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Total MC-LR levels between July and October 2018 (left) and between August 2019 and October 2019 (right)
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Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic d...
Citations
... Harmful algae blooms (HABs) are a pervasive and escalating concern for marine ecosystems, public health, and economies worldwide [1][2][3]. These phenomena occur when nutrient and temperature conditions cause microalgae to multiply rapidly and dominate a water body [4][5][6]. HABs are characterized by the rapid accumulation of algae cells, which can produce a range of toxins harmful to both marine and terrestrial life [7][8][9][10]. These events manifest in various forms, often named for their appearance, such as "red tides", "brown tides", and "green tides" [11,12]. ...
Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, and biodiversity impacts. These blooms, increasingly exacerbated by climate change, compromise water quality in both marine and freshwater ecosystems, significantly affecting marine life and coastal economies based on fishing and tourism while also posing serious risks to inland water bodies. This article examines the role of hyperspectral imaging (HSI) in monitoring HABs. HSI, with its superior spectral resolution, enables the precise classification and mapping of diverse algae species, emerging as a pivotal tool in environmental surveillance. An array of HSI techniques, algorithms, and deployment platforms are evaluated, analyzing their efficacy across varied geographical contexts. Notably, hyperspectral sensor-based studies achieved up to 90% classification accuracy, with regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients of determination (R2) above 0.80. These quantitative findings underscore the potential of HSI for robust HAB diagnostics and early warning systems. Furthermore, we explore the current limitations and future potential of HSI in HAB management, highlighting its strategic importance in addressing the growing environmental and economic challenges posed by HABs. This paper seeks to provide a comprehensive insight into HSI’s capabilities, fostering its integration in global strategies against HAB proliferation.
... Consequently, water retention times have increased and flow rates have decreased because the river's dynamics have shifted to those of a lentic water body [10]. Additionally, the upstream region of the Yeongsan River, which includes the densely populated Gwangju Metropolitan City (with approximately 1.4 million residents) and its industrial complexes, has encountered challenges such as eutrophication, excessive phytoplankton proliferation, foul odor, and fish kills due to pollution discharge [11,12]. ...
The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO4-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO4-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management.
... Excessive phosphate levels can lead to eutrophication (Kumararaja et al., 2019). Eutrophication can be a trigger for algal bloom (Abbas et al., 2023). These algal blooms can result in reduced dissolved oxygen levels in the seawater, a deterioration in seawater quality, and a decrease in biodiversity (Kumararaja et al., 2019;Lin et al., 2020). ...
The bio-physicochemical conditions of seawater are critically important in the rate of expansion of mangrove forests. This study aims to assess the driving factors of mangrove forest expansion with bio-physicochemical water quality analysis using the Maximum Entropy (MaxEnt) method in Laikang Bay, Indonesia. Water quality analysis included measurements of NO3, PO4, kH, salinity, current speed, brightness (D3), NO2, pH, and chlorophyll-a levels (bio-physicochemical factors). This research adopts quantitative methods, with data collected from 42 specific locations between 12:00 a.m. and 3:00 p.m. The observation data was gathered using the stratified random sampling method. Spatial distribution mapping of mangroves and observation data were analyzed using Euclidean nearest neighbor distance with ArcGIS software version 8.1. The MaxEnt method was applied to investigate the percentage contribution of water quality on the distribution of mangroves. The results of this study indicate that the most significant factor contributing to the growth and expansion of mangrove forests in Laikang Bay is the PO4 content, with a contribution value of 47.4%. The PO4 concentration ranges from 0.10 to 1.40 mg.100g-1, with a concentration of approximately 0.10 mg.100g-1 having the greatest impact. Meanwhile, the less influential factor is brightness (D3), with a contribution value of 0.3%. These results indicate that to maintain the growth and expansion of mangrove forests in Laikang Bay, it is necessary to maintain the levels of these influential variables.
... Algal overgrowth on the surface of water prevents sunlight from reaching the bottom, causing changes in the aquatic environment and leading to drastic changes in the flora and fauna. These changes harm drinking water supply and increase the risk of water-borne infections [37,1,31]. ...
... The Paldang Watershed experiences a significant blue-green algae (Cyanophyta) surge post-rainfall during the high-temperature months of July and August (24.4 ± 1.6 • C), likely leading to high Chl-a [35]. ...
Organic matter in lakes is categorized into allochthonous organic matter, such as leaves and sewage effluent, and autochthonous organic matter, generated by microorganisms within the water system. In this study, organic matter composition was analyzed using UV-vis spectroscopy and liquid chromatography-organic carbon detection (LC-OCD). Several allochthonous natural organic matter substances were collected including leaves, green leaves, forest soils, and paddy soils. The organic matter composition analysis in our study sites revealed that humic substances comprised the highest proportion (36.5–42.3%). Also, individual samples at each site exhibited distinct characteristics. This study used a humic substance-diagram (HS-diagram) and principal component analysis (PCA) to trace the sources affecting the river water quality and identify their origins. The humic substances of soil origin predominantly influenced the water quality, with the impact of organic matter significantly pronounced during the July rainfall period. Compared with the PCA results, the contribution of the humic substance (HS, 48.9%) and building block (BB, 42.0%) indices appeared higher between June and July in summer, likely due to non-degradable substances released by heavy rain. In fall, the contribution of low molecular weight neutrals increased from 71.2% to 85.2%, owing to a humic substance influx and decomposition. This study demonstrated the application of estimating the relative contributions of source materials in lakes utilized for drinking and agricultural water to identify sources, aiding in the development of efficient watershed management plans.
... Vol.: (0123456789) water release from the dam, and variability in evaporation and infiltration (Abbas et al., 2023;Deutsch, 2020). These changes along with the seasonal variability in lake temperatures and nutrients levels, resulted in large inter-seasonal variabilities in Chl-a levels. ...
... These elevated temperatures boost cyanobacterial blooms, particularly Microcystis, which has an optimum growth temperature ranging between 25 and 32 °C (Deutsch, 2020;Jiang et al., 2008;Liu et al., 2011). Algal blooms were also observed during the non-growing season, when surface water temperatures exceeded 17 °C and/or when the surface nutrient concentrations peaked following Fall turnover and/or large external loads brought about by the first flush after a long dry summer season (Abbas et al., 2023). Similar to Chl-a, TSS levels were significantly higher in 2019 (median = 21 mg/L) as compared to 2018 (median = 7.33 mg/L). ...
Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R²) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.
Harmful Algal Blooms (HABs) can originate from a variety of reasons, including water pollution coming from agriculture, effluent from treatment plants, sewage system leaks, pH and light levels, and the consequences of climate change. In recent years, HAB events have become a serious environmental problem, paralleling population growth, agricultural development, increasing air temperatures, and declining precipitation. Hence, it is crucial to identify the mechanisms responsible for the formation of HABs, accurately assess their short- and long-term impacts, and quantify their variations based on climate projections for developing accurate action plans and effectively managing resources. From this point of view, this present study utilizes empirical dynamic modeling (EDM) to predict chlorophyll-a concentration of Lake Erie. This method is characterized by its nonlinearity and nonparametric nature. EDM has a key advantage in that it overcomes the limitations of traditional statistical modeling by utilizing data-driven attractor reconstruction. Chlorophyll-a is a critical parameter in the prediction of HAB events. Lake Erie is an inland water body that experiences frequent HAB phenomena due to its location. The EDM demonstrated exceptional performance, and these findings imply that the EDM model can effectively capture the underlying dynamics of chlorophyll-a changes.