Towards establishing the national referential on exposure factors in France

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BACKGROUND AND AIM: Availability of up-to-date methods, models, and exposure data is critical to realistically estimate human exposure to environmental contaminants. Exposure factors (EF) are human characteristics and behaviors that are needed for reliable exposure and, thus, risk assessments (RA). These factors determine exposure time, frequency, and intensity. They are usually considered as effect modifiers in epidemiological studies. While the US-EPA Exposure Factors Handbook is under updating, there is no European equivalent that meets RA needs. Therefore, the French Agency for Food, Environmental and Occupational Health & Safety (ANSES) launched a working group to define the reference values of specific EFs for the French population. METHODS: We defined priority EFs based on an inventory of needs identified by ANSES. For each EF, we defined the method, including a systematic review, quality assessment of studies, individual data retrieval, and specific modeling approaches, including descriptive statistics and generalized additive models for location, scale and shape, to derive population distributions for reference values by sex, age, and region. We also conducted uncertainty analyses. RESULTS:We prioritized three EFs: body mass, time-activity-location patterns, and inhalation rate (IR). For body mass, we pooled individual data from nine large studies to yield age-related distribution per sex, region, and socio-professional categories. For time-activity-location patterns, we estimated the practising individuals fraction and duration of all activities/locations, and frequency of some, as reported in the French Time Use Survey (2009-2010) (FTUS), per age-group (from 11 years), sex and region. For IR, we applied an energy-based approach. We determined energy expenditure per activity (professional and personal) from the FTUS using metabolic equivalent tasks and derived 24-hour IR and per physical activity level (sedentary, light, moderate, vigorous) by age-group, sex, and BMI. CONCLUSIONS:The output will allow more precise RA, facilitate the implementation of probabilistic RA approaches and improve comparability of RA results at the international level. KEYWORDS: body mass, time-activity-location patterns, inhalation rate, energy expenditure, (pooled) analysis of individual data, reference percentiles