Ergosterol , arabitol and mannitol as tracers for biogenic aerosols in the eastern Mediterranean

Abstract. Aerosols containing biological components can have a significant effect on human health by causing primarily irritation, infection and allergies. Specifically, airborne fungi can cause a wide array of adverse responses in humans depending on the type and quantity present. In this study we used chemical biomarkers for analyzing fungi-containing aerosols in the eastern Mediterranean region during the year 2009 in order to quantify annual fungal abundances. The prime marker for fungi used in this study was ergosterol, and its concentrations were compared with those of mannitol and arabitol which were recently suggested to also correlate with fungal spores concentrations (Bauer et al., 2008a). Back trajectory analysis, inorganic ions, humidity and temperature were used in an attempt to identify sources as well as the dependence on seasonal and environmental conditions. We found that the ambient concentrations of ergosterol, arabitol and mannitol range between 0 and 2.73 ng m −3 , 1.85 and 58.27 ng m −3 , 5.57 and 138.03 ng m −3 , respectively. The highest levels for all biomarkers were during the autumn, probably from local terrestrial sources, as deduced from the inorganic ions and back trajectory analysis. Significant correlations were observed between arabitol and mannitol during the entire year except for the winter months. Both sugars correlated with ergosterol only during the spring and autumn. We conclude that mannitol and arabitol might not be specific biomarkers for fungi and that the observed correlations during spring and autumn may be attributed to high levels of vegetation during spring blossoms and autumn decomposing.


Introduction
Airborne particles that originate from living organisms, plants, cell parts, pollen, bacteria and fungal spores are termed bioaerosols.Uplifting of biological particles to the atmosphere is possible when mechanical disturbances such as dust storms, activated sludge systems and irrigation with reclaimed water occur (Crook et al., 1997;Schlesinger et al., 2006;Taha et al., 2006).Bioaerosols have been implicated in many environmental processes such as modification of clouds by serving as cloud and ice condensation nuclei (Hoose, 2009;Kurup et al., 2000;Kurup, 2003;Möhler et al., 2007;Vali et al., 1996) as well as affecting human health by causing primarily irritation, infection and allergies (Pei-Chih et al., 2000;Shelton et al., 2002).
Measurements of aerosol phospholipids content suggest that pollen and fungal spores contribute 4 to 11% of the total mass in particulate matter less than 2.5µm in aerodynamic diameter (PM 2.5 ) or 12 to 22% of the total organic carbon fraction of ambient aerosols (Womiloju et al., 2003).More recent estimations suggest that fungal spores account for about 60% of the coarse atmospheric organic carbon, and for 40 ± 5% of the coarse PM mass as was measured in Vienna during spring and summer 2005 (Bauer et al., 2008b).
Fungi are important microorganisms in nature for they decompose most of the organic material.The vast majority of fungi have terrestrial habitats, in soil or dead plant matter (Madigan et al., 1997).Most fungi emit spores that range between 1.5 and 12 µm (Cox and Wathes, 1995;Ruzer and Harley, 2005).These spores constitute a significant portion of the natural bioaerosols.These particles have long retention time in the atmosphere and can travel long distances.
Fungal spores can penetrate into the human airways and irritate the respiratory system, cause allergies and infectious acute diseases such as alvioletis and upper airway blockage as well as chronic disease such as asthma and bronchitis Published by Copernicus Publications on behalf of the European Geosciences Union.(Douwes et al., 2003;Fung and Hughson, 2003;Kurup et al., 2000;Kurup, 2003;Ring, 2001;Ruzer and Harley, 2005).Emissions of volatile organic compounds from microorganisms such as fungi can also cause human health problems such as irritation of mucous membranes and damage to the central nerve system (Fung and Hughson, 2003;Ruzer and Harley, 2005).Glucose polymers in the cell walls of most fungi such as (1-3)-β-D-glucans can induce inflammatory responses and cause respiratory symptoms (Douwes et al., 2003;Shelton et al., 2002).
The adverse responses to fungi depend on their type and quantity.Since dose response is highly individual, the sensitivity of an exposed person is an important consideration (Ruzer and Harley, 2005).The abundance of bioaerosols in the ambient air and their health impacts depend on the season and on the environmental conditions.For example, the release of spores and their allergenic effects depend on water content (Ruzer and Harley, 2005).
Estimating the concentration of fungi in the atmosphere is commonly conducted using viable samplers or spore traps.With viable samplers it is possible to enumerate only live species, while spore traps followed by microscopic examination can provide total spore counts regardless of their viability.Both approaches provide number concentrations and species identification, but they are labor-intensive and time consuming.They do not provide the contribution of fungal spores to atmospheric aerosols in terms of mass loading or impacts (Lau et al., 2006).To quantify and identify fungi contribution to atmospheric aerosols and the impact to public health, it has been suggested to use biomarkers in chemical analysis of collected aerosols.An often used biomarker for determining the fungal biomass is ergosterol (Lau et al., 2006), which is a primary fungal membrane sterol shown in Fig. 1a.Ergosterol is almost exclusively found in fungi and is therefore an efficient biomarker.Photochemical degradation can cause a significant decrease of ergosterol content in living fungi, therefore it is necessary to avoid light exposure once the fungi are collected (Mille-Lindblom et al., 2004).Gas Chromatography-Mass Spectrometry (GC-MS) is the most efficient mean for determination ergosterol (Miller and Young, 1997).Trimethylsilyl derivatization of ergosterol is often used for increasing the detection sensitivity.The derivatization occurs by replacing the hydrogen from the OH group by a trimethylsilyl group (Saraf et al., 1997).
Recently, Bauer et al. (2008a) suggested that mannitol and arabitol (Fig. 1b, c) concentrations are correlated with the fungal spore counts in atmospheric PM 10 (mass of airborne particles that have an aerodynamic diameter smaller then 10 µm) (Bauer et al., 2008a).This finding was confirmed by Zhang et al. (2010)  common in fungi, is also a common sugar alcohol in plants; it is particularly abundant in algae and has been detected in at least 70 higher plant families.This suggests that mannitol may not be a specific biomarker for fungi as ergosterol (Loescher et al., 1992).Inorganic cations (Na + , NH + 4 , K + , Mg 2+ , Ca 2+ ) and anions (Cl − , NO − 3 , SO 2− 4 ) are common markers for identifying aerosol sources and processes.Inorganic ions can often provide important information about the origin of the aerosols and are often used for source identification (Hays et al., 2002).
To date, limited research on the abundance and sources of biogenic aerosols in the Mediterranean basin in general and in Israel in particular has been conducted.While there is pioneering research on bioaerosols occurrence in this region (Schlesinger et al., 2006;Waisel et al., 2008), the biomarkers approach has not been applied yet.Fungi biomarkers in aerosols have not yet been studied and therefore the seasonal fungi abundance and the potential health effects, apart from dust storms (Schlesinger et al., 2006), are not well known.
In this study, ergosterol, arabitol, mannitol and inorganic ions were quantified in ambient aerosols in the Eastern Mediterranean region.Several goals were set: 1. Quantify ergosterol concentrations in ambient PM 10 aerosol in order to study the annual and seasonal behavior of fungi-containing bioaerosols.
2. Test whether arabitol and mannitol are good predictors of ergosterol and hence of fungal containing aerosols.
3. Explore correlations between the tracers' abundance with inorganic ions, humidity, temperature, and synoptic data in order to identify dominant sources of fungal spores.
2 Experimental methods The excess water and hexane were evaporated under a gentle stream of nitrogen.The samples were re-dissolved in hexane-dichloromethane solution (1:1 v/v) (Sigma-Aldrich, 99.8%) and were applied to disposable silica gel columns (Phenomenex 100 mg/1.5 ml 100 pk) pre-conditioned with hexane:dichloromethane (1:1 v/v) mixture that decreased the analysis error by filtering unwanted components present in the sample.Extraction was performed with 2ml hexane:dichloromethane mixture (1:1 v/v) followed by 2ml ethyl acetate (Sigma-Aldrich, 99.9%), the samples were again evaporated under a stream of nitrogen.Ergosterol and DHC derivatization was performed using Bis (Trimethylsilyl) trifluoro-acetamid (BSTFA) (Aldrich, 99.0+).Pyridine (Aldrich 99.8%) was used as a catalyst.Derivatization improves the ergosterol signal to noise ratio (Lau et al., 2006;Saraf et al., 1997).After reacting at 65 • C for 45 minutes the samples were injected to a Varian GC-MS Saturn 2000 equipped with a Restek column (RXi -5 ms. 5% diphenyl 95% dimethyl polysiloxane.30 m, 0.25 mm ID, 0.25 µm).A sensitive GC-MS/MS procedure was developed for obtaining a high sensitivity.The second MS stage was used to detect the ergosterol at 363 m/z and DHC at 351 m/z.Injector split ratio was adjusted to the splitless mode to enhance sensitivity.Column flow rate was constant at 1.4 PSI except from a pressure pulse at 40 PSI for the first minute.Ergosterol retention time was between 25.6 to 26.1 min.Calibration curve is presented in Fig. 2.

Mannitol and arabitol
Mannitol and arabitol were detected and quantified by ion chromatography using the following procedure: the sampled filter was weighted and for each sample, a 3 cm 2 piece was weighed separately.All samples were analyzed in duplicates.6 ml of de-ionized water were added to each sample and put in an ultrasonic bath for 45 min at room temperature.The samples were filtrated in 0.45 µm syringe filter (Acrodisk) and were injected to the Ion Chromatograph (Dionex) with a CarboPac column (Dionex, MA1; 4×250 mm & guard).The column temperature was set to 30 • C, eluent flow (NaOH) was set to a rate of 0.4 ml s −1 and the injection volume was 50 µl.The detector was an electrochemical detector with a gold electrode as a work electrode and silver-chloride as reference electrode (Dionex).Arabitol (Sigma, >98.0%) and mannitol (Riedel de Haen, extra pure) were used as standards.Calibration curve is presented in Fig. 2.

Inorganic ions
A 3 cm 2 piece of the filter was used for each analysis; all samples were analyzed in duplicates.8 ml of de-ionized water were added to each sample.The samples were put in an ultra sonic bath for 45 min at room temperature; they were then filtrated with a 0.45 µm syringe filter (Acrodisc) and injected to an Ion Chromatograph (Dionex) All laboratory glassware was washed carefully with de-ionized water prior to use.
Anions were detected using the following conditions: the flow rate of mobile phase, NaOH 13 mM was 1 ml min −1 with injection volume of 50 µl and suppressor current of 40 mA.An IonPac column (AS11A 4 × 250 mm & guard IonPac AG11A 4 × 50 mm) was used, were the column temperature was set to 25 • C. A suppressor (ASRS 300 4 mm self regeneration), a conductivity detector (Dionex) and an automated autosampler (Dionex) were utilized.

Meteorological information
Humidity and temperature data was obtained from the meteorology station at the Hebrew University in Rehovot and the meteorological station in Nes-ziona on a daily basis.Recorded dust storms events were taken from the ministry of environmental protection.

Back trajectory analysis
Back trajectories were calculated for each sampled filter using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) (Draxler and Rolph, 2010) developed by the United States National Oceanic and Atmospheric Administration (NOAA).The model produced back trajectories for 3 days for the sampling dates at 3 different heights (500, 1000, 1500 m) providing estimate of the source direction for each sample separately with the model vertical velocity, coordinates of the Weizmann institute are 31 • 54 27 • N and 34 • 48 33 • E. The HYSPLIT model was commonly used in different occasions to estimate PM 10 sources (Dayan and Levi, 2005;Escudero et al., 2006;Falkovich et al., 2001;Waisel et al., 2008).

Calibration curves for ergosterol, arabitol and mannitol
The analytical tools used in this study were all verified and calibrated with standards in concentration ranges relevant for the samples.Ergosterol, arabitol and mannitol were detected with good precision, calibration curves for are shown in Fig. 2. For ergosterol each peak area was divided by the DHC peak area giving a relative response factor (RRF).An internal standard was not needed for mannitol and arabitol, because they are stable species.

Ergosterol, arabitol and mannitol concentrations during 2009
In Figs. 3 and 4, the variation of ergosterol, mannitol and arabitol in our sampling site from December 2008 to December 2009 are shown.The gaps in the data were caused by technical issues.The data covers 46% of the year.Table 1 provides detailed concentrations.The data contains 14 winter samples (December, January, February), 8 spring samples (March, April, May), 17 summer samples (June, July, August) and 17 autumn samples (September, October, November) totaling 56 samples.The ambient concentrations of ergosterol, arabitol and mannitol range between 0 and 2.73 ng m −3 , 1.85 and 58.27 ng m −3 , 5.57 and 138.03 ng m −3 , respectively.A visible correlation is noticeable between arabitol and mannitol during this year; the highest levels for all biomarkers are seen in the autumn when the vegetation decomposes and fungal population increase.The concentrations of the three biomarkers were in the same order of magnitude and range to those reported for Hong Kong and Vienna.Ergosterol concentrations in Hong Kong were between 30.7 to 407.3 pg m −3 (Lau et al., 2006), sugars levels in Vienna during the autumn months were between 7 and 63 ng m −3 , and between 8.9 and 83 ng m −3 for arabitol and mannitol respectively (Bauer et al., 2008a).The slight difference in the biomarkers' concentrations between Israel, Hong Kong and Austria may be attributed to the difference in the types of fungi species collected, the climatic regions and amount of surrounding vegetation.

Seasonal correlations between the biomarkers
Table 2 details the Pearson correlation coefficient (r) between mannitol and arabitol during the different seasons.Apart from the winter months, arabitol and mannitol are highly correlated throughout the year.Ergosterol is highly correlated with arabitol and moderately correlated with mannitol during spring and autumn but shows no correlation with both sugars during summer and winter.
Ergosterol is an established biomarker for fungi (Gessner and Schmitt, 1996;Lau et al., 2006;Lee et al., 2007;Mille-Lindblom et al., 2004;Miller and Young, 1997;Saraf (Bauer et al., 2008a), our data show that in our region, these sugars correlate with ergosterol during spring and autumn but not during winter and summer.Bauer's measurements were conducted only during the autumn, when high concentrations of the sugars were observed in both sites and a significant correlation was found between them and the fungal spores (Bauer et al., 2008a).Although there are differences in environmental conditions between Israel and Austria, the arabitol levels detected were usually lower than mannitol levels in Israel, similarly to the observations in Austria.
Mannitol and arabitol, although common in fungi, are most frequently occurring sugar alcohol in plants; mannitol is particularly abundant in algae and has been detected in at least 70 higher plant families (Cheng et al., 2009;Lafay-Ette et al., 2004;Loescher et al., 1992)     mannitol and arabitol are not specific biomarkers for fungi.The observed correlation is confusing and might be attributed to high levels of vegetation during spring blossoms and autumn decomposing and not necessarily have a direct relation with fungi levels.Therefore we suggest that further studies should be conducted in order to establish whether they are good predictors of fungal spores.

Effects of ambient conditions on biomarkers concentration
We have investigated the correlation between the temperature and relative humidity (at the sampling point) and the biomarkers.Figures 5-6 show the correlation of concentration with relative humidity and temperature.The highest ergosterol concentrations were observed when the relative humidity was below 58% and between 18-21 • C. At higher temperatures and RH, ergosterol concentrations decrease.During the measurements, average relative humidity in our site was between 55 and 70% (Fig. 6).Lau (2006) reported that the highest ergosterol levels were detected when the RH was below 75% (Lau et al., 2006).For Vienna the   relative humidity was not reported.Arabitol and mannitol show high levels between 18-21 • C, as ergosterol does.Relative humidity levels did not show significant influence on arabitol, while mannitol levels are the highest when the RH is between 62-64%.The different influence of the relative humidity on the biomarkers can be another indication for the different sources of each of them.Humidity was previously found to be a significant factor in effecting fungi concentrations.It was found that fungi are more abundant when the humidity is high in both indoor and outdoor environments (Arundel et al., 1986;Cox and Wathes, 1995;Elbert et al., 2007;Li and LaMondia, 2010).However, ergosterol was negatively correlated with relative humidity in Hong Kong (Lee et al., 2007), our results show the same trend, meaning that high humidity levels do not have a straight relation with high biomarker concentrations.This could be explained by the facts that the source of the spores is from air mass from inland where the humidity is relatively low when compared to the humidity when the air mass arrives from the Mediterranean Sea.We estimate that this explains the observed negative correlation with the humidity, since the sea may be a weaker source of spores.

Biomarkers concentrations by geographical sectors
72 h back trajectories were calculated for each sample using HYSPLIT model (Draxler and Rolph, 2010).The back trajectory model ran for 72 h back for each sampling date in order to capture the 72 h time frame of the sampling.In a research conducted recently in Maryland, the transport and characterization of ambient biological aerosol were studied using the back trajectories model.They found that the backtrajectory analysis indicate that samples that originated from both the ocean and land mass, were consistent with genetic material found in the samples, which show evidence of both soil and oceanic origins (Santarpia et al., 2010).
The trajectories were broadly separated into 4 sectors (Dayan and Levi, 2005), each sector was categorized according to the major synoptic scenarios common in Israel: southern Europe & Mediterranean Sea, Turkey & central western Asia, Arabian Peninsula and North Africa & Sahara (Fig. 7).Typical trajectories for each of the sections are shown in Fig. 8.
We found that the higher concentrations of mannitol and arabitol are associated with terrestrial trajectories or by mixed trajectories -trajectories that contain both terrestrial and marine sources (Fig. 9), while ergosterol has the highest concentrations only when the trajectories are from terrestrial source (Fig. 10).Ergosterol has the higher levels when originating from terrestrial sources also in Hong Kong (Lee et al., 2007).When investigating the trajectories according to sectors and seasons division (Fig. 11), generally, all biomarkers have the highest values when arriving from North Africa -Sahara region and Turkey.Mannitol and ergosterol show high levels when coming from the Turkey region mainly during autumn.The same qualitative conclusions are obtained when investigating 24 or 48 h trajectories.

Statistical analysis for biomarkers and inorganic ions
The Pearson χ 2 test was chosen for its simplicity in explaining the relations between several inorganic ions and biomarkers coordinated with seasonality.Since we were interested in examining the correlations separately for each season and we only have several samples from each season (between 8 and 17), the most suitable statistical analysis was the Pearson test.
When we applied a principle component analysis (PCA) we could not observe any significant correlations, probably due to lack of measurements.The disadvantage of the Pearson test is that we could not identify any patterns that hide within the data but we were able to reveal the most significant correlations.
During 8 dust storm events that were sampled during 2009 we observed higher than average Ca 2+ levels, as was previously reported (Falkovich et al., 2001;Schlesinger et al., 2006).The biomarkers levels (Table 4), in those dusty sampling days had very low correlation with the Ca 2+ levels measured (Table 3).The correlations suggest that the average concentrations of biomarkers are low at most dust events, suggesting that ergosterol and hence fungi concentrations are not related to dust storms events.This finding may indicate that the biomarkers origin is not from remote desert regions but is probably local, possibly from the southern plain area, a region with ample agriculture and vegetation.These conclusions partially contradict the conclusion of Schlesinger et al. in which higher than average concentrations of fungi were found during dust storms (Schlesinger et al., 2006).However, they agree with the conclusions of Lee et al. who did not find a distinct correlation between ergosterol and Ca 2+ levels (Lee et al., 2007).
A negative or negligible correlation can be seen between SO 2− 4 , NH + 4 and NO − 3 with all markers for the entire period of measurements.SO 2− 4 , NH + 4 and NO − 3 are associated with aged aerosols (Lee et al., 2007) or with industrial emissions.This observation may indicate a low survival time of biogenic materials in the atmosphere, and the sensitivity of the airborne organisms to air pollution.
During the spring season, all biomarkers are correlated with all ions that are markers for marine indicating aerosols: Na + , Cl − and Mg 2+ .However, we cannot conclusively determine the source of the biomarkers.It is difficult to assess whether the prevailing western winds carried biological material from algae sources in the Mediterranean Sea or from close by terrestrial sources.

Summary
Ergosterol, mannitol and arabitol were measured during the year 2009 in order to quantify fungal distribution in the eastern Mediterranean region as well as understanding the seasonal behavior and estimate possible sources.For this purpose we established analytical methods to quantify the biomarkers.Back trajectory analysis and inorganic ions have been investigated in order to obtain source identification.We find that ergosterol, which is an established biomarker for fungi, correlates with arabitol and mannitol only during the spring and autumn months.This correlation might be related to high levels of vegetation during springs blossoms and autumn decomposition and not necessarily have a direct relation with fungi levels.
Ergosterol concentrations were found to be the highest in air masses arriving from local terrestrial sources during the autumn with an average of 0.94 ng m −3 , while arabitol and N. Burshtein et al.: Ergosterol, arabitol and mannitol as tracers for biogenic aerosols mannitol have the highest levels in air masses arriving from local terrestrial and mixed sources during the autumn with an average concentration of 18.90 ng m −3 and 49.15 ng m −3 , respectively.
Although ergosterol is an established biomarker for atmospheric fungal spores (Lau et al., 2006), specific conversion factors should be determined for each geographic region, fungal species and environmental conditions for further use of ergosterol as a biomarker and for establishing the actual fungi concentration.Ergosterol abundance is dependent on the fungal species present (Pasanen et al., 1999) therefore further investigation is needed in order to establish these factors for our region.

Fig. 3 .
Fig. 3. Arabitol and mannitol concentrations during 2009.Missing samples are due to technical failures of the high volume sampler.High concentrations of the biomarkers are seen during the autumn.

Table 1 .
Biomarkers levels were detected in each of the sampling dates (ng m −3 ).Each date shown represents the starting date of the sampling period during 72 h.Averages are shown for each biomarker in all seasons.Arabitol, mannitol and ergosterol show the highest average concentrations during the autumn season, the lowest levels are seen during summer for ergosterol and mannitol and during winter for arabitol.

Table 2 .
Pearson coefficient (r) was used in order to determine the correlation between the markers during each season.Arabitol and mannitol are highly correlated during the year apart from the winter season.Ergosterol shows moderate correlation with both arabitol and mannitol during spring and autumn.

Table 3 .
Pearson correlation coefficient (r)between Ca 2+ levels and the measured biomarkers as was calculated for 8 dust events sampled during 2009.Low correlation is observed suggesting that the biomarkers are from a local source.