POLLEN SPECTRUM VARIATIONS IN THE ATMOSPHERE OF SANTINIKETAN, WEST BENGAL AND INFLUENCE OF METEOROLOGICAL PARAMETERS & AIR POLLUTION

Abstract

The present study deals with the survey of airborne pollen spectrum in the atmosphere of Santiniketan, West Bengal (Eastern part of India) using a Burkard personal volumetric sampler for the two-years period 2014– 2015, with the aim of supplementing and extending the knowledge of the city air plankton. A total of 66 pollen taxa belonging to 37 angiosperm families and one gymnosperm have been identified, of which pollen of Grasses (Poaceae) showed a maximum frequency followed by Cassia sp., Acacia sp., Solanaceae, Asteraceae and Cyperaceae. Pollen grains of Cycas sp., Lagerstroemia sp., Spathodea campanulata, Lantana camara, Eucalyptus sp., Malvaceae, Liliaceae, Parthenium hysterophorus, Carica papaya, Peltophorum pterocarpum, Areca catechu, Catharanthus roseus were also predominant in the air of Santiniketan. Total pollen catch in these 2 years’ survey was 1,57,400 /m3 of air which reflects the rich pollen diversity of this famous sub-urban tourist spot of West Bengal. The place has the highest annual concentrations of pollen in April (13024/m3 in 2014 and 12160/m3 in 2015) in both of the study years. Peak pollen season was observed twice in a year i.e. one in pre-monsoon time and other in post- moonsoon time. The studies carried out disclosed the relationship between air pollen concentration, meteorological factors an air quality data (pollutants). Yearly variations on these seasons could be related to the influence of meteorological factors such as temperature, rainfall, relative humidity and wind speed which have been proven statistically by correlation analysis. PM10 and PM 2.5 were found to be statistically significant with total pollen count, while other pollution parameters like NO2 and SO2 were found to be non-significant. So, we have analysed the air quality data as independent variables, with pollen counts as dependent variables for 2014 and 2015 employing a Multiple Regression Analysis (MRA) model using MiniTab 17.0 and fitted a regression equation for prediction of weekly pollen counts for future years. Employing the MRA we also have plotted Fitted line plot for two significant air pollutants PM10 and PM 2.5 separately. We also compiled a pollen calendar showing data on monthly pollen concentrations, from which a great deal of useful and important information has been obtained. Pollen calendars are useful in the prevention and diagnosis of hay fever, in that they enable the timing and severity of the pollen season to be clearly defined. This aeropalynological survey may serve as guide for allergologiests to predict and manage the source and the incidence of allergic diseases among local inhabitants.