Methods for interpolating missing data in aerobiological databases

A Picornell; J Oteros; R Ruiz-Mata; M Recio; M M Trigo; M Martínez-Bracero; B Lara; A Serrano-García; C Galán; H García-Mozo; P Alcázar; R Pérez-Badia; B Cabezudo; J Romero-Morte; J Rojo

doi:10.1016/j.envres.2021.111391

Methods for interpolating missing data in aerobiological databases

Environ Res. 2021 Sep:200:111391. doi: 10.1016/j.envres.2021.111391. Epub 2021 May 28.

Authors

A Picornell¹, J Oteros², R Ruiz-Mata³, M Recio³, M M Trigo³, M Martínez-Bracero⁴, B Lara⁵, A Serrano-García⁵, C Galán², H García-Mozo², P Alcázar², R Pérez-Badia⁵, B Cabezudo³, J Romero-Morte⁵, J Rojo⁶

Affiliations

¹ Department of Botany and Plant Physiology, University of Malaga, Campus de Teatinos s/n, E-29071, Malaga, Spain. Electronic address: picornell@uma.es.
² Department of Botany, Ecology and Plant Physiology, Agrifood Campus of International Excellence CeiA3, University of Cordoba, Cordoba, Spain; Andalusian Inter-University Institute for Earth System IISTA, University of Cordoba, Spain.
³ Department of Botany and Plant Physiology, University of Malaga, Campus de Teatinos s/n, E-29071, Malaga, Spain.
⁴ Department of Botany, Ecology and Plant Physiology, Agrifood Campus of International Excellence CeiA3, University of Cordoba, Cordoba, Spain; Andalusian Inter-University Institute for Earth System IISTA, University of Cordoba, Spain; School of Chemical and Pharmaceutical Sciences, Technological University Dublin, Dublin, Ireland.
⁵ University of Castilla-La Mancha, Institute of Environmental Sciences (Botany), Toledo, Spain.
⁶ University of Castilla-La Mancha, Institute of Environmental Sciences (Botany), Toledo, Spain; Department of Pharmacology, Pharmacognosy and Botany, Complutense University, Madrid, Spain.

PMID: 34058184
DOI: 10.1016/j.envres.2021.111391

Abstract

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.

Keywords: Aerobiology; Bioaerosols; Environmental sampling; Interpolation; Missing data; Modelling; Time-series.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Databases, Factual
Pollen*
Seasons