ArticleJournal of environmental health science & engineering2022
Prediction of airborne pollen concentrations by artificial neural network and their relationship with meteorological parameters and air pollutants.
Article in Journal of environmental health science & engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 17 citations in OpenAlex.
- An advanced framework for predicting pollen outbreaks using multi-input multi-output temporal convolutional networks and firefly algorithm.Scientific reports · 2026Article
- A Review of Airborne Pollen and Its Interactions With Air Pollutants, Urbanization, and Climate Change: Implications for Human Health and Monitoring Gaps.Current allergy and asthma reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
After the early rainfall in the autumn of 2013, respiratory syndromes spread in the Khuzestan province of Iran with the most severity in Ahvaz. There have been recurring outbreaks in recent years. Considering that pollen-derived airborne allergens are regarded as key aeroallergens and the main cause of allergic rhinitis and asthma, this work aimed to forecast total pollen concentration in Ahvaz through an artificial neural network (ANN), followed by evaluating the pollen spatial distribution across the city and the association between pollen concentrations and environmental parameters. The utilized ANN in this work included an input layer with 13 parameters, a hidden layer of five neurons, and an output layer. Data were classified into training, validation, and testing sets. The ANN was implemented with 70% and 80% of data for training. The value of the correlation coefficient for the data validation of these two networks was 0.89 and 0.92, respectively. The results also indicated that despite the difference in the mean concentration of the pollens in various areas of Ahvaz, this difference was not statistically significant (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.