The basic principles of monitoring and characterization are described for different environments, considering their most relevant processes. Initially, sampling protocols are described, followed by documentation of quality control issues and statistical methods for data analysis. Methods for making field measurements in soil, vadose zone, water, and atmospheric environments are described. This includes real-time monitoring, temporal and spatial issues, and the issues of scale of measurement.

1. Environment
al
Monitoring &
Evaluation
Week 2: Sampling & Data Quality
Objectives

2. LEARNING OUTCOMES
The following are the learning outcomes:
1. To describe the characteristics of the environment;
2. To know the types of environmental sampling and sampling plan.
3. The know the requirements of analytical data quality;
4. To describe the quality control checks, reporting data and units of measure.

3. Environmental
Characteristics
0
1
Environmental
Sampling, Sampling
Locations &
Sampling Plan
0
2
Analytical Data
Quality
0
3
Quality Control
and Reporting
0
4

4. 0
1
Environment
al
Characteristi
cs

5. ENVIRONMENTAL CHARACTERISTICS
● Most environments have unique features or special
characteristics that help environmental scientists choose
and ultimately select one sampling approach over
another.
● On a global scale, one can distinguish between land- and
water-covered areas and separate them with ease. On a
watershed scale, aerial photographic and topographic
maps may be used to identify the location of streams,
agricultural fields, or industrial activities that further
subdivide the land environment.
● At the field scale, information on soil series and
soil horizons can be used by scientists to design
soil sampling plans for waste contaminated sites.
These examples illustrate that a priori knowledge
of the general physical, chemical, and biological
characteristics of an environment is indispensable
in environmental monitoring.

6. ENVIRONMENTAL CHARACTERISTICS

7. SPATIAL PROPERTIES
● The earth environment is defined by two or three spatial dimensions. Measurements
at the interface between two environments have two dimensions (X–Y) along a plane
or surface. The third dimension is the Z axis away from the X–Y plane. The third
dimension is the Z axis away from the X–Y plane.
● Thus, the Z dimension comprises height or depth and incorporates environments such as the
atmosphere, the earth’s subsurface, and the ocean depths. The collection of samples at multiple
depths or altitude intervals adds a third dimension (Z) to two-dimensional (2-D) sampling. It is
possible to collect samples at random intervals down a soil/geological profile.
● For temporal dimensions, usually sample collection or measurements over time are defined with
natural cycles such as daily, seasonal, or yearly intervals. Additionally, more precise intervals are
sometimes simply defined in convenient time units such as seconds (or fractions), minutes,
hours, weeks, or months. Therefore, most temporal sampling programs can be defined as
systematic because they are usually carried out at regular intervals.

8. TEMPORAL PROPERTIES
● Environmental monitoring often has a temporal component. Therefore, knowledge of
the dominant cycles that affect an environment or a parameter of interest is also
indispensable. For example, information about the degradation rate of a pesticide in
soil may help scientists design a cost-effective series of soil sampling events. Pesticide
in the soil environment is known to be approximately
6 months, it may be sufficient to collect a soil sample every 3 months over 2 to 3 years to monitor and
quantify degradation rates. However, if the pesticide’s half-life is closer to 30 days in the soil environment,
then weekly sampling for up to 6 months may be needed to obtain useful results.
● For temporal dimensions, usually sample collection or measurements over time are defined with natural
cycles such as daily, seasonal, or yearly intervals. Additionally, more precise intervals are sometimes
simply defined in convenient time units such as seconds (or fractions), minutes, hours, weeks, or
months. Therefore, most temporal sampling programs can be defined as systematic because they are
usually carried out at regular intervals.

9. REPRESENTATIVE UNITS
● Environments do not always consist of clearly defined units. For example,
although a forest is composed of easily recognizable discrete units (trees),
a lake is not defined by a discrete group of water units. The lake in fact
has a continuum of units that have no beginning or end in themselves.
● However, these ‘‘water’’ units (like the forest units) occupy specific volumes in space at any given
time. Furthermore, the water units in total reside within fixed boundaries defined by intersections with
other components of the environment.
● No unique definition for a representative sample or unit exists. Each
environment and scale has a different unit definition. Ideally, the sample
support should be equal to the unit. However, this is not always the case.

10. REPRESENTATIVE UNITS
● Because of this ambiguity, a few examples of this concept will be
presented. Note that a unit is defined as the smallest sample or
observation that has or is believed to have all the attributes of the
targeted environment. In other words, it is ‘‘representative’’ of the target
component.
● The size or dimensions of a sample are constrained by two important aspects.
○ First, the sampling technique applied to the problem must be defined, with consideration to
the physical limitations of the environment, which in turn limits the type of equipment
available for use, sensor resolution, and mass of the material removed.
○ Thus, the second important aspect is the collection of an adequate and representative
number of samples that are critical to the science of environmental monitoring.

11. REPRESENTATIVE UNITS

12. 0
2
Environment
al Sampling,
Sampling
Locations &
Sampling
Plan

13. SAMPLING LOCATION: CONSIDERATIONS
● Ideally, each sampling location should be selected at random. Also, the number of samples must be
defined with a maximum-accepted level of error in the results.
● Sample location and number of samples must be considered in concert with several other important
aspects unique to environmental science.
For example:
● Costs
● Accessibility
● Time
● These can be associated with sampling and analysis often limit the application of rigorous statistics in
environmental monitoring. may constrain statistical schemes and result in unintentional bias.

14. SAMPLING LOCATION: BIASES
● The degree of the bias varies with the type of knowledge available to the designer(s) of the sampling
plan. Some bias is expected, acceptable, and even necessary to reduce costs. The use of
previously acquired knowledge about an environment to select a specific location, soil depth, or plant
species is acceptable.
● However, this process, if left unchecked, can quickly become judgment sampling, which has some
inherent shortcomings. Judgment sampling assumes that the sampler ‘‘knows best’’ and that the
location or time of the sample selected by the sampler is ‘‘representative.’’
● Often this approach produces biased data that have no defined relevance. Nonetheless, some forms
of this approach are often used in environmental monitoring to reduce costs and save time.

15. SAMPLING DEFINITIONS

16. SAMPLING DEFINITIONS

17. TYPES OF ENVIRONMENTAL SAMPLING
● Environmental monitoring is paradoxical in that many measurements cannot be
done without in some way affecting the environment itself.
● When samples are physically removed from an environment it is called
destructive sampling and usually has a long lasting and often permanent
impact on the environment.
An example is drilling a deep well to collect groundwater samples. Although
here the groundwater environment itself suffers little disturbance, the overlaying
geological profile is irreversibly damaged.
● Also, soil cores collected in the vadose zone disrupt soil profiles and can create
preferential flow paths. When biological samples are collected, the specimen must
often be sacrificed. Thus, sampling affects an environment when it damages its
integrity or removes some of its units.

18. TYPES OF ENVIRONMENTAL SAMPLING
● Nondestructive sampling, often called noninvasive sampling, is becoming
increasingly important as new sensors and technologies are developed.
● Two major techniques are remote sensing, which records electromagnetic
radiation with sensors, and liquid-solid or gas-solid sensors, which
provide an electrical response to changes in parameter activity at the
interface.
● The first sampling technique is best illustrated by satellite remote sensing that uses reflected visible,
IR, and UV light measurements of the earth’s surface.
● The second technique is commonly used in
the direct measurement of water quality
parameters such as E.C. or pH with electrical
conductivity and H+ activity–sensitive
electrodes.

19. NONDESTRUCTIIVE
SAMPLING
ENIVRONMENTAL SAMPLING
TECHNIQUES

20. SAMPLING PLAN: OBJECTIVES
● Several objectives must be defined in a good sampling
plan. Examples include what is needed to quantify the
daily amount of a pollutant being discharged into a
river, to determine the percent of vegetative cover in a
watershed area, or to measure the seasonal changes in
water quality in a reservoir. Each of these objectives
requires different sampling approaches in terms of
● Location;
● Number of samples; and;
● Sampling intensity.
● Therefore, it is important that the objective be clearly
stated, that it be attainable, and that its successful
completion produce data that are usable and
transferable.
● However, there are three issues that often limit efficacy
and objectiveness in environmental monitoring:
● Number of samples (n), which is usually limited
by sample analysis and/or collection costs.
● Amount of sample, which is often limited by the
technique used.
● Sample location, which is often limited by
accessibility.

21. SAMPLING PLAN

22. SAMPLING PLAN

23. 0
3
Analytical
Data Quality

24. ANALYTICAL DATA QUALITY REQUIREMENTS
● A critical component of environmental monitoring is the type of analytical equipment used to analyze
the samples. The choice of methods is usually dictated by the following:
● environment monitored;
● the parameter of interest, and;
● the data quality requirements.
● Typically, we must select a scientifically sound method, approved by a regulatory agency. For
example, drinking water quality methods require a specific laboratory technique such as analysis of
total soluble lead in drinking water, U.S. EPA Method 239.2 should be used.
● This includes the use of graphite furnace atomic absorption spectroscopy. Additionally, the method
provides a detailed laboratory operation procedure and quality control requirements for use with water
samples.

25. ANALYTICAL DATA QUALITY REQUIREMENTS
● Many analytical methods are available for
the analysis of air, water, soil, wastes,
plants, and animal samples. These
methods can be found in standard
references for the analysis of soil, water,
and wastes presented in the box here:

26. ANALYTICAL DATA QUALITY REQUIREMENTS
● Standard operating procedures (SOPs) are used in environmental monitoring, but field and
laboratory methods are not usually interchangeable, although they are often complementary.
● When no direct methods of analysis exists, then sampling and analysis become two separate tasks.
Field analysis procedures are often adapted from laboratory methods.
● Standard laboratory methods are only introduced when needed to complement a field protocol.
Because samples are collected in the field but analyzed in the laboratory, these standards may be
applicable only to laboratory procedures.

27. PRECISION & ACCURACY
● Measurements are limited by the intrinsic ability of each
method to detect a given parameter. These limitations are
dependent on the instrument(s) and the method used, as
well as the characteristics of the sample (type, size, matrix)
and the human element.
● Precision or Resolution
● Precision is a measure of the reproducibility of a
measurement done several times on the same
sample or identical samples. A measure of the
closeness of measurements is given by the
distribution and its standard deviation.
● In most chemical measurements, instrument/ method
precision is computed under controlled conditions
with no fewer than 30 replicate measurements.
● These measurements are done with standards
near the detection limits of the instrument.
Analytical measurements are usually assumed to
have a normal distribution.
● Resolution is a term sometimes used
interchangeably with precision and is applicable to
modern measuring devices that convert a continuous
analog (A) signal into a discrete digital (D)
response.
● Thus, all instruments, including cameras; volt, amp,
and resistance meters; and photometers, have an
intrinsic resolving power. Resolution is the smallest
unit that provokes a measurable and reproducible
instrument response.

28. PRECISION & ACCURACY
● Accuracy-point of reference
● The instruments used in environmental monitoring and analysis are often extremely
sophisticated, but without a proper calibration, their measurements have no meaning.
Thus, most instruments require calibration with a point of reference because measurements are
essentially instrument response comparisons.
● Reference & Calibration
● A reference is usually a standard such as a fixed point, a length, a mass, a cycle in time, or a
space that we trust does not change.
● Field and laboratory instruments must be calibrated using ‘‘certified’’ standards. Calibration
is a process that requires repeated measurements to obtain a series of instrument responses. If
the instrument produces a similar response for a given amount of standard, then we trust the
instrument to be calibrated.

29. PRECISION & ACCURACY
● Detection Limit
● All techniques of measurement and measuring devices have limits of detection. Furthermore,
most instruments can be calibrated to produce predictable responses within only a specified
range or scale.
● At the low end of the range, a signal generated from a sample is indistinguishable from
background noise. At the upper range, the sample signal generates a response that exceeds
the measuring ability of the instrument. When measurements are made at or near the detection
limits, the chances of falsely reporting either the presence or absence of a signal increase.

30. PRECISION & ACCURACY
● Detection Limit
● Lower detection limits are very important in environmental monitoring and must be
determined for each method-instrument-procedure combination before field use. These
detection limits should be determined under controlled laboratory conditions.
● There is no consensus on how to measure detection limits, and they are still a subject of
debate. The most common method is based on the standard deviation(s) of the lowest
signal that can be observed or measured generated from the lowest standard available.
● Note that blank, instead of standard, readings can be used, but this is not recommended
because blank and standard values often do not have the same distribution. It is also
important to remember that detection limits are unique for each environment (matrix),
method, and analyte.

31. PRECISION & ACCURACY
● Detection Limit
● Consecutive standard readings should be made to determine detection limits no less than
30s. From these values the mean and standard deviation(s) should be computed. We can then
proceed with the analysis of an unknown sample and set a reliable detection limit (RDL) to be
equal to the method or minimum detection limit (MDL) of no less than 3s.
● However, even setting an RDL at 3s has problems in that 50% of the time, data that are the
same as the MDL will be discarded.
● This equates to a 50% chance of making a Type II error (false negative), as compared with a
less than 0.15% chance of making a Type I error (false positive).
● If the RDL is increased to 6s units, then the chances of having either a Type I or Type II error
are now both equal to or less than 0.15%.
● RDL is increased to 10s units, no chance exists of making a Type I or Type II error. This is
also called the limit of quantification (LOQ ).

33. ● Instrument blank and real sample values can be misinterpreted when these are close to each
other. (A) When reliable detection limits are set to equal minimum or method detection limits = 3
sigma (σ) units, blank and sample values overlap. (B) If reliable detection limits are set at 6σ units at
least, blank and sample reading overlap minimally. (C) Quantifiable detection limit should be set at
least at 10 s units above average blank to prevent overlap between blank and sample values.

34. TYPES OF ERRORS
● Field instruments with poor precision and accuracy produce biased measurements. Three types of
errors can occur when making measurements:
● Random errors are usually due to an inherent dispersion of samples collected from a population,
defined statistically by variance or standard deviation about a ‘‘true’’ value. As the number of replicate
measurements increases, this type of error is reduced. Precision increases as n (number of
measurements) increases. Random errors include Type I and Type II errors, which were discussed
previously.
● Instrument calibration errors are associated with the range of detection of each instrument.
Uncertainty about the calibration range varies. Typically, as the analyte concentration increases,
so does the standard deviation.

35. TYPES OF ERRORS
● Also, if extrapolation is used, at either end of the calibration curve, the standard deviation of the
confidence intervals increases quickly. For example, the common linear regression used to
interpolate instrument response versus analyte concentration assumes that all the standards used
have the same standard deviation. Because this is not true, often modern instruments incorporate the
weighted regressions to optimize calibration curves.
● Systematic errors or constant errors are due to a variety of reasons that include the following:
● Biased calibration-expired standards
● Contaminated blank; tainted sample containers
● Interference: complex sample matrix
● Inadequate method: does not detect all analyte species
● Unrepresentative subsampling: sample solids/sizes segregate, settle
● Analyte instability: analyte degrades due to inadequate sample preservation

36. COMBINED ASPECTS OF PRECISION & ACCURACY
● Environmental data include all the factors that affect precision and accuracy. The previous sections focused on the
analytical aspects of data precision and accuracy, but there are also inherent field variations (spatial and temporal
variabilities) in the samples collected. There are also inherent variations in the methods chosen for the sample
preparation before analysis. Thus, it is useful to discuss in final report, the precision, accuracy, and detection limits
associated with each step of the monitoring process. The following is a suggested sequence of data quality
characterization steps:
● Instrument precision and detection limits.
● Type of sample and sample preparation (method) precision and detection limits.
● Combined sample spatial and temporal or random variations. Note that if the goal is to measure field spatial
and temporal variabilities, this step should be omitted (see Chapter 3).
● Overall precision of the data based on sum of all or some errors from the steps 1–3.

37. 0
4
Quality
Control and
Reporting

38. QUALITY CONTROL CHECK
● Routine instrument, method precision checks, or both can be done in the field by analyzing the same
sample or standard twice or by analyzing two samples that are known to be identical. This process
should be repeated at regular intervals every 10–20 samples.
● In this case the percent absolute difference (PAD) is given by the following formula:
where abs = absolute value
● Similarly, we can check the accuracy of the method if one of the two sample values is known to be
the true value. The accuracy as a percent relative difference (PRD) of the measurement can simply
be defined as:
where A is the unknown value and B is the true value.
𝑃𝐴𝐷𝐴𝐵 = 𝑎𝑏𝑠
𝐴 − 𝐵
A + B
∗ 200
𝑃𝑅𝐷𝐵 = (A − B)/B ∗ 100

39. QUALITY CONTROL CHECK
● Conversely, accuracy could also be checked by measuring
the percent recovery (%R) of an unknown value against a
true value:
● Analytical limits of precision and accuracy may be
determined and updated in ongoing field projects that
require numerous measurements over long periods. For
example, plotting the equations above over time with
%𝑅𝐵 =
𝐴
𝐵
∗ 100
preset upper and lower limits would provide a visual indication of the precision and accuracy of each
measurement over time. More commonly, control charts are made by plotting individual values by
date against an axis value scale.

40. QUALITY CONTROL CHECK
● More commonly, control charts are made by plotting individual values by date against an axis value
scale. If the precision of each measurement is needed, then the center line is a running mean of all
the QC measurements.
● The upper and lower control limits can be defined in terms of confidence limits as mean ±2s or
warning limits (WLs) and mean ±3s or control limits (CLs). If the accuracy of each measurement is
needed, then the central line represents the true value and the %R values are plotted with WL and CL
lines.

41. REPORTING DATA
● Most chemical, physical, and biological measurements have inherent limitations that limit their
precision and consequently their accuracy to four or five significant digits.
● In the digital age, methods often use highly precise processing algorithms with more than 128 bits
(significant digits) of precision. This high level of precision has meaning only in the context of the
computational power (speed) of a computer and serves to reduce rounding-off errors that can
become significant when performing a series of repetitive computations.
● Digital processing does not add more digits of precision than those imposed by sensor (analog
or digital), human, and environmental factors. Therefore, computer processing does not add digits
of precision to external data such as values entered in spreadsheets and graphs.
● Atomic clocks routinely achieve eleven digits of precision by measuring highly stable frequency
energies from light-emitting gaseous molecules. These examples of high-precision data and data
processing are the exception rather than the rule.

42. REPORTING DATA
● Data manipulation often combines numbers of different precision. For precision biases to be
reduced during data manipulation, round-off rules should be always be followed. These rules are
listed in the box

43. UNIT OF MEASUREMENT
● Use of appropriate units or dimensions in the results is important to have transferability and
applicability. There are several systems of measurement units, the most common being the
British/American system and the metric system.
● The Systeme Internationale d’Unite´ (SI) incorporates the metric system and combines the most
important units and unit definitions used in reporting and processing environmental monitoring
● data.
● Scientists and engineers involved in monitoring and characterization activities must pay careful
attention to units and often spend significant amounts of time converting data, from
British/American to metric and SI, to/from non-SI units.

44. UNIT OF MEASUREMENT
● This process undoubtedly adds transcription and rounding-off errors to data. It is common to add
more significant figures to data values that have been converted, but because most conversion
factors are multiplications or divisions.

45. UNIT OF MEASUREMENT
● This process undoubtedly adds
transcription and rounding-off errors
to data. It is common to add more
significant figures to data values that
have been converted, but because
most conversion factors are
multiplications or divisions. It is
unfortunate that there is no uniform
or mandated use of the SI, and in
particular the use of the metric
system in such countries like the
United States.

46. UNIT OF
MEASURE
MENT
(Review)
DIMENSION UNIT ABBREVIATION
Length kilometer
meter
centimeter
micrometer
nanometer
km
m
cm
μm
nm
Mass metric tonne
kilogram
gram
milligram
microgram
nanogram
pound
t (metric)
kg
g
mg
μg
ng
lb
Volume cubic meter
liter
milliliter
microliter
gallon
m3
L
mL
μL
gal
Concentration milligrams per L
mg L-1
parts per million
part per billion
milliequivalents per L
moles of charge per L
mg kg-1
mg L1 mg L1
ppm
ppb
mEq L-1
molc L-1
Plane Angle radian
degrees
rad
º

47. UNIT OF
MEASURE
MENT
(Review)
DIMENSION UNIT ABBREVIATION
Density grams per cubic cm g cm-3
Temperature degrees Centigrade
degrees Kelvin
ºC
K
Radioactivity curie Ci
Pressure atmosphere at
pounds per square inch
pascal
at
psi
Pa
Application
rate
kilograms per hectare pounds per acre kg ha-1
lb acre-1
Energy joule
British thermal unit
calorie
J
Btu
cal

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