Analyzing airborne fungal concentration in Kolkata, India: temporal distribution, the effect of atmospheric parameters and health impact

SenGupta, Koyel; Karmakar, Bijoya; Roy, Sangeeta; Kaur, Amarjeet; Bhattacharya, Swati Gupta

doi:10.1007/s11869-023-01316-1

Cited by 2 publications

(2 citation statements)

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“…The results regarding relative humidity are also similar to other studies [58,62,64,67,68,70,71]. Hasnain et al [67] and Quintero et al [70] assert that high humidity and light precipitation facilitate the sporulation of basidiospores.…”

Section: Discussionsupporting

confidence: 88%

“…The high response of ascospores to the precipitation obtained in the logit model and regression tree analyses could be due to fungal spore release being favored by the rain, provoking higher emissions after rain ceased [5,[51][52][53][54][55]. Likewise, the results agree with other studies in terms of relative humidity but differ with respect to minimum temperature [56][57][58][59]. However, our results agree with Toscano-Underwood et al [60], who assert that ascoma can develop in a wide range of temperatures (5-20 • C), but it is optimal between 15 and 20 • C. This is corroborated by Kaczmarek et al [61], who assert that an increase in temperature is associated with an increasing trend of ascospores in Poland.…”

Section: Discussionsupporting

confidence: 87%

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A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores

Vélez-Pereira

Linares

Canela³

et al. 2023

Atmosphere

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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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