1. Considerations for Optimal Monitoring Program Design
CASQA 9th Annual Conference, Squaw Valley, CA
Monitoring Workshop
September 9, 2013

2. Design Challenges Size and complexity of MS4s
Spatial and temporal variability
Ephemeral nature of many conditions
Difficult sampling logistics
More complex problems with
interacting causes

3. The Use and Abuse of QAPPs
99.99% of attention focused on sampling and lab methods
0.01% * of attention focused on
Right questions?
Right design to answer these questions?
Right data analysis method?
Does design meet requirements of data analyses?
Do data analysis results actually address the questions?
* Estimates accurate to +/ %

4. Data Quality What is data quality? What are data quality objectives?
The ability of the study and its data to answer core questions
What are data quality objectives?
Characteristics of the data that ensure its ability to answer core questions

5. Aspects of Data Quality
USEPA’s PARCCS construct
Precision
Accuracy
Representativeness
Completeness
Comparability
Sensitivity
But even these are not enough
Presume that already have goals, questions, appropriate designs

6. Larger Context for Data Quality
All elements must be completed
Elements must be coordinated
Goals & questions of paramount importance
Technical aspects driven by goals & questions

7. Data Quality?
Have conditions changed over time at Huntington Beach?

8. How Much Do We Understand?

9. Organizing Our Understanding

10. The Right Amount of Complexity

11. Collaborative Modeling

12. The Tool Bag A wide range of approaches to choose from
Choice depends on state of knowledge, questions, logistics, time constraints, etc.
No single right answer for all situations

13. The Right Conceptual Model
Power analysis for regression of diazinon trends assuming steady decline over time
Data courtesy of Alameda County

14. Or, Maybe Not
Power analysis for change in diazinon concentrations assuming a step-function decline
Data courtesy of Alameda County

15. Design Options Monitor all sites Monitor random subset of sites
Monitor only fixed trend sites
Monitor only known problems
Compare all data to standards
Implement special studies
Use hybrid designs

16. Monitor All Sites Pros Comprehensive picture of conditions
Simple sampling strategy
Track trends over time
Cons
Expensive and time consuming
Lacks frame of reference for comparison

17. Monitor Random Subset of Sites
Pros
Statistically valid estimate of conditions
Track trends in overall condition
Design can be optimized for efficiency
Cons
May miss problem areas
Unable to track trends at specific sites
Lacks frame of reference for comparison

18. Monitor Only Fixed Trend Sites
Pros
Tracks trends over time
Design can be optimized for efficiency
Cons
Does not describe overall conditions
Lacks spatial frame of reference for comparison

19. Monitor Only Known Problems
Pros
Efficiently focuses monitoring effort
Measures progress in solving problems
Cons
Does not describe overall conditions
Lacks broader frame of reference
for comparison

20. Compare All Data to Standards
Pros
Provides consistent basis for comparison
Assesses compliance with regulations
Cons
Difficult to prioritize actions if exceedances widespread
Standards may not provide meaningful action thresholds for local conditions

21. Implement Special Studies
Pros
Focus on specific issues
Improve understanding
More efficient
Defined endpoint
Cons
Applicability may be limited
May require specialized expertise

22. Look at Deviations Bacteria in Aliso Creek Predictable patterns
Focus monitoring on subset of year
Improved $$ and statistical efficiency of monitoring

23. Control for Covariates

24. Focus on Derived Variables

25. Enterococci Aliso Creek: Exponential Regression
Drop in mean concentration over 11 years of 1 – 2 orders of magnitude
Drop from mean of 2500 to 90

26. Questions?