Introduction and objectives
Short-term extreme increases in desert-derived particulate-matter with aerodynamic diameter below 10 μm (PM
10
) may affect emergency department (ED) visits due to COPD exacerbations.
Research question
Our aim was to identify the effect of extreme increases in desert-derived PM
10
on ED visits for dyspnea and COPD exacerbations and on the related hospital admissions.
Methods
We performed a retrospective analysis of dyspnea-related ED visits and hospital admissions in Heraklion, Crete, during four consecutive storms of desert-derived PM
10
that happened during March 2018. We collected data from over 17,000 ED visits and recorded patients with atopic symptoms, COPD exacerbations, and dyspnea, as well as admissions to the departments of pulmonary medicine, internal medicine, and cardiology. PM
10
data were collected from a monitoring station in the same geographic area.
Results
Four desert dust storms were recorded during the study period with 238, 203, 1138, and 310 μg/m
3
average-daily PM
10
and 652, 308, 4262, and 778 μg/m
3
hourly mean day-peak PM
10,
respectively. There was no clinically important increase in total ED visits, total admissions or admissions to the departments of cardiology, pulmonary medicine, or internal medicine, during PM
10
peaks. However, during the desert dust storm with daily-average PM
10
above 500 μg/m
3
, there was a striking increase in dyspnea-related ED visits (including COPD exacerbations, 3.6-fold increase), while there was no clinically important increase in non-asthma allergy-related ED visits.
Conclusion
Extreme desert dust storm episodes may cause meaningful increases in ED visits for dyspnea and COPD exacerbations/admissions.
The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.