The document summarizes Dr. John Norton's efforts to collect operational and financial benchmarking data from the 250 largest wastewater treatment plants in the US. His preliminary analysis found that complete data was only available for about 20% of plants due to challenges collecting data from utilities. The data collected so far shows wide variations in treatment capacities, costs per million gallons treated, and staffing levels between plants. Benchmarking this type of data across utilities could help identify best practices and innovations to improve performance. Further refinement of the data collection process is needed to develop a more complete benchmarking dataset.

1. Preliminary analysis of the 250 largest
WWTP in the US:
Initial findings, benchmarking results, and next steps
February 29 - March 2, 2016
Dr. John Norton, PE
IWEA Annual Conference 2016

2. Description of the effort
• Selection of the largest 350 cities in the
country by population, plus the largest 25 or
so in each state.
– Tried to capture WWTP in sanitary districts serving
multiple communities
• City by city examination of every wastewater
treatment plant
– Thousands of calls/emails to utilities, State EPAs,
DEPs, etc., reviews of NPDES permits, hundreds of
masterplans read

3. Staggering challenges
• Available treatment data
– Range from “non-existent” public data to full disclosure.
– Numerous data sets contradict each other, e.g., average daily flow will
not match recent reporting, NPDES permitted levels, posted
information, etc.
– Dozens of permits had errors, omissions, etc.
– Inconsistent data sets, CSOs, plant excursions, often not available
• Social resistance
– “Why do you want our data?” “Call the EPA if you want our data.”
“Send us a letter.” (Letter sent, no response despite numerous
followups)
• Costing data
– Goal: “simple” summary of all administrative, O&M, capital costs….
– Occasionally available as part of comprehensive masterplans/CIP,
roughly 1/3 of the utilities post their masterplans

4. Five largest plants, by design flow
0 250 500 750 1000 1250 1500
DC WASA (Blue Plains)
Los Angeles (Hyperion)
Detroit
Boston (Deer Island)
Chicago (Stickney)
MGD (design flow)

5. Data (so far)
• Complete data for only about 20% of those WWTP
examined (about 60/320)
• Basic load and flow data
• O&M data, other costing data, very deficient
• Plant treatment processes are almost unique on a plant
by plant basis, typically basic data available ~ 40% of
the WWTP (135/320)
• BEST SYSTEM: Chicago! All treatment data posted
online (limited financial data though)
• Worst system: New York City! (They require a FOIA
request for EVERYTHING.)

6. Initial data (data set NOT COMPLETE)
NOTE: “Percent less than” is the percentage of values less than a particular value. For instance, this
graph shows that the 50th percentile plant is roughly 30 MGD, meaning that 50% of the plants
measured are smaller than 30 MGD.

7. Treatment capacity: actual to design
Stickney

8. Why benchmarking?
• Search for innovative ideas
– Internal: year over year performance comparison
– External: drill down into criteria to reveal success factors:
• e.g., energy reduction efforts, employee retention, health and safety
practices, equipment performance, etc
• Establish best practices
– Comprehensive and data-based comparison of efforts
• Gain broader, more accurate, organizational perspective
– Since it is based on what the best are doing it takes the emotion
out of arguments about the need to change

9. Data set: materials and methods
• Types of data
– Operational aspects such as flow and loading, treatment goals, and permit
compliance
– Economic aspects such as treatment cost, energy use, and capital investment
– Managerial aspects such as utility metrics, employee training, and
procurement systems
• Sources of data
– Facility websites, operations reports, master plans, NPDES permits, posted
data, personally provided data.
– This data is being collected into a comprehensive database.
– All data is being confirmed via multiple methods, including
• review with facility personnel,
• direct assessment of operational and other published data, and
• discussion and review with regulatory officials
• Current data set is preliminary and is provided as an example

10. Everyone is unique? Yes!

11. Employees per MGD

12. Note the difference when accounting for
economies of scale
Staff/MGD, as a function of MGD

13. Cost of treatment

14. Cost per MDG – accounting for
economies of scale

15. Challenges of benchmarking
• Economies of scale – “artificially” outstanding
performance
• Hidden correlations – dry climates
• Using the results – gaining traction for positive
change

16. Technology examples
• Inflow and infiltration reduction programs
• Energy use per unit treated
• Solids generation per unit treated
• Biogas utilization

17. Economics examples
• Cost per unit treated
• Employees per unit treated
• Capital investment over time
• Electrical energy rate agreements

18. Organizational examples
• Training expenditure per employee
• Organizational structure and type
– Integrated city service, independent political
agency, privately run
• Internal versus external laboratory services
• Operator scheduling, shift rate, etc.

19. Successfully enabling change needs
“The Whole Story”
REASONS
TARGETS
ACTIONS

20. Examples of data driving
organizational performance
• School systems:
– Graduation/placement data informs parents
• The World Bank –
– http://www.doingbusiness.org/, motivated countries to
initiate reforms, e.g., Namibia, Zambia, Singapore, etc.
• Toxics Release Inventory
– public information drove
huge reductions in
industrial pollution

21. Next steps
• “Stick with the effort” - >2,500 hours
personally invested
• Focus on a specific area to get an initial “win”
to share, motivate further collaboration
• Refine/streamline the approach based on
lessons learned
• Standardized data request?

22. Personal note
“I believe this type effort, these types of data and resulting
analysis, are a critical missing link in the management and
evolution of our country’s water infrastructure.
I feel that, so far, I have failed in my efforts to deliver even a
fraction of the promise that may yet come to pass.
I will never give up.”
- Dr. John, W. Norton, Jr., PE
March, 2016

23. Questions?
www.clarkdietz.com
John W. Norton, Jr., Ph.D., P.E.
977 N. Oaklawn Avenue, Suite 106
Elmhurst, IL 60126
630.413.4130 - office
312.550.1274 - cell
john.norton@clarkdietz.com