Semivolatile organic compounds in French schools: Partitioning between the gas phase, airborne particles and settled dust.

The indoor environmental quality in classrooms can largely affect children's daily exposure to indoor chemicals in schools. To date, there has not been a comprehensive study of the concentrations of semivolatile organic compounds (SVOCs) in French schools. Therefore, the French Observatory for Indoor Air Quality (OQAI) performed a field study of SVOCs in 308 nurseries and elementary schools between June 2013 and June 2017.

Predicting the rate constants of semivolatile organic compounds with hydroxyl radicals and ozone in indoor air.

Semivolatile organic compounds (SVOCs) in air can react with hydroxyl radicals (OH), nitrate radicals (NO) and ozone (O). Two questions regarding SVOC reactivity with OH, NO and O in the gas and particle phases remain to be addressed: according to the existing measurements in the literature, which are the most reactive SVOCs in air, and how can the SVOC reactivity in the gas and particle phases be predicted? In the present study, a literature review of the second-order rate constant (k) was carried out to determine the SVOC reactivity with OH, NO and O in the gas and particle phases in ambient and indoor air at room temperature. Measured k values were available in the literature for 90 polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), organophosphates, dioxins, di(2-ethylhexyl)phthalate (DEHP) and pesticides including pyrifenox, carbamates and terbuthylazine.

Machine learning and statistical models for predicting indoor air quality.

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ.