A comparison of models for the forecast of daily concentration thresholds of airborne fungal spores
Keywords: 
Aerobiology
Logistic regression
Mycology
Prediction
Regression tree
Issue Date: 
13-Jun-2023
Publisher: 
MDPI
Project: 
info:eu-repo/grantAgreement/MINECO/Proyectos de Investigación Fundamental No-Orientada/CGL2012-39523-C02-01/ES/BIOAEROSOLES ATMOSFERICOS: NIVELES, TRANSPORTE E IMPACTOS
ISSN: 
2073-4433
Note: 
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Citation: 
Vélez-Pereira, A.M. (Andrés M.); Linares, C. (Concepción) de; Canela, M.A. (Miguel A.); et al. "A comparison of models for the forecast of daily concentration thresholds of airborne fungal spores". Atmosphere. 14 (6), 2023-06-13, 1016
Abstract
Aerobiological predictive model development is of increasing interest, despite the distribution and variability of data and the limitations of statistical methods making it highly challenging. The use of concentration thresholds and models, where a binary response allows one to establish the occurrence or non-occurrence of the threshold, have been proposed to reduce difficulties. In this paper, we use logistic regression (logit) and regression trees to predict the daily concentration thresholds (low, medium, high, and very high) of six airborne fungal spore taxa (Alternaria, Cladosporium, Agaricus, Ganoderma, Leptosphaeria, and Pleospora) in eight localities in Catalonia (NE Spain) using data from 1995 to 2014. The predictive potential of these models was analyzed through sensitivity and specificity. The models showed similar results regarding the relationship and influence of the meteorological parameters and fungal spores. Ascospores showed a strong relationship with precipitation and basidiospores with minimum temperature, while conidiospores did not indicate any preferences. Sensitivity (true-positive) and specificity (false-positive) presented highly satisfactory validation results for both models in all thresholds, with an average of 73%. However, seeing as logit offers greater precision when attempting to establish the exceedance of a concentration threshold and is easier to apply, it is proposed as the best predictive model.
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