1fed6055-8072-4a9f-b72c-beea93a2f19e 1.0 Pollen Trends Analysis with AeRobiology 2023-12-31T00:00:00 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> José Francisco Aldana Montes University of Malaga jfaldana@uma.es Francisco Manuel SÁNCHEZ-CANO LifeWatch ERIC ICT Core franciscom.sanchez@lifewatch.eu Antonio José SÁENZ-ALBANÉS LifeWatch ERIC ICT Core aj.saenz@lifewatch.eu ICT Core Group LifeWatch ERIC ICT Core ict.coordination@lifewatch.eu Phytosociological Analysis Vegetation Inventories Statistical Analysis Data Preprocessing Interactive Graphs Species Dominance Altitudinal Distribution Species Coverage Similarity Dendrogram Species Interactions Fidelity Index Environmental Management Copyright 2023 Khaos Research Group Import file from CSV Input CSV dataset https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/core/ImportFile/0.0.5 iPot Pollen Interactive graph of the pollen data during one season https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotPollen/1.0.0 iPot Year Interactive graph of the pollen data during several seasons https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotYear/1.0.0 Quality Control Check the quality of several pollen types https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/QualityControl/1.0.0 Calculate PS Calculate the main parameters of the pollen season regarding phenology and pollen intensify https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/CalculatePs/1.0.0 Plot PS Plot the main pollen season from a single pollen type https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotPs/1.0.0 Pollen Calendar Calculate the pollen calendar from several pollen types https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PollenCalendar/1.0.0 Plot Abundance Bar plot based on the relative abundance in the air of pollen types with respect of the total amount https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IplotAbundance/1.0.0 Analyse Trend Graphical representation of the pollen trends https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/AnalyseTrend/1.0.0 Plot Pheno Graphical representations of the phenological parameters https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/IPlotPheno/1.0.0 Plot Trend Graphical representations of the trends in some main pollen season variables https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotTrend/1.0.0 Plot Summary Graphical representation of average, maximum and minimum pollen concentration by type https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotSummary/1.0.0 Plot Normsummary Graphical representation of pollen concentration by type and year https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/data-sink/PlotNormsummary/1.0.0 https://helpdesk.lifewatch.eu DOI