Estimating indoor galaxolide concentrations using predictive models based on objective assessments and data about dwelling characteristics

Abstract

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.

Keywords:

• Galaxolide
• predictive model
• data mining
• supervised learning
• machine learning
• building characteristics
• indoor air

Acknowledgements

We are indebted to the participants of the study for accepting the survey and filling the questionnaire. The authors thank Delphine Pelle, Gaëlle Raffy, Gaëlle Saramito, Aude Caraes, Karine Elandaloussi, Emilie Surget, Marylise Houitte, Julien Gastine, Mathieu Alain and Eric Daniel from the LERES (Environment and health Research Laboratory) and Olivier Ramalho from the CSTB (Scientific and technical Centre for Building) for their contributions to this work. We also thank Cara Henson Maesano for having reviewed the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by a grant from the French Environment and Energy Management Agency (ADEME) and the French Agency for Food, Environmental and Occupational Health & Safety (ANSES).