Air, Water and Land Pollution
Chapter 3:
Environmental Sampling Design
Copyright © 2010 by DBS
Contents
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Planning and Sampling Protocols
Sampling Environmental Population
Environmental Sampling Approaches: Where and When
Estimating Sample Numbers: How Many Samples Are Required
Environmental Sampling Design
Planning and Sampling Protocols
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Types of study:
– Remediation
– Investigation
– Site assessment
– Waste management
– Risk assessment
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Planning is critical for overall data quality and project completion
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Outcomes:
– Data quality objectives
– Sampling and analysis work plan
Environmental Sampling Design
Planning and Sampling Protocols
Environmental Sampling Design
Planning and Sampling Protocols
Environmental Sampling Design
Planning and Sampling Protocols
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This plan uses experienced field personnel, analytical chemist and data analysis by a
mathematician
Engineer may be called in for complex field sampling devices or hard to reach areas
QA/QC representative reviews the applicability of standard operating proceedure
(SOP), determines QA/QC samples (blanks, spikes etc.) and document the accuracy
and precision of the resulting data
Data user ensures that data objectives are understood and incorporated into the plan
Environmental Sampling Design
Planning and Sampling Protocols
Data Quality Objectives (DQOs)
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Developed during planning process
“Qualitative and quantitative statements that define the appropriate type of data, and
specify the tolerable levels of potential decision errors that will be used as basis for
establishing the quality and quantity of data needed to support a decision”
(EPA, 2000)
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Main idea – least expensive data collection but not at the price of providing answers
that have too much uncertainty
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Agreed upon by all stakeholders
Environmental Sampling Design
Planning and Sampling Protocols
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Data Quality Objectives (DQOs)
Environmental Sampling Design
Planning and Sampling Protocols
Data Quality Objectives (DQOs)
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DQOs in simple terms:
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What is the project’s purpose?
What is the problem that requires data collection?
What types of data are relevant for the project?
What is the intended use of data?
What are the budget, schedule, and available resources?
What decisions and actions will be based on the collected data?
What are the consequences of a wrong decision?
What are the action levels?
What are the contaminants of concern and target analytes?
What are the acceptable criteria for the PARCC parameters?
Who are the decision-makers?
Who will collect data?
Why do we need to collect the particular kind of data and not the other?
When will we collect the data?
Where will we collect the data?
How will we collect the data?
How will we determine whether we have collected a sufficient volume of data?
How will we determine whether the collected data are valid?
How will we determine whether the collected data are relevant?
Environmental Sampling Design
Planning and Sampling Protocols
Basic Considerations of Sampling Plan
Environmental Sampling Design
Planning and Sampling Protocols
Basic Considerations of Sampling Plan
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Four primary factors:
– Objectives
– Variability
– Cost Factors
– Nontechnical factors
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Objective is the determining factor in sampling design
e.g. water monitoring trend analysis requires long-term sampling scheme, whereas
background levels (baseline) can be taken during a one-time event
e.g. required data quality affects the number of samples, increases with higher quality
Environmental Sampling Design
Sampling Environmental Populations
Where (Space) and When (Time) to Sample
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1-D (x), 2-D (x,y) and 3-D (x,y,z)
e.g. outfall of industrial wastewater discharge – concentration vs. distance (1-D)
e.g. lead content in surface soil downwind of a smelter (2-D)
e.g. large body of water, or solid/hazardous landfill where depth is important (3-D)
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In many cases contaminant variation in BOTH space and time are important
Representative Samples
Environment is variable
(sampling strategy needs to
account for this)
– no two organisms
exposed in exactly the
same way
– day/night cycling of
factories
– hour by hour, day by
day, seasonal
e.g. NO3- in river water
Different results would be found a few km
downstream due to physical, chemical and biological
transformations
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
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“Representativeness” is one of the 5 “Data Quality Indicators” (DQIs)
“a measure of the degree to which data accurately and precisely represent a
characteristic of a population, a parameter variation at a sampling point, a process
condition, or an environmental condition” (US EPA)
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
Representative Solids Samples
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Depends on sample matrix
Contaminants in soils vary more vertically than horizontally
Sample preparation process very important for representative samples (subsampling,
mixing, grounding, sieving)
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
Representative Air Samples
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Potentially large variations and heterogeneity
Important to determine whether air sample is representative of “typical” or “worst
case” site conditions both spatially and temporally
Contaminant concentrations may vary within minutes depending on meteorology and
topography
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
Representative Water Samples
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Water samples show typical seasonal variations depending on water balance due to
recent precipitation and water usage
Surface waters can be very heterogeneous both spatially and temporally as a result
of stratification
Stratification is common in oceans, deep-lakes and slow-moving streams
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
Representative Biological Samples
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Differences in species, size, sex, mobility, tissue variations leads to large
heterogeneity
Migratory and transitory species should be avoided
Tissues are required to be well homogenized
Environmental Sampling Design
Sampling Environmental Populations
Obtain Representative Samples from Various Matrices
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“representativeness” depends on the project objective
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Analytical results of soil samples from sites A,B,C are representative if the objective
is to address whether the pipe released a particular contaminant
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These data are not representative if the objective is to estimate the average
concentration in the entire lagoon
Environmental Sampling Design
Environmental Sampling Approaches
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Where and when?
– Judgmental sampling
– Random sampling
– Stratified sampling
– Systematic sampling
Environmental Sampling Design
Environmental Sampling Approaches
Judgmental Sampling
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Subjective selection based on professional judgment using
– Prior information
– Visual information
– Personal knowledge and experience
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Preferred sampling approach for:
– Tight schedule and budget (e.g. emergency response)
– Early stages of site investigations
– Screening for presence or absence of contaminants
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Primary approach used for selecting ground water monitoring wells in groundwater
assessment (due to cost of installation, monitoring wells must be placed in areas at
threat of contamination
Environmental Sampling Design
Environmental Sampling Approaches
Random Sampling (a)
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Arbitrary collection of samples
Each sample unit in the population has the same probability of being chosen
A random process is used to select each sampling point independently from all the
other points (random.org)
Is NOT haphazard sampling (“any location will do”)
Not recommended by EPA since it ignores prior site information/professional
judgment
Environmental Sampling Design
Environmental Sampling Approaches
Stratified Random Sampling (b)
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Divides a population into several nonoverlapping strata
Within each stratum a random sample is taken
Strata could be temporal or spatial
e.g. day/evening, weekday/weekend, seasons, depth, ages and sex, topography,
geographical regions, land use, wind direction etc.
Environmental Sampling Design
Environmental Sampling Approaches
Stratified Random Sampling (b)
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Within each stratum the formulas for mean and SD are the same as for simple
random sampling
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Suppose we have r strata (k = 1, 2, …r), the stratum mean and stratum SD are
combined as follows to calculate the population mean and SD:
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Where wk = the fraction or weight of the population represented by stratum k,
( = nk/n for proportional allocation, nk =total sample units in stratum k, n = total
sample units.)
Environmental Sampling Design
Environmental Sampling Approaches
Systematic Sampling (c an d)
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Selecting sample units according to a specified pattern
Grid sampling divides the area into squares
In ‘systematic grid sampling’ samples are collected from nodes or center of squares
In ‘systematic random sampling’ samples are collected from each grid cell using
simple random sampling
Environmental Sampling Design
Environmental Sampling Approaches
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Systematic Sampling
– Easier to implement
– Convenient for field personnel
– More uniform distribution over space/time domain – ensures all areas are
represented
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Grid spacing ‘L’ is very important
Should be small enough to detect spatial/temporal patterns or search for hot spots
Environmental Sampling Design
Environmental Sampling Approaches
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In a 1-dimensional systematic sampling, e.g. concentration vs. time
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Calculate spacing interval k,
K = N/n
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Where N = total population units and n = predetermined no. samples
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Round k to nearest integer, use k for spacing
e.g. a 3-day sample scheme in a month of 31 days:
k = 10,
- randomly chose a number from 1 to 31, for example 15
- sampling days are 15, 25, 4
Environmental Sampling Design
Environmental Sampling Approaches
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In a 2-dimensional area:
L = √(A/n)
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Where A = area sampled, n = total number of samples collected
e.g. in an area 100 km2 with 10 samples collected L = ?
3 km
Environmental Sampling Design
Environmental Sampling Approaches
Other Sampling Designs
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Composite Sampling - used to estimate average concentration rather than variability
or extremes
– Mixing will provide the same degree of precision and accuracy as the average of
all samples
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Search Sampling
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Transect Sampling – uses transects rather than grids
Environmental Sampling Design
Environmental Sampling Approaches
EPA, 1995
Environmental Sampling Design
Environmental Sampling Approaches
http://vsp.pnl.gov/
EPA, 1995
Environmental Sampling Design
Estimating Sample Numbers
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Best sample number is the largest number possible
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Limited time and budget resources
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Too few samples make data unreliable!
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Sample number (n) is a function of:
– Project goal
– Type of sampling
– Environmental variability
– Cost
– Etc.
Environmental Sampling Design
Estimating Sample Numbers
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e.g. judgmental sampling to determine the presence or absence of a contaminant
requires few samples whereas grid sampling requires a great deal many more
samples to examine the extent of contamination
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No universal formula for calculating sample size
References
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EPA (2002) Guidance on Choosing a Sampling Deign for Environmental Data Collection
for Use in Developing a Quality Assurance Project Plan, EPA-QA/G-5S. Office of
Environmental Information, EPA/240/R-02/005.
Gilbert, R.O. (1987) Statistical Methods for Environmental Pollution Monitoring, Van
Nostrand Reinhold, New York, NY.
Keith, L.H. (1990) Environmental Sampling: A Summary. Environmental Science and
Technology, Vol. 24, No. 5, pp. 610-617.
Kratochvil, B., Wallace, D. and Taylor, J.K. (1984) Sampling for chemical analysis.
Analytical Chemistry, Vol. 56, pp. 113-129.
Popek E.P. (2003) Sampling and Analysis of Environmental Pollutants: A Complete
Guide. Academic Press, San Diego, CA.
Environmental Sampling Design
Questions
6. Describe the differences between: (a) haphazard sampling; judgmental sampling; and (c) random sampling.
11. Why the standard deviation is typically smaller for stratified random sampling than simple random sampling, particularly for a
heterogeneous population with a geographical (spatial) or temporal pattern?
12. In a site assessment for the identification of contaminated source, which one of the following is likely the best and least favorable: (a)
judgmental sampling; (b) simple random sampling, and (c) systematic sampling? Briefly explain.
Environmental Sampling Design
Questions
16. A former small pesticide manufacturing facility was surveyed for pesticide residues in surrounding soils. Historical data have shown
that the pesticide is very stable in soil, concentration is in the range of 40-200 ppb with a standard deviation of 5 ppb.
(a) If an error level of ± 2 ppb is acceptable, how many samples are needed to be 95 % confident that the requirement is met?
For 95 % confidence z = 1.96,
n ≥ (zs/E)2 ≥ (1.96 x 5/2)2 = 24 samples
(b) If an area of 1 km 2 is to be surveyed (see the figure below), design the locations using the method of simple random sampling. Use
Excel to generate random numbers and use the coordinate as shown below (i.e., x = 0, y = 0 for the manufacturing facility, x = -500 ~ 500;
y = -500 ~ 500). Attach the random number from your Excel output and plot a x-y scatter plot showing the locations of all the samples
calculated from (a).
Generate random numbers using the formula =RAND()*1000-500
Environmental Sampling Design
Questions
10. A former small pesticide manufacturing facility was surveyed for pesticide residues in surrounding soils. Historical data have shown
that the pesticide is very stable in soil, concentration is in the range of 40-200 ppb with a standard deviation of 5 ppb.
(b) If an area of 1 km2 is to be surveyed (see the figure below), design the locations using the method of simple random sampling. Use
Excel to generate random numbers and use the coordinate as shown below (i.e., x = 0, y = 0 for the manufacturing facility, x = -500 ~ 500;
y = -500 ~ 500). Attach the random number from your Excel output and plot a x-y scatter plot showing the locations of all the samples
calculated from (a).
Environmental Sampling Design
Questions
18. Thermal stratification is common in lakes located in climates with distinct warm and cold seasons. It divides lakes into three zones
(top: epilimnion; middle: thermocline; bottom: hypolimnion). Because of the stratification, the vertical mixing of the water is prohibited. A
stratified random sampling is designed to collect water samples for nitrogen concentrations. The following data were obtained.
(a) One of the objectives was to estimate the mean, standard deviation, and confidence interval of the entire lake based on this stratified
random sampling plan. Use 80 % confidence level.
wE = nk/n = 8/20 = 0.4,
wT = 2/20 = 0.1,
X-barE = 10.5, x-barT = 13.5,
x-barH = 15.2
wH = 10/20 = 0.5
Environmental Sampling Design
Questions
18. Thermal stratification is common in lakes located in climates with distinct warm and cold seasons. It divides lakes into three zones
(top: epilimnion; middle: thermocline; bottom: hypolimnion). Because of the stratification, the vertical mixing of the water is prohibited. A
stratified random sampling is designed to collect water samples for nitrogen concentrations. The following data were obtained.
(a) One of the objectives was to estimate the mean, standard deviation, and confidence interval of the entire lake based on this stratified
random sampling plan. Use 80 % confidence level.
wE = nk/n = 8/20 = 0.4,
wT = 2/20 = 0.1,
X-barE = 10.5, x-barT = 13.5,
x-barH = 15.2
wH = 10/20 = 0.5
Combine stratum mean to calculate the population mean:
X-bar = wE x X-barE + wT x X-barT + wH x X-barH
X-bar = (0.4)(10.5) + (0.1)(13.5) + (0.5)(15.2) = 4.2 + 1.35 + 7.6 = 13.15
Combine stratum standard deviation to calculate the population standard deviation:
s2 = (wE2 x SE2)/nE + (wT2 x ST2)/nT + (wH2 x SH2)/nH
s2 = (0.4)2(4.6291…)2/8 + (0.1)(3.5355)2/2 + (0.5)2(5.316752…)2/10 = 0.42857…+ 0.06249… + 0.70669… = 1.19788…
= 1.20
s = 1.09
Environmental Sampling Design
Questions
18. Thermal stratification is common in lakes located in climates with distinct warm and cold seasons. It divides lakes into three zones
(top: epilimnion; middle: thermocline; bottom: hypolimnion). Because of the stratification, the vertical mixing of the water is prohibited. A
stratified random sampling is designed to collect water samples for nitrogen concentrations. The following data were obtained.
(a) One of the objectives was to estimate the mean, standard deviation, and confidence interval of the entire lake based on this stratified
random sampling plan. Use 80 % confidence level.
wE = nk/n = 8/20 = 0.4,
wT = 2/20 = 0.1,
X-barE = 10.5, x-barT = 13.5,
x-barH = 15.2
80 % Confidence interval:
CI = ± tn-1,1-α/2 (s/√n)
From Appendix C1 the 2-sided CI for 20 samples = 1.325
CI = ± 1.325 x 1.09/√20 = ± 0.32
wH = 10/20 = 0.5
Environmental Sampling Design
Questions
18. Thermal stratification is common in lakes located in climates with distinct warm and cold seasons. It divides lakes into three zones
(top: epilimnion; middle: thermocline; bottom: hypolimnion). Because of the stratification, the vertical mixing of the water is prohibited. A
stratified random sampling is designed to collect water samples for nitrogen concentrations. The following data were obtained.
(b) If the above nitrogen concentrations were obtained from simple random sampling (i.e., total number of samples = 8 +2 + 10 = 20),
calculate the mean, standard deviation, and confidence interval at a 80 % confidence level.
X-bar = 13.15
s = 5.20
80 % Confidence interval:
CI = ± tn-1,1-α/2 (s/√n)
From Appendix C1 the 2-sided CI for 20 samples = 1.328
CI = ± 1.328 x 5.20/√20 = 1.54
Answers show that strata expected to be more variable should be sampled more intensively, provides greater precision
(lower s, 1.09 vs. 5.20)
Also provides additional data regarding the mean and standard deviation within each stratum.
Environmental Sampling Design
Questions
21. A lagoon waste pit has the following historical data for the barium concentration based on a simple random sampling (n = 4): 86, 90,
98, 104 mg/kg (the lower two thirds of lagoon). The regulatory threshold for barium is 100 mg/kg. The waste on this site was categorized
to be hazardous, and therefore a more thorough sampling plan is needed. Determine the number of samples required so that the reported
mean has a 90 % confidence level.
X-bar = 94.5
s = 8.06
100 - 94.5 = 5.5 mg/kg
Acceptable error is < 5.5 mg/kg
90 % Confidence interval:
CI = ± tn-1,1-α/2 (s/√n)
Environmental Sampling Design
Questions
21. A lagoon waste pit has the following historical data for the barium concentration based on a simple random sampling (n = 4): 86, 90,
98, 104 mg/kg (the lower two thirds of lagoon). The regulatory threshold for barium is 100 mg/kg. The waste on this site was categorized
to be hazardous, and therefore a more thorough sampling plan is needed. Determine the number of samples required so that the reported
mean has a 90 % confidence level.
CI = ± tn-1,1-α/2 x 8.06/√n = 5.5
So tn-1,1-α/2 = 2.353 (from Appendix C1 ) and therefore n = [(2.353 x 8.06)/5.5]2 = 12 samples