# Non-Accidental Health Impacts of Wildfire Smoke

Hassani Youssouf, Catherine Liousse, Laurent Roblou, Eric-Michel Assamoi, Raimo O. Salonen, Cara Maesano, Soutrik Banerjee, Isabella Annesi-Maesano

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## To cite this version:

Hassani Youssouf, Catherine Liousse, Laurent Roblou, Eric-Michel Assamoi, Raimo O. Salonen, et al.. Non-Accidental Health Impacts of Wildfire Smoke. International Journal of Environmental Research and Public Health, 2014, 11 (11), pp.11772-11804. 10.3390/ijerph11111772. hal-01328538

HAL Id: hal-01328538

https://hal.sorbonne-universite.fr/hal-01328538

Submitted on 8 Jun 2016

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Review

# Non-Accidental Health Impacts of Wildfire Smoke

Hassani Youssouf<fnref n="1" />,<fnref n="2" />, Catherine Liousse<fnref n="3" />, Laurent Roblou<fnref n="3" />, Eric-Michel Assamoi<fnref n="3" />, Raimo O. Salonen<fnref n="4" />, Cara Maesano<fnref n="1" />,<fnref n="2" />, Soutrik Banerjee<fnref n="1" />,<fnref n="2" /> and Isabella Annesi-Maesano<fnref n="1" />,<fnref n="2" />,*

External Editor: Paul B. Tchounwou

Received: 25 July 2014; in revised form: 23 October 2014 / Accepted: 29 October 2014 /

Published: 14 November 2014

**Abstract:** Wildfires take a heavy toll on human health worldwide. Climate change may increase the risk of wildfire frequency. Therefore, in view of adapted preventive actions, there is an urgent need to further understand the health effects and public awareness of wildfires. We conducted a systematic review of non-accidental health impacts of wildfire and incorporated lessons learned from recent experiences. Based on the literature, various studies have established the relationship between one of the major components of wildfire, particulate matter (particles with diameter less than 10 µm (PM₁₀) and less than 2.5 µm (PM₂.₅)) and cardiorespiratory symptoms in terms of Emergency Rooms visits and hospital

<footnote n="1">¹ Department of Epidemiology of Respiratory and Allergic Disease (EPAR), UMR-S 1136, Institute Pierre Louis of Epidemiology and Public Health, National Institute for Health and Medical Research (INSERM), 27 Rue Chaligny, 75012 Paris, France; E-Mails: hassani.youssouf@upmc.fr (H.Y.); carahenson@gmail.com (C.M.); soutrik.banerjee@upmc.fr (S.B.)</footnote>

<footnote n="2">² Department of Epidemiology of Respiratory and Allergic Disease (EPAR), UMR-S 1136, Institute Pierre Louis of Epidemiology and Public Health, University Pierre and Marie Curie (UPMC)—Sorbonne University, 27 Rue Chaligny, 75012 Paris, France</footnote>

<footnote n="3">³ Laboratory of Aerology, National Center for Scientific Research (CNRS), University of Toulouse, 14 Avenue Edouard Belin, 31400 Toulouse, France; E-Mails: catherine.liousse@aeroobs-mip.fr (C.L.); laurent.roblou@aeroobs-mip.fr (L.R.); eric-michel.assamoi@aeroobs-mip.fr (E.-M.A.)</footnote>

<footnote n="4">⁴ Environmental Epidemiology Unit, National Institute for Health and Welfare, P.O. Box 95, FI-70701 Kuopio, Finland; E-Mail: raimo.salonen@thl.fi</footnote>

<footnote>* Author to whom correspondence should be addressed; E-Mail: isabella.annesi-maesano@inserm.fr; Tel.: +33-1-4473-8449; Fax: +33-1-4473-8454.</footnote>
admissions. Associations between wildfire emissions and various subclinical effects have also been established. However, few relationships between wildfire emissions and mortality have been observed. Certain segments of the population may be particularly vulnerable to smoke-related health risks. Among them, people with pre-existing cardiopulmonary conditions, the elderly, smokers and, for professional reasons, firefighters. Potential action mechanisms have been highlighted. Overall, more research is needed to better understand health impact of wildfire exposure.

**Keywords:** wildfires emissions; wildfire exposure; health impact; cardiorespiratory disease; particulate matter

# 1. Introduction

A wildfire is an uncontrolled fire of combustible vegetation that spreads quickly over woodland or brush. Wildfire smoke composition depends on multiple factors, such as fuel type, moisture content, fire temperature and oxygenation, wind conditions and other weather-related influences [1]. A large percentage of wildfires are caused directly or indirectly by human actions [2]. According to a 2010 European Commission report on forest fires, more than 95% of forest fires in many countries in Europe were of human origin and 53% were caused by smoking and fire making in Estonia [2]. However, wildfire spreads is influenced by environmental factors such as high temperatures, drought and temporary dry spells. In northern countries of the boreal hemisphere, fire danger conditions are generally observed in the summer when rain precipitation usually decreases. In tropical areas, fires occur during the dry season, from December to February in boreal hemisphere and from June to September in austral hemisphere. Due to climate change, wildfires have become more problematic for public health and ecosystems in the past decades [2].

It is estimated that wildfire smoke is composed by more than thousands individual chemical compounds and its composition depends on the fuel type, the temperature of the fire, and the wind conditions [1]. Primarily, wildfire smoke is composed of carbon dioxide and water vapor. Other common components of smoke present in lower concentrations are carbon monoxide (CO), formaldehyde, acrolein, polyaromatic hydrocarbons, benzene and particle Matter (PM), namely small particulates suspended in air, which include particles with diameters less than 2.5 micrometers (PM<sub>2.5</sub>) and 10 micrometers (PM<sub>10</sub>). However, about 80%–90% of mass particulate matter produced by wild land is within the fine particles (PM<sub>2.5</sub>) range with high black carbon, organic carbon [3] and brown carbon contents [4].

Many of wildfire emissions can have acute or long term health implications on the exposed populations, according to the official Organizations for health protection [1,5,6]. Among the major components of wildfire smoke, fine particles (PM<sub>2.5</sub>) affects ambient air quality and has various effects on human health [7]. These effects are expected to further increase during wildfire smoke episodes where PM<sub>2.5</sub> and PM<sub>10</sub> concentrations above air quality standards can occur [8]. Standard values according to the United States Environmental Protection Agency (USEPA) are fixed in 24 h exposure, to 35 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 150 µg/m<sup>3</sup> for PM<sub>10</sub> [9]. Mean annual standard values according to the
World Health Organization (WHO) are 12 µg/m³ for PM₂.₅ and 35 µg/m³ for PM₁₀ respectively. In our observational data, we have found values 75 times higher than the WHO standard for PM₂.₅ during a wildfire in Spain. Fine particles have been observed to causes changes in lung functions, leading to increases in respiratory and cardiovascular mortality and morbidity including asthma. Fine particles may reach the alveoli, and if not sufficiently cleared in the lungs and greats concentration may enter the bloodstream or remain in the lung, resulting in chronic lung disease such as emphysema. Other wildfire emissions like volatile organic compounds (VOCs) may cause skin and eye irritation, drowsiness, coughing and wheezing, while other like benzene may be carcinogenic [10,11].

The health effects of wildfires are less widely known [12]. So far, despite the fact that wildfires take a heavy toll on human health worldwide, few studies reported health effects of wildfires, in particular health effects of trace gases and aerosols emitted by wildfires remain poorly quantified due to uncertainties on wildfire emissions assessment, and the taking into account of their transport and chemical evolution during the long range transport (ozone production, interactions between ozone and aerosols) [11].

Some epidemiological studies have established the relationship between exposure to PM from wildfire smoke and increased visits to hospital emergency rooms and hospital admissions for cardiorespiratory diseases [13–18]. Exposure to wood smoke PM₂.₅ from wild land fires and agricultural burning has been linked with cardiovascular effects, including increased cardiovascular mortality, risk of developing cardiovascular disease and risk of myocardial infarction [19]. However, with the exception of two literature reviews [8,11], limited to papers published until January 2012 [8,11,14], data on wildfire related health effects are sparse and many questions remain unanswered.

The aim of the present article was to contribute to the knowledge on health impacts of wildfires by providing a systematic review of non-accidental health outcomes related to wildfire exposure according to the literature, including subclinical effects and health effect in firefighters in order to understand mechanisms. In the present review, non-accidental has been defined any outcome where there is no identifiable incident, trauma, stress or other mental conditions resulting directly from the fire.

## 2. Methods

### 2.1. *Search Strategy*

A literature search of online databases (PubMed, ISI, and Google Scholar) up to April 2014 without specifying the start date was performed using the terms, “wildfire smoke exposure”, “bushfire smoke exposure”, “forest fire smoke exposure”, “wildfire emissions”, “bushfire emissions”, “forest fire emissions”, “health effects of wildfire”, “health effects of bushfire”, “health effects of forest fire”. In addition, references of the retrieved articles were examined to identify further relevant articles. A sifting process identified (from study titles, abstracts and the full paper) those studies suitable for inclusion in the review.

### 2.2. *Eligibility Criteria*

Published studies were included in this review if they met the following criteria: peer-reviewed original article, review article, population study (cohort, case-crossover, time-series, case-control or
cross-sectional study, meta-analysis (none)), written in English and reporting on non-accidental health impacts of wildfire. Figure 1 shows a flow diagram describing the study selection process.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Flow diagram of study selection process.</figcaption></figure>

## 2.3. Data Abstraction

All but the studies meeting the above mentioned criteria were discarded. Data on health impact of wildfires were then systematically summarized in tables where the following characteristics were recorded for each study included: location, publication reference (first author's last name, year of publication), study population, health outcomes (hospital admissions, mortality), statistical methods, exposure assessment and findings. The percentage increase of health outcomes after exposure to particulate matter was extracted if available.

# 3. Results

## 3.1. Study Selection

The initial search by key words yielded 255 papers, 94 of which were considered as relevant to the original research question (Figure 1).

## 3.2. Wildfire Exposure Assessment

Several methods have been used to quantify exposure to wildfire emissions in epidemiologic studies investigating health impact of wildfires [11,14]. Self-administrated questionnaires which estimated the exposure to wildfire based on the declaration of the individuals threatened by flames miscalculated real exposure [16] due to report bias in the absence of a quantitative measure [16,20]. Usual objective methods included the number of known wildfires as a proxy for smoke episodes [11,21], the daily burned area as a proxy for daily smoke exposure [14] or more rarely PM measurements to identify single smoke episodes caused by known fires [22]. Air quality monitoring stations estimated the PM
concentration from all sources including those emitted by a fire. However in some cases, monitoring stations were not able to monitor an entire wildfire area [13]. Furthermore, the stations are sometimes far from wildfire sources and may be not representative of the burnt area. Recently, wildfire smoke exposure assessment has been estimated through multiple sources of information [14] as ground-based air quality monitoring [23,24], remote sensing through satellite imagery [18,25] and chemical transport models [26,27]. In particular satellite data were used to evaluate wildfire exposure because spatially-resolved particle mass data provided by satellites is superior to using data provided only by the nearest available monitoring station data [13]. In addition, satellites allow detections of plume etc. in which case daily variation in urban pollution is not be confounded with smoke exposure.

### 3.3. Study Design and Statistical Approaches to Evaluate Wildfire Health Effects

There has been considerable variability in study design and statistical approaches used to evaluate the health effects from wildfire smoke exposure. This can challenge the comparison of results, especially in regards to urban background PM and acute toxicity from wildfire. Concerning the study design, different approaches were taken in order to better distinguish the health impacts of air pollution from wildfire smoke to those from other sources of pollution and to better evaluate health outcomes before, during and after the wildfire exposure [13] and in exposed and non-exposed area [13,21]. A lot of statistical approaches were applied, among them paired *t*-tests and multiple regression [28], time-series analysis [29], logistic regression with repeated measures was used to estimate associations with each outcome [13], generalized linear model with negative binomial distribution and Poisson distribution [13], time-series analysis combined with generalized linear Poisson regression models [30]. Poisson distribution was used in several studies to take wildfire smoke exposure and daily health outcomes into account. Other studies use Case-crossover analysis [31] and ecological studies [21,32]. For all statistical methods, potential confounding factors were taken into account in the multivariate models [33].

### 3.4. Non-Accidental Health Effects Related to Wildfire

Based on the literature, non-accidental health outcomes that have been found related to wildfire emissions include: (1) all-causes and cardiorespiratory mortality (Table 1), (2) cardiorespiratory morbidity (Table 2), and (3) birth weight. In addition, it is well documented that certain segments of the population are particularly vulnerable to wildfire smoke-related health risks [34]. Among them people with pre-existing cardiopulmonary conditions [35], the elderly [32], smokers and firefighters [20].

#### 3.4.1. Non-Accidental Health Effects at the Population Level

Table 1 and Table 2 present data showing links between wildfire and non-accidental morbidity and mortality.
<table><caption>**Table 1.** Exposure to wildfire and non-accidental mortality.</caption><thead><tr><th>Location</th><th>Authors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td>Sydney</td><td>Morgan et al. 2010 [24]</td><td>1994–2002</td><td></td><td>All causes mortality, Respiratory mortality and cardiovascular mortality, respiratory diseases admissions</td><td>Monitoring stations using TEOM instruments in 8 monitoring sites Wildfire PM<sub>10</sub> is calculated by subtracting total PM<sub>10</sub> by background PM<sub>10</sub> (due to others sources) calculated as the 30-days moving average of PM<sub>10</sub> without wildfire</td><td>PM<sub>10</sub> was associated with small increase in all causes mortality at lag0 (0.80% CI: -0.24% to 1.86%) but not associated with respiratory mortality or cardiovascular mortality</td></tr><tr><td>Denver</td><td>Vedal and Duton [36]</td><td>2 days fire in Denver 9 June and 18 June 2002</td><td>Denver population area (2 millions)</td><td>All-cause mortality data for 2001 and 2002</td><td>PM was obtained from daily air pollution from network of monitoring sites for Colorado</td><td>9 June 2002: PM<sub>10</sub> and PM<sub>2.5</sub> peak of 1 h concentration: 372 and 200 mg/m<sup>3</sup>, respectively 18 June 2002: PM<sub>10</sub> and PM<sub>2.5</sub> peak of 1 h concentration: 316 and 200 mg/m<sup>3</sup>, respectively No perceptible increases in daily mortality could be attributed to the increase PM concentrations from wildfire</td></tr><tr><td>Kuala Lampur</td><td>Sastry et al. 2002 [37]</td><td>April and November 1997</td><td>Population of Kuala Lumpur (2.5 millions)</td><td>All-cause mortality data from 1994–1997</td><td>Daily measurement from Malaysian Meteorological Bureau</td><td>PM<sub>10</sub> > 210 µg/m<sup>3</sup> is associated with increase of total non-trauma mortality (relative risk = 1.72 for 65–74)</td></tr><tr><td>Sydney</td><td>Johnston et al. 2011 [31]</td><td>1997–2004</td><td>Population of Sydney</td><td>All-cause mortality data from the Australian Bureau of Statistics</td><td>Events were defined as days for which the 24 h city-wide concentration of PM<sub>10</sub> exceeded the 99th percentile</td><td>A recent study conducted by Johnston and colleagues in Sydney looked at the effects of bushfires between 1994 and 2007 and mortality. This study revealed that a 5% increase in non-accidental mortality at lag of 1 day (OR 1.05 (95% CI: 1.00–1.10)) was observed on days of high air pollution from bushfire smoke</td></tr></tbody></table>
<table><caption>**Table 2.** Exposure to wildfire and cardiorespiratory diseases.</caption><thead><tr><th>Location</th><th>Study Authors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td rowspan="3">North Carolina</td><td rowspan="3">Rappold et al. 2011 [18]</td><td rowspan="3">June 2008</td><td rowspan="3">Population of 42 North Carolina counties</td><td>Respiratory diseases</td><td>Use Aerosol Optical Depth (AOD) measured by satellite GEOS</td><td>In the counties exposed significant increase in cumulative RR for asthma (RR = 1.65 (95% CI: 1.25–2.1)), COPD (RR = 1.73 (95% CI: 1.06–2.83)) and pneumonia and acute bronchitis (RR = 1.59 (95% CI: 1.07–2.34))</td></tr><tr><td>Emergency departments visits</td><td>AOD scale from 0–2, high density of plume if AOD > 1.25<br>Counties with 25% of areas with AOD > 1.25 were defined as exposed to the smoke plume for each day in high-expose window</td><td>ED visits of all respiratory diagnosis were elevated in the exposed counties (RR = 1.66 (95% CI: 1.38–1.91))<br>Significant increase for Emergency department visits for cardiopulmonary symptoms (RR = 1.23 (95% CI: 1.06–1.43)) and heart failure (RR = 1.37 (95% CI: 1.01–1.85))</td></tr><tr><td>Respiratory diseases</td><td>Webmail questionnaire to assess smoke exposure and occurrence of symptoms</td><td>Prevalence rates of reported outcomes were much higher among individuals with asthma</td></tr><tr><td rowspan="2">Southern California</td><td rowspan="2">Kunzli et al. 2006 [16]</td><td rowspan="2">October 2003</td><td rowspan="2">873 high school students and 551 elementary-school children from 16 communities in California</td><td>Medication usage</td><td>Exposure duration were quantified by the number of days of exposure during the two weeks (not at all, 1–2 d, 3–5 d, 6–10 d, all days)</td><td>Dry cough, medication and physician visits were more frequently reported by parents of elementary school children. High school students report eye symptoms<br>Six or more days of fire smell was significantly associated with all outcomes</td></tr><tr><td>Physician visits</td><td></td><td>Six days or more of fire smell is associated with more than four-fold higher rates of eyes symptoms, 3 fold dry cough and sneezing 2 for cold, sore throat, wet cough, medication use, physician visits and missed school due symptoms</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>Location</th><th>StudyAuthors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td rowspan="3">Southern California</td><td rowspan="3">Mirabelli et al. 2009 [17]</td><td rowspan="3">October 2003</td><td rowspan="3">465 high school students from 12 communities</td><td rowspan="3">Respiratory diseases</td><td>Webmail questionnaire assess smoke exposure and the occurrence of symptoms</td><td>Forty percent (186 of 465) of population reported the odor of wildfire smoke at home</td></tr><tr><td>Log-binomial regression to evaluate associations between smoke exposure and fire-related health symptoms</td><td rowspan="2">Increase respiratory and eye symptoms with increasing frequency of wildfire smoke exposure</td></tr><tr><td>Ratio of maximum midexpiratory flow to forced vital capacity as marker of airway size</td></tr><tr><td rowspan="2">Three Provinces of Netherlands (Groningen, Friesland and Drenthe)</td><td rowspan="2">Greven et al. 2011 [20]</td><td rowspan="2">12 months</td><td rowspan="2">1330 firefighters</td><td>General respiratory symptoms</td><td rowspan="2">Questionnaire web-based version of European community Respiratory Health Survey questionnaire, added question to identify the number of incidents, the type, the onset, and the duration of symptoms and possible exposure during the incident</td><td rowspan="2">OR of general respiratory symptoms were estimated between 1.2 (95% CI: 1.0–1.4) and 1.4 (95% CI: 1.2–1.7) per 25 fires. An inhalation incident is strongly associated with the presents respiratory symptoms OR between 1.7 (95% CI: 1.1–2.7) and 3.0 (95% CI: 1.9–4.7).</td></tr><tr><td>Atopy and bronchial hyper-responsiveness</td></tr><tr><td>Brazilian Amazon Region</td><td>Ignotti et al. 2010 [32]</td><td>2004–2005</td><td>Population of Brazilian amazon region</td><td>Rates of respiratory hospitalization among children, elderly and intermediate age group and due to childbirth</td><td>Annual hours (AH%) of PM<sub>2.5</sub> > 80 µg/m<sup>3</sup> AH = (sum of hours with PM<sub>2.5</sub> > 80 µg/m<sup>3</sup>/sum hours PM<sub>2.5</sub> is measured in the year) × 100 Use of a coupled aerosol and gas transport model to estimate atmospheric emission Use of satellite observations of fires to obtain several gas and aerosols particles from biomass burnings</td><td>1% of increase of the exposure indicator was associated to an increase of 8% of child hospitalization (children < 5 years), 10% increase in hospitalization of elderly, 5% increase of the intermediate age group</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>Location</th><th>Study Authors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td rowspan="2">Singapore</td><td rowspan="2">Emmanuel et al. 2000 [38]</td><td rowspan="2">1997</td><td rowspan="2"></td><td>Respiratory diseases</td><td rowspan="2">PM<sub>10</sub>, PM<sub>2.5</sub>, and other compounds(nitrogen dioxide, ozone, CO) were measured by 15 stations located through the Island linked via public telephone network to a central control station</td><td>Air quality was into the unhealthy range (PSI > 100) on 12 days, the highest PSI was 138.94% of particles observed were PM<sub>2.5</sub><br/>Haze from the Indonesian forest fire was responsible of 30% increase in outpatient attendances</td></tr><tr><td>Outpatient attendances, accident and emergencies, inpatient care, mortality data</td><td>Increase in PM<sub>10</sub> levels from 50 µg/m<sup>3</sup> to 150 µg/m<sup>3</sup> was significantly associated with increase of 12% of upper respiratory tract illness, 19% asthma and 26% rhinitis<br/>No significant increase in hospitalization or mortality due to the smoke haze</td></tr><tr><td>Victoria, Australia</td><td>Tham et al. 2009 [39]</td><td>2002–2003</td><td></td><td>Hospital admissions, emergency attendances, air quality and meteorological data</td><td>Air pollution from the Aplington air quality monitoring station which had the most complete data and was located away from the coast freeways and industrial settings</td><td>Daily levels PM<sub>10</sub> were strongly associated with respiratory emergency department attendances (p < 0.001)<br/>No association with hospital admissions (p = 0.06)<br/>After adjusting for confounding effects of maximum temperature and relative humidity, the strongest associations were observed between PM<sub>10</sub> and daily respiratory emergency department attendances in Melbourne (RR = 1.018, 95% CI: 1.004–1.033, p = 0.01)</td></tr><tr><td>Vilnius</td><td>Ovadnevaite et al. 2006 [40]</td><td>August–September 2002</td><td>The population of Vilnius</td><td>Respiratory diseases, bronchial asthma</td><td>Air pollution data from Vilnius monitoring network</td><td>Significant increase of average hourly values of PM<sub>10</sub>, NO<sub>2</sub>, CO and SO<sub>2</sub> during several episodes in 2002<br/>The number of respiratory diseases and bronchial asthma in September (after longer exposure) was up to 20 times higher (by comparison in July) in some regions about 3 times higher all over the city</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>Location</th><th>Study Authors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td rowspan="2">Australia</td><td rowspan="2">Reisen et al. 2011 [41]</td><td rowspan="2">2005–2008</td><td rowspan="2">130 firefighters</td><td rowspan="2">Air toxics within the breathing zone of firefighters</td><td>One-way analyses of variance, Student t-tests</td><td>30% of firefighters had a high exposure risk i.e., exposure to hazardous substance, CO, RP, and formaldehyde exceeds the occupational exposure standard (OES) to 5% to 20% of time, 6% had a very high exposure risk i.e., exposure to hazardous substance exceeds OES for more than 20% of time</td></tr><tr><td>CO values were Log-transformed in all tests to meet the assumption of normal distribution of variables</td><td>The majority of firefighters (60%) were exposed in low to moderate levels</td></tr><tr><td>Galice, Spain</td><td>Caamano-Isorna et al. 2011 [21]</td><td>2006</td><td>156 municipalities</td><td>Consumption drugs for anxiolytics-hypnotics and drugs for obstructive airway disease (DOADs) for respiratory health</td><td>Additive model for time series analysis</td><td>Higher consumption of DOADs among pensioners during the months after the wildfires. The defined DDDs increased by 17 69 DDDs (95%CI: 0.86–34.51) for male pensioners (10.29% increase) p < 0.05 in comparison to male pensioners in municipality unaffected. For the female pensioners of the municipality affected, the increase was 12.9% of DDDs (p < 0.05) For anxiolytics-hypnotics consumption, there was a significant increase in the DDDs among men (pensioners and no pensioners) in affected municipality (15.88% p < 0.05 vs. 12.2% p < 0.05)</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>Location</th><th>StudyAuthors</th><th>Study Period</th><th>Population</th><th>Health Outcomes</th><th>Exposure Assessment/ Analytical Methodology</th><th>Findings</th></tr></thead><tbody><tr><td>Sydney</td><td>Jalaludin et al. 2000 [15]</td><td>January 1994</td><td>Children with a reported history of wheezing in the previous 12 months (32 children recruited)</td><td>Peak expiratory flow rates (PEFR)</td><td>Generalized estimating equation models</td><td>After adjusting for the wildfire period and potential confounders, there was no significant association between mean PM<sub>10</sub> and PEFR Children without bronchial hyperactivity had a significant negative association between PEFR and PM<sub>10</sub></td></tr><tr><td>Southern California</td><td>Delfino et al. 2009 [13]</td><td>October 2003</td><td>n = 40,856 (hospital admissions)</td><td>Respiratory admissions, cardiovascular admissions</td><td>Generalized estimating equation models for Poisson data</td><td>Average increases of 70 µg/m<sup>3</sup> PM<sub>2.5</sub> during heavy smoke conditions was associated with 34% increase asthma admissions The strongest association between wildfire PM<sub>2.5</sub> and asthma admissions was observed among elderly aged 65–99 (10.1% increase per 10 µg/m<sup>3</sup> PM<sub>2.5</sub>, 95%CI: 3%–17.8%) and children aged 0–4 (8.3% increase, 95% CI: 2.2%–14.9%) followed by adults aged 20–64 (4.1% 95% CI: 0.5% to 9%), no association for age 5–18 No evident association between wildfire related PM<sub>2.5</sub> on cardiovascular admissions</td></tr><tr><td rowspan="2">Sao Paulo State Brazil</td><td rowspan="2">Abrex et al. 2007 [30]</td><td rowspan="2">23 March 2003– 27 July 2004</td><td rowspan="2">Population admitted for asthma in main hospital of Araraquara</td><td rowspan="2">Asthma hospital admissions</td><td>Time series analysis</td><td>Asthma hospital admission during burning period were 50% higher than those observed during the non-burning period (p &lt; 0.001)</td></tr><tr><td>Generalized linear Poisson regression models</td><td>After stratification to non-burning and burning periods, it was observed that for the same variation of 10 µg/m<sup>3</sup> in TSP concentration, asthma hospital admissions increased by 9.7% (95% CI: 2.6–17.2) and 12.7% (95% CI: 2.2–24.3) respectively</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><td>Location</td><td>StudyAuthors</td><td>Study Period</td><td>Population</td><td>Health Outcomes</td><td>Exposure Assessment/ Analytical Methodology</td><td>Findings</td></tr></thead><tbody><tr><td>Brisbane</td><td>Chen et al. 2006 [23]</td><td>1 July 1997– 31 December 2000</td><td>Patients admitted in Brisbane</td><td>Respiratory hospital admissions</td><td>generalized linear model with negative binomial distribution</td><td>An increase of PM<sub>10</sub> from low (&lt;15 µg/m<sup>3</sup>) to high level (&gt;20 µg/m) level, is accompanied by an increase of 19% in respiratory hospital admissions for wildfire days vs. 13% for background days</td></tr><tr><td>Indonesia</td><td>Kunii et al. 2002 [42]</td><td>29 September– 7 October 1997</td><td>n = 543</td><td>Respiratory diseases</td><td>8 monitoring sites between Jakarta (Java) and Jambi (Sumatra) were used to air quality measurements, Health effects measured by a face to face structured interview</td><td>Concentration of CO and PM<sub>10</sub>: very unhealthy and hazardous levels Concentration of PAH were 6–14 times higher in the unaffected area, 91.3% of responders had respiratory symptoms due to the haze Elderly had a serious deterioration of overall health</td></tr><tr><td>Kuching, Malaisia</td><td>Mott et al. 2005 [35]</td><td>1 January 1995– 31 December 1998, fire period 1 August– 31 October 1997</td><td>Population of Kuching region in Malaysia (7 hospitals)</td><td>Hospitalizations, all causes, respiratory admissions, cardiovasuclar admissions</td><td>Comparison of health outcomes in the wildfire period or post-fire period basing on forecasting estimates established from a historical baseline period of 1 January 1995 through 31 July 1997</td><td>Increase respiratory hospitalizations specifically for patients with COPD and asthma patients Persons over aged 65 year with previous hospital admissions for any cause any cardiorespiratory disease, any respiratory disease or COPD were significantly more likely to be re-hospitalized during the follow-up period in 1997 than in the follow-up period in the pre-fire years of 1995 or 1996.</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><td>Location</td><td>StudyAuthors</td><td>Study Period</td><td>Population</td><td>Health Outcomes</td><td>Exposure Assessment/ Analytical Methodology</td><td>Findings</td></tr></thead><tbody><tr><td>Australia</td><td>Morgan et al. 2010 [24]</td><td>1994–2002</td><td></td><td>Respiratory diseases, respiratory mortality and cardiovascular mortality, respiratory diseases admissions</td><td>Monitoring stations using TEOM instruments in 8 monitoring sites Wildfire PM<sub>10</sub> is calculated by subtracting total PM<sub>10</sub> by background PM<sub>10</sub> (due to others sources) calculated as the 30-days moving average of PM<sub>10</sub> without wildfire</td><td>A 10 µg/m<sup>3</sup> increase in wildfire PM<sub>10</sub> is associated with: 1.24% (95% CI 0.22% to 2.27%) increase in all respiratory diseases admissions (at lag 0) 3.8% (1.4 to 6.26) increase in COPD admissions at lag 25.02 (1.77 to 8.37) increase in adult asthma at lag (0)</td></tr><tr><td>Darwin, Australia</td><td>Hanigan et al. 2008 [43]</td><td>April–November 1995–2005</td><td></td><td>Respiratory diseases admissions</td><td>Daily PM<sub>10</sub> exposure level is determined using the visibility data to build a predictive model Data from 2005 and 1995 were used to assess the performance of the model Predictive peaks of PM<sub>10</sub> during 2000 and 2001 were mapped against wildfire activity records for this period</td><td>An increase of 10 µg/m<sup>3</sup> in same-day estimated PM<sub>10</sub> was associated with 4.81% (95% CI: –1.04%–11.1%) increase in total respiratory admissions<br>A strong association of wildfire PM<sub>10</sub> and respiratory admission among indigenous people than non-indigenous people (15.02%, 95% CI: 3.73%–27.54% vs. 0.67%, 95% CI: 7.55%–9.6%)</td></tr><tr><td>Central Florida</td><td>Sorenson et al. 1999 [44]</td><td>June–July 1998</td><td>All ages</td><td>Emergency room visits, hospital admissions</td><td>descriptive statistics</td><td>Increased emergency-room visits and hospital admissions for asthma and bronchitis during fire period relative to same period in previous year</td></tr><tr><td>Malaysia</td><td>Brauer, 1998 [45]</td><td></td><td>All ages</td><td>Outpatient visits</td><td>Not specified</td><td>Increased visits for asthma, upper respiratory tract symptoms, and rhinitis during vegetation fire episode periods of elevated PM<sub>10</sub> in Malaysia</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><td>Location</td><td>Study Authors</td><td>Study Period</td><td>Population</td><td>Health Outcomes</td><td>Exposure Assessment/ Analytical Methodology</td><td>Findings</td></tr></thead><tbody><tr><td>Singapore</td><td>Chew et al. 1995 [46]</td><td></td><td>Children less than 12 years old</td><td>Emergency room visits</td><td>Multiple regression analysis</td><td>Increased asthma visits with PM<sub>10</sub> during episode of exposure to biomass burning emissions in Singapore</td></tr><tr><td>Denver</td><td>Sutherland et al. 2005 [47]</td><td>June–July 2002</td><td>Adult with COPD</td><td>Symptoms</td><td>Standard descriptive statistics, repeated measurements ANOVA</td><td>Significant increase in symptom index (dyspnea, cough, chest tightness, wheezing, sputum production) on two days of elevated PM2.5 (65 µg/m<sup>3</sup>) relative to control days (14 µg/m<sup>3</sup>). Days of elevated PM attributed to fire smoke by satellite imaging</td></tr><tr><td>Kelowna and Kamloops Regions British Columbia</td><td>Moore et al. 2006 [48]</td><td>2003</td><td>All ages</td><td>Physician visits for respiratory, cardiovascular, and mental illness</td><td>Particulate matter obtained from monitoring network of the BC Ministry of Water</td><td>A 46% to 78% increase in physician visits for respiratory illness during a 3-week forest fire period in Kelowna, British Columbia</td></tr><tr><td>Malaysia</td><td>Hisham-Hashim et al. 1998 [49]</td><td>1997</td><td>Children</td><td>Lung function</td><td>Not specified</td><td>Decreased lung function in children during vegetation fire episode compared to preepisode measurements</td></tr><tr><td>Malaysia</td><td>Tan et al. 2000 [50]</td><td>1997</td><td>Adult military recruits</td><td>Blood markers of inflammation</td><td>Not specified</td><td>Bone marrow stimulated to release immature polymorphonuclear leukocytes into blood during period of exposure to forest fire smoke relative to period following smoke exposure</td></tr><tr><td>Isfahan rural areas, Iran</td><td>Golshan et al. 2002 [51]</td><td>1–80 years olds</td><td>Adults</td><td>Asthma medication, lung function, asthmatic and other respiratory symptoms</td><td>physician-administered health questionnaire, physical examinations and spirometry in symptomatic cases</td><td>Increased prevalence of respiratory symptoms and various asthma indicators, decreased lung function post-rice stubble burning period relative to period prior to burning in three communities in Iran</td></tr><tr><td>Darwin, Australia)</td><td>Johnston et al. 2002 [52]</td><td>April– 31 October 2000</td><td>All Ages</td><td>Emergency room visits</td><td>Mean atmospheric concentration PM<sub>10</sub> per cubic metre per 24-h period</td><td>Increased asthma visits associated with PM<sub>10</sub>, especially for concentrations exceeding 40 µg/m<sup>3</sup></td></tr><tr><td>California</td><td>Duclos et al. 1990 [53]</td><td>August 1987</td><td>All ages</td><td>Emergency room visits</td><td>descriptive statistics</td><td>Increased respiratory visits in communities exposed to fire smoke</td></tr></tbody></table>
### 3.4.1.1. All-Cause and Cardiorespiratory Mortality

Few studies have evaluated the effects of wildfire emissions on mortality (Table 1). In a study conducted by Vedal and Duton [36], no effect was observed. However, small sample size probably led to the null result. Haenninen [54] showed that Vedal and Duton were not able to detect mortality effects from the observed data because their study could not in any realistic case produce a positive finding, and the expected negative finding should therefore not by any means be considered as evidence of lack of mortality risk from smoke particles.

In 11 Southern Finnish provinces exposed to an additional population-weighted average PM<sub>2.5</sub> level of 15.7 µg/m<sup>3</sup> from wildfire smoke between 26 August and 8 September 2002, an increase of 10 µg/m<sup>3</sup> of urban PM<sub>2.5</sub> was associated with a 0.5% to 2% increase in mortality [55]. A larger study conducted after a widespread series of fires in Indonesia between April and November of 1997 showed high PM<sub>10</sub> levels (&gt;210 µg/m<sup>3</sup>) associated with total non-trauma mortality [37]. When the authors extended the study period from 1994 to 1996 by using visibility instead of PM<sub>10</sub> as the model predictor, severely impaired visibility (about 0.9 km, corresponding with the chosen PM<sub>10</sub> limit value) was associated not only with increased total mortality, but also with increased cardiovascular and infant mortality. Another study conducted by Morgan and colleagues assessed the effects of bushfire smoke on daily mortality and hospital admissions in Sydney, Australia. This study distinguished the effect of bushfire PM<sub>10</sub> from urban PM<sub>10</sub>. Bushfire PM<sub>10</sub> was associated with a small increase in all-cause mortality (0.80%) (95% CI 0.24%–61.86%) but not with respiratory or cardiovascular mortality at lag 0. A recent study conducted by Johnston and colleagues in Sydney investigated the effects of bushfires between 1994 and 2007 and mortality. This study revealed that a 5% increase in non-accidental mortality at lag 1 day (OR 1.05, 95% CI: 1.00–1.10) was observed on days with high air pollution from bushfire smoke [31]. Another recent study conducted by Johnston and colleagues estimated the global mortality attributable to smoke from wildfires and daily and annual exposure to PM<sub>2.5</sub> from fire emissions between 1997 and 2006. The authors combined outputs from a chemical transport model with satellite based-observations of aerosol optical depth (AOD). Study data indicated that the estimated average annual mortality associated with exposure to wild fire smoke was 339,000 deaths worldwide. Regions most affected were sub-Saharan Africa, with 157,000 wildfire related deaths, and South East Asia, with 110,000 wildfire related deaths [56]. This study did have some regional uncertainty in exposure measurements.

### 3.4.1.2. Cardiorespiratory Morbidity

Figure 2 presents the percent increase in adverse health outcomes except mortality as discussed in the literature per various increases in PM. According to the study, this ranged from 1 to 19. Increased respiratory symptoms were reported in connection with the 1997 Southeast Asian haze episode [42]. Ninety-one percent of the interviewed persons reported respiratory symptoms. The evidence of increased cardiovascular symptoms, e.g., palpitations (23% of respondents), is noteworthy. In both the USA and the Asian study, wearing masks was associated with decreased occurrence of symptoms. Aditama *et al.* [57] who studied in another analyze the health impact of this same episode of haze in the Southeast Asian by comparing data during September 1997 and June 1998 to data for 2 years
earlier between 1995 and 1996 found a significant increase of respiratory outcomes during the forest fire period. This 1997 Southeast Asian haze episode was also investigated by Frankenberg et al. [58] who combined data from a population-based longitudinal survey with satellite measures of aerosol levels to assess the impact of smoke on adult health. To account for unobserved differences between haze and nonhaze areas, they compared changes in the health of individual respondents. They found that between 1993 and 1997, individuals who were exposed to haze experienced greater increases in difficulty with activities of daily living than did their counterparts in nonhaze areas. The results for respiratory and general health, although more complicated to interpret, suggest that haze had a negative impact on these dimensions of health.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** % increase¹ of respiratory morbidity outcomes as discussed in the literature per various increases in particle matter (PM) (Mean rate and 95% CI).</figcaption></figure>

Notes: \* Increase in PM is 10 µg/m³, if it's not precised; \*\* increase of 30 µg/m³ of PM₂.₅ in Yao *et al.* (2013) [59]; \*\*\* increase > 20 µg/m³ of PM₁₀ in Chen *et al.* (2006) [23]; \*\*\*\* Elliott *et al.* (2013) [60].

In the Children's Health study conducted in California, USA on the effect of smoke from a 2003 wildfire on respiratory symptoms, questionnaires were given to exposed children (age 6–7 years) and teenagers (age 16–19 years). Both respiratory and eye symptoms were observed to increase in children, and those children with the smallest airways had a 2-fold risk of experiencing respiratory symptoms (morning or nighttime dry cough, wet cough, wheezing) compared to other children [17]. Among teenagers, all symptoms (nose, eyes, throat irritation, cough, bronchitis, cold, wheezing,
and asthma attack) were increased in relation to smoke episodes [16]. Risk increased monotonically with the number of reported smoky days. There was also increased use of medication and physician's visits. Since both exposure assessment and evaluation of the health effects were based on questionnaires in the study, it should be noted that participant recall of symptoms might be biased, as they link exposure with worsening health. In the same year, a study of 465 non-asthmatic teenagers affected by 2003 wildfires in Spain revealed that individuals with smaller airways and poorer pre-existing lung function were more vulnerable to smoke effects [10].

Increased emergency department visits for cardiopulmonary symptoms were associated with exposure to smoke from a peat bog wildfire in North Carolina, USA, when smoke covered 42 rural counties. Daily counts of emergency department visits for all respiratory diagnoses were assessed and were found to be elevated in the exposed counties with a relative risk (RR) equal to 1.66 (95% CI: 1.38–1.91). Significant increase in cumulative RR for asthma (RR = 1.65, 95% CI: 1.25–2.1), COPD (RR = 1.73, 95% CI: 1.06–2.83) and pneumonia and acute bronchitis (RR = 1.59, 95% CI 1.07–2.34) was observed in exposed counties. There also was a significant increase in emergency department visits for cardiopulmonary symptoms (RR = 1.23, 95% CI: 1.06–1.43) and heart failure (RR = 1.37, 95% CI: 1.01–1.85) [18]. Morgan et al. [24] found that a 10 µg/m³ increase in bushfire PM₁₀ was associated with the following: a 1.24% increase (95% CI: 0.22%–2.27%) in all respiratory disease admissions and a 5.02% (95% CI: 1.77%–8.37%) increase in adult asthma admissions at lag 0; and a 3.8% (95% CI: 1.4%–6.26%) increase in COPD admissions at lag 2.

A considerable impact of wildfire smoke on respiratory and cardiovascular health outcomes was established in British Columbia, Canada, when numerous fires burned in 2003. The association over a 92-day study period were examined between respiratory and cardiovascular physician visits and hospital admissions and three measures of smoke exposure: (1) tapered element oscillating microbalance (TEOM) based air quality PM monitoring, (2) smoke-related PM₁₀ from a California puff (CALPUFF) dispersion model, and (3) smoke exposure metric for plumes visible in satellite images [61]. Respiratory physician visits were positively associated with all exposure metrics. A 30 µg/m³ increase in total PM₁₀ based on TEOM was found associated with increased respiratory physician visits (OR = 1.05, 95% CI: 1.03–1.06), asthma specific visits (OR = 1.16, 95% CI: 1.09–1.23) and respiratory related hospital admissions (OR = 1.15, 95% CI: 1.0–1.29) [61]. Limitations of the study included the TEOM air quality monitors measuring PM₁₀ from all sources. While PM₁₀ related to smoke was obtained with the help of the CALPUFF model, this method performs poorly under low wind conditions, when fires are typically smoldering and PM₁₀ levels are high [61].

Elevated respiratory symptoms have also been associated with the burning of agricultural waste. In Winnipeg, Canada, a group of people with mild to moderate airway obstruction received a symptom questionnaire three weeks after a smoke episode caused by burning of straw and stubble. Forty-two percent of subjects reported having new or exacerbated symptoms (cough, wheezing, chest tightness, shortness of breath) during the smoke episode, and 20% reported having breathing trouble [62]. In Iran [51], burning of residues from rice farming was associated with the increased prevalence of respiratory symptoms, e.g., asthma attacks, use of asthma medication, and cough. Symptomatic subjects underwent spirometric testing. Both FEV1 (forced expiratory volume in 1 sec) and PEFR (peak expiratory flow rate) were decreased in association with the rice burning episode.
In Brazil, wildfires have been associated with increased rates of respiratory hospital admissions and emergency room visits (Table 2). Asthmatics in particular, but also those with COPD, are especially susceptible. Because levels of particulate air pollution are clearly more elevated during smoke episodes than the levels of gaseous pollutants, it is reasonable to assume that observed health effects are primarily due to PM. Unfortunately, very little information is available on any link between wildfire PM and cardiovascular hospital admissions. In some studies, effects have been observed [53,63], while in others the effects were stronger for respiratory admissions [13,43].

Using the incidence of daily counts of hospital admissions for respiratory and cardiovascular diagnoses Crabbe *et al*. [64] calculated the risk of hospitalization from wildfire exposure with respect to PM₁₀, in Darwin, Australia. The results suggest that respiratory admissions were associated with exposure to PM₁₀ with a lag of 1 day when adjusted for flu and other confounders (RR = 1.025, 95 % CI: 1.000–1.051, *p* < 0.05). This effect is strongest for exposure to fine particulate matter concentrations (RR = 1.091, 95 % CI: 1.023–1.163, *p* < 0.01) when adjusted for flu. Respiratory admissions were also associated with black carbon concentrations recorded the previous day (RR = 1.0004, 95 % CI: 1.000–1.0008, *p* < 0.05), which did not change strength when adjusted for flu. Cardiovascular admissions had the strongest association with exposure to same-day PM and highest RR for exposure to fine particulate matter when adjusted for confounders (RR = 1.044, 95 % CI: 0.989–1.102).

Martin *et al*. [65] examined the association between validated bushfire smoke pollution events and hospital admissions in three eastern Australian cities from 1994 to 2007. Smoke events were defined as days on which bushfire smoke caused the 24-h citywide average concentration of airborne particles to exceed the 99th percentile of the daily distribution for the study period. They used a time-stratified case-crossover design to assess the association between smoke events and hospital admissions and compared health outcomes on event days with non-event days. This study showed that in Sydney, events were associated with a 6% (OR = 1.06, 95% CI: 1.02–1.09) same day increase in respiratory hospital admissions. Same day chronic obstructive pulmonary disease admissions increased 13% (OR = 1.13, 95% CI: 1.05–1.22) and asthma admissions by 12% (OR = 1.12, 95% CI: 1.05–1.19). Events were also associated with increased admissions for respiratory conditions in Newcastle and Wollongong.

Delfino *et al*. [13] analyzed the relationship between respiratory and cardiovascular hospital admissions and exposure to the southern California wildfires of 2003. Analyses were performed in terms of PM exposure and wildfire period. They found that several respiratory symptoms increased in relation with PM₂.₅ during the wildfire period. For example, asthma admissions across all ages increased by 4.8% (95% CI: 2.1%–7.6%) in relation to PM₂.₅ during the wildfire period. The strongest wildfire-related PM₂.₅ associations with asthma admissions were for the elderly, ages 65–99 years (10.1% increase), and children ages 0–4 years (8.3%), followed by adults ages 20–64 years (4.1%). A small relative increase in admission rates for total cardiovascular outcomes in people aged 45–99 years in relation to PM₂.₅ during the fires was observed. Considering the wildfire period, there were significantly increased risks for all respiratory hospital admissions after the fires compared with the pre-fire period. Admissions increased for all ages by 17% (*p* < 0.001), and in age groups 5–19 years by 37% (*p* < 0.008) and 65–99 years by 15% (*p* < 0.004). Respiratory admissions concerned were for asthma, acute bronchitis and bronchiolitis and pneumonia. There was a 6.1% increased
risk of combined cardiovascular admissions (*p* < 0.05), and an 11.3% increased risk of congestive
heart failure admissions after the fires (*p* < 0.06). However, risk of cardiovascular admissions was
lower during the fires by 4.4%. A recent reviews through 2010 [8] and 2012 [12] provides a
background for respiratory symptoms from wildfire smoke in different regions in the world.
As discussed in this review, although using different methods, all studies looking at Emergency
Department presentations in relation to a wildfire smoke event have found associations and most
studies have also found an association with hospital admissions. However, only a few studies have
distinguished between the effects of wildfire PM₁₀ and background PM₁₀. These studies suggest that
PM₁₀ from wildfire smoke is at least as toxic as urban PM₁₀, but more research is needed.

Some studies used modeling to develop quantitative assessment and prediction of respiratory health outcomes as hist relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. This approach were used by Thelen *et al*. [66] to study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County. Using coupled empirical and deterministic models describing PM emissions and atmospheric dispersion linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization system and a Generalized Additive Modeling (GAM) statistical approach, they found that the model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Other studies addressed the impact of fire smoke but they considered only indoor settings and some of them experimented health impact of exposure wood smoke under controlled environmental conditions. In these studies significant health effects were reported [67–70].

3.4.1.3. Wildfire Exposure during Pregnancy and Birth Weight

Few studies analyzed the impact of wildfire exposure during pregnancy. One of them conducted by Holstius *et al*. [71] analyzed the potential impact of maternal exposure to wildfire emissions from the October 2003 California wildfire on birth weight. Potential wildfire exposure windows were defined using MODIS satellite imagery from 21 October to 10 November 2003. Holstius *et al*. used records for singleton term births delivered to mothers residing in California’s South Coast Air Basin (SoCAB) during 2001–2005 (n = 886,034) and compared birth weights from pregnancies that took place entirely before or after the wildfire event with those pregnancies that occurred during the wildfire. Census tracts located close to air pollution monitors that measured an average daily PM₁₀ level of <40 µg/m³ during the fire were classified as low exposure, and census tracts with average daily PM₁₀ level >40 µg/m³ were classified as high exposure. Of the 886,034 births analyzed, 84.4% (n = 747,590) were unexposed in utero. Of the 138,444 babies that received in utero exposure, 43.5% were exposed in the first trimester, 28.5% were exposed in the second trimester, and 28% were exposed in the third trimester. Adjusted models revealed that mean birth weight was 6.1 g lower (95% CI: −8.7–−3.5) among infants exposed in utero during any trimester compared with unexposed
infants. Among those exposed in the third trimester, there was a reduction of 7 g (95%CI -11.8–-2.2). The largest estimated effect was observed in the second trimester, with a reduction of 9.7 g (95% CI: -14.5–-4.8). Kessler performed separate analyzes on the same birth records of babies delivered between 2001 and 2005 in the South Coast Air Basin in the same period and found the same results [72].

### 3.4.1.4. Subclinical Effects

In contrast to what has been observed for urban air pollution, there is only limited information on the subclinical effects of particles from wildfires. It has generally been assumed that health effect mechanisms are the same for biomass burning PM as for urban PM (e.g., combustion of fossil fuels). However, during wildfires PM concentrations are higher than those usually observed in urban settings so that a greater effect of PM is expected. Indeed we estimated a PM<sub>2.5</sub> concentration 75 higher than the WHO annual mean standard in Mangualde in Spain in 2002 (data not shown). The literature reports that biomass particles are at the origin of acute inflammation, oxidative stress and in analogy with biomass burning for domestic cooking, diminished response to infections.

#### 3.4.1.4.1. Animal Models

In a recent study conducted in a mouse lung exposed to PM from wildfire, a very rapid cytotoxicity occurred in pulmonary macrophages and oxidative stress responses are seen after wildfire coarse PM instillation. Indeed, at 1 h after PM instillation, authors observed cytotoxicity because both decreased numbers of viable macrophages and increased dead macrophage percentages as compared to controls were observed. An increase in free isoprostanes, an indicator of oxidative stress, from control values of 28.1 ± 3.2 pg/mL to 83.9 ± 12.2 pg/mL was observed a half-hour after PM instillation [73].

Another study conducted during the 2008 wildfire season in California on mouse bioassay analyzed the Toxicity of PM<sub>10–2.5</sub> (PM with mass median aerodynamic diameter>2.5 µm and <10µm) and PM<sub>2.5</sub> obtained during the time of peak concentrations of smoke in the air and compared with PM samples collected under normal conditions from the region during the month of June 2007. Results showed that concentrations of PM were higher during the wildfire episodes and much more toxic to the lung on an equal weight basis than was PM collected from normal ambient air in the region. Toxicity was manifested as increased neutrophils and protein in lung lavage and by histologic indicators of increased cell influx and edema in the lung [74].

#### 3.4.1.4.2. Human Beings and Firefighters

One study having evaluated the effect of air pollution from wildfires on systemic inflammation [50], proposed main pathway from exposure to cardiovascular health effects. Among U.S. servicemen, the 1997 Southeast Asian smoke-haze was associated with an increased neutrophil count in the blood, indicative of a bone marrow response to PM<sub>10</sub>. In a Finnish study, conducted among patients, a 10-day forest fire smoke transport episode from Russia increased median values of IL-12 in ischemic heart disease patients’ plasma by 227% but had no effect on other inflammatory markers such as IL-8, C-reactive protein, fibrinogen, white blood cell count and myeloperoxidase in blood circulation [75].
There are no studies on the possible effects of biomass PM on autonomous nervous function, another health effect mechanism proposed for PM.

3.4.1.4.3. Exposure Trials

Other investigations in a controlled human wood smoke exposure assessed the impact of exposure to wood smoke on systemic inflammation, oxidative stress. Wood smoke exposure was controlled by air filters [67,69,70] and climate controlled chamber [68], under different controlled environmental conditions. Indeed, portable air filters were used in a randomized crossover intervention study of 45 healthy adults exposed to consecutive 7-day periods of filtered and nonfiltered air to investigate the impact of wood smoke exposure and the endothelial function and the underlying role of oxidative stress and inflammation in relation to exposure reductions among healthy adults in a wood smoke impacted community. Air filters reduced indoor fine particle concentrations by 60%. Filtration was associated with a 9.4% (95% CI: 0.9%–18%) increase in reactive hyperemia index and a 32.6% (4.4%–60.9%) decrease in C-reactive protein. Decreases in particulate matter and the wood smoke tracer levoglucosan were associated with reduced band cell counts. There was limited evidence of more pronounced effects on endothelial function and level of systemic inflammation among males, overweight participants, younger participants, and residents of wood-burning homes. However, no associations were noted for oxidative stress markers [67].

In order to test the postulate that healthy volunteers exposed to wood smoke particles would demonstrate evidence of both pulmonary and systemic inflammation, ten healthy volunteers were exposed to filtered air and, 3 weeks or more later, to wood smoke particles. Each exposure included alternating 15 min of exercise and 15 min of rest for a total duration of 2 h. Wood smoke was generated by heating an oak log on an electric element and then delivered to the exposure chamber. Endpoints measured in the volunteers included symptoms, pulmonary function tests, measures of heart rate variability and repolarization, blood indices and analysis of cells and fluid obtained during broncho-alveolar lavage. The results showed that at 20 h after wood smoke exposure, blood tests demonstrated an increased percentage of neutrophils, and bronchial and broncho-alveolar lavage revealed a neutrophilic influx. This study conclude that exposure of healthy volunteers to wood smoke particles may be associated with evidence of both systemic and pulmonary inflammation [69].

Another study assessed the effect of systemic inflammation, oxidative stress and microvascular function after controlled wood smoke exposure, was assessed in a randomized, double-blinded, cross-over study among 20 non-smoking atopic subjects were exposed at rest to 14,220, or 354 µg/m³ of particles from a well-burning modern wood stove for 3 h in a climate controlled chamber with 2 week intervals. The investigations have focused on the level of oxidatively damaged DNA, inflammatory markers and adhesion molecules before and 0, 6 and 20 h after exposure. Six hours after exposure, measured microvascular function non-invasively by digital peripheral artery tonometry following arm ischemia was measured. The microvascular function score was unaltered after inhalation of clean air (1.58 ± 0.07; mean ± SEM), low (1.51 ± 0.07) or high (1.61 ± 0.09) concentrations of wood smoke particles in atopic subjects, whereas unexposed non-atopic subjects had higher score (1.91 ± 0.09). The level of oxidatively damaged DNA, mRNA of ITGAL, CCL2, TNF, IL6, IL8, HMOX1, and OGG1 and surface marker molecules ICAM1, ITGAL and L-selectin in
peripheral blood mononuclear cells were not affected by inhalation of wood smoke particles. Exposure to wood smoke had no effect on markers of oxidative stress, DNA damage, cell adhesion, cytokines or MVF in atopic subjects [68].

### 3.4.2. Wildfire Health Effects in Firefighters and Mechanisms

Studies conducted among forest firefighters shed light on the physiological responses to wildfire smoke. Typically, health status right after the work-shift is compared to other time periods, but there is little information on exposure. Reinhardt *et al.* [76] summarized measurements of smoke exposure among wildland firefighters and showed that firefighters can be exposed to significant levels of CO and respiratory irritants, including formaldehyde, acrolein, and respirable particulate matter. Extensive research conducted during forest firefighting in the United States and Australia identified CO and respiratory irritants as the major wildfire pollutants of concern to firefighters [77].

To characterize the acute pulmonary and systemic inflammatory effects of exposure to forest fire smoke, Swiston *et al.* [78], analyzed 52 seasonal forest firefighters recruited before and/or after a day of firefighting. Exposure was assessed by questionnaires and measurement of CO levels. The pulmonary response was assessed by questionnaires, spirometry, and sputum induction. Peripheral blood cell counts and inflammatory cytokines were measured to define the systemic response. The authors found that estimate respirable particulate matter exposure was high (peak levels > 2 mg/m³) during firefighting activities. Respiratory symptoms were reported by 65% of firefighters. The percentage sputum granulocytes increased from 6.5% to 10.9% (*p* < 0.02) following firefighting shifts with concurrent increases in circulating white blood cells (5.55 × 10⁹ to 7.06 × 10⁹ cells/L, *p* < 0.0001) and band cells (0.11 × 10⁹ to 0.16 × 10⁹ cells/L, *p* < 0.01). Serum IL-6, IL-8 and MCP-1 levels increased following firefighting (*p* < 0.05). There were no changes in band cells, IL-6, and IL-8 following strenuous physical exertion without firefighting. There was an association between changes in sputum macrophages containing phagocytized particles and circulating band cells (*p* < 0.05). These results showed that acute exposure to air pollution from forest fire smoke elicits inflammation within the lungs as well as a systemic inflammatory response.

In a study conducted to investigate the effect of occupational woodsmoke exposure on inflammatory biomarkers in firefighters, Twelve U.S. Forest Service wildland firefighters at the Savannah River Site, South Carolina, volunteered to give blood samples during four prescribed burns between February and March 2011. Concurrent personal PM₂.₅ and CO monitoring of firefighters was conducted. IL-8 showed a significant cross-work shift difference as indicated by a post/pre-work shift ratio of 1.70 (95% CI: 1.35–2.13; *p* = 0.0012). Concentrations of IL-8, CRP, and ICAM-1 increased in >50% of samples across work shift. Firefighters who lighted fires as opposed to other work tasks had the largest cross-work shift increase in IL-8. A significant cross-work shift increase in IL-8 in blood samples was observed in healthy wildland firefighters working at prescribed burns [79].

In order to analyze the CO poisoning among firefighters, Brotherhood *et al.* [80] assessed the carboxyhemoglobin saturation (COHb%) levels from alveolar CO levels in 24 firefighters working with hand tools and in 12 accompanying scientific observers, before and after firefighting (duration 37–187 min) on 15 experimental bushfires. Carboxyhemoglobin levels increased on average
by 0.7% per hour in the firefighters and by 0.3% per hour in the observers that indicate that firefighters
are generally unlikely to experience hazardous levels of CO exposure.

Benzene was also measured and found to be well below permissible exposure limits, with the highest concentrations occurring among firefighters working with engines and torches burning petroleum-based fuel. Most of these compounds had a health effects so that smoke inhalation is one of the greatest concerns for firefighter health [81].

Firefighting has been associated with decreased lung function, increased systemic and pulmonary
inflammation, and respiratory symptoms [28,78,82]. By measuring pulmonary functions on
24 non-smoking United States Forest Service (USFS) firefighters and 2 current smokers recruited
during the dormant winter burn seasons of 2003 and 2004, Adetona *et al*. [83] showed that for a given
point in time during the season, each additional day of exposure was estimated to be associated with
declines of 24 mL in pre-shift FVC and 24 mL in pre-shift FEV1 (p < 0.01). Furthermore, cardiovascular
disease is the leading cause of on-duty death among firefighters and a major cause of morbidity [84,85].
According to the 2010 firefighter fatality report of the US Fire Administration, sudden cardiac death
accounts for 49% of total firefighter fatalities at work [86]. Generalizability of the results to the
general population is limited, however, because firefighters are exposed to fresh combustion particles
and gaseous pollutants in the proximity of the fire, whereas populations located further away from fires
are exposed to more aged particles and considerably lower levels of pollutants.

A risk assessment of firefighter exposure during prescribed burns and wildfires was conducted in Australia [41]. This study monitored air toxins within the breathing zone of the firefighters and showed that 30% of firefighters were exposed to high levels of a hazardous substance (i.e., CO, respirable particles, formaldehyde) that exceeded the occupational exposure standard (OES) for 5% to 20% of time. Six percent of the firefighters were observed to have very high exposures (exceeding OES for more than 20% of time). However, the majority of firefighters (60%) had low or moderate exposures. Firefighters patrolling on top of ridges of steep burn areas or in downwind sectors tended to have high (44%-62%) or very high (31%-44%) exposures, with a large percentage of very high exposures experienced by supervisors (17%). This study was limited by the fact that monitoring was performed at only a small fraction of the burns conducted each year across Australia and may not be representative of the extensive program of fuel reduction burns carried out each year. Monitoring was restricted to tanker-based crews and to the later stages of campaign fire and did not include the initial more intense stage of the fires or hand crews. Lastly, neither firefighter work experience was taken into account, assuming that less experienced firefighters may endure higher smoke conditions, nor climatic factors influencing the smoke plume.

In another study, 64 career firefighters from the Fire Emergency Service Authority of Western Australia were allocated one of three filter types (particulate, organic vapor or organic vapor/formaldehyde). Data on spirometer, audiometry, self-reported symptoms and personal air sampling were collected before, during and after exposure to bushfire smoke from prescribed burns. Declines in lung function (Forced Expiratory Volume in One Second (FEV1) and oxygen saturation (SaO₂)) were demonstrated with exposure. Firefighters in the particulate filter group reported significantly more respiratory symptoms compared to the other filter groups after 60 and 120 min of exposure, respectively (67% and 64%, particle group; 22% and 18%, organic vapor group; 11% and 18%, organic vapor/formaldehyde group) [87].
A screening health risk assessment was performed to assess the upper-bound risks of cancer and noncancer adverse health effects among wildland firefighters performing wildfire suppression and prescribed burn management. Of the 15 substances in smoke that were evaluated, only benzene and formaldehyde posed a cancer risk greater than 1 per million, while only acrolein and respirable particulate matter exposures resulted in hazard indices greater than 1.0. The estimated upper-bound cancer risks ranged from 1.4 to 220 excess cancers per million, and noncancer hazard indices ranged from 9 to 360, depending on the exposure group. These values only indicate the likelihood of adverse health effects, not whether they will or will not occur [88].

Concerning the heart attack among firefighters, it's was reported by Aisebett *et al.*, in a study on wildfire suppression and its impact on firefighters' health in Australia, that fire agency may not completely illustrate the cardiovascular strains of firefighting. Fire agencies do not collect data on cardiac events that occur after the firefighter has completed their assigned shift. Furthermore, early warning signs such as chest pain/angina, increased fatigue, sensation of indigestion/heartburn, and excessive breathlessness that often precede fatal cardiac events are frequently ignored and not reported. The risk of a cardiac event during physical exertion is increased for individuals who possess two or more cardiovascular risk factors [89].

In order to protect firefighters against high particulate exposures which have been demonstrated to decrease lung function among firefighters, Edwards *et al.* [90] demonstrated the feasibility of using small real-time particle sensors to inform wildland firefighters so they may make informed decisions on the use of personal respiratory protection. Using 1 mg/m³ as an indicator point for use of appropriately designed respiratory protection; such sensors could help prevent 16% to 74% of particulate exposure during prescribed burns when firefighters assess exposure as low or medium. Adherence to such a guideline for the use of respiratory protection would involve its deployment during 3% to 22% of individual 8-hour shifts. In addition, data-logging sensors would provide a valuable tool for tracking exposure to particulates among wildland firefighters for occupational health monitoring [90].

## 4. Public Awareness

Even when technical solutions exist to reduce exposure to wildfire smoke, the effectiveness of an intervention largely depends on public awareness and compliance with air pollution advisories. A recent USA study demonstrated that only 10-15% of those aware of public alerts for hot weather and urban air pollution episodes actually changed their behavior [91]. It was the personal perception of poor air quality or high temperature, rather than public advisories that typically led to the change in behavior.

It is unclear how effective public air pollution warning systems are in the case of wildfire smoke. Deterioration of air quality is usually easy to recognize, and a possible link between irritant symptoms with simultaneous high smoke concentrations can be identified. At the same time, there is probably a tendency to consider smoke from wildfires as something “natural” and thus, less harmful than, for example, vehicle exhaust. In a study conducted during a wildfire smoke episode among residents of Hoopa Valley National Indian Reservation in California [35], 66% of those residents who recalled a public service announcement took some action to reduce exposure. Most residents (83%) stayed indoors, while 16% left the area, and only 1% used an air filter, likely because air filters are not
common in homes. During the episode, ambient PM₁₀ concentrations exceeded the US EPA's 24-h air quality standard (150 µg/m³) for 15 days.

**5. Discussion**

Wildfires can cause significant health effects both in the population in the immediate vicinity as well as in those far from the fire [12] and several studies have sought to establish the link between exposure to wildfire and health outcomes. Among wildfire emissions, PM₁₀ and PM₂.₅ are the most studied as those having various effects on human health [7]. Furthermore, local daily and hourly PM₂.₅ and PM₁₀ concentrations can increase dramatically from a wildfire even if it is located hundreds of kilometers away because of long-transportation of the aerosol [38,92]. Different methods have been used to measure wildfire exposure but only the most recent enable us to avoid exposure miss-classification. In terms of health, several studies have found a significant association between PM and respiratory symptoms [16,18,35], increased respiratory hospital admissions [32] and increased emergency department visits. Studies have also found an association between daily mortality from wildfires for all-causes of death, including cardiovascular disease [37]. The effects of exposure to wildfire on cardiovascular disease and mortality, however, have not been sufficiently studied to support general conclusions [19].

Not everyone who is exposed to thick smoke will have health problems. The level and duration of exposure, age, individual susceptibility, including the presence or absence of pre-existing lung or heart disease, and other factors play significant roles in determining whether someone will experience smoke-related health problems [1]. Indeed, the elderly, people with pre-existing cardiopulmonary conditions, smokers and, for professional reasons, firefighters, may experience more severe short-term and chronic symptoms [13,41,47]. In addition, individuals with smaller airways may be more susceptible to the respiratory health effects of wildfire smoke [10]. Some epidemiological studies have observed various health effects among these specific groups [13,19,41,47]. More research is needed to evaluate long term health effects from exposure to wildfires.

Two previous reviews had dealt with health impact of wildfires [10,12]. In contrast with these reviews, our review covered more largely the literature on non-accidental health impact and included firefighters, a vulnerable subgroup exposed to fires for professional reasons. Existing data show that in addition to potential injuries, firefights may be concerned by various non-accidental health effects.

A limitation in all studies is exposure assessment. Even for conclusive studies, there is a large uncertainty on the evaluation of personal exposure because there is difficulty in distinguishing wildfire emissions from emissions due to other sources of pollution [8,11,14]. In addition, although available information suggests that wildfire smoke penetrates readily into indoor environments during air pollution episodes caused by wildfires, this has not been measured objectively. Lastly, there are no published studies on personal exposures to PM from wildfires in community settings.

Another limitation results from the paucity of statistical methods that have been used to relate wildfire exposure to health conditions. Other methods that could be applied include semi-parametric regressions for estimating associations between day-to-day variations in air pollution and mortality after controlling for confounding [93,94]. Such methods using Generalized Additive Model (GAM) to time-series health outcomes like mortality, morbidity, or hospitalization and studying the impact of PM
is well established in the last decade with numerous studies cited in the literature [93–96]. Hierarchical
Models for estimating: National-average relative rates, national-average exposure-response relationship
exploring heterogeneity of air pollution effects across the country are also possible [97]. Longitudinal
methods have to be implemented to evaluate long term health effects from exposure to wildfires.
However these methods which generally imply following individuals over time could be limited by the
period and the variation of wildfire exposure area. In summary, various studies have established the
relationship between one of the major components of wildfire, PM₁₀ and PM₂.₅, and cardiorespiratory
in terms of hospital emergency rooms visits and hospital admissions. Associations between wildfire
emissions and mortality or other diseases have been less investigated. Prevention against fires has to be
implemented to protect the population, in particular the segments of the population vulnerable to
smoke-related health risks, such as the elderly, people with pre-existing cardiopulmonary conditions,
smokers and firefighters.

**6. Conclusions**

This review has demonstrated the impact of wildfire emissions on public health in terms of non-accidental effects at population level and among vulnerable subgroups. The main challenge for epidemiological studies that evaluate the impact of wildfire emissions during a given event is the estimation of personal exposure. So far, most of the used methods have not distinguished air pollution from wildfires from other source-oriented pollution. Various statistical approaches can be used in the epidemiological studies to take into account this uncertainty. However, so few have been applied. Main health effects of wildfire smoke are cardiorespiratory and associations between exposure to wildfire emissions and increasing of hospital admissions and emergency room visits for respiratory and cardiovascular diseases has been observed in the most studies. Few studies reported on cardiorespiratory mortality. Only a small number of studies found other non-accidental effects like the reduction of birth weight. However, various subclinical effects have been associated to wildfire exposure. Some these effects were tested healthy volunteers using a controlled human exposure, animal exposure or *in vitro* studies.

**Acknowledgments**

This publication arises from the project PHASE (contract number EAHC 20101103) which has
received funding from the European Union, in the framework of the Health Programme.

**Author Contributions**

Isabella Annesi-Maesano conceptualized the study. Youssouf Hassani and Isabella Annesi-Maesano searched the articles, collected the data and drafted the manuscript. Soutrik Banerjee implemented the discussion on statistical methods. All co-authors read, edited and commented the paper.

**Conflicts of Interest**

The authors declare no conflict of interest.
References

1. Lipsett, M.; Materna, B.; Lyon Stone, S.; Therriault, S.; Blaisdell, R.; Cook, J. *Wildfire Smoke—A Guide for Public Health Officials*; California Department of Public Health: Sacramento, CA, USA; U.S. Environmental Protection Agency: Washington, DC, USA; California Office of Environmental Health Hazard Assessment: Sacramento, CA, USA; Missoula County Health Department: Missoula, MT, USA; California Air Resources Board: Sacramento, CA, USA, 2008.

2. European Commission. *Forest Fires in Europe 2010*; Institute for Environment and Sustainability: Ispra, Italy, 2010.

3. Ottmar, R.D. Smoke source characteristics. In *Smoke Management Guide for Prescribed and Wildland Fire*, 2001 ed.; Hardy, C.C., Ottmar, R.D., Peterson, J.L., Core, J.E., Seamon, P., Eds.; National Wildfire Coordinating Group, Fire Use Working Team: Boise, ID, USA, 2001; pp. 89–105.

4. Gyawali, M.; Arnott, W.P.; Lewis, K.; Moosmüller, H. *In situ* aerosol optics in Reno, NV, 5 USA during and after the summer 2008 California wildfires and the influence of absorbing and non-absorbing organic coatings on spectral light absorption. *Atmos. Chem. Phys.* **2009**, *9*, 8007–8015.

5. WHO Regional Office for Europe. *Health Risks of Particulate Matter from Long-Range Transboundary Air Pollution*; European Centre for Environment and Health: Bonn, Germany, 2006.

6. WHO Regional Office for Europe. *Health Effects of Particulate Matter*; WHO: Copenhagen, Denmark, 2013.

7. World Health Organization. *Health Guidelines for Vegetation Fire Events*; United Nations Environment Programme, WHO: Geneva, Switzerland, 1999.

8. Dennekamp, M.; Abramson, M.J. The effects of bushfire smoke on respiratory health. *Respirology* **2011**, *16*, 198–209.

9. U.S. Environmental Protection Agency. National ambient air quality standards for particulate matter. *Fed. Register* **2013**, *78*, 3086–3287.

10. Malilay, J. *A Review of Factors Affecting the Human Health Impacts of Air Pollutants from Forest Fires*; WHO: Geneva, Switzerland; UNEP: Nairobi, Kenya; WMO: Geneva, Switzerland, 1999; pp. 255–270.

11. Youssouf, H.; Lioussec, C.; Roblouc, L.; Assamoic, E.M.; Salonend, R.O.; Maesanoa, C.; Banerjeea, S.; Annesi-Maesano, I. Quantifying wildfires exposure for investigating health-related effects. *Atmos. Environ.* **2014**, *97*, 239–251.

12. Finlay, S.E.; Moffat, A.; Gazzard, R.; Baker, D.; Murray, V. Health impacts of wildfires. *PLoS Curr. 2012*, *4*, doi:10.1371/4f959951cce2c.

13. Delfino, R.J.; Brummel, S.; Wu, J.; Stern, H.; Ostro, B.; Lipsett, M.; Winer, A.; Street, D.H.; Zhang, L.; Tjoa, T.; et al. The relationship of respiratory and cardiovascular hospital admissions to the Southern California wildfires of 2003. *Occup. Environ. Med.* **2009**, *66*, 189–197.

14. Henderson, S.B.; Johnston, F.H. Measures of forest fire smoke exposure and their associations with respiratory health outcomes. *Curr. Opin. Allergy Clin. Immunol.* **2012**, *12*, 221–227.
15. Jalaludin, B.; Smith, M.; O'Toole, B.; Leeder, S. Acute effects of bushfires on peak expiratory flow rates in children with wheeze: A time series analysis. *Aust. N. Z. J. Public Health 2000*, *24*, 174-177.

16. Künzli, N.; Avol, E.; Wu, J.; Gauderman, W.J.; Rappaport, E.; Millstein, J.; Bennion, J.; McConnell, R.; Gilliland, F.D.; Berhane, K.; et al. Health effects of the 2003 Southern California wildfires on children. *Amer. J. Respir. Crit. Care Med. 2006*, *174*, 1221-1228.

17. Mirabelli, M.C.; ünzli, N.; Avol, E.; Gilliland, F.D.; Gauderman, W.J.; McConnell, R.; Peters, J.M. Respiratory symptoms following wildfire smoke exposure: Airway size as a susceptibility factor. *Epidemiology 2009*, *20*, 451-459.

18. Rappold, A.G.; Stone, S.L.; Cascio, W.E.; Neas, L.M.; Kilaru, V.J.; Carraway, M.S.; Szykman, J.J.; Ising, A.; Cleve, W.E.; Meredith, J.T.; et al. Peat bog wildfire smoke exposure in rural North Carolina is associated with cardiopulmonary emergency department visits assessed through syndromic surveillance. *Environ. Health Perspect. 2011*, *119*, 1415-1420.

19. Naeher, L.P.; Brauer, M.; Lipsett, M.; Zelikoff, J.T.; Simpson, C.D.; Koenig, J.Q.; Smith, K.R. Woodsmoke health effects: A review. *Inhal. Toxicol. 2007*, *19*, 67-106.

20. Greven, F.E.; Rooyackers, J.M.; Kerstjens, H.A.; Heederik, D.J. Respiratory symptoms in firefighters. *Amer. J. Ind. Med. 2011*, *54*, 350-355.

21. Caamano-Isorna, F.; Figueiras, A.; Sastre, I.; Montes-Martínez, A.; Taracido, M.; Piñeiro-Lamas, M. Respiratory and mental health effects of wildfires: An ecological study in Galician municipalities (north-west Spain). *Environ. Health 2011*, *10*, doi:10.1186/1476-069X-10-48.

22. Kolbe, A.; Gilchrist, K.L. An extreme bushfire smoke pollution event: health impacts and public health challenges. *N. S. W. Public Health Bull. 2009*, *20*, 19-23.

23. Chen, L.; Verrall, K.; Tong, S. Air particulate pollution due to bushfires and respiratory hospital admissions in Brisbane, Australia. *Int. J. Environ. Health Res. 2006*, *16*, 181-191.

24. Morgan, G.; Sheppeard, V.; Khalaj, B.; Ayyar, A.; Lincoln, D.; Jalaludin, B.; Beard, J.; Corbett, S.; Lumley, T. Effects of bushfire smoke on daily mortality and hospital admissions in Sydney, Australia. *Epidemiology 2010*, *21*, 47-55.

25. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P.J. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. *Environ. Health Perspect. 2010*, *118*, 847-855.

26. Hodzic, A.; Madronich, S.; Bohn, B.; Massie, S.; Menut, L.; Wiedinmyer, C. Wildfire particulate matter in Europe during summer 2003: Meso-scale modeling of smoke emissions, transport and radiative effects. *Atmos. Chem. Phys. 2007*, *7*, 4043-4064.

27. Liousse, C.; Guillaume, B.; Grégoire, J.M.; Mallet, M.; Galy, C.; Pont, V.; Akpo, A.; Bedou, M.; Castéra, P.; Dungall, L.; et al. Updated African biomass burning emission inventories in the framework of the AMMA-IDAF program, with an evaluation of combustion aerosols. *Atmos. Chem. Phys. 2010*, *10*, 9631-9646.

28. Betchley, C.; Koenig, J.Q.; van Belle, G.; Checkoway, H.; Reinhardt, T. Pulmonary function and respiratory symptoms in forest firefighters. *Amer. J. Ind. Med. 1997*, *31*, 503-509.

29. Analitis, A.; Georgiadis, I.; Katsouyanni, K. Forest fires are associated with elevated mortality in a dense urban setting. *Occup. Environ. Med. 2012*, *69*, 158-162.
30. Arbex, M.A.; Martins, L.C.; de Oliveira, R.C.; Pereira, L.A.; Arbex, F.F.; Cançado, J.E.; Saldiva, P.H.; Braga, A.L. Air pollution from biomass burning and asthma hospital admissions in a sugar cane plantation area in Brazil. *J. Epidemiol. Community Health* **2007**, *61*, 395-400.

31. Johnston, F.; Hanigan, I.; Henderson, S.; Morgan, G.; Bowman, D. Extreme air pollution events from bushfires and dust storms and their association with mortality in Sydney, Australia 1994–2007. *Environ. Res.* **2011**, *111*, 811–816.

32. Ignotti, E.; Valente, J.G.; Longo, K.M.; Freitas, S.R.; de Souza Bacon, S.; Netto, P.A. Impact on human health of particulate matter emitted from burnings in the Brazilian Amazon region. *Rev. Saúde Públ.* **2010**, *44*, 121-130.

33. Gasparrini, A. *Distributed lag linear and non-linear models in R: The package dlnm*. *J Stat. Softw* **2011**, *43*, 1–20.

34. Fowler, C.T. Human health impacts of forest fires in the southern United States: A literature review. *J. Ecol. Anthropol.* **2003**, *7*, 39–63.

35. Mott, J.A.; Mannino, D.M.; Alverson, C.J.; Kiyu, A.; Hashim, J.; Lee, T.; Falter, K.; Redd, S.C. Cardiorespiratory hospitalizations associated with smoke exposure during the 1997, southeast Asian forest fires. *Int. J. Hyg. Environ. Health* **2005**, *208*, 75-85.

36. Vedal, S.; Dutton, S.J. Wildfire air pollution and daily mortality in a large urban area. *Environ. Res.* **2006**, *102*, 29-35.

37. Sastry, N. Forest fires, air pollution, and mortality in southeast Asia. *Demography* **2002**, *39*, 1-23.

38. Emmanuel, S.C. Impact to lung health of haze from forest fires: The Singapore experience. *Respirology* **2000**, *5*, 175-182.

39. Tham, R.; Erbas, B.; Akram, M.; Dennekamp, M.; Abramson, M.J. The impact of smoke on respiratory hospital outcomes during the 2002–2003 bushfire season, Victoria, Australia. *Respirology* **2009**, *14*, 69–75.

40. Ovadnevaitė, J.; Kvietkus, K.; Maršalka, A. 2002 summer fires in Lithuania: Impact on the Vilnius city air quality and the inhabitants health. *Sci. Total Environ.* **2006**, *356*, 11–21.

41. Reisen, F.; Hansen, D.; Meyer, C.P.M. Exposure to bushfire smoke during prescribed burns and wildfires: Firefighters' exposure risks and options. *Environ. Int.* **2011**, *37*, 314-321.

42. Kunii, O.; Kanagawa, S.; Yajima, I.; Hisamatsu, Y.; Yamamura, S.; Amagai, T.; Ismail, I.T. The 1997 haze disaster in Indonesia: Its air quality and health effects. *Arch. Environ. Health.* **2002**, *57*, 16-22.

43. Hanigan, I.C.; Johnston, F.H.; Morgan, G.G. Vegetation fire smoke, indigenous status and cardio-respiratory hospital admissions in Darwin, Australia, 1996–2005: A time-series study. *Environ. Health* **2008**, *7*, doi:10.1186/1476-069X-7-42.

44. Sorensen, B.; Fuss, M.; Mulla, Z.; Bigler, W.; Wiersma, S.; Hopkins, R. Surveillance of morbidity during wildfires—Central Florida, 1998. *MMWR* **1999**, *48*, 78–79.

45. Brauer, M. *Health Impacts of Biomass Air Pollution*; Regional Offices for Southeast Asia and the Western Pacific, World Health Organization: Kuala Lumpur, Malesia, 1998.

46. Chew, F.T.; Ooi, B.C.; Hui, J.K.; Saharom, R.; Goh, D.Y.; Lee, B.W. Singapore's haze and acute asthma in children. *Lancet* **1995**, *346*, doi:10.1016/S0140-6736(95)92443-4.
47. Sutherland, E.R.; Make, B.J.; Vedal, S.; Zhang, L.; Dutton, S.J.; Murphy, J.R.; Silkoff, P.E. Wildfire smoke and respiratory symptoms in patients with chronic obstructive pulmonary disease. *J. Allergy Clin. Immunol.* **2005**, *115*, 420–422.

48. Moore, D.; Copes, R.; Fisk, R.; Joy, R.; Chan, K.; Brauer, M. Population health effects of air quality changes due to forest fires in British Columbia in 2003: Estimates from physician-visit billing data. *Can. J. Public Health* **2006**, *97*, 105–108.

49. Hisham-Hashim, J.; Hashim, Z.; Jalaludin, J.; Lubis, S.H.; Hashim, R. Respiratory function of elementary school children exposed to the 1997 Kuala Lumpur haze. *Epidemiology* **1998**, *9*, doi:10.1097/00001648-199807001-00330.

50. Tan, W.C.; Qiu, D.; Liam, B.L.; Ng, T.P.; Lee, S.H.; van Eeden, S.F.; D'Yachkova, Y.; Hogg, J.C. The human bone marrow response to acute air pollution caused by forest fires. *Amer. J. Respir. Crit. Care Med.* **2000**, *161*, 1213–1217.

51. Golshan, M.; Faghihi, M.; Roushan-Zamir, T.; Masood Marandi, M.; Esteki, B.; Dadvand, P.; Farahmand-Far, H.; Rahmati, S.; Islami, F. Early effects of burning rice farm residues on respiratory symptoms of villagers in suburbs of Isfahan, Iran. *Int. J. Environ. Health Res.* **2002**, *12*, 125–131.

52. Johnston, F.H.; Kavanagh, A.M.; Bowman, D.M.; Scott, R.K. Exposure to bushfire smoke and asthma: an ecological study. *Med. J. Aust.* **2002**, *176*, 535–538.

53. Duclos, P.; Sanderson, L.M.; Lipsett, M. The 1987 forest fire disaster in California: Assessment of emergency room visits. *Arch. Environ. Health* **1990**, *45*, 53–58.

54. Haenninen, O. Challenges in estimating the health effects of biomass smoke—Response to Sverre Vedal and Steven J. Dutton: Wildfire air pollution and daily mortality in a large urban area—Commentary. *Environ. Res.* **2008**, *106*, 423–424.

55. Hanninen, O.O.; Salonen, R.O.; Koistinen, K.; Lanki, T.; Barregard, L.; Jantunen, M. Population exposure to fine particles and estimated excess mortality in Finland from an east European wildfire episode. *J. Expo. Sci. Environ. Epidemiol.* **2009**, *19*, 414–422.

56. Johnston, F.H.; Henderson, S.B.; Chen, Y.; Randerson, J.T.; Marlier, M.; Defries, R.S.; Kinney, P.; Bowman, D.M.; Brauer, M. Estimated global mortality attributable to smoke from landscape fires. *Environ. Health Perspect.* **2012**, *120*, 695–701.

57. Aditama, T.Y. Impact of haze from forest fire to respiratory health: Indonesian experience. *Respirology* **2000**, *5*, 169–174.

58. Frankenberg, E.; McKee, D.; Thomas, D. Health consequences of forest fires in Indonesia. *Demography* **2005**, *42*, 109–129.

59. Yao, J.; Brauer, M.; Henderson, S.B. Evaluation of a wildfire smoke forecasting system as a tool for public health protection. *Environ. Health Perspect.* **2013**, *121*, 1142–1147.

60. Elliott, C.T.; Henderson, S.B.; Wan, V. Time series analysis of fine particulate matter and asthma reliever dispensations in populations affected by forest fires. *Environ. Health* **2013**, *12*, doi:10.1186/1476-069X-12-11.

61. Henderson, S.B.; Brauer, M.; Macnab, Y.C.; Kennedy, S.M. Three measures of forest fire smoke exposure and their associations with respiratory and cardiovascular health outcomes in a population-based cohort. *Environ. Health Perspect.* **2011**, *119*, 1266–1271.
62. Long, W.; Tate, R.B.; Neuman, M.; Manfreda, J.; Becker, A.B.; Anthonisen, N.R. Respiratory symptoms in a susceptible population due to burning of agricultural residue. *Chest* **1998**, *113*, 351–357.

63. Johnston, F.H.; Bailie, R.S.; Pilotto, L.S.; Hanigan, I.C. Ambient biomass smoke and cardio-respiratory hospital admissions in Darwin, Australia. *BMC Public Health* **2007**, *7*, doi:10.1186/1471-2458-7-240.

64. Crabbe, H. Risk of respiratory and cardiovascular hospitalisation with exposure to bushfire particulates: New evidence from Darwin, Australia. *Environ. Geochem. Health* **2012**, *34*, 697–709.

65. Martin, K.L.; Hanigan, I.C.; Morgan, G.G.; Henderson, S.B.; Johnston, F.H. Air pollution from bushfires and their association with hospital admissions in Sydney, Newcastle and Wollongong, Australia 1994–2007. *Aust. N. Z. J. Public Health* **2013**, *37*, 238–243.

66. Thelen, B.; French, N.F.N.; Koziol, B.W.; Billmire, M.; Owen, R.C.; Johnson, J.; Ginsberg, M.; Loboda, T.; Wu, S. Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling. *Environ. Health* **2013**, *12*, doi:10.1186/1476-069X-12-94.

67. Allen, R.W.; Carlsten, C.; Karlen, B.; Leckie, S.; van Eeden, S.; Vedal, S.; Wong, I.; Brauer, M. An air filter intervention study of endothelial function among healthy adults in a woodsmoke-impacted community. *Amer. J. Respir. Crit. Care Med.* **2011**, *183*, 1222–1230.

68. Forchhammer, L.; Møller, P.; Riddervold, I.S.; Bønløkke, J.; Massling, A.; Sigsgaard, T.; Loft, S. Controlled human wood smoke exposure: Oxidative stress, inflammation and microvascular function. *Part Fibre Toxicol.* **2012**, *9*, doi:10.1186/1743-8977-9-7.

69. Ghio, A.J.; Soukup, J.M.; Case, M.; Dailey, L.A.; Richards, J.; Berntsen, J.; Devlin, R.B.; Stone, S.; Rappold, A. Exposure to wood smoke particles produces inflammation in healthy volunteers. *Occup. Environ. Med.* **2012**, *69*, 170–175.

70. Riddervold, I.S.; Bønløkke, J.H.; Mølhave, L.; Massling, A.; Jensen, B.; Grønborg, T.K.; Bossi, R.; Forchhammer, L.; Kjærgaard, S.K.; Sigsgaard, T.; et al. Wood smoke in a controlled exposure experiment with human volunteers. *Inhal. Toxicol.* **2011**, *23*, 277–288.

71. Holstius, D.M.; Reid, C.E.; Jesdale, B.M.; Morello-Frosch, R. Birth weight following pregnancy during the 2003 Southern California wildfires. *Environ. Health Perspect.* **2012**, *120*, 1340–1345.

72. Kessler, R. Followup in Southern California: Decreased birth weight following prenatal wildfire smoke exposure. *Environ. Health Perspect.* **2012**, *120*, doi:10.2307/41601744.

73. Williams, K.M.; Franzi, L.M.; Last, J.A. Cell-specific oxidative stress and cytotoxicity after wildfire coarse particulate matter instillation into mouse lung. *Toxicol. Appl. Pharmacol.* **2013**, *266*, 48–55.

74. Wegesser, T.C.; Pinkerton, K.E.; Last, J.A. California wildfires of 2008: Coarse and fine particulate matter toxicity. *Environ. Health Perspect.* **2009**, *117*, 893–897.

75. Huttunen, K.; Siponen, T.; Salonen, I.; Yli-Tuomi, T.; Aurela, M.; Dufva, H.; Hillamo, R.; Linkola, E.; Pekkanen, J.; Pennanen, A.; et al. Low-level exposure to ambient particulate matter is associated with systemic inflammation in ischemic heart disease patients. *Environ. Res.* **2012**, *116*, 44–51.

76. Reinhardt, T.E.; Ottmar, R.D. Baseline measurements of smoke exposure among wildland firefighters. *J. Occup. Environ. Hyg.* **2004**, *1*, 593–606.
77. Aisbett, B.; Wolkow, A.; Sprajcer, M.; Ferguson, S.A. "Awake, smoky, and hot": Providing an evidence-base for managing the risks associated with occupational stressors encountered by wildland firefighters. *Appl. Ergon.* **2012**, *43*, 916–925.

78. Swiston, J.R.; Davidson, W.; Attridge, S.; Li, G.T.; Brauer, M.; van Eeden, S.F. Wood smoke exposure induces a pulmonary and systemic inflammatory response in firefighters. *Eur. Respir. J.* **2008**, *32*, 129–138.

79. Hejl, A.M.; Adetona, O.; Diaz-Sanchez, D.; Carter, J.D.; Commodore, A.A.; Rathbun, S.L.; Naeher, L.P. Inflammatory effects of woodsmoke exposure among wildland firefighters working at prescribed burns at the Savannah River Site, SC. *J. Occup. Environ. Hyg.* **2013**, *10*, 173–180.

80. Brotherhood, J.R.; Budd, G.M.; Jeffery, S.E.; Hendrie, A.L.; Beasley, F.A.; Costin, B.P.; Wu, Z.E. Fire fighters' exposure to carbon monoxide during Australian bushfires. *Amer. Ind. Hyg. Assoc. J.* **1990**, *51*, 234–240.

81. Leonard, S.S.; Castranova, V.; Chen, B.T.; Schwegler-Berry, D.; Hoover, M.; Piacitelli, C.; Gaughan, D.M. Particle size-dependent radical generation from wildland fire smoke. *Toxicology* **2007**, *236*, 103–113.

82. Van Eeden, S.F.; Tan, W.C.; Suwa, T.; Mukae, H.; Terashima, T.; Fujii, T.; Qui, D.; Vincent, R.; Hogg, J.C. Cytokines involved in the systemic inflammatory response induced by exposure to particulate matter air pollutants (PM₁₀). *Amer. J. Respir. Crit. Care Med.* **2001**, *164*, 826–830.

83. Adetona, O.; Hall, D.B.; Naeher, L.P. Lung function changes in wildland firefighters working at prescribed burns. *Inhal. Toxicol.* **2011**, *23*, 835–841.

84. Soteriades, E.S.; Smith, D.L.; Tsismenakis, A.J.; Baur, D.M.; Kales, S.N. Cardiovascular disease in U.S. firefighters: A systematic review. *Cardiol Rev.* **2011**, *19*, 202–215.

85. Yang, J.; Teehan, D.; Farioli, A.; Baur, D.M.; Smith, D.; Kales, S.N. Sudden cardiac death among firefighters ≤45 years of age in the United States. *Amer. J. Cardiol.* **2013**, *112*, 1962–1967.

86. Choi, B.; Schnall, P.L.; Dobson, M.; Garcia-Rivas, J.; Kim, H.; Zaldivar, F.; Israel, L.; Baker, D. Very long (>48 h) shifts and cardiovascular strain in firefighters: A theoretical framework. *Ann. Occup. Environ. Med.* **2014**, *26*, doi:10.1186/2052-4374-26-5.

87. De Vos, A.J.; Cook, A.; Devine, B.; Thompson, P.J.; Weinstein, P. Effect of protective filters on fire fighter respiratory health during simulated bushfire smoke exposure. *Amer. J. Ind. Med.* **2006**, *49*, 740–750.

88. Booze, T.F.; Reinhardt, T.E.; Quiring, S.J.; Ottmar, R.D. A screening-level assessment of the health risks of chronic smoke exposure for wildland firefighters. *J. Occup. Environ. Hyg.* **2004**, *1*, 296–305.

89. Aisbett, B.; Phillips, M.; Sargeant, M.; Gilbert, B.; Nichols, D. Fighting with fire—How bushfire suppression can impact on fire fighters' health. *Aust. Fam. Physician* **2007**, *36*, 994–997.

90. Edwards, R.; Johnson, M.; Dunn, K.H.; Naeher, L.P. Application of real-time particle sensors to help mitigate exposures of wildland firefighters. *Arch. Environ. Occup. Health* **2005**, *60*, 40–43.

91. Semenza, J.C.; Wilson, D.J.; Parra, J.; Bontempo, B.D.; Hart, M.; Sailor, D.J.; George, L.A. Public perception and behavior change in relationship to hot weather and air pollution. *Environ. Res.* **2008**, *107*, 401–411.
92. Sillanpää, M.; Saarikoski, S.; Hillamo, R.; Pennanen, A.; Makkonen, U.; Spolnik, Z.; van Grieken, R.; Koskentalo, T.; Salonen, R.O. Chemical composition, mass size distribution and source analysis of long-range transported wildfire smokes in Helsinki. *Sci. Total Environ.* **2005**, *350*, 119–135.

93. Dominici, F.; McDermott, A.; Zeger, S.L.; Samet, J.M. On the use of generalized additive models in time-series studies of air pollution and health. *Amer. J. Epidemiol.* **2002**, *156*, 193–203.

94. Kim, S.-Y.; Peel, J.L.; Hannigan, M.P.; Dutton, S.J.; Sheppard, L.; Clark, M.L.; Vedal, S. The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations. *Environ. Health Perspect.* **2012**, *120*, 1094–1099.

95. Dominici, F.; Peng, R.D.; Bell, M.L.; Pham, L.; McDermott, A.; Zeger, S.L.; Samet, J.M. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. *JAMA* **2006**, *295*, 1127–1134.

96. Fraga, J.; Botelho, A.; Sá, A.; Costa, M.; Quaresma, M. The lag structure and the general effect of ozone exposure on pediatric respiratory morbidity. *Int. J. Environ. Res. Public Health* **2011**, *8*, 4013–4024.

97. Peng, R.D.; Dominici, F. *Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health Series*; Springer: New York, NY, USA, 2008.

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