San Diego Bay Debris Study - California · San Diego Bay Debris Study Presentation at the Trash Amendment Monitoring Workshop April 18, 2017 Ted Von Bitner, Associate Scientist Amec

San Diego Bay Debris Study Presentation at the Trash Amendment Monitoring Workshop

April 18, 2017

Ted Von Bitner, Associate Scientist

Amec Foster Wheeler

Presentation Outline

1) Project Review

2) Results

3) Lessons Learned

2

Study Goals

3

► Complete first comprehensive survey of bay and watershed receiving waters

► (Apr 2014 to Oct 2016)

► Establish a baseline to assess against future changes

► Assist municipalities in prioritizing locations for future trash controls

4

Stakeholder Workgroup

Dr. Sherry Lippiatt California Regional Coordinator at

NOAA Marine Debris Program

Dr. Brock Bernstein Independent Consultant

Program Design and Evaluation

Shelly Moore, M.S. Bight ‘13 Marine Debris

Lead Scientist

Technical Advisors

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Study Implementation Framework

6

San Diego Regional Board, Practical Vision 2013

Water-Body Oriented Monitoring and Assessment Metrics (M)

Condition and Assessment

(M1) • Are habitats and ecosystem

healthy?

Stressor Identification

(M2)

Source Identification

(M3)

BMP Performance Monitoring

(M4)

Study Questions

1) (Status) How do the quantities and types of debris in different habitats vary during dry and wet season?

2) (Transport) What types of riverine debris do wet weather flows transport to the bay?

3) (Fate) What species caught in the bay has ingested plastic pieces?

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Study Design

► Probabilistic and targeted based sites within key habitats of interest

► Pre- and post-storm surveys in open water, intertidal, and riverine habitats

► Continuous collection in bay to record seasonal variations

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Methods

► Standard methods from:

► (Riverine) SWAMP Rapid Trash Assessment

► (Shoreline) NOAA Marine Debris Program

► (Marina) Automated trash skimmers

► (Open Water) So Cal Bight Program Trawls

► Trash type (e.g. plastic bags), count, and volume

► Debris sizes

► macro-plastics(>25 cm),

► meso-plastic (25 cm – 5 mm),

► micro-plastic ( 5 mm – 0.35 mm)

► Tested alternative methods in small number of habitats

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Study Locations

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Paradise Marsh

Conditions Monitoring (M1): Bay

11

12

0

20

40

60

80

100

Pre-storm Post-storm

% A

rea

Entire Bay (Pre-Storm) Entire Bay (Post-Storm)

Percent Bay Area Covered by One or More Plastic Pieces

Trash Characterization within the Bay

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Trash Characterization along Intertidal

Highest debris amounts located along wrack line

14

Count Rain (in.)

0

2

4

6

0

1

2

3

4

5

Southern skimmer

(5mm – 25cm) mean count

Trash Characterization at Skimmers

Count

Rain (in.)

0

2

4

6

0

25

50

75

100

Northern skimmer

(5mm – 25cm) mean count

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Trash Characterization on Open Water

>4.75 mm

2.00 mm 1.00 mm

710 mm 500 mm 355 mm

0

100

200

300

400

500

600

700

800

900

1000

South bay Mid-bay North bay Ocean

Pie

ce

s p

er

Tra

wl (a

ve

rag

e)

Micro-debris (<5mm) in Every Trawl

Condition Monitoring (M1): Riverine

Chollas Creek

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17

Chollas Creek

Sweetwater River

Otay River

Trash characterization and hot spot identification

Trash Characterization in Chollas Creek

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0

500

1000

A B C DSW

AM

P R

TA

M

ean

Co

un

ts

Visual Survey Score

30 sites 28 miles

Quantitative Survey Qualitative Survey

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0

0.1

0.2

0.3

0.4

0.5P

re-S

torm

Post-

Sto

rm

Pre

-Sto

rm

Post-

Sto

rm

Pre

-Sto

rm

Post-

Sto

rm

Chollas Otay Sweetwater

Liters

/mete

r2

(>25 cm) Volume

0

0.1

0.2

0.3

0.4

0.5

Pre

-Sto

rm

Post-

Sto

rm

Pre

-Sto

rm

Post-

Sto

rm

Pre

-Sto

rm

Post-

Sto

rm

Chollas Otay Sweetwater

Liters

/mete

r2

(5mm – 25 cm) Volume

Stressor Identification Monitoring (M2)

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Stressor Identification Monitoring (M2)

0

10

20

30

40

50

60

70

80

90

100

PolystyreneFoam Pieces,

Pellets, &Cups

PersistentPlastics (hard

and softplastic pieces)

Wrappers(Food and

Other)

CigaretteButts

Bags (single-use)

% o

f P

las

tic

De

bri

s

River Intertidal Marina

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Lessons Learned

Lesson Learned 1. Need to manage complexities of current methods and design tiered approaches for different end users.

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Lesson Learned 2. Labor intensive methods makes surveys challenging and volunteers less likely to return

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Standard Method

Rapid Method (4x more)

Lesson Learned 3. Rapid methods could improve representativeness and increase survey efficiency

Lesson Learned 4. Qualitative survey improved assessments and increased management options

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Green

(Clean)

3.85 miles (14.1%)

Yellow

(Few Pieces)

13.21 miles (48.5 %)

Orange

(Small to Moderate)

6.75 miles (24.8 %)

Red

(Moderate to High)

3.43 miles (12.6 %)

Illegal Dumping

Other Pathways

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Lesson Learned 5. Quantitative survey methods should be limited to countable key trash items

Degraded polystyrene

pieces were often too

numerous to count

Project Scientists

Terra Miller-Cassman, Amec Foster Wheeler

Dr. Theresa Talley, California Sea Grant

Travis Pritchard, San Diego Coastkeeper

Chad Loflen, San Diego RWQCB

Heather Krish, City of San Diego

Christiana Boerger, US Navy

Project Management, SWAMP

Dr. Betty Fetscher

Dr. Lilian Busse

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Questions

Contact Information

Ted Von Bitner

Amec Foster Wheeler, Environment and Infrastructure

9177 Sky Park Court, San Diego CA, 92672

(858) 514-6401

[email protected]

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