Methods for interpolating missing data in aerobiological databases

Picornell, Antonio; Oteros, José; Ruiz-Mata, Rocío; Recio, Marta; Trigo, M.M.; Martínez-Bracero, Moisés; Lara, Beatriz; Serrano-García, Alicia; Galán, Carmen; Garcı́a-Mozo, Herminia; Alcázar, Pilar; Pérez-Badía, Rosa; Cabezudo, Baltasar; Romero-Morte, Jorge; Rojo, Jesús

doi:10.1016/j.envres.2021.111391

Cited by 20 publications

(6 citation statements)

References 52 publications

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“…We interpolated the missing data with the moving mean function because it adapted better to our data than linear regression did in preliminary tests. We confirm that for aerobiological data, the moving mean interpolation performs better as compared to other methods as suggested by Picornell et al (2021). We present the weekly pollen concentration average of our studied years, not considering this presentation a pollen calendar for which at least five years of data are required (Galán et al, 2017).…”

Section: Discussionsupporting

confidence: 78%

The duration and severity of the allergenic pollen season in Istanbul, and the role of meteorological factors

2022

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Information on the allergenic pollen season provides insight on the state of the environment of a region and facilitates allergy symptom management. We present a retrospective analysis of the duration and severity of the allergenic pollen season and the role of meteorological factors in Istanbul, Turkey. Aerobiological sampling from January 2013 to June 2016, pollen identification and counting followed current standard methodology. Pollen seasons were defined according to 95% of the Annual Pollen Integral (APIn) and the season start date was compared with the first day of 5 day consecutive non-zero records. Generalized additive models (GAMs) were created to study the effect of meteorological factors on flowering. The main pollen contributors were taxa of temperate and Mediterranean climates, and neophytic Ambrosia. Cupressaceae, Poaceae, Pinaceae, Quercus and Ambrosia had the greatest relative abundance. The pollen season defined on 95% of the APIn was adequate for our location with total APIns around 10.000 pollen*day*m−3. Woody taxa had generally shorter seasons than herbaceous taxa. In trees, we see precipitation as the main limiting factor for assimilate production prior to anthesis. A severe tree pollen season in 2016 suggests intense synchronous flowering across taxa and populations triggered by favourable water supply in the preceding year. GAM models can explain the effect of weather on pollen concentrations during anthesis. Under the climatic conditions over the study period, temperature had a negative effect on spring flowering trees, and a positive one on summer flowering weeds. Humidity, atmospheric pressure and precipitation had a negative effect on weeds. Our findings contribute to environmental and allergological knowledge in southern Europe and Turkey with relevancy in the assessment of impacts of climate change and the management of allergic disease.

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Section: Discussionsupporting

confidence: 78%

The duration and severity of the allergenic pollen season in Istanbul, and the role of meteorological factors

2022

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“…In this study, we selected the linear interpolation method for filling the pollen gaps. Linear interpolation is one of the most commonly used methods to complete missing data, although it is more extended in other disciplines than in Aerobiology [84]. Other interpolation methods, such as moving mean interpolation or using data of nearby locations, are rarely applied.…”

Section: Discussionmentioning

confidence: 99%

“…Other interpolation methods, such as moving mean interpolation or using data of nearby locations, are rarely applied. These two methods, together with another three (i.e., linear, spline and temporal series interpolation), were tested by Picornell et al [84] for the assessment of their accuracy and performance. These authors found that the moving mean interpolation method obtained the highest success rate on average.…”

Section: Discussionmentioning

confidence: 99%

Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa

González-Fernández

Álvarez-López

Piña-Rey

et al. 2021

Forests

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Variations in the airborne pollen load are among the current and expected impacts on plant pollination driven by climate change. Due to the potential risk for pollen-allergy sufferers, this study aimed to analyze the trends of the three most abundant spring-tree pollen types, Pinus, Platanus and Quercus, and to evaluate the possible influence of meteorological conditions. An aerobiological study was performed during the 1993–2020 period in the Ourense city (NW Spain) by means of a Hirst-type volumetric sampler. Meteorological data were obtained from the ‘Ourense’ meteorological station of METEOGALICIA. We found statistically significant trends for the Total Pollen in all cases. The positive slope values indicated an increase in pollen grains over the pollen season along the studied years, ranging from an increase of 107 to 442 pollen grains. The resulting C5.0 Decision Trees and Rule-Based Models coincided with the Spearman’s correlations since both statistical analyses showed a strong and positive influence of temperature and sunlight on pollen release and dispersal, as well as a negative influence of rainfall due to washout processes. Specifically, we found that slight rainfall and moderate temperatures promote the presence of Pinus pollen in the atmosphere and a marked effect of the daily thermal amplitude on the presence of high Platanus pollen levels. The percentage of successful predictions of the C5.0 models ranged between 62.23–74.28%. The analysis of long-term datasets of pollen and meteorological information provides valuable models that can be used as an indicator of potential allergy risk in the short term by feeding the obtained models with weather prognostics.

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“…Few references compare the precision and accuracy of various interpolation methods, and many previous studies simply selected a single method without stated justification or intercomparisons of alternative methods (Li and Heap 2011, 2014; Louvet et al 2016). However, intercomparison studies of interpolation are beginning in fields of study outside of limnology (Penone et al 2014; Miao et al 2021; Picornell et al 2021) and within limnology (Lottig and Carpenter 2012; Song et al 2016). Intercomparison studies and available analysis scripts will guide aquatic scientists on choosing the most promising tools among many methods available for their data.…”

Section: Figmentioning

confidence: 99%

Reconstructing missing data by comparing interpolation techniques: Applications for long‐term water quality data

Larson,

Bungula,

Lee

et al. 2023

Limnology & Ocean Methods

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Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long‐term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L‐TP)−1, TN had a MAE of 0.39 mg (L‐TN)−1, and water velocity had a MAE of 0.10 m s−1. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

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Methods for interpolating missing data in aerobiological databases

Cited by 20 publications

References 52 publications

The duration and severity of the allergenic pollen season in Istanbul, and the role of meteorological factors

The duration and severity of the allergenic pollen season in Istanbul, and the role of meteorological factors

Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa

Reconstructing missing data by comparing interpolation techniques: Applications for long‐term water quality data

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