Data mining assessment of Poaceae pollen influencing factors and its environmental implications

Poaceae pollen is highly allergenic, with a marked contribution to the pollen worldwide allergy prevalence. Pollen counts are defined by the species present in the considered area, although year-to-year oscillations may be triggered by different parameters, among which are weather conditions. Due to the predominant role of Poaceae pollen in the allergenicity in urban green areas, the aim of this study was the analysis of pollen trends and the influence of meteorology to forecast relevant variations in airborne pollen levels. The study was carried out during the 1993–2020 period in Ourense, in NW Iberian Peninsula. We used a volumetric Lanzoni VPPS 2000 trap for recording Poaceae airborne pollen grains, and meteorological daily data were obtained from the Galician Institute for Meteorology and Oceanography. The main indexes of the pollen season and their trends were calculated. A correlation analysis and ‘C5.0 Decision Trees and Rule-Based Models’ data mining algorithm were applied to determine the influence of meteorological conditions on pollen levels. We detected atmospheric Poaceae pollen during 139 days on average, mainly from April to August. The mean pollen grains amount recorded during the pollen season was 4608 pollen grains, with the pollen maximum peak of 276 pollen/m3 on 27 June. We found no statistically significant trends and slight slopes for the seasonal indexes, similarly to previous Poaceae studies in the same region. The calculated C5.0 model offered defined results, indicating that the combination of mean temperature above 17.46 °C and sunlight exposure higher than 12.7 h is conductive to significantly high pollen levels. The obtained results make possible the identification of risk moments during the pollen season for the activation of protective measures for sensitized population to grass pollen. [Display omitted] •To forecast relevant variations in airborne pollen levels by data mining as novelty•The obtained results show the identification risk moments during the pollen season.•The results allowed us to propose measures for sensitized population to grass pollen.