Consumer product exposures calculation details

The long term (chronic) consumer product exposure assessment through oral ingestion, dermal contact and inhalation via personal care products, household cleaning products and do-it-yourself products is following the methodology as developed in PACEM and RSExpo. MCRA applies in principle the equations as used in PACEM and RSExpo.

Consumer product exposures are generated using an existing set of individuals, see module consumer product use frequencies. Together with information on the application amounts the exposure of personal care products, household cleaning products and do-it-yourself products in various European countries is estimated.

Two approaches are available, depending on the Consumer product exposure calculation method setting:

  • Use concentration models and survey

  • Use percentiles

Use consumer product concentration models and survey

The calculation for exposure to consumer products is:

\[\mathtt{E_{consumer-product}} = \mathtt{frequency} \cdot \mathtt{amount} \cdot \mathtt{concentration} \cdot \frac{ \sum_{route=1}^{p} EF_{route}} {BW} \cdot \mathtt{occurrence \% /100}\]

Parameters used in the equation

\(\mathtt{E_{consumer-product}}\): average daily external exposure of an individual to substances in consumer products (default in \(\mu g/kg \, bw /day\))

\(\mathtt{BW}\): body weight of the individual (default in \(kg\))

\(\mathtt{amount}\): application amount (\(mg\))

\(\mathtt{concentration}\): substance concentration in consumer product (\(mg/kg\))

\(\mathtt{frequency}\): use frequency of een individual

\(\mathtt{occurrence \%}\): occurence percentage, % of products with substance (default 100%)

\(\mathtt{EF_{route}}\): exposure fraction of a specific route (substance specific)

Use consumer product percentiles

For each exposure set a lognormal distribution is fitted to the percentile data using R package rriskDistributions. Parameters \(\mu\) and \(\sigma\), which represent the mean and standard deviation on log scale, are used to simulate the exposure for each individual. Note that the total exposure is the sum of all exposures of the underlying products as defined for the exposure set.

Select a Consumer product filter method to match individual properties, such as gender and age, with the the population characterists describing an assessment.

  • Filter by gender and age option is the most restrictive,

  • No filtering selects any assessments regardsless of its specified population.

  • Filter by age, this option matches the individual’s age with the population criteria of the assessments, selecting any assessment that fits the age group regardless of gender.

  • Filter by gender, this option matches the individual’s gender with the population criteria of the assessments, selecting any assessment that fits the gender regardless of age.

Individuals are generated based on the primary entity settings in module populations. The Compute option generates individuals with gender categories “male” and “female” and ages between 0 and 100 year. For a specific population, such as females between 18 and 65 years old, select Use data and specify the population characterics in a file.