Influence of weather and air pollution on the occurrence of idiopathic pulmonary embolism in the region of Sousse (Tunisia).

Pulmonary embolism (PE) is the most serious manifestation of thromboembolic conditions. Its incidence varies considerably between countries, suggesting its interaction with the external environment. To analyze the influence of climate and air pollution on the occurrence of idiopathic PE in the region of Sousse (Tunisia). A total of 142 patients with idiopathic PE at two academic hospitals in Sousse (Tunisia) were enrolled in the study over a 7-year period. An analysis of two time series (environmental data and PE cases) was performed. Climatic data were collected from the National Institute of Meteorology. Air pollution data were obtained from the modeling platform of the National Agency for Protection of the Environment. The year 2015 was marked by the occurrence of the highest number of cases (24.6%). A statistically significant decrease in PE risk of 41.9% was observed during the summer with an OR of 0.59 (95% CI [0.36-0.94] and p = 0.026), compared with other seasons. Poisson GLM regression showed a significant increased risk of PE of 3.3% for each 1 °C temperature drop. After multiple binary logistic regression, the elevation of PM10 concentration was independently associated with an increased risk of PE (p < 10-3, OR 79.55, 95% CI [42.28-149.6]). Some environmental parameters may predispose to the onset of idiopathic PE. Understanding their accurate influence may have preventive and curative implications.

A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection.

Air pollution is considered one of the biggest threats for the ecological system and human existence. Therefore, air quality monitoring has become a necessity in urban and industrial areas. Recently, the emergence of Machine Learning techniques justifies the application of statistical approaches for environmental modeling, especially in air quality forecasting. In this context, we propose a novel feature ranking method, termed as Ensemble of Regressor Chains-guided Feature Ranking (ERCFR) to forecast multiple air pollutants simultaneously over two cities. This approach is based on a combination of one of the most powerful ensemble methods for Multi-Target Regression problems (Ensemble of Regressor Chains) and the Random Forest permutation importance measure. Thus, feature selection allowed the model to obtain the best results with a restricted subset of features. The experimental results reveal the superiority of the proposed approach compared to other state-of-the-art methods, although some cautions have to be considered to improve the runtime performance and to decrease its sensitivity over extreme and outlier values.