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Estimating indoor galaxolide concentrations using predictive models based on objective assessments and data about dwelling characteristics.
Inhalation Toxicology 2017 October
BACKGROUND: Galaxolide (HHCB) is used for fragrance in many consumer products. The aim of the current study was to use objective assessments of HHCB to build a predictive model in order to estimate indoor-measured HHCB concentrations from questionnaire-based data on dwelling characteristics and occupants' habits and activities.
METHODS: Environmental assessments of indoor HHCB, dwelling characteristics were carried out in 150 dwellings in Brittany (France). Among the various models that were tested, the best predictive model for the reduced set of characteristics was identified on the basis of the coefficient of determination (R2 ) criterion.
RESULTS: Linear regression model showed among the best performances (R2 = 0.48), together with some more complex models. According to the estimated results, the main variables that significantly increased HHCB concentrations were: living in rural area, drying clothes inside dwellings, painted walls, chipboard furniture, double glazing, damaged floors and duration of bathroom door being kept open. Laminated floors and presence of indoor plants were found to significantly decrease HHCB concentrations.
DISCUSSION: The linear model based on objective assessments and questionnaire-derived data about dwelling characteristics and occupants' activities constituted an easy method for predicting indoor air HHCB concentrations. For studies including a large number of dwellings, modeling of HHCB concentrations is cheaper than measuring it in every location. Our methodological procedure can be applied to other indoor air pollutants.
METHODS: Environmental assessments of indoor HHCB, dwelling characteristics were carried out in 150 dwellings in Brittany (France). Among the various models that were tested, the best predictive model for the reduced set of characteristics was identified on the basis of the coefficient of determination (R2 ) criterion.
RESULTS: Linear regression model showed among the best performances (R2 = 0.48), together with some more complex models. According to the estimated results, the main variables that significantly increased HHCB concentrations were: living in rural area, drying clothes inside dwellings, painted walls, chipboard furniture, double glazing, damaged floors and duration of bathroom door being kept open. Laminated floors and presence of indoor plants were found to significantly decrease HHCB concentrations.
DISCUSSION: The linear model based on objective assessments and questionnaire-derived data about dwelling characteristics and occupants' activities constituted an easy method for predicting indoor air HHCB concentrations. For studies including a large number of dwellings, modeling of HHCB concentrations is cheaper than measuring it in every location. Our methodological procedure can be applied to other indoor air pollutants.
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