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The present work is the first of two reviews on applied modeling in the field of aerobiology. The aerobiological predictive models for pollen and fungal spores, usually defined as predictive statistical models, will, amongst other objectives, forecast airborne particles' concentration or dynamical behavior of the particles. These models can be classified into Observation Based Models (OBM), Phenological Based Models (PHM), or OTher Models (OTM). The aim of this review is to show, analyze and discuss the different predictive models used in pollen and spore aerobiological studies. The analysis was performed on published electronic scientific articles from 1998 to 2016 related to the type of model, the taxa and the modelled parameters. From a total of 503 studies, 55.5% used OBM (44.8% on pollen and 10.7% on fungal spores), 38.5% PHM (all on pollen) and 6% OTM (5.4% on pollen and 0.6% on fungal spores). OBM have been used with high frequency to forecast concentration. The most frequent model of OBM was linear regression (18.5% out of 503) on pollen and artificial neural networks (4.6%) on fungal spores. In the PHM, the principal use was to characterize the main pollen season (flowering season) based on the model of growth degree days. Finally, OTM have been used to estimate concentrations at unmonitored areas. Olea (14,5%) on pollen and Alternaria (4,8%) on fungal spores were the taxa most frequently modelled. Daily concentration was the most modelled parameter by OBM (25.2%) and season start day by PHM (35.6%). The PHM approaches include greater model diversity and use fewer independent variables than OBM. In addition, PHM show to be easier to apply than OBM; however, the wide range of criteria to define the parameters to use in PHM (e.g.: pollination start day) makes that each model is used with a lesser frequency than other models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Alternaria and Cladosporium are the most common airborne fungal spores responsible for health problems, as well as for crop pathologies. The study of their behavior in the air is a necessary step for establishing control and prevention measures. The aim of this paper is to develop a logistic regression model for predicting the daily concentrations of airborne Alternaria and Cladosporium fungal spores from meteorological variables. To perform the logistic regression analysis, the concentration levels are binarized using concentration thresholds. The fungal spore data have been obtained at eight aerobiological monitoring stations of the Aerobiological Network of Catalonia (NE Spain). The meteorological data used were the maximum and minimum daily temperatures and daily rainfall provided by the meteorological services. The relationship between the meteorological variables and the fungal spore levels has been modeled by means of logistic regression equations, using data from the period 1995-2012. Values from years 2013-2014 were used for validation. In the case of Alternaria, three equations for predicting the presence and the exceedance of the thresholds 10 and 30 spores/m 3 have been established. For Cladosporium, four equations for the thresholds 200, 500, 1000, and 1500 spores/m 3 have been established. The temperature and cumulative rainfall in the last 3 days showed a positive correlation with airborne fungal spore levels, while the rain on the same day had a negative correlation. Sensitivity and specificity were calculated to measure the predictive power of the model, showing a reasonable percentage of correct predictions (ranging from 48 to 99%). The simple equations proposed allow us to forecast the levels of fungal spores that will be in the air the next day, using only the maximum and minimum temperatures and rainfall values provided by weather forecasting services.

Airborne fungal spores are prevalent components of bioaerosols with a large impact on ecology, economy and health. Their major socioeconomic effects could be reduced by accurate and timely prediction of airborne spore concentrations. The main aim of this study was to create and evaluate models of Alternaria and Cladosporium spore concentrations based on data on a continental scale. Additional goals included assessment of the level of generalization of the models spatially and description of the main meteorological factors influencing fungal spore concentrations. Aerobiological monitoring was carried out at 18 sites in six countries across Europe over 3 to 21 years depending on site. Quantile random forest modelling was used to predict spore concentrations. Generalization of the Alternaria and Cladosporium models was tested using (i) one model for all the sites, (ii) models for groups of sites, and (iii) models for individual sites. The study revealed the possibility of reliable prediction of fungal spore levels using gridded meteorological data. The classification models also showed the capacity for providing larger scale predictions of fungal spore concentrations. Regression models were distinctly less accurate than classification models due to several factors, including measurement errors and distinct day-to-day changes of concentrations. Temperature and vapour pressure proved to be the most important variables in the regression and classification models of Alternaria and Cladosporium spore concentrations. Accurate and operational daily-scale predictive models of bioaerosol abundances contribute to the assessment and evaluation of relevant exposure and consequently more timely and efficient management of phytopathogenic and of human allergic diseases., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Alternaria alternata (AA) is an important mould in respiratory allergic diseases. The objective is to determine the prevalence of AA sensitization in respiratory allergic patients and to examine the annual variation of Alternaria spores. 824 patients between 5 and 65 years old with allergic rhinitis and /or asthma were enrolled. Prick tests were performed with two different standardized AA extracts with quantification of Alt a 1. Alternaria spores concentrations were provided. 151 patients (18.3%) were sensitized to AA. The patients sensitized to AA were affected more frequently by asthma when compared to the patients not sensitized to AA (chi-square test = 7.34; p = 0.003). The prevalence of AA sensitization in paediatric population was statistically significantly higher than in patients older than 13. Atmospheric levels of Alternaria spores showed two periods of maximum concentration: July/August and October/November. The sensitization prevalence of AA in patients with respiratory allergy is meaningful, fundamentally in paediatric patients and/or allergic asthma patients. There is a significance spore concentration of Alternaria in the studied area, with a seasonal behaviour.

Abstract: Alternaria is a common airborne phytopathogenic fungus that may affect crops in the field or can cause decay of plant products. It can also cause diseases in animals and humans. The study of airborne Alternaria conidia is a necessary step for the control and prevention of the agricultural damage they can provoke. The aim of this paper is to contribute to model the presence and levels of Alternaria conidia in the air using a logistic regression model. Our study is conducted in 12 monitoring stations in Spain corresponding to three geographic regions with different bio-climatic characteristics, which show three different patterns of Alternaria conidia dynamics: a unique main sporulation season from mid spring to autumn in NE Spain, two defined periods (spring and autumn) in SE Spain and a uniform and constant presence in the Canary Islands. Regarding the abundance, NE Spain shows the highest values and the Canary Islands the lowest. Daily Alternaria conidia concentration is positively correlated to daily minimum temperature and daily temperature variation and negatively correlated to daily precipitation. Also, the occurrence of rain in the 3 previous days has a positive effect on Alternaria levels. These effects are modelled in this paper by means of logit regression equations. The three equations used apply to the presence of Alternaria conidia, and to the exceedance of thresholds of 10 and 30conidia/m3. The model is calibrated in the 12 stations using data from years 1995 to 2008 and validated with data from 2009 in 7 stations, showing a reasonable percentage of right prediction (average 78.6%, ranging from 61.3% to 92.5%). [ABSTRACT FROM AUTHOR]

Fungal spores are universal atmospheric components associated to allergic reactions. Alternaria (Ascomycota) is considered the most allergenic spore taxa. Alt a 1 is the major allergen of Alternaria and is present also in other Pleosporales. In this study, standard Hirst-based sampling and analyzing methods for measuring spore daily concentrations of Alternaria, Curvularia, Drechslera-Helminthosporium, Epicoccum, Leptosphaeria, Pithomyces, Pleospora and Stemphylium (all included in the taxon Pleosporales) have been used as well as two high-volume samplers, Burkard Cyclone (2017) and MCV CAV-A/mb (2019–2020), and ELISA kits for measuring the allergen. The detection and quantification of Alt a 1 was only possible in the samples from the MCV sampler. Although Alt a 1 was better correlated with Alternaria spores than with Pleosporales spores, the three of them showed high correlations. It is shown that there is a high and significant correlation of Alt a 1 with temperature, a negative correlation with relative humidity and no correlation with precipitation. The aerobiological monitoring of these three elements ensures the best information for understanding the affectation to allergy sufferers, but, if this is not possible, as a minimum public health service aimed at the detection, treatment and prevention of allergies, the study of the airborne Alternaria spores should be ensured. [ABSTRACT FROM AUTHOR]