MAINTAINING RECYCLED WATER QUALITY IN STORAGE AND DISTRIBUTION OUTCOMES OF WATEREUSE FOUNDATION PROJECT 08-04

Transcription

1 MAINTAINING RECYCLED WATER QUALITY IN STORAGE AND DISTRIBUTION OUTCOMES OF WATEREUSE FOUNDATION PROJECT Timothy M. Thomure, P.E., P.M.P. and Rezaur Rahman, Ph. D. HDR Engineering Damon S. Williams, P.E. Jacobs Engineering Group Channah M. Rock, Ph.D. and Ian Pepper, Ph.D. U of Arizona - Dept of Soil, Water and Environmental Science Jean McLain, Ph.D. U of Arizona - Water Resources Research Center (WRRC) Kevin Lansey, Ph.D. U of Arizona - Dept of Civil Engineering and Engineering Mechanics Chris Choi, Ph.D. The University of Wisconsin-Madison - Dept of Biological Systems Engineering

2 Distribution System Research OBJECTIVE: Evaluate existing water infrastructure and management and provide insight to minimize water age and improve water quality in distribution systems and storage Microbial water quality of recycled water via WateReuse Research Foundation grant (WRRF-0804)

3 Recycled Water Uses Urban Reuse Agricultural Irrigation Industrial Reuse Environmental Reuse Recreational Reuse Groundwater Recharge Indirect/Direct Potable Reuse

4 Recycled Water Evolution Disposal Treated effluent delivered to golf course(s) Product Networked distribution system to multiple users Cross connection control programs Public education Tailored Waters Indoor Use Indirect Potable Reuse Emerging: Direct Potable Reuse

5 Regulations and Criteria No Federal Regulations 60% of States Have Water Reuse Regulations 2012 U.S. EPA Guidelines for Water Reuse: Planning and Management Types of Reuse State Regulatory Programs and Regional Variations Treatment Technologies Funding Options Public Participation Global Experiences Lack of Consistency is a Challenge

6 Summary of Water Quality Parameters of Concern for Water Reuse Parameter Range in Secondary Effluents Treatment Goal in Reclaimed Water US EPA Guideline Suspended solids 5 mg/l 50 mg/l <5 mg SS/L 30 mg SS/L Turbidity 1 NTU 30 NTU <0.1 NTU 30 NTU 2 NTU BOD5 10 mg/l 30 mg/l <10 mg BOD/L 45 mg BOD/L 10 mg/l COD 50 mg/l 150 mg/l <20 mg COD/L 90 mg COD/L TOC 5 mg/l 20 mg/l <1 mg C/L 10 mg C/L Total coliforms <10 cfu/100ml <1 cfu/100ml 10 7 cfu/100ml 200 cfu/100ml Fecal coliforms < cfu/100ml <1 cfu/100ml 10 3 cfu/100ml 14 for any sample, 0 for 90% Helminth eggs <1/L 10/L <0.1/L 5/L Viruses <1/L 100/L <1/50L Heavy metals <0.001 mg Hg/L <0.01 mg Cd/L <0.1 mg Ni/L 0.02 mg Ni/L Inorganic <450 mg TDS/L Chlorine residual 0.5 mg Cl/L >1 mg Cl/L 1 mg/l Nitrogen 10 mg N/L 30 mg N/L <1 mg N 30mgN/L Phosphorus 0.1 mg P/L 30 mg P/L <1 mg P/L 20 mg P/L ph 6 9

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8 Objectives Water quality monitoring coupled with distribution system modeling to develop guidelines and recommendations towards successful recycled water management. Distribution System Monitoring AZ (2) Recycled Water Storage AZ (2) Case Study Evaluation Historical data (3) Storage and Distribution System Monitoring (2)

9 Setting an Industry Baseline Questionnaire including the following Treatment Operation Distribution Storage End users Water quality monitoring Past problems Historical data Willingness of sampling and analyzing water samples from the selected storage/distribution system points for the research team.

10 Respective Location of the Selected Utilities

11 Treatment Technology DS-1 MF UV Chlorination Distribution DS-2 Conventional Chlorination Distribution Chlorination Distribution DS-3 MF RO Chloramination Distribution DS-4 Conventional UV Distribution

12 Treatment Technology DS-1 MF UV Chlorination Distribution DS-2 Conventional Chlorination Distribution Chlorination Distribution DS-3 MF RO Chloramination Distribution DS-4 Conventional UV Distribution Actively manage residual disinfectant in storage and distribution.

13 Water Microbiology Channah Rock

14 Known Potential Microbial Hazards in Water Distribution Systems Enteric pathogens via leakage or intrusion events Environmental pathogens Legionella pneumophila Mycobacterium avium complex Helicobacter pylori Naegleria fowleri

15 Monitoring Non-traditional Microbial Indicators Aeromonas Mycobacteria Legionella Amoebic Activity

16 Frequency of occurrence 100% 90% 80% 70% 60% 50% 40% 30% 20% DS 1 (MF UV) DS 2 (Cl) DS 3 (MF RO) DS 4 (UV) 10% 0% Opportunistic pathogens, such as Aeromonas, Legionella, and Mycobacterium, seemed to occur more frequently in recycled water systems that did not maintain residual disinfectant in their distribution system regardless of treatment technology.

17 Disinfection Effects : Distribution System DS-2 :Chlorine Disinfection DS-4 :UV Disinfection Public Company Private Company 24 years 6 years 18 golf courses, 39 parks, 52 schools Managed residual, booster stations, etc. Municipal parks, golf courses, lakes No residual disinfectant 255 Samples 150 Samples >25 miles 7.6 miles

18 1.00E+05 DS 2 (Cl) 1.00E+04 CFU/100 ml 1.00E E+02 Total Coliform E.coli Enterococci Aeromonas Legionella Mycobacterium 1.00E E+00 Distance (miles)

19 1.00E+05 DS 2 (Cl) 1.00E+04 CFU/100 ml 1.00E E+02 Total Coliform E.coli Enterococci Aeromonas Legionella Mycobacterium 1.00E E+00 Distance (miles)

20 DS 4 (UV) 1.00E E+03 Total Coliform CFU/100 ml 1.00E+02 E.coli Enterococci Aeromonas Legionella Mycobacterium 1.00E E Distance (miles)

21 DS 4 (UV) 1.00E E+03 Total Coliform CFU/100 ml 1.00E+02 E.coli Enterococci Aeromonas Legionella Mycobacterium 1.00E E Distance (miles)

22 Amoebic Activity Recycling Facility Number Sampled Number Positive % Positive DS-4 (UV) % DS-2 (Cl) %

23 100% 90% 80% 70% 60% 50% HPC Aeromonas Total coliforms Somatic coliphage Legionella Indicator organisms were uncommon in the chlorinated system while opportunistic pathogens were detected quite frequently. 40% 30% 20% 10% 0% 100% 90% 80% 70% 60% 50% 40% 30% DS 2 (Cl) Mycobacterium Enterococci Male specific Presump. Amoebic E. coli HPC Aeromonas Total coliforms Somatic coliphage Legionella Mycobacterium Enterococci There were numerous instances in which opportunistic pathogens were present in the recycled water distribution systems in the absence of indicator organisms (E.coli). The decline of residual disinfectant in the system(s) was accompanied by an increase in the level of bacteria. 20% 10% 0% DS 4 (UV) Male specific Presump. Amoebic E. coli This data lends itself to the usefulness of certain indicators based on treatment technology.

24 Distribution System Modeling Kevin Lansey

25 Conceptual Diagram of Chemical Species and Physical Phases Within a Pipe Operational performance Physical parameters Nutrient indicators Specific microbial indicators

26 Governing Equations (Monod kinetics with limited nutrient substrate) Parameter Symbol Unit Bulk First order kinetic constant for bulk chlorine decay k b h -1 Maximum free bacteria growth rate u b,max h -1 Bacterial mortality rate k d h -1 Monod half saturation coefficient k s mg C/L Fraction of dead biomass converted to AOC after lysis k ly mg/mg Growth yield coefficient for bacteria Y g mg/mg Characteristic chlorine concentration Cl 2,c mg/l Chlorine threshold for free bacteria Cl 2,tb mg/l Wall Maximum attached bacteria growth rate μ a,max h -1 First order kinetic constant for detachment k det h -1 First order kinetic constant for deposition k dep h -1 (m/s) -1 Zero order kinetic constant for wall chlorine k w mg/m 2 /h Chlorine threshold for attached bacteria Cl 2,ta mg/l

27 Storage tank study results, Class A Biomass [log(cells)/l] HPC Model Sample AOC AOC [mg/l] Chlorine Cl2 [mg/l] Time [days]

28 Storage tank study results, Class A+ Biomass [log(cells)/l] 10 8 HPC Model Sample AOC AOC [mg/l] Chlorine Cl2 [mg/l] Time [days]

29 Microbial regrowth & decay parameters

30 Available data sets from SCADA VE VW SS L S VW VE SS

31 Demand Correction Step1 - Converting original data sets for uniform time step, t=1 min (original data sets were measured when there is a change in flow and pressure) Step 2 - Recovering SS" flow data based on outlet pressure (Original data didn't match with monthly billing record due to dropped signal and latching) Before modification - June 2010 After modification - June 2010 Step 3 - Recovering VE" flow data based on mass balance in the system L S SS

32 Reconstructed flows at each location June 19 ~ 24, 2010 SS VE VW S L Sabino Vent. East Vant. West Skyline La Paloma Date (t=1min)

33 Chlorine residual Chlorine (mg/l) Sample Model-Bulk & Wall Model-Bulk only Skyline S Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Ventana VE East Chlorine (mg/l) Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Sabino SS Springs Chlorine (mg/l) Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Sampling Date

34 Water Age 100 S Skyline Water Age (hrs) Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Ventana VE East Water Age (hrs) Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 SS Sabino Springs Water Age (hrs) Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Sampling Date

35 Biomass [log(cells)] Biomass [log(cells)] Biomass [log(cells)] HPC concentrations Skyline S Sample Model-Bulk & Wall Model-Bulk only Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Ventana VE East Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 SS Sabino Springs Nov17 Dec9 Dec17 Jan21 Feb8 Feb17 Mar9 Apr7 Apr29 May26 Jun21 Jun24 Jul15 Aug30 Aug31 Sep16 Oct12 Oct25 Sampling Date

36 HPC vs. water age (DS-2) Consistent with tank study with decrease over time log(hpc) Water Age [hr]

37 HPC vs. water age (DS-4) 10 8 log{hpc) Water Age [hr]

38 HPC vs. water age (DS-4) 10 8 log{hpc) Water Age [hr]

39 Microbial regrowth & decay parameters

40 Modeled HPC s for Class A and Class A+ Summer Winter log(hpc [cfu/100ml]) A Skyline A+ Skyline A Ventana East Solid A, Dashed A+ A+ Ventana East A Sabino A+ Sabino July log(hpc [cfu/100ml]) A Skyline A+ Skyline A Ventana East Solid A, Dashed A+ A+ Ventana East A Sabino A+ Sabino February Time (hr) Time (hr) % Difference log(hpc) Skyline S Ventana VE East Sabino SS % Difference log(hpc) Skyline S Ventana VE East Sabino SS Simulation Duration [hours] Time (hr) Simulation Duration [hours] Time (hr)

41 Modeled HPC s for changing chlorine dosage % Difference in log(hpc) General Demand Pattern most distant point Sabino SS - Initial Cl2 = 3.33 mg/l Cl2 [mg/l] % 100% 150% 200% 250% 300% Time [hr]

42 Modeling Conclusions Distance traveled is not key indicator for microbial Water age is better predictor Water quality modeling is possible and provides reasonable parameters Predictive changes can suggest modifying operations/treatment Little benefit of higher treatment or increasing chlorine dosage.

43 Microbial Diversity and Summary Jean McLain

44 Microbial Diversity in Storage Tanks Species richness and abundance similar to the patterns in total C and N over the same time period, most notably in the Class A water, indicating that C and N control heterotrophic populations in storage. Changes in diversity over time may allow researchers to identify critical time points at which remediation efforts may be applied most effectively to stored reclaimed water. Species Abundance Species Abundance c) Class A c) Class Day A of Sampling (Fill Date = Day 1) Day of Sampling (Fill Date = Day 1)

45 Key Findings Waterbased pathogens (Legionella, Mycobacterium, Aeromonas) routinely found in utility distribution systems regardless of treatment. Fecal indicator organisms (E. coli, Enterococcus) were rarely detected, suggesting effective treatment (UV, Cl) vs. waterborne pathogens.

46 Key Findings Booster station re-chlorination reduced the concentration of waterbased organisms, but regrowth occurred rapidly. Amoebic activity found in approximately 30% of samples collected, regardless of treatment technology. Microbial growth in storage systems is strongly dependent on an equilibrium with assimilable organic carbon.

47 Utility Perspective E.coli and Enterococcus are effective indicators of recycled water quality the point of entry, but not within the distribution system. Identification of waterbased pathogens in water distribution systems (reclaimed or potable) is not novel (e.g., Alonso et al., 2006; Jjemba et al., 2010). Key finding: maintaining disinfectant residual is the key to controlling microbial growth and regrowth.

48 Follow-Up Work Risk Assessment: In the absence of a risk assessment, no conclusions regarding public health risk can be drawn from the findings of this study. Pathogen mitigation within the storage and distribution system may be achieved by reducing the total organic carbon (TOC) concentration. Specific removal of the assimilable organic carbon (AOC) fraction (e.g, via ozone) may aid in realizing the same endpoint.

49 Acknowledgements WateReuse Foundation (WRF-0804) Municipal Recycled Water Partners Rezaur Rahman, HDR UA Water Environment and Technology (WET) Center USDA ARS Internship Programs

50 Tim Thomure, P.E. Channah Rock, Ph.D. Kevin Lansey, Ph.D. Jean McLain, Ph.D.