air, water and land pollution chapter 3: environmental sampling design copyright © 2010 by dbs

Air, Water and Land Pollution

Chapter 3:Environmental Sampling Design

Copyright © 2010 by DBS

Contents

• Planning and Sampling Protocols• Sampling Environmental Population• Environmental Sampling Approaches: Where and When• Estimating Sample Numbers: How Many Samples Are Required

Environmental Sampling DesignPlanning and Sampling Protocols

• Types of study:– Remediation– Investigation– Site assessment– Waste management– Risk assessment

• Planning is critical for overall data quality and project completion

• Outcomes:– Data quality objectives– Sampling and analysis work plan


• 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


Data Quality Objectives (DQOs)

• 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)

• Main idea – least expensive data collection but not at the price of providing answers that have too much uncertainty

• Agreed upon by all stakeholders


• Data Quality Objectives (DQOs)


Data Quality Objectives (DQOs)

• DQOs in simple terms:

– 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?


Basic Considerations of Sampling Plan


Basic Considerations of Sampling Plan

• Four primary factors:– Objectives– Variability– Cost Factors– Nontechnical factors

• 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 DesignSampling Environmental Populations

Where (Space) and When (Time) to Sample

• 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)

• 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


Obtain Representative Samples from Various Matrices

• “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)



Representative Solids Samples

• Depends on sample matrix• Contaminants in soils vary more vertically than horizontally• Sample preparation process very important for representative samples (subsampling,

mixing, grounding, sieving)



Representative Air Samples

• 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



Representative Water Samples

• 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



Representative Biological Samples

• 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



• “representativeness” depends on the project objective

• 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

• These data are not representative if the objective is to estimate the average concentration in the entire lagoon

Environmental Sampling DesignEnvironmental Sampling Approaches

• Where and when?– Judgmental sampling– Random sampling– Stratified sampling– Systematic sampling


Judgmental Sampling

• Subjective selection based on professional judgment using– Prior information– Visual information– Personal knowledge and experience

• Preferred sampling approach for:– Tight schedule and budget (e.g. emergency response)– Early stages of site investigations– Screening for presence or absence of contaminants

• 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


Random Sampling (a)

• 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


Stratified Random Sampling (b)

• 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.


Stratified Random Sampling (b)

• Within each stratum the formulas for mean and SD are the same as for simple random sampling

• 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:

• 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.)


Systematic Sampling (c an d)

• 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


• Systematic Sampling– Easier to implement– Convenient for field personnel– More uniform distribution over space/time domain – ensures all areas are

represented

• Grid spacing ‘L’ is very important• Should be small enough to detect spatial/temporal patterns or search for hot spots


• In a 1-dimensional systematic sampling, e.g. concentration vs. time

• Calculate spacing interval k,

K = N/n

• Where N = total population units and n = predetermined no. samples

• 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


• In a 2-dimensional area:

L = √(A/n)

• Where A = area sampled, n = total number of samples collected

e.g. in an area 100 km2 with 10 samples collected L = ?

3 km


Other Sampling Designs

• 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

• Search Sampling

• Transect Sampling – uses transects rather than grids


EPA, 1995


EPA, 1995http://vsp.pnl.gov/

Environmental Sampling DesignEstimating Sample Numbers

• Best sample number is the largest number possible

• Limited time and budget resources

• Too few samples make data unreliable!

• Sample number (n) is a function of:– Project goal– Type of sampling– Environmental variability– Cost– Etc.

Environmental Sampling DesignEstimating Sample Numbers

• 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

• No universal formula for calculating sample size

References

• 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 DesignQuestions

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.


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?

(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).

For 95 % confidence z = 1.96,

n ≥ (zs/E)2 ≥ (1.96 x 5/2)2 = 24 samples

Generate random numbers using the formula =RAND()*1000-500


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).


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, wH = 10/20 = 0.5

X-barE = 10.5, x-barT = 13.5, x-barH = 15.2




wE = nk/n = 8/20 = 0.4, wT = 2/20 = 0.1, 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 SE

2)/nE + (wT2 x ST

2)/nT + (wH2 x SH

2)/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




wE = nk/n = 8/20 = 0.4, wT = 2/20 = 0.1, wH = 10/20 = 0.5


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



(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


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.


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


CI = ± tn-1,1-α/2 (s/√n)


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

air, water and land pollution chapter 3: environmental sampling design copyright © 2010 by dbs

Documents

data objectives

collected data

data analysis

types of data

resulting data data

quantity of data

overall data quality

expensive data collection