Environmental factors and asthma hospitalization in Montreal, Canada, during spring 2006–2008: a synergy perspective

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.

Study area

Montreal is an island and the largest city of the province of Québec (Canada) with roughly 1.5 million inhabitants (3.5 million for the whole metropolitan area, see www.ville.montreal.qc.ca/portal/). The daily mean temperature during the birch pollen season spans from 6.4 °C in April, 13.4 °C in May up to 19.3 °C in June. Montreal has its heavy industrial activities including refineries in the eastern parts of the island (noted E in Fig. 1). Traffic pollution and population have both the highest density in the central parts (noted cn and cs). Finally, the western part of the island (noted w) has little industries, less traffic density, and some minor agricultural activities, and therefore presents the less frequency of days with poor air quality (Ville de Montréal 2019). Montreal is an interesting region to study the impact of birch pollen since most of it has a foreign origin (Robichaud and Comtois 2017). Therefore, health impacts due to the local pollen emitted on the island is not significant here. However, pollen of remote origin can be transported from source regions outside Montreal² and interact with local urban pollution. Allergic symptoms caused by long-distance transport of birch pollen have been documented for a long time (Wallin et al. 1991; Sofiev et al. 2006). Moreover, since Montreal lies within the St-Lawrence River valley, pollutants, pollen, and bio-aerosols tend to be trapped within the valley exacerbating the aerosol exposure to the population.

Open image in new window

Air pollution data

Pollen data

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

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.

Statistical analysis

Principal component analysis (PRINCOMP procedure of SAS®: SAS, 1989) was used to examine the relation of the weather types between each other and their proximity (redundancy check) in the principal component analysis (PCA) space. PCA can be viewed as a technique that transforms the data to a new coordinate system in terms of principal component or eigenvectors (Jolliffe 2002). Figure 2 shows the validation of the classification method for weather types of Table 2 using PCA. The following loading factors were used in the PCA: afternoon maximum temperature, mid-afternoon relative humidity and dew point temperature, pressure, wind speed, wind direction, and visibility observations. As shown in Fig. 2, weather types are generally non-redundant and well distributed among four domains in the PCA space: warm and dry, warm and wet, cool and wet, and cool and dry. Some of the weather types could be re-grouped as seen later (C1, C2, C3, C4) for some analyses. The possibility of pollen rupture and synergy between environmental factors will be evaluated in terms of these groups. The computation of correlation is performed using Pearson’s correlation coefficient. To disentangle the role of synergy, the procedure GLM (generalized linear model) with unbalanced ANOVA (analysis of variance with sample of different sizes) of SAS® ( 2008) has been used to evaluate the significance of the differences between weather types and statistical interaction between variables. Interactions between pollen level, pollution level (ozone and fine particulate matter), and weather type (for both the current day and the previous day) are also investigated for their statistical significance. The study presented here is not a cohort study but an ecological descriptive study (see definition in NRC 1985). Variables in an ecologic analysis may be aggregate measures, environmental measures, or global measures (see for example, Robichaud 2019).

Open image in new window

Asthma hospitalization, pollen, pollution, and weather type

Stratification according to weather type (Table 2) allows reducing the effect of meteorological variability giving rise to better association between the main atmospheric co-stressors (pollen and pollutants) and the frequency of daily hospitalization. Figure 3 a shows the total count of daily hospitalization due to asthma over the Montreal island during the pollen season versus the synoptic weather type. Results clearly show that the health risk associated with asthma is the highest in a situation of warm front (weather type 12), ridge of high pressure (weather type 7) or trowal (weather type 9) with an average of hospitalizations per day in the range 3.70–3.85 in these meteorological situations which is significantly higher (p < 0.01) as compared with the remaining group of weather types. Interestingly, the meteorological situation usually associated with relatively high air pollution (i.e., highest values of AQHI, see Fig. 3b) that is weather type 1 (heat wave and photochemical smog situation) shows surprisingly fairly modest average hospitalization counts for asthma (Fig. 3a). In fact, an average of 2.36 hospitalizations is reported in a situation of photochemical smog during the Betula pollen season in Montreal as opposed to 3.85 for a situation of warm front (weather type 12), 3.7 for occluded front (weather type 9), and 3.79 hospitalizations as an average for ridge of high pressure (weather type 7) during the same period.

Open image in new window

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

Correlations stratified by grouping weather situations

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

Betula pollen concentration during the spring pollen season as a predictor is better related with the frequency of hospitalization for asthma than does air pollution concentration in Montreal as seen in Table 3. Using the same classification of weather type previously presented (Table 2), we now group together warm fronts and trowals weather types (so-called group A) for the city south-center sector of Montreal (where air pollution is the highest) and compared with all other remaining weather types in the same sector (which forms group B, i.e., all other meteorological situations). Note that the pollen load is not considered as a confounder here given that the pollen load is similar within different sectors of the city center (due to very little birch pollen sources in Montreal). The result found is that the daily average hospitalization counts in the two groups were significantly different (p = 0.0008) under such stratification (Fig. 4a). Higher hospitalization count was found in the weather group A with an average of 3.78 hospitalizations per day (CI 95% 2.95–4.61) as compared with an average of 2.49 (CI 95% 2.37–2.71) for weather group B. This indicates that the two weather types associated with group A are more prone to asthma crisis in Montreal than other meteorological situation grouped together (group B) during the Betula pollen season. Figure 4 b suggests that ozone is also higher in group A versus group B (p = 0.075, i.e., marginally significant). Similarly, Fig. 4c shows that PM_2.5 is higher as well in group A (p = 0.045). Finally, on Fig. 4d the logarithm of pollen count load is also higher in group A as well (p < 0.05). Overall, these results suggest that ozone, fine particulate matter, and allergenic pollen could contribute to increase asthma in group A (likely as a form of synergy). Supplementary material S3 shows result and tests of significance for interaction between environmental variables using procedure GLM (generalized liner models, SAS®). Among significant interactions (p < 0.05), we find on many occasions combinations of weather type (classmet), ozone (ozoclass), PM_2.5 (pmclass), and pollen class (betclass), and their lag (previous day class: i.e., lclassmet, lozoclass, lpmclass, lbetclass). Results indicate many potential interactions (or synergy between hospitalization count versus environmental factors) which are significant and cannot be ignored. Moreover, when the weather types prone to synergy are removed (weather types 7, 9, and 12), no significant interaction were found (p > 0.10). This confirms stronger synergy in the latter weather types.

Open image in new window

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, PM_2.5, or AQHI and hospitalization are noted. Moreover, unrealistic negative correlation was found between fine particulate matter (PM_2.5) 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 PM_2.5 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

For allergy sufferers, it is important to know under which meteorological condition the health risk is the highest. One might intuitively think that a higher number of hospitalization probably happens on days with high pollen counts and/or smog situation. However, the reality is much more complex. Despite high level of asthma hospitalization in weather type 12 (warm front, Fig. 3a) pollen grain count is low (see Fig. 3c) with modest air pollution found (see Fig. 3b). In fact, the sudden rise of absolute humidity, intermittent convective rainfall, convergence of aerosols, and likely increase of electrical field accompanying active fronts would considerably increase the likelihood of pollen rupture and therefore liberation of small allergens inside pollen carriers (Bet v1, v2, and v4 in the case of birch) based on numerous laboratory experiments (see literature review in Introduction). It is suggested here that it is for these reasons why atmospheric abrupt changes (often frontal situation) tend to augment asthma risk as noted in the hospital communities although rarely addressed in peer-review literature. One study by Mireku et al. (2009) showed that a rapid increase of humidity or temperature (such as found associated with warm fronts or trowal during the birch pollen season) was associated with asthma rise among children. Overall, based on the evidence presented above, a new concept so-called frontal asthma is now proposed which offers a plausible explanation to the rise of asthma hospitalization in active frontal or any other abrupt weather changes (trowals, squall lines, thunderstorms, etc.). Figure 5 illustrates the new concept (here for the case of a warm front). It is well known that a trough of barometric pressure usually accompanies fronts causing wind convergence (Environment Canada 1996) leading to accumulation of primary aerosols, pollen, and spores near the front. Frontal clouds and precipitation would humidify the pollen to the point of rupture and electrical field higher in convective precipitation (if any) would possibly enhance further the likelihood of rupture. Given the similarity with the phenomenon of thunderstorm asthma (see D’Amato et al. 2015; D'Amato et al. 2016), it is suggested here that the latter could be a special case of frontal asthma but at a different spatiotemporal scale (i.e., at local scale for the former) instead of at synoptic scale (for the latter as depicted in Fig. 5). In fact, the sudden rise of asthma cases noted in different parts of the world (D’Amato et al. 2015, D'Amato et al. 2016) is also somehow linked with a frontal system at the local scale (i.e., thunderstorm gust front or outflow). The thunderstorm outflow is a front just like synoptic front but occurring at a smaller spatial and temporal scale.⁶ In fact, the schematic of Fig. 5 could also be applied for a thunderstorm outflow (gust front) but with cold air pushing behind the front instead of warm air. The thunderstorm asthma phenomenon is certainly more violent and occurs when the thunderstorm gust front passes over producing epidemic asthma hospitalization as reported in London, England, and Australia (see Taylor and Jonsson 2004; Marks and Bush 2007). In both cases (synoptic front or thunderstorm gust front), the probability of pollen rupture tends to increase under enhanced electrical filed (Vaidyanathan et al. 2006; de Weger et al. 2013) particularly when it is combined with higher humidity which is also known to cause pollen rupture (Miguel et al. 2006; Zhou 2014). All the above factors combined could explain the fact that higher rate of hospitalization than average is observed in warm front passing over Montreal despite low value of pollen counts for this weather type (see Fig. 3 and Supplementary material S2). Note that cold fronts are potentially candidate for enhanced synergy as well. However, in our case, cold fronts were found not active (only wind with little or no precipitation) during the entire study period in Montreal (spring pollen season of years 2006–2008). For example, Table S3a (Supplementary material S2) shows an example of an inactive cold front (on May 27, 2006).

Open image in new window