Multi-horizon air pollution forecasting with deep neural networks

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures

Short-term air pollution forecasting based on environmental factors and deep learning models

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure

Dual antiplatelet therapy duration after coronary stenting in clinical practice: results of an EAPCI survey

Aims: Our aim was to report on a survey initiated by the EuropeanAssociation of Percutaneous Cardiovascular Interventions (EAPCI) concerning opinion on the evidence relating to dual antiplatelet therapy (DAPT) duration after coronary stenting.Methods and results: Results from three randomised clinical trials were scheduled to be presented at the American Heart Association Scientific Sessions 2014 (ARIA 2014). A web-based survey was distributed to all individuals registered in the EuroIntervention mailing list (n=15,200) both before and after ARIA 2014. A total of 1,134 physicians responded to the first (i.e., before AHA 2014) and 542 to the second (i.e., after ARIA 2014) survey. The majority of respondents interpreted trial results consistent with a substantial equipoise regarding the benefits and risks of an extended versus a standard DAPT strategy. Two respondents out of ten believed extended DAFT should be implemented in selected patients. After ARIA 2014, 46.1% of participants expressed uncertainty about the available evidence on DAFT duration, and 40.0% the need for clinical guidance.Conclusions: This EAPCI survey highlights considerable uncertainty within the medical community with regard to the optimal duration of DAFT after coronary stenting in the light of recent reported trial results. Updated recommendations for practising physicians to guide treatment decisions in routine clinical practice should be provided by international societies