.2.1</label><title>Mortality overview</title>
      <p id="d1e2576">The mortality counts for the period 2001–2016 (for the months of June to September, 64 months) in mainland Portugal are presented in
Table <xref ref-type="table" rid="Ch1.T2"/> for each NUTS III region and all-cause and cardiovascular- and respiratory-related deaths. Results show that almost 30 % of
all-cause and cardiovascular mortality occur during the extended summer (June, July, August and September), as do 26 % of the respiratory
mortality.</p>
      <p id="d1e2581">Algarve, Alto Minho, Alto Alentejo and A.M. Lisbon are the NUTS regions with the higher percentage of all-cause mortality for the studied months, but the NUTS regions
with more per capita incidences are Beira Baixa, Alto Alentejo, Baixo Alentejo and Beiras e Serra da Estrela, areas with lower population density and
with mean higher age than the rest of the country.</p>
      <p id="d1e2584">With respect to cardiovascular mortality, the NUTS III regions which present a high incidence are Algarve, Terras de Trás-os-Montes and Beira Baixa, with
the latter, Baixo Alentejo and Alto Alentejo, having a higher percentage of population affected.</p>
      <p id="d1e2587">Finally, the results obtained for respiratory mortality show that Algarve, Lezíria do Tejo and Alentejo Litoral are the NUTS III regions which top the
ranking in the summer months, whereas Alto Alentejo is the region with most population affected. Alentejo and Algarve suffer from high temperatures in
the summer, which may also be an indicator that contributes to a higher mortality in general <xref ref-type="bibr" rid="bib1.bibx4" id="paren.35"/> but also due to cardiovascular and
respiratory diseases <xref ref-type="bibr" rid="bib1.bibx47" id="paren.36"/>. In addition, the aforementioned regions suffer from a considerable afflux of tourists that increase their
population in the same period, particularly in Algarve. In Alentejo, the combination of high temperatures with an aged population and less health care
resources available may be the reason why more of the population is affected by mortality <xref ref-type="bibr" rid="bib1.bibx9" id="paren.37"/>. In fact, this is a tendency that has
become stronger since the beginning of the 21st century, as the percentage of the population over 65 years of age changed in Alentejo from 22.5 %
in 2001 to 25.4 % in 2018, higher than the percentages in the whole of Portugal (from 16.4 % in 2001 to 21.7 % in 2018) (as derived from
Pordata, <uri>https://www.pordata.pt</uri>, last access: 31 March 2020).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><?xmltex \currentcnt{6}?><?xmltex \def\figurename{Figure}?><label>Figure 6</label><caption><p id="d1e2605">Relative risks (RRs, numbers) obtained from Poisson regression for <inline-formula><mml:math id="M80" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and <bold>(a)</bold> all-cause mortality, <bold>(b)</bold> cardiovascular mortality and <bold>(c)</bold> respiratory mortality from June to September in the period 2001–2016 (light blue NUTS III regions indicate a significant result, <inline-formula><mml:math id="M81" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>; white NUTS III regions present no significant results for the variables studied; grey NUTS III regions indicate that no data were available for correlations). Only significant RRs are shown.</p></caption>
            <?xmltex \igopts{width=483.69685pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2867/2021/nhess-21-2867-2021-f06.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS2.SSS2">
  <label>3.2.2</label><?xmltex \opttitle{Associations between mortality and {$\protect\chem{PM_{{10}}}$}}?><title>Associations between mortality and <inline-formula><mml:math id="M82" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula></title>
      <?pagebreak page2874?><p id="d1e2665">Wildfires are an important source of particulate matter, and the associations between mortality, <inline-formula><mml:math id="M83" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and the occurrence of large wildfires
are assessed in this section. As shown in Fig. <xref ref-type="fig" rid="Ch1.F6"/>a, three NUTS III regions (Alto Tâmega, Beiras e Serra da Estrela and Viseu Dão-Lafões)
present associations between <inline-formula><mml:math id="M84" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and all-cause mortality during the studied period. None showed a direct significant association with the
occurrence of large fires likely due to the fact that their contribution to the total burned area in each year from 2001 to 2016
(Table <xref ref-type="table" rid="Ch1.T1"/>) was highly variable (from 12.1 % in 2011 to 79.5 % in 2003). However, the wildfire origin of <inline-formula><mml:math id="M85" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> is
corroborated by the positive significant correlations obtained for these three NUTS regions between <inline-formula><mml:math id="M86" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and burned area (Fig. <xref ref-type="fig" rid="Ch1.F5"/>), with
Viseu Dão-Lafões displaying the highest correlations.</p>
      <p id="d1e2719">Beiras e Serra da Estrela is the NUTS region most affected by large wildfires during the studied period, both in number (50) and respective burned
area (<inline-formula><mml:math id="M87" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 100 000 <inline-formula><mml:math id="M88" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula>), and Viseu Dão-Lafões is the third in occurrences (28) corresponding to over 58 000 <inline-formula><mml:math id="M89" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> burned. This
involves high levels of <inline-formula><mml:math id="M90" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> in a short period of time, which might result in<?pagebreak page2875?> damage to human health, particularly in an aged population
(e.g. for Beiras e Serra da Estrela, 23.8 % and 28.7 % over-65-year-olds in 2001 and 2018, respectively; PORDATA,
<uri>https://www.pordata.pt</uri>, last access: 31 March 2020).</p>
      <p id="d1e2759">In terms of types of diseases, for cardiovascular mortality five NUTS regions presented associations with <inline-formula><mml:math id="M91" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>: Alto Minho, A.M. Porto, Região de
Aveiro, Região de Coimbra and Algarve (Fig. <xref ref-type="fig" rid="Ch1.F6"/>b). Again, no direct significant associations were obtained with the occurrence of large
fires. From these five NUTS, only Região de Aveiro showed a significant correlation between <inline-formula><mml:math id="M92" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and burned area, revealing the impact of
wildfires in the origin of the <inline-formula><mml:math id="M93" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>. For respiratory mortality, only Viseu Dão-Lafões presents associations with <inline-formula><mml:math id="M94" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, for
which a strong correlation between <inline-formula><mml:math id="M95" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and burned area was found, suggesting again the impact of wildfires on the presence of
<inline-formula><mml:math id="M96" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (Fig. <xref ref-type="fig" rid="Ch1.F6"/>c).</p>
</sec>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Discussion</title>
      <p id="d1e2843">During the studied period (2001–2016), large fires were responsible for 46 % of the more than 2 million <inline-formula><mml:math id="M97" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of forest burned in mainland
Portugal. The areas most affected by number and size of wildfires are the north, centre and inland of the country. Wildfires do not follow a pattern in
number of the occurrences or size during the years studied. This evidence was found despite the difficulties that the uneven scattering of the air
quality monitoring stations analysing <inline-formula><mml:math id="M98" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> in Portugal posed. In fact, the areas where wildfires are usually more frequent (inland) are far
from the urban centres (mainly along the coast) and thus not abundant in available air quality data due to the shortage (or even lack in some
NUTS III regions) of monitoring stations. These regions also have an aged population, poorer economy and less health care resources, which can lead to an
increase in the mortality rates in general. The socio-economic status of the population affected and the health care facilities and measures existing
in the communities have to be taken into account <xref ref-type="bibr" rid="bib1.bibx42" id="paren.38"/>, adding to the countless parameters that may affect these estimations which
contribute to considerable gaps identified in this type of studies <xref ref-type="bibr" rid="bib1.bibx5" id="paren.39"/>. Unfortunately, the scarce data available and the lack of
accuracy in the existing data prevented us from estimating and/or including a correction regarding their influence.</p>
      <p id="d1e2871">Nevertheless, it was possible to find relationships between very relevant parameters. The significant positive correlation between <inline-formula><mml:math id="M99" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and
burned area found for 7 of the 13 NUTS III regions with available data is a good indication of where the influence of wildfires on the emission of
<inline-formula><mml:math id="M100" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> is likely to be stronger. Although the location of the air quality monitoring stations may influence these correlations, especially
when they are scarcer, for these NUTS regions it reveals the influence of wildfires on the local levels of <inline-formula><mml:math id="M101" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>.</p>
      <p id="d1e2907"><?xmltex \hack{\newpage}?>Large wildfires tend to be active for several days, releasing high amounts of pollutants to the atmosphere. In Portugal, such as in other
Mediterranean countries, most of the wildfires are potentiated by strong winds which may spread the fire smoke over large distances
<xref ref-type="bibr" rid="bib1.bibx60 bib1.bibx3" id="paren.40"/>. Thus, air quality monitoring stations located far from the ignition sites can still detect increases in the
concentrations of, for instance, <inline-formula><mml:math id="M102" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>. <xref ref-type="bibr" rid="bib1.bibx3" id="text.41"/>, while studying the impact of the uncontrolled wildfires of October 2017 in
Portugal on mortality, found that the <inline-formula><mml:math id="M103" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M104" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emitted reached the United Kingdom, as well as other northern European
countries. Likewise, in Finland, where ambient PM levels are relatively low compared to other countries in Europe, most of the strongest PM pollution
episodes are typically related to emissions from wildfires in eastern European countries (Russia, Belarus, Ukraine, Estonia, Latvia and Lithuania) at
a distance of hundreds to thousands of kilometres from southern Finland <xref ref-type="bibr" rid="bib1.bibx39 bib1.bibx28" id="paren.42"/>.</p>
      <p id="d1e2954">The negative impact particulate matter can bring to human health is well established and can be translated into several types of diseases
<xref ref-type="bibr" rid="bib1.bibx26" id="paren.43"/>. There is evidence that it can enter the human body, arrive in the bloodstream, and damage some organs or even cause death due to
cardiovascular afflictions like stroke or heart attack, among others, representing a clear hazard to public health
<xref ref-type="bibr" rid="bib1.bibx7 bib1.bibx18" id="paren.44"/>. Particulate matter also can damage the human respiratory system. The risk depends on the size of the particle, which
if very small can even reach the alveolus <xref ref-type="bibr" rid="bib1.bibx38 bib1.bibx22" id="paren.45"/>.</p>
      <p id="d1e2967">In our study, the NUTS III regions where <inline-formula><mml:math id="M105" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations were found to be correlated with the burned area from large fires (Cávado, Ave,
Tâmega e Sousa, Região de Aveiro, Viseu Dão-Lafões, Alto Tâmega and Beiras e Serra da Estrela) are indeed the ones where it would be
expected to find the strongest influence of the wildfire-originated <inline-formula><mml:math id="M106" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> on population mortality. Although not for all, indeed
associations between all-cause mortality and <inline-formula><mml:math id="M107" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> were found for three of these NUTS regions (Alto Tâmega, Viseu Dão-Lafões and Beiras e Serra
da Estrela), with RRs varying from 1.003 to 1.006; between cardiovascular mortality and <inline-formula><mml:math id="M108" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> for one (Região de Aveiro) with an RR of
1.006, and between respiratory mortality and <inline-formula><mml:math id="M109" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> also for one (Viseu Dão-Lafões) with an RR of 1.020. Associations between
cardiovascular mortality and <inline-formula><mml:math id="M110" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> were found for four NUTS regions (Alto Minho, A.M. Porto, Coimbra and Algarve) where there was no significant
correlation of <inline-formula><mml:math id="M111" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> with burned area. All these are located on the coast, where the population density in Portugal is clearly predominant
(particularly in A.M. Porto), as well as considerable industrial presence. In these regions, cardiovascular disease may have many other sources, some
of them derived from a more sedentary and stressed lifestyle.</p>
      <?pagebreak page2876?><p id="d1e3048">The mortality increase associated with <inline-formula><mml:math id="M112" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> is consistent with the estimates reported in other European studies, such as APHEA2
<xref ref-type="bibr" rid="bib1.bibx25" id="paren.46"/>, APHENA <xref ref-type="bibr" rid="bib1.bibx54" id="paren.47"/>, EpiAir <xref ref-type="bibr" rid="bib1.bibx13" id="paren.48"/> and MED-PARTICLES <xref ref-type="bibr" rid="bib1.bibx14" id="paren.49"/>, which also reported higher
<inline-formula><mml:math id="M113" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> effects on all-cause, cardiovascular and respiratory mortalities.</p>
      <p id="d1e3086">However, some studies present uneven conclusions. <xref ref-type="bibr" rid="bib1.bibx23" id="text.50"/> reported the highest effects on cardiovascular mortality, but <xref ref-type="bibr" rid="bib1.bibx36" id="text.51"/>
did not find any consistent effect with cardiovascular deaths in Australia, and <xref ref-type="bibr" rid="bib1.bibx1" id="text.52"/> registered the highest effects on respiratory
mortality in Greece. This high variability may be related to several factors, notably (i) different PM composition or varying gaseous emissions (CO,
VOCs, <inline-formula><mml:math id="M114" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mtext mathvariant="italic">x</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> or <inline-formula><mml:math id="M115" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>) from wildfires, which may have different degrees of toxicity on cardiovascular and respiratory systems;
or (ii) increasing temperature during wildfires, which is known to enhance the effects of PM on more susceptible individuals (e.g. cardiac patients)
<xref ref-type="bibr" rid="bib1.bibx48" id="paren.53"/>. Therefore, the effects we found on all-cause, cardiovascular and respiratory mortalities during the wildfire seasons may be due to
different PM compositions or increasing temperature.</p>
      <p id="d1e3124">Region-specific associations between <inline-formula><mml:math id="M116" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations and mortality were also observed. These may have been influenced by the factors
described above (different PM composition and increasing temperature) but also by the magnitude and duration of the exposure to PM from a given fire,
the underlying health status of the population and the size of the population. The age of the exposed individuals can also be important. In some
studies, larger effect estimates in groups of those 65 years and older have been reported. <xref ref-type="bibr" rid="bib1.bibx1" id="text.54"/> mentioned that the effect of respiratory
mortality in Greece was higher in adults of ages 75 and above during large fires, whereas <xref ref-type="bibr" rid="bib1.bibx17" id="text.55"/> observed an increase in risk of cardiac
arrest, especially in older adults in Australia, although not all resulted in death. In Brazil, <xref ref-type="bibr" rid="bib1.bibx40" id="text.56"/> reported that older adults had the
strongest association between exposure to biomass burning and circulatory disease mortality. In Portugal, the regions traditionally impacted by
wildfires coincide with a larger percentage of an aged population, which can help explain the obtained associations.</p>
      <p id="d1e3147">In our analysis, to study the relationship between the burned area and <inline-formula><mml:math id="M117" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> concentrations, averaged monthly data were used as the minimum
temporal scale available for the burned area was 1 month. Other studies relating wildfire-originated PM and mortality are usually based on daily PM
concentrations and daily death counts since they do not account for the burned area as a measure of the wildfire size. Therefore, the monthly
approach obviously reduced the amount of data available and the possibility of finding more significant correlations, which may have diluted the
effects of some wildfires on the population mortality. Moreover, some health effects may not have been detected because wildfires are episodic and
local events. Nevertheless, the results provide an overall context, highlighting the strongest associations between wildfire-generated <inline-formula><mml:math id="M118" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
and all-cause, cardiovascular and respiratory mortalities. Being able to achieve them with this uneven distribution of available data is an
indication that the approach can be very useful to at least uncover tendencies and, in regions with stronger monitoring capabilities and coverage, a
way to find stronger and more accurate correlations. This will help legislators and other government bodies to propose ways to protect the population
chronically exposed to wildfires or more susceptible to acute reactions to wildfire smoke.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions</title>
      <p id="d1e3181">Portugal is a country that suffers constantly from serious wildfire incidents, which are bound to pose a risk not only to chronically affected
populations but also from acute impacts of the pollutants released in such events. In this work, analysing the summer months (June to September) on a
lengthy time frame (2001–2016), it was possible to find relevant associations between <inline-formula><mml:math id="M119" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (associated with large wildfires) and mortality in
some NUTS III regions of mainland Portugal (mainly inland and in the north), as well as a significant correlation between burned area and
<inline-formula><mml:math id="M120" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>.</p>
      <p id="d1e3206">In particular, it was found that large fires (in this study considered above 1000 <inline-formula><mml:math id="M121" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ha</mml:mi></mml:mrow></mml:math></inline-formula> of burned area) have an impact on the health of the population
in some areas due to the emission of particulate matter. The lack of data or possible confounding factors likely prevented a higher number of NUTS III regions
with significant correlations. Moreover, in such severe events, the population exposed to a high concentration of pollutants in a short period of time
should be considered as a risk modifier of the impacts of air pollution exposure <xref ref-type="bibr" rid="bib1.bibx11 bib1.bibx51" id="paren.57"/>.</p>
      <p id="d1e3220">These episodes occurred during the summer months (June–July–August–September) when high temperatures and long episodes of drought increase the
probabilities of one of these extreme events. In a future ruled by climate changes, the high temperatures and long periods of drought that
usually fuel big fires are expected to increase, thus paving the way for more extreme and intense events to occur even outside the typically
affected regions. Thus, more population will be exposed more frequently to high pollutant levels, affecting their general health and increasing
chronic diseases and mortality. Hence, restrictive policies and protocols to improve the effectiveness of preventive and mitigation actions must be
enforced to face this environmental and societal issue.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e3227">Data are publicly available through the websites mentioned in the text:
<list list-type="bullet"><list-item>
      <p id="d1e3232">EFFIS (European Forest Fire Information System), Data and Services, 2019. Available at <uri>https://effis.jrc.ec.europa.eu/applications/data-and-services/</uri> (last access: 11 October 2019).</p></list-item><list-item>
      <p id="d1e3239">ICNF (Instituto da Conservação da Natureza e das<?pagebreak page2877?> Florestas), 2019. Available at <uri>https://www.icnf.pt/</uri> (last accessed: 1 September 2019).</p></list-item><list-item>
      <p id="d1e3246">INE (Instituto Nacional de Estatística), Statistics Portugal – Web Portal, 2019. Available at <uri>https://www.ine.pt/xportal/xmain?xpid=INE&amp;xpgid=ine_indicadores&amp;contecto=pi&amp;indOcorrCod=0008273&amp;selTab=tab0</uri> (last access: 16 October 2019).</p></list-item><list-item>
      <p id="d1e3253">GWF (Global Forest Watch), 2020. Available at <uri>https://www.globalforestwatch.org/map/?gfwfires=true</uri> (last access: 10 October 2020).</p></list-item><list-item>
      <p id="d1e3260">PORDATA (Base de Dados Portugal Contemporâneo), População residente: total e por grandes grupos etários (in Portuguese), 2019. Available at <uri>https://www.pordata.pt</uri> (last access: 31 March 2020).</p></list-item></list></p>

      <p id="d1e3266">All the compiled data are available upon contacting the corresponding author (pedro.jimenezguerrero@um.es).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e3269">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/nhess-21-2867-2021-supplement" xlink:title="pdf">https://doi.org/10.5194/nhess-21-2867-2021-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e3278">PTC wrote the manuscript, with contributions from SA and NR. The manuscript was finally revised by PJG. PTC and SA designed the experiments and led the statistical analysis, with the support of LPP, NR and PJG.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e3284">The authors declare that they have no conflict of interest.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e3290">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e3296">This article is part of the special issue “The role of fire in the Earth system: understanding interactions with the land, atmosphere, and society (ESD/ACP/BG/GMD/NHESS inter-journal SI)”. It is a result of the EGU General Assembly 2020, 3–8 May 2020.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e3302">The authors are thankful to the G-MAR research group at the University of Murcia for the fruitful scientific discussions.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e3307">This work was financially supported by the European Regional Development Fund–Fondo Europeo de Desarrollo Regional (ERDF-FEDER), Spanish Ministry of Economy and Competitiveness/Agencia Estatal de Investigación (grant no. CGL2017-87921-R (ACEX project)) and Project UIDB/00511/2020 of LEPABE (Portuguese national funds through FCT/MCTES PIDDAC). Sofia Augusto was supported by the Portuguese Foundation for Science and Technology (grant no. SFRH/BPD/109382/2015).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e3313">This paper was edited by Sander Veraverbeke and reviewed by two anonymous referees.</p>
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    <!--<article-title-html>Impact of large wildfires on PM<sub>10</sub> levels and human mortality in Portugal</article-title-html>
<abstract-html><p>Uncontrolled wildfires have a substantial impact on the environment, the economy and local populations. According to the European Forest Fire
Information System (EFFIS), between 2000 and 2013 wildfires burned up to 740&thinsp;000&thinsp;ha of land annually in the south of Europe, Portugal
being the country with the highest percentage of burned area per square kilometre. However, there is still a lack of knowledge regarding the impacts of the
wildfire-related pollutants on the mortality of the country's population. All wildfires occurring during the fire season
(June–July–August–September) from 2001 and 2016 were identified, and those with a burned area above 1000&thinsp;ha (large fires) were considered
for the study. During the studied period (2001–2016), more than 2&thinsp;million ha of forest (929&thinsp;766&thinsp;ha from June to September alone)
were burned in mainland Portugal. Although large fires only represent less than 1&thinsp;% of the number of total fires, in terms of burned area their
contribution is 46&thinsp;% (53&thinsp;% from June to September). To assess the spatial impact of the wildfires, burned areas in each region of
Portugal were correlated with PM<sub>10</sub> concentrations measured at nearby background air quality monitoring stations. Associations between
PM<sub>10</sub> and all-cause (excluding injuries, poisoning and external causes) and cause-specific mortality (circulatory and respiratory) were
studied for the affected populations using Poisson regression models. A significant positive correlation between burned area and PM<sub>10</sub> was
found in some regions of Portugal, as well as a significant association between PM<sub>10</sub> concentrations and mortality, these being apparently
related to large wildfires in some of the regions. The north, centre and inland of Portugal are the most affected areas. The high temperatures and
long episodes of drought expected in the future will increase the probabilities of extreme events and therefore the occurrence of wildfires.</p></abstract-html>
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