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Table 3. Ranges of various physical/chemical and biological parameters in water from Baltimore Harbor,
in comparison to ETV and G8 listed challenge conditions
Parameter
ETV
G8
Historic Ranges
Port of Baltimore
Temperature (
o
C)
10 - 35
–
4 - 28
Salinity (psu)
0 - 31
Two salinities, >10
PSU difference
5 - 15
Total Suspended Solids (mg/l)
> 15
> 50
1 - 60
Particulate Organic Carbon
(mg/l)
> 1
> 5
0.5 - 6.0
Dissolved Organic Carbon
(mg/l)
> 3
> 5
2 - 10
Zooplankton (> 50
µ
m) / m
3
> 10,000
> 100,000
10,000 - 300,000
Phytoplankton (10 - 50
µ
m) / ml
> 100
> 1,000
500 - 15,000
Heterotrophic Bacteria cfu / ml
> 1,000
> 10,000
10,000 - 10,000,000
A.7.
Quality Objectives and Criteria for Measurement Data
In performing BWTS tests, MERC and all participating laboratory staff will follow the technical and QA
procedures specified in this QAPP and will comply with the data quality requirements in the MERC QMP
(Section 8.2.3). Data quality objectives (DQOs) have been established as test conditions to ensure that
MERC tests provide suitable data for robust evaluations of performance.
A.7.1.
Data Quality Objectives
The development of the DQOs follows U.S. EPA’s Guidance for the Data Quality Objectives Process
(EPA QA/G-4, 2006). DQOs are qualitative and quantitative statements that clarify study objectives,
define the appropriate types of data, and specify the tolerable levels of potential decision errors that will
be used as the basis for establishing the quality and quantity of data needed to support decisions. DQOs
therefore provide the criteria to design a sampling program within cost and resource constraints or
technology limitations. DQOs are typically expressed in terms of acceptable uncertainty associated with a
point estimate at a desired level of statistical confidence. Acceptance criteria are specifications intended
to evaluate the adequacy of one or more existing sources of data as being acceptable to support the
project’s intended use. Data quality objectives and acceptance criteria vary by analysis type and will be
specified in specific test plans. In general, only data that meet or exceed these criteria are deemed valid,
thereby ensuring that all data generated is of the highest quality.
A.7.2.
Measurement Quality Objectives
Measurement Quality Objectives (MQOs) are a subset of DQOs. MQOs are designed to evaluate and
control various phases (sampling, preparation, and analysis) of the measurement process to ensure that
total measurement uncertainty is within the range prescribed by the project’s DQOs. MQOs define the
acceptable quality (data validity) of field and laboratory data for the project. MQOs are defined in terms
of the following data quality indicators:
•
Accuracy;
•
Precision;
•
Bias;