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Text description for Data Management Protocols

Figure 8.1: Process elements that provide data quality assurance

The flowchart shows the essential elements of a data quality assurance system.

Data Quality Assurance has two main elements: Quality Control and External Quality Assessments.
Some elements included under Quality Control are:

  • Training
  • Technical competence
  • Good measurement practices
  • Standard operating procedures
  • Proper facilities and equipment
  • Instrument co-location studies
  • Use of standard reference materials
  • Internal audits
  • On-going systems improvements
  • Data quality assessments
  • Use of standard methods
  • Calibration and maintenance.

External Quality Assessments are composed of:

  • External data audits
  • External technical systems audit
  • Network reviews

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Figure 8.2: Flow diagram of acceptance process for routine air quality measurements

The flowchart shows the acceptance process for routine air quality measurements.  It shows a sample and a known concentration (reference material) going through the measurement process. This results in sample data and reference material data.  These two sets of data will go through a quality assessment process before the sample data is accepted.  If quality assessment results in data that is out of specification, it is noted in the quality control process so that system improvements can be made. Invalid data can be excluded from the final data set.  

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Figure 8.3: Response curves used to calculate actual concentrations from recorded instrument response R(x) at time T(x)

The figure illustrates how a datalogger’s response curve is used to calculate actual concentrations from recorded instrument response. For a known concentration of 40 ppm, the datalogger will give a response of R(T-1) on the curve at time T-1 and a response of R(T) from the curve at time T. Using this, a concentration of 40 ppm will give a response of R(x) at time T(x). 

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Figure 8.4: Example of baseline drift in CO data

The figure is an example of a carbon dioxide data graph with a gradually increasing baseline, within a four-month period.  This is shown by the baseline drift line. 

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Figure 8.5: Example of a sudden change in data baseline

The figure is an example of a monitoring results graph showing a sudden baseline increase/change. The data in the graph shows monitoring results for the month of December.  Data baseline was even from 1 to 14 December. The sudden increase occurred on 15 December onwards.

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Figure 8.6: Incorrectly applying ramp correction

The figure illustrates why it is not advisable to use a linear equation for ramp correction when there is a sudden baseline shift.  The example uses the graph in figure 8.5 with a straight line drawn between the first and last result for the period.  This shows it is not representative of the data.

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Figure 8.7: Extrapolating a calibration

The figure shows an example when extrapolation of the response curves may be used if the drift period is short enough.

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