1. WP3 - Quality Control survey findings and gaps M. Vinci, A. Giorgetti

2. Quality Control “The quality of the data is a key issue when merging heterogeneous data coming from different: sources periods geographic areas. “ 1.QA/QC ex ante  ISPRA survey 2.QA/QC ex post  OGS survey Quality controls levels: Metadata Data  Regional QC and aggregation (P35)  Products (source and NODCs) Local experts QC? “…to ensure that the quality and errors of the data are apparent to the user who has sufficient information to assess its suitability for a task.”(IOC/CEC Manual, 1993)

3. The QC Survey Results All 33 institutes that replied perform quality checks for the water column matrix. 16 partners of the 33 are performing quality checks in the sediment matrix. 12 partners of the 33 are performing quality checks in the biota matrix The partnership of EMODNET Chemistry 2 project is composed by 46 partners but five of them are not involved in the quality check work. We received 33 responses from our partners, out of 41 institutes involved in data collection.

4. QC survey findings and gaps: water column 29/33 for range checks ( Fertilizers- -Ammonium concentration parameters in the water column ) 26/33 for date checks (Fertilizers- -Nitrite concentration parameters in the water column ) 24/33 for visual and manual checks (Fertilizers- -Phosphate concentration parameters in the water column) 23/33 for position checks (Fertilizers- -Ammonium concentration parameters in the water column) 22/33 for missing value checks (Fertilizers- -Ammonium concentration parameters in the water column) 19/33 for spike checks (Fertilizers- -Ammonium concentration parameters in the water column) 10/33 for pre-existing statistics checks (Fertilizers- -Nitrate concentration parameters in the water column)

5. QC survey findings and gaps: sediment 9/16 11/16 for position checks (Pesticides and biocides- Dichlorodiphenyltrichloroethane (DDT) -Pesticide concentrations in sediment) 11/16 for date checks (Pesticides and biocides- Dichlorodiphenyltrichloroethane (DDT) -Pesticide concentrations in sediment) 10/16 for visual and manual checks (Pesticidides and biocides- Dichlorodiphenyltrichloroethane (DDT) -Pesticide concentrations in sediment) 9/16 for range checks (Heavy metals- Mercury (Hg) -Metal concentrations in sediment) 7/16 for missing value checks (Pesticides and biocides- Hexachlorobenzene (HCB) -Pesticide concentrations in sediment) 5/16 for spike checks (Pesticides and biocides- Dichlorodiphenyltrichloroethane (DDT) -Pesticide concentrations in sediment) 4/16 for pre-existing statistics checks (Pesticides and biocides- Dichlorodiphenyltrichloroethane (DDT) -Pesticide concentrations in sediment) ?

6. QC survey findings and gaps: biota 10/12 10/12 for date checks (Heavy metals Cadmium (Cd) Metal concentrations in biota) 10/12 for range checks (Heavy metals Cadmium (Cd) Metal concentrations in biota) 9/12 for visual and manual checks (Heavy metals Mercury (Hg) Metal concentrations in biota) 8/12 for position checks (Pesticides and biocides Dichlorodiphenyltrichloroethane (DDT) Pesticide concentrations in biota) 7/12 for missing value checks (Heavy metals Cadmium (Cd) Metal concentrations in biota) 3/12 for pre-existing statistics checks (Pesticides and biocides Dichlorodiphenyltrichloroethane (DDT) Pesticide concentrations in biota) 2/12 for spike checks (Pesticides and biocides Dichlorodiphenyltrichloroethane (DDT) Pesticide concentrations in biota) ?

7. QC survey findings and gaps Is it feasible (and reasonable!) to define QC protocols (ranges, pre-existing statistics?) for sediment & biota, standard for EMODnet database? For which parameters? At which scale (local, regional,…)? At what level (P01???) Heterogeneity in sediment sizes Extremely high heterogeneity in biota (species, target tissue, body size,…) Very high spatial heterogeneity (coastal vs transitional vs off-shore sediments, organic matter contents, hot spots,…) Only few partners do range and pre-existing statistics QC checks (sediment and biota)!

8. QC survey findings and gaps Is it feasible (and reasonable!) to define QC protocols (ranges, pre-existing statistics,…) for contaminants? For which parameters? At which scale (local, regional,…)? At what level (P01???) We asked which kind of range controls are performing the institutes that positively replied in the QC survey…but… -we received just some examples -a lot of doubts about feasibility -some misunderstandings Can these ranges be used in other areas? BSH - Germany

9. QC survey findings and gaps Is it feasible (and reasonable!) to define QC protocols (ranges, pre-existing statistics,…) for contaminants? For which parameters? At which scale (local, regional,…)? At what level (P01???) Some examples from some institutes: Others? IHP – Portugal IHP – Portugal summary: No Min values for water and sediment Water column: Max values depending on coastal/open waters Sediment: Max values depending on 2 sets of factors: Coastal/transitional Organic matter content

10. QC survey findings and gaps ICES: For the contaminants data our current range check is comparing against the values already reported in our database. If a reported value is outside ± 2*standard deviation from the average value in our database (same contaminant, matrix and basis of determination) then they will be given a warning when screening the dataset in our online data screening utility. We are only doing this check if we have 50 or more measurements to compare with in the database. Screening for outliers for contaminants may be tricky … concentrations may be highly variable even over smaller distances. Also concentrations are highly depending on matrix (eg. liver in a fish) or species measured. Our intension is to identify typical calculation errors resulting in factors of difference (10*, 1000*). On the other hand we do not want to issue too many warnings, so that it get unmanageable. Our current setup could be improved and we are this year looking into ways to develop a facility to examine the whole database and time series to identify outliers. This should be a tool for the national contacts / data submitters to address national data issues before assessments. This work is supported by OSPAR, but we expect that some of the developments will have more general use.

11. QC survey findings and gaps Following the ICES suggestions could be a good idea to ask to Maris a table describing the number of P01? (P01 consider different grain sizes...species...tissues ) per regions? In this way we should be able to highlight which parameters have a sufficient number of measures/area to think to a (even simple) quality control...

12. QC conclusions Water Column matrix seems well covered by quality controls of: 29/33 for range checks 26/33 for date checks 24/33 for visual and manual checks 22/33 for missing value checks … but there are chances to improve the situation : 19/33 for spike checks 10/33 for pre-existing statistics For sediment and biota matrix: Spikes checks should be avoided! Controls at metadata level should be extended to all the institutes that are dealing with these matrixes: position checks date checks missing values Is it feasible/useful/make sense to extend controls at data level: ranges, pre-existing statistics!? Our idea was to find best practices from our partnership to analyze and discuss if is possible to extend to other areas?...seems a really sensitive point…

13. QC conclusions Let’s discuss it!