1. Assessing the value of measured data Day 1.
Adam Peters and Graham Merrington
2017

2. Why measure a chemical in the environment? Challenges in measuring
What is measured data?
Why measure a chemical in the environment?
Challenges in measuring
Challenges in interpreting
Check list for measured data?
Good practice
Bad practice
Summary
2017

3. What is measured data The purpose of measuring something?
Representative sample of…………?
Environmental sample
Of all sorts of things?!
It is not modelled…..but might be used in a model or to validate a model
It tends to be…..expensive to get in the short-term (this can result in compromise and a complete waste of budget….no DOC).
2017

4. Why measure a chemical in the environment?
For compliance assessment
To assess or benchmark the state of the environment
To classify the environment
To determine reasonable worst case (RWC) exposures of that chemical…..
Perhaps, refinement of risk assessment (Scale)?
Where is it likely to be (check the phys-chem)? Make sure the monitoring data is for the matrix of relevance (WFD: BFRs in water column?)
ISO
2017

5. Challenges in measuring
Can you measure it?
Is it ethical (eels, vertebrates, apex predators…..)?
Is the methodology robust and certified?
Can you find a commercial lab to do it?
Ask yourself…..will it meet my objective?
Analytical error (+/- X%?), normally a relatively minor contributor to the overall sampling error
2017

6. How representative (ISO 5667-1)?
Challenges in interpreting (this should really be established BEFORE the monitoring begins)
Correct matrix?
How representative (ISO )?
Summary statistic – appropriate? RWC? Stage of Risk Assessment?
LoD/LoQ? (Cu EQS – Cu Regulatory data?)
Sample for which chemical determinand received is what you think it is?
Sampling record, site locations?
QA/QC?
2017

7. Challenges in interpreting…. Existing measured data
Collected for a different purpose than which you are about to use it….by someone else.
Often not always a good fit….summary statistics, meta data, limited reporting, treatment of LoDs/LoQs, no audit trail.
Regulatory data….large data sets, formatting issues, searching issues, structuring to be able to use in risk assessment, methods, purpose, quality.
Be very explicit and transparent about the uncertainties and the assumptions you are making – all the way through the assessment.
2017

8. Check list for measured data. (ECHA R16. APPENDIX A. 16-7
Check list for measured data? (ECHA R16. APPENDIX A SELECTION OF MEASURED DATA)
Quality of the sampling and analytical techniques;
Selection of data representative for the environmental compartment of concern and for the addressed exposure scenarios;
Outliers;
Treatment of values below the limit of quantification (LOQ);
Data comparability.
Score the data…….for reliability and relevancy
2017

9. Good practice?
Silver…
Not too much reliable ecotoxoicty data, but a probabilistic approach (>10 NEOCs/EC10s)
Low value derived for the annual average - 40 ng L-1. An EQS?
Are there silver exposures (can we find it in the water)?
No. The maximum mean dissolved silver concentration recorded was 19.8 ng L-1 from 425 samples from 84 sites in England.
Almost all monitoring data for silver, too high an LoD!
Therefore, no silver EQS implemented in the UK…………no exposures
2017

10. Bad practice? Move toward hazard based assessment
Focus falls upon the ecotoxicity data ……completely
Datasets that are:
Composed of many (and different) LoDs/LoQs,
Collated from mixed methods of determination (none of which match the method used for your hazard data!)
Collected from mixed methods of extraction
Collected for a specific purpose (which was not yours!),
Collected from a limited spatial or temporal scale
Treatment of these data critical – NOT “½ LoD will be fine!”. Highly censored datasets need to be carefully treated….
Quinoxyfen detected in 2 MS, Bifenox detected in 3 MS – 28 MS now must measure it.
2017

11. Summary
The aim of the environmental risk assessment is to provide environmental benefit.
That benefit would be……….in the environment then (measures).
Measured data – purpose, problem definition.
Ensure quality and reliability for YOUR purpose.
Be clear about uncertainties – through the life of the assessment.
Be aware of highly censored datasets. There are techniques to deal with them (e.g. Kaplan-Meier).
2017

12. 2016