CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms

Mishra, Deepak R.; Kumar, Abhishek; Ramaswamy, Lakshmish; Boddula, Vinay; Das, Moumita; Page, Benjamin P.; Weber, Samuel J.

doi:10.1016/j.hal.2020.101828

Cited by 36 publications

(14 citation statements)

References 39 publications

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“…Fig 1B). In the USA, the CyanoTRACKER project is based on the use of a smartphone application by volunteers to report algae blooms in their neighborhood pond and/or lake [12]. In the same way, citizen science programs based on the collection of water samples have been implemented for the monitoring of lake water quality and the survey of blooms in the province of Saskatchewan (Canada) [13] and in numerous lakes located in the state of Michigan (USA) [14].…”

Section: Introductionmentioning

confidence: 99%

Can participatory approaches strengthen the monitoring of cyanobacterial blooms in developing countries? Results from a pilot study conducted in the Lagoon Aghien (Ivory Coast)

et al. 2020

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Monitoring of cyanobacteria in freshwater ecosystems is a complex task, which is time consuming and expensive due to the chaotic population dynamics and highly heterogeneous distribution of cyanobacteria populations in water bodies. The financial cost constitutes a strong limitation for the implementation of long-term monitoring programs in developing countries, particularly in Africa. The work presented here was performed in the framework of an international project addressing the sustainable monitoring and management of surface water resources used for the production of drinking water in three African countries. We tested the potential of a citizen approach for monitoring cyanobacterial blooms, which are a growing threat to the drinking water supply. This pilot study was designed, implemented and evaluated in close interaction with the Pasteur Institute of the Ivory Coast and with the populations of three villages located on the shoreline of a freshwater lagoon located near Abidjan city. Based on the use of a smartphone application, the citizens of the three villages were invited to report water color changes, as these changes could reflect cyanobacteria proliferations. A two-year experimentation period has shown that it is possible to mobilize the local populations to monitor cyanobacterial blooms. The data collected by citizens were consistent with the data obtained by a classical monitoring of cyanobacteria performed over seven months, but it appeared that new approaches were needed to validate the citizen data. This participatory approach also provided great improvements to the understanding and awareness of local populations regarding water quality and cyanobacterial bloom issues. Finally, we discuss some of the difficulties and limitations of our participatory monitoring approach that should be considered by further implementations. Despite these difficulties, our work

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Section: Introductionmentioning

confidence: 99%

Can participatory approaches strengthen the monitoring of cyanobacterial blooms in developing countries? Results from a pilot study conducted in the Lagoon Aghien (Ivory Coast)

et al. 2020

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“…Many studies have tried to predict HABs by collecting massive data through manual sampling 51 , 54 , 56 , AHFM systems 16 , 17 , 60 , and satellite imagery 76 – 78 . However, such data-driven models are useful for the water bodies for which their equation is derived but present large uncertainties when applied to different water bodies 79 .…”

Section: Discussionmentioning

confidence: 99%

“…New technologies and methods have been developed to support manual sampling, as they track the spatial-temporal distribution of HABs more efficiently 15 . One of the major breakthroughs for continuous and autonomous water monitoring are the automatic high frequency monitoring (AHFM) systems 16 , 17 . These systems allow to collect a massive water quality data by deploying wireless sensor networks on floating platforms or buoys, thus facilitating decision-making based on real-time data.…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll soft-sensor based on machine learning models for algal bloom predictions

Mozó

Morón-López

Vakaruk

et al. 2022

Sci Rep

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Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor. Massive data for water temperature, pH, electrical conductivity (EC) and system battery were taken for three years at intervals of 15 min from two different areas of As Conchas freshwater reservoir (NW Spain). We designed a set of soft-sensors based on compact and energy efficient ML algorithms to infer Chl-a fluorescence by using low-cost input variables and to be deployed on buoys with limited battery and hardware resources. Input and output aggregations were applied in ML models to increase their inference performance. A component capable of triggering a 10 $$\upmu$$ μ g/L Chl-a alert was also developed. The results showed that Chl-a soft-sensors could be a rapid and inexpensive tool to support manual sampling in water bodies at risk.

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“…Chlorophyll-a; a photosynthetic green pigment present in photoautotrophic bacteria and plants, emerges as one of the main indicators of water quality, biophysical status, and the level of eutrophication of a body of water (Ha et al, 2017;Matthews, 2017;nsper and Alikas, 2019), in addition to being used to detect the proliferation of algae (Pereira-Sandoval et al, 2019), cyanobacteria (Mishra et al, 2020) and aquatic plants (Singh et al, 2020).Rivers are an important source of highly dynamic freshwater, varying spatially and temporally. Regular monitoring of these ecosystems is increasingly necessary to broaden the understanding of biogeochemical processes and to accompany rapid environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Detection and Mapping of Eutrophication and Aquatic Plants in a River in the Northeast Region of Brazil Using Sentinel-2 Data

Pereira

Lopes

Espíndola

et al. 2022

Rev. Bras. Geog. Fis.

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The urban expansion of Teresina has caused environmental impacts on the Poti River due to the occurrence of eutrophication and proliferation of aquatic plants in the dry season of the year. Considering the characteristics related to water-quality monitoring in the Northeast region of Brazil, from remote sensing, this study aimed to evaluate the performance of semi-empirical algorithms in Sentinel-2 data in the detection and mapping of eutrophication and aquatic plants in the river Poti in Teresina, Piaui, Brazil. The eutrophication detection methodology involved the study of the reflectance of the water surface in the Sentinel-2 images and the respective correlation within situ data of chlorophyll-a, using the MCI, MPH and NDCI indexes. The NDCI index showed superior one-off performance than the MCI and MPH indexes. In this sense, with the NDCI, the spatio-temporal variation of eutrophication in the Poti River was identified broadly and no longer specific. In relation to aquatic plants, the NDVI index proved to be appropriate for the detection and mapping of water hyacinths, demonstrating the location of the areas covered by aquatic plants. At the beginning of proliferation, the maximum expansion rate of 854.7% was evaluated. At the end of the dry period, there was a peak of covered area of 570,145.6 m² and a production of 6,408.4 tons of fresh biomass from water hyacinths. Therefore, in both cases, it was found that the MSI sensor was suitable for the detection, mapping and monitoring of eutrophication and aquatic plants in the Poti River.

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CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms

Cited by 36 publications

References 39 publications

Can participatory approaches strengthen the monitoring of cyanobacterial blooms in developing countries? Results from a pilot study conducted in the Lagoon Aghien (Ivory Coast)

Can participatory approaches strengthen the monitoring of cyanobacterial blooms in developing countries? Results from a pilot study conducted in the Lagoon Aghien (Ivory Coast)

Chlorophyll soft-sensor based on machine learning models for algal bloom predictions

Detection and Mapping of Eutrophication and Aquatic Plants in a River in the Northeast Region of Brazil Using Sentinel-2 Data

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