A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores
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 occurre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/6/1016 |
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| author | Andrés M. Vélez-Pereira Concepción De Linares Miquel A. Canela Jordina Belmonte |
| author_facet | Andrés M. Vélez-Pereira Concepción De Linares Miquel A. Canela Jordina Belmonte |
| author_sort | Andrés M. Vélez-Pereira |
| collection | DOAJ |
| description | 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 (<i>Alternaria</i>, <i>Cladosporium</i>, <i>Agaricus</i>, <i>Ganoderma</i>, <i>Leptosphaeria</i>, and <i>Pleospora</i>) 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. |
| first_indexed | 2024-03-11T02:46:27Z |
| format | Article |
| id | doaj.art-152ae958a9264e2bac94a7983df8ce6c |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T02:46:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-152ae958a9264e2bac94a7983df8ce6c2023-11-18T09:15:03ZengMDPI AGAtmosphere2073-44332023-06-01146101610.3390/atmos14061016A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal SporesAndrés M. Vélez-Pereira0Concepción De Linares1Miquel A. Canela2Jordina Belmonte3Departamento de Ingeniería Mecánica, Facultad de Ingeniería, Universidad de Tarapacá, Avenue 18 de Septiembre 2222, Arica 1000007, ChileDepartment of Botany, Universidad de Granada, 18071 Granada, SpainDepartment of Managerial Decision Sciences, IESE, Business School, 08034 Barcelona, SpainInstitute of Environmental Science and Technology, Universitat Autònoma de Barcelona, 08193 Barcelona, SpainAerobiological 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 (<i>Alternaria</i>, <i>Cladosporium</i>, <i>Agaricus</i>, <i>Ganoderma</i>, <i>Leptosphaeria</i>, and <i>Pleospora</i>) 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.https://www.mdpi.com/2073-4433/14/6/1016aerobiologylogistic regressionmycologypredictionregression tree |
| spellingShingle | Andrés M. Vélez-Pereira Concepción De Linares Miquel A. Canela Jordina Belmonte A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores Atmosphere aerobiology logistic regression mycology prediction regression tree |
| title | A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores |
| title_full | A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores |
| title_fullStr | A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores |
| title_full_unstemmed | A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores |
| title_short | A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores |
| title_sort | comparison of models for the forecast of daily concentration thresholds of airborne fungal spores |
| topic | aerobiology logistic regression mycology prediction regression tree |
| url | https://www.mdpi.com/2073-4433/14/6/1016 |
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