ArticleScientific reports2025
Spatiotemporal modelling and monitoring of harmful algal blooms using IoT in Lake Victoria Basin Kenya.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The escalating global concern of harmful algal blooms (HABs) in aquatic environments, poses significant challenges in the context of sustainable development goals (SDGs). The impacts of HABs extend beyond increased waterborne diseases, causing high aquaculture mortality, diminished aesthetic appeal, elevated drinking water treatment costs, and negative effects on tourism and gross domestic product (GDP). Traditional HAB assessment methods involving field sampling and laboratory analysis are inefficient due to their labor intensiveness and cost. This study introduces a novel approach, combining near real-time satellite remote sensing with a low-cost in-situ internet of things (IoT) system that adds a layer of real-time data collection and timely reporting. The method utilizes the ocean color algorithm and the mono-window lake surface air temperature (LSAT) algorithm, enabling widespread detection and mapping of cyanobacteria-induced HABs with a 30 m spatial resolution landsat imagery. The research involved deployment of immobile in-situ IoT system for continuous near real-time LSAT monitoring at select locations which were as well Kenya marine and fisheries research institute (KMFRI) HAB sampling sites that were prone to early HAB occurrence. From 2015 to 2020, the study used Landsat 8 data to monitor chlorophyll-a (Chl-a) concentrations as proxies for HABs. The Ocean Colour 2 algorithm, combined with landsat 8 thermal infrared (TIR) sensor data, estimated LSAT during blooms. Results are validated against datasets from Copernicus' Sentinel-3 and national aeronautics and space administration's (NASA) moderate resolution imaging spectroradiometer (MODIS) missions. Findings showed significant increases in Chl-a values (31 to 57.1 Mg/M
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.