1. Today’s Lecture  Sampling Design

2. Announcements  Lecture  today: sampling design  Tuesday: subsurface sampling  Lab  Lab 2 surface water  report due Wed, Oct 7, before lab  Lab 3 soil sampling  Wednesday, Oct 7  HAZWOP component

3. Environmental Sampling in the News  Two major U.S. aquifers contaminated by natural uranium ScienceDaily, Aug 17 2015, Scott Schrage ScienceDaily  naturally-occurring uranium mobilized by nitrate  nitrate source is agricultural; up to 189  EPA standard  uranium up to 89  EPA standard  one in six wells contaminated  about 2 million users  High Plains aquifer  largest in the U.S.  Central Valley aquifer

5. Sampling Design  Colorado gubernatorial election, Sept 17, 2014 Beauprez, +10%

6. Sampling Design  Colorado gubernatorial election, Sept 17, 2014 Beauprez, +10% Hickenlooper, +7%

7. Sampling Design  Colorado gubernatorial election, Sept 17, 2014 Beauprez, +10% Hickenlooper, +7% Colorado governor: Hickenlooper, Beauprez tied in Denver Post poll By John Frank, The Denver Post Once-charmed Colorado Gov. John Hickenlooper is fighting for his political life as the Denver Post poll in the governor's race shows him tied with Republican rival Bob Beauprez. Hickenlooper and Beauprez are deadlocked at 46 percent, according to a SurveyUSA poll this week that reached likely voters and those who had completed mail ballots. Four third-party candidates combined for 4 percent and another 4 percent of voters remain undecided. The poll — conducted Monday through Wednesday — has a 4 percentage point margin-of- error.

8. Sampling Design  Colorado gubernatorial election, Sept 17, 2014 Statement of Methodology: The SurveyUSA poll was conducted via landline and cell phone. All respondents indicated that they were very likely to vote or had already voted in the Nov. 4 election. The margin of error is +/-4.4 percent at a 95 percent level of confidence. Check the calculations: Sample Size Calculator

10. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?

11. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level:  confidence interval:  population:  sample size:

12. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level: certainty of sampling result; 95% confidence level a common choice  confidence interval:  population:  sample size:

13. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level: certainty of sampling result; 95% confidence level a common choice  confidence interval (margin of error): range of certainty of sample estimate;  10 %  population:  sample size:

14. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level: certainty of sampling result; 95% confidence level a common choice  confidence interval (margin of error): range of certainty of sample estimate;  10 %  population: number of possible samples; size of population irrelevant unless sample size > a few % of population size  sample size:

15. Sampling Design  Valmont Butte area: 103 acres (one sample per acre?) = 41.7 hectares 417,000 m 2 (one sample per m 2 ?) 4,170,000,000 cm 2 (one sample per cm 2 ?)

16. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level: certainty of sampling result; 95% confidence level a common choice  confidence interval (margin of error): range of certainty of sample estimate;  10 %  population: 417,000  sample size:

17. Sampling Design  How many samples should I take to determine the concentration of lead in the shallow soil at Valmont Butte to a 10% margin of error?  Sample Size Calculator Sample Size Calculator  confidence level: certainty of sampling result; 95% confidence level a common choice  confidence interval (margin of error): range of certainty of sample estimate;  10 %  population: 417,000  sample size: 96

18. Sampling Design  When and where to sample  time: one dimension  changes in release, degradation  space: three dimensions  x and y  shallow surface soil contamination  surface water, well-mixed  z  surface soil, groundwater contamination  surface water, stratified

19. Sampling Design  Representativeness

20. Sampling Design  Representativeness

21. Sampling Design  Representativeness  (representativity?)  degree to which data accurately and precisely represent a characteristics of an environmental condition  ultimate representativeness  scale: entire site, entire population  sampling point representativeness  scale: a soil horizon, a location in a lake, groundwater near a monitoring well  collected sample representativeness  scale: within a sample during laboratory analysis

22. Sampling Design  Representativeness  solid samples  vertical variations  grain-scale variations  water samples  seasonal variations  stratification; mixing of streams  air samples  meteorological conditions  topographic factors  biota samples  species, size, sex, mobility

23. Sampling Design  Sampling approaches  judgmental  prior information, visual inspection, personal knowledge  simple random  arbitrary collection; each sample unit has same probability of being chosen  stratified random  sampling strata (temporal, spatial), random sampling in each stratum  systematic  specified pattern or grid in time, space; systematic or random within grid

24. Sampling Design  Sampling approaches  judgmental  simple random  stratified random  systematic

25. Sampling Design  Simple random sampling  temporal or spatial  not “haphazard” or convenient  appropriate for homogeneous populations  appropriate for little background information  minimum sample size needed to support statistical analyses  calculators (e.g., Raosoft)Raosoft

26. Sampling Design  Stratified random sampling  identify homogeneous strata  simple random sampling within each strata  prior knowledge of environment  temporal or spatial  intensity varied by strata  equal  proportional (#  size)  optimal (#  cost)

27. Sampling Design  Systematic sampling  specified pattern or grid  systematic or random within grid  samples from nodes or centers  temporal or spatial  e.g., specified time intervals corresponding to seasons  grid spacing, two-d

28. Sampling Design  Systematic sampling  advantages  easy to implement  disadvantages  parameter being measured has periodicity in space or time similar to sampling periodicity  collect a sample for dissolved oxygen at 3 pm every day; miss diel cycle  inefficient  possible collection of large number of samples over areas of little variation

29. Sampling Design https://www.e-education.psu.edu/geog486/l6_p6.html

30. Sampling Design  Number of samples  more is better  more is more expensive, more time-consuming  more may not improve understanding  number should fit project goals and data quality objectives  for normally-distributed data  standard deviation  half-width of 95% c.i. on mean

31. Sampling Design https://www.e-education.psu.edu/geog486/l6_p6.html

32. Sampling Design https://www.e-education.psu.edu/geog486/l6_p6.html

35. Next Lecture  Subsurface Sampling