Impact of large wildfires on PM10 levels and human mortality in Portugal

Impact of large wildfires on PM10 levels and human mortality in Portugal Patricia Tarín-Carrasco1, Sofia Augusto2,3, Laura Palacios-Peña1,4, Nuno Ratola5, and Pedro Jiménez-Guerrero1,6 1Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum", University of Murcia, Spain. 2EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal 3Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciencias, Universidade de Lisboa (CE3C-FC-ULisboa), Lisboa, Portugal. 4Dept. of Meteorology, Meteored, Almendricos, Spain. 54. LEPABE-Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. 6Biomedical Research Institute of Murcia (IMIB-Arrixaca), Spain. Correspondence: Pedro Jiménez-Guerrero ([email protected]) Abstract. Uncontrolled wildfires have a substantial impact on the environment, the economy and local populations. According to the European Forest Fire Information System (EFFIS), between the years 2000 and 2013 wildfires burnt about 170,000-740,000 ha of land annually on the south of Europe (Portugal, Spain, Italy, Greece and France). Although most southern European countries have been impacted by wildfires in the last decades, Portugal was the most affected, having the highest percentage of5 burned area comparing to its whole territory. For this reason, it deserves a closer attention. However, there is a lack of knowledge regarding the impacts of the wildfire-related pollutants on the mortality of the population. All wildfires occurring during the fire seasons (June-July-August-September) from 2001 and 2016 were identified and those with a burned area above 1000 ha were considered for the study. To assess the spatial impact of the wildfires, these were correlated with PM10 concentrations measured at nearby background air quality monitoring stations, provided by the Portuguese Environment Agency (APA). Associations10 between PM10 and all-cause (excluding injuries, poisoning and external causes) and cause-specific mortality (circulatory and respiratory), provided by Statistics Portugal, were studied for the affected populations, using Poisson regression models. During the studied period (2001-2016), more than 2 million ha of forest were burned in mainland Portugal and the 48% of wildfires occurred were large fires. A significant correlation between burned area and PM10 have been found in some NUTS III (regions) on Portugal, as well as a significant correlation between burned area and mortality. North, centre and inland of Portugal are the15 most affected areas. The high temperatures and long episodes of drought expected on the future will increase the probabilities of extreme events and therefore, the occurrence of wildfires. 1 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. 1 Introduction The existence of wildfires constitutes a considerable impact on the environment and humans living in numerous regions worldwide. Climate change has lately been identified as a very important variable to be considered in this matter and global warming20 scenarios are forecasting an increase of the number and intensity of wildfires during the next years (Bowman et al., 2017). Global warming will produce changes in temperature and precipitation patterns which will increase the prevalence and severity of wildfires (Settele et al., 2015), consequently impacting future air quality (Schär et al., 2004). In fact, an increase on the number of droughts, heat waves and dry spells is suggested by climate change projections, which could not only extend the burnt area in chronically impacted areas, but also affect new ones (Gillett et al., 2004), as was the case of Sweden in the summer25 of 2018 (Lidskog et al., 2019). One of the areas of the world most fustigated by wildfires is the Mediterranean basin (Portugal being the most impacted country), which needs to be studied carefully to address the concerns of local populations. Although there has been a slight decreasing trend in the burnt area in this region since 2000 after an increasing period in the previous 20 years (European Environment, Agency, https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-3/ assessment), recent extreme events like the 2017 fires in Portugal and the 2018 fires in Greece which even resulted in a severe30 loss of human lives are confirming the worst projections. In fact, a recent study by Turco et al. (2019) showed a relationship between drought and the occurrence of wildfires and suggested an increase of both due to future climate change. But already some years before, the PESETA (Projection of Economic impacts of climate change in Sectors of the EU based on bottom-up Analysis) study estimated an increase of the burnt area in southern Europe in the future (Ciscar et al., 2014). Uncontrolled wildfires emit numerous pollutants derived from the incomplete combustion of biomass fuel, which cause35 damage to human health, particularly the respiratory system (World Health Organization, 2010). Examples include particulate matter (PM), carbon monoxide, methane, nitrous oxide, nitrogen oxides, volatile organic compounds (VOCs), and other secondary pollutants (Cascio, 2018) 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, as for instance the interaction of PM with solar radiation which can prompt a modification of the temperature depending on the characteris-40 tics of the aerosol (Trentmann et al., 2005). 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 (Knorr et al., 2016), 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 ignition45 points due to the atmospheric transport of the pollutant plumes. A number of studies (e.g. Lin et al. (2012); Im et al. (2018); Liang et al. (2018); Augusto et al. (2020), among others) report the influence of natural and anthropogenic emissions on air quality composition across different countries, especially PM and tropospheric O3. 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 (Lazaridis et al., 2008). An increase of cardiovascular and respiratory morbidity and50 mortality are some of the impact these contaminants can have on humans (Johnston et al., 2012; Tarín-Carrasco et al., 2019). 2 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. For instance, there is a strong evidence of the relationship between PM in general and mortality, especially from cardiovascular diseases, for both long-term and short-term exposure (Anderson et al., 2012). 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 (Liu et al., 2015; Reid et al., 2016), the impacts on human health are difficult to quantify and the real55 effects still poorly known. Regarding PM, a recent study focusing on 10 southern European cities revealed that cardiovascular and respiratory mortality associated to PM10 (particles with aerodynamic diameter below 10 µm) was higher on days affected by wildfires’ smoke than in smoke-free days (Faustini et al., 2015). The authors also found that PM10 from forest fires increased mortality more than PM10 from other sources. So, the estimation of mortality due to exposure to wildfire-generated pollutants is key to60 manage health resources and the necessary public funds towards prevention and remediation, setting up appropriate policies and protocols (Rappold et al., 2012). 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 burnt area). When the wildfire occurs close to a large conurbation, the population exposed is higher. But as Analitis et al. (2012) showed in their study, small fires do not seem to have an effect on mortality, whereas65 medium and large episodes (with burnt areas >1000 ha) 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 PM10 on the Portuguese population mortality. The70 relationship between the size of the wildfire (characterised by the respective burned area) and PM10 and this same pollutant and mortality has been 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 has been studied. These causes have been selected due to their well-known connection with air75 pollution. 2 Methodology and data In this study, the effects of short-term pollutants exposure due to wildfires on human mortality are quantified. The forest fire pollutant emissions were estimated for the period 2001-2016 during the summer months (June-July-August-September) in Portugal mainland (23 NUTS III and more than 10 million people). For this quantification, two steps have been followed.80 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. PM10 and burned area has been correlated through linear regression, while the mortality data and PM10 was correlated with Poisson regression. Data was processed and ordered by NUTS and by month and year. Finally, the correlation between the pollutants emitted by forest fires, the wildfires burned area and the 3 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. different causes of mortality during the period 2001-2016 for the summer months was studied. The study is focused in PM85 since it is one of the main pollutants emitted by wildfires, which can increase PM concentrations up to 50% and more (Lazaridis et al., 2008). Moreover, there is a clear relation with several effects on human health (including mortality), in particular with respiratory and circulatory diseases (Kollanus et al., 2016; Reid et al., 2016; Liang et al., 2018). There was not enough PM2.5 data collected from the Portuguese air quality management network to establish a correlation (only 20 stations measure PM2.5 in the mainland). For all these reasons this study is focused on PM10.90 2.1 Target area With 89 015 km2(9.11 Mha) 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 Figure 1,left), the country has most of its mountain ranges in the north, reaching 1993 m in Serra da Estrela. Although showing a Mediterranean95 climate, this topographic display leads to various climate patterns along the country, with increasing temperature and decreasing rainfall from northwest to southeast (Moreira et al., 2011; Oliveira et al., 2017). In terms of land cover, Figure 1,left shows a predominance of agriculture by 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 allied with high temperatures in the summer months represent a potential fire hazard, which unfortunately has been often verified almost every summer for many years.100 Figure 1. (Left) Land cover in mainland Portugal in 2015 (Global Forest Watch, https://www.globalforestwatch.org; (Right) Mainland Portugal NUTS III regions as included in this contribution. 4 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. 2.2 Datasets 2.2.1 NUTS III boundary data The target domain was divided by NUTS (Nomenclature of Territorial Units for Statistics) level 3 (Figure 1,right) for Portugal mainland. NUTS is a geocode standard for referencing the subdivisions of countries for statistical purposes developed by the European Union, and are divided in three levels, established by each EU member country. The boundaries of the NUTS III105 files from mainland Portugal (in total, 23) were retrieved from the Eurostat web page (https://ec.europa.eu/eurostat/web/gisco/ geodata/reference-data/administrative-units-statistical-units/nuts) with QGIS3 software. The downloaded data are for the 14 March 2019 version at a 1:60 million scale. 2.2.2 Wildfires data The wildfire data, collected in the period from 2001 and 2016, was obtained from the Portuguese Institute for Nature Conserva-110 tion and Forests (https://www.icnf.pt/). For this study, forest fires occurring in the months of June, July, August and September 2001-2016 (the months with highest temperatures and drier conditions when more than 65% of fires happened) with more than 1000 ha of total burned area were selected and considered large fires. In total, there were 331 events under that category. This data was divided by month and year and the respective monthly and yearly sums were considered for each NUTS III level region. Ave, Alto Tâmega, Tâmega e Sousa, Oeste, Médio Tejo and Alentejo Litoral are the NUTS III where large fires during115 the study period were not found. 2.2.3 Pollution data The information available on the levels of pollutants was obtained from the Portuguese Environment Agency air quality network (https://qualar.apambiente.pt/qualar/index.php), established to monitor the concentrations of pollutants according to the European Legislation requirements (European Directive 2008/50/EC of 21 May 2008). The network is irregularly scattered120 throughout the country, with a stronger presence in the most populated areas. The isolation of pollutant emissions due to burnt biomass is quite complicated as it depends on parameters, such as vegetation type, the weather conditions on the burnt moment or the contribution of other sources, among others. For this reason, in this study, background stations (specifically urban, suburban and rural background) were selected, so that the direct impact of other urban and industrial sources such as road traffic, building heating and manufacturing combustions was avoided. Considering all the pollutants measured on the background125 stations, PM was the one with a potentially higher link to forest fires. And although some stations also measured PM2.5, the coverage was insufficient to draw any significant correlations, so PM10 was chosen in the end. As for the wildfires data, also here the time range was from 2001 to 2016 and only the months of June to September were considered, with monthly means used for the correlations. Concentrations of PM10 were obtained for mainland Portugal in all types of background stations (a total of 91 which cover 17 NUTS III, as shown in Figure 2)- Given the uneven coverage of the130 target domain, most stations are located in the metropolitan areas of Oporto (14 stations) and Lisbon (24 stations) and in the 5 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. rest of the coastal areas, where the higher population (NUTS III commonly above 250,000 inhabitants, Figure 2) demands a tighter control of the air quality, but where, in turn, not a lot of large wildfires occur due to the urbanized land use. Figure 2. Population of each NUTS III according to the 2011 Census (https://www.ine.pt) and respective number of monitoring stations for PM10. 2.2.4 Mortality data Mortality data covering the period from 2001 to 2016 was obtained from Statistics Portugal (https://www.ine.pt). Monthly135 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 all-age residents. These mortality causes were selected since they have been reported previously in literature as important in their connection with air pollution (Hoek et al., 2013; Liu et al., 2015; Kollanus et al., 2016; Münzel et al., 2018), in particular with particulate matter (PM). Other relevant mortality causes, such as Chronic140 Obstructive Pulmonary Disease (COPD, codes J40-J45) and asthma (ICD-10, code J47), were also considered, however the reduced number of deaths due to these diseases in the study period prevented the establishment of correlations. 6 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. 2.3 Statistical analysis 2.3.1 Correlations between PM10 and burned area Correlations between PM10 and the total burned area per month by NUTS III were estimated using Pearson correlations145 coefficients (Pearson and Galton, 1895). Pearson correlation is used to correlate two continuous variables having a normal distribution, while Poisson coefficients are used to correlate a count variable with a continuous variable. These methodologies are widely used in studies covering the topic of health impacts of air pollution (e.g. Islam and Chowdhury (2017); Pallarés et al. (2019); Rahman et al. (2019); Rovira et al. (2020); among many others). Results were considered statistically significant if the p-value was p<0.05. Correlations were performed using the detrended data series of burned area and PM10 in order to remove150 the strong seasonal cycle of these variables and avoid spurious correlations. The detrending method follows Tarín-Carrasco et al. (2019), using the first-time difference time series. 2.3.2 Associations between burnt area, PM10 and mortality The associations of monthly average PM10 levels, and the size of the wildfires (burnt area >1000 ha and burnt area <1000 ha) with mean monthly mortalities (all-cause, respiratory and cardiovascular causes) were studied for the months June, July, August155 and September for the period between 2001 and 2016. The effect estimates were obtained for each NUTS III region using Pearson regression models. 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. 3 Results and discussion160 The results obtained in the study are presented as follows. First, the description of the situation in Portugal in terms of geographical distribution of the burned area is presented. Then, a summary of the PM10 concentrations during the summer months of the years 2001-2016 is presented. Finally, the correlations between burned area and PM10 and the potential associations of wildfire-derived PM10 and all-cause mortality is presented in this section. 3.1 Spatio-temporal patterns of wildfires165 3.1.1 Burned area From 2001 to 2016 period more than 2 million ha of forest were burned in mainland Portugal. Around 48% due to large fires (>1000 ha). During this period, the wildfires occurred on different areas in Portugal, as shown in Figure 3. The north, centre and inland of Portugal are the areas with the highest number of wildfires and the highest burned area, being Beiras e Serra da Estrela and Beira Baixa. Also Lezíria do Tejo (in Alentajo) and Algarve (in the south) (Figure 1) can170 be found among the most affected areas (in number of wildfires and burned area). As seen in Figure 3, the north and centre of 7 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. Figure 3. Burned area and number of fires (>1000 ha) per NUTS III from June to September in the period 2001-2016. Portugal is where the most extensive forests in the country (Nunes et al., 2019), particularly abundant in pine and eucalyptus trees, two species that have been associated with extreme wildfire events (Maia et al., 2014). Additionally, dense Mediterranean forests over hard-to-reach mountains can also be found in these areas, which combined enhance the difficulty of the firefighting efforts. The Algarve, which despite being located in the south coast, also has some mountains with forests, surrounded by a175 considerably dry and arid terrain, especially in the summer (Nunes et al., 2019). Beira Baixa is the region which presented the most burned area during the period 2001-2016, with almost 124,000 ha in total. But Beira e Serra da Estrela is the area where the highest number of wildfires was found, 47 in total. On the other hand, Lezíria do Tejo is the NUTS III region most affected by wildfires, considering the number of large fires and burned area together. Oeste and Area Metropolitana de Lisboa are the areas with a smaller number of large fires, only one during the target timeframe (since these are mainly non-forested areas).180 Table 1 presents an overview of the burned area and occurrences of large fires by NUTS III areas in mainland Portugal. On the other hand, Table 2 shows the yearly variability during the studied period of the number of the occurrences and burned area. In 2008 no wildfires over 1,000 ha of burned area occurred, whereas 2003 accounted for 81 of occurrences of large fires, which were responsible for 80% of the total burned area in that year. The data in Table 2 suggests that it is not possible to perceive a yearly pattern of wildfires in Portugal regarding the occurrences, burned area or the contribution of large185 fires to the burned area, but other studies have shown a relationship with high temperatures and drought periods (Turco et al., 2019). 8 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. Table 1. Total burned area and occurrences of large fires by NUTS III areas in mainland Portugal in the period 2001-2016 in the months of June, July, August and September (total of 64 target summer months). NUTS Months with large fires (N) Number of large fires (N) Total burned area (ha) North Alto Minho 7 14 53918 Cávado 3 4 7731 Ave 4 4 5410 Alto Tâmega 10 21 48415 Terras de Trás-os-Montes 7 10 28050 A. M. Porto 5 8 40840 Tâmega e Sousa 4 4 5706 Douro 14 27 48907 Centre Aveiro 3 6 17481 Viseu Dão-Lafões 11 31 60744 Coimbra 8 16 45673 Beiras e Serra da Estrela 17 49 115503 Leiria 6 13 28347 Médio Tejo 11 31 96947 Beira Baixa 7 14 59032 Oeste 1 1 1700 A.M.Lisboa 1 1 2756 Alentejo Leizíria do Tejo 2 3 24404 Alto Alentejo 4 12 70657 Alentejo Central 3 6 1970 Alentejo Litoral 3 5 16176 Baixo Alentejo 2 2 9240 Algarve 9 19 107273 9 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. centre and inland of Portugal. Wildfires do not follow a pattern in number of the occurrences or size during the years studied. These evidences were found despite the difficulties that the uneven scattering of the air monitoring stations analysing PM10 in Portugal posed, since the areas where wildfires are usually more frequent (inland) are far from the urban centres (mainly275 along the coast), and thus, not abundant in air quality data availability due to the shortage (or even lack in some NUTS III) 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 (Oliveira et al., 2017), adding to the countless parameters that may affect these estimations which contribute to considerable gaps identified in this type of studies (Black280 et al., 2017). During the summer months occur almost 30% of the deaths of all diseases and cardiovascular while for respiratory diseases mortality is around a 26%, so we can conclude that cold temperatures are in more responsible for all cause deaths than warm ones. These episodes occurred during the summer months (June-July-August-September), when high temperatures and long285 episodes of drought increase the probabilities of undergo one of these extreme events. On a future ruled by climate changes, the high temperatures and long periods of drought that usually fuel big fires are expected to increase, thus leading 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 this290 environmental and societal issue. Data availability. Data is publicly available through the websites mentioned in the text: –EFFIS (European Forest Fire Information System). Data and Services, 2019. Available at https://effis.jrc.ec.europa.eu/applications/ data-and-services/ (last accessed 11/10/2019). –ICNF (Instituto da Conservação da Natureza e das Florestas), 2019. Available at https://www.icnf.pt/(last accessed: 01/09/2019).295 –INE (Instituto Nacional de Estatística). Statistics Portugal - Web Portal, 2019. Available at: https://www.ine.pt/xportal/xmain?xpid= INE&xpgid=ine_indicadores&contecto=pi&indOcorrCod=0008273&selTab=tab0 (last accessed 16/10/2019). –GWF (Global Forest Watch), 2020. Available at https://www.globalforestwatch.org/map/?gfwfires=true (last accessed, 10/10/2020). –PORDATA (Base de Dados Portugal Contemporâneo). População residente: total e por grandes grupos etários (in Portuguese), 2019. Available at https://www.pordata.pt (last accessed 31/03/2020).300 All the compiled data is available upon contacting the corresponding author ([email protected]) Author contributions. PT-C wrote the manuscript, with contributions from SA and NR. The manuscript was finally revised by PJ-G. P-TC and SA designed the experiments and led the statistical analysis, with the support of LP-P, NR and PJ-G. 16 https://doi.org/10.5194/nhess-2021-38 Preprint. Discussion started: 12 February 2021 c Author(s) 2021. CC BY 4.0 License. Competing interests. The authors declare no conflict of interest. Acknowledgements. The authors are thankful to the G-MAR research group at the University of Murcia for the fruitful scientific discussions.305 Financial support. 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