1 Factors influencing the dynamics of excessive algal blooms Richard F. Ambrose Environmental 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 Engineering Brett Jordan, undergraduate student - Mechanical Engineering

2 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.

3 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.

4 Sample site NIMS-RD site N Sampling locations in Malibu Creek Watershed

5 NIMS RD Site Medea Creek NIMS RD Deployment

6 NIMS RD Rapidly Deployable Class

7 NIMS RD at Medea Creek field site Temperature pH Conductivity Nitrate Ammonium

8 Medea Creek NIMS RD sampling path Sample cycle: 16 minutes

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25 Spatial distribution within the stream

26 Changes at water surface over time Nitrate Conductivity Ammonium Temperature

27 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

28 Multiple-scale temporal and spatial variation October 2004 Daylight Nitrate (mg/L – N)

29 Multiple-scale temporal variation

30 Multiple-scale temporal variability

31 Multiple-scale temporal variability

32 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

33 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