Previous Page  57 / 59 Next Page
Information
Show Menu
Previous Page 57 / 59 Next Page
Page Background

Ref. No. [UMCES] CBL 2016-011

ACT VS16-02

57

Data validation uses the outputs from data verification and included inspection of the verified

field and laboratory data to determine the analytical quality of the data set. A representative set of

approximately 10% of the data on core parameters was traced in detail from raw data from field and

laboratory logs and instrument readouts through data transcription or transference through data

manipulation through data reduction to summary data, data calculations, and final reported data.

Data validation established:

Required sampling methods were used;

Sampling procedures and field measurements met performance criteria;

Required analytical methods were used;

Data validation confirmed that ACT’s sample measurement system performed in accordance

with the performance goals specified in the ACT QAPP and the DO Test Protocols and that the data

were accumulated, transferred, reduced, calculated, summarized, and reported correctly. There is

sufficient documentation of all procedures used in the data collection and analysis to validate that

the data were collected in accordance with the verification’s quality objectives.

A Data Quality Assessment (DQA), the third and final process of the overall data assessment,

is a scientific and statistical evaluation of validated data to determine if the data are of the right type,

quality, and quantity to support conclusions on the performance of the DO sensors. The DQA

determined that ACT’s data quality objectives, described in Section 3.4 of the ACT QAPP, were

achieved. This evidence supports conclusions that:

The sampling design performed very well and was very robust with respect to changing

conditions.

Sufficient samples were taken to enable the reviewer to see an effect if it were present.

Data on the performance of the DO sensors are unambiguous, and the vendors and buyers can

make informed choices about the performance of a sensor with a high level of certainty.

REFERENCES

Bittig, H.C.,Fiedler, B., Scholz, R., Krahmann, G., and Kortzinger, A. 2014. Time response of

oxygen optodes on profiling platforms and its dependence on flow speed and temperature. Limnol.

Oceanogr. Methods, 12: 617-636.

Carignan, R., Blais, A.M., and Vis, C. 1998. Measurement of primary production and community

respiration in oligotrophic lakes using the Winkler method, Canada Journal of Fisheries and

Aquatic Sciences 55:1078-1084.

Carritt, D.E., and Carpenter, J.H. 1966. Comparison and evaluation of currently employed

modifications of the Winkler method for determining dissolved oxygen in seawater; a NASCO

report. J. Mar. Res. 24: 286–318.