# Temporal and spatiotemporal autocorrelation of daily concentrations of Alnus, Betula, and Corylus pollen in Poland

## TL;DR

This study analyzed how pollen concentrations of Alnus, Betula, and Corylus in eight Polish cities vary over time and space, finding factors influencing their spread.

## Contribution

The study introduces a novel approach to model pollen count variations using spatiotemporal autocorrelation over an 8-year period.

## Key findings

- Pollen count variations are influenced by air mass exchange after weather fronts.
- Long-lasting factors account for 50–60% of pollen count variation.
- Random factors like diurnal changes and measurement errors contribute 10%.

## Abstract

The aim of the study was to determine the characteristics of temporal and space–time autocorrelation of pollen counts of Alnus, Betula, and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001–2005 and 2009–2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus, Betula, and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30–40 % of pollen count variation); (2) long-lasting factors (50–60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.