Environmental factors and asthma hospitalization in Montreal, Canada, during spring 2006–2008: a synergy perspective
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The aim of this study is to analyze the synergy between environmental factors (pollutant, allergenic birch pollen, weather) and its relationship with asthma hospitalization in Montreal, Canada. The data is stratified into weather types and the study restricted to spring season to limit the impact of multiple confounders. Results shows that the daily count of asthma hospitalization (spring 2006–2008) in situation of warm fronts or trowals (daily average of 3.78 counts, CI 95% 2.95–4.61) was much higher (p < 0.001) than in other situations (2.49 counts, CI 95% 2.37–2.71). Moreover, the explained variance of asthma hospitalization due to air pollution rises from about less than 7% (in the case of no stratification) to about 28% (R = 0.53, p < 0.05 with stratification). Statistical tests for interaction and overall results point towards a synergy between environmental factors which exacerbates asthma. A new concept named frontal asthma is proposed to explain several results found here and in the open literature.
KeywordsEnvironmental pollution Allergenic pollen Birch (Betula spp.) Asthma hospitalization Weather type Warm front Trowal Frontal asthma Thunderstorm asthma Synergy
Over many decades, massive evidence across the world has shown associations between outdoor environmental factors and respiratory health. These factors include atmospheric pollution (White et al. 1994; Asgari et al. 1998; Delfino et al. 2003; Shima et al. 2003; D’Amato et al. 2015), aeroallergens such as pollen and/or molds (Behrendt and Becker 2001; Dales et al. 2004, 2008; Thibaudon 2007; Sofiev and Bergmann 2013), atmospheric air mass type (Lee et al. 2012; Helbern and Cakmak 2015), or specific weather conditions related to asthma (Schappi et al. 1997; Marks and Bush 2007; Mireku et al. 2009; D’Amato et al. 2015; D'Amato et al. 2016). We review the critical environmental factors below.
Impact of air pollution
Particulate matter (PM), ozone, SO2, NO2, and formaldehyde have been shown to have an adverse impact on respiratory health (von Ehrenstein 2002; Tamburlini 2002; D’Amato et al. 2015; ALA 2019; see Table S1 of Supplementary material for details). In Asia, France, and Iran, synergistic interaction between atmospheric pollutant and pollen grain has been demonstrated (see Okuyama et al. 2007; Sénéchal et al. 2015 and Sedghy et al. 2018, respectively). These studies are in line with previous studies proposing the neologism “polluen” (pollutant+pollen; Peltre 1998). Chemical pollution interacts with pollen in four ways: (1) it impacts the quantity of emitted pollen by plants, (2) it increases the allergenicity of the pollen released, (3) it changes the patient individual sensitivity to pollen and allergens, and (4) it favors pollen rupture or cracks in its surface liberating hundreds of small allergens or pollen fragments (size 30 nm–5 μm) which could penetrate deeper in the lungs and exacerbate respiratory problems such as asthma (Gervais 1994; Taylor and Jonsson 2004; Miguel et al. 2006; Thibaudon 2007; Laaidi et al. 2011; Sénéchal et al. 2015; Sedghy et al. 2018).
Impact of birch (Betula spp.) pollen and its allergens
The birch (Betula spp.) pollen is studied here because (1) it is the most allergenic tree pollen (Guérin 1993), (2) it could be transported over long distances (Comtois 1996; Sofiev et al. 2006), and (3) it is characterized by one of the highest pollen production among common trees (Guérin 1993). While studying impact on respiratory health, only few studies in North America have focused on this taxon and even less on trying to appropriately control confounding factors (e.g., seasonality, weekly cycle, see Hondula et al. 2013 for details). Tiny bio-aerosols (about 1 μm diameter or less) containing Bet v1 antigens1 have been found in the ambient atmosphere likely due to birch pollen grain rupture (Rantio-Lehtimäki et al. 1994; Puc et al. 2016). Several researchers (Schappi et al. 1997; Chathurika et al. 2017; Wozniak et al. 2018) found that in light rain conditions, tree pollen grain releases antigens or sub-pollen inner particles contained within the grain into the atmosphere. Lacroix (2005) pointed out that laboratory work made by his team revealed that if pollen is exposed to pollution (O3, NO2, SO2), an increase of the frequency of rupture, pollen surface cracks, or deformation of the exine is observed which facilitate the release of allergens (Traidl-Hoffmann et al. 2003). Moreover, the percentage of pollen which ruptures whenever immersed in water (or exposed to high humidity found within clouds and fog) is significantly higher if the grain has been pre-exposed to pollutant.
Impact of meteorological conditions
The study of environmental factors and its health impact cannot be separated from their meteorological context (Laaidi et al. 1997; D’Amato et al. 2015). The links between air mass type and asthma has been analyzed by few Canadian authors (Lee et al. 2012; Hebbern and Cakmak 2015). However, considering rather daily meteorological situations (fronts, troughs, highs and lows, etc.) as presented in here offers another point of view in analyzing the links between respiratory health and environmental conditions. Ideal weather conditions for airborne pollen release and dispersion are warm temperatures, sunny skies, dry air conditions with moderate winds (Laaidi et al. 1997; Laaidi 2001). Among studies pointing towards the impact of rapid changes in weather and asthma include Taylor and Jonsson (2004), Marks and Bush (2007), and Mireku et al. (2009) which have related sudden weather changes (thunderstorms, fronts, etc.) and asthma exacerbation whereas Schappi et al. (1997) showed the importance of rain in the release of Bet v1 allergen from the birch pollen grain. Overall, three specific factors are favorable to pollen rupture and release of tiny bio-aerosols: high relative humidity (Miguel et al. 2006; Zhou 2014) or rain (Schappi et al. 1997; Chathurika et al. 2017; Wozniak et al. 2018), presence of pollution (Lacroix 2005), and the presence of enhanced atmospheric electrical field (Vaidyanathan et al. 2006). These factors can work in synergy and can occur in the atmosphere under various conditions.
Focus on asthma during spring season
This paper also focuses on asthma which is a common and chronic lung disease (Public Health Agency of Canada 2018). It is related to the atmospheric environment (D’Amato et al. 2015) and has a strong allergic component. Grossman (1997) estimated that up to 78% of asthma patients also suffer from allergic rhinitis. Bergeron and Hamid (2005) provide evidence that asthma and allergies are strongly related at any ages. The etiology of asthma is complex and presents both genetic and environmental components which interacts each other in a way which is not well understood (EPA 2008). According to the Asthma and Allergy Foundation of America, 14 of the 15 cities listed as the “most challenging places to live with asthma” have overlapping risks of ozone and ragweed pollen (EPA 2008) suggesting dangerous synergy between pollen and pollution. A key atmospheric factor to trigger asthma crisis is the presence of airborne bio-aerosols (e.g., allergens Bet v1) which can penetrate deeper into the lungs while pollen grain (usually about 22 μm of diameter for birch) is of too large dimension to penetrate the barrier of the upper respiratory track (Taylor et al. 2004). While monitoring of allergens is out of the scope of this research (no observations available in Canada yet), it is rather suggested here that estimating in each weather type the likelihood of pollen rupture (based on information from the open literature) would be more appropriate if one wants to provide a better assessment of the health risk. Sudden changes of environmental condition (air pollution, pollen, abrupt weather changes) seems to be the key in creating a synergy (Taylor and Jonsson 2004; Mireku et al. 2009; Abou Chakra et al. 2010; Sénéchal et al. 2015). This research paper will analyze asthma hospitalization counts in terms of the synergy pollen-pollution-weather type in Montreal, Canada, during the spring season 2006–2008 with special attention to the control of confounding factors. In this context, this study presented here is the first report on the impact of Betula pollen and its synergy with other environmental factors in the province of Québec, Canada.
Material and methods
Air pollution data
Description and location of the air quality monitoring station used in this study (see Fig. 1 for the location of the stations). ID stands for station identifier. AQHI stands for Air Quality Health Index (see text for more details)
Birch (Betula spp.) pollen data was collected using a Lanzoni (VPPS) pollen trap based on the simple idea of Hirst (1952). The instrument is located at about 20 m above the street level on the rooftop of Department of Geography of University of Montreal (see location indicated by a white X in Fig. 1). More details about the set-up measurement are given elsewhere (see Robichaud and Comtois 2017) and the general methodology could be found in Mandrioli et al. (1998). Note that the pollen count for a given day corresponds to a daily measurement from noon the previous day up to noon the current day. The pollen season is defined following Garcia-Mozo et al. (2002): whenever pollen concentration is greater than one grain or more for at least three consecutive days during the spring (defined here as 1st April–June 30th).
Meteorological data and weather type
Description of synoptic meteorological classes (i.e., weather type) over Montreal with its impact on clouds and weather. The last column indicates the number of cases and frequency of each type during spring periods of years 2006–2008 (observed at 18UTC, i.e., mid-afternoon). Typical origin of back-trajectories (computed from HYSPLIT tool) expressed in terms of wind direction (V variable, SW south-west, NW northwest, E east, etc.)
Weather types impacting Montreal
Clouds and weather in Montreal
Nb. cases and frequency (%)
Back of the high pressure located on the eastern US seabord
Sunny with temperature much above normal(i.e., heat wave)
Flat pressure gradient
Calm winds (temperature above normals)
Temperature above normal (unstable situation) with southerly winds
Cold front passage
Sudden changes of wind (speed and direction) and temperatures turning colder
Behind cold front
Northwest winds and colder
Approaching high pressure
Sunny, windy, and cool
Ridge of high pressure
Sunny skies, dry, and windy
Clouds and steady light rain
Unstable, cloudy, showers and turbulence
Increasing cloudiness and winds
Windy, cloudy, unstable with risk of showers
Cloudy, unstable, showers, increasing temperature and humidity
Data on asthma hospitalization and control of confounding factors
Daily data for asthma hospitalization (code 493.0, classification ICD-9-CM) was obtained for 4 different sectors of Montreal (west, center-north, center-south and east) during the spring season of years 2006 through 2008 from INSPQ (Institut National de Santé Publique, Gov. of Québec). A total of 609 asthma hospitalizations was reported during the study period. Hospitalization is an indicator of poorly controlled asthma because the disease could be prevented with proper measures (HSC 2008). Nevertheless, asthma hospitalization fluctuations are an appropriate indicator of rapid change of adverse environmental atmospheric condition although the presence of infection of the superior respiratory tract is reported to influence the decision of hospitalizing an asthmatic patient (Lajoie et al. 1994). However, our study is performed during the spring period (where the prevalence of infection is low compared to winter, see Assaad and Cockburn 1974) so the potential bias introduced here is minimized. Note that restricting the analysis to only one season (spring) eliminates the need to control confounding factors related to seasonal variations (e.g., summer grass pollen and ragweed, spores more numerous in summer and fall). Finally, confounding factors associated with other type of asthma (such as COPD, chronic obstructive pulmonary disease, infections, bronchitis) are filtered by selecting asthma with the code 493.0. Contrary to many other studies, asthma hospitalization was preferred here to asthma emergency room visits. In fact, the former has more direct correlation and less delay for an asthma reaction following exposition to co-stressors than the latter which could be associated with delay up to 1 week as observed by Garty et al. (1998). This choice adopted here makes it easier to trace back the original cause of asthma attack and its relation to environmental conditions. One limitation of the methodology is possible misclassification of COPD as asthma particularly among smokers. However, it is believed that this does not significantly influences the results of this study since this misclassification is not expected to occur frequently.
Results and discussion
Asthma hospitalization, pollen, pollution, and weather type
To better elucidate how environmental factors trigger asthma, we also need to include allergenic pollen into the picture. Figure 3 c shows the average daily count of Betula pollen grains versus the predominant meteorological class of the current day (obtained from weather maps at 12 and 18 UTC). First, note that a large variability of Betula count is observed in each weather type as given by confidence interval (at level 95%). Second, it is interesting to note that in weather type 1 (heat wave and photochemical smog), the pollen count is very low and modest hospitalization counts are reported in this class despite the highest concentration of pollutants (i.e., higher AQHI, p < 0.05 as shown in Fig. 3b). Consequently, this suggests that the low allergenic pollen load in weather type 1 would provide some explanation for the low hospitalization. Weather types 7 (ridge of high pressure) and 9 (occluded front) present among the highest average daily values of Betula count which is consistent with higher hospitalization rate (Fig. 3a) but not necessarily associated with the highest pollution (see Fig. 3b). Note that the maximum pollen count in a situation of high pressure (Fig. 3c) is not surprising since low relative humidity, very little precipitation, and windy afternoons are to be expected in this class and, in the latter, meteorological conditions are found optimum for pollen emission and dispersion (Laaidi et al. 1997; Helbig et al. 2004). The case of the warm front (type 12) is apparently enigmatic. It shows the highest average hospitalization count (3.85, see Fig. 3a) while presenting one of the lowest Betula count (Fig. 3c) and modest values of air pollution (Fig. 3b). To elucidate the case of warm front further, supplementary material presents a detailed case study (Table S3a, Supplementary material S2) analyzing further the link between environmental variables. Intermittent rain and higher humidity associated with warm front could explain grain pollen rupture and release of respirable Bet v1 allergens from the ruptured grain triggering severe asthma (Schappi et al. 1997; Taylor et al. 2004) and therefore a lower probability of pollen grains remaining airborne after they are ruptured. Since it has been suggested that the pollen potency varies with the weather conditions (Buters et al. 2010, 2012), the warm front situation could represent such a situation where the pollen potency would be stronger although the pollen count is lower. Conversely, some meteorological situations are associated with higher pollen (e.g., weather type 10) but with modest hospitalization rates. It is known than in type 10, (approaching low pressure system), winds bring easterly flow in Montreal (i.e., airborne pollen from the Eastern Townships region birch sources, region 5, see Fig. 1) but with rain which has not started yet so there is likely low release of airborne sub-pollen particles (containing the allergens) due to pollen rupture (Wozniak et al. 2018). On the other hand, trowals could be associated with pollen rupture as well since they are considered upper fronts so they are similar to warm fronts from a meteorological point of view. In both meteorological situations, the presence of intermittent rain (including cycle of wet and dry period) is common (Environment Canada 1996; Schultz and Vaughan 2011). These situations are associated to high probability of birch pollen rupture based upon independent observations (Schappi et al. 1997; Taylor et al. 2004; Zhou 2014). A case study is also presented for occluded front (Table S3b, Supplementary material S2). We will discuss pollen potency in more details later and relate it in terms of a new concept (frontal asthma: situation in which the likelihood of pollen rupture is enhanced, see below).
Correlation between asthma and environmental factors
Pearson’s coefficient (correlation matrix) between the daily total count of asthma hospitalization and environmental parameters by geographical sectors (N ~ 240).. Betula stands for daily birch pollen count, LBetu logarithm of Betula daily count, Ozone daily maximum surface concentration, PM2.5 daily maximum surface concentration of fine particulate matter, AQHI daily maximum of the Air Quality Health Index (3-h moving average), T daily maximum temperature, Td mean afternoon dew point temperature, WS mid-afternoon wind speed, Press mid-afternoon atmospheric pressure. In the West sector, Lag1 and Lag2 entries were found not statistically significant (p > 0.1) and therefore not shown. No significant correlations were found beyond Lag2 and therefore not shown. p < 0.05 unless otherwise stated, *0.05 ≤ p ≤ 0.10. Empty entries in the table means no significant correlation (i.e., p > 0.1)
Correlations stratified by grouping weather situations
Pearson’s coefficient (correlation matrix) between the daily total count of asthma hospitalization and environmental parameters. Betula stands for daily birch pollen count, LBetu logarithm of Betula daily count, Ozone daily maximum surface concentration, PM2.5 daily maximum surface concentration of fine particulate matter, AQHI daily maximum of the Air Quality Health Index (3-h moving average), T daily maximum temperature, Td mean afternoon dew point temperature, WS mid-afternoon wind speed, Press mid-afternoon atmospheric pressure. By weather group: C1: weather types 7, 9, and 12 only (N = 42), C2: all weather types excluding C1 (N = 201), C3: all weather types except 2, 5, 8, and 11 (N = 165), C4: weather types with warm temperatures: 1, 2, 3, 9, 10, and 12 (N = 74). aall sectors, nCenter-north, sCenter-south, eEast, wWest of Montreal Island. No significant correlations were found beyond Lag2 and therefore not shown. p < 0.05 unless otherwise stated, *0.05 ≤ p ≤ 0.10. Empty entries in the table means no significant correlation (i.e., p > 0.1)
According to a panel of expert (Landrigan et al. 2002), outdoor air pollution explains acute asthma in about 30% of the cases. In here, it was found that this proportion is apparently similar (up to about 28% of the variance explained for asthma hospitalization by AQHI in Montreal, see Table 4, R = 0.53). However, it is likely that this correlation is not only due to pollution but also due to synergy between pollen-pollution-weather type considering that such high correlation occurs only in the center of the city (high traffic) and in C1 weather situations (situation likely favorable to pollen-pollutant synergy). A similar conclusion was reached by Rosas et al. (1998) in Mexico City who pointed out that most studies have ignored important confounding factors such as aeroallergens and interaction with pollution in the analysis of asthma related to environmental factors.
The coefficients of correlation are now re-computed for all meteorological classes but excluding classes of the group C1 (i.e., group C2, C3, and C4). The results (see Table 4 under group C2, C3, and C4, respectively) show that even though some correlations remain significant (p < 0.05) or marginally significant (0.05 < p < 0.10) between environmental variables and asthma hospitalization, the strength of the correlation is generally weaker than that of group C1 suggesting that some kind of synergy is acting in weather types belonging to group C1 and less in other groups.
Higher asthma risk associated with warm fronts and trowals
The best predictor of asthma hospitalization is the air pollution (AQHI) according to Table 4. The best second predictor is Betula pollen concentration. The latter relationship is not intuitive since pollen grains are of too large dimension to reach the lower airways where asthmatic reactions occur (Taylor et al. 2004). Pollen is known to cause rhinitis and conjunctivitis but its role in asthma is controversial (EPA 2008). However, respirable-sized particles released by pollen grain rupture have been detected in the atmosphere especially after rainfall (Schappi et al. 1997; Puc et al. 2016; Chathurika et al. 2017) or associated with wind-induced mechanical rupture (Visez et al. 2015). Wozniak et al. (2018) have provided modeling equations for pollen rupture in rain situation. Rupture of pollen grain producing a release of respirable bio-aerosols is highly consistent with our results (Fig. 3a) which show that warm front, occluded front (trowal), and ridge of high pressure meteorological situations were the most often encountered weather categories with uncontrolled asthma (requiring hospitalization) in Montreal during the birch tree pollen season (spring 2006–2008). We report here that in the context of this research, the use of conventional methods using classical statistics (without stratification into meteorological classes as presented in Table 3) has failed to establish any robust connection between environmental variables and asthma hospitalization. Only weak correlation (R < 0.3) appears between pollen count and asthma hospitalization and very low coefficient of correlation between ozone, PM2.5, or AQHI and hospitalization are noted. Moreover, unrealistic negative correlation was found between fine particulate matter (PM2.5) and asthma hospitalization (Table 3) which is unlikely from a biological perspective. Similar negative correlation with air pollutants has also been obtained in Israel between emergency room visits of asthmatic children and ozone (Garty et al. 1998) the latter is believed to be due to uncontrolled weather confounding factors and could be explained by the Simpson’s paradox.5 With meteorological stratification into groups of weather types (such as in Table 4) robust and moderate correlations (R > 0.3, p < 0.05) start appearing between air pollution and asthma hospitalization for certain weather situations (i.e., group C1) including positive correlation with PM2.5 and much higher correlation with ozone than previously found. Note that in the eastern sector of Montreal (industrial sector E in Fig. 1), according to Table 4, no significant correlation was found with any environmental factors in weather situations C1, C2, C3, or C4 despite higher levels of sulfur dioxide usually found in this sector of the city (Ville de Montréal 2015). In another study, absence of association between asthma emergency visits and sulfur dioxide was also reported for the region of Quebec City (Lajoie et al. 1994). According to Behrendt and Becker (2001) and de Weger et al. (2013), in presence of pollen, curiously enough sulfur dioxide seems to have a protective effect which would reduce the probability of pollen rupture (likely due to its high pH) therefore diminishing the risk of liberation of allergenic bio-aerosols and asthma attack. Finally, although spores could exacerbate asthma, its impact is not discussed here since the highest activity of spores does not occur in spring but rather in summer and fall in Montreal (see https://theweathernetwork.com/Spores).
Frontal asthma: a new hypothesis
This research has addressed the complex interaction between environmental conditions and asthma. Overall, warm front, trowal, and ridge of high pressure weather types were the most often encountered situations with uncontrolled asthma (requiring hospitalization) in Montreal during the birch tree pollen season. First, in a situation of ridge of high pressure (weather type 7), the relatively high rate of hospitalization (Fig. 3a) is explained by the fact that the airborne pollen load is higher high (Fig. 3c) due to favorable pollen emission and efficient atmospheric dispersion in this weather type (Laaidi et al. 1997). A fraction of pollen is ruptured by wind-induced mechanical impact releasing airborne allergens (Visez et al. 2015) in this case. On the other hand, the case of warm front (including trowal) is more complex and the frontal asthma concept (Fig. 5) has been proposed based on Figs. 3a, c and 4a−c, Supplementary material S2 (two case studies analysis) and a synthesis of many reports in the literature (e.g., Schappi et al. 1997; Taylor et al. 2004; Mireku et al. 2009; Klein et al. 2012). Our analysis used a synoptic weather type-based approach to reduce the effect of meteorological confounding factors in the analysis (minimizing variability of temperature, sunlight, dew point temperature, or relative humidity within a given weather type). Moreover, the study was restricted to only spring season to avoid confounding factors related to seasonal changes and impact of other allergenic pollen types (grass or ragweed) or molds and spores (which have peaks in summer and fall in Montreal, but have low values in the study period). In this research, it was hypothesized that active fronts may present similar environmental conditions than that of thunderstorms although less in intensity (in terms of showers, humidity, vertical lifting of particles, electrical field, convergence of pollutants and aerosols, etc.) but with fronts occurring more frequently (24 days of warm or occluded fronts noted during the study period and only few cases of brief thunderstorms reported7). In the future, we suggest that effort and studies should emphasize on measuring the counts of allergens since these tiny bio-aerosols describe better exposure to allergens causing asthma than the pollen itself as pointed out by Buters et al. (2010, 2012). Nevertheless, the concept of frontal asthma as suggested if correct could provide a science advance since (1) it relates pollen upture likelihood with synoptic frontal meteorological conditions (wind convergence of pollutants and pollen, higher humidity and precipitation, enhanced atmospheric electrical field, barometric pressure changes), (2) it re-conciliates many observations from literature and summarizes the results of the statistical analysis presented in this research, and finally (3) it possibly generalizes and include the already known concept of thunderstorm asthma (i.e., asthma attack associated with the outflow gust front), since, in all cases, either synoptic (frontal asthma) or sub-synoptic front (thunderstorm asthma), there are sudden changes of atmospheric conditions (although more violent and sudden in the thunderstorm case). In the hospital community, it is known that sudden atmospheric changes promote asthma (Beer et al. 1991; Lajoie et al. 1994 and references therein; Mireku et al. 2009) but details are lacking relative to the weather type in which it occurs. Although this research is exploratory, it provides some evidence that some specific weather situations are more prone to asthma and likely pollen releasing allergens. Further research is needed to replicate the concept of frontal asthma to other locations and for other pollen types. However, a true validation would require the monitoring of allergens, not only that of pollen concentrations.
This allergen is considered having the major health impact associated with birch pollen grain (de Weger et al. 2013).
Birch source regions around Montreal are located mostly in regions 04, 05, 07, and 14 through 17 (see top of Fig. 1) which are mostly mountainous regions.
Confounding factors are source of heterogeneous variations of a given variable which seeks to explain another variable or to establish cause and effect link.
The Simpson’s paradox is a common problem when analyzing data and occurs when a confounding factor is present. In extreme cases, it can reverse the sign of the correlation when the confounding is properly controlled or its impact eliminated (Julious and Mullee 1994).
According to Fujita (1986), thunderstorm gust front has a scale of 10–300 km, a duration of 0.5 to 6 hours and maximum wind speed of 35 m/s whereas a warm front has scale of 300–1000 km, duration of 1 to 3 days and maximum wind speed of 15 m/s. Hence, thunderstorms can be viewed as a sub-synoptic disturbance whereas a front is at synoptic scale.
Thunderstorm’s season usually starts after the birch pollen season in Montreal (i.e., not under the study period).
The authors would like to thank Denis Hamel from the “Institut National de Santé Publique”, Gov. of Québec for providing data on asthma hospitalization and Dr. R. Pampalon for some advices.
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