Air Quality e-reporting QA/QC and feedback: current & future. Jaume Targa (ETC/ACM)

Transcription

1 EEA-ETC/ACM 16 th of November th IPR Air Quality Meeting, Copenhagen Air Quality e-reporting QA/QC and feedback: current & future Jaume Targa (ETC/ACM)

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3 Data quality Automated QA/QC checks Would you like to see all your deliveries on our maps?

4 Data quality Automated QA/QC checks Do we want to see a quick overview of Attainment status over Europe? Do I have similar exceedance as my neighbour? IGNORE colours

5 Data quality Automated QA/QC checks We all need good quality deliveries

6 Data quality Automated QA/QC checks In 2014, QA checks on CDR were done on: Correct link to EEA vocabulary Pure XML validation to schema ALL delivered XMLs were released & processed

7 Data quality Automated QA/QC checks Consequences MS: Why is my data not on the map? MS: Why there is no NO 2 data on this output? DG-Env/EEA: Why is the attainment empty for? ETC/ACM: Why dataset C has not been harvested? ETC/ACM: Why do we have duplicated timeseries?

8 Data quality Automated QA/QC checks Early 2015, manual follow up was carried out Time consuming by ETC/ACM & EEA Feedback late for countries to re-act

9 Data quality Automated QA/QC checks To improve this situation Great improvement on QA cdr: Hard work by countries, EEA & ETC/ACM

10 Data quality Automated QA/QC checks To improve this situation Countries Great improvement on QA cdr: Input by countries during summer 2015 to further improve QA/QC and remove errors United Kingdom, Germany, Spain, Luxembourg, Italy, Croatia, Austria etc EEA & ETC/AC M

11 Data quality Automated QA/QC checks Applied rules in CDR already documented in QA document Rules follow the IPR Decision, xsd, Excel Mapping table, XML Guidance Revision of rules in CDR

12 Data quality Automated QA/QC checks Categorisation of rules: Info, warning, Error & Turned off Intensive follow up with during submission If QA was causing problems, checks have been downgraded or envelopes FORCED in Majority checks required a 1-off correction

13 Data quality Automated QA checks Benefits in Sept 2015 reporting Errors in FORMAT & CONTENT detected earlier EEA-ETC/ACM able to focus on CRITICAL issues Better and quicker assistance to countries Earlier detection of issues between meta-data information (D) & primary data (E1a)

14 Data quality Automated QA checks Some feedback by countries: QA has made it HARDER The good thing about the QA is when you pass it Increased reporting time (cost)

15 Data quality Automated QA checks Useful because you can check your own data before doing the official submission Sometimes you make mistakes which are difficult to detect once the xml file has been generated Cross checks between elements within a dataflow are specially interesting

16 Data quality Automated QA checks Examples of errors detected during reporting: Wrong reporting year **** Invalid files, invalid XML, missing files Missing Sampling Points or Stations Wrong network identification or wrong UTC Important inconsistencies between data-flows

17 Data quality Examples of errors detected Wrong reporting year **** Data might be ignored (not harvested) XML XML D D Updated XML D 2013 EEA Content Registry Latest FULL D from 2014

18 Data quality Examples of errors detected Wrong reporting year **** Data might be ignored (not harvested) Data gets harvested in the wrong place XML C 2013 as 2013 XML C 2014 as 2015 EEA Content Registry C 2013 C 2015

19 Data quality Examples of errors detected Wrong reporting year **** Data might be ignored (not harvested) Data gets harvested in the wrong place Previous data gets overwritten XML C 2013 as 2013 XML C 2014 as 2013 EEA Content Registry C C

20 Data quality Examples of errors detected Invalid files, invalid XML, missing files: If an EXCEL file is submitted instead of an XML, we can not process it correctly Invalid XML will not be harvested (or incorrectly) If shape files are not provided, we can not assess the geometry of your zone

21 Data quality Examples of errors detected Missing Stations, Sampling Points, Process : Can not process Primary Data (E1a) XML D Missing INFO Aggregation Output missing XML E1a Partially processed EEA database Map Output missing

22 Data quality Examples of errors detected Missing Stations, Sampling Points, Process : Can not process Primary Data (E1a) Assessment Regime (C) or Attainment (G) might xlink to an INEXISTENT Sampling Point C and G might not be harvested correctly

23 Data quality Examples of errors detected Wrong network identification or wrong UTC Can not generate any statistics for linked SamplingPoint as database is missing the aggregation TimeZone (key information!) SamplingPoint/Sations xlinked to a missing Network do not get harvested in CR

24 Data quality Examples of errors detected Important inconsistencies between data-flows

25 Data quality Examples of errors detected Important inconsistencies between data-flows Assessment Regimes states that a model is used for assessing a particular zone information on the model is NOT PROVIDED

26 Data quality Automated QA/QC checks What s next?

27 Data quality Automated QA/QC checks What s next? Make outputs more user-friendly

28 Data quality Automated QA/QC checks What s next? Make outputs more user-friendly Any zone with a timeextension for NO 2 must be designated (at least) for the combination NO2 & Health

29 Data quality Automated QA/QC checks What s next? Remove unnecessary checks Improve geometry check (B23)

30 Data quality Automated QA/QC checks What s next? Fine tune some checks and when necessary implement further checks

31 Data quality Automated QA/QC checks What s next? Example: Equivalent information is mandatory

32 Data quality Automated QA/QC checks What s next? Check consistency of identifiers against previous submissions To prevent: duplicates, overwriting,

33 Data quality Automated QA/QC checks What s next? Revise critical checks that are currently just warnings a) Ensure that they perform correctly b) Assess if critical according to IPR & data processing c) Some checks might result in suspending further processing of the XML d) some checks might be lightened

34 Data quality Automated QA/QC checks What s next? Further looking at cross-consistency checks This might need strengthening (request by DG-Env): Example: on number of exceedances declared (G) vs exceedances calculated from reported data (E)

35 Data quality Automated QA checks Content checks Improve content checks to benefit us all Quicker and automatic if possible Improve checks on UTD data

36 Data quality Automated QA checks Jaume Targa Principal consultant 4sfera Innova m e. skype jaume.targa.4sfera web You can follow us on

37 Data quality 100% valid XML schema compliant

38 Data quality 100% valid XML schema compliant

39 Data quality 100% valid XML schema compliant