Methodology for public monitoring of total coliforms, Escherichia coli and toxicity in waterways by Canadian high school students

Au, Jason S.; Bagchi, P.; Chen, B.; Martínez, Rodríguez; Dudley, Susan A.; Sorger, George J.

doi:10.1006/jema.2000.0323

Cited by 67 publications

(51 citation statements)

References 28 publications

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“…Benchmarking studies of the accuracy of the data collected by citizen scientists have reported differing outcomes (Au et al, 2000;Nicholson et al, 2002;Canfield et al, 2002;Shelton, 2013). Nevertheless, new sensing technologies open up perspectives for a wider range of data collection.…”

Section: Water Qualitymentioning

confidence: 99%

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

et al. 2014

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The participation of the general public in the research design, data collection and interpretation process together with scientists is often referred to as citizen science. While citizen science itself has existed since the start of scientific practice, developments in sensing technology, data processing and visualization, and communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. This paper reviews the state of citizen science in a hydrological context and explores the potential of citizen science to complement more traditional ways of scientific data collection and knowledge generation for hydrological sciences and water resources management. Although hydrological data collection often involves advanced technology, the advent of robust, cheap, and low-maintenance sensing equipment provides unprecedented opportunities for data collection in a citizen science context. These data have a significant potential to create new hydrological knowledge, especially in relation to the characterization of process heterogeneity, remote regions, and human impacts on the water cycle. However, the nature and quality of data collected in citizen science experiments is potentially very different from those of traditional monitoring networks. This poses challenges in terms of their processing, interpretation, and use, especially with regard to assimilation of traditional knowledge, the quantification of uncertainties, and their role in decision support. It also requires care in designing citizen science projects such that the generated data complement optimally other available knowledge. Lastly, using 4 case studies from remote mountain regions we reflect on the challenges and opportunities in the integration of hydrologically-oriented citizen science in water resources management, the role of scientific knowledge in the decision-making process, and the potential contestation to established community institutions posed by co-generation of new knowledge.

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Section: Water Qualitymentioning

confidence: 99%

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

et al. 2014

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“…Recently, the iSPUW project was initiated to integrate data from advanced weather radar systems, innovative wireless sensors, and crowdsourcing of data via mobile applications in order to better predict flood events for the Dallas-Fort Worth Metroplex urban water systems (ISPUW, 2015;Seo et al, 2014). Other examples of crowdsourced water-related information include the so-called Crowdmap platform for collecting and communicating the information about the floods in Australia in 2011 (ABC, 2011) and informing citizens about the proper time for water supply in an intermittent water system (Alfonso, 2006;Au et al, 2000;Roy et al, 2012). Wehn et al (2015) stressed the importance and need of public participation in water resources management to ensure citizens' involvement in the flood management cycle.…”

Section: Introductionmentioning

confidence: 99%

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

Mazzoleni

Verlaan

Alfonso

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

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“…Many examples of citizen science for monitoring water quality exist in the peer-reviewed scientific literature (Au et al 2000;Buytaert et al 2014;Canfield et al 2002;Fore at al. 2001;Lottig et al 2014;Maas et al 1991;Nicholson et al 2002;Overdevest et al 2004;Peckenham et al 2012;Savan et al 2003) and more certainly exist in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Fore et al (2001) and Canfield et al (2002) both found no significant difference between volunteer collected biological and chemical water quality data, while Maas et al (1991) chose to run their water chemistry samples through a university lab to avoid volunteer error. Au et al (2000) found that local high school students were able to evaluate toxicity of Escherichia coli (E. coli) similarly to experts after they were trained, and Peckenham et al (2012) determined that middle to high-school aged students were able to accurately analyze pH and conductivity, but that additional quality assurance was needed for hardness, chloride, and nitrate testing. There is still a need to assess current water monitoring programs and provide examples of applications of citizen science to collect and analyze water quality data for improved watershed management.…”

Section: Introductionmentioning

confidence: 99%

The Wabash Sampling Blitz: A Study on the Effectiveness of Citizen Science

Muenich

Peel²,

Bowling

et al. 2016

CSTP

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||The increasing number of citizen science projects around the world brings the need to evaluate the effectiveness of these projects and to show the applicability of the data they collect. This research describes the Wabash River Sampling Blitz, a volunteer water-quality monitoring program in Central Indiana developed by the Wabash River Enhancement Corporation (WREC). Results indicate that field test strips for nitrate+nitrite-N read by volunteers generally agree with lab-determined values. Orthophosphate results are less transferable owing to low observed concentrations, although the field test strip values from unfiltered samples consistently over-predicted the lab values. Hierarchical cluster analysis (HCA) applied to volunteer-collected data groups sampling sites into meaningful management clusters that can help to identify water-quality priorities across the watershed as a proof of concept for watershed managers. Results of the HCA provide an opportunity for WREC to target future programs, education, and activities by analyzing the data collected by citizen scientists. Overall this study demonstrates how citizen science water quality data can be validated and applied in subsequent watershed management strategies.

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Methodology for public monitoring of total coliforms, Escherichia coli and toxicity in waterways by Canadian high school students

Cited by 67 publications

References 28 publications

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

The Wabash Sampling Blitz: A Study on the Effectiveness of Citizen Science

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