1 / 7

Surveillance monitoring - PowerPoint PPT Presentation

  • Uploaded on

QSAR. CQW. MIKE. OMEGA. Chemical fate fugacity model. Surveillance monitoring. Select substance Are physical data and toxicity information available ?. No. Yes. Operational and investigative monitoring. Ecological status - multiple substances. Dispersion of substance.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Surveillance monitoring' - ciqala

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Surveillance monitoring





Chemical fate

fugacity model

Surveillance monitoring

  • Select substance

  • Are physical data and toxicity information available ?



Operational and investigative monitoring

Ecological status

- multiple substances

Dispersion of substance

Screening monitoring


Surveillance monitoring

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

Surveillance monitoring

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).


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)


Surveillance monitoring

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

Surveillance monitoring

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

Surveillance monitoring

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)


Link to REBECCA project homepage


Surveillance monitoring

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.