Surveillance monitoring

1
Surveillance monitoring

• Select substance
• Are physical data and toxicity information
  available ?

No
Yes
Operational and investigative monitoring
Ecological status - multiple substances
Dispersion of substance
Screening monitoring programme
2
Data required for the Fugacity model
Environmental Properties areas and depths for
all bulk media volume fractions for all
subcompartments densities for all
subcompartments organic carbon content (soil,
sediment suspended sediment only) fish lipid
content (Type I chemicals only) advective flow
residence times for air (including aerosols), and
water (including suspended sediment and aquatic
biota) advective flow residence time for
sediment burial transport velocities - air side
air-water mass transfer coefficient - water side
air-water mass transfer coefficient - rain rate -
aerosol deposition velocity (wet and dry
combined) - soil air phase diffusion mass
transfer coefficient - soil water phase diffusion
mass transfer coefficient - soil air boundary
layer mass transfer coefficient - sediment-water
mass transfer coefficient - sediment deposition
velocity - sediment resuspension velocity - soil
water runoff rate - soil solids runoff rate
Chemical Properties chemical name molecular
mass data temperature reaction half-life
estimates for - air- water- soil- sediment-
aerosols- suspended sediment- aquatic
biota Type 1 chemicals - water solubility-
vapour pressure- log Kow- melting point Type 2
and 3 chemicals - partition coefficients
3
Fugacity model
The fugacity modelling approach was introduced by
Mackay et al., and can be considered to be a
distribution-modelling tool based on the concept
of fugacity (Mackay, 1991 and 2001). The
fugacity, f Pa, is a normalised measure of the
concentration of a substance within a phase
(solid, liquid or gaseous). fZiCi Zi is
defined as the fugacity capacity mol m-3 Pa-1,
the escaping tendency of a substance from the
phase i. Each phase has a set of defined
transport velocity parameters, their D values
mol Pa-1 h-1. When combined with mass flow
equations, degradation kinetics (e.g., the
half-lives of the substance in the included
phases) and the spatial parameterisation of a
certain area, the fugacity modelling approach can
give valuable information on the distribution of
the substance between different phases
(compartments) in the environment. The models
also provide information on residence time,
accumulation and concentrations. In order to
include the unsteady-state dynamics of the
substances being emitted to the Compartments of
the model, the calculating algorithm needs to
handle and solve the Differential equation
Vi being the volume of the compartment i, Zi its
bulk fugacity capacity, Ii is the input rate,
each term Djifj represents intermediary input
transfers and DTifi is the total output.
Link to level III fugacity model EQC (Mackay et
al, downloadable freeware) http//www.trentu.ca/ac
ademic/aminss/envmodel/models/VBL3.html
4
Output of the Fugacity model
partition coefficients (Type 1) Z values
fugacity of each medium intermedia transport
rates and D values reaction and advection D
values and loss rates residence times or
persistences (overall, reaction, and advection)
concentrations and amounts for each medium a
summary diagram charts of key results
5
Input for QSAR
A measured descriptor is usually a physical
property of the compound, e.g. partition
coefficients, refractive index or light
absorption, and requires that the substance is
available
Calculated descriptors on the other hand, do not
require that the substance is isolated in the
laboratory it may not even have been
synthesized, since all that is needed is the
chemical structure. A further classification of
calculated descriptors is into zero, one, two and
three dimensional depending on how they are
dependent on the chemical structure. Zero and
one-dimensional descriptors only depend on the
number of different atoms and functional groups.
Two dimensional descriptors depend on the
connectivity between atoms while
three-dimensional also depend on the
conformation of the molecule.
The single most important descriptor used in QSAR
is hydrophobicity, which is usually measured as
the logarithm of the octanol/water partition
coefficient, log KOW
6
QSAR models
Descriptor calculation can be made using the
software Dragon (Talete srl, Italy). Dragon
requires a 3-D structure as input. Often,
quantum or molecular mechanics software is used
for 3D optimization of chemical structures.
However, such software is often expensive and
optimization can be very time consuming for large
structures. An alternative is rule-based 3D
structure estimation, which is faster and
considered to be sufficiently accurate. A
summary of the methods and software used for
descriptor calculation is as follows 1. CAS
number is transformed to SMILES strings using
information from public databases. 2. CORINA is
used to transform the SMILES string to 3D mol
files. 3. DRAGON was used to calculate
descriptors from the mol files Partial least
squares (PLS) regression is used, which is a
latent variable regression method. One of the
main advantages of latent variable regression
methods are the possibility for prediction
outlier detection offered. It is extremely
important to note that empirical models are not
validated outside the domain in which they are
trained, i.e. a QSAR model cannot be applied to
substances that are too dissimilar to the
substances in the training data.
Link to level III fugacity model EQC (Mackay et
al, downloadable freeware) http//www.trentu.ca/ac
ademic/aminss/envmodel/models/VBL3.html Link to
REBECCA project homepage http//www.environment.fi
/default.asp?contentid230500lanEN
7
Output of QSAR models
For many substances, physical properties and
chronic toxicity may not be available. A QSAR
model is a relation between chemical structure
and a property of the chemical compound.