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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
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Methods for interpolating missing data in aerobiological databases.

A Picornell1, J Oteros2, R Ruiz-Mata1

  • 1Department of Botany and Plant Physiology, University of Malaga, Campus de Teatinos s/n, E-29071, Malaga, Spain.

Environmental Research
|May 31, 2021
PubMed
Summary
This summary is machine-generated.

Missing environmental data can be filled using interpolation methods. The moving mean approach showed the highest success rate for pollen and spore data, especially for shorter gaps. A new Variation Index helps estimate interpolation errors.

Keywords:
AerobiologyBioaerosolsEnvironmental samplingInterpolationMissing dataModellingTime-series

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Area of Science:

  • Environmental science
  • Aerobiology
  • Data science

Background:

  • Environmental time series data often contain missing values due to collection difficulties and equipment failures.
  • Gaps in environmental databases hinder accurate data analysis.
  • Interpolation is a common strategy to address missing data in environmental research.

Purpose of the Study:

  • To assess and compare the accuracy of different interpolation methods for environmental time series data.
  • To evaluate the influence of gap size, interpolation method, and seasonal periods on interpolation accuracy.
  • To develop a method for estimating potential interpolation errors before application.

Main Methods:

  • Utilized daily pollen/spore concentration data from six aerobiological sampling stations.
  • Randomly removed data points to simulate missing values.
  • Applied various interpolation methods to fill data gaps.
  • Compared interpolated data with observed data to quantify errors.
  • Introduced a novel Variation Index to predict interpolation error.

Main Results:

  • The moving mean interpolation method demonstrated the highest average success rate.
  • A 70% success rate was achieved with the moving mean method when considering pollen alert system risk classes.
  • Interpolation errors increased with longer data gaps (over 5 days) and during high concentration periods (pre-peak and peak).
  • High oscillations in daily concentrations led to greater interpolation errors.

Conclusions:

  • The moving mean interpolation method is effective for filling missing environmental data, particularly for shorter gaps.
  • For extended data gaps, alternative interpolation strategies should be explored.
  • The developed Variation Index provides a valuable tool for estimating potential interpolation errors, guiding method selection.