Can we improve pollen season definitions by using the symptom load index in addition to pollen counts?

Bastl, Katharina; Kmenta, Maximilian; Geller-Bernstein, C; Berger, Uwe; Jäger, Siegfried

doi:10.1016/j.envpol.2015.04.016

Cited by 26 publications

(39 citation statements)

References 12 publications

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“…In terms of air quality, aerobiologists focus on plant species whose pollen is most harmful for pollen allergy sufferers (e.g., birch, alder, mugwort, grass, and so on) and emphasize that their co-occurrence with PMs is affected by other factors, such as increasing urban air temperature (Grewling et al, 2019). Bastl et al (2015Bastl et al ( , 2017 described pollen as one of the "green pollutants", which are signi cant components of the atmosphere and are relevant to air quality information for pollen allergy sufferers. This distinction is important for the comprehensive understanding of APS.…”

Section: Extending the Paradigm Of Urban Air Pollutionmentioning

confidence: 99%

“…Ensuring measurement credibility becomes a key scienti c challenge in this context. To this end, we carried out research on the example of air pollution health symptoms, an emerging trend particularly related to odour (Arias et al, 2018) and green pollutant (Bastl et al, 2015) crowdsensing (Dutta et al, 2017, Feng et al, 2018. As crowdsensing (or, more generally, crowdsourcing) methods for health symptom mapping are subject to data bias (Zupančič and Žalik 2019), we developed and tested the quality assurance mechanism (QAm) framework (section 2.2), which can be transferred to similar health symptom-based studies.…”

Section: Introductionmentioning

confidence: 99%

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Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

Samulowska

Chmielewski

Raczko

et al. 2020

Preprint

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The United Nations (UN) sustainable development goals (SDGs), a strategy to guide the world’s social and economic transformation, highlight the issue of urban air pollution in SDG 11. Open data, as an output of citizen science (CS), are needed to supply and improve the SDG indicator system. Therefore, we propose a CS framework to extend the paradigm of urban air pollution monitoring from particulate matter concentration levels to air quality-related health symptom load, and foster the development of a tier-3 SDG indicator (which we call indicator 11.6.3). Building this new perspective for CS contributions to the achievement of SDGs, we address the problem of crowdsourced data bias as a prerequisite for better quality open data output. The aim of this study is to propose an air pollution symptom mapping framework for citizen-driven research and to find the most robust data quality assurance system (QAs) in this field. The method includes a GeoWeb application as well as data quality assurance mechanisms based on conditional statements, in order to reduce crowdsourced data bias. A four-month crowdsourcing campaign, released in Lubelskie voivodship (Poland), resulted in 1823 outdoor reports with a rejection rate of up to 28%, depending on the applied QA system (QAs). Testing the QAs variants, we find the most robust data bias solving method in survey-based symptom mapping. The framework output is shared via GeoWeb dashboards, including the 11.6.3 indicator evaluation. By familiarizing the public with citizen science, a city can track the progress of its SDG achievements and increase the transparency of the process through the use of GeoWeb.

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Section: Extending the Paradigm Of Urban Air Pollutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

Samulowska

Chmielewski

Raczko

et al. 2020

Preprint

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“…As such it is a tool with a direct connection to those who suffer from pollen allergies and provides crucial information. The SLI proved to be a robust index through different pollen seasons or years [ 15 ] and is applicable even on a local level [ 16 ]. Pollen measurements are needed for the definition of a pollen season and for comparisons with the SLI (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Pollen measurements are needed for the definition of a pollen season and for comparisons with the SLI (e.g. [ 15 , 17 ]). Thus, the SLI as a number on its own is independent of pollen data and calculated only on symptom data, but is in need for pollen data when a pollen season has to be defined as a prerequisite for a mean SLI in a certain period.…”

Section: Introductionmentioning

confidence: 99%

The connection of pollen concentrations and crowd-sourced symptom data: new insights from daily and seasonal symptom load index data from 2013 to 2017 in Vienna

Bastl

Kmenta

Berger

et al. 2018

World Allergy Organization Journal

Self Cite

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BackgroundOnline pollen diaries and mobile applications nowadays allow easy and fast documentation of pollen allergy symptoms. Such crowd-sourced symptom data provides insights into the development and the onset of a pollen allergy. Hitherto studies of the symptom load index (SLI) showed a discrepancy between the SLI and the total pollen amount of a season, but did not analyze the daily data.MethodsThe Patient’s Hayfever Diary (PHD) was used as data pool for symptom data. Symptom data of Vienna (Austria) was chosen as a large and local sample size within the study period of 2013 until 2017. The city was divided into three different areas based on equal population densities and different environmental factors. Correlation factors, regression lines, locally weighted smoothing (LOESS) curves and line plots were calculated to examine the data.ResultsDaily SLI and pollen concentration data correlates well and the progress of the SLI within a pollen season is mirrored by the pollen concentrations. The LOESS curves do not deviate much from the regression line and support the linearity of the symptom-pollen correlation on a daily basis. Seasonal SLI data does not follow the same pattern as the respective seasonal pollen indices. Results did not vary in the three areas within Vienna or when compared with the Eastern region of Austria showing no significant spatial variation of the SLI.DiscussionResults indicate a linear relationship of the SLI and pollen concentrations/seasonal polllen index (SPIn) on a daily basis for both in general and throughout the season, but not on a seasonal basis. These findings clarify the frequent misinterpretation of the SLI as index that is tightly connected to pollen concentrations, but reflects as well the seasonal variation of the burden of pollen allergy sufferers.ConclusionMore than just the seasonal pollen index has to be considered when the SLI of a selected pollen season has to be explained. Cross-reactivity to other pollen types, allergen content and air pollution could play a considerable role. The similar behavior of the SLI in Vienna and a whole region indicate the feasibility of a possible symptom forecast in future and justifies the use of a single pollen monitoring station within a city of the size of Vienna.

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“…Along with pollen from enthomophilous species, anemophilous pollen with allergenic properties often gets into bee products [2][3][4]. Accidental inclusion of airborne pollen in bee products can be due to several reasons: bee nutrition habits, weather conditions, plant diversity across an area, and others.…”

Section: Introductionmentioning

confidence: 99%

The comparison of pollen abundance in air and honey samples

Šaulienė

Šukienė

Noreikaite-Merkeliene

et al. 2015

Acta Agrobot

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Honey as a food has long been used in human nutrition and is still popular. Honey is important because of its therapeutic, prophylactic and strengthening value. Pollen is one of the most decisive components that ensure the quality and type of honey. Modern society becomes more and more sensitive to airborne pollen. Therefore, it is crucial to determine the composition of allergenic plant pollen in natural honey. For this purpose, we studied and compared pollen abundance in honey and air samples collected in Lithuania. Standard methods for pollen investigation in air and honey were used in this study. The botanical diversity of pollen identified in honey and air samples indicates 10 morphotypes: 8 of woody plants and 2 of herbaceous plants, in both the honey and air samples. Salix pollen counts in the honey were found to be highest among airborne pollen from May to September. The anemophilous allergenic pollen constituted 44 % of the total pollen detected in the honey.

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Can we improve pollen season definitions by using the symptom load index in addition to pollen counts?

Cited by 26 publications

References 12 publications

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

The connection of pollen concentrations and crowd-sourced symptom data: new insights from daily and seasonal symptom load index data from 2013 to 2017 in Vienna

The comparison of pollen abundance in air and honey samples

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