UNIVERSITY OF HOUSTON - CLEAR LAKE 2015. Quality product (or service) as one that is free of defects...
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INDUSTRIAL HYGIENE QUALITY CONTROL FOR SAMPLING AND LAB ANALYSIS UNIVERSITY OF HOUSTON - CLEAR LAKE 2015
UNIVERSITY OF HOUSTON - CLEAR LAKE 2015. Quality product (or service) as one that is free of defects and performs those functions for which it was designed
Quality product (or service) as one that is free of defects and
performs those functions for which it was designed and constructed
and produces Client satisfaction. (Juran) Quality Control: System
of activities whose purpose is to control the quality of a product
or service so that it meets the needs of users. (Taylor)
Slide 3
An additional QC system implemented to assess the efficacy of
the QC system monitoring the product. This control system is
referred to as a QA program. Thus, quality assurance could be
defined as quality control on quality control.
Slide 4
Defines mission, goals, and values of organization. Also
provides: financial systems, HR, and administrative functions. The
quality management system provides policies, procedures, and
organization that defines the quality assurance programs and how
they interact with and are supported by the overall management
system.
Slide 5
Elements: -Organization -Management System -Document Control
-Review of Requests/Tenders/Contracts -Subcontracting -Service to
Customer -Purchasing
Slide 6
Elements: -Control of Nonconformance -Complaints -Improvement
-Corrective Action -Preventive Action -Control of Records -Internal
Audits -Management Reviews and Reports
Slide 7
Topics: -Selection and Training of Personnel -Selection of
Methods -Estimation of Uncertainty -Control of Data -Equipment and
Instrumentation -Traceability
Slide 8
Topics: -Sampling -Handling of Test Items -Quality Assurance of
Results -Reporting of Results Issues scaled and adjusted to
organization size and scope of processes involved.
Slide 9
Purpose of quality is to provide a level of assurance that the
result of a process will meet specifications. The terms: accuracy,
bias, and precision are terms often used to describe how close a
result is to the true or expected value.
Slide 10
Accuracy is qualitative term referring to whether there is
agreement between a measurement made on an object and its true
(target or reference) value. [NIST]
Slide 11
Quantitative term describing the difference between the average
of measurements made on the same object and its true value. Bias is
the difference between the average of observed results and the true
value, and is determined over a period of time.
Slide 12
Quantitative measurement of normal distribution of results due
to random error in the system. Standard error used to describe
precision measurements. The smaller the standard error, the more
precise are the measurements. Precision is a measurement of the
variability or standard error observed between the average value
and the individual readings. Measures of variability include
statistics like the range, variance, standard deviation,
coefficient of variation, and the standard error.
Slide 13
Measures of variability that are often used to evaluate
precision are: -range maximum minus minimum; -sample variance
differences between average of a series of measurement and the
individual measurements; -sample standard deviation square root of
variance; -coefficient of variation standard deviation divided by
the mean; and, -standard error estimate of expected error in sample
estimate of population mean or the sample SD divided by the square
root of size.
Slide 14
In order to draw conclusions about airborne contaminant
concentration, the extent of current or future worker exposure,
efficacy of control measures, samples must be properly collected
and analyzed. Sample collection and analysis are inter- related,
and both are critical components of accurate data production. There
must be goals and objectives for each operation.
Slide 15
Caused by several factors: -Training, attitude, and attention
-Representative !!! samples -Environmental factors T/%RH/BP;
sampling handling and transport; contaminant concentrations
assessed -Sample collection factors flow rate, time, and collection
efficiency
Slide 16
Rigid adherence to written sampling methods can reduce inherent
variability. Materials must be consistent in quality and use.
Equipment and instruments used must be appropriate for the
procedures employed. Metrics for monitoring the sampling plan is
through use of samples that produce results that provide
comparisons: duplicate, split, spiked, and blank samples.
Slide 17
Control samples: -Duplicate samples evaluate method -Split
samples e.g. bulk samples to labs -Spiked samples most common;
apply known mass of contaminant on media -Blank samples field
blanks; transport blanks; and, media blanks.
Slide 18
Use of reference method NIOSH/OSHA Documentation of
modifications, etc. Validated methods. Identify variables that
cannot be controlled. Written sampling method/protocol equipment;
sampling time intervals; personal/area; handling and transport;
blanks; recordkeeping; decontamination process; data check
sequences; and personnel training.
Slide 19
Testing of supplies and materials QA programs Statistical
sampling protocols Labels lot-specific Certificates of Analysis
Material QA/QC issues sampling media Lab/field blanks
Slide 20
Calibration set of operations used to determine the accuracy of
the reading of a test device to a stated uncertainty [AIHA]
Equipment calibration and recordkeeping Description of
environmental conditions Realistic pre- and post-calibration
intervals Written methodology for calibration Mechanisms used for
establishing traceability of calibration standards (i.e. NIST) or
other recognized organizations.
Slide 21
Portable instruments = laboratory function. Purpose to provide
immediate results useful to help make decisions. Subject to many of
same QA as laboratory. Users trained on equipment. Calibration
before and after use; standard and routine maintenance. QC samples
for accuracy and precision on a regular basis with appropriate data
analysis.
Slide 22
Formal recognition by a national or international authority of
capability of a lab to perform testing and measurements. Purpose to
provide information that will help make informed decisions
regarding laboratory selection. Demonstrates lab competence and
capabilities. (e.g. AIHA) AIHA voluntary program; ISO/IEC Standard
17025; inter-laboratory proficiency programs, and other technical
requirements.
Slide 23
Normal distribution properties: Symmetrical distribution in
which the mean, median, and the mode all have the same value. See:
Figure 13.3 +/- 1 SD = 68% +/- 2 SD = 95% +/- 3 SD = 99.7%
Slide 24
For random samples of size n drawn from a population with mean
and SD, as n increases: -mean of samples approaches population
mean; -SD of samples approaches SE of mean; - shape of the
distribution will approach the normal.
Slide 25
Extend lines that segment the distribution curves by standard
deviation, then rotate by 90 degrees to form a control chart. See:
Figure 13.4 mean +/- 3 sigma of average is UCL/LCL mean +/- 2 sigma
of average is UWL/LWL
Slide 26
Two general types in data-producing systems: -assignable (or
determinate) causes is systematic error (i.e. control chart data)
-unassignable (indeterminate) causes is random error Need two types
of control charts one to deal with bias and another for
precision.
Slide 27
Since bias is related to central tendency, a common type of
control chart for bias plots MEANS (xbar). Precision is a measure
of variability, and is commonly monitored by the use of RANGES.
Combination of charts is referred to an xbar and r chart.
Slide 28
Defined as a data point that appears to be markedly different
from other members of the sample in which it occurs. Not discarded
or deleted, but indicated in set. Data could be: - an extreme value
in the distribution; - results from some gross deviation from
analytical method or math error; so, investigate process and
calculations first.
Slide 29
Most IH methods used address both sampling and lab analysis.
Validation. Sampling part of methods is often accepted as published
and then evaluated further based on field studies and comparison
with other methods. Lab portion of methods should be validated for
the analytes, instrumentation, and the procedures involved (i.e.
spiked samples).
Slide 30
Sample to which has been added a known amount of analyte. The
analysis of spike samples can be used to determine the bias and
precision of a test method, the accuracy of a lab measurement
process, and/or to detect changes in the analytical process. Need
to know ranges of concentrations of interest and the relationship
between recovery and concentration(s).
Slide 31
AIHA definition: the lowest concentration of an analyte in a
sample that can be reported with a defined, reproducible level of
certainty. Environmental chemistry limits: -Critical Limit analyte
detection -Detection Limit distinguish from zero -Quantitation
Limit relatively close to the true value.
Slide 32
Labs report results to reflect the true value. Number of
significant figures implies the precision that can be attributed to
the result. General rules to apply: -The least precise measurement
determines the number of significant figures. -All digits are
retained during the calculation and the final result is rounded to
significant digits. -Other rules for significant figures on page
324 of third edition.
Slide 33
Two types of error that contribute to uncertainty: random
errors and biases. -Biases contributors that can be corrected or
minimized (e.g. calibration of standards or references by labs,
material prep, environ conditions). Overall average deviation.
-Random errors results of contributors that cannot be corrected
(e.g. instruments, inability to repeat a process, variability,
etc.). Predominant contributor to the precision control chart. It
can be measured but cannot be corrected.
Slide 34
Proficiency Testing Programs by: American Industrial Hygiene
Association (AIHA) Proficiency Analytical Testing (PAT) evaluate
labs analyzing workplace samples by use of reference samples (i.e.
metals, silica, organics, asbestos, lead, microbial). Statistical
data analysis to assess proficiency according to defined criteria.
Round-robin approach.
Slide 35
Statistics Normal distributions QA/QC Control charts