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(searched for: doi:10.1016/j.scitotenv.2017.09.061)
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Published: 26 September 2022
Journal: Aerobiologia
Aerobiologia, Volume 38, pp 413-428; https://doi.org/10.1007/s10453-022-09756-5

Abstract:
The goal of this study is to determine if the annual pollen integral (APIn) for the top tree allergens in the City of Albuquerque is correlated with meteorological variables. This analysis would be the first of its kind for this area. We used 17 consecutive years from 2004 to 2020 and data collected by the city of Albuquerque using a Spore Trap (Burkard) volumetric air sampler in a location designed to represent a typical desert environment. The pollen studied include Juniper, Elm, Ash, Cottonwood, and Mulberry. We found a negative linear correlation with early summer temperatures of the previous year and APIn for Elm, Cottonwood, and Mulberry, and early fall temperatures for Juniper. Linear regression models developed for Elm, Cottonwood, and Mulberry used the monthly mean maximum temperature for the month of June of the prior year as the independent variable to yield a R squared statistic (R2) of 0.88, 0.91 and 0.78, respectively. For Juniper, the average monthly mean minimum temperature for the previous September and October served as the independent variable and yielded the R2 value of 0.80. We also observed a positive trend for the annual maximum temperature over time and a negative trend for the total APIn. Summers in New Mexico are hot and dry, and they may be getting hotter and drier because of climate change. Our analysis predicts that climate change in this area may lead to reduced allergies if temperatures continue to increase and if precipitation patterns remain the same.
, Rostislav Kouznetsov, Lucie Hoebeke, Nicolas Bruffaerts, Mikhail Sofiev, Andy W. Delcloo
Published: 1 June 2022
Agricultural and Forest Meteorology, Volume 320; https://doi.org/10.1016/j.agrformet.2022.108942

Published: 29 September 2021
Journal: Aerobiologia
Aerobiologia, Volume 37, pp 843-860; https://doi.org/10.1007/s10453-021-09727-2

Abstract:
Airborne allergenic pollen affects a significant part of the population and the information on pollen load is a valuable tool for public health prevention. The messages should be provided in a form easily understandable for the population. The study provides new insight for the categorisation of pollen load by defining thresholds solely from aerobiological data. Using the long-term airborne pollen data of Corylus, Alnus, Betula, Poaceae, and Artemisia have been evaluated the regionality of pollen concentrations in Lithuania. SPIn and peak values of the main pollen season highlighted as regionality indicators. The largest differences between stations were found in the cases of Corylus and Artemisia.The principle enabling a group of pollen concentrations into levels has been analysed based on retrospective aerobiological data of five pollen types. Thresholds were determined by employing the lowest peak value of the pollen season and applying the 25% principle for selected pollen types. The results were verified by performing associations of defined thresholds with retrospective morbidity data of allergic rhinitis and allergic asthma in Lithuania. Determined pollen thresholds can be used in epidemiological studies requiring associations with pollen concentration. Thresholds could also complement air quality information by integrating pollen load data into public messages or contribute to the development of mHealth systems.
, Cecilia M. Bitz, Jeremy J. Hess
Published: 1 June 2021
Science of the Total Environment, Volume 773; https://doi.org/10.1016/j.scitotenv.2021.145590

Abstract:
Pollen allergies have negative impacts on health. Information about airborne pollen concentration can improve symptom management by guiding choices affecting timing of medicines and pollen exposure. Observations provide accurate pollen concentrations at point locations. However, in the contiguous United States and southern Canada (CUSSC), observations are sparse, and sampling is often seasonal, intermittent or both. Modeling pollen concentration can fill in the gaps with estimates where direct observations are unavailable and also provide much-needed forecasts. The goal of this study is to develop and evaluate statistical models that predict daily pollen concentrations using a machine learning Random Forest algorithm. To evaluate our methods, we made retrospective forecasts of four pollen types (Quercus, Cupressaceae, Ambrosia and Poaceae), each in one of four CUSSC locations. Meteorological and vegetation conditions were input to the models at city and regional scales. A data augmentation technique was investigated and found to improve model skill. Models were also developed to forecast pollen in locations where there are no observations. Forecast skill in these models were found to be greater than in previous models. Nevertheless, the skill is limited by the spatiotemporal resolution of the pollen observations.
J.M. Maya-Manzano, , M. Smith, P. Dowding, R. Sarda-Estève, D. Baisnée, E. McGillicuddy, G. Sewell, D.J. O'Connor
Published: 1 March 2021
Agricultural and Forest Meteorology, Volume 298-299; https://doi.org/10.1016/j.agrformet.2020.108298

, Yuliia Palamarchuk, Annabelle Bédard, Xavier Basagana, Josep M. Anto, Rostislav Kouznetsov, Rodrigo Delgado Urzua, Karl Christian Bergmann, Joao A. Fonseca, Govert De Vries, et al.
Journal of Mechanical Engineering, Volume 133, pp 1561-1567; https://doi.org/10.1097/cm9.0000000000000916

Abstract:
This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.
, Josep M. Anto, Tari Haahtela, Pekka Jousilahti, Marina Erhola, Xavier Basagaña, Wienczyslawa Czarlewski, Mikaëla Odemyr, Susanna Palkonen, Mikael Sofiev, et al.
Published: 19 June 2020
Clinical and Translational Allergy, Volume 10; https://doi.org/10.1186/s13601-020-00321-2

Abstract:
In December 2019, a conference entitled "Europe That Protects: Safeguarding Our Planet, Safeguarding Our Health" was held in Helsinki. It was co-organized by the Finnish Institute for Health and Welfare, the Finnish Environment Institute and the European Commission, under the auspices of Finland's Presidency of the EU. As a side event, a symposium organized as the final POLLAR (Impact of air POLLution on Asthma and Rhinitis) meeting explored the digital transformation of health and care to sustain planetary health in airway diseases. The Finnish Allergy Programme collaborates with MASK (Mobile Airways Sentinel NetworK) and can be considered as a proof-of-concept to impact Planetary Health. The Good Practice of DG Santé (The Directorate-General for Health and Food Safety) on digitally-enabled, patient-centred care pathways is in line with the objectives of the Finnish Allergy Programme. The ARIACARE-Digital network has been deployed in 25 countries. It represents an example of the digital cross-border exchange of real-world data and experience with the aim to improve patient care. The integration of information technology tools for climate, weather, air pollution and aerobiology in mobile Health applications will enable the development of an alert system. Citizens will thus be informed about personal environmental threats, which may also be linked to indicators of Planetary Health and sustainability. The digital transformation of the public health policy was also proposed, following the experience of the Agency for Health Quality and Assessment of Catalonia (AQuAS).
, Josep M. Anto, , , , Wienczyslawa Czarlewski, Anna Bedbrook, Sinthia Bosnic‐Anticevich, G. Walter Canonica, , et al.
Published: 8 June 2020
Journal: Allergy
Allergy, Volume 76, pp 168-190; https://doi.org/10.1111/all.14422

Abstract:
Digital anamorphosis is used to define a distorted image of health and care that may be viewed correctly using digital tools and strategies. MASK digital anamorphosis represents the process used by MASK to develop the digital transformation of health and care in rhinitis.It strengthens the ARIA change management strategy in the prevention and managementof airway disease. The MASK strategy is based on validated digital tools. Using the MASK digital tool and the CARAT online enhanced clinical framework, solutions for practical steps of digital enhancement of care are proposed.
, , , Mikhail Sofiev, Annika Saarto, Elena Severova, Sergei Smyshlyaev,
Published: 26 February 2020
Atmospheric Chemistry and Physics, Volume 20, pp 2099-2121; https://doi.org/10.5194/acp-20-2099-2020

Abstract:
Information about distribution of pollen sources, i.e. their presence and abundance in a specific region, is important, especially when atmospheric transport models are applied to forecast pollen concentrations. The goal of this study is to evaluate three pollen source maps using an atmospheric transport model and study the effect on the model results by combining these source maps with pollen data. Here we evaluate three maps for the birch taxon: (1) a map derived by combining a land cover data and forest inventory, (2) a map obtained from land cover data and calibrated using model simulations and pollen observations, and (3) a statistical map resulting from analysis of forest inventory and forest plot data. The maps were introduced to the Enviro-HIRLAM (Environment – High Resolution Limited Area Model) as input data to simulate birch pollen concentrations over Europe for the birch pollen season 2006. A total of 18 model runs were performed using each of the selected maps in turn with and without calibration with observed pollen data from 2006. The model results were compared with the pollen observation data at 12 measurement sites located in Finland, Denmark, and Russia. We show that calibration of the maps using pollen observations significantly improved the model performance for all three maps. The findings also indicate the large sensitivity of the model results to the source maps and agree well with other studies on birch showing that pollen or hybrid-based source maps provide the best model performance. This study highlights the importance of including pollen data in the production of source maps for pollen dispersion modelling and for exposure studies.
, Daria Bilińska, , , Małgorzata Malkiewicz
Published: 12 February 2020
Journal: Aerobiologia
Aerobiologia, Volume 36, pp 261-276; https://doi.org/10.1007/s10453-020-09629-9

Abstract:
The influence of atmospheric circulation conditions on pollen concentrations of two taxons (Betula and Alnus) in Wroclaw, Poland, for the years 2005–2014 was analysed. Pollen concentration was analysed separately for twenty circulation types that were determined using objective classification. The results indicate the atmospheric circulation conditions favourable for both low and high pollen concentrations over Central Europe. Pollen concentrations vary significantly according to circulation types. The highest pollen concentrations for both taxons are typical for warm, sunny, and dry anticyclonic circulation types with anticyclone in the lower and upper troposphere, especially for types with advection from the SW. The lowest pollen concentrations are observed for cold, wet, and cloudy cyclonic types with advection from the northern sectors. There is also a positive and statistically significant trend in the frequency of circulation types favourable for high concentrations of Betula and Alnus.
Yi-Ting Tseng, , Satoshi Kobayashi, Shinji Takeuchi, Kimihito Nakamura
Published: 3 September 2019
Science of the Total Environment, Volume 698; https://doi.org/10.1016/j.scitotenv.2019.134246

Abstract:
The seasonal pollen index (SPI) is a continuing concern within the fields of aerobiology, ecology, botany, and epidemiology. The SPI of anemophilous trees, which varies substantially from year to year, reflects the flowering intensity. This intensity is regulated by two factors: weather conditions during flower formation and the inner resource for assimilation. A deterministic approach has to date been employed for predicting SPI, in which the forecast is made entirely by parameters. However, given the complexity of the masting mechanism (which has intrinsic stochastic properties), few attempts have been made to apply a stochastic model that considers the inter-annual SPI variation as a stochastic process. We propose a hidden Markov model that can integrate the stochastic process of mast flowering and the meteorological conditions influencing flower formation to predict the annual birch pollen concentration. In experiments conducted, the model was trained and validated by using data in Hokkaido, Japan covering 22 years. In the model, the hidden Markov sequence was assigned to represent the recurrence of mast years via a transition matrix, and the observation sequences were designated as meteorological conditions in the previous summer, which are governed by hidden states with emission distribution. The proposed model achieved accuracies of 83.3% in the training period and 75.0% in the test period. Thus, the proposed model can provide an alternative perspective toward the SPI forecast and probabilistic information of pollen levels as a useful reference for allergy stakeholders.
, Cecilia M. Bitz, David S. Battisti,
Published: 17 July 2019
Journal: Aerobiologia
Aerobiologia, Volume 35, pp 613-633; https://doi.org/10.1007/s10453-019-09601-2

Abstract:
Pollen is a common allergen that causes significant health and financial impacts on up to a third of the population of the USA. Knowledge of the main pollen season can improve diagnosis and treatment of allergic diseases. Our objective in this study is to provide clear, quantitative visualizations of pollen data and make information accessible to many disciplines, in particular to allergy sufferers and those in the health field. We use data from 31 National Allergy Bureau (NAB) pollen stations in the continental USA and Canada from 2003 to 2017 to produce pollen calendars. We present pollen season metrics relevant to health and describe main pollen season start and end dates, durations, and annual pollen integrals for specific pollen taxa. In most locations, a small number of taxa constitute the bulk of the total pollen concentration. Start dates for tree and grass pollen season depend strongly on latitude, with earlier start dates at lower latitudes. Season duration is correlated with the start dates, such that locations with earlier start dates have a longer season. NAB pollen data have limited spatiotemporal coverage. Increased spatiotemporal monitoring will improve analysis and understanding of factors that govern airborne pollen concentrations.
Published: 13 July 2019
by MDPI
Journal: Remote Sensing
Remote Sensing, Volume 11; https://doi.org/10.3390/rs11141671

Abstract:
Airborne fungal spores (AFS) represent the major fraction of primary biological aerosol particles (PBAPs), and they are studied worldwide largely due to their important role within the Earth system. They have an impact on climate and human health, and they contribute to the propagation of diseases. As their presence in the air depends largely on studied ecosystems, a spore trap was used to monitor their atmospheric concentrations from 2014 to December 2018 in Saclay, a suburban area in the megacity of Paris. The main objective of this work was: (1) to understand the atmospheric variability of AFS in relation to different variables such as meteorological factors, agricultural practice, and (2) to identify their geographical origin by using a source receptor model. During our period of observation, 30 taxa have been identified under a light microscope. In order of importance, Ascospores, Cladosporium, Basidiospores, Tilletiopsis, Alternaria were found to be the most abundant types respectively (50.8%, 33.6%, 7.6%, 1.8%, and 1.3%) accounting for 95% of the atmospheric concentrations. We observed a general decrease associated with a strong interannual variability. A bimodal seasonal cycle was identified with a first maximum in July and a second in October. The main parameters driving the atmospheric concentration are temperature and precipitation. The daily variability is strongly activated by successive periods of hot weather and rainfall, multiplying the concentration by a factor of 1000 in less than 12 hours. Results from the source receptor model ZeFir point out unambiguous different origins of AFS due to specific sources impacting the observation site. Our study also indicated that a hydrological stress has a direct effect on the daily concentrations. This last point should be taken into account for every stressed ecosystem studied in a global warming context. This is particularly important for Mediterranean areas where water is a key control of the growth and dispersion of fungal spores.
Published: 1 December 2018
by MDPI
Journal: Remote Sensing
Remote Sensing, Volume 10; https://doi.org/10.3390/rs10121932

Abstract:
The study of the origin and dispersion processes associated with airborne pollen grains are important to understand due to their impacts on health. In this context, a Hirst-type spore trap was utilized over the period 2015–2018 to monitor ambient pollen grains at Saclay, France, a receptor site influenced by both clean air masses originating from the Atlantic Ocean and polluted air masses under anticyclonic conditions. The objective of this work was to use ZeFir (a user-friendly, software tool recently-developed to investigate the geographical origin and point sources of atmospheric pollution) as a method to analyse total and allergenic airborne pollen grain concentrations. Strong interannual variability was exhibited for the total pollen grains concentrations and it was determined that this was mainly driven by Betulaceae pollen, with a general increasing trend displayed. The start of the pollen season was seen to be triggered by particular synoptic conditions after a period of dormancy and two maximums were displayed, one in April and a second in June. Results from the ZeFir tool, fed with on-site hourly meteorological and pollen measurements, demonstrate that the dominant pollen grains inputs to Saclay are favoured by non-prevailing winds originating from East and North in association with dry air, moderate winds, mild temperature and enhanced insolation.
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