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. The concentrations of 52 SVOCs, including phthalates, polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), synthetic musks, and pesticides, were measured in air and settled dust (40 SVOCs in both air and dust, 12 in either air or dust). The results showed that phthalates had the highest concentrations among the SVOCs in both the air and dust. Other SVOCs, including tributyl phosphate, fluorene, phenanthrene, gamma-hexachlorocyclohexane (gamma-HCH, lindane), galaxolide, and tonalide, also showed high concentrations in both the air and dust. Theoretical equations were developed to estimate the SVOC partitioning between the air and settled dust from either the octanol/air partition coefficient or the boiling point of the SVOCs. The regression constants of the equations were determined using the data set of the present study for phthalates and PAHs.

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 (NO3) and ozone (O3). Two questions regarding SVOC reactivity with OH, NO3 and O3 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, NO3 and O3 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. PAHs and organophosphates were found to be more reactive than dioxins and PCBs. Based on the obtained data, quantitative structure-activity relationship (QSAR) models were developed to predict the k value using quantum chemical, molecular, physical property and environmental descriptors. Eight linear and nonlinear statistical models were employed, including regression models, bagging, random forest and gradient boosting. QSAR models were developed for SVOC/OH reactions in the gas and particle phases and SVOC/O3 reactions in the particle phase. Models for SVOC/NO3 and SVOC/O3 reactions in the gas phase could not be developed due to the lack of measured k values for model training. The least absolute shrinkage and selection operator (LASSO) regression and random forest models were identified as the most effective models for SVOC reactivity prediction according to a comparison of model performance metrics.

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. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM2.5 and PM10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.