Environmental sampling involves observing changing environmental states through direct or indirect observations, utilizing various data capture and processing methods. Recommendations for effective sampling include formulating clear data collection goals, understanding variability sources, and ensuring representative samples through statistical analysis. Composite sampling is vital for estimating average values over time, while grab sampling focuses on immediate performance indicators, each having distinct advantages and disadvantages.

1. Environmental sampling

2. Environmental sampling
 Environmental sampling is defined as an observation of
changing environmental states.
 Ecological data are obtained by field samples and/or
laboratory analysis.
 They are directly observed (direct observations) or indirectly
observed (due to calibration of analytical instruments and
sensors).
 Summary data are derived from statistics or by restricted
observable indicators.
 Simulated data are obtained by simulation models.

3. Environmental sampling
Recommendations for environmental sampling
 The goals and needs for environmental data collection should be
formulated explicitly for each application.
 Prior knowledge of the factors that effect environmental variables to be
sampled should be given.
 During sampling significant changes of external and internal driving forces
should not be taking place.
 Existing estimates may be sufficient if they were obtained by unbiased
sampling design.

4. Sampling design
Phases of data flow in environmental sampling are given by
 data capture,
 data processing,
 data storage, and
 data analysis.

5. Sampling design
 A prerequisite for process or system identification is the
availability of appropriate records of the observed process
or system dynamics.
 Important aspects of experimental design are
1. major process time-constants,
2. sampling (measurement) frequency,
3. duration of observations and experiment,
4. choice of initial (start) values of observations,
5. noise level, and
6. process nonlinearities.

6. Sources of variability in sampling programs
Sources of variability in sampling programs are given by
 the variability caused by environmental factors (between sites and dates,or
between dates at the same sites),
 the variability caused by intrinsic factors between samples (same site and
same date),
 the variability caused by different chemical analytical methods (between
determination), and
 the variability caused by different chemical pre-treatments (same
determinations).

7. Samples vs. Populations
 The population is the total or all of the possible
answers we might get by sampling.
 We sample because we can’t count the whole
population.

8. Environmental sampling
 Proper sample technique is critical for good
analysis results. Trash in = Trash out.

9. Representative Samples
What is representative?
 Sample should represent or be typical of the wastewater it
is collected from.
 If the true value of BOD5 in the wastewater is 280mg/L,
then the sample should be close to this value. (BOD5 –
Biological Oxygen Demand – indicates amount of oxygen
which bacteria and other microorganisms consume in
water sample during the period of 5 days at a temperature
of 20o
C to degrade the water contents aerobically –
indirect measure of the sum of all biodegradable organic
substances in water)

10. Representative Samples
How do we know the sample is representative?
 Answer: Statistics
 Representative samples should be very close to
the mean value of the population.
 How do we know we are close to the
mean?
 Look at the sample standard deviation.

11. Population Characteristics

12. Sample Standard Deviation
 – x bar
 is the number of
degrees of freedom –
indicates the number
of independent results
that enter into the
computation of the
standard deviation.
 The standard deviation
tells us how spread out
the data are.
 If the mean is 20 and std
dev is 2, then 68% of all
measurements are
between 18 and 22.

13. Sample Containers and Preservation for
Common Parameters.

14. Grab Sampling

15. Grab Sampling
 Exactly what it sounds like. One sample collected
at a particular point and time.
 Reflects performance only at the point in time
that the sample was collected, and then only if
the sample was properly collected.
 May be used where population is not changing
suddenly or changing a great deal over time.

16. Grab Sampling
 Grab sampling allows the analysis of specific types of
unstable parameters such as pH, dissolved oxygen,
residual chlorine, Fecal coliform, nitrites and
temperature.
 Also applicable for estimating performance under a given
set of conditions.
 However, the most widely used indicators of treatment
plant performance, including CBOD5 (five day
carbonaceous biochemical oxygen demand), TSS (total
suspended solids) and TN (total nitrogen) require the use
of composite sampling techniques.

17. Composite Sampling

18. Composite Sampling
 Composite sampling involves taking a number of
small samples, called sub-samples.
 Multiple samples collected and added together to
make one sample.
 Time Composite.
 Space Composite.
 Flow Proportional Composite.

19. Composite Sampling

20. Composite Sampling
 Frequently used to estimate average values over a 24-hour
period.
BOD5 loading to aeration tanks.
Total suspended solids (TSS) leaving the wastewater
treatment plant (WWTP) in the effluent.
 Gives information over a longer period of time or space.

21. Composite Sampling
 Consideration must be given to sample handling
and storage during compositing.
 We don’t want the sample characteristics to
change while we are sampling.
 Refrigeration often used to slow activity.
 Chemicals may also be added as preservatives.

22. How to Composite
 Simple Composite – Add equal volumes of samples
collected from different times or locations. Mix
thoroughly.
 Flow Proportional Composite – Volume of each
subsample based on flow.
 Estimate total volume of sample required.
 Estimate total flow over sampling period.
 Calculate sample volume per flow.

23. Advantages of composite sampling
 One can select a large number of sample units to
ensure the desired sample support, so that the
results of the analysis will be sufficiently reliable.
 By compositing several individual samples into
fewer composites, one reduces the number of
analytical measurements so that a study can be
carried out without affecting the available
financial resources.

24. Disadvantages of composite sampling
 Compositing may dilute peak values of concern
 Therefore, if peak concentrations of analytes are
important, compositing should be supplemented with
grab samples taken at sites and times where high values
are suspected.

25. Remote surveillance

26. Remote surveillance
 The word ‘surveillance’ means to observe a specific
area or to monitor the activities of individual or a
group.