Abstract
Microbial species such as bacteria and fungi can be transported by dust storms over long distances, and may change the mycobiota in downwind. This study aimed to evaluate phenotypes and genotypes of airborne fungi during the Middle Eastern dust (MED) events and normal days in Khorramabad, Iran. The samples were collected regularly every six days at three locations during April 2018–March 2019, with additional samplings during MED days. For phenotypic analyses, the Petri dishes were incubated at 25 °C for 72–120 h. Molecular identification of fungi was carried out using polymerase chain reaction (PCR). The average (±SD) of total fungal concentration was 460.9 (±493.2) CFU/m3. The fungi with the highest average concentrations included Cladosporium cladosporioides, Penicillium brevicompactum, and Cladosporium iridis, respectively. The average concentration of fungi during dust days (967.65 CFU/m3) was 3.6 times higher than those in normal days (267.10 CFU/m3). During normal and dust days, 61 and 45 species were detected, respectively. Aspergillus and Cladosporium spp. were relatively more dominant during normal and dust days, respectively. Eight fungal species were only observed during MED days, including Talaromyces albobiverticillius that was detected for the first time in Iran. Despite air temperature, relative humidity and wind speed were associated to the fungal concentrations. Dust events lead to the changes in the air pollutants composition and mycobiota, identification of new fungi, and elevated fungal concentrations that may extremely affect the public health.
Electronic supplementary material
The online version of this article (10.1007/s40201-019-00428-0) contains supplementary material, which is available to authorized users.
Keywords: Bioaerosols, Aerobiology, Particulate matter, Dust episode
Introduction
Dust events have been reported as a major health concern, and may cause cardiovascular and respiratory mortality and hospitalizations [1–3]. One of the main sources of air quality deterioration in the Middle East, and especially in Iran, is Middle Eastern dust (MED) events [4]. These dust storms originate from arid areas in Saudi Arabia, Iraq, Kuwait, and Iran, passing from industrial and petrochemical complexes, and affecting a large part of the Middle East especially southern and western areas of Iran mostly during April to September [5]. These events have worsened in recent years due to the extensive drought and improper water management in the region [6]. Therefore, the health of tens of millions of people in the Middle East is at risk.
Physicochemical and biological characteristics of dust storms are affected by the characteristics of their origin and pathway areas, and influence the air quality of downwind areas such as Ahvaz, Iran [7]. In Ahvaz as a city affected by MED events, the average concentrations of PM with an aerodynamic diameter < 10 μm (PM10), <2.5 μm (PM2.5), and < 1 μm (PM1) were 319.6, 69.5, and 37.02 μg/m3, respectively [5]. During the six months of study period, a total of 72 dust events were recognized that raised PM10, PM2.5, and PM1 concentrations to 5337.6, 910.9, and 495 μg/m3, respectively [5]. In Khorramabad, during a three-month period, the monthly averages of PM10 and PM2.5 in Khorramabad have been reported to be 100.1–199.8 and 190.8–215.7 μg/m3, respectively [8]. In 2015, a total of 172 and 9 days in Khorramabad were reported with dust condition and MED storm, respectively. These led to about 93 cases of cardiopulmonary hospitalization in this city [9]. Other studies also have reported that dust events are increasing particulate air pollution and excess attributable deaths in Khorramabad [10].
In general, the physicochemical air quality of cities with MED episodes in Iran is well documented [9, 11], but limited studies have investigated the biological aspects of MED events. Microbial agents such as bacteria and fungi can be transported by dust storms over long distances [12]. In a study by Honda et al. (2017), Bjerkandera adusta as a fungus associated with Asian dust storms exacerbated asthma in vitro [13]. In the southwestern United States, the incidence of Valley fever that is caused by the fungal species Coccidioides immitis and C. posadasii was related to the occurrence of dust storms [14]. Asian dust storms are associated with higher concentrations of fungal species [7].
In case of MEDs, some studies have been conducted to explore the fungal species dominant during the dust events [12, 15, 16], but none of these studies have carried out in Khorramabad. In addition, conducting a comprehensive study with large sample size over a long period of time is required. This study aimed to evaluate and compare the phenotypes and genotypes of airborne fungi during the MED and non-MED days in Khorramabad, Iran. Additionally, the associations and correlations of concentrations and number of fungal species with meteorological parameters and criteria air pollutants were aimed to be assessed.
Methods
Study design
This study was carried out in Khorramabad city, which is located in southwestern areas of Iran (33° 29′ 16″ N; 48° 12′ 21″ E) and has a population about 370,000. The mountainous topography of city exacerbates the potential for air pollution trapping. During the recent years, Khorramabad is affected by severe MED events that has worsened the air quality. Figure 1 shows the location of Khorramabad in Iran, and also contains two pictures taken during one MED and one normal day (without dust events) in this city. The samples were collected regularly every six days at 3 locations illustrated in Fig. 1 in 4–6 P.M. (local time) during April 2018–March 2019. Based on the predictions by Meteorological Organization of Iran on occurrence of dust episodes (using satellite images and minimum optical depth), additional samples were collected on MED days. In total, 74 days were sampled. Six dust storms were identified during the study period, and 13 samples were collected from these events. The air temperature and relative humidity were measured (Barometer Model: PHB-318) during the samplings.
Fig. 1.
The approximate location of Khorramabad city and sampling locations
Sampling and analysis
The sampling procedure was according to a previous study [15]. Briefly, the samples were collected using an Andersen Impactor for single stage sampling (SKC, US) and a 10-cm Petri dish with Sabouraud Dextrose Agar (SDA), and Chloramphenicol (100 μg/L) was used to inhibit bacterial growth. The Petri dish was placed at the height of 1.5 m. Afterward, the samples were immediately transferred to the laboratory and maintained under the proper conditions. For phenotypic analyses, the Petri dishes were incubated at 25 °C for 72–120 h, and then, the colonies were counted. To evaluate slow-growing fungi, the cultures were maintained for 4 weeks, and then re-counted. Some suspected colonies were isolated, and subcultures and slide cultures were prepared for more precise microscopic, macroscopic, and molecular evaluations. The species or genera were identified based on the morphological characteristics approved by standard taxonomic keys [17]. The incubation temperature was evaluated using a calibrated thermometer (in a container of sterilized water) placed in the incubator. After 30 min, the thermometer’s temperature was read, and the incubator was adjusted accordingly. The samples were collected in duplicate. In this case, the number of colonies were averaged. The colony counts were converted to the concentrations (CFU/m3) using the following equation:
Concentration (CFU/m3) = (CFU)/(sampling duration × pump flow) Eq. 1.
Where pump flow was set to 0.0283 m3/min, and sampling durations were in the range of 1–2 min.
For molecular identification, DNA was extracted from fresh and pure culture colonies using the method described by Makimura et al., with some modifications [18]. Briefly, small amount (approximately 5 cubic millimeter) of the fresh colony was allocated in 100 μL lysis buffer (100 mM Tris-HCl, PH = 7.5, 30 mM EDTA, 0.5% w/v SDS) and then crushed with a conical grinder (Micro Multi Mixer, IEDA Co. Ltd., Tokyo, Japan) for 1 min, incubated for 15 min at 100 °C, then mixed with 100 μL of 2.5 M sodium acetate, kept at −20 °C for 60 min, and centrifuged at 12000 g for 5 min. The supernatants were removed and DNA was precipitated with an equal volume of isopropanol, kept at −20 °C for 30 min, and centrifuged at 8000 g for 15 min. Then, the pellet was washed with 300 μL of 100% ethanol at a centrifuge force of 10,000 g. The pellet was again washed with 300 μL of 70% ethanol at a centrifuge force of 10,000 g. After air-drying, DNA was resuspended in 50 μL of ultrapure water, and kept at −20 °C until using as template for PCR.
The ITS1–5.8 s-1TS2 regions of rDNA gene were amplified using primers ITS1 (5′- TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5’-TCCTCCGCTTATTGATATGC-3′) (22). PCR reactions contained 12.5 μL of premix (Ampliqon, Denmark), 2 μL of DNA template, 0.5 μM of each primer, and enough water to reach a final reaction volume of 30 μL. Two negative controls (water instead of fungal DNA) were added to each PCR. The mixture was initially denatured at 95 °C for 5 min, followed by 35 cycles of temperature changes, including: 94 °C for 30 s, 58 °C for 45 s, 72 °C for 45 s, and a terminal extension step of 72 °C for 5 min. PCR products were separated via electrophoresis on 1.5% agarose gels and visualized using staining with ethidium bromide (0.5 μg/mL) and photographed under UV irradiation. After genotypic analyses, BLAST algorithm was used to identify the fungi ((http://www.ncbi.nlm.nih.gov/).
Air quality and meteorological data
In order to investigate the effect of meteorological factors on the concentrations and types of fungi, the values of air temperature, relative humidity, and wind speed on sampling days were acquired from Meteorological Organization of Iran. In addition, to evaluate the correlation between fungi and other air pollutants during dust and non-dust days, hourly concentrations of PM10, PM2.5, sulfur dioxide (SO2), nitrogen oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), ozone (O3), and carbon monoxide (CO) were kept from Department of Environment of Iran.
Data analysis
To compare the concentrations of fungi and air pollutants between dust and non-dust days, first, normality of distributions and equity of variances were analyzed using Kolmogorov-Smirnov test and Levene’s test, respectively. Independent t-test and Mann-Whitney U test were used for parametric and non-parametric analyses, respectively. To evaluate the effect of meteorological variables on fungal concentrations and number of species, backward multiple linear regressions were used. In addition, Spearman correlation was used to assess the correlation between meteorological variables, fungal indices, and air pollutants. Data handling and analysis were carried out by Excel 2016 (Microsoft office) and SPSS v.24 (IBM), respectively.
Results
In this study, 13 and 61 dust and normal days were sampled, respectively. Table 1 presents the descriptive statistics of 25 most observed fungal species during the study period. Table S1 in the Supplementary Materials provide the complete descriptive statistics of all identified fungi. The average (±SD) of total fungal concentration was 460.9 (±493.2) CFU/m3. In total, Sixty-nine (69) fungal species were identified within all samplings. The five most observed fungi were Cladosporium cladosporioides, Alternaria alternate, Penicillium chrysogenum that were identified in 48.9%, 46.8%, and 38.2% of days, and had the average concentrations of 221.2, 65.8, and 80.4 CFU/m3, respectively. The fungi with the highest average concentrations i.e. the most prevalent species included Cladosporium cladosporioides (221.2 CFU/m3), Penicillium brevicompactum (134.9 CFU/m3), and Cladosporium iridis (96.4 CFU/m3), respectively. In case of diversity, 12 species of Aspergillus, 10 species of Alternaria, 9 species of Penicillium, and 8 species of Cladosporium were detected. More importantly, Talaromyces albobiverticillius that is not a member of normal fauna of Iran was identified during a dust storm event.
Table 1.
Descriptive statistics of all fungal species observed during the study period
| Genre/Specie | Day | No. of days (%) a | CFU/m3 | |||
|---|---|---|---|---|---|---|
| Mean (SD) | Median | Min. | Max. | |||
| Cladosporium cladosporioides | Both | 23 (48.9%) | 221.2 (262.1) | 106.01 | 35.34 | 883.39 |
| Alternaria alternata | Both | 22 (46.8%) | 65.8 (53.7) | 53.00 | 35.34 | 282.69 |
| Penicillium chrysogenum | Both | 18 (38.2%) | 80.4 (62.7) | 70.67 | 35.34 | 247.35 |
| Cladosporium pseudocladosporioides | Both | 14 (29.7%) | 78.2 (68.1) | 53.00 | 35.34 | 282.69 |
| Aspergillus niger | Both | 13 (27.6%) | 57 (46.8) | 35.34 | 35.34 | 176.68 |
| Aspergillus flavus | Both | 13 (27.6%) | 43.4 (21.1) | 35.34 | 35.34 | 106.01 |
| Cladosporium iridis | Both | 11 (23.4%) | 96.3 (61.3) | 70.67 | 35.34 | 212.01 |
| Penicillium brevicompactum | Both | 11 (23.4%) | 134.9 (196.6) | 70.67 | 35.34 | 706.71 |
| Penicillium sp. | Both | 8 (17%) | 39.7 (12.4) | 35.34 | 35.34 | 70.67 |
| Fusarium | Both | 8 (17%) | 48.5 (18.2) | 35.34 | 35.34 | 70.67 |
| Cladosporium sp. | Both | 7 (14.8%) | 70.6 (35.3) | 70.67 | 35.34 | 141.34 |
| Sporobolomyces poonsookiae | Both | 7 (14.8%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Alternaria arborescens | Both | 6 (12.7%) | 41.2 (14.4) | 35.34 | 35.34 | 70.67 |
| Alternaria terricola | Both | 6 (12.7%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Alternaria sp. | Both | 6 (12.7%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Acremonium sp. | Both | 6 (12.7%) | 47.1 (28.8) | 35.34 | 35.34 | 106.01 |
| Cladosporium tenuissimum | MED | 5 (10.6%) | 70.6 (61.2) | 35.34 | 35.34 | 176.68 |
| Cladosporium cucumerinum | Both | 5 (10.6%) | 70.6 (49.9) | 35.34 | 35.34 | 141.34 |
| Penicillium simplicissimum | Both | 5 (10.6%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Naganishia adeliensis | Both | 5 (10.6%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Yeast sp. | Normal | 5 (10.6%) | 42.4 (15.8) | 35.34 | 35.34 | 70.67 |
| Aspergillus fumigatus | Both | 4 (8.5%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Alternaria japonica | Normal | 4 (8.5%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Ulocladium dauci | Normal | 4 (8.5%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
| Penicillium piscarium | Both | 4 (8.5%) | 35.3 (0) | 35.34 | 35.34 | 35.34 |
aNumber of days
Figure S1 in the Supplementary Materials illustrates the front and reverse view of the colonies of some selected fungi. Figure 2 shows the front and reverse view of culture of isolate Talaromyces albobiverticillius. Fig. S2 (Supplementary Materials) illustrates the phylogenetic analysis of Talaromyces albobiverticiiius strain Y1005 based on the partial sequence of rDNA genes isolated in the present study. Talaromyces albobiverticiiius with accession numbers: KJ775732.1, KJ775723.1, KJ775711.1, KP017805.1 and KX832942.1 were used as the comparison group. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model. The name of species and accession number of other fungi observed in molecular analyses are presented in Table S2 (Supplementary Materials). Fig. S3 illustrates the electrophoregram of genomic DNA of 16 selected species.
Fig. 2.
Front and reverse view of culture of isolate Talaromyces albobiverticillius on (SC) sabouraud dextrose agar with chloramphenicol
Figure 3 illustrates the average concentrations (standard deviation) of the most observed fungal species in dust and non-dust days separately. The navy blue bars show the species common in the ten leading ranked fungi in both sets of days. The black and green bars denote to the species only present among the 10 leading ranks in dust and non-dust days, respectively. The red bar represents the species that only observed during dust day. The fungal species during dust days had 3.6 times higher average concentration (967.65 CFU/m3), comparing to those in normal days (267.10 CFU/m3). During normal and dust days, 61 and 45 different species were detected, respectively, indicating more diversity under normal conditions.
Fig. 3.
The average concentrations (standard deviation) of the most observed fungal species during (a) non-MED and (b) MED days
Among the top ranked species, only five species were observed in both MED and non-MED days: C. cladosporioides, A. alternate, C. pseudocladosporioides, C. iridis, P. chrysogenum, Cladosporium sp., and P. brevicompactum. In total, during MEDs, the concentrations of several fungi were increased such as C. cladosporioides, A. alternate, C. pseudocladosporioides, C. iridis, P. chrysogenum, Cladosporium sp., P. brevicompactum, C. tenuissimum, Alternaria sp., and C. cucumerinum. This showed that MED episodes elevate the prevalence of Cladosporium species; while during the normal days, Aspergillus species such as Aspergillus niger and flavus were dominant.
Eight fungal species were only observed during MED days, not normal days. These species (average concentration) were Aspergillus ruber (35.34 CFU/m3), Aspergillus cristatus (35.34 CFU/m3), Cladosporium tenuissimum (70.67 CFU/m3), Cladosporium herbarum (70.67 CFU/m3), Curvularia sp. (35.34 CFU/m3), Kluyveromyces marxianus (35.34 CFU/m3), Talaromyces albobiverticillius (35.34 CFU/m3), and Phoma sp. (70.67 CFU/m3).
Table 2 presents the descriptive statistics of air pollutants including PM10, PM2.5, SO2, O3, NO2, NO, NOx, and CO and meteorological parameters including temperature, relative humidity, and wind speed. The average concentrations of PM10 during dust days, non-dust days, and the whole period were calculated to be 158.3, 92.2, and 137.4 μg/m3, respectively. The concentrations of PM10 in dust days were 1.72 times higher than those in non-dust days. The PM2.5:PM10 ratio was 0.37. In addition to particulate matter, levels of SO2 and relative humidity during dust days were statistically higher than those in non-dust days (p < 0.05). Air temperature during dust days was greater than those during non-dust days (p < 0.05). No statistically significant differences were found between the levels of other pollutants and meteorological parameters in dust and non-dust days.
Table 2.
Descriptive statistics of air pollutants and meteorological parameters
| Parameter | Unit | Ave (±SD) | Median | Min | Max |
|---|---|---|---|---|---|
| Overall | |||||
| PM10 | μg/m3 | 137.42 (±42.69) | 151.51 | 25.96 | 189.34 |
| PM2.5 | μg/m3 | 38.45 (±22.85) | 38.43 | 5.92 | 70.51 |
| SO2 | ppb | 63.37 (±92.68) | 12.64 | 3.67 | 242.27 |
| O3 | ppb | 10.04 (±9.51) | 4.36 | 2.42 | 29.20 |
| NO2 | ppb | 23.95 (±5.32) | 23.18 | 16.88 | 32.29 |
| NO | ppb | 27.33 (±31.87) | 14.37 | 6.37 | 91.82 |
| NOx | ppb | 51.28 (±29.58) | 40.25 | 24.62 | 108.69 |
| CO | ppm | 1.67 (±0.77) | 1.52 | 0.27 | 2.60 |
| Temperature | °C | 17.99 (±10.32) | 17.35 | 3.30 | 32.95 |
| Relative humidity | % | 45.98 (±23.44) | 53.00 | 14.50 | 97.50 |
| Wind speed | km/h | 6.16 (±2.67) | 6.00 | 3.00 | 15.00 |
| MED days | |||||
| PM10 | μg/m3 | 158.28 (±12.59) | 159.52 | 151.51 | 189.34 |
| PM2.5 | μg/m3 | 58.94 (±10.62) | 60.5 | 44.56 | 70.2 |
| SO2 | ppb | 117.56 (±112.96) | 112.39 | 10.81 | 242.27 |
| O3 | ppb | 9.2 (±7.16) | 8.46 | 2.42 | 18.95 |
| NO2 | ppb | 24.54 (±6.2) | 24.89 | 16.88 | 31.25 |
| NO | ppb | 25.83 (±32.6) | 14.29 | 6.37 | 91.82 |
| NOx | ppb | 50.37 (±29.99) | 43.14 | 24.62 | 108.69 |
| CO | ppm | 1.77 (±0.63) | 1.86 | 1.01 | 2.36 |
| Temperature | °C | 13.53 (±6.98) | 15.60 | 3.60 | 27.40 |
| Relative humidity | % | 58.92 (±16.63) | 58.00 | 18.50 | 79.00 |
| Wind speed | km/h | 7.3 (±3.72) | 6.00 | 3.00 | 15.00 |
| non-MED days | |||||
| PM10 | μg/m3 | 92.21 (±50.96) | 115.32 | 25.96 | 132.96 |
| PM2.5 | μg/m3 | 32.14 (±22.02) | 23.14 | 5.92 | 70.51 |
| SO2 | ppb | 30.86 (±64.01) | 11.74 | 3.67 | 212.77 |
| O3 | ppb | 10.55 (±11.03) | 4.36 | 2.73 | 29.20 |
| NO2 | ppb | 23.6 (±5.03) | 22.82 | 17.76 | 32.29 |
| NO | ppb | 28.23 (±33.16) | 14.37 | 7.36 | 90.61 |
| NOx | ppb | 51.82 (±30.94) | 40.05 | 27.65 | 108.38 |
| CO | ppm | 1.63 (±0.85) | 1.48 | 0.27 | 2.60 |
| Temperature | °C | 19.69 (±10.96) | 24.13 | 3.30 | 32.95 |
| Relative humidity | % | 40.89 (±23.97) | 38.00 | 14.50 | 97.50 |
| Wind speed | km/h | 5.66 (±1.93) | 6.00 | 3.00 | 12.00 |
Table 3 presents the results of linear regression modeling to investigate the effects of meteorological variables on the concentrations and number of fungal species. Air temperature did not affect concentrations and number of fungal species; however, relative humidity and wind speed were significantly associated with fungi. Wind speed had a higher impact on the concentrations of fungi (coefficient = 57.05) and types of species (coefficient = 0.44), comparing to the relative humidity. The adjusted R-squared of regression models were in the range of 0.17–0.2, which can be acceptable, considering the number of variables included in the models.
Table 3.
Results of linear regression for the effects of meteorological variables on the concentrations and number of fungal species
| Response | Constant | Relative humidity | Wind speed | R2 | Adj. R2 | ||
|---|---|---|---|---|---|---|---|
| Coefficient | P | Coefficient | P | ||||
| Concentration | −250.18 | 8.03 | 0.016 | 57.05 | 0.041 | 0.213 | 0.173 |
| No. of species | 2.16 | 0.05 | 0.024 | 0.44 | 0.011 | 0.240 | 0.201 |
Spearman correlation was conducted between meteorological parameters, the concentrations and number of fungal species, and air pollutants including PM10, PM2.5, NO2, NO, NOx, O3, SO2, and CO. The results of Spearman correlation analysis are presented in Table 4. A high correlation coefficient (0.916) was found between the concentrations and number of fungal species. Relative humidity (r = 0.36–0.48) and wind speed (r = 0.31–0.34) showed to be fairly correlated with the indices of fungi. Temperature was not correlated with the concentrations and number of fungal species. Among the air pollutants, only PM10 (r = 0.55) and PM2.5 (r = 0.51) were significantly correlated with the concentrations of fungi. Number of species were not correlated with any of the air pollutants. PM10 and PM2.5 showed to have correlations with most of the air pollutants, except for O3 and NO. In addition, PM10 and PM2.5 correlated with relative humidity and wind speed, not air temperature.
Table 4.
Results of Spearman correlation between meteorological variables and the concentrations and number of fungal speciesa,b
| No. of species | O3 | NO | NO2 | NOx | PM10 | SO2 | CO | PM2.5 | Temp. | RH | SW | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Concentrations | 0.916 | −0.112 | −0.174 | 0.086 | −0.126 | 0.552 | 0.415 | 0.227 | 0.512 | −0.284 | 0.477 | 0.341 |
| No. of species | 0.029 | 0.475 | 0.045 | −0.410 | 0.308 | 0.405 | 0.077 | 0.428 | −0.153 | 0.364 | 0.308 | |
| O3 | 0.734 | 0.123 | 0.634 | 0.301 | 0.493 | 0.640 | 0.681 | 0.438 | 0.215 | 0.228 | ||
| NO | 0.188 | 0.808 | 0.149 | 0.531 | 0.575 | 0.385 | 0.411 | 0.140 | 0.125 | |||
| NO2 | 0.570 | 0.504 | 0.479 | 0.523 | 0.479 | 0.443 | 0.734 | 0.627 | ||||
| NOx | 0.414 | 0.468 | 0.814 | 0.589 | 0.600 | 0.387 | 0.432 | |||||
| PM10 | 0.556 | 0.566 | 0.837 | 0.257 | 0.537 | 0.529 | ||||||
| SO2 | 0.476 | 0.575 | 0.121 | 0.507 | 0.447 | |||||||
| CO | 0.901 | 0.500 | 0.550 | 0.562 | ||||||||
| PM2.5 | 0.099 | 0.693 | 0.715 | |||||||||
| Temperature | −0.382 | 0.501 | ||||||||||
| Relative humidity | 0.217 |
aThe statistically significant (p < 0.05) correlations are bold
bTemp temperature, RH relative humidity, WS wind speed
Discussion
Ambient air of Khorramabad was sampled to collect and evaluate the fungal species present during normal days and MED days. The concentrations of fungi during MED days were extremely higher than those in normal days. This is consistent with the results of other studies. This may be due to several reasons. Fungi from upwind regions could have been brought to the area, and increase the microbial load in atmosphere. In addition, higher wind speed increases aerosolization of phylloplane and soil fungi from their reservoirs during dust events [19]. Soleimani et al. (2013) reported that the concentrations of different fungal species in dust days of Ahvaz -as another Iranian city affected by MED- were 1.3–13.4 times higher than those in normal days. The average concentrations of total fungi in dust days of summer, autumn, and winter 447, 1504, and 1026 CFU/m3, respectively; while the corresponding averages in normal days were 300, 551, and 621 CFU/m3, respectively [15]. In another study in Ahvaz, the average concentrations of fungi during normal and MED days were 596 and 1116 CFU/m3, respectively [16]. In another city affected by MEDs, the average concentrations of fungal species during dust events were about 4 times higher than those in normal days [12]. In case of Asian dust storms in Taiwan, the average concentration of all fungal species during dust events was 1829 CFU/m3 [7].
We observed many changes in types and proportion of airborne fungal species during dust events, comparing to the normal days. Eight new fungi were detected during MED events, indicating that long-range transport of airborne particles can affect the mycobiota in an area. In addition, Aspergillus spp. and Cladosporium spp. were relatively more dominant during normal and dust days, respectively. Soleimani et al. (2013) showed that Cladosporium spp. were the dominant species during both normal and MED days in Ahvaz [15]. In another MED-affected city, Cladosporium spp. was the most prevalent fungi during dust episodes, after Mycosporium spp. In case of normal days, Cladosporium spp. were still the dominant fungi, although in extremely lower concentrations [12]. The difference in the dominant species during dust events is a conventional phenomenon, and is repeatedly reported in other areas of the world, especially when comparing the findings with the studies from other cities with different topography, meteorology, and fauna [20]. In a study on Asian dust storms, Cladosporium (average = 706 CFU/m3) was the dominant species, followed by non-sporulating fungi, Penicillium, Curvularia, Alternaria, and Aspergillus. They also reported that the proportion of species during dust and non-dust days was different [7]. The differences in types and proportion of dominant fungi during dust and non-dust days could be due to the different meteorological conditions, local vegetation, human activities, and also the characteristics of dust storm’s pathway. These factors separately or aggregately could promote the growth of specific types of fungi [7, 21, 22]. Future studies could investigate the upwind sources to associate their fauna to the affected areas during dust events.
Among the 8 fungi observed only during dust events, Talaromyces albobiverticillius was recorded for the first time in Iran and the countries that dust originate from. At first, we identified the cultured colony as Talaromyces marneffei, but molecular identification proved the presence of T. albobiverticillius. In general, Talaromyces is a genus of fungi in the family Trichocomaceae. Talaromyces albobiverticillius that was previously classified in the genus Penicillium, is a species producing red pigment and industrial usage as a food-coloring agent [23]. In some documents, it has been considered as an indoor air pollutant [24]. To our knowledge this is the first time that T. albobiverticillius is reporting in the Middle East, and especially in Iran. One reason can be the lack of comprehensive studies in different areas of Iran and other neighbor countries. Besides that Talaromyces species have been frequently reported in many parts of the world, T. albobiverticillius is also observed in Eastern Asian countries such as China, Taiwan, African dust, and European countries such as Italy, and [24–26]. It can be entered to the Middle East through human transportations from other parts of the world. In one study on desert dust in Turkey, molecular analysis of one colony was led to equal identification of Penicillium, Eupenicillium and Talaromyces sp. [27].
Our results showed that O3, NO, NO2, NOx, and CO concentrations during the dust events were not statistically different from those in normal days. This can be due to the contribution of upwind sources such as of these pollutants (oil refineries, petrochemical plants and other industries). This shows that in Ahvaz and maybe in some other areas, local conditions may exacerbates the air quality deterioration during dust events, and the major concerns in such episodes may not be limited to particulate matter. In a study in China, concentrations PM10, PM2.5, SO2, and O3 in dust days were higher than normal days. However, the average temperature, RH, NO, and NOx were lower. In addition, no significant differences were found in the levels of CO and NO2 [28]. Despite out results, in a study on Asian dust storms, concentrations of SO2, CO, NO2, and non-methane hydrocarbons were lower during dust periods. The authors claimed that it is possibly because of the accumulation of local pollution after dust events caused by atmospheric stagnancy brought about by the anticyclonic outflow [7]. These differences in the results of studies from different areas are due to local characteristics, anthropogenic sources of air pollution, and season [29, 30].
Among the air pollutants, only PM10 and PM2.5 were significantly correlated with the concentrations of fungi. This means that the variations in concentrations of fungi had been following the fluctuations of particulate matter, indicating the effect of dust events on the increase of fungi. The results also showed that wind speed and relative humidity were significantly associated with the fungi. The same relationship was not found in case of air temperature. In general, meteorological conditions affect the viability and growth of fungi. Suitable conditions promote the growth of different types of fungal species. Soleimani et al. (2013) reported that fungal concentrations were associated with relative humidity during Middles Eastern dust storms [15]. In another study also reported that particulate matter and wind speed were significantly higher during dust periods, but temperature and the concentrations of SO2, NO2 and hydrocarbons were higher during normal days [7]. Temperature drop and relative humidity increase during dust events have been reported repeatedly [31, 32]. In previous studies, temperature and wind speed have been positively correlated with fungal concentrations [7]. Several studies have reported that temperature and dew point are the important factors that determine the types of fungal spores found in outdoor air [20]. Despite the non-significant effect of temperature in our study, this factor is known as one of the most essential environmental factors affecting fungal growth and survival. In addition, relative humidity determines the availability of water, which is a critical factor for growth [33, 34].
Conclusions
Airborne fungi were evaluated in a city affected by severe Middle Eastern dust storms during both dust and non-dust days. Dust days showed higher concentrations of fungi. Relatively different mycobiota was observed between dust and non-dust days. Eight fungal species were only observed during MED days, including Talaromyces albobiverticillius was detected for the first time in Iran. Our results indicated that dust events lead to the changes in the air pollutants composition and mycobiota, and elevated fungal concentrations. This condition may extremely affect the public health. The identification of a new species during dust events reflects the conveyance of dust from far areas. Therefore, special considerations should be undertaken for the air quality deteriorations during MED events. Regarding the different types and proportion of fungi during dust and non-dust days, future studies could investigate the upwind dust sources to associate their fauna to the affected areas during dust events. In addition, further studies are required to investigate whether the airborne fungi during dust events could induce greater health effects than those in normal days.
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Acknowledgments
The authors wish to thank Tehran University of Medical Sciences for their full support of this PhD thesis at Mycology (#thesis number: 38506-27-02-97, and ethical code: IR.TUMS.SPH.REC.1397.268).
This work was conducted as a Ph.D. student thesis of Mohammad Yarahmadi at Mycology. The authors wish to thank Tehran University of Medical Sciences (TUMS) for their full support of this study (Grant number: 97-02-27-38506, and ethical code: IR.TUMS.SPH.REC.1397.268).
Compliance with ethical standards
Conflict of interests
The authors declare that they have no conflict of interest.
Footnotes
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