Search Results (112)

Pollution of rivers and large water bodies, including reservoirs, by wastewater from various sources is one of the most critical issues in the Donetsk region, requiring continuous monitoring and assessment of surface water quality. The research aims to assess the state of the Kalmius River under anthropogenic pressure, as well as to find correlations between the species composition, photosynthetic activity of phytoplankton, and the degree of water pollution. This study presents the results of biomonitoring of the Kalmius River and its tributaries within Donetsk City, which are under intense anthropogenic pressure. Pollution of the river channel by phenol, anionic surfactants, Ferrum ions, chlorides, and sulfates was identified. Based on the combinatorial pollution index, the water in the Kalmius River and its tributaries can be classified as polluted. The pigment composition of water samples was analyzed, and the species composition of river phytoplankton was determined. Dominant species include Chlorella vulgaris Beij., Dictyosphaerium pulchellum H.C.Wood, Scenedesmus quadricauda Brébisson, and Oscillatoria agardhii M.A.Gomont. Photosynthetic activity of the river’s algal flora was assessed based on chlorophyll fluorescence induction curves of natural phytoplankton. A correlation was established between surface water pollution levels and changes in the photosynthetic apparatus of microalgae cells. A strong negative correlation was found between the content of nitrate nitrogen in the aquatic environment and the photosynthetic activity, pigment composition, and abundance of the main dominant forms of phytoplankton, particularly the microalgae of the genus Cyclotella. The data obtained shows that the Kalmius River’s pollution has a significant impact on phytoplankton biodiversity, leading to the growth of cyanobacteria species. Full article

Phytoplankton size classes (PSCs) fundamentally regulate ocean productivity, biogeochemical cycling, and carbon export, yet their distribution and optical variability across the Arabian Sea remain poorly constrained. This study develops and validates a regionally tuned absorption-based approach for phytoplankton size class estimation using in situ phytoplankton absorption spectra (a_ph(λ)) collected during six research cruises between 2016 and 2024. A significant power-law relationship between a_ph(443) and the spectral slope (S_443–510) (R² = 0.963, p < 0.001) provided a consistent optical basis for distinguishing PSCs. Co-located HPLC pigment data were used to derive empirical a_ph(443) thresholds for pico- (≤0.011 m⁻¹), nano- (0.011–0.059 m⁻¹), and micro-phytoplankton (>0.059 m⁻¹). Class-specific mean spectra showed clear optical distinctions consistent with size-dependent pigment packaging. Model evaluation showed reduced error and improved regression agreement relative to existing a_ph- and chl-a-based models when applied to the Arabian Sea dataset, with regression slopes close to unity (0.78–0.81) across all PSCs. This regional model also improved representation of transitional nano communities, which are commonly associated with higher uncertainties in global models. The empirical relationships developed in this study were applied to VIIRS Level 3 a_ph(443) data for 2024 to generate PSC distributions. Satellite-derived PSC fields revealed pronounced spatial gradients and regional contrasts across the Arabian Sea, including micro-phytoplankton blooms in the northern Arabian Sea and mixed nano-dominated communities along the western Arabian Sea (Somali coast). Pico-phytoplankton dominated the low-absorption oligotrophic offshore waters, while nano-phytoplankton were most common in transitional regions influenced by moderate nutrient inputs. Taken together, these results demonstrate that the combined a_ph(443)-S_443–510 framework provides a practical, regionally optimized method for retrieving PSCs at synoptic scales across the Arabian Sea, supporting improved bio-optical modelling and satellite-based monitoring of phytoplankton community structure in this region. Full article

Mining wastewater contains complex mixtures of regulated and emerging contaminants that challenge treatment technologies. This study evaluates the bioremediation potential of 10 phytoplankton species, including Chlorella vulgaris, and the aquatic fern Salvinia natans for removing contaminants from synthetic and mine outflow water. Batch screening experiments were conducted using synthetic wastewater containing regulated elements, rare earth elements (REEs), or selected organic flotation reagents, followed by validation using acidic mine outflow water from a decommissioned mine (Romania). All tested phytoplankton species and Salvinia natans showed high removal efficiencies for several priority elements, including Pb, Ag, Cr, Th, U, and multiple REEs. Organic flotation reagents were efficiently removed by all phytoplankton species. Chlorella vulgaris and Salvinia natans emerged as high-performing species and were further evaluated in mine outflow, where species-specific and matrix-dependent removal behavior was observed. Here, Chlorella vulgaris showed a higher average removal. Time-resolved analyses indicated a rapid initial removal followed by equilibrium phases, suggesting biosorption and bioaccumulation mechanisms. Li and Se showed limited removal capacities across all species. Photosynthetic pigment analysis revealed stress responses in Salvinia natans under acidic, multielement exposure. Overall, phycoremediation and phytoremediation represent effective low-chemical treatment strategies with potential for integration into a complementary mining wastewater treatment workflow. Full article

In order to determine the condition of drinking water sources in Donetsk Region and assess potential threats related to water pollution from mining and industrial wastewater, it is extremely important to monitor surface waters, which should include an assessment of the condition of gydrobionts. Additionally, declining surface water quality in the region contributes to pollution in the coastal waters of the Sea of Azov. This study presents the monitoring results for the southern part of the Kalmius River basin. Analysis of water samples revealed contamination by phenol, sulfates, chlorides, anionic surfactants, iron, elevated water hardness, and significant exceedances of suspended solids and total dissolved solids. The iron concentration at the Kalmius River estuary reached 0.81 mg∙L⁻¹, exceeding the permissible limit by 2.5-fold. Sulfate and total dissolved solids concentrations attained 1673 and 160 mg∙L⁻¹, respectively. Changes in the species composition of phytoplankton were observed in response to variations in iron, manganese, and phenol concentrations in the water. Specifically, elevated iron levels led to increased abundance of the metal-sensitive species Cyclotella meneghiniana Kützing. Principal component analysis of the data revealed a relationship between increased phenol concentrations in the aquatic environment and a mean 20% reduction in phytoplankton cell photosynthetic activity, as well as the influence of manganese ions on cell abundance and photopigment content. Thus, phytoplankton cell fluorescence, alongside shifts in species composition and photosynthetic pigment content, can serve as an additional indicator of surface water pollution by iron and phenol. Full article

This study investigated the spatial and seasonal dynamics of phytoplankton communities in Jaran Bay, inner Hansan Bay, and outer Hansan Bay, with particular emphasis on size structure and pigment-based indicators of productivity and physiological status. Water sampling was conducted during May, August, and October in 2020, 2022, and 2023 and phytoplankton communities were analyzed using size-fractionated chlorophyll a measurements and high-performance liquid chromatography (HPLC) pigment analysis. Chlorophyll a concentrations exhibited pronounced seasonality, with consistently elevated values in August across all bays. Diatoms were predominant throughout the study period; however, their relative contribution declined in outer Hansan Bay during summer, coinciding with increased contributions from cryptophytes and cyanobacteria. Size-fractionated analyses revealed that large-sized phytoplankton (>20 µm) predominantly consisted of diatoms, whereas small-sized phytoplankton (<20 µm) were composed of diatoms and cryptophytes. Comparisons between fluorometric and pigment-based approaches indicated that pigment-based diagnostics overestimated microphytoplankton contributions, attributable to the presence of small-sized diatoms. Pigment indices further revealed that large-sized phytoplankton were characterized by higher photosynthetic carotenoid concentrations and lower photoprotective carotenoid ratios, indicative of enhanced photosynthetic activity and productivity. Overall, these findings highlight the critical role of phytoplankton size structure in regulating productivity and physiological responses in aquaculture-dominated coastal bays. Full article

Understanding the composition of phytoplankton assemblages and monitoring changes in their diversity is a key factor in the comprehension of global biogeochemical cycles, climate regulation and marine ecosystem health, especially in the context of increasing global warming. Regional empirical algorithms for phytoplankton satellite estimates of size classes (PSCs) and functional types (PFTs) in the Mediterranean Sea have been developed and implemented in the EU Copernicus Marine Service since 2019. Here, we present an update of the PSC and PFT algorithms operational in the Copernicus catalogue since the end of 2024. Results show an overall improvement in the model performance, in line with Copernicus Marine Service requirements focused on the continuous enhancement of the accuracy of distributed biogeochemical variables. Finally, the new algorithms were applied to a time series of over 25 years of satellite data (1998–2024), enabling the identification of key changes in phytoplankton composition at both monthly and basin scales. These insights were made possible by an algorithm re-calibration based on updated and more comprehensive regional pigment ratios. Full article

The bloom-forming dinoflagellates and euglenophyceae were observed in the coastal waters of Hammam-Lif (Southern Mediterranean), during a green tide event on 3 June 2023. The bloom was dominated by Lepidodinium chlorophorum, identified through ribotyping with densities reaching 2.3 × 10⁷ cells·L⁻¹. Euglena spp. and Eutrepsiella spp. contributed to the discoloration, with abundances up to 2.9 × 10⁷ cells·L⁻¹. Environmental data revealed significant depletion of nitrite and nitrate, coinciding with a rapid increase in sunlight duration, likely promoting the proliferation of L. chlorophorum and euglenophyceae. By 5 June, two days after the bloom, nutrient stocks were exhausted. Diatoms appeared limited by low silicate concentrations (<0.05 µmol·L⁻¹), while dissolved inorganic phosphate and Nitrogen-ammonia were elevated during the bloom (0.88 and 4.8 µmol·L⁻¹, respectively), then decreased significantly afterward (0.23 and 1.06 µmol·L⁻¹, respectively). Low salinity (34.0) indicated substantial freshwater input from the Meliane River, likely contributing to nutrient enrichment and bloom initiation. After the event, phytoplankton abundance and chlorophyll levels declined, with a shift from dinoflagellates to diatoms. The accumulation of pigments (chlorophyll b and carotenoids) and the presence of Mycosporine-like amino acids (MAAs) during and after the bloom suggest that UV radiation and Nitrogen-ammonia were key drivers of this green tide. Full article

Cyanotoxins in agricultural waters pose a human and animal health risk. These toxins can be transported to nearby crops and soil during irrigation practices; they can remain in the soil for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds, cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the random forest algorithm in estimating microcystin concentrations using eight in situ water quality measurements at one active livestock water pond and two working irrigation ponds in Georgia and Maryland, USA. Measurements of microcystin along with eight in situ-sensed water quality parameters were used to train and test the machine learning model. The models performed better at the Georgia ponds compared to the Maryland pond, and interior models performed better than nearshore or whole-pond models. The most important variables for microcystin prediction were water temperature and phytoplankton pigments. Overall, the random forest algorithm(RF), augmented with a ‘trainControl’ function to perform repeated cross validations, was able to explain 40% to 70% of the microcystin concentration variation in the three agricultural ponds. Water quality measurements showed potential to aid water monitoring/sampling design by predicting the microcystin concentrations in the studied ponds by using readily available and easy to collect in situ data. Full article

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized as ecological indicators that enable a quantitative assessment of the status of marine ecosystems in response to environmental change. Here, using an extensive, updated in situ pigment dataset collated from numerous past research campaigns across the Mediterranean Sea, we re-parameterized an abundance-based phytoplankton size class model that infers Chl-a concentration in three phytoplankton size classes: pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm). Following recent advancements made within this category of size class models, we also incorporated information of sea surface temperature (SST) into the model parameterization. By tying model parameters to SST, the performance of the re-parameterized model was improved based on comparisons with concurrent, independent in situ measurements. Similarly, the application of the model to remotely sensed ocean color observations revealed strong agreement between satellite-derived estimates of phytoplankton size structure and in situ observations, with a performance comparable to the current regional operational datasets on size structure. The proposed conceptual regional model, parameterized with the most extended in situ pigment dataset available to date for the area, serves as a suitable foundation for long-term (1997–present) analyses on phytoplankton size structure and ecological indicators (i.e., phenology), ultimately linking higher trophic level responses to a changing Mediterranean Sea. Full article

Chydorus sphaericus is often a dominant cladoceran zooplankton species in water bodies experiencing harmful cyanobacterial blooms. However, its relationship with toxin-producing algae remains largely unexplored. In this study, the feeding behavior of C. sphaericus on colonial cyanobacteria and potentially toxic Microcystis was investigated in a temperate, shallow, eutrophic lake. Liquid chromatographic analyses of phytoplankton marker pigments in C. sphaericus gut content revealed that pigments characteristic of cyanobacteria (identified a zeaxanthin, echinenone, and canthaxanthin) comprised the majority of its diet. Among them, colonial cyanobacteria (marked by the pigment canthaxanthin) were the highly preferred food source despite their minor contribution to phytoplankton biomass. qPCR targeting Microcystis genus-specific mcyE synthase genes, which are involved in microcystin biosynthesis, indicated that potentially toxic strains of Microcystis were present in C. sphaericus gut content throughout its temporal and spatial presence in the lake. The results suggest that the common small cladoceran in eutrophic waters, C. sphaericus, has a close trophic interaction with colonial cyanobacteria (including Microcystis) and may represent an important vector for transferring toxigenic Microcystis to the food web, even under conditions of low Microcystis biomass in the lake water. Full article

An oceanographic cruise with 34 stations was conducted in the central-southwestern region of the Gulf of Mexico from February 19 to 10 March 2013. This study included the measurement of hydrographic and phytoplankton bio-optical parameters, and pigment samples were collected at two depth levels (10 and 50 m). Our results showed a warm and nutrient-depleted water column associated with low chlorophyll a (<1 mg Chla m⁻³) and average values of a_ph440 (0.01 ± 0.008, m⁻¹) and a_d350 (0.04 ± 0.02, m⁻¹). In addition, nano-microphytoplankton abundance and pigments were analyzed using a light microscope and HPLC, respectively. Overall, the Gulf of Mexico exhibited oligotrophic characteristics, with Chla (0.17 ± 0.11 mg m⁻³) and NO₃⁻ (0.03 ± 0.001 µM), except at 50 m depth in some stations north of Yucatán and in Campeche Bay and at surface level off the Tamaulipas shelf. In these three regions, values of a_ph(440), a_d(350), (Chla) and phytoplankton abundance (>12 × 10³ cells L⁻¹) were observed near river mouths and under seasonal oceanographic forcings, which increased the growth and diversity of phytoplankton. The most relevant pigments found were DVchla (0.06 ± 0.13 mg m⁻³), Chlb (0.16 ± 0.21 mg m⁻³), Zea (0.06 ± 0.03 mg m⁻³), and Hex-fuco (0.02 ± 0.02 mg m⁻³); these are associated with the presence of Prochlorococcus, chlorophytes, Synechococcus, prymnesiophytes, and diatoms. Through the bio-optical variability, we determined the ecological geography of phytoplankton in four different spectral shapes, where M1 and M2 represent the group of cyanobacteria (Prochlorococcus and Synechococcus) and M3 and M4 represent a mixture of diatoms, dinoflagellates, and chlorophytes. In conclusion, we consider that oceanographic processes such as cyclonic and anticyclonic structures and permanent rivers determine the favorable changes in phytoplankton (>nutrients, Chla, a_ph440) and an increment in the number of phytoplankton spectral shapes). Full article

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology. Full article

The Great Salt Lake (GSL) is the largest saline lake in the Western Hemisphere. It supports billion-dollar industries and recreational activities, and is a vital stopping point for migratory birds. However, little is known about the spatiotemporal variation of phytoplankton biomass in the lake that supports these resources. Spectral reflectance provided by three remote sensing products was compared relative to their relationship with field measurements of chlorophyll a (Chl a). The MODIS product MCD43A4 with a 500 m spatial resolution provided the best overall ability to map the daily distribution of Chl a. The imagery indicated significant spatial variation in Chl a, with low concentrations in littoral areas and high concentrations in a nutrient-rich plume coming out of polluted embayment. Seasonal differences in Chl a showed higher concentrations in winter but lower in summer due to heavy brine shrimp (Artemia franciscana) grazing pressure. Twenty years of imagery revealed a 68% increase in Chl a, coinciding with a period of declining lake levels and increasing local human populations, with potentially major implications for the food web and biogeochemical cycling dynamics in the lake. The MCD43A4 daily cloud-free images produced by 16-day temporal composites of MODIS imagery provide a cost-effective and temporally dense means to monitor phytoplankton in the southern (47% surface area) portion of the GSL, but its remaining bays could not be effectively monitored due to shallow depths, and/or plankton with different pigments given extreme hypersaline conditions. Full article

Marine diatoms are essential members of both phytoplankton and phytobenthic communities, able to colonize submerged artificial and natural surfaces, contributing to benthic microbial biomass. Diatoms have developed different adaptative mechanisms to cope with various environmental stresses, including high concentrations of heavy metals. The aim of this study was to investigate the arsenic resistance of diatoms, isolated from microbial mats collected from an arsenic-rich brackish pond (Lake Mergolo della Tonnara, Italy), by evaluating (i) their ability to form biofilms in the presence of arsenite (As^III) or arsenate (As^V), and (ii) the variations in the photosynthetic pigments’ contents (i.e., chlorophyll a and c) in their biofilms. The mats were dominated by members of the genus Amphora, and isolates were affiliated with species of A. capitellata, A. coffeaeformis, and A. montana. The strains grew better in the presence of As^V than As^III, which is generally less toxic. After seven days of incubation, each strain exhibited a different ability to form biofilms on glass surfaces in the presence of arsenic (25 ppm), with A. montana strain 27 being the most effective (86%) in the presence of As^III, and A. coffeaeformis strain 26 (74%) with As^V. Photosynthetic pigment levels (chlorophyll a and c) differed in each biofilm, being poorly reduced by As^III in strain 27, and by As^V in strain 26, indicating a species-specific response to arsenic stress. Our results indicated that Amphora species thriving in this environment can form biofilms as an As-resistance mechanism, maintain their levels of photosynthetic pigments, and support the functioning of the pond ecosystem, with A. montana being favored in the presence of As^III, whereas A. coffeaeformis 26 in the presence of As^V. As producers of biofilms, these strains could be useful to develop new strategies to remediate arsenic pollution. Full article

Cyanobacterial blooms in lakes have increased in frequency and intensity over the past two decades, negatively affecting ecological and biogeochemical processes. This study focuses on the phytoplankton composition of Lake Maggiore, with a special emphasis on cyanobacteria detection through pigment composition. While microscopy is the standard method for phytoplankton identification, pigment-based methods provide broader spatiotemporal coverage. Between May and September 2023, five measurement campaigns were conducted in Lake Maggiore, collecting bio-geochemical and bio-optical data at 27 stations. The total Chlorophyll-a (TChl a) was measured, with concentrations ranging from 1.13 to 6.9 mg/m³. Phytoplankton pigment composition was analyzed using High-Performance Liquid Chromatography (HPLC) and the CHEMTAX approach was applied for phytoplankton classification. The results were cross-validated using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and microscopic counts. Cyanobacteria were identified based on unique pigment markers, such as carotenoids. The HPLC-derived pigment classification results aligned well with both PCA and HCA and microscopic counts verified the accuracy of the pigment-based chemotaxonomy. The study demonstrates that pigment-based classification methods, when combined with statistical analyses, offer a reliable alternative for identifying cyanobacteria and other phytoplankton groups, with potential applications in support of remote sensing algorithm development. Full article