Correlation of spring spore concentrations and meteorological conditions in Tulsa, Oklahoma

Different spore types are abundant in the atmosphere depending on the weather conditions. Ascospores generally follow precipitation, while spore types such as Alternaria and Cladosporium are abundant in dry conditions. This project attempted to correlate fungal spore concentrations with meteorological data from Tulsa, Oklahoma during May 1998 and May 1999. Air samples were collected and analyzed by the 12-traverse method. The spore types included were Cladosporium, Alternaria, Epicoccum, Curvularia, Pithomyces, Drechslera, smut spores, ascospores, basidiospores, and other spores. Weather variables included precipitation levels, temperature, dew point, air pressure, wind speed, wind direction and wind gusts. There were over 242.57 mm of rainfall in May 1999 and only 64.01 mm in May 1998. The most abundant spore types during May 1998 and May 1999 were Cladosporium, ascospores, and basidiospores. Results showed that there were significant differences in the dry-air spora between May 1998 and May 1999. There were twice as many Cladosporium in May 1998 as in May 1999; both ascospores and basidiospores showed little change. Multiple regression analysis was used to determine which meteorological variables influenced spore concentrations. Results showed that there was no single model for all spore types. Different combinations of factors were predictors of concentration for the various fungi examined; however, temperature and dew point seemed to be the most important meteorological factors. Received: 5 July 2000 / Revised: 20 December 2000 / Accepted: 22 December 2000     

Effects of meteorological conditions on spore plumes

Fungal spores are an ever-present component of the atmosphere, and have long been known to trigger asthma and hay fever symptoms in sensitive individuals. The atmosphere around Tulsa has been monitored for airborne spores and pollen with Burkard spore traps at several sampling stations. This study involved the examination of the hourly spore concentrations on days that had average daily concentrations near 50,000 spores/m(3) or greater. Hourly concentrations of Cladosporium, Alternaria, Epicoccum, Curvularia, Pithomyces, Drechslera, smut spores, ascospores, basidiospores, other, and total spores were determined on 4 days at three sites and then correlated with hourly meteorological data including temperature, rainfall, wind speed, dew point, air pressure, and wind direction. On each of these days there was a spore plume, a phenomenon in which spore concentrations increased dramatically over a very short period of time. Spore plumes generally occurred near midday, and concentrations were seen to increase from lows around 20,000 total spores/m(3) to highs over 170,000 total spores/m(3) in 2 h. Multiple regression analysis of the data indicated that increases in temperature, dew point, and air pressure correlated with the increase in spore concentrations, but no single weather variable predicted the appearance of a spore plume. The proper combination of changes in these meteorological parameters that result in a spore plume may be due to the changing weather conditions associated with thunderstorms, as on 3 of the 4 days when spore plumes occurred there were thunderstorms later that evening. The occurrence of spore plumes may have clinical significance, because other studies have shown that sensitization to certain spore types can occur during exposure to high spore concentrations.     

The effects of meteorological factors on the occurrence of <Emphasis Type="Italic">Ganoderma</Emphasis> sp. spores in the air

Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.—one of the most abundant fungal taxa in the area—were developed. Aerobiological sampling was conducted over 2004–2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors.     

Artificial neural network models of relationships between <Emphasis Type="Italic">Alternaria</Emphasis> spores and meteorological factors in Szczecin (Poland)

Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004–2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and log(x+1) transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.     

Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom

Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000–2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257–265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.     

Hourly predictive artificial neural network and multivariate regression tree models of <Emphasis Type="Italic">Alternaria</Emphasis> and <Emphasis Type="Italic">Cladosporium</Emphasis> spore concentrations in Szczecin (Poland)

A study was made of the link between time of day, weather variables and the hourly content of certain fungal spores in the atmosphere of the city of Szczecin, Poland, in 2004–2007. Sampling was carried out with a Lanzoni 7-day-recording spore trap. The spores analysed belonged to the taxa Alternaria and Cladosporium. These spores were selected both for their allergenic capacity and for their high level presence in the atmosphere, particularly during summer. Spearman correlation coefficients between spore concentrations, meteorological parameters and time of day showed different indices depending on the taxon being analysed. Relative humidity (RH), air temperature, air pressure and clouds most strongly and significantly influenced the concentration of Alternaria spores. Cladosporium spores correlated less strongly and significantly than Alternaria. Multivariate regression tree analysis revealed that, at air pressures lower than 1,011 hPa the concentration of Alternaria spores was low. Under higher air pressure spore concentrations were higher, particularly when RH was lower than 36.5%. In the case of Cladosporium, under higher air pressure (>1,008 hPa), the spores analysed were more abundant, particularly after 0330 hours. In artificial neural networks, RH, air pressure and air temperature were the most important variables in the model for Alternaria spore concentration. For Cladosporium, clouds, time of day, air pressure, wind speed and dew point temperature were highly significant factors influencing spore concentration. The maximum abundance of Cladosporium spores in air fell between 1200 and 1700 hours.     

Changes in concentration of Alternaria and Cladosporium spores during summer storms

Fungal spores are known to cause allergic sensitization. Recent studies reported a strong association between asthma symptoms and thunderstorms that could be explained by an increase in airborne fungal spore concentrations. Just before and during thunderstorms the values of meteorological parameters rapidly change. Therefore, the goal of this study was to create a predictive model for hourly concentrations of atmospheric Alternaria and Cladosporium spores on days with summer storms in Szczecin (Poland) based on meteorological conditions. For this study we have chosen all days of June, July and August (2004–2009) with convective thunderstorms. There were statistically significant relationships between spore concentration and meteorological parameters: positive for air temperature and ozone content while negative for relative humidity. In general, before a thunderstorm, air temperature and ozone concentration increased, which was accompanied by a considerable increase in spore concentration. During and after a storm, relative humidity increased while both air temperature ozone concentration along with spore concentrations decreased. Artificial neural networks (ANN) were used to assess forecasting possibilities. Good performance of ANN models in this study suggest that it is possible to predict spore concentrations from meteorological variables 2 h in advance and, thus, warn people with spore-related asthma symptoms about the increasing abundance of airborne fungi on days with storms.     

Airborne ascospores in Tetouan (NW Morocco) and meteorological parameters

Ascospores are frequently found as airborne fungal spores and recognized in various areas as an important cause of respiratory allergies. The main objective of the study was to determine the relationship between airborne ascospores and meteorological parameters using multivariate canonical correspondence analysis (CCA) and Spearman correlation. The aerobiological monitoring of fungal spores was performed over 5 years (2009–2013) using a Burkard volumetric spore traps. Seven main types of ascospores were identified: Leptosphaeria, Pleospora, Venturia, Diatrype, Chaetomium, Sporormiella and Ascobolus. The CCA results showed that all applied variables accounted for 27.4 % of the total variance in the spore data in the 5 years. The largest contribution to the total variance was explained in this period by the maximum air temperature (10.3 %). The effect of meteorological factors varied among years. The highest values of the total variance in the spore data, explained by the statistically significant variables, were observed in 2012 (28.6 %), with the highest contribution to minimum relative humidity (8.0 %). Most ascospores showed positive and statistically significant correlation with relative humidity and rainfall. In contrast, ascospores of Chaetomium were negatively correlated with precipitation and the relative humidity and positively with temperature. Based on these results, epidemiological and allergological studies must deserve more attention to estimate the allergenic potential of the ascospores.     

Airborne <Emphasis Type="Italic">Aspergillus</Emphasis> and <Emphasis Type="Italic">Penicillium</Emphasis> in the atmosphere of Szczecin, (Poland) (2004–2009)

The investigation into airborne fungal spore concentrations was conducted in Szczecin (Poland) between 2004 and 2009. The objective of the studies was to determine a seasonal variation in concentrations of amerospores on the basis of meteorological parameters. The presence of spores in Szczecin was recorded using a volumetric method. Fungal spores were present in the air in high numbers in late summer and early autumn. The highest concentrations were noted in September, October and November. The peak period was recorded in August, September, October and November. The highest annual number of spores occurred in 2005 and 2007 and the lowest in 2006. High values of daily concentration of amerospores occurred during the afternoon and late at night. In 2005 and 2007 the late-night maximum was overdue about 1 or 2 h. For daily values of dew point temperature and relative humidity, the coefficients were positive, significant for p = 0.001 and ranged from 0.342 to 0.258. The average wind speed was positively correlated for p = 0.01 and the coefficient was 0.291. The similar relations were noted for hourly values of spore concentrations for p = 0.05, p = 0.01 and p = 0.001. For these spore types, the dew point temperature and relative humidity appeared to be the most influential factor.     

Airborne Alternaria spores in SE Spain (1993-98)

The annual, seasonal and hourly distribution of Alternaria spores in the air of Murcia, SE España, was studied on a six-year period. The relationships between Alternaria spore concentrations and meteorological factors were investigated. Alternaria is a late afternoon taxon in the Murcia city, with maximum spore concentrations occurring between 13.00h and 21.00h. Alternaria spores are present in the atmosphere throughout the year, with a main spore season extending from March to October, and showing two peaks as a consequence of the summer drop in concentration. Alternaria spore concentrations correlate well with Poaceae and Chenopodiaceae pollen counts, suggesting these plants could be important hosts, but not the only ones, because many crops are growing just when peaks occur. Low wind velocities favoured high spore counts. Correlation with temperature was positive in five of the six years. Mean temperature is the factor which best explain spore levels. The best prediction model obtained explains 74% of the observed variance in Alternaria levels (in a five steps scale) by using mean temperature alone.     

Atmospheric mold spore counts in relation to meteorological parameters

 Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P<0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens. Received: 14 October 1996 / Revised: 17 February 1997 / Accepted: 28 February 1997     

石羊河流域干旱荒漠绿洲区不同滴灌模式下葡萄茎液流变化及其与环境因子的关系

研究了石羊河流域干旱荒漠绿洲区交替滴灌（ADI）、固定滴灌（FDI）和常规滴灌（CDI）模式下葡萄茎液流的变化规律及其与气象因子和土壤含水率的相关关系.结果表明：研究区葡萄茎液流表现出与太阳辐射同步的昼夜变化节律;新梢生长期和开花期CDI处理的茎液流量显著大于其它两个处理;影响瞬时茎液流的主要气象因子是太阳辐射和气温,日茎液流量与平均气温和风速具有线性相关关系;不同灌溉方式下葡萄茎液流与气象因素的相关程度依次为：CDI>ADI>FDI;葡萄日茎液流量与参考作物蒸发蒸腾量（ET₀）呈显著线性相关关系.与CDI相比,ADI节省50%水量,而茎液流总量仅降低6.56%,且其葡萄茎液流和水分传导具有明显的补偿效应.     

A 10-year study of <Emphasis Type="Italic">Alternaria</Emphasis> and <Emphasis Type="Italic">Cladosporium</Emphasis> in two Polish cities (Szczecin and Cracow) and relationship with the meteorological parameters

Alternaria and Cladosporium spores belong to the most frequent and allergenic particles in bioaerosol in the temperate climate. The investigation of Alternaria and Cladosporium spore concentrations was performed in two cities in Poland, Szczecin and Cracow, in 2004–2013. The meteorological parameters taken to assess their impact on fungal spores were average, maximum and minimum temperature, relative humidity and average wind velocity. In order to reveal whether changes in dynamics of spore seasons are driven by meteorological conditions, ordination methods were applied. Canonical correspondence analysis was used to explore redundancy among the predictors (meteorological parameters). Prior to ordination analyses, the data were log(x)-transformed. Concentrations of Alternaria and Cladosporium spores were significantly higher in Szczecin comparing to Cracow, but it was also observed the decreasing trend in the spore concentrations in Szczecin. As regards temperature, it was higher in Cracow and was still increasing in the studied years. Relative humidity and wind velocity were significantly lower in Cracow. In Szczecin meteorological conditions did not explain changes in spore season characteristics (insignificant redundancy analysis models), while in Cracow’s redundancy analysis models indicated that spore season parameters were in over 40 % determined by meteorological conditions, mainly air temperature and wind velocity. If they increase, the peak value, total number of spores and their average concentrations in a season will also increase.     

Effects of wind speed and direction on monthly fluctuations of <Emphasis Type="Italic">Cladosporium</Emphasis> conidia concentration in the air

This study determined the relationship between airborne concentration of Cladosporium spp. spores and wind speed and direction using real data (local wind measured by weather station) and modelled data (air mass flow computed with the aid of HYbrid Single Particle Lagrangian Trajectory model). Air samples containing fungal conidia were taken at an urban site (Worcester, UK) for a period of five consecutive years using a spore trap of the Hirst design. A threshold of ≥6000 s m^?3 (double the clinical value) was applied in order to select high spore concentration days, when airborne transport of conidia at a regional scale was more likely to occur. Collected data were then examined using geospatial and statistical tools, including circular statistics. Obtained results showed that the greatest numbers of spore concentrations were detected in July and August, when C. herbarum, C. cladosporioides and C. macrocarpum sporulate. The circular correlation test was found to be more sensitive than Spearman’s rank test. The dominance of either local wind or the air mass on Cladosporium spore distributions varied between examined months. Source areas of this pathogen had an origin within the UK territory. Very high daily mean concentrations of Cladosporium spores were observed when daily mean local wind speed was v _s ≤ 2.5 m s^?1 indicating warm days with a light breeze.     

MicroclimOz – A microclimate data set for Australia,with example applications

Many problems in pure and applied ecology require the quantification of above‐ and below‐ground microclimates. Here I describe a data set of hourly microclimates for the Australian continent, simulated from the years 1990 to 2017 across a grid of 1893 locations approx. 60 km apart. The data were generated with the NicheMapR microclimate model, driven by 0.05° gridded daily meteorological forcing data (air temperature, wind speed, humidity, cloud cover, rainfall), 0.025° elevation and 0.008° soil texture data. The above‐ground microclimate variables include horizontal plane solar radiation, solar zenith angle, sky temperature (from which down‐welling longwave radiation can be computed), air temperature, relative humidity and wind speed at 1 and 120 cm height, and snow depth. The below‐ground variables include soil temperature, pore humidity, soil moisture and soil water potential for 0, 2.5, 5, 10, 15, 20, 30, 50, 100 and 200 cm below‐ground. The computations are for four shade levels (0%, 50%, 70% and 90%). The data set can be used for a wide variety of applications, including the computation of heat and water budgets of organisms, the potential for vegetation growth, and the computation of stress and growth indices. The use of daily forcing data also allows assessments of the consequences of extreme events including heat waves. Example applications are provided for computing plant growth potential, grasshopper egg development, lizard body temperature and mammalian energy and water requirements.     

Study of airborne fungal spores in Madrid, Spain

The concentration of fungal spores in the atmosphere of Madrid was recorded and analyzed for the year 2003. Airborne spores were sampled continuously with a Hirst-type spore trap located on the roof of a building of the School of Pharmacy, at about 8 m above ground level. Correlation between the mean daily spore concentrations and meteorological variables were explored by means of Spearman’s correlation analyses. Seventy spore types were identified, of which the most numerous were Cladosporium, Aspergillaceae (conidia), Coprinus, Agaricales (basidiospores), Ustilago (teliospores) and Pleospora (ascospores). These six types of spores represented more than 70% of the total. Cladosporium represented 41% of the total fungal spores, while Ustilago spores, the concentrations of which in May and June exceeded 47% of the monthly total spore count, constituted the second most important group. Spores reached their highest concentrations in the spring months, and in the autumn, mainly in October. A␣positive significant correlation was found between airborne spore counts and temperature and relative humidity. The results provide a picture of the spectrum of airborne fungal spores present in the atmosphere of Madrid and of the `peak' periods of their presence. Future studies will provide more detailed information on the seasonal dynamics of the spores most frequently found in the air as well as on the extent to which atmospheric conditions influence their release, dispersion and sedimentation processes.     

Long term trends in outdoor <Emphasis Type="Italic">Aspergillus/Penicillium</Emphasis> spore concentrations in Derby,UK from 1970 to 2003 and a comparative study in 1994 and 1996 with the indoor air of two local houses

Aspergillus/Penicillium spore concentrations have been monitored in Derby since 1970 using a volumetric spore trap, with full year data from 1991. In addition a short comparative study with the indoor air was undertaken at two local houses in 1994 and 1996. Aspergillus/Penicillium spores were present in the Derby air throughout the year and often reached maximum monthly cumulative concentrations in the autumn, although they were occasionally the dominant spores in the winter when total spore concentrations were low. Very high daily concentrations could occur at any time of year with a count of over 5000 recorded. Peak days in the autumn and winter of 2002–2003 were examined on a two hourly basis showing higher concentrations in the middle of the day. There was a positive correlation of cumulative monthly Aspergillus/Penicillium totals with maximum temperature. Indoor data from the two houses was examined on a daily basis and compared with simultaneously sampled outdoor daily spore concentrations. The elevated Aspergillus/Penicillium spore levels found in the older of the two houses occurred on all of the days sampled. Compared to the modern house, the Aspergillus/Penicillium spore concentrations in the old house represented a much higher percentage of the total spore count than in the modern one. The correlation between outdoor Aspergillus/Penicilliumspore concentrations and the indoor air of the old house was 0.62, whereas in the modern house it was 0.31. Peak hourly samples of Aspergillus/Penicillium spore counts occurred at times of greatest activity.     

An analysis of local wind and air mass directions and their impact on Cladosporium distribution using HYSPLIT and circular statistics

Ecological studies that examine species-environment relationships are often limited to several meteorological parameters, i.e. mean air temperature, relative humidity, precipitation, vapour pressure deficit and solar radiation. The impact of local wind, its speed and direction are less commonly investigated in aerobiological surveys mainly due to difficulties related to the employment of specific analytical tools and interpretation of their outputs. Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). We, therefore, performed an analysis of 24 h intra-diurnal cycle of Cladosporium spp. spores from an urban site in connection with both the local wind direction and overall air mass direction computed by HYSPLIT. The observational method was a volumetric air sampler of the Hirst design with 1 h time resolution and corresponding optical detection of fungal spores with light microscopy. The atmospheric modelling was done using the on-line data set from GDAS with 1° resolution and circular statistical methods. Our results showed stronger, statistically significant correlation (p ≤ 0.05) between high Cladosporium spp. spore concentration and air mass direction compared to the local wind direction. This suggested that a large fraction of the investigated fungal spores had a regional origin and must be located more than a few kilometers away from the sampling point.     

Estimation of fungal spore concentrations associated to meteorological variables

A 1-year time series of fungal spore concentrations has been used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated to observed meteorological variables. Analysis of the results revealed that the daily average values of these meteorological variables are suitable to predict with high confidence the number of fungal spores that are actually observed. The calibrated neural network has also been used randomizing each single input parameter in order to evaluate which meteorological variable contributes more to the formation and the depletion of the selected fungal spores. Emphasis is given to the possibility of using the proposed model for operational activities, predicting the future spore concentrations on the basis of meteorological forecasts.     

Analysis of the predicting variables for daily and weekly fluctuations of two airborne fungal spores: <Emphasis Type="Italic">Alternaria</Emphasis> and <Emphasis Type="Italic">Cladosporium</Emphasis>

Alternaria and Cladosporium are two fungal taxa whose spores (conidia) are included frequently in aerobiological studies of outdoor environments. Both spore types are present in the atmosphere of Malaga (Spain) throughout almost the entire year, although they reach their highest concentrations during spring and autumn. To establish predicting variables for daily and weekly fluctuations, Spearman's correlations and stepwise multiple regressions between spore concentrations (measured using a volumetric 7-day recorder) and meteorological variables were made with results obtained for both spore types in 1996 and 1997. Correlations and regressions were also made between the different taxa and their concentrations in different years. Significant and positive correlation coefficients were always obtained between spore concentrations of both taxa, followed by temperature, their concentrations in different years, sunshine hours and relative humidity (this last in a negative sense). For the two spore types we obtained higher correlation and regression coefficients using weekly data. We showed different regression models using weekly values. From the results and a practical point of view, it was concluded that weekly values of the atmospheric concentration of Alternaria spores can be predicted from the maximum temperature expected and its concentrations in the years sampled. As regards the atmospheric concentration of Cladoposrium spores, the weekly values can be predicted based on the concentration of Alternaria spores, thus saving the time and effort that would otherwise be employed in counting them by optical microscopy.