Predicting the gas-phase concentration of semi-volatile organic compounds from airborne particles: Application to a French nationwide survey

Wei, Wenjuan; Mandin, Corinne; Blanchard, Olivier; Mercier, Fabien; Pelletier, Maud; Bot, Barbara Le; Glorennec, Philippe; Ramalho, Olivier

doi:10.1016/j.scitotenv.2016.10.074

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…Distribution of Indoor Air SVOCs in Gas Phase, Three Sizes of Particles and House Dust Gas/particle partitioning of organic compounds is reported to be affected by changes in atmospheric temperature 42,43) and some other studies reported gas/particle correlations between indoor air and house dust for some SVOCs. 44,45) In the previous study, sampling was performed from October to January (in the cold season), 22) while in this study it was performed from July to September (in the hot season). There was a difference of more than 10°C in the median room temperature between the two studies.…”

Section: Discussionmentioning

confidence: 99%

Distribution of 58 Semi-Volatile Organic Chemicals in the Gas Phase and Three Particle Sizes in Indoor Air and House Dust in Residential Buildings During the Hot Season in Japan

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Distribution of 58 Semi-Volatile Organic Chemicals in the Gas Phase and Three Particle Sizes in Indoor Air and House Dust in Residential Buildings During the Hot Season in Japan

et al. 2020

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“…To increase the SVOC detection rate, the air samples were analyzed without separating the gas and particle phases. Thus, the gas‐ and particle‐phase SVOC concentrations were calculated using a probabilistic method 22 . If equilibrium partitioning has been reached, the SVOC concentrations in the gas phase and airborne particles can be described by 2

C_{normalp} = C_{normalg} \times T S P \times K_{normalp}

Section: Methodsmentioning

confidence: 99%

“…If equilibrium partitioning has been reached, the SVOC concentrations in the gas phase and airborne particles can be described by 2

C_{normalp} = C_{normalg} \times T S P \times K_{normalp}

where C p (ng/m 3 ) and C g (ng/m 3 ) are the SVOC concentrations in the airborne particles and gas phase, respectively; TSP (µg/m 3 ) is the airborne particle concentration; and K p (m 3 /µg) is the particle/gas partition coefficient. Based on Equation (1), Wei et al developed a probabilistic method applying Monte Carlo simulation to determine the distribution of SVOC concentrations in the gas phase from their measured values in airborne particles to study the SVOC concentrations in French dwellings at the nationwide scale 22 . The model was based on one‐week measurements of SVOC concentrations in airborne particles and assumed that most of the SVOCs reached equilibrium partitioning between the gas and particle phases during the week in most of the dwellings.…”

Section: Methodsmentioning

confidence: 99%

“…Studies have shown that the indoor SVOC partitioning between the gas and particle phases and between the gas phase and settled dust can be predicted assuming that the indoor SVOC concentrations have reached steady state in the different phases 2,20 . This assumption is valid for studies of large data sets from buildings 21,22 . The characterization of SVOCs in the gas phase, airborne particles, and settled dust allows studies of SVOC exposure through different pathways including inhalation, dust ingestion, and dermal exposure 2 .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Later, the model was improved by considering the deposition of airborne particles, the resuspension of settled dust [14,15], and the reactivity of SVOCs with hydroxyl radicals, nitrate radicals, and ozone [16]. For the prediction of SVOC concentrations for a large dataset of buildings, a probabilistic approach based on Monte Carlo simulation was developed to predict the distribution of the SVOC concentrations under equilibrium conditions [17].…”

Section: Introductionmentioning

confidence: 99%

A long-term dynamic model for predicting the concentration of semivolatile organic compounds in indoor environments: Application to phthalates

Wei

Ramalho

Mandin

2019

Building and Environment

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Semivolatile organic compounds (SVOCs) in indoor environments can partition into the gas phase, airborne particles, and settled dust and onto available surfaces. A long-term dynamic model was developed to predict the hourly concentrations of SVOCs over a year in the gas phase, airborne particles, and settled dust and on each sink surface. The model takes into account mass transfer mechanisms, the reactivity of SVOCs with oxidants indoors, and the influence of four indoor environmental factors (the air temperature, relative humidity, concentration of indoor airborne particles, and air exchange rate) on the mass transfer parameters. The model was validated for DEHP (di-2-ethylhexyl phthalate) and BBzP (butyl benzyl phthalate) by comparing the predicted concentrations in all the phases with the measured concentrations obtained in an environmental chamber and a test house. The model was then used to predict the hourly averaged concentration of BBzP in all the phases under real environmental conditions over a year. More than 52% of the variance in the BBzP concentration was found to be associated with the covariance of the environmental factors. The air exchange rate contributed to 16% of the variance in the concentration. In addition, the indoor air temperature and relative humidity contributed 9% of the variance in the gas-phase concentration of BBzP and 7% of the variance in the settled dust concentration of BBzP. The variance in the concentration of the total suspended particles contributed 10% of the variance in the BBzP concentration on the walls and windows.

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Predicting the gas-phase concentration of semi-volatile organic compounds from airborne particles: Application to a French nationwide survey

Cited by 20 publications

References 35 publications

Distribution of 58 Semi-Volatile Organic Chemicals in the Gas Phase and Three Particle Sizes in Indoor Air and House Dust in Residential Buildings During the Hot Season in Japan

Distribution of 58 Semi-Volatile Organic Chemicals in the Gas Phase and Three Particle Sizes in Indoor Air and House Dust in Residential Buildings During the Hot Season in Japan

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

A long-term dynamic model for predicting the concentration of semivolatile organic compounds in indoor environments: Application to phthalates

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