Quantifying the contribution of environmental variables to cyclists' exposure to PM using machine learning techniques.

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models.

Indoor persistent organic pollutants in agricultural areas from Argentina.

Persistent Organic Pollutants (POPs) are anthropogenic chemicals extensively used in the past for industrial and agricultural purposes, characterized by their lipophilicity, ubiquity, volatility and environmental persistence. By other hand, chlorpyrifos is the most widely used current pesticide (CUPs) being the main insecticide used for crops in Argentina. The aim of this work was to assess levels of POPs and CUPs in different fractions of airborne particles collected indoor in agricultural areas from Argentina.