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Factors influencing the dynamics of excessive algal blooms

Richard F. AmbroseEnvironmental Science and Engineering Program

Department of Environmental Health Sciences, School of Public Health

Center For Embedded Networked Sensing

Public Health and Water Quality

Robert Gilbert, Ph.D. student – Environmental Health Sciences Gerald Kim, Yeung Lam, undergraduate students - Electrical Engineering

Victor Chen, Michael Stealey, M.S. students - Electrical EngineeringBrett Jordan, undergraduate student - Mechanical Engineering

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Excessive algal blooms

• “Nuisance” algal blooms impair the “beneficial uses” of streams and rivers

• Urban runoff is rich in nutrients that can lead to algal blooms, but many factors are involved– Nutrients, light, substrate,

water flow

• Complex interaction among factors means uncertainty about how and why algal blooms form– Especially important in

REGULATORY context

Malibu Creek, July 2005

Los Angeles Regional Water Quality Control Board is proposing a Total Maximum Daily Load (TMDL) limit of 1.0 mg/L for nitrate. The major discharger is arguing that this limit is excessively strict and may not solve the problem with nuisance algae, and will be extremely expensive to meet.

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Hypotheses and Questions

• Do weather, urban runoff, and biological activity affect nutrient levels in streams temporally and spatially?

• Do these dynamics affect algal conditions? • Where and when are the appropriate times to

sample nutrients and other water parameters in these systems?

We are using NIMS to sample much more intensely in space and time than is possible with conventional sampling, providing a high resolution description of the dynamics of this complex system.

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Sample site NIMS-RD site

N

Sampling locations in Malibu Creek Watershed

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NIMS RD Site

Medea CreekNIMS RD

Deployment

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NIMS RDRapidly Deployable Class

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NIMS RD at Medea Creek field site

TemperaturepHConductivityNitrateAmmonium

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Medea Creek NIMS RD sampling path

Sample cycle: 16 minutes

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10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

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Spatial distribution within the stream

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Changes at water surface over time

Nitrate

Conductivity

Ammonium

Temperature

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Future sampling for Medea Creek study

• Multiple temporal scales: minutes, days, months• Monthly sampling for one year

– NIMS RD: 24-hour deployment– Samples at 3 additional sites along Medea

Creek/Malibu Creek• Traditional sampling for nutrients, algal cover, light, etc.• Stable isotope analysis of water to determine source

(natural versus imported)

• “Fill-in” temporal sampling– NIMS RD: 48-hour deployment– Single-point continuous water quality measurements

for 1 week

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Multiple-scale temporal and spatial variation

Medea Creek -Estimated NO3 PPM

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

1.18

1.2

10/8/080:00

10/8/084:48

10/8/089:36

10/8/0814:24

10/8/0819:12

10/9/080:00

10/9/084:48

10/9/089:36

10/9/0814:24

10/9/0819:12

10/10/080:00

Time

NO

3 P

PM

October 2004

Daylight Daylight

Nitrate (mg/L – N)

April 23, 2005

SpCond (mS) NH4+(ppm) NO3-(ppm)

Va

lue

0

1

2

3

4

5

Cold Creek (reference)Oak Creek Park Medea Creek Park NIMS RD siteBelow Tapia

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Multiple-scale temporal variation

Conductivity

2400

2500

2600

2700

2800

2900

3000

3100

3200

3300

0 100 200 300 400 500 600 700 800 900 1000

Time (10min intervals)

Con

du

cti

vit

y (

mic

roS

eim

en

s)

August

J uly

Tu

e 1

2am

Wed 1

2am

Fri 1

2am

Su

n 1

2am

Mon

12

am

Th

u 1

2am

Sat

12

am

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Multiple-scale temporal variability

Nitrate

0.600

0.700

0.800

0.900

1.000

1.100

1.200

1.300

1.400

1.500

0 100 200 300 400 500 600 700 800 900 1000

Time (10min intervals)

NO

3- August

J uly

Tu

e 1

2am

Wed 1

2am

Th

u 1

2am

Fri 1

2am

Su

n 1

2am

Mon

12

am

Sat

12

am

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Temperature

Tem

pera

ture

(o C

)12

14

16

18

20

22

Oak Creek ParkMedea Creek ParkNIMSTapiaCold Creek (reference)

Nitrate

Nitr

ate

(ppm

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Conductivity

Jul Aug Sep

Con

duct

ivity

(m

icro

siem

ens)

500

1000

1500

2000

2500

3000

3500

4000

Multiple-scale temporal variability

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Future directions

• Expand sensor capability– Stream flow, light, dissolved oxygen, depth,

oxidation-reduction potential (ORP), turbidity– Supplemental measurements (fecal indicator

bacteria)

• Laboratory experiments to evaluate dynamics under controlled conditions– Experimental streams

• Nutrient additions, varying amounts and schedule of delivery

• Different algal species

– NIMS 3D

• Field deployment of NIMS 3D

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Conclusions

• NIMS RD provides an efficient platform for temporally and spatially intensive measurements of water quality

• Initial results are already providing insight into the dynamic nature of water quality parameters, as well as raising new hypotheses to explore– Small scale variation– Temporal trends

• Implications for sampling protocols