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Quality Assurance/Quality Control

What are quality assurance and quality control?

Quality assurance is a system of management activities performed to ensure that a process, item, or service is of the type and quality needed by the user. It deals with creating management controls that cover planning, implementation, and review of data collection activities.

Quality control, on the other hand, is technical in nature and is implemented at the project level. It includes all the scientific precautions, such as calibrations and duplications, which are needed to ensure that data is of the proper quality and accuracy.

The Integrated Atmospheric Deposition Network (IADN) is required to implement a quality assurance strategy so that the monitoring data produced is valid, defensible, and of known precision and accuracy. The quality of the data must be high enough for its purpose–to determine atmospheric trends and loadings of toxic chemicals to the Great Lakes.

Trends and loadings figures will be used to determine if reduction efforts are successful and if further steps should be taken.

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Quality Control Activities at the Integrated Atmospheric Deposition Network(IADN)

Quality control within IADN laboratories is ongoing, involving side-by-side replicate samples, use of analyte standards and blanks, calibrations, and the exchange of standards, samples, and extracts.

In order to perform quality control of the data collected, all of the agencies in IADN participate in the use of the Research Data Management and Quality Assurance System ™ (RDMQ™). RDMQ™ is a menu-driven program from Statistical Analysis System (SAS) © with capabilities for loading data, applying quality control checks, adding validity flags, viewing and editing data, producing user-defined tables and graphs, and exporting data in ASCII files. These tasks are performed through a set of menu-driven SAS© programs and macros. RDMQ™ was developed by Environment Canada’s, Air Quality Research Division .

The program can also merge and link data from different sources. For example, field observations, results of lab analyses, and meteorological data can be combined into one record and used in the quality control evaluation process. Having all observations in one record makes it easier to determine whether the data is usable.

In addition, supplementary values (detection limits, for example) can be linked to values in the data set for reference by the user. Corrections to measured values, including blank corrections and calibrations, can be performed through the use of an add-on SAS program. Seasonal and Annual Statistics Programs are designed according to the requirements in the Quality Assurance Program Plan.

Data may be assigned validity flags that refer to a specific data issue or problem. Flags may be labeled with "warning", "corrective action required", or "no further action required". Each dataset has specific conditions that must be met. These conditions are written into the program. When a condition is violated, the appropriate data point(s) are labeled. This ensures that the end-user of the data can refer to and evaluate abnormalities in the data. Corrective actions or other changes to data points are documented in an audit log for future reference.

In addition, the individual who defines a flag may cite the usual cause and suggested corrective action. This is especially helpful when different individuals are flagging, reviewing, and using the data over the course of the program.

The implementation and use of RDMQ™ ensures that all the data entering the IADN loading calculation is treated in a consistent manner. RDMQ™ allows the large amounts of data acquired through IADN to be quality controlled in a timely, efficient manner. Each year of data from each site undergoes several evaluation steps before being accepted as valid. RDMQ™ was first used for IADNdata quality control in 1996.

Examples of other projects using RDMQ™ are:

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Quality Assurance Activities

Quality assurance efforts include the improvement of method and instrument detection limits, development and use of Data Quality Objectives, and updating of the Quality Assurance Project Plan (QAPP). The Standard Operating Procedures manuals for each agency are updated as necessary to accommodate procedure changes.

In addition, the round-robin laboratory inter comparison series will be continued to ensure that analyses by the different laboratories involved in IADN are comparable and consistent. Further work on field audits and inter comparisons are being conducted through site visits and multi-agency sampling at one IADN site (Point Petre). This activity is designed to link the measurements made by the various agencies contributing to IADN.  Findings from quality assurance activities from IADN have been published by Wu et al., Journal of Environmental Monitoring, 2009.

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