The Baseline - Mining and the Environment: From Ore to Metal - page 379

TABLE 8.8
Performance Characteristics Used in Analytical Chemistry and How These Might be Translated to Biological Methods

Performance Characteristic Analytical Chemical Methods Biological Methods

Precision Replicate samples Multiple taxonomists identifying 1 sample; split sample for sorting, identifi ca-

tion, enumeration; replication samples within sites; duplicate reaches

Bias Matrix spiked samples; standard reference

materials; performance evaluation samples

Taxonomic reference samples: 'spiked' organism samples

Performance range Standard reference materials at various

concentrations; evaluation of spiked
samples by using different matrices

Effi ciency of fi led sorting procedures under different sample conditions
(mud detritus, sand, low light)

Interferences Occurrence of chemical reactions involved

in procedure; spiked samples; procedural
blanks; contamination

Excessive detrital material or mud in sample; identifi cation of young life
stages; taxonomic uncertainty

Sensitivity Standards; instrument calibration Organism-spiked samples; standards level of identifi cation

Accuracy Performance standards, procedural blanks Confi rmation of identifi cation percentage of 'missed' specimens

Data quality and performance characteristics of methods for analytical chemistry are typically validated through the use of quality
control samples including blanks, calibration standards, and samples spiked with a known quantity of the analyte of interest.

Source:
Diamond et al. 1996

8.5 CONVERTING DATA TO INFORMATION

The goal of collecting baseline data is to make the results available for decision-making,
and to others - the mine proponent, interested community members, or regulatory agen-
cies. However, raw data has little meaning until transferred into a format and context
which can be readily understood. Clearly, some data is readily interpreted, while other
data may be very difi cult to interpret. Deductions concerning the data and its mean-
ing are interpreted based on scientii c principles. This section provides some guidance in
converting data to information, based on relevant text in the 'Volunteer Surface Water
Monitoring Guide', Minnesota Pollution Control Agency (2003).

Raw data has little meaning until
transferred into a format and
context which can be readily
understood.

CASE 8.9
Freeport's Environmental Laboratory

At the time that PT Freeport Indonesia
(PTFI) was developing its Grasberg Mine
in the Papua Province of Indonesia, there
were no laboratories in Indonesia capable
of accurately analyzing dissolved metals
at concentrations in the parts per billion
range. Accordingly, PTFI established its
own state-of-the-art laboratory which is
located at Timika, more than 100 km
from the mine and process plant areas.
The 1,000 square metre lab building
was completed in January 1994 at a total

cost of more than $US 3 million, and
has been used for environmental labora-
tory operations since then. Hundreds of
samples and thousands of measurements
are conducted at the lab each month. The
environmental laboratory provides analyses
of samples from mine water, river water,
groundwater, tailing, soil, plant tissues, and
fi sh for heavy metals (both dissolved and
total), suspended solids, pH, conductivity,
alkalinity, hardness and other parameters
which are of environmental concern.