In 2011, CVC began using long-term data loggers to monitor pollution events in streams within the Kiski-Conemaugh River Basin. As part of the program, CVC collected macro-invertebrate samples from each logger site in the spring and fall of the year to begin assessing biological integrity of the monitored streams and attaining baseline data. Throughout the program, several unknown impacts were recorded using the data loggers and confirmed with macro-invertebrates. In 2014, CVC began to sample fish at logger monitoring locations to complete biological baseline data collection, confirm and diagnose stream pollutants and assess reclamation efforts. The comparison of the logger and biological data is allowing CVC to more accurately diagnose pollutants and pinpoint critical areas for reclamation efforts within the Kiski-Conemaugh River Basin, while building a chemical and biological baseline for the Basin.

1. Incorporating Data Logger and Biological
Monitoring to Diagnose Stream Pollutants
and Aid in Reclamation Efforts
By Eric Null
Aquatic Biologist
Conemaugh Valley Conservancy

2. Data Logger and Biological Monitoring
• Two Very Powerful Tools for Pollution
Monitoring
• Biological Sciences and Data Logging
Technology are Advancing Rapidly
• Both are Long Term Monitoring Practices
• When Used Together They can Produce
Powerful Data

3. An Introduction on How to Drill Into
the Data

4. Data Logger Data

5. The Unique Qualities of Data Logger
Data
• Data Logger Data Sets are Immense
• Logging Intervals Must Be Short To Capture
Episodes (15 min)
• This Data can not be Looked at Like Grab Data
• Averages Change Drastically
• Full Stream Behavior is Seen
• Eyes Going Crossed and Migraines are
Symptoms

6. Every Stream is Different

7. Spikes and Valleys
• Can be Caused Naturally or by Disturbance
• Frequency and Duration can Determine
Between Causes

8. Stream A

9. Stream B

10. Biological Data

11. Macroinvertebrates
• Macroinvertebrate Taxa Act Like Letters in the
Alphabet that can Spell Out Pollutants
• Certain Taxa only Thrive in Certain Polluted
Conditions
• Abundance and Diversity Can Determine the
Type of Pollutant

12. Spelling Test
• Your Stream is Dominated by the following
Taxa, What is the Pollutant ?
– Amphinemura
– Cheumatopshche
– Ilybius
– Diptera
– ACID Impacts

13. Another Stream
• Your Stream is Dominated by the following
Taxa, What is the Pollutant ?
– Hydropsyche
– Odonates
– Tabanus
– This Stream has Thermal Pollution, It is HOT

14. One More
• Take a Guess what is Wrong Here
– Psilotreta
– Oligochaeta
– Ochlerotatus
– You guessed it Organics and Sewage

15. Fish Data
• Fish Abundance and Diversity can Determine
Pollution
• Fish Disappear Before Macroinvertebrates in
Polluted Streams
• Different Fish Life Stages are impacted by
Different Pollutants

16. Cross Referencing Biological and Data
Logger Data to Diagnose the Pollutant
• This is When Both Make More Sense
• Conductivity and Other Parameters Influence
Community Structure
• The Community Structure Indicates What is
causing the Conductivity and Other
Parameters to Behave the way they are
Behaving

17. Stream A
Logger Data Biological Data
• Macroinvertebrates
– Extremely Low
Numbers of
Individuals
– Poor Diversity
– Acid Tolerant Taxa
• Fish
– All Juveniles
– Low Numbers of
Individuals
– Ok Diversity

18. Stream A
• Pulsing Spikes with a Constant Occurrence
• Depressed Biological Communities
• No Adult Fish
• Pollution Tolerant Macroinvertebrates
• ACID and METALS

19. Stream B
Data Logger Data Biological Data
• Macroinvertebrates
– High Biomass and
Individuals
– Low Diversity
– Organic and Acid
Tolerant Black Fly
Taxa were Dominant
– Most Taxa Collected
were Pollution
Tolerant

20. Stream B
• Large Conductivity Spikes
• High Biomass and Abundance
• Low Diversity of Macros
• Acidophilic Macroinvertebrates
• ORGANICS AND ACID

21. Stream C (The Hard One)
Data Logger Data Biological Data
• Macroinvertebrates
– Low Diversity and
Numbers
– Acid Tolerant Taxa
• Fish
– Low Diversity
– Acid Tolerant Taxa
– White Sucker/Creek
Chub

22. Stream C
• Consistent Mid Level Conductivity
• Low Macroinvertebrate Diversity and
Abundance
• Low Fish Abundance and Diversity
• Pollution Tolerant Taxa (Fish and Macros)
• Episodic Acidification with Alkalinity
Replacement by Metals and Acidity

23. Stream D
Data Logger Data Biological Data
• Macroinvertebrate
– Very High Diversity
– Very High
Abundance
– Dominated by
Pollution Intolerant
Taxa
– No Organic Loading

24. Stream D
• Very Consistent and Low Conductivity
• Very Diverse Macroinvertebrate Community
• Volunteers and Staff Very Excited to
Electrofish in 2015
• HIGH QUALITY H2O

25. Conclusions
• Data Logger and Biological Data on their Own
are Powerful Assessment Tools
• When Combined they can be used Very
Effectively to Isolate Individual Pollutants
• Data Sets May Appear Confusing at First, but
Over Time Become Easier to Interpret
• Using Both can Better Interpret Each
Individual Data Set

26. Questions?