Carmel-by-the-Sea, CA, United States
Carmel-by-the-Sea, CA, United States

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Thoe W.,Stanford University | Gold M.,University of California at Los Angeles | Griesbach A.,Heal the Bay | Grimmer M.,Heal the Bay | And 2 more authors.
Water Research | Year: 2014

Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take ~24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the world's most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared to 28% by the current method. Artificial neural network is the second best model which minimizes the number of incorrect beach postings. The binary logistic regression model also gives promising results, comparable to classification tree, by adjusting the posting decision thresholds to maximize correct beach postings. This study indicates that predictive models hold promise as a beach management tool at Santa Monica Beach. However, there are opportunities to further refine predictive models. © 2014 Elsevier Ltd.


Thoe W.,Stanford University | Gold M.,University of California at Los Angeles | Griesbach A.,Heal the Bay | Grimmer M.,Heal the Bay | And 2 more authors.
Environmental Science and Technology | Year: 2015

Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models: multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices. © 2014 American Chemical Society.


PubMed | Stanford University, University of California at Los Angeles and Heal the Bay
Type: | Journal: Water research | Year: 2014

Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take 24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the worlds most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared to 28% by the current method. Artificial neural network is the second best model which minimizes the number of incorrect beach postings. The binary logistic regression model also gives promising results, comparable to classification tree, by adjusting the posting decision thresholds to maximize correct beach postings. This study indicates that predictive models hold promise as a beach management tool at Santa Monica Beach. However, there are opportunities to further refine predictive models.


Stevenson C.,Heal the Bay | Sikich S.A.,Heal the Bay | Gold M.,Heal the Bay
Marine Policy | Year: 2012

Ecosystem-based management is more successful when a great diversity of stakeholders is engaged early in a decision-making process. Implementation of the California Marine Life Protection Act (MLPA) has been stakeholder-based, coordinating the participation of a wide range of people including divers, fishermen, conservationists, local officials, business owners and coastal residents. Although commercial and recreational fishermen have actively participated throughout the MLPA implementation process, and research related to California's sport and commercial fisheries has been integrated into the process, pier and shore anglers have been relatively unengaged as stakeholders. This study was completed to generate information about pier angler understanding and sentiment towards marine protected areas (MPAs), as well as to educate anglers on the MLPA implementation process in southern California and inform them on involvement opportunities. Of the 3030 pier anglers surveyed over 12 months, 78% only fish for subsistence from piers and from shore (never from boats); 84.6% are of non-White/Euro-American ethnicity and speak English as a second language; and 82% indicated that they were supportive of establishing a strong network of MPAs in southern California, specifically fully-protective no-take marine reserves. This study is an example of an alternative and customized method of outreach designed to reach a unique and previously unengaged stakeholder group, which stands to be affected by the implementation of the MLPA in California. Engaging such non-traditional stakeholders in public policy may be critical for decision makers to gauge all views from those standing to be affected by a policy-not just the views of those that regularly attend policy meetings-and for the ultimate success of policy implementation and community support. © 2011 Elsevier Ltd.


Colford J.M.,University of California at Berkeley | Schiff K.C.,Southern California Coastal Water Research Project | Griffith J.F.,Southern California Coastal Water Research Project | Yau V.,University of California at Berkeley | And 13 more authors.
Water Research | Year: 2012

Background: Traditional fecal indicator bacteria (FIB) measurement is too slow (>18 h) for timely swimmer warnings. Objectives: Assess relationship of rapid indicator methods (qPCR) to illness at a marine beach impacted by urban runoff. Methods: We measured baseline and two-week health in 9525 individuals visiting Doheny Beach 2007-08. Illness rates were compared (swimmers vs. non-swimmers). FIB measured by traditional (Enterococcus spp. by EPA Method 1600 or Enterolert™, fecal coliforms, total coliforms) and three rapid qPCR assays for Enterococcus spp. (Taqman, Scorpion-1, Scorpion-2) were compared to health. Primary bacterial source was a creek flowing untreated into ocean; the creek did not reach the ocean when a sand berm formed. This provided a natural experiment for examining FIB-health relationships under varying conditions. Results: We observed significant increases in diarrhea (OR 1.90, 95% CI 1.29-2.80 for swallowing water) and other outcomes in swimmers compared to non-swimmers. Exposure (body immersion, head immersion, swallowed water) was associated with increasing risk of gastrointestinal illness (GI). Daily GI incidence patterns were different: swimmers (2-day peak) and non-swimmers (no peak). With berm-open, we observed associations between GI and traditional and rapid methods for Enterococcus; fewer associations occurred when berm status was not considered. Conclusions: We found increased risk of GI at this urban runoff beach. When FIB source flowed freely (berm-open), several traditional and rapid indicators were related to illness. When FIB source was weak (berm-closed) fewer illness associations were seen. These different relationships under different conditions at a single beach demonstrate the difficulties using these indicators to predict health risk. © 2012 Elsevier Ltd.


Viviano C.M.,Loyola Marymount University | Alderete M.R.,Loyola Marymount University | Boarts C.,Heal the Bay | McCarthy M.,Heal the Bay
ACS Symposium Series | Year: 2012

We draw on our experiences designing and implementing a SENCER course for future science teachers to discuss the benefits of an integrated and relevant community-based learning experience for future educators. Broad issues related to water and the environment form the core of our capstone course; students collaborate with environmental educators from a local non-profit environmental agency and teachers from area high schools in the design of service-learning projects that enable high school students to address environmental issues within their community. As a result, both groups of students strengthen their ties to the community and begin to appreciate the connection between environmental health and community health. The core principle of our approach has been to create sustainable, collaborative partnerships where all stakeholders are involved in the development process from planning through implementation and evaluation. By blurring the boundaries between formal and informal education, we have found that students are more engaged, they connect more effectively with the community and they begin to develop the tools they need to make science relevant to their own students. © 2012 American Chemical Society.

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