Chronic single value dietary exposure assessment

The long term (chronic) exposure assessment is usually the exposure related to a consumption over a longer period of time. MCRA applies in principle the TMDI, IEDI or NEDI (Rees-Day) equations as shown in EFSA PRIMo revision 3 (EFSA (2018)). However, the equations are extended with factors $P{F}_i$ and $O{F}_i$ to allow adaptation for processing factors and occurrence frequencies lower than 1. Also, the inputs to the equations are not necessarily the same as used in PRIMo. For example, the consumption statistics ($MC$, $p_{97.5}$) and body weight ($BW$) can be computed instead of just being standard values. Note that TMDI, IEDI and NEDI (Rees-Day) estimates are summations over foods (raw agricultural products). In addition to the summations, MCRA will also report the individual terms (single value dietary exposures per food).

TMDI (Theoretical Maximum Dietary Intake)

$$\sum _{X=i}^n\frac{{\mathit{MRL}}_i\cdot {\mathit{CF}}_i\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot {\mathit{MC}}_i}{BW}$$

$i,j,k,...n$: individual raw agricultural products

IEDI (International Estimated Dietary Intake)

$$\sum _{X=i}^n\frac{{\mathit{STMR}}_i\cdot {\mathit{CF}}_i\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot {\mathit{MC}}_i}{BW}$$

$i,j,k,...n$: individual raw agricultural products

NEDI (National Estimated Dietary Intake): Rees-Day model (I)

$$\sum _{X=i}^j\frac{{\mathit{MRL}}_i\cdot C{F}_i\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot p_{97.5}{\mathit{consumption}}_i}{BW}+\sum _{X=k}^n\frac{{\mathit{MRL}}_k\cdot {\mathit{CF}}_k\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot {\mathit{MC}}_k}{BW}$$

$i,j$: two raw agricultural products leading to the highest intake;

$k,l,m,...n$: remaining raw agricultural commodities consumed

NEDI (National Estimated Dietary Intake): Rees-Day model (II)

$$\sum _{X=i}^j\frac{{\mathit{STMR}}_i\cdot {\mathit{CF}}_i\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot p_{97.5}{\mathit{consumption}}_i}{BW}+\sum _{X=k}^n\frac{{\mathit{STMR}}_k\cdot {\mathit{CF}}_i\cdot {\mathit{PF}}_i\cdot {\mathit{OF}}_i\cdot {\mathit{MC}}_k}{BW}$$

$i,j$: two raw agricultural products leading to the highest intake;

$k,l,m,...n$: remaining raw agricultural commodities consumed

Parameters used in the equations

${\mathit{MRL}}_i$: Maximum residue level for the RAC concerned (default in mg/kg);

${\mathit{STMR}}_i$: Supervised Trials Median Residue for raw agricultural commodity (RAC) concerned (default in mg/kg);

${\mathit{CF}}_i$: Conversion factor residue definition enforcement to residue definition risk assessment (calculated as the ratio of residues according to the residue definition for risk assessment divided by the residue concentration according to the residue definition for enforcement);

${\mathit{MC}}_i$: mean consumption for a given raw agricultural product (RAC) calculated for the whole survey/subgroup of the survey, including processed products (recalculated to the unprocessed RAC) (default in kg/day);

$p_{97.5}$ ${\mathit{consumption}}_i$ for a given raw agricultural product (RAC), calculated from the individual consumption reported by the participants of the whole survey/subgroup of the survey, including processed products (recalculated to the unprocessed RAC) (default in kg/day);

$BW$: mean body weight of the population related to the $LP$ or mean consumption (default in kg).

${\mathit{OF}}_i$: Occurrence Frequency of the substance on the food (typically, a raw agricultural commodity, RAC),

${\mathit{PF}}_i$: Processing factor or peeling factor (calculated as the ratio of residues in processed/peeled product, divided by residue concentration in unprocessed/unpeeled product);

Alternative TMDI-, IEDI- or NEDI-styled assessments

If consumption survey data for a specific population are available, the $MC,p_{97.5}\mathit{consumption}$ values in the IESTI equations may be replaced by statistics calculated from these data (at the consumed modelled food level).

If concentration monitoring data (retrospective) or concentration field trial data (prospective) are available, the $MRL,STMR$ values in the IESTI equations may be replaced by statistics calculated from these data (at the consumed modelled food level).

In the current use of IESTI, the occurrence frequency (use frequency) $OF$ is assumed to be 1. In alternative assessments, a more realistic estimate may be used. Such an estimate could be derived for example as the highest occurrence frequency observed in a retrospective assessment for either the same substance or the same food.