Exposure mixtures settings

Calculation settings

Table 135 Calculation settings for module Exposure mixtures.
Name Type Description
Exposure type
ExposureType The type of exposure considered in the assessment; acute (short term) or chronic (long-term).
Target level
TargetLevelType Select to express hazard characterisations at external or internal exposure level. For an aggregate assessment, that is dietary and nondietary exposure data are combined, the target dose level is always internal. When only dietary exposures are available, the target dose level is optional, i.c. external or internal.
Internal concentration
InternalConcentrationType Internal concentrations are derived form dietary and/or non-dietary concentrations and aggregated using a kinetic or absorption factor model or are human monitoring concentrations.
Substance weighting in mixtures
ExposureApproachType Risk based: exposures in equivalents of the reference substance; standardised: standardised exposures per substance have variance 1; or unweighted exposures: RPFs are equal to 1.
Sparseness parameter

Numeric

Sparseness parameter. Value between 0 (not sparse, many substances) and 1 (sparse, few substances).
SNMU: number of components

Numeric

The number of components to select in SNMU.
Iterations

Numeric

Maximum number of iterations, e.g. 1000.
Seed for pseudo-random number generator.

Numeric

Random seed for initialising matrix W and H.
Convergence criterion

Numeric

Convergence criterion for factorisation algorithm.
Cutoff MCR

Numeric

For selection of individual(day) exposures with maximum cumulative ratio (MCR = total exposure/maximum) above the cutoff.
Cutoff percentage in population ranked on total exposure

Numeric

For selection of individual(day) exposures above the cutoff percentage in the set of individual(day)s ranked on total exposure.
Number of clusters

Numeric

Number of clusters for hierarchical cluster analysis or clustering minimizing within-cluster variance (k-means).
Method
ClusterMethodType After component selection, a clustering maybe applied on the individuals coefficents matrix V (or H) using K-Means or hierarchical clustering to identify populations sub-groupings.
Determine number of clusters automatically

Boolean

For hierarchical clustering the number of clusters is determined automatically.
Network analysis type
NetworkAnalysisType Network analysis on the substance x component (U) matrix.
Apply log transformation

Boolean

Network analysis: apply log transform on exposures.