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Abstract

Influence of meteorological parameters and air pollutants on the airborne pollen of city Chandigarh, India.

Abstract

Pollen, climatic variables and air pollutants coexist in nature with the potential to interact with one another and play a crucial role in increasing allergic diseases. The current study evaluates the influence of meteorological parameters and air pollutants on the airborne pollen in an urban city, Chandigarh, situated in the Indo-Gangetic Plains. Airborne pollen monitoring was done following Spanish Aerobiological Network guidelines and dynamics of daily total pollen and six most abundant taxa were studied from June 2018 to June 2020. Among meteorological parameters, temperature and wind were the most correlated and influential parameters to airborne pollen concentration. Annual Pollen Integral (APIn) of Cannabis sativa (r = 0.52), Parthenium hysterophorus (r = 0.27), Poaceae (r = 0.32) and total pollen concentration (r = 0.30) showed a statistically significant positive correlation with temperature. In contrast, precipitation and relative humidity negatively correlated with APIn of total pollen concentration, Eucalyptus sp. and Poaceae except for Parthenium hysterophorus and Celtis occidentalis. Similar results were found with Seasonal Pollen Integral (SPIn) of total pollen concentration, six major taxa and meteorological variables. Spearman correlation performed for NOx showed a significant positive correlation among APIn and SPIn of Celtis occidentalis and insignificant among APIn and SPIn of Eucalyptus sp. and Morus alba. In contrast, except for Eucalyptus sp., PM10 and PM2.5 were negatively correlated among APIn and SPIn of total pollen concentration and other major taxa. Spearman's correlation of APIn and SPIn for each pollen taxon, meteorological parameters and air pollutants suggests that each taxon has a different pattern in response to all parameters. The study findings suggest that pollen response must be examined at the taxon level, not the assemblage level, having long time-series data. This will help to compute future scenarios of changing environmental factors and comprehend the relationships and trends among meteorology, air pollutants and aerobiology.