quality assurance project plan (qapp) for the deployment ... plan/qapp... · deployment of...

Division of Surface Water June 10, 2019

Quality Assurance Project Plan (QAPP) for

the Deployment of

Continuous Temperature Data Loggers

in 12 Watershed Study Areas

in Southeast, Southwest and Northeast Ohio

2019

Celeste and Voinovich Counties

12 Watersheds – Temperature Probe Placement 2019 QAPP June 10, 2019

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Quality Assurance Project Plan (QAPP) for the Deployment of Continuous Temperature Data Loggers

in Selected Streams in 12 Watershed Study Areas in Southeast, Southwest and

Northeast Ohio 2019

June 10, 2019

Prepared by

State of Ohio Environmental Protection Agency

Division of Surface Water Lazarus Government Center

50 West Town Street, Suite 700 P.O. Box 1049

Columbus, Ohio 43216-1049

Northeast District Office 2110 East Aurora Road Twinsburg, Ohio 44087

Southeast District Office

2195 Front Street Logan, Ohio 43138

Southwest District Office

401 East Fifth Street Dayton, Ohio 45402

Central District Office

50 West Town Street, Suite 700 Columbus, Ohio 43216

Mike DeWine, Governor Laurie A. Stevenson, Director

State of Ohio Ohio Environmental Protection Agency


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SECTION A – PROJECT MANAGEMENT

A1 - Quality Assurance Project Plan for the Deployment of Continuous Temperature Data

Loggers in Selected Streams in 12 Watershed Study Areas in Southeast, Southwest and

Northeast Ohio (2019)

____________________________________________ Date: ___________ Brian Hall, Assistant Chief ___________________________________________ Date: ___________ Marianne Piekutowski, Modeling and Assessment Manager ___________________________________________ Date: ___________ Chris Skalski, Standards and Technical Support, Project Leader ____________________________________________ Date: ___________ Ellie Hagen, Ohio EPA Quality Assurance Officer ____________________________________________ Date: ___________ Bill Zawiski, Northeast District Water Quality Supervisor ____________________________________________ Date: ___________ Rachel Taulbee, Southeast District Water Quality Supervisor ____________________________________________ Date: ___________ Joby Jackson, Southwest District DSW Manager ____________________________________________ Date: ___________ Jeff Lewis, Central District Water Quality Supervisor

6/12/19

6/12/2019

6/12/2019

06/13/2019

6/13/2019

6-14-19

6/14/19

6/17/19

10053489

Stamp


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A2 - Table of Contents SECTION A – PROJECT MANAGEMENT ......................................................................................................... 2

A1 - Quality Assurance Project Plan for the Deployment of Continuous Temperature Data Loggers in

Selected Streams in 12 Watershed Study Areas in Southeast, Southwest and Northeast Ohio (2019) .. 2

A3 - Distribution List.................................................................................................................................. 5

A3.1 - Table 1. Ohio EPA Central Office Staff ........................................................................................ 5

A3.2 - Table 2. Ohio EPA District Office Staff ........................................................................................ 5

A4 – Project/Task Organization and Communication ............................................................................... 6

A4.1 - Table 3. Roles & Responsibilities ................................................................................................ 6

A5 – Problem Definition/Background ....................................................................................................... 7

A5.1 - Total Maximum Daily Load (TMDL) ............................................................................................ 7

A5.2 - Aquatic Life Use and Human Health Designations ..................................................................... 7

A6 – Project/Task Description ................................................................................................................... 8

A7 – Quality Objectives and Criteria ....................................................................................................... 13

A8 – Special Training/Certification ......................................................................................................... 13

A9 – Documents and Records ................................................................................................................. 13

A9.1 - Document/record control ........................................................................................................ 14

A9.2 - Document storage .................................................................................................................... 14

SECTION B – DATA GENERATION AND ACQUISITION ................................................................................. 14

B1 – Sampling Process Design ................................................................................................................. 14

B2 – Sampling Methods .......................................................................................................................... 15

B3 – Instrument/Equipment Testing and Calibration, Inspection, and Maintenance ............................ 15

B4 – Inspection/Acceptance of Supplies and Consumables ................................................................... 15

B5 – Data Management .......................................................................................................................... 15

SECTION C: ASSESSMENT AND OVERSIGHT ................................................................................................ 16

C1 – During Sampling Assessments/Analysis and Response Actions ..................................................... 16

C1.1 – Assessments ............................................................................................................................. 16

C1.2 - Response Actions ...................................................................................................................... 16

C1.3 - Reporting and Resolution of Issues .......................................................................................... 16

C1.4 - Data Completeness ................................................................................................................... 16

C2 – Reports to Management ................................................................................................................. 17


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SECTION D: DATA VALIDATION AND USABILITY ......................................................................................... 17

D1 – Data Review, Verification, and Validation ...................................................................................... 17

D2 – Verification and Validation Methods .............................................................................................. 17

D3 – Reconciliation with User Requirements ......................................................................................... 17

APPENDICES ................................................................................................................................................ 18

Appendix A -- Standard Operating Procedure for Continuous Temperature Data Logger Deployments

................................................................................................................................................................ 19

Appendix B -- Standard Operating Procedure for the Analysis of Continuous Temperature Data for

Determining Coldwater Habitats ............................................................................................................ 25

Appendix D – Safety Contacts and Hospital Locations ........................................................................... 33

REFERENCES ................................................................................................................................................ 40


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A3 - Distribution List

A3.1 - Table 1. Ohio EPA Central Office Staff

Name/Title Contact E-mail/Phone

Tiffani Kavalec, Environmental Administrator [email protected] (614) 644-3538

Brian Hall, Asst. Environmental Administrator [email protected] (614) 644-2033

Mari Piekutowski, Environmental Manager [email protected] (614) 644-2876

Audrey Rush, Environmental Manager [email protected] (614) 644-2035

Chris Skalski, Environmental Specialist 3 [email protected] (614) 644-2144

Ellie Hagen, Environmental Specialist 2 [email protected] (614) 705-1011

Keith Orr, Environmental Supervisor [email protected] (614) 644-2885

Jordan Jenkins, Environmental Specialist 2 [email protected] (614) 836-8786

A3.2 - Table 2. Ohio EPA District Office Staff

Name/Title Contact Phone/E-mail

DSW Southeast District Office

Rachel Taulbee, Environmental Supervisor [email protected] 740-380-5433

Kelly Capuzzi, Environmental Specialist 2 [email protected] 740-380-5283

Randy Spencer, Environmental Specialist 2 [email protected] 740-380-5240

DSW Southwest District Office

Joby Jackson, Environmental Manager [email protected] 937-285-6029

Laura Marshall, Environmental Specialist 2 [email protected] 937-285-6031

Michelle Waller, Environmental Specialist 2 [email protected] 937-285-6028

DSW Northeast District Office

Bill Zawiski, Environmental Supervisor [email protected] 330-963-1134

Gregory Orr, Environmental Specialist 2 [email protected] 330-963-1189

DSW Central District Office

Mike Gallaway, DSW Manager [email protected] 614-728-3843

Jeff Lewis, CDO Water Quality Supervisor [email protected] 614-728-3841

Chloe Welch, Environmental Specialist 2 [email protected] 614-728-3852

mailto:[email protected]

















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A4 – Project/Task Organization and Communication

A4.1 - Table 3. Roles & Responsibilities

Individual(s) Assigned: Responsible for: Authorized to:

Tiffani Kavalec/Brian Hall DSW Chief/Assistant Chief

Overall administration of division.

Confirm project existence; approve staff and capital resources; approve plans; edit reports.

Mari Piekutowski Assessment & Modeling Section Manager

Overall management of monitoring section.

Assign staff; approve plans; edit reports.

Keith Orr Modeling & Assessment Unit Supervisor

Supporting modeling field crews with supplies, equipment and training.

Obtain approvals and signatures; develop budgets; conduct field audits; edit reports.

Jordan Jenkins Modeling & Assessment Unit Staff

Data set reduction. Data review, analysis, verification, database population and transmission.

Tabulate and analyze data; write discussion for technical report.

Audrey Rush Standards and Tech Support Section Manager

Quality management (QAPPs, SOPs); staff training; water quality standard rules.

Approve plans and edit reports.

Chris Skalski Standards and Tech Support Lead Worker

Project Leader Water quality standard criteria development and rule updates.

Study planning. Evaluate data and write discussion for technical report. Make recommended beneficial use changes.

Ellie Hagen Standards and Tech Support QA Officer

DSWs quality management program.

Develop and implement field QA/QC guidelines. Track field QA/QC and staff training.

Marco Deshaies, SEDO DSW District Manager Rachel Taulbee, SEDO District Water Quality Supervisor Rich Blasick, NEDO DSW District Manager Bill Zawiski, NEDO District Water Quality Supervisor Joby Jackson, SWDO DSW District Manager Michael Gallaway, CDO DSW District Manager Jeff Lewis, CDO District Water Quality Supervisor

Staff assignment, signatures, payments, and reporting.

Review documents and reports; suggest changes and edits; obtain approvals and signatures.

STUDY TEAMS

*Kelly Capuzzi, SEDO District Water Quality Randy Spencer, SEDO District Water Quality *Laura Marshall, SWDO District Water Quality Michelle Waller, SWDO District Water Quality *Gregory Orr, NEDO District Water Quality Chloe Welch, CDO District Water Quality

Complete field activities and quality control; field sampling and analysis, data collection, Assist with project planning.

Prepare documents and reports. Arrange for external training. Schedule and conduct field activities.

*Study Team Leader


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A5 – Problem Definition/Background

As part of the TMDL process and in support of the basin approach for NPDES permitting, intensive ambient assessments were conducted during several field sampling seasons (2014-2018) in various watersheds in southwest, southeast and northeast Ohio. In twelve of the watershed study areas, collection of additional continuous temperature data will be used to provide supplementary information to potentially support a Coldwater Habitat (CWH) Aquatic Life Use (ALU) designation for 90 streams where existing biological data demonstrates CWH potential.

A5.1 - Total Maximum Daily Load (TMDL)

Information collected as part of this project will support TMDL development for the study areas. The objectives of the TMDL process are to estimate pollutant loads from the various sources within the basin, define or characterize allowable loads to support the various beneficial uses, and to allocate pollutant loads among different pollutant sources through appropriate controls (e.g., NPDES permitting, storm water management, 319 proposals, NPS controls or other abatement strategies). The components of the TMDL process supported by this survey are primarily the identification of impaired waters, verification (and redesignation if necessary) of beneficial use designations, gathering ambient information that will factor into the wasteload allocation, and ascribing causes and sources of use impairment. These data are necessary precursors to the development of effective control or abatement strategies. This project will provide data to support the determination and/or verification of the CWH beneficial use for the study streams.

A5.2 - Aquatic Life Use and Human Health Designations

The aquatic life use has been previously verified or otherwise affirmed for some streams in this study while unverified (identified in the WQS but have not been subjected to biological and habitat field assessment) or not listed (absent from the WQS) for other streams. Ohio EPA is obligated to review, evaluate, or recommend (where appropriate) beneficial uses prior to basing any permitting actions on existing, unverified designations, or wholly unclassified water bodies. A5.3 - Coldwater Habitat Designations This study will provide temperature data to supplement existing biological data that will be used to determine if the selected streams meet the conditions described in a working revision of the CWH (native fauna) ALU designation as described below: “CWH – Native Fauna” means waters that support coldwater fauna as demonstrated by either of the

following:

1. Thermal regimes in which the rolling thirty-day average of the daily average water

temperatures measured July 1 through August 31 of a calendar year is less than 70 degrees

Fahrenheit and any one of the following:

a. Four or more coldwater macroinvertebrate taxa.

b. One or more coldwater fish species.

c. Larval salamanders in the genus Eurycea in instances where the fish assemblage is

rudimentary due to natural physiographic or zoogeographic constraints.


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2. The presence of either of the following:

a. Nine or more coldwater macroinvertebrate taxa.

b. Any of the following salamander species:

i. Northern spring salamander (Gyrinophilus porphyriticus porphyriticus)

ii. Kentucky spring salamander (Gyrinophilus porphyriticus duryi)

iii. Midland mud salamander (Pseudotriton montanus diastictus)

iv. Northern red salamander (Pseudotriton ruber ruber); or

v. Cave salamander (Eurycea lucifiga).

The streams identified in this project have previously been found to have a biological signature consistent with that described in A5.3 as part of recent watershed surveys but the thermal regime component in A5.3 is unknown. The purpose of this project is to collect the temperature data necessary to determine if the streams exhibit a temperature profile consistent with that described in A5.3.

A6 – Project/Task Description

The general objective of this study is to gather temperature data from various water bodies where recent biological sampling suggests the potential to support a CWH designation. The resulting data will be used to support aquatic life use designation recommendations. The study area is composed 52 HUC 12 watershed assessment units (Table 4). A total of 95 sampling stations are allocated to this effort and will provide for the assessment of 90 streams. Continuous temperature data will be collected using Onset HOBO Water Temperature Pro v2 Data Loggers. A datapoint will be collected every 30 minutes from July 1 through August 31 to determine the temperature regime for the streams during this time period and whether it is consistent with that described in A5.3. Data loggers will be deployed in a manner consistent with the Ohio EPA’s Standard Operating Procedure for Continuous Temperature Data Logger Deployments (see Appendix A). The location that the data loggers are placed in stream will be dependent on the best professional judgement of the field staff. Field staff have been trained to take multiple factors into consideration when determining final placement. Placement should be designed to conceal the data logger as best as possible to avoid theft/vandalism. Preference should be given to shaded locations, but this is not always possible. It is important that loggers be placed close to the location the biological assessment was conducted, for this reason staff who are placing the loggers will have access to location notes from the biological staff and will have their contact information should questions arise in the field. For additional notes on placement of temperature probes, see Appendix A. Data will go through an initial review process to flag anomalous data that could indicate a malfunction of the probe or that the probe was not reading accurate information. Data will be evaluated in a manner consistent with Ohio EPA’s Standard Operating Procedure for the Analysis of Continuous Temperature Data for Determining Coldwater Habitats (see Appendix B). The results of this project will be incorporated into various biological and water quality reports currently in preparation or will be incorporated as an appendix to biological and water quality reports already completed. The study area watersheds are located in northeastern Ohio (Ashtabula, Carroll, Cuyahoga, Holmes, Lake, Stark and Summit counties), southeastern Ohio (Adams, Coshocton, Harrison, Gallia, Guernsey, Monroe, Muskingum, Scioto, Vinton and Washington counties) and southwestern Ohio (Brown, Darke, and Preble counties).


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Table 4. Locations where temperature probes will be placed within the study area.

Station ID

River Code Site Name

River Mile

Drainage Area (mi2) HUC Latitude Longitude

2014 Wills Creek Survey (5 probes)

R18S19 17-825-000 Turkey Run 1.19 2.2 05040005-04-05 40.1644 -81.4106

302769 17-810-001 Trib to Johnson Fork (1.04) 0.20 0.7 05040005-05-07 40.1780 -81.5764

302777 17-800-009 Trib to Wills Cr (RM 34.43) at RM 2.35 0.50 3.3 05040005-05-08 40.1461 -81.6626

302536 17-800-008 Marlatt Run (Trib To Wills Cr at RM 24.0) 0.40 7.1 05040005-06-02 40.2105 -81.6642

R18P07 17-803-000 White Eyes Creek at RM 11.9 11.9 4.2 05040005-06-03 40.0731 -81.7740

2015 Little Muskingum River Survey (21 probes)

204084 06-452-000 Left Prong Rich Fork 0.1 4 05030201-06-01 39.7131 -81.1800

303321 06-458-004 Tributary to Cranenest Fork (RM 7.27) 0.1 2.2 05030201-06-02 39.7241 -80.9975

303320 06-458-003 Laurel Run 0.1 1.1 05030201-06-02 39.7262 -81.0386

303318 06-458-002 Wolfpen Run 0.05 1.6 05030201-06-02 39.7363 -81.1066

303319 06-458-001 Willison Run 0.3 1.7 05030201-06-02 39.7168 -81.1426

C01L02 06-457-000 Wolfpen Run 0.32 3.3 05030201-06-03 39.6872 -81.1431

303322 06-456-000 Buhrs Run 0.17 2 05030201-06-03 39.6632 -81.1274

609400 06-440-000 Witten Fork 7.82 7.9 05030201-06-04 39.6756 -81.0078

C01K91 06-448-000 Woods Run 0.12 2 05030201-06-04 39.6854 -81.0012

303323 06-446-000 Alum Run 0.17 1.8 05030201-06-04 39.6619 -81.0071

C01K90 06-445-000 Walnutcamp Run 0.44 2.3 05030201-06-04 39.6475 -81.0156

C01K83 06-437-000 Biglick Run 0.2 4.4 05030201-06-05 39.6125 -81.1078

303325 06-429-000 Brown Run 0.2 2.8 05030201-06-05 39.6193 -81.1208

303326 06-433-001 Little Indian Run 0.3 2.1 05030201-07-01 39.6269 -81.2430

C01K80 06-432-000 Rias Run 0.2 2.3 05030201-07-01 39.6419 -81.2125

204059 06-426-000 Wilson Run 0.3 3.4 05030201-07-03 39.5581 -81.1656

C01K61 06-416-000 Wingett Run 0.05 5.3 05030201-07-03 39.5386 -81.2400

C01K58 06-412-000 Deans Fork 0.02 2.2 05030201-07-04 39.5247 -81.2889

303328 06-415-000 Hog Run 0.15 0.8 05030201-07-05 39.4859 -81.2704


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Station ID


River Mile


C01K59 06-413-000 Sycamore Fork 0.1 4.5 05030201-07-05 39.5306 -81.2811

303329 06-400-001 Little Eightmile Creek 0.95 1.5 05030201-07-05 39.4573 -81.3466

2015 - Lake Erie Central Tributaries Survey (8 probes)

303276 07-006-000 Wheeler Creek 2.75 6.8 04110003-02-02 41.8259 -80.9828

303278 07-011-003 Trib to Arcola Creek (4.32) 0.65 4.9 04110003-02-04 41.7978 -81.0130

A01P05 07-100-001 Smokey Run 0.2 3.8 04120101-06-05 41.9361 -80.5608

303294 07-100-008 Trib to Smokey Run at RM 0.31 0.55 3.3 04120101-06-05 41.9360 -80.5488

303289 07-100-003 Trib to Conneaut Creek (17.1) 0.3 3.6 04120101-06-05 41.8836 -80.6187



303297 07-012-000 Whitman Creek 1.2 1.6 04120101-06-06 41.9089 -80.7058

2016 Raccoon Creek Survey (1 probe)

303688 09-528-000 McConnel Run 1.98 0.9 05090101-04-01 39.221375 -82.516245

2016 Symmes Creek Survey (2 probes)

303586 09-031-000 Teens Run 1.20 2.5 05090101-07-04 38.690088 -82.205701

303643 09-095-000 Big Creek 0.48 1.6 05090101-07-09 38.603102 -82.270155

2016 Southern Ohio River Tributaries Survey (19 probes)

303610 10-508-000 Turkey Creek 12.9 1.6 05090201-02-01 38.7494 -83.220097

302770 10-518-000 Rock Lick 0.5 0.4 05090201-02-02 38.6934 -83.17646

302183 10-516-000 Odell Creek 0.3 5.3 05090201-02-02 38.7060 -83.116886

303613 10-099-000 Pond Run

4.64 3.2 05090201-02-03

38.6659 -83.197219

303602 0.38 11.3 38.6601 -83.129213

303601 10-507-000 Nace Run 0.43 1.4 05090201-02-04 38.6768 -83.113329

X04P01 10-085-000 Upper Twin Creek 2.55 12.8 05090201-02-05 38.6376 -83.224548

303597 10-089-000 East Fork Upper Twin Creek 0.1 3.9 05090201-02-05 38.6601 -83.225298

303600 10-082-000 Lower Twin Creek

5.2 6.1 05090201-02-06

38.6787 -83.310233

X99Q02 2.1 14.1 38.6481 -83.2739


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Station ID


River Mile


303604 10-081-000 Rock Run 1.8 0.8 05090201-02-07 38.6291 -83.2784

303603 10-096-000 McAtee Run 0.66 1.6 05090201-02-07 38.6282 -83.1594

200741 10-067-000 Stout Run 12.9 5.4 05090201-02-09 38.6936 -83.3792

302195 10-071-000 Puntenney Run 1.35 0.5 05090201-02-09 38.7123 -83.3890

303599 10-075-000 Long Lick Run 1.23 2.7 05090201-02-10 38.6670 -83.3470

303608 10-077-000 Sulphur Creek 0.35 2.4 05090201-02-10 38.6394 -83.3230

302194 10-202-000 Waggoner Run 1.04 0.3 05090201-05-06 38.7197 -83.4322

301574 10-051-000 Lickskillet Branch 0.6 0.6 05090201-06-01 38.6620 -83.6882

302956 10-302-000 Rangle Run 0.3 1 05090201-08-03 38.7793 -83.9077

2016 Conotton Creek Survey (6 probes)

303624 17-120-000 Irish Creek @ RM 5.00 5.0 6.7 05040001-07-02 40.4296 -81.0262

303625 17-121-000 Lick Fork 0.2 5 05040001-07-02 40.4108 -81.0486

303626 17-118-000 Dining Fork @ RM 6.30 6.3 3.1 05040001-07-03 40.4608 -81.0316

303634 17-108-000 Bear Hole Run 1.7 1.4 05040001-07-05 40.5290 -81.1370

303631 17-106-001 Long Run 0.7 1.7 05040001-07-06 40.4901 -81.0717

303646 17-114-000 Pleasant Valley Run 1.5 6.5 05040001-08-01 40.5764 -81.1512

2017 Sugar Creek Survey (4 probes)

R05S50

17-412-000 Indian Trail Creek

7.45 2

05040001-10-03

40.5864 -81.7422

R05S49 6.33 4.8 40.5889 -81.7219

R05S46 5.72 7.3 40.5897 -81.7114

R05S77 17-406-003 Trib. to Middle Fork Sugar Creek (RM 6.00) 0.17 2.3 05040001-11-02 40.6589 -81.6694

2017 Upper Tuscarawas Survey (10 probes)

R06S32 17-500-017 Metzgers Ditch 0.45 9.7 05040001-01-01 40.9886 -81.4367

R06G39 17-541-000 Hudson Run at RM 3.66 3.66 4.9 05040001-01-03 41.026 -81.662

R06G36 17-541-001 Trib. To Hudson Run at RM 3.02 0.96 4.1 05040001-01-03 41.017 -81.668

R06P82 17-533-000 Sippo Creek 1.4 17.5 05040001-03-08 40.8003 -81.5072

R06G29 17-500-012 West Sippo Creek 1.22 9.8 05040001-03-09 40.803 -81.544


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Station ID


River Mile


R06W85 17-532-000 Pigeon Run

0.01 9.5 05040001-12-01

40.7428 -81.5281

R06G04 4.11 6 40.764 -81.581

R06G27 17-500-000 Sherman Creek 1.83 8 05040001-12-03 40.686 -81.462

303810 17-500-033 Trib. to Tuscarawas R. (RM 79.9) 0.5 3.8 05040001-12-03 40.6760 -81.4917

R06W88 17-530-000 Wolf Creek 0.01 4.3 05040001-12-03 40.7261 -81.5297

2017 Lower Tuscarawas Survey (1 probe)

R10G06 17-501-000 Morgan Run 0.6 3.9 05040001-19-04 40.276 -81.807

2017 Whitewater River Survey (7 probes)

H11S02

14-308-000 East Fork Whitewater River

41.4 31.3

05080003-07-02

39.8664 -84.7908

H11S01 39.48 36.8 39.8439 -84.8053

H11S03 46.89 11.6 39.9392 -84.7719

303801 14-313-000 Little Creek 0.7 8.7 05080003-07-02 39.8736 -84.7784

303561 14-313-001 Brinley Fork 0.1 3.4 05080003-07-02 39.8917 -84.7504

H11S05 14-308-002 Welker Lateral 0.9 1.2 05080003-07-02 39.9531 -84.7047

303802 14-312-000 Rocky Fork 0.1 2 05080003-07-04 39.8534 -84.7989

2018 Cuyahoga River Survey (11 probes)

303970 19-020-004 Trib to Furnace Run (1.2) 0.1 1.9 04110002-04-03 41.2052 -81.5878

303997 19-001-012 Spring Creek 0.9 1.3 04110002-04-05 41.2583 -81.5734

F01P23 19-001-014 Sand Run 0.13 3.1 04110002-04-05 41.1387 -81.5616

F01G33 19-012-000 Slipper Run 0.16 1.4 04110002-04-05 41.2424 -81.5557

F01G27 19-019-000 Robinson Run 0.1 1.4 04110002-04-05 41.2083 -81.5603

F01G34 19-019-000 Stanford Run 0.9 1.9 04110002-04-05 41.2704 -81.5508

F01G29 19-017-000 Dickerson Run 0.6 2.5 04110002-04-05 41.2184 -81.5520

302647 19-009-003 Trib to Chippewa Creek (0.8) 0.1 1.2 04110002-05-03 41.3156 -81.6020

F01G38 19-001-011 Sagamore Creek 0.2 6.2 04110002-05-05 41.3514 -81.5923

303971 19-001-045 Trib to Cuyahoga R. (25.41) 0.15 2 04110002-04-05 41.2756 -81.5680

F01G26 19-023-000 Woodward Creek 0.6 2.9 04110002-04-05 41.1594 -81.5649


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A7 – Quality Objectives and Criteria A7.1 - QC Performance criteria for ALU determination Continuous temperature measurements will be recorded using Onset HOBO Water Temperature Pro v2 Data Loggers. Per manufacturer specifications, the loggers are accurate to approximately 0.2°C with an expected drift of approximately 0.1°C per year. Battery-life and quality assurance of the loggers will be tested prior to deployment and upon retrieval. QA of the loggers will be tested by a method called heard calibration. This is done by placing groupings of the loggers into a homogenized water bath and recording the temperature every minute for approximately 15 – 20 minutes. The data will be put into an excel program that will assess whether the deviation of each individual logger data is within an acceptable range from the group average. Loggers that fail the QA test during the pre-deployment test will not be deployed. The same procedure will be implemented upon retrieval. Data that falls out of acceptable range when tested upon retrieval will go through further evaluation to determine if data is able to be used in any capacity for this project or if it must be rejected. 2019 is the first year this procedure will be implemented for HOBO water temperature loggers, so re-evaluation/refinement of this protocol may be necessary during or after implementation. If re-evaluation of the current QA plan is necessary, DSW’s QA officer and project supervisors/ management must approve any changes. Upon receipt of temperature data, the data will go through an initial review process to flag/remove anomalous data. Anomalous data could be a result of a malfunction of the probe or an indication that the probe was not reading accurate information which could occur if the probe was not fully submerged underwater for a period of time, became buried under fine sediment, etc. (Table 5). Methods employed will take advantage of the ContDataQC R package developed by Tetra Tech, Inc (https://github.com/leppott/ContDataQC). Table 5. Quality control thresholds to be used on collected temperature data.

Analyte Flag Gross Spike Rate of Change Flat Line

Temperature, Water (oC)

Fail > 33 (oC) or

< -2 (oC) ≥ 1.5 (oC) (±) NA

>30 consecutive measurements within 0.01 units of one another

Temperature, Water (oC)

Suspect > 28 (oC) or < -0.1 (oC)

≥ 1 (oC) (±) ≥ 3 standard deviations

within 25 hours

>20 consecutive measurements within 0.01 units of one another

A8 – Special Training/Certification DSW has developed an Access database called “TrainTrack” to document initial trainings and refreshers. All staff involved in collecting any type of environmental sample must complete training associated with that sampling method. The first line supervisors shall ensure staff have the necessary safety and skill set training (initial and refresher training) prior to sampling. The study teams are required to receive training on Hobo datalogger set up and deployment before beginning field work for this project. The training will include an in-house webinar and a field demonstration workshop.

A9 – Documents and Records The final Quality Assurance Project Plan (QAPP) will be provided to the appropriate project personnel by email as detailed in the distribution list. As the plan is updated, each person on the distribution list will be sent an email with the most current document. The most current date of revision will be included in the document name and in the header of the document.

https://github.com/leppott/ContDataQC


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The field logs will be maintained in their original form and information from those forms will be included in Agency databases. The databases are backed up on secure servers. The format for all data recording will be consistent with the requirements and procedures used for data validation and assessment described in this QAPP. Files generated according to applicable and attached standard operating procedures (such as raw data, results of QC checks, problems encountered, etc.) will be documented and reported to the study team. Datalogger temperature data files will be created for each stream location and stored on the Agency network drive as CSV files. The district study team will download the data files from the dataloggers and transmit them to the Modeling and Assessment Section staff for data reduction and analysis. All communications regarding study plan changes or refinements, such as changes to sites, staff, parameters, etc. will be filed in the SharePoint project file by the Project Leader. Other major actions which might affect the DQOs, project leader changes, etc. will require an updated QAPP with a new signoff sheet. A9.1 - Document/record control The recording media for the project will be a combination of paper and electronic means to document site conditions. Data gathered using paper will be recorded using indelible ink on waterproof paper, and changes to such data records will be made by drawing a single line through the error with an initial by the responsible person. Similar methods will be used for electronic data recording. The Project Leader shall retain the most recent version of the QAPP and be responsible for distribution of the current version of the QAPP to the project team. Agency management and the QAO will approve updates to the QAPP, as needed. The Project Leader shall retain copies of all management reports, memoranda, and all correspondence between team members identified in Section A. Retention of records should emphasize any deviations from the signed QAPP including the rationale for those changes. A9.2 - Document storage The Project Leader will maintain a central project file, which will act as a repository for all data collected or generated as part of this project. The project file will include both hardcopy and electronic data and will be stored at the Ohio EPA office. Project photos will be moved to and stored in the Lynx Photo System. All files will be retained by Ohio EPA indefinitely (for a minimum of 10 years).

SECTION B – DATA GENERATION AND ACQUISITION B1 – Sampling Process Design This study is designed as part of the TMDL process and in support of the basin approach for NPDES permitting. Determination of Use Designation attainment is based on the living biota within the waterway; fish, macroinvertebrates, and in some cases, salamanders, are the indicator groups. Habitat evaluation, water chemistry and temperature data provide supporting information which help explain the biotic findings. B1.1 Design of the sampling


15

The sampling is designed to span multiple watersheds. Sampling stations are in streams that potentially support the working definition of coldwater habitat ALU based on biota previously collected.

B1.2 Sampling locations and frequencies

A Hobo temperature data logger will be installed at each stream sampling station listed in Table 1. Temperature readings will be collected every 30 minutes over the critical summer temperature indicator period from July 1 through August 31, 2019.

B2 – Sampling Methods All data processing and data analysis methods and procedures adhere to those specified in the Surface Water Field Sampling Manual – Appendix II for water quality parameters and flows (Ohio EPA 2019). The sampling will be conducted in accordance with procedures outlined in Appendix A, Standard Operating Procedure for Continuous Temperature Data Logger Deployments. The loggers will be placed as close as possible to the macroinvertebrate sampling collection point, where coldwater taxa have been found.

B3 – Instrument/Equipment Testing and Calibration, Inspection, and Maintenance The Team Leaders have operated and maintained most of the equipment to be used during this project for several years. The Team Leaders will inspect the equipment prior to and during the sampling. They will also ensure that all equipment remains in functional working condition.

B4 – Inspection/Acceptance of Supplies and Consumables Supplies and consumables will be inspected upon receipt by the field sampling teams. Nearly all the supplies utilized for this project are maintained and used during the normal business operations of the Ohio EPA. Battery-life and quality assurance of the loggers will be tested prior to deployment and upon retrieval by a method called heard calibration. For more information on QA protocol see Section A7.

B5 – Data Management The Project Leader will maintain the project file in a dedicated folder on SharePoint. The goal or objective is to have a complete record of all decisions about modifications of data collection, validation or interpretation between the QAPP signoff and project report completion. To achieve this, the Project Leader will need to be included on emails or otherwise receive summaries of all actions that meet the above description. Project photos should all be filed in the Lynx photo management system. File names for the temperature data downloaded from the probes are created using the following convention for consistency and to promote efficient file management: StationID_Project Name_YYYYMMDD whereby the project name is the five letter project abbreviation found in the TMDL project tracking spreadsheet on the DSW water quality share point site and the YYYYMMDD is the date the probe is programmed to begin recording temperature data. Note that this is slightly different than the suggested naming convention in the SOP, which used the date as the first part of the file name.


16

The data files will be transferred to the Modeling and Assessment section for calculations of rolling 30-day averages using R scripts. Data reduction will be done according to Appendix B, Standard Operating Procedure for Analysis of Continuous Temperature Data for Determining Coldwater Habitats

SECTION C: ASSESSMENT AND OVERSIGHT C1 – During Sampling Assessments/Analysis and Response Actions C1.1 – Assessments Periodic assessment of field sites, field equipment, and laboratory equipment is necessary to ensure that sampling goes smoothly, and data obtained meet project needs. This is an ongoing process that continues every day during which the project is implemented as well as larger scale assessments that take place less frequently (e.g., annually). The assessments generally will focus on readiness and consistency of implementation but also are looking for continual improvement opportunities. Daily assessments (for each day of project activities, as applicable) will include assessment of field equipment and supplies, laboratory equipment and supplies, completeness of the day’s samples and associated field notes, future needs, etc. C1.2 - Response Actions Despite best preparations, situations may arise that require corrective actions (CAs). Small day-to-day level assessment findings are often addressed by the individual doing the assessment in the field or in the lab and are common enough to the process. Corrective action implementation will be determined by the likelihood that the situation may affect the quality of the data. Field corrective actions will be brought to the attention of the Study Team for consideration as to their impact on the data, their potential interest to other sampling teams/subcontractors, any future considerations for process improvement, and for their potential inclusion to the quarterly reports.

The datalogger installations will be visited periodically over the summer to make sure the logger remains in place within the stream. The dataloggers will be checked as soon as possible after large storm events which may dislodge or bury a logger. It is critical to reset the logger as soon as possible to prevent the loss of no more than several days of the entire data record. Weather and stream conditions will also be monitored if streams are at risk of going dry. Loggers may need to be relocated to deeper pools in the same area. C1.3 - Reporting and Resolution of Issues Any audits or other assessments that reveal findings of practice or procedure that do not conform to the written QAPP will be corrected as soon as possible. The Study Team and QA Officer will be notified regarding deviations. Management will be contacted as necessary. C1.4 - Data Completeness Overall success of the project will require that most of the described sampling produces useable results. Potential data gaps will be monitored as the project progresses and the project schedule will be revised to fill these gaps where they are determined to be significant or to potentially impact the fulfillment of project objectives.


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C2 – Reports to Management Biweekly or monthly oral progress reports are to be provided to management on the survey/study and what steps are being taken to resolve any issues or problems. This may include access problems early on that lead to changes of sites and weather or resource problems during sampling. After the samples have been evaluated, the Project Leader and project members will have a meeting to evaluate the use recommendations. They will also generate a written report that will document the project conclusions and accompany the ensuing rulemaking process. The project summary report will contain discussions of specific QA/QC issues, including any changes to the QAPP, significant QA/QC problems, recommended solutions, detailed information on how well the QA objectives were met, any sites that need to be re-visited due to reasons such as the loss or malfunction of a temperature probe and any limitations on the use of project data. The information from this project will provide information to support aquatic life habitat use designation recommendations. The results of this project will be incorporated into various biological and water quality reports currently in preparation or will be incorporated as an appendix to biological and water quality reports already completed.

SECTION D: DATA VALIDATION AND USABILITY D1 – Data Review, Verification, and Validation Data verification will be conducted by the Study Team with assistance from other DSW staff.

D2 – Verification and Validation Methods The Study Team will make final decisions regarding the validity and usability of the and will evaluate the sample collection, analysis, and data reporting processes to determine if the data are of sufficient quality to meet the project objectives. Data validation involves all procedures used to accept or reject data after collection and prior to use. These include screening, editing, verifying, and reviewing. Data validation procedures ensure that objectives for data precision and bias will be met, that data will be generated in accordance with the QAPP and SOPs, and that data are traceable and defensible. The process is both qualitative and quantitative and is used to evaluate the project.

D3 – Reconciliation with User Requirements Issues related to data uncertainty will be assessed by field staff and their management. For most situations, issues can be addressed with acknowledgement of factors captured in the sample metadata which can confirm, explain, and document the data quality concern. Significant, persistent, or unresolved issues will be brought to the attention of the project Study Team, division QC personnel, and EAS and/or DSW management for further evaluation. This combination of personnel will assess how to best label affected data for storage in the EA3 database and how to eliminate or limit any similar problems going forward. Consideration will also be given on how best to memorialize data limitations or anomalies as the data is transferred to other databases, including the WQ Portal, so that future users of the sampling data are aware of any data quality issues or limitations.


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APPENDICES

Appendix A: SOP for Continuous Temperature Data Logger Deployments

Appendix B: SOP for Analysis of Continuous Temperature Data for Determining Coldwater

Habitats

Appendix C: Contacts

Appendix D: Safety Contacts and Hospital Locations


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Appendix A -- Standard Operating Procedure for Continuous Temperature Data

Logger Deployments

Subsection G.1 Introduction

This section outlines best practices for the deployment of continuous temperature data loggers

(loggers). Temperature loggers are used to collect temperature data in surface waters to document the

ambient temperature profile over a long time period, recorded at pre-programmed time intervals.

These devices come in various shapes and sizes but in general they are small having a length of around

4.5 inches, a diameter of around 1.2 inches, and a weight of about 1.5 ounces. These devices come in

various styles and can be referred to in different ways such as a probe, sensor or logger. Although the

resulting data may be utilized for various analyses, one potential purpose for collection of these data is

to support a coldwater habitat (CWH) aquatic life habitat use designation to a stream. Plans to deploy

temperature data loggers will typically be documented in the QAPP for a specific project. The

procedures described within this document are intended for long-term deployments ranging from

weeks to months.

Subsection G.2 Equipment preparation

G.2.a. Calibration: Calibration of the logger is normally unnecessary but consult the directions and

specifications provided by the manufacturer to verify this for the specific model being used. The

accuracy of the logger should be approximately 0.2oC with an expected drift of approximately 0.1oC per

year. Accuracy of the logger should be tested against a calibrated thermometer prior to each

deployment, ensuring the readings are within the accuracy range provided by the manufacturer. The

internal clock is not expected to deviate more than about one minute per month. The internal battery

should be checked prior to deployment of the logger to ensure there is enough remaining charge to last

throughout the planned deployment. The battery check is usually conducted during programming.

Note that most of these devices typically have no user-serviceable parts, including the battery.

G.2.b. Programming: The loggers are programmed by staff prior to field deployment to begin taking

readings on a specified date and time and at prescribed time intervals. A measurement interval of 30

minutes is recommended but should be at least hourly to adequately characterize the thermal regime of

the water body over the length of the deployment. Record the programmer’s name, programming date,

the date and time the device is programmed to begin measurements, along with the programmed

measurement interval on the field data sheet (G.7.a) in the space provided in the “Lab Use Only” box.

Update the deployment tracking spreadsheet as the loggers are deployed recording the deployer’s

name and the deployment location and date.


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Subsection G.3 Deployment

G.3.a. Equipment List

____ Programmed Temperature Logger ____ Zip Ties ____ Cable Cutters

____ Wire Cable or Cord ____ Field Sheet ____ Thermometer or

Field Meter

____ Padlock and Key ____ GPS Device ____ Camera

____ Cinder Block, half size ____ Meter Stick ____ Flagging Tape

G.3.b Deployment considerations: Temperature loggers need to be placed, secured and concealed to

maximize the chance of obtaining a continuous record of ambient water temperature for an extended

time period lasting from weeks to months. Place the logger as close spatially to the biological sampling

locations as possible and avoid exposure to direct sunlight where possible while accounting for other

factors such as water depth and concealment opportunities. Avoid placing the logger near or

immediately downstream of road crossings or recently disturbed areas unless this is unavoidable. A

prime consideration for placement in smaller streams is water depth. The goal is for the logger to

remain submerged throughout the deployment period. Additionally, placement should avoid areas with

potential for significant deposition of fine silts or sands that may bury the logger in these materials. For

deployments made to support a potential CWH designation, plans should be made to deploy the logger

by June 30th and for it to remain in the stream through August 31st, after which it can be retrieved when

time permits.

G.3.c. Deployment method: Secure the pre-programmed logger to the inside hollow center of a half

cinder block using a zip tie or similar fastener as shown in Figure 1 below. Place the logger in a location

as close as practical to the collection of biological sampling. Consult Part C of the Volume III biocriteria


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manual updates (June 2015) for tips on qualitative macroinvertebrate sampling and the macrohabitats

the biologists will look for when sampling.

Figure 1. Temperature Logger Affixed to a Cinder Block

Place the block with attached logger onto the stream bed with the long axis of the block parallel to the

flow in a pool or other location that is likely to remain deep enough for the logger to remain submerged

for the duration of the deployment. Choose a location out of direct sunlight where possible and within

the main channel. Place in an area out of view from casual observation and where maximum

concealment opportunities exist to minimize vandalism or theft. This could include log jams, root mats,

boulder slabs, or undercut banks, for example. Secure the block to a stable object such as a log or tree

using a wire cable and padlock. In some streams such as those completely dominated by bedrock or

that are extremely shallow, it may be necessary to use alternative methods and some ingenuity to place

and secure the logger, such as tightly wedging it into the crevice of the bedrock or cabling it directly to

the underside of a large boulder or slab. Alternative methods not using a cinder block must: 1)

Sufficiently anchor the logger; 2) Shield the logger from solar radiation; and 3) Protect the logger from

physical damage.

Using a thermometer or field meter, measure and record the water temperature at the time of

deployment. Take photos of the stream and deployment location, document the latitude/longitude of

the deployment location, and record any field notes that may be useful in the recovery of the logger.

See Figure 2 for an example of in-stream logger deployment.

G.3.d. Documentation (field notes): A field data sheet is completed for every temperature logger

deployment (G.7.a). The field data sheet is used to document the date, time, exact location of

deployment, and qualitative environmental data (such as depth, flow type, velocity, substrate, stream

shading, flow regime, and weather condition) at the time of deployment. The field data sheet is also

used to document information regarding the location of the logger’s placement so that it may retrieved

following a long deployment.


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Figure 2. Example of a deployed temperature logger

Subsection G.4 Equipment Retrieval

G.4.a. Inspection: When possible, a periodic check of the logger is recommended during the deployment

period to verify that the logger is fully submerged and that it has not been lost, stolen, damaged, or

buried by fine materials such as muck, silt, or fine sand. Time permitting, these inspections are

especially recommended shortly after a large storm event where higher flows have a greater chance of

dislodging the logger. If any inspections are made of the logger while deployed, record any notes made

along with the date(s) in the space provided on the field sheet. Upon retrieval, the logger should be

visually inspected for any obvious damage and for battery operation with notes made to the field sheet

in the “retrieval notes” section.

G.4.b. Documentation (field notes): Using the same field sheet used for deployment, document the

date, time and qualitative environmental data at the time of retrieval along with any inspection notes.

Measure and record the temperature at the time and location the logger is retrieved using a

thermometer or field meter.


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Subsection G.5 Data management

G.5.a. Data download and storage: The raw data files are downloaded from the loggers and saved in a

comma separated value (.csv) format. The raw files are stored locally on the state of Ohio servers. Raw

data are organized by year and project. The downloaded file names are created using the start date of

the deployment (YYYYMMDD), watershed or project name, and the station ID. (e.g.

20180724_Swan_123456.csv). Ensure that the unit is turned off once the data have been successfully

downloaded. Record the date the logger is returned along with associated information on the tracking

spreadsheet.

G.5.b. Data review: Staff within the Assessment and Modeling Section at Ohio EPA review the raw data

with regards to collected QC data. The raw datafiles are trimmed so the start and end time correspond

to the deploy and retrieval times. Other anomalies in the data are considered and field notes are

reviewed. If data are removed from the dataset, a specific quality assurance record (QAR) note is

developed. QARs are recorded in files stored on the state of Ohio’s servers. Once all data for a site are

reviewed, the field sheet is initialed and dated by the reviewer. At this point, the data are approved and

are ready to be used. A total of 2,976 temperature data points will be logged during a July 1st through

August 31st deployment period programmed for a thirty-minute measurement interval.

Subsection G.6 Maintenance and Care

G.6.a. Routine maintenance

There is no routine maintenance necessary. Equipment should be cleaned after retrieval to remove any

debris, deposits or algal growth.

G.6.b. Storage: The equipment should be stored according to the manufacturer’s recommendations,

which may vary slightly depending on the model. Storage outside of a range from 32o-77oF may reduce

expected battery life.

Subsection G.7 Supporting Documents

The following forms are used by Ohio EPA staff.

G.7.a. Field Sheet


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Appendix B -- Standard Operating Procedure for the Analysis of Continuous

Temperature Data for Determining Coldwater Habitats

Introduction

This document is intended to outline steps for the evaluation of temperature data collected to support

Coldwater Habitat (CWH) aquatic life use designations. Temperature data can provide a direct measure

of the thermal conditions of a water body and its ability to host fauna with an affinity for coldwater or

that depend on a constant connection to groundwater or perennial flow to complete their life cycle.

Waters that have thermal regimes that maintain a rolling thirty-day average temperature less than 70

degrees Fahrenheit measured July 1 through August 31 of a calendar year are candidates for a CWH

designation provided that a CWH biological signature is also present.

Step 1. Data Collection

The first step in the process is to collect the raw water temperature data of the water body. Proper

planning and equipment are required to ensure that a robust set of temperature data is collected to

properly characterize the thermal regime of the water body during the target period of July and August.

A data logging device capable of accurately measuring and recording water temperatures for a long

duration should be used to collect the raw data. Details of the deployment of temperature data loggers

can be found in Section G of Appendix 2 of the Ohio EPA DSW field manual. General deployment steps

and considerations are outlined here:

1) Program a temperature data logger to record temperature measurements at regular

intervals. A measurement interval of at least hourly is required (30-minute intervals are

recommended). Shorter intervals such as fifteen or twenty minutes are also acceptable.

2) Deploy the data logger into the water body by June 30th and leave the data logger in the

water throughout the entire months of July and August. After August 31st, the logger may

be retrieved.

3) Keep the following considerations in mind during deployment. Place the logger:

• As close spatially to the biological collections as practical.

• In a shaded location where possible.

• In a well concealed location to minimize vandalism or theft.


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• Where it most likely to remain under water throughout the deployment period.

• Where it is least likely to become buried in fine silts or sands within depositional

areas.

4) Retrieve the logger as soon as logistically possible after August 31st. Download and save the

raw data. Assuming a deployment of July 1st through August 31st at a measurement interval

of 30 minutes, the device should log 2,976 individual temperature measurements. A time

series of temperature data from a single day recorded at a 30-minute interval might look

similar to this:

Step 2. Data Analysis

Once the data have been collected, stored, and reviewed, analysis of the data can take place. This

analysis is intended to test whether the collected temperature data set is consistent with the following

temperature profile:

Thermal regimes that maintain a rolling thirty-day average temperature less than 70 degrees

Fahrenheit measured July 1 through August 31 of a calendar year

Outlined below are the steps used to calculate the rolling thirty-day temperature average as applied to

the statement above. Note that scripts can be written to automate this analysis and produce graphs for

visual inspection. An example script is presented at the end of this document.

1) Compute the arithmetic average of all the individual temperature measurements collected

on each calendar day. The arithmetic average of the temperature measurements in the

table above is 65.2oF. For a deployment period of July 1st through August 31st, there will be

62 daily averages.

2) Calculate the first 30-day arithmetic average of the individual daily arithmetic average

values determined in the first step. The first thirty-day average for a deployment that

included July 1st will be made from all the daily averages from July 1st through July 30th.

7/10/2015 0:00 64.3 7/10/2015 6:00 63.6 7/10/2015 12:00 65.5 7/10/2015 18:00 66.9

7/10/2015 0:30 64.2 7/10/2015 6:30 63.6 7/10/2015 12:30 65.5 7/10/2015 18:30 66.8

7/10/2015 1:00 64.0 7/10/2015 7:00 63.6 7/10/2015 13:00 65.8 7/10/2015 19:00 66.6

7/10/2015 1:30 63.9 7/10/2015 7:30 63.6 7/10/2015 13:30 66.1 7/10/2015 19:30 66.5

7/10/2015 2:00 63.9 7/10/2015 8:00 63.7 7/10/2015 14:00 66.6 7/10/2015 20:00 66.4

7/10/2015 2:30 63.8 7/10/2015 8:30 63.8 7/10/2015 14:30 66.9 7/10/2015 20:30 66.2

7/10/2015 3:00 63.7 7/10/2015 9:00 64.0 7/10/2015 15:00 67.2 7/10/2015 21:00 66.0

7/10/2015 3:30 63.7 7/10/2015 9:30 64.3 7/10/2015 15:30 67.2 7/10/2015 21:30 65.8

7/10/2015 4:00 63.7 7/10/2015 10:00 64.5 7/10/2015 16:00 67.2 7/10/2015 22:00 65.7

7/10/2015 4:30 63.6 7/10/2015 10:30 64.9 7/10/2015 16:30 67.2 7/10/2015 22:30 65.5

7/10/2015 5:00 63.6 7/10/2015 11:00 65.3 7/10/2015 17:00 67.2 7/10/2015 23:00 65.3

7/10/2015 5:30 63.6 7/10/2015 11:30 65.4 7/10/2015 17:30 67.1 7/10/2015 23:30 65.2


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3) Calculate additional thirty-day averages from successive 30-day windows (i.e., July 2nd

through July 31st, etc.) through the final date of August 31st. This “rolling” or “moving” 30-

day average results in a series of 30-day averages in which the data used to compute each

individual 30-day average is from the previous thirty-day period.

4) Assuming a deployment period of July 1st through August 31st and no missing dates, there

will be a total of 33 thirty-day averages. Once all the thirty-day averages have been

calculated, they are compared to the 70oF threshold. If all the thirty-day averages values are

below 70oF, a thermal regime supportive of the CWH use designation is documented. If one

or more of the thirty-day average values are greater than or equal to 70oF, the thermal

regime does not support a CWH designation.

30-day Avg

July 1 July 2 July 3 July 4 …… July 30

Daily Avg 67.1 67.3 67.3 67.5 68.2

30-day Avg #3

30-day Avg #2

30-day Avg #1

July 1 July 2 July 3 July 4 …… July 30 July 31 August 1 August 2

Daily Avg 67.1 67.3 67.3 67.5 68.2 68.3 68.3 68.4


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Example Script

A 30-day average from a record of daily stream temperatures can be computed in R using the following

functions:

# Description ------------------------------------------------------------- # thirty_day_rolling_average.R # # A script to process temperature data from longterm data loggers into a thirty-day # rolling average by site. # #' @author Ohio Environmental Protection Agency #' @version 20190418 # # Requires as input a tab-delimited text file where the first column contains # the data and time in mm/dd/yyyy hh:mm format. Each subsequent column are vectors # of temperature data with a location id as the header. # Required Packages ------------------------------------------------------- list_of_packages <- c("zoo") # Install missing packages new_packages <-


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list_of_packages[!(list_of_packages %in% installed.packages()[, "Package"])] if (length(new_packages)) { install.packages(new_packages) } # Add packages to workspace lapply(list_of_packages, FUN = require, character.only = TRUE) # Data Import and Pre-Processing ------------------------------------------ # Find your working directory getwd() # Enter file path or, if in working directory, file name between the quotes below fileName <- "temp_raw.txt" temp_data <- read.table(fileName, sep = "\t", header = TRUE) # Extract date as new column date_value <- as.character(temp_data[, 1]) temp_data$collection_date <- as.Date(date_value, format = "%m/%d/%y") # Calculate Rolling Average and Store in Result Matrix -------------------- # Create list of days day_list <- as.Date(unique(temp_data$collection_date)) # First result will be on the 31st day, remove preceding 30 days day_list <- as.Date(day_list[30:length(day_list)]) #' Function to calculate the rolling average of data that has been aggregated #' to an average value for each day #' #' @param column continous data from a single location #' @param aggregate_on vector defining the data aggregation #' @param num_days number of days on which to calculate a rolling average, #' the default is 30 days. #' roll_avg <- function(column, aggregate_on, num_days = 30) { agg_mean <- aggregate(column, aggregate_on, mean)$x rollapply(agg_mean, FUN = mean, width = num_days, na.rm = TRUE) }


30

# Implementation of the roll_avg function on the dataset temp_data_only <- temp_data[, 3:length(temp_data) - 1] collection_date_list <- list(temp_data$collection_date) result_matrix <- apply( temp_data_only, MARGIN = 2, # apply to columns FUN = function(column) roll_avg(column = column, aggregate_on = collection_date_list)) result_matrix <- data.frame(day_list, result_matrix) #Save results to working directory write.csv(x = result_matrix, file = "30_day_avgs.csv", row.names = F) # Plot Temperature Averages ----------------------------------------------- #' Plot temperature values #' #' @param column a vector of rolling averages for a single location #' @param day_list a list of days to be plotted plot_fun <- function(column, day_list) { col_name <- colnames(column) label <- substr(col_name, start = nchar(col_name) - 5, stop = nchar(col_name)) plot( as.matrix(column) ~ day_list, type = "l", las = 2, ylim = c(15, 25), main = label, cex.main = 0.9, ylab = expression("Temperature" ~ degree ~ C), xlab = "Date" ) MAX_TEMP <- 21.111 abline(h = MAX_TEMP, col = "red", lty = 2) }


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# create a pdf in working directory pdf("30_day_avg_plots.pdf") for (i in 2:ncol(result_matrix)) { plot_fun(column = result_matrix[i], day_list = day_list) } dev.off()


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Appendix C – Contacts

Ohio EPA Contacts

Study Team

Central Office Chris Skalski 614-644-2144 Jordan Jenkins 614-836-8786 Keith Orr 614-644-2885 Northeast District Office: Bill Zawiski 330-963-1134 Greg Orr 330-963-1189

Southeast District Office:

Kelly Capuzzi 740-380-5285 Randy Spencer 740-380-5240 Rachel Taulbee 740-380-5433

Southwest District Office:

Laura Marshall 937-285-6031 Michelle Waller 937-285-6028 Joby Jackson 937-285-6029

Central District Office Michael Gallaway 614-728-3842 Jeff Lewis 614-728-3841 Chloe Welch 614-728-3852

Other Contacts: ODNR Wildlife Officers:

County Wildlife Officer Phone Number

Adams Houston Wireman 937-372-5639 x5205

Ashtabula Jason Warren 330-245-3036

Brown Eric Lamb 937-372-5639 x5220

Carroll Scott Cartwright 330-245-3048

Coshocton Jerrod Allison 740-589-9982

Cuyahoga Matt Madgar 330-245-3033

Darke Jeff Wenning 937-372-5639 x5208

Gallia Roy Rucker 740-589-9983

Guernsey Anthony Lemle 740-589-9984


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Harrison Nick Turner 330-245-3049

Holmes Jeremy Carter 330-245-3045

Lake Ryan Pawlus 330-245-3034

Monroe Wes Feldner 740-589-9989

Muskingum Jeff Berry 740-589-9991

Preble Brad Tuner 937-372-5639 x5219

Scioto 740-589-9996

Stark Evan Huegel 330-245-3041

Summit Daniel Shroyer 330-245-3042

Vinton Jared Abele 740-589-9997

Washington Ryan Donnelly 740-589-9998

County Sheriff Offices:

County Sheriff Office

Adams 937-544-2314

Ashtabula 440-576-9046

Brown 937-378-4435

Carroll 330-627-2141

Coshocton 740-622-2411

Cuyahoga 216-443-6000

Darke 937-548-2020

Gallia 740-446-1221

Guernsey 740-439-4455

Harrison 740-942-2197

Holmes 330-674-1936

Lake 440-350-5500

Monroe 740-472-1612

Muskingum 740-455-7134

Preble 937-456-6301

Scioto 740-355-8277

Stark 330-430-3800

Summit 330-643-2154

Vinton 740-596-5242

Washington 740-376-7070

Appendix D – Safety Contacts and Hospital Locations

Hospitals:

Adams County Ashtabula County

Adams County Regional Medical Center 230 Medical Center Drive Seaman, Ohio 45679 (937) 386-3400

Ashtabula County Medical Center 2420 Lake Avenue Ashtabula, Ohio 44004 (440) 997-2262


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Brown County Carroll County

Mercy Health – Mount Orab Medical Center 154 Health Partners Circle Mount Orab, Ohio 45154 (937) 444-4000

Trinity Hospital Twin City 819 N. 1st Street Dennison, Ohio (740) 922-2800

Coshocton County Cuyahoga County

Coshocton County Memorial Hospital 1460 Orange Street Coshocton, Ohio 43812

The MetroHealth System 2500 MetroHealth Drive Cleveland, Ohio 44109

Darke County Gallia County

Wayne Healthcare 835 Sweitzer Street Greenville, Ohio 45331

Holzer Medical Center 100 Jackson Pike Gallipolis, Ohio 45631

Guernsey County Harrison County

Southeastern Ohio Regional Medical Center 1341 Clark Street Cambridge, Ohio 43725

Harrison Community Hospital 951 East Market Street Cadiz, Ohio 43907

Holmes County Lake County

Pomerene Hospital 981 Wooster Road Millersburg, Ohio 44654

Lake Health West Medical Center 36000 Euclid Avenue Willoughby, Ohio 44094

Monroe County Muskingum County

Barnesville Hospital 639 West Main Street Barnesville, Ohio 43713

Genesis Hospital 2951 Maple Avenue Zanesville, Ohio 43701

Preble County Scioto County

Preble County Medical Center 450-B Washington Jackson Road Eaton, Ohio 45320

Southern Ohio Medical Center 1805 27th Street Portsmouth, Ohio 45662

Stark County Summit County

Mercy Medical Center 1320 Mercy Drive Northwest Canton, Ohio 44708

Cleveland Clinic Akron General 1 Akron General Avenue Akron, Ohio 44307

Vinton County Washington County

Selby General Hospital 1106 Colegate Drive Marietta, Ohio 45750


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Adams County Regional Medical Center, 230 Medical Center Drive, Seaman, OH

Ashtabula County Medical Center, 2420 Lake Avenue, Ashtabula, Ohio


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Mercy Health – Mount Orab Medical Center, 154 Medical Partners Circle, Mt. Orab, Ohio

Trinity Hospital Twin City – 819 North 1st Street, Dennison, Ohio 44621


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Wayne Healthcare – 835 Sweitzer Street, Greenville, Ohio 45331

Holzer Medical Center – 100 Jackson Pike, Gallipolis, Ohio 45631

Southeastern Regional Medical Center – 1341 Clark Street, Cambridge, Ohio 43725


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Harrison Community Hospital – 951 East Market Street, Cadiz Ohio 43907


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REFERENCES Ohio Environmental Protection Agency. 2018. Surface water field sampling manual for water column

chemistry, bacteria and flows. Div. of Surface Water, Columbus, Ohio. Version 6.0, March 30, 2018. . 2015a. 2015 Updates to Biological criteria for the protection of aquatic life: Volume II and Volume II

Addendum. User’s manual for biological field assessment of Ohio surface waters. Div. of Surface Water, Ecol. Assess. Sect., Columbus, Ohio. May 8, 2015.

. 2015b. Biological criteria for the protection of aquatic life: Volume III. Standardized biological field

sampling and laboratory methods for assessing fish and macroinvertebrate communities. Tech. Rept. EAS/2015-06-01. Div. Of Surface Water, Ecol. Assess. Sect., Columbus, Ohio. September 23, 2015.

. 1987. Biological criteria for the protection of aquatic life: Volume II. User’s manual for biological field

assessment of Ohio surface waters. Division of Water Quality Monitoring and Assessment, Surface Water Section, Columbus, Ohio.