Harmful Algal Bloom Monitoring: Challenges and Lessons Learned

Anne Wilkinson, PhD

Wenck Associates

Colorado Lake and Reservoir Management Association Fall Meeting 2018

November 19, 2018

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October 31, 2018

Presentation Outline

• Part 1: Introduction to Harmful Algal Bloom Risk Factors & Monitoring Strategies

• Part 2: CO Harmful Algal Bloom Monitoring and Management • Water Research Foundation Grant Overview• Common Concerns• Opportunities for Communal Resources• Lessons Learned• What’s Next?

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Part 1:Introduction to Harmful Algal Bloom Risk Factors & Monitoring Strategies

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Madison Lake, MN July 2016

200 μm

Freshwater microscopic photosynthetic microorganisms that have

the potential to form Harmful Algal Blooms (HAB)

4

Harmful Algal Blooms and Public Health

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OHIO SEA GRANT AND STONE LABORATORY

Dioxin (0.000001 mg/kg-d)

Microcystin LR (0.000003 mg/kg-d)

Saxitoxin (0.000005 mg/kg-d)

PCBs (0.00002 mg/kg-d)

Cylindrospermopsin (0.00003 mg/kg-d)

Methylmercury (0.0001 mg/kg-d)

Anatoxin-A (0.0005 mg/kg-d)

DDT (0.0005 mg/kg-d)

Selenium (0.005 mg/kg-d)

Alachlor (0.01 mg/kg-d)

Cyanide (0.02 mg/kg-d)

Atrazine (0.04 mg/kg-d)

Fluoride (0.06 mg/kg-d)

Chlorine (0.1 mg/kg-d)

Aluminum (1 mg/kg-d)

Ethylene Glycol (2 mg/kg-d)

Botulinum toxin A (0.001 mg/kg-d)

Toxin Reference Doses

Toxic

ity

Neurotoxin

(nervous system)

Hepatotoxin

(liver)

Dermatoxin (skin)

Saxitoxin

Anatoxin

BMAA

Microcystin

Cylindrospermopsin

Lyngbyatoxin

Lipopolysaccharides

• Microcystin is regulated in drinking

water by the EPA and World Health

Organization

• Microcystin and other cyanotoxins

cause cancer, GI illness, and skin

rash

• Cyanotoxins present in HABs are

fatal to pets and wildlife

Possible causes of HABs

• Excess macro-nutrients-(Phosphate, Nitrate) • e.g. Paerl et al. 2016

• Excess inorganic carbon• e.g. Song et al. 2016

• Warm temperatures• e.g Elliot 2012 ;You et al. 2018

• Stable Stratification• Paerl and Huisman 2009, Visser et al. 2015

Anthropogenic Influences

Industrialized Farming

Climate Change

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Cyanobacteria blooms and their risk factors are increasing

across the globe!

Cyanobacteria Blooms in Lakes

• Driven by codependent environmental conditions rather than a single variable

• Cyanobacteria accumulation is highly spatially and temporally transient

• Prediction and management is difficult

Beach

Madison Lake July, 2016

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Importance of Understanding HAB Vertical Variability• Risk of drinking water

contamination is dependent on depth of intakes

• Current monitoring strategies could be underestimating HABs

• We need to understand Where, When and How much to sample to get a representative data to accurately observing HAB dynamics

Seasonal high resolution, high frequency monitoring of cyanobacteria biomass

concurrent with high resolution seasonal meteorological, temperature and water

quality data is necessary to capture complex bloom dynamics 8

Research StationMeasurements:

Meteorological Station (every 5 minutes)

- wind speed, wind direction, precipitation,

Air temperature, Ambient light

Thermistor Chain (every 5 minutes)

Water Temperature

Profiler (every 2hours; every 0.5m)

PAR penetration

pH

Dissolved oxygen

Specific Conductivity

Phycocyanin (cyanobacteria)

Water Samples (every week; every1m)

Cyanobacteria composition

Nutrients

Cyanotoxins (Total Microcystin)

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Seasonal Water Quality Monitoring Site

2 km10.50

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Madison Lake

South Center Lake

Nutrient Concentrations ConditionsMadison Lake

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South Center Lake

• The nitrate+nitrite conditions were all

<0.05 mg/L during our observation

periods

• Phosphate concentrations were higher

in Madison Lake as compared to South

Center Lake

• Phosphate concentrations are high in

the epilimnion

The variability in either hypo/epilimnetic

phosphate concentrations cannot entirely

describe the variability in cyanobacteria

biovolume (BV)

BV Distribution in the water column

• During the stratified period, the

BV is accumulated above the

thermocline

• However, when the

stratification weakens the BV is

uniformly distributed

throughout the water column in

Madison Lake

We would like to describe this

vertical BV distribution.

Madison Lake

7/19/16 7/29/16 8/8/16 8/18/16 8/28/16 9/7/16 9/17/16 9/27/16

South Center Lake

2

4

6

8

z (m

)

10

8

6

4

2

(µm

3/m

L)

12

BV Heterogeneity

BV/BVave

• BVmax /BVave quantifies BV

stratification in the water

column

• Overall, Madison Lake has

lower BV heterogeneity

• BV is uniform during the

weak stratification in Madison

Lake

ML ML SC

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BV Heterogeneity vs Thermal Structureunstable

stable

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• Higher temperature and stratification in the water column means more stratified distribution of Cyanobacteria

• Easily measurable parameters can inform BV distribution

BV distribution above the thermocline

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Heterogeneity in Surface Layer

• There are two distributions observed in

the BV above the thermocline

• In terms of sampling having this peak is a

problem because BV can be under-

sampled

peakuniform

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Predicting BV distributions

• Using wind and the depth of the

surface layer we can predict when a

peak will occur.

• We the mixing is higher there is a

greater probably of uniform

distribution

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0.0

0.5

1.0

1.5

2.0

2.5

3.0

z/z T

0.0 0.2 0.4 0.6 0.8 1.0

MC (g/L)

BV and Microcystin Distribution

• MC is higher in South Center Lake

• BV and MC are distributed above the thermocline

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South Center Lake

Madison Lake

2.0x107

4.0x107

6.0x107

8.0x107

1.0x108

0.0

0.5

1.0

1.5

2.0

2.5

3.0

7/29/16

8/3/16

8/12/16

z/z T

BV (m3/mL)

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

MC (g/L)

z/z T

2.0x107

4.0x107

6.0x107

8.0x107

1.0x108

0.0

0.5

1.0

1.5

2.0

2.5

3.0

7/29/16

8/3/16

8/12/16

z/z T

BV (m3/mL)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

z/z T

2.0x107

4.0x107

6.0x107

8.0x107

1.0x108

8/11/17

6/15/17

7/21/17

8/2/17

8/4/17

8/7/17

BV (m3/mL)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

z/z T

2.0x107

4.0x107

6.0x107

8.0x107

1.0x108

8/11/17

6/15/17

7/21/17

8/2/17

8/4/17

8/7/17

BV (m3/mL)

Microcystin vs Cyanobacteria BV

• MC and BV are highly correlated

The vertical distributions of BV and MC are the statistically similar

• Still, The regression is different probably because of cyanobacteria composition

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0 1x107

2x107

3x107

4x107

5x107

6x107

7x107

8x107

9x107

1x108

0.0

0.2

0.4

0.6

0.8MC=9.02x10

-9BV

R2=0.84

MC

(

g/L

)

BV (m3/mL)

Equation y = a + b*x

Plot MC_ave

Weight No Weighting

Intercept 0.06804 ± 0.0403

Slope 7.74662E-9 ± 9.2857E-10

Residual Sum of Squares 0.2174

Pearson's r 0.88631

R-Square(COD) 0.78555

Adj. R-Square 0.77426

MC=1.5x10-9BV

R2=0.31

Madison Lake

South Center Lake

Recommendations: Sampling Protocolk

When?: We saw BV and MC anytime from ice out to ice over Where and How many samples?:

SAMPLE

ReSL thermistor chain,

wind

StcpTs

thermistor chain

BV PROFILE

ABOVE THE THERMOCLINE

UNIFORM IN THE SURFACE

MIXING LAYER

ONE SAMPLE ANYWHERE ABOVE THE

THERMOCLINE

FORMING LOCAL PEAKS

MANY SAMPLES ABOVE THE

THERMOCLINE

UNIFORM IN THE WATER

COLUMN

ONE SAMPLE ANYWHERE IN

THE WATER COLUMN

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Part 2: CO Harmful Algal Bloom Monitoring and Management

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Risks of Harmful Algal Blooms in Reservoirs

• Managing HABs in reservoirs is particularly important as they serve as a drinking water source

• Cyanotoxins can be difficult to predict, detect and remove from raw water.

• Quick response is necessary to protect human health

• Frequent consistent monitoring and a response plan are necessary

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Taste and Odor and Cyanobacteria

• Cyanobacteria can co-produce taste and odor compounds and cyanotoxins

• Cyanotoxins and taste and odor compounds do co-occur.

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WRF grant overview

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Goal: To provide a

critical evaluation of

sampling plans before a

bloom occurs to increase

confidence and

minimize risk of missing

something potentially

harmful.

Utility Partners

• NALMS had 10 local reservoir managers and water utilities in the Denver area

• All committed to examining HAB monitoring and Management strategies in their reservoirs

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Common Concerns

• Sampling frequency

• Choice of data analysis

• Notification strategy

• Analysis strategy

• Management Strategies and Success

• What can I do with all this data?

• Unpreparedness or feeling behind the “curve”

• Link with Taste and Odor

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Variety within the Partner Network

Monitoring

• Frequency

Data Analysis

• Toxins

• Cyanobacteria Biomass

Management

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Opportunities for Communal Resources• Sampling and Monitoring Plans

• Response and Public Awareness strategies

• Recommendations for Data Analysis

• HAB Management Successes/Challenges

• Network for Equipment Training/Knowledge

• Photo Library for FlowCAM

• Sample sharing

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Lessons Learned

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• There has been a lot work put in to address HABs locally, across the region.

• It is now time to share our collective experiences to improve HAB monitoring and response for everyone.

What’s Next?

• Utility Partnership

• Revisit CO HAB work group

• HAB workshop/special session

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Utah Lake, 2016 Desert News

Thank you!

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