Methods for interpolating missing data in aerobiological databases.

Missing data is a common problem in scientific research. The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.

Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.

Betula pollen is frequently found in the atmosphere of central and northern Europe. Betula pollen are health relevant as they cause severe allergic reactions in the population. We developed models of thermal requirements to predict start, peak and end dates of the Betula main pollen season for Bavaria (Germany). Betula pollen data of one season from 19 locations were used to train the models. Estimated dates were compared with observed dates, and the errors were spatially represented. External validation was carried out with time series datasets of 3 different locations (36years in total). RESULTS: The temperature requirements to detonate the main pollen season proved non-linear. For the start date model (error of 8,75days during external validation), daily mean temperatures above a threshold of 10°C from 28th of February onwards were the most relevant. The peak model (error of 3.58days) takes into account mean daily temperatures accumulated since the first date of the main pollen season in which the daily average temperature exceeded 11°C. The end model (error of 3.75days) takes into account all temperatures accumulated since the start of the main pollen season. CONCLUSION: These models perform predictions that enable the allergic population to better manage their disease. With the established relationship between temperatures and pollen season dates, changes in the phenological behaviour of Betula species due to climate change can be also estimated in future studies by taking into account the different climate scenarios proposed by previous climate change studies.

Increasing resolution of airborne pollen forecasting at a discrete sampled area in the southwest Mediterranean Basin.

Daily fluctuations of the airborne pollen concentrations produce variations on symptomatology in allergic population. Such fluctuations are influenced by local vegetal coverage, flowering phenology, geography and climatology. Since 1991, airborne pollen of Malaga province (southern Spain) has been monitored in 7 different locations. Malaga station has been kept operational uninterruptedly throughout the studied period, while the rest of the stations only worked in periods of 2-4 years. Weekly, its pollen information is updated online to inform the population in order to prevent allergic diseases. Increasing the spatial resolution of pollen information would be very useful for allergic population living at unsampled locations. Due to the impossibility of keeping operational a high number of pollen stations covering the whole province of Malaga, the aim of this study is to create spatial models to extrapolate and forecast the pollen concentrations to Malaga province by using the concentrations registered at the capital as unique input. To do so, the relationships obtained between the airborne pollen concentrations detected at Malaga city and those detected at the other stations have been used to elaborate models for the main pollen types registered at the province. These models were spatially interpolated all over the province by using co-kriging techniques and the Compensated Thermicity Index as covariable. As result of this work, pollen distribution of the 8 most prevalent taxa has been depicted all over the whole Malaga province and an allergy alert system has been set up to extrapolate pollen information from Malaga to the whole province.