Search Results (18)

Polyethylene microplastics (PE-MPs) and glyphosate (GLY) are widespread aquatic contaminants, but their combined effects on microalgae remain poorly understood. This study assessed the individual and joint toxicity of GLY and PE-MPs to the model microalga Chlorella vulgaris. Acute (3-day) and chronic (7-day) exposures were performed using GLY at 1–40 mg/L, alone or combined with PE-MPs (10 mg/L). A four-parameter log-logistic (4PL) model was applied to estimate median effect concentrations (EC₅₀). After 72 h, the EC₅₀ values were 9.77 mg/L for the GLY single system and 2.31 mg/L for the GLY-PE combined system, confirming enhanced toxicity in combined exposures. Co-exposure reduced pigment levels (chlorophyll a, chlorophyll b, and carotenoids) by up to 65% and significantly increased oxidative stress markers, including reactive oxygen species production and malondialdehyde accumulation, compared with single treatments. Antioxidant enzymes (superoxide dismutase and catalase) showed concentration- and time-dependent responses, indicating activation of cellular defense mechanisms. Scanning Electron Microscopy revealed PE-induced aggregation and structural damage to algal cells, particularly at higher GLY concentrations. These findings demonstrate that PE-MPs can amplify the toxic effects of GLY on microalgae and highlight the need for further studies at environmentally relevant concentrations and with different polymer types. Full article

Cocultivation of microalgae and aerobic methanotrophs represents an emerging biotechnology platform to produce high-protein biomass, yet quantifying individual species in mixed cultures remains challenging. Here, we present a rapid, low-cost method—differential sedimentation, optical density, and fluorescence (DSOF)—to determine the abundance of coculture members. DSOF exploits differences in cell size and pigment autofluorescence between the thermoacidophilic microalga and methanotrophic species Galdieria sp. RTK37.1 and Methylacidiphilum sp. RTK17.1, respectively, to selectively sediment algal cells and estimate population contributions via OD₆₀₀ and phycocyanin fluorescence. Evaluation with model suspensions across a wide cell density range (0 ≤ [Galdieria]: ≤ 3.23 A.U., and 0 ≤ [Methylacidiphilum] ≤ 1.54 A.U.) showed strong agreement with known values, with most absolute errors < 0.1 A.U. and relative errors < 10% at moderate biomass levels. Application to live batch cocultures under microalga or methanotroph growth-suppressed conditions, and during simultaneous growth, demonstrated accurate tracking of population dynamics and revealed enhanced methanotroph growth in the presence of oxygenic microalgae. While DSOF accuracy decreases at very concentrated biomass (>2.0 A.U. for Galdieria) or under nitrogen-limiting conditions, the model provides a practical, scalable alternative to more complex, invasive or expensive techniques, enabling near real-time monitoring of microalgae–methanotroph cocultures. Full article

Synthetic non-metallic nanoparticles (NMNPs) such as carbon nanotubes (CNTs), carbon nanodots (CNDs), and cell-penetrating peptides (CPPs) have been explored to treat harmful algal blooms. However, their strain-specific algicidal activities have been rarely investigated. Here we determined their acute toxicity to nine freshwater cyanobacterial strains belonging to seven genera, including Microcystis aeruginosa UTEX 2386, M. aeruginosa UTEX 2385, M. aeruginosa LE3, Anabaena cylindrica PCC 7122, Aphanizomenon sp. NZ, Planktothrix agardhii SB 1810, Synechocystis sp. PCC 6803, Lyngbya sp. CCAP 1446/10, and Microcoleus autumnale CAWBG635 ATX. We prepared in-house three batches of CNDs using glucose (CND-G) or chloroform and methanol (CND-C/M) as the substrate and one batch of single-walled CNTs (SWCNTs). We also ordered a commercially synthesized CPP called γ-Zein-CADY. The axenic laboratory culture of each cyanobacterial strain was exposed to an NMNP at two dosage levels (high and low, with high = 2 × low) for 48 h, followed by measurement of five endpoints. The endpoints were optical density (OD) at 680 nm (OD₆₈₀) for chlorophyll-a estimation, OD at 750 nm (OD₇₅₀) for cell density, instantaneous pigment fluorescence emission (FE) after being excited with 450 nm blue light (FE₄₅₀) for chlorophyll-a or 620 nm red light (FE₆₂₀) for phycocyanin, and quantum yield (QY) for photosynthesis efficiency of photosystem II. The results indicate that the acute toxicity was strain-, NMNP type-, dosage-, and endpoint-dependent. The two benthic strains Microcoleus autumnale and Lyngbya sp. were more resistant to NMNP treatment than the other seven free-floating strains. SWCNTs and fraction A14 of CND-G were more toxic than CND-G and CND-C/M. The CPP was the least toxic. The high dose generally caused more severe impairment than the low dose. OD₇₅₀ and OD₆₈₀ were more sensitive than FE₄₅₀ and FE₆₂₀. QY was the least sensitive endpoint. The strain dependence of toxicity suggested the potential application of these NMNPs as a target-specific tool for mitigating harmful cyanobacterial blooms. Full article

In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R² values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. Full article

Harmful algae blooms (HABs) have been reported with greater frequency in lakes across New York State (NYS) in recent years. In situ sampling is used to assess water quality, but such observations are time intensive and therefore practically limited in their spatial extent. Previous research has used remote sensing imagery to estimate phytoplankton pigments (typically chlorophyll-a or phycocyanin) as HAB indicators. The primary goal of this study was to validate a remote sensing-based method to estimate cyanobacteria concentrations at high temporal (5 days) and spatial (10–20 m) resolution, to allow identification of lakes across NYS at a significant risk of algal blooms, thereby facilitating targeted field investigations. We used Google Earth Engine (GEE) as a cloud computing platform to develop an efficient methodology to process Sentinel-2 image collections at a large spatial and temporal scale. Our research used linear regression to model the correlation between in situ observations of chlorophyll-a (Chl-a) and phycocyanin and indices derived from Sentinel-2 data to evaluate the potential of remote sensing-derived inputs for estimating cyanobacteria concentrations. We tested the performance of empirical models based on seven remote-sensing-derived indices, two in situ measurements, two cloud mitigation approaches, and three temporal sampling windows across NYS lakes for 2019 and 2020. Our best base model (R² of 0.63), using concurrent sampling data and the ESA cloud masking—i.e., the QA60 bitmask—approach, related the maximum peak height (MPH) index to phycocyanin concentrations. Expanding the temporal match using a one-day time window increased the available training dataset size and improved the fit of the linear regression model (R² of 0.71), highlighting the positive impact of increasing the training dataset on model fit. Applying the Cloud Score+ method for filtering cloud and cloud shadows further improved the fit of the phycocyanin estimation model, with an R² of 0.84, but did not result in substantial improvements in the model’s application. The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. Future work will focus on exploring alternative algorithms that can incorporate diverse data sources and lake characteristics, contributing to a deeper understanding of the relationship between remote sensing data and water quality parameters. This research provides a valuable tool for cyanobacteria parameter estimation with confidence quantification to identify lakes at risk of algal blooms. Full article

Chemical compounds, such as the CS gas employed in military operations, have a number of characteristics that impact the ecosystem by upsetting its natural balance. In this work, the toxicity limit and microorganism’s reaction to the oxidative stress induced by O-chlorobenzylidenemalonitrile, a chemical found in CS gas, were assessed in relation to the green algae Chlorella pyrenoidosa. A number of parameters, including the cell growth curve, the percent inhibition in yield, the dry cell weight, the percentage viability and productivity of algal biomass flocculation activity, and the change in oxygen production, were analyzed in order to comprehend the toxicological mechanisms of O-chlorobenzylidenemalonitrile on algal culture. Using fluorescence and Fourier transform infrared spectroscopy (FTIR), the content of chlorophyll pigments was determined. The values obtained for pH during the adaptation period of the C. pyrenoidosa culture were between 6.0 and 6.8, O₂ had values between 6.5 and 7.0 mg/L, and the conductivity was 165–210 µS/cm. For the 20 µg/mL O-chlorobenzylidenemalonitrile concentration, the cell viability percentage was over 97.4%, and for the 150 µg/mL O-chlorobenzylidenemalonitrile concentration was 74%. The EC_b50 value for C. pyrenoidosa was determined from the slope of the calibration curve; it was estimated by extrapolation to the value of 298.24 µg/mL. With the help of this study, basic information on the toxicity of O-chlorobenzylidenemalonitrile to aquatic creatures will be available, which will serve as a foundation for evaluating the possible effects on aquatic ecosystems. The management of the decontamination of the impacted areas could take the results into consideration. Full article

Algal Organic Matter (AOM) is derived from the dissolved organic matter composition of the algal species being observed. In this study, excitation–emission fluorescence spectroscopy was used to determine Chlorella sp.’s AOM and pigment characteristics in varying algal biomass concentrations. The AOM and pigment characteristics were observed at 400–600 nm and 600–800 nm fluorescence emission, respectively, with an excitation spectrum of 300–450 nm. F450/680 was computed based on the ratio between the dissolved organic matter contribution at 450 nm and chlorophyll-a at 680 nm. F450/680 positively correlated with algal biomass (r = 0.96) at an excitation wavelength of 405 nm. This study is a good reference for those interested in algal biomass estimation and production in natural waters. Full article

Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum $R^2$ of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates. Full article

The real-time chlorophyll-a pigment monitoring of C. reinhardtii is studied using our developed LED fluorescence light detection and ranging (LiDAR) system. It features a portable set-up that uses a pulsed LED module with an excitation wavelength of 385 nm. We were able to monitor the different growth phases of C. reinhardtii with specific cultivation parameters. The developed fluorescence LiDAR system showed the linear correlation of its chlorophyll-a signal with the optical density and EEM fluorescence measurements at 680 nm emission wavelength. Water quality and weather parameters were also measured, which explains the variation in the growth dynamics of C. reinhardtii during the sampling period. The results from the monitoring demonstrated a different technique that can be used in estimating algal biomass in the environment. Full article

The bio-optical properties of coral reef waters were examined across coral reef ecosystems not influenced by land-derived run-off, in the Great Barrier Reef lagoon (Heron Island) and the Coral Sea (the Coringa-Herald and Lihou Reefs). The aim was to determine whether the absorption properties, the concentration-specific absorption properties, and the phytoplankton and non-algal pigmented particle (NAP) absorption concentrations varied from the ocean waters flushing onto the reef at high tide to those waters on the reef or flushing off the reef at low tide. The optical and biogeochemical properties of on-reef waters systematically differed from the surrounding ocean waters. The chl a concentration values varied up to 7-fold and the NAP concentrations up to 29-fold; for the reef samples, the chl a values were on average 2 to 3 times lower than for the oceans whilst the NAP values were slightly higher on the reefs. The spectral absorption values of the chl a, NAP, and colored dissolved organic matter (CDOM) varied up to 6-fold for reef waters and up to 15-fold for ocean waters. The spectral absorption for chl a was up to 3-fold lower on the reef waters, the absorption by the CDOM was up to 2-fold higher and the NAP absorption was 1.6-fold higher on the reef waters. The concentration-specific absorption coefficients for chl a and NAP varied up to 9-fold in reef waters and up to 30-fold in ocean waters. In the case of Heron Island and Coringa-Herald cays, this concentration-specific absorption was on average 1.3 to 1.7-fold higher for chl a and up to 2-fold lower for NAP on the reefs. The Lihou Reef measurements were more ambiguous between the reef waters and ocean waters due to the complex nature and size of this reef. Based on our results, the assumption that the optical properties of on-reef waters and the adjacent ocean waters are the same was shown to be invalid. Ocean waters flowing on to the reef are higher in phytoplankton, whilst waters on the reef or flowing off the reefs are higher in CDOM and NAP. We found differences in the pico,- nano-, and microplankton distributions as well as in the ratios of photosynthetic to photoprotective pigments. The variability in the bio-optical properties between the reef waters and adjacent ocean waters has implications for the estimations of sunlight absorption along the water column, the UV penetration depth, the temperature distributions, and the nutrient and carbon fluxes in coral reef ecosystems. As Earth observation algorithms require proper parameterization for the water column effects when estimating benthic cover, the actual optical properties need to be used. These results will improve the use of Earth observation to systematically map the differences in the water quality between reefs and the adjacent ocean. Full article

Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility. Full article

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPD_CV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPD_CV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations. Full article

Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed. Full article

This work is an extension of the MicroPhytoBenthos Optical Model (MPBOM) workflow. The model was based on the observation that the biofilm itself has a negligible inherent reflectance and can be described solely by the ratio between its apparent reflectance (R_A) and background reflectance (R_B), allowing a straightforward calculation of the absorption coefficient (α). This coefficient is directly related to pigment concentrations estimated by High Performance Liquid Chromatography (HPLC). To run the model, assess and extend the use of α, the background contribution is a critical step. This work shows that: (i) indices based on reflectance and absorption coefficient spectra derived from the optical model correctly identified the main microphytobenthos (MPB) groups covering a pixel; (ii) contrary to the R_A index each new α index was insensitive to biomass variations; (iii) for each MPB group there was a significant linear relation between the biomass estimated by HPLC and α peak at 673 nm; (iv) indices based on α spectra were almost insensitive to mixing constraints at a subpixel level. Knowing the background reflectance contribution of MPB biofilms, α can therefore be used to map MPB algal composition and biomass at any scale from MPB synthetized in laboratory to intertidal mudflat airborne observations. Full article

The green microalga Chromochloris zofingiensis can accumulate significant amounts of valuable carotenoids, mainly natural astaxanthin, a product with applications in functional food, cosmetics, nutraceuticals, and with potential therapeutic value in cardiovascular and neurological diseases. To optimize the production of astaxanthin, it is essential to monitor the content of astaxanthin in algal cells during cultivation. The widely used HPLC (high-performance liquid chromatography) method for quantitative astaxanthin determination is time-consuming and laborious. In the present work, we present a method using flow cytometry (FCM) for in vivo determination of the astaxanthin content and the carotenoid-to-chlorophyll ratio (Car/Chl) in mixotrophic C. zofingiensis. The method is based on the assessment of fluorescent characteristics of cellular pigments. The mean fluorescence intensity (MFI) of living cells was determined by FCM to monitor pigment formation based on the correlation between MFI detected in particular channels (FL1: 533 ± 15 nm; FL2: 585 ± 20 nm; FL3: >670 nm) and pigment content in algal cells. Through correlation and regression analysis, a linear relationship was observed between MFI in FL2 (band-pass filter, emission at 585 nm in FCM) and astaxanthin content (in HPLC) and applied for predicting astaxanthin content. With similar procedures, the relationships between MFI in different channels and Car/Chl ratio in mixotrophic C. zofingiensis were also determined. Car/Chl ratios could be estimated by the ratios of MFI (FL1/FL3, FL2/FL3). FCM is thus a highly efficient and feasible method for rapid estimation of astaxanthin content in the green microalga C. zofingiensis. The rapid FCM method is complementary to the current HPLC method, especially for rapid evaluation and prediction of astaxanthin formation as it is required during the high-throughput culture in the laboratory and mass cultivation in industry. Full article