Negotiation of knowledge for coastal management? Reflections from a transdisciplinary experiment in South Africa

Hybrid science-society approaches for knowledge production are often framed by a transdisciplinary approach. Most forms of “linear” progression of science informing policy or the “production” of knowledge as a one-way process are increasingly being challenged. This is also true for coastal and marine sciences informing decision-making to support sustainable development of coastal areas. From the early 2010s, South Africa had one of the most progressive and well-structured frameworks for the establishment of integrated coastal management (ICM) in order to achieve societal objectives for its valuable coastal area. Even so, the implementation of the legislation, policies and guidelines remain a challenge, especially at the local level in municipalities. This paper reports on a social experiment that was intended to examine the possibility for a new knowledge negotiation process to unsettle the highly structured, nested and regular policy process, which forms the basis of ICM in South Africa. This paper reflects on an experimental application of a participatory methodology known as a “competency group” to co-produce knowledge for coastal and marine management. The group members, a combination of codified, tacit and embedded knowledge holders, agreed to serve on a competency group and met on six occasions over a 12-month period in 2013. This group “negotiated” amongst themselves to achieve a common understanding of knowledge useful for the management of beach water quality on the Golden Mile, the prime beachfront of Durban, a South African city. The paper provides a novel lens into a potentially distinctive, challenging and imminently useful approach of co-producing knowledge for coastal governance, especially in a middle-income country where the social and political context is complex.

U.S. beach water quality monitoring

Coastal water quality is an important factor influencing public health and the quality of our nation’s beaches. In recent years, poor water quality has resulted in increased numbers of beach closures and corresponding negative impacts on tourism. This paper addresses some of the issues surrounding the management challenge of coastal water quality, in particular, beach water quality monitoring. For this effort, data on beach water quality monitoring activities conducted by states were assessed and synthesized. In total, 29 states were surveyed: 16 reported information for seawater; six reported for freshwater only; eight reported for both seawater and freshwater. Thresholds for advisories and closure vary nationally; however, all 29 states have established an online presence for their monitoring programs and display advisories and closures in real time, most often on spatial information (GIS) portals. Challenges in monitoring, prediction, and communication are assessed and discussed. Based on this assessment, the committee offers the following recommendations, as detailed in the text: • Standardization of water quality data and the distribution medium; • Enhanced public access to water quality monitoring data; • Consistent thresholds for swim advisories; • Water quality regulation reviews with stakeholder participation; • Enhanced predictive models incorporating rapid testing results; • Holistic water quality monitoring that includes indicators beyond fecal indicator bacteria; • Managing contaminants of emerging concern through identification, monitoring and control; and • Funding for water quality monitoring and reporting -- from federal, state, and local governments.

Physicochemical interaction with faecal bacteria in characterisation of beach water quality, Gulf of Guinea, Ghana

Human activities such as industrial and agricultural waste discharges directly in the coastal areas increasingly contribute to pollution in coastal waters of Western Africa. The study employed physicochemical and faecal analysis to understand water pollution along the coast of Ghana. The physicochemical parameter such as temperature, salinity, electrical conductivity, pH, dissolved oxygen concentration, dissolved oxygen saturation, total dissolved solids, and redox potential) were measured in situ while water samples were collected determination of total suspended solids, nutrients, chlorophyll-a, and faecal bacteria. The abundance of total coliforms (4061.6 ± 4159.14 CFU/100 ml water), Escherichia coli, and Enterococcus spp. varied significantly (p < 0.05) among the beaches. The high amount of faecal bacteria suggest microbial contamination, possible ecosystem, and health risks to water resource users. This baseline study provides evidence of coastal water contamination to improve beach water quality standards to ensure safe environmental health.

Artificial neural network based nowcasting model for beach water quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

Artificial neural network based nowcasting model for beach water quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

Waterborne illness and injury: a feasibility study of a site-specific predictive model for beach water quality at Beachway Park in the City of Burlington

It is commonly assumed that the persistence model, using day-old monitoring results, will provide accurate estimates of real-time bacteriological concentrations in beach water. However, the persistence model frequently provides incorrect results. This study: 1. develops a site-specific predictive model, based on factors significantly influencing water quality at Beachway Park; 2. determines the feasibility of the site-specific predictive model for use in accurately predicting near real-time E. coli levels. A site-specific predictive model, developed for Beachway Park, was evaluated and the results were compared to the persistence model. This critical performance evaluation helped to identify the inherent inaccuracy of the persistence model for Beachway Park, which renders it an unacceptable approach for safeguarding public health from recreational water-borne illnesses. The persistence model, supplemented with a site-specific predictive model, is recommended as a feasible method to accurately predict bacterial levels in water on a near real-time basis.

Investigation of the relationship between wave action and the transport of bacteria in freshwater : implications for the management of aquatic and human health risk

The sampling regime used to monitor the microbiological quality of water typically involves the collection of whole water samples, where bacteria are assumed to be planktonic. This practice ignores sedimentary pathogen sources and highlights the lack of understanding regarding the effect of shear stress on the erosion of bacteria from sediment particles. This study utilized a wave flume and an environmental test bacterial strain to examine the effect of increasing wave energy on bacterial loading and the partitioning of free-floating and floc-associated bacteria in water. A positive correlation was found between wave energy, total suspended solids, and bacterial loading in water. Experiments examining free-floating and floc-associated bacteria under low (0.60 N/s) and high (5.35 N/s) wave energy demonstrated the importance of floc as a vector for the transport of bacteria. These results imply that current beach sampling and analysis methods may not reflect overall beach water quality.