1. Procedures for QA/QC on the air quality monitoring data
 Libor Černikovský, Blanka Krejčí Czech Hydrometeorological Institute, CZ

2. Procedures for AQ/QC on the air quality monitoring data
Content
QA/QC
definition
objective
plan
Legislation
Data quality objectives (DQO)
Collecting and reporting data
Examples
Procedures for AQ/QC on the air quality monitoring data

3. Procedures for AQ/QC on the air quality monitoring data
Definition
Quality assurance (QA): all planned and systematic actions necessary to provide adequate confidence that a product, process or service will satisfy given requirements for quality
Quality control (QC): operational techniques and activities that are used to fulfil given requirements for quality
ISO Publications, ISO 8402, Quality Management and quality assurance - Vocabulary, International Organization for Standardization,1994
In relation to air quality network operation:
Quality assurance refers to the overall management of the process involved in obtaining the data, i.e. relates to the measurement process
Quality control refers to the activities undertaken to check and optimise data accuracy and precision after collection, i.e. concerned primarily with outputs
Procedures for AQ/QC on the air quality monitoring data

4. Objective = usable data
The measured data must allow an objective and quantitative assessment of AQ, whether it complies with relevant national and European standards, limit values, guidelines and other rules. All tools used for AQ data acquisition, processing and evaluation has to generate data which is acceptable by the national authority, European Commission and European Environmental Agency in terms of quantity, quality and format in which is data stored, presented and transmitted.
Procedures for AQ/QC on the air quality monitoring data

5. Procedures for AQ/QC on the air quality monitoring data
QA/QC plan 1/3
A document that
shall specify all the QA/QC activities required to achieve the data quality objectives (DQOs)
should describe how the data is assessed for precision, accuracy, representativeness, completeness (combined data capture and time coverage) and comparability
mechanisms used when corrective actions are necessary
The QA/QC plan should assure that
the quality of the data is known
the total measuring uncertainty can be quantified
and is available to users of the data
Procedures for AQ/QC on the air quality monitoring data

6. Procedures for AQ/QC on the air quality monitoring data
QA/QC plan 2/3
The QA/QC plan of the AQ monitoring network must explicitly define
the unambiguous responsibility and authority for each of the activities contributing to the data quality
co-ordination between them
Key elements are
organizational rules
operational rules
appropriate staff
Procedures for AQ/QC on the air quality monitoring data

7. Procedures for AQ/QC on the air quality monitoring data
QA/QC plan 3/3
Measured data QA/QC has to be guaranteed by
methods of measurements
standard operating procedures (SOPs)
hardware and software tools
maintenance, calibration and emergency plans
appropriate staff in terms of quality and quantity
personnel training and education
Network design, station siting and instrument selection are crucial before the monitoring launch!
Correct sampling is crucial, as well as storage and transport of samples...
Procedures for AQ/QC on the air quality monitoring data

8. Procedures for AQ/QC on the air quality monitoring data
Legislation
… QA/QC plan shall specify all the QA/QC activities required to achieve the data quality objectives (DQOs)
The system for acquisition, processing, evaluation and reporting AQ data has to be in accordance with the EU legislation on AQ as well as with EU standards, regulations and existing guidelines, i.e. primarily with
New Air quality directive 2008/50/EC
AQ Framework Directive (FWD)
Daughter Directives (DD 1-4)
Exchange of Information Decision 97/101/EC
Commission Decision 2004/461/EC (annual reporting on ambient air quality assessment)
Directive On ambient AQ and cleaner air for Europe (AQD)
see
Procedures for AQ/QC on the air quality monitoring data

9. New Air quality directive 2008/50/EC
… includes the following key elements:
The merging of most of existing legislation into a single directive (except for the fourth daughter directive) with no change to existing air quality objectives*
New air quality objectives for PM2.5 (fine particles) including the limit value and exposure related objectives – exposure concentration obligation and exposure reduction target
The possibility to discount natural sources of pollution when assessing compliance against limit values
The possibility for time extensions of three years (PM10) or up to five years (NO2, benzene) for complying with limit values, based on conditions and the assessment by the European Commission.
* Framework Directive 96/62/EC, 1-3 daughter Directives 1999/30/EC, 2000/69/EC, 2002/3/EC, and Decision on Exchange of Information 97/101/EC.
Procedures for AQ/QC on the air quality monitoring data

10. Data quality objectives (AQD, Annex 1) 1/4
Specify
uncertainty
minimum data capture
minimum time coverage
for each pollutant covered by the Directive
Apply to individual analysers and samplers at individual station
Procedures for AQ/QC on the air quality monitoring data

11. Data quality objectives (AQD, Annex 1) 2/4
Notes:
Member states may apply random measurements instead of continuous measurements for benzene, lead and particulate matter if they can demonstrate to the Commission that the uncertainty, including the uncertainty due to random sampling, meets the quality objective of 25 % and the time coverage is still larger than the minimum time coverage for indicative measurements. Random sampling must be evenly distributed over the year in order to avoid skewing of results. The uncertainty due to random sampling may be determined by the procedure laid down in ISO (2002) "Air Quality – Determination of the Uncertainty of the Time Average of Air Quality Measurements". If random measurements are used to assess the requirements of the PM10 limit value, the 90.4 percentile (to be lower than or equal to 50 µg/m³) should be evaluated instead of the number of exceedances, which is highly influenced by data coverage.
Distributed over the year to be representative of various conditions for climate and traffic.
One day's measurement a week at random, evenly distributed over the year, or 8 weeks evenly distributed over the year.
One measurement a week at random, evenly distributed over the year, or 8 weeks evenly distributed over the year.
Procedures for AQ/QC on the air quality monitoring data

12. Data quality objectives (AQD, Annex 1) 3/4
The uncertainty (expressed at a 95 % confidence level) of the assessment methods will be evaluated in accordance with the principles of the CEN Guide to the Expression of Uncertainty in Measurement (ENV ), the methodology of ISO 5725:1994 and the guidance provided in the CEN report "Air Quality – Approach to Uncertainty Estimation for Ambient Air Reference Measurement Methods" (CR 14377:2002E)... The uncertainty for modelling ... The uncertainty for objective estimation is defined as the maximum deviation of the measured and calculated concentration levels, over the period considered, by the limit value (or target value in the case of ozone), without taking into account the timing of the events. The requirements for minimum data capture and time coverage do not include losses of data due to the regular calibration or the normal maintenance of the instrumentation.
Procedures for AQ/QC on the air quality monitoring data

13. Data quality objectives (AQD, Annex 1) 4/4
To ensure accuracy of measurements and compliance with the DQO the appropriate competent authorities and bodies ... shall ensure the following:
that all measurements ... are traceable;
that institutions operating networks and individual stations have an established QA/QC system which provides for regular maintenance to assure the accuracy of measuring devices;
that a QA/QC process is established for the process of data collection and reporting and that institutions appointed for this task actively participate in the related Community-wide quality assurance programmes;
that the national laboratories ... are taking part in Community-wide intercomparisons... are accredited according to EN/ISO or are in the process of accreditation.
All reported data shall be deemed to be valid except data flagged as provisional, i.e. all faulty data, zero/span checks, calibrations etc. must be removed from the reported dataset.
Procedures for AQ/QC on the air quality monitoring data

14. AQD, Annex VII Ozone target values and long-term objectives
The following criteria shall be used for checking validity when aggregating data and calculating statistical parameters:
Procedures for AQ/QC on the air quality monitoring data

15. AQD, Annex XI Limit values for the protection of human health
Without prejudice to Annex I, the following criteria shall be used for checking validity when aggregating data and calculating statistical parameters:
(1) The requirement for the calculation of annual mean do not include losses of data due to the regular calibration or the normal maintenance of the instrumentation.
Procedures for AQ/QC on the air quality monitoring data

16. Procedures for AQ/QC on the air quality monitoring data
DQO - remarks 1/2
In order for the measurements to constitute a compliant overall assessment of AQ in the Member State also need to be met
requirements for the appropriate numbers of monitoring points in Zones and Agglomerations (Annex V and IX)
locations and macro and micro siting of monitoring points (Annex III and VIII)
reference methods for assessment (Annex VI)
calculation of uncertainty: the CEN standards provide a specific methodology for calculation of the uncertainty of measurement for direct comparison with the Directive DQO. The CEN standard requires that this is calculated annually and DEG is likely to require that this is calculated individually from the QA/QC test data for each analyser and reported annually to the Commission along with the corresponding annual data set.
Procedures for AQ/QC on the air quality monitoring data

17. Procedures for AQ/QC on the air quality monitoring data
DQO - remarks 2/2
Standardisation (Annex VI, C.):
all the results for gaseous pollutants have to be expressed at the following conditions of temperature and pressure:
293 K and kPa.
for particle bound components, data shall be reported at ambient conditions.
Correct metadata (geographical co-ordinates, altitude, station’s classification,...) are important, too.
Procedures for AQ/QC on the air quality monitoring data

18. EU Data Exchange Group (DEG)
Implementing Provisions (IP) for reporting under AQD are currently being developed by the DEG (current status: draft, under preparation).
Subject:
to provide the (technical) requirements for the AQ information flow under AQD
to serve as the main basis for the Commission's preparation of the IP for reporting under AQD and any related guidance
Main IP elements:
specification of the reported information
information flow requirements (deadlines/periodicity, reporting scheme etc.)
common data format and metadata description (all data-flows)
description of tools for checking the format, data and metadata consistency and integrity
description of tools for merging, aggregation and rendering of the data
Procedures for AQ/QC on the air quality monitoring data

19. Procedures for AQ/QC on the air quality monitoring data
EMEP QA/QC
EMEP Manual for sampling and chemical analysis
Data Quality Objectives
Flagging data
Detection limits and precision
Results from laboratory intercomparison
Results from field intercomparison
Data quality reports
Procedures for AQ/QC on the air quality monitoring data

20. EMEP data quality objectives
for the acidifying and eutrophying compounds
10 % accuracy or better for oxidized sulphur and oxidized nitrogen in single analysis in the laboratory
15 % accuracy or better for other components in the laboratory
0.1 units for pH
15-25 % uncertainty for the combined sampling and chemical analysis (components to be specified later)
90 % data completeness of the daily values
the targets, with respect to precision and detection limit follow the DQO of the WMO/GAW precipitation programme (WMO, 2004)
the targets for the wet analysis of components extracted from air filters are the same as for precipitation. For SO2 the limit above for sulphate is valid for the medium volume method with impregnated filter. For NO2 determined as NO2- in solution the accuracy for the lowest concentrations is 0.01 mg N/l.
for heavy metals
90% completeness
30% accuracy in annual average
accuracy in laboratory...
Procedures for AQ/QC on the air quality monitoring data

21. Collecting and reporting data
Theory: all of the QA activities are undertaken correctly, in compliance with the relevant CEN standards and SOPs  the measurements will fulfil the requirements of the EU Directives without further checking Practice: there is a need to QC the data by careful data management and checking, analyser / sampler faults must be identified and addressed quickly in order to fulfil the DQO for data capture Prior to submission of data to the data user, any suspect data must be identified and investigated; in addition there is the need to ensure that the data are reported correctly
Procedures for AQ/QC on the air quality monitoring data

22. QC scheme - data flow & feedback
1. Monitoring station
2. Measurement data centre
a) Laboratory (manual meas.)
b) Automatic measurements
3. Monitoring data centre
4. National data centre
5. European data centre
The roles and responsibilities must be unambiguous as well as the feedback between persons / institutions.
Procedures for AQ/QC on the air quality monitoring data

23. QC - level 1: monitoring station
The hardware and software tools on monitoring station must guarantee a correct data storage (results of measurements and also supported parameters such as temperature, pressure, sampled air volume etc.) and a transmission to the data centre
The first (automatic) check of completeness of data has to be implemented on automatic station
Software should use automatic data flags (valid data, faulty data due to incompleteness, zero / span checks, calibrations, maintenance,...)
 provisional data
Procedures for AQ/QC on the air quality monitoring data

24. QC - level 2a: laboratory
The hardware and software tools in laboratory as well as organisational rules must guarantee correct samples storage and analysis, results of analysis evaluation, inspection and storage.
Evaluation of conditions and rules during:
sampling (temperature, air volume and flow continuity,...)
storage and transportation of samples (temperature,...)
laboratory analysis
should be done, too
 provisional data
Procedures for AQ/QC on the air quality monitoring data

25. QC - level 2b: automatic meas. data centre
The hardware and software tools as well as organisational rules must guarantee:
assurance of the integrity of transmission of data from the station to data centre
correct data storage
storage and use all supported information from station (e.g. zero / span check and calibration records, information about (ir)regular analyzers inspection,...)
review of data completeness and refill non-complete data series
review of data correctness from technical perspective:
identify and inspect out-of-range, negative, constant, extreme, above thresholds, rapidly changed and other suspicious data
reject invalid data
correct data (e.g. if shift of measured level is known)
flag the data
provisional data
All procedures must ensure that data capture is maximised, i.e. the data must be analysed frequently (ideally daily) so that measurements affected by instrument fault and other faults could be recognised quickly.
Some of reviews have to work automatically to allow real-time data release.
Procedures for AQ/QC on the air quality monitoring data

26. QC - level 3: monitoring data centre
The hardware and software tools as well as organisational rules must guarantee advanced review of data correctness, reliability and consistency:
identify and inspect negative, constant, extreme, above thresholds, rapidly changed and other suspicious data
consider data integrity
compare data series from different stations
compare data series from one station to inspect relationship between different pollutants
provisional data – daily
valid data - monthly, quarterly, yearly
All procedures must ensure that data capture is maximised, i.e. the basic review must be done frequently (ideally daily) so that measurements affected by instrument fault and other faults would be recognised quickly.
Procedures for AQ/QC on the air quality monitoring data

27. QC - level 4 & 5: national and European data centre
The hardware and software tools as well as organisational rules must guarantee advanced review of data correctness, reliability and consistency on national and European level:
identify and inspect negative, constant, extreme, above thresholds, rapidly changed and other suspicious data
consider data integrity
compare data series from different stations and regions
compare data series from one station to inspect relationship between different pollutants
Frequency:
national level: monthly, quarterly, yearly
European level: yearly
Procedures for AQ/QC on the air quality monitoring data

28. Examples: suspicious data  invalid data
Zero / span check
Procedures for AQ/QC on the air quality monitoring data

29. Examples: suspicious data  valid data
CO peak on traffic station
Procedures for AQ/QC on the air quality monitoring data

30. Examples: suspicious data  valid data
Emergency SO2 outflow from chemical factory (30min values)
Procedures for AQ/QC on the air quality monitoring data

31. Examples: suspicious data  valid data
O3 peak, 7th March 2005
Procedures for AQ/QC on the air quality monitoring data

32. Examples: suspicious data  valid data
Dust above central Europe from eastern Ukraine
Procedures for AQ/QC on the air quality monitoring data

33. Examples: suspicious data  checks
e.g. alien values
Procedures for AQ/QC on the air quality monitoring data

34. Examples: suspicious data  checks
Simple outliers or outliers based on statistical methods
Procedures for AQ/QC on the air quality monitoring data

35. Examples: suspicious data  checks
Cumulative concentrations
Procedures for AQ/QC on the air quality monitoring data

36. Examples: relationships
some pollutant levels will be expected to rise and fall together or against
diurnal peaks of NO are usually associated with traffic during rush hours
diurnal behaviour of O3
...
Procedures for AQ/QC on the air quality monitoring data

37. Examples: relationships
NO2 [ppb] + NO [ppb] = NOX [ppb]
Procedures for AQ/QC on the air quality monitoring data

38. Examples: relationships
NO vs. O3
Procedures for AQ/QC on the air quality monitoring data

39. Examples: relationships
Different ratio PM10 vs. PM2.5
Procedures for AQ/QC on the air quality monitoring data

40. Examples: especial events
Special weather conditions (usefull meteorological data support)
Service intervention
Unusual day run …
Procedures for AQ/QC on the air quality monitoring data

41. Thank you for attention...
The CAFE Programme/ implementation of the Thematic Strategy on Air Pollution
Ambient Air Quality
New Air Quality Proposal
Existing Air Quality Legislation
Implementation of existing AQ legislation
Meetings & Workshops - CIRCA website
EU Focus on Clean Air
Useful links
Feedback
Procedures for AQ/QC on the air quality monitoring data