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Pollen Trends Analysis with AeRobiology

The workflow "Pollen Trends Analysis with AeRobiology" leverages the AeRobiology library to manage and analyze time-series data of airborne pollen particles. Aimed at understanding the temporal dynamics of different pollen types, this workflow ensures data quality, profiles seasonal trends, and explores temporal variations. It integrates advanced features for analyzing pollen concentrations and their correlation with meteorological variables, offering comprehensive insights into pollen behavior over time. The workflow enhances data accessibility, facilitating broader research and public health applications.<div><br></div><div>Background</div><div>In the dynamic landscape of environmental research and public health, the AeRobiology library ( https://cran.r-project.org/web/packages/AeRobiology/index.html) emerges as a potent instrument tailored for managing diverse airborne particle data. As the prevalence of airborne pollen-related challenges intensifies, understanding the nuanced temporal trends in different pollen types becomes imperative. AeRobiology not only addresses data quality concerns but also offers specialized tools for unraveling intricate insights into the temporal dynamics of various pollen types.</div><div><br></div><div>Introduction</div><div>Amidst the complexities of environmental research, particularly in the context of health studies, the meticulous analysis of airborne particles—specifically various pollen types—takes center stage. This workflow, harnessing the capabilities of AeRobiology, adopts a holistic approach to process and analyze time-series data. Focused on deciphering the temporal nuances of pollen seasons, this workflow aims to significantly contribute to our understanding of the temporal dynamics of different airborne particle types.</div><div><br></div><div>Aims</div><div>The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of time series pollen samples:

- Holistic Data Quality Assurance: Conduct a detailed examination of time-series data for various pollen types, ensuring completeness and accuracy to establish a robust foundation for subsequent analysis.

- Pollen-Specific Seasonal Profiling: Leverage AeRobiology's advanced features to calculate and visually represent key parameters of the seasonal trends for different pollen types, offering a comprehensive profile of their temporal dynamics.

- Temporal Dynamics Exploration: Investigate the temporal trends in concentrations of various pollen types, providing valuable insights into their evolving nature over time.

- Enhanced Accessibility: Employ AeRobiology's interactive tools to democratize the exploration of time-series data, making complex information accessible to a broader audience of researchers and professionals.</div><div><br></div><div>Scientific Questions</div><div>This workflow addresses critical scientific questions related to pollen analysis:</div><div>- Distinct Temporal Signatures: What are the discernible patterns and trends in the temporal dynamics of different airborne pollen types, especially during peak seasons?

- Pollen-Specific Abundance Variability: How does the abundance of various pollen types vary throughout their respective seasons, and what environmental factors contribute to these fluctuations?

- Meteorological Correlations: Are there statistically significant correlations between the concentrations of different pollen types and specific meteorological variables, elucidating the influencing factors unique to each type?

- Cross-Annual Comparative Analysis: Through the lens of AeRobiology, how do the temporal trends of different pollen types compare across different years, and what contextual factors might explain observed variations?</div>

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Date ( Publication)
2023-12-31T00:00:00
Status
On going / operational
Principal investigator
  University of Malaga - José Francisco Aldana Montes

Publisher
  LifeWatch ERIC ICT Core - Francisco Manuel SÁNCHEZ-CANO

Custodian
  LifeWatch ERIC ICT Core - Antonio José SÁENZ-ALBANÉS

Principal investigator
  LifeWatch ERIC ICT Core - ICT Core Group

Keywords

Phytosociological Analysis

Keywords

Vegetation Inventories

Keywords

Statistical Analysis

Keywords

Data Preprocessing

Keywords

Interactive Graphs

Keywords

Species Dominance

Keywords

Altitudinal Distribution

Keywords

Species Coverage

Keywords

Similarity Dendrogram

Keywords

Species Interactions

Keywords

Fidelity Index

Keywords

Environmental Management

Access constraints
Copyright
Other constraints

Copyright 2023 Khaos Research Group

Protocol

DOI

Service Name

Import file from CSV

Service Description

Input CSV dataset

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/core/ImportFile/0.0.5

Service Name

iPot Pollen

Service Description

Interactive graph of the pollen data during one season

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotPollen/1.0.0

Service Name

iPot Year

Service Description

Interactive graph of the pollen data during several seasons

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotYear/1.0.0

Service Name

Quality Control

Service Description

Check the quality of several pollen types

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/QualityControl/1.0.0

Service Name

Calculate PS

Service Description

Calculate the main parameters of the pollen season regarding phenology and pollen intensify

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/CalculatePs/1.0.0

Service Name

Plot PS

Service Description

Plot the main pollen season from a single pollen type

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotPs/1.0.0

Service Name

Pollen Calendar

Service Description

Calculate the pollen calendar from several pollen types

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PollenCalendar/1.0.0

Service Name

Plot Abundance

Service Description

Bar plot based on the relative abundance in the air of pollen types with respect of the total amount

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IplotAbundance/1.0.0

Service Name

Analyse Trend

Service Description

Graphical representation of the pollen trends

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/AnalyseTrend/1.0.0

Service Name

Plot Pheno

Service Description

Graphical representations of the phenological parameters

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotPheno/1.0.0

Service Name

Plot Trend

Service Description

Graphical representations of the trends in some main pollen season variables

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotTrend/1.0.0

Service Name

Plot Summary

Service Description

Graphical representation of average, maximum and minimum pollen concentration by type

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotSummary/1.0.0

Service Name

Plot Normsummary

Service Description

Graphical representation of pollen concentration by type and year

Service Reference (id)

https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotNormsummary/1.0.0

Workflow Helpdesk

https://helpdesk.lifewatch.eu

Metadata

File identifier
1fed6055-8072-4a9f-b72c-beea93a2f19e XML
Metadata language
en
Hierarchy level
Workflow
Metadata Schema Version

1.0

 
 

Overviews

Spatial extent

Keywords



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