Particulate matter 1µm (PM) dataset collected by low-cost sensors in residential and industrial areas at the neighborhood level.

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions of particles (PM, PM, and PM) allows the characterization of the concentration levels of specific sources or events, including the analysis of ultrafine fractions (PM). Several studies have documented adverse effects on human health due to exposure to PM, such as morbidity and mortality from respiratory, cardiovascular, and, in some cases, carcinogenic diseases. Hence, studying the concentration levels and the sources that cause PM is imperative.

Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election.

Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations.