A comprehensive assessment of photosynthetic acclimation to shade in C4 grass (Cynodon dactylon (L.) Pers.).

BACKGROUND: Light deficit in shaded environment critically impacts the growth and development of turf plants. Despite this fact, past research has predominantly concentrated on shade avoidance rather than shade tolerance. To address this, our study examined the photosynthetic adjustments of Bermudagrass when exposed to varying intensities of shade to gain an integrative understanding of the shade response of C4 turfgrass. RESULTS: We observed alterations in photosynthetic pigment-proteins, electron transport and its associated carbon and nitrogen assimilation, along with ROS-scavenging enzyme activity in shaded conditions. Mild shade enriched Chl b and LHC transcripts, while severe shade promoted Chl a, carotenoids and photosynthetic electron transfer beyond QA- (ET0/RC, φE0, Ψ0). The study also highlighted differential effects of shade on leaf and root components. For example, Soluble sugar content varied between leaves and roots as shade diminished SPS, SUT1 but upregulated BAM. Furthermore, we observed that shading decreased the transcriptional level of genes involving in nitrogen assimilation (e.g. NR) and SOD, POD, CAT enzyme activities in leaves, even though it increased in roots. CONCLUSIONS: As shade intensity increased, considerable changes were noted in light energy conversion and photosynthetic metabolism processes along the electron transport chain axis. Our study thus provides valuable theoretical groundwork for understanding how C4 grass acclimates to shade tolerance.

Cohesive phycoremediation of pyrene by freshwater microalgae Selenastrum sp. and biodiesel production and its assessment.

In this study, the freshwater microalgae Selenastrum sp. was assessed for the effective degradation of pyrene and simultaneous production of biodiesel from pyrene-tolerant biomass. The growth of algae was determined based on the cell dry weight, cell density, chlorophyll content, and biomass productivity under different pyrene concentrations. Further, lipids from pyrene tolerant culture were converted into biodiesel by acid-catalyzed transesterification, which was characterized for the total fatty acid profile by gas chromatography. Increased pyrene concentration revealed less biomass yield and productivity after 20 days of treatment, indicating potent pyrene biodegradation by Selenastrum sp. Biomass yield was unaffected till the 20 mg/L pyrene. A 95% of pyrene bioremediation was observed at 20 days of culturing. Lipid accumulation of 22.14%, as evident from the estimation of the total lipid content, indicated a marginal increase in corroborating pyrene stress in the culture. Fatty acid methyl esters yield of 63.06% (% per 100 g lipids) was noticed from the pyrene tolerant culture. Moreover, fatty acid profile analysis of biodiesel produced under 10 mg/L and 20 mg/L pyrene condition showed escalated levels of desirable fatty acids in Selenastrum sp., compared to the control. Further, Selenastrum sp. and other freshwater microalgae are catalogued for sustainable development goals attainment by 2030, as per the UNSDG (United Nations Sustainable Development Goals) agenda. Critical applications for the Selenastrum sp. in bioremediation of pyrene, along with biodiesel production, are enumerated for sustainable and renewable energy production and resource management.

Assessment of the Cu phytoremediation potential of Chrysanthemum indicum L. and Tagetes erecta L. using analysis of growth and physiological characteristics.

In the current study, the Cu phytoremediation ability of two ornamental plants, Chrysanthemum indicum L. and Tagetes erecta L., was tracked concerning the growth and physiological responses. Plants were subjected to varying concentrations of Cu (0, 100, 200, and 400 mg/kg) under the pot experiment for 8 weeks. The results showed that the measured growth and physiological characteristics declined in T. erecta shoots and roots at all tested treatments compared with the control. However, in C. indicum at 100 mg/kg, shoot biomass, shoot total soluble protein, and leaves number remained equal to that of the control and then reduced by rising Cu concentrations, compared with the control. Also, results indicated that in C. indicum, after 56 days of exposure to Cu, the chlorophyll pigments content markedly increased and reached a maximum level at 100 mg/kg dose and gradually declined with enhancing Cu concentrations, compared with the control. Other measured growth and physiological parameters decreased in both tissues of C. indicum in response to Cu usage in the growth medium. The carotenoid content of T. erecta decreased in all studied Cu levels in comparison to the control, but in C. indicum remained unaffected up to 200 mg/kg Cu in comparison to the control and then enhanced with increasing Cu level. The augmentation of antioxidant enzyme activity in two species, especially in roots, reflected the incident of Cu stress as demonstrated by elevated MDA and ion leakage levels. Data concerning copper accumulation in tissues, TF, and BAF showed T. erecta is a weak Cu accumulator and seems not to be an appropriate candidate for Cu phytoremediation. However, the Cu content in shoots and roots of C. indicum increased significantly with an increment in applied Cu level. Also, C. indicum accumulated higher Cu concentrations in the roots than in shoots and exhibited TF < 1, 0.1 < BAF root < 1, and can be considered as a Cu excluder by the phytostabilization mechanism.

Salicylic acid and Tocopherol improve wheat (Triticum aestivum L.) Physio-biochemical and agronomic features grown in deep sowing stress: a way forward towards sustainable production.

BACKGROUND: The rate of germination and other physiological characteristics of seeds that are germinating are impacted by deep sowing. Based on the results of earlier studies, conclusions were drawn that deep sowing altered the physio-biochemical and agronomic characteristics of wheat (Triticum aestivum L.). RESULTS: In this study, seeds of wheat were sown at 2 (control) and 6 cm depth and the impact of exogenously applied salicylic acid and tocopherol (Vitamin-E) on its physio-biochemical and agronomic features was assessed. As a result, seeds grown at 2 cm depth witnessed an increase in mean germination time, germination percentage, germination rate index, germination energy, and seed vigor index. In contrast, 6 cm deep sowing resulted in negatively affecting all the aforementioned agronomic characteristics. In addition, deep planting led to a rise in MDA, glutathione reductase, and antioxidants enzymes including APX, POD, and SOD concentration. Moreover, the concentration of chlorophyll a, b, carotenoids, proline, protein, sugar, hydrogen peroxide, and agronomic attributes was boosted significantly with exogenously applied salicylic acid and tocopherol under deep sowing stress. CONCLUSIONS: The results of the study showed that the depth of seed sowing has an impact on agronomic and physio-biochemical characteristics and that the negative effects of deep sowing stress can be reduced by applying salicylic acid and tocopherol to the leaves.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Assessment of in-situ monitoring and tracking the vertical migration of cyanobacterial blooms using LISST-HAB.

Cyanobacterial harmful algal blooms (cyanoHABs) are becoming increasingly common in aquatic ecosystems worldwide. However, their heterogeneous distributions make it difficult to accurately estimate the total algae biomass and forecast the occurrence of surface cyanoHABs by using traditional monitoring methods. Although various optical instruments and remote sensing methods have been employed to monitor the dynamics of cyanoHABs at the water surface (i.e., bloom area, chlorophyll a), there is no effective in-situ methodology to monitor the dynamic change of cell density and integrated biovolume of algae throughout the water column. In this study, we propose a quantitative protocol for simultaneously measurements of multiple indicators (i.e., biovolume concentration, size distribution, cell density, and column-integrated biovolume) of cyanoHABs in water bodies by using the laser in-situ scattering and transmissometry (LISST) instrument. The accuracy of measurements of the biovolume and colony size of algae was evaluated and exceeded 95% when the water bloom was dominated by cyanobacteria. Furthermore, the cell density of cyanobacteria was well estimated based on total biovolume and mean cell volume measured by the instrument. Therefore, this methodology has the potential to be used for broader applications, not only to monitor the spatial and temporal distribution of algal biovolume concentration but also monitor the vertical distribution of cell density, biomass and their relationship with size distribution patterns. This provides new technical means for the monitoring and analysis of algae migration and early warning of the formation of cyanoHABs in lakes and reservoirs.

Influence of low-cost Thai leucoxene minerals on the growth, bioactive compounds, and antibacterial activities of Chrysanthemum indium L. cuttings in in vitro culture.

The effects of low-cost Thai leucoxene mineral (LM) at different concentrations (10, 20, 30, 40, 50, and 60 mg/L) on the growth and antibacterial properties of Chrysanthemum indium L. cuttings under in vitro were evaluated. The primary chemical composition of LM was approximately 86% titanium dioxide (TiO2), as determined by dispersive X-ray spectroscopy. The crystalline structure, shape, and size were investigated by X-ray diffraction and scanning electron microscopy. LM at 40 and 50 mg/L significantly increased plant height, leaf number, node number, and fresh and dry weight. These growth-promoting properties were accompanied by improved chlorophyll and carotenoid contents and antioxidant enzyme activities and reduced malondialdehyde levels. Additionally, LM treatment at 40 and 50 mg/L had positive effects on antibacterial activity, as indicated by the lowest minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values. The high levels of phenolic compounds in the plants contributed to the MIC and MBC values. In conclusion, these findings provide evidence for the effectiveness of LM in enhancing the growth of Chrysanthemum plants in in vitro culture and improving their antibacterial abilities.

Three-state mathematical model for the assessment of DCMU-treated photosystem II heterogeneity.

Photosystem II (PSII) is one of the main pigment-protein complexes of photosynthesis which is highly sensitive to unfavorable environmental factors. The heterogeneity of PSII properties is essential for the resistance of autotrophic organisms to stress factors. Assessment of the PSII heterogeneity may be used in environmental monitoring for on-line detection of contamination of the environment. We propose an approach to assess PSII oxygen-evolving complex and light-harvesting antenna heterogeneity that is based on mathematical modeling of the shape of chlorophyll a fluorescence rise of 3-(3,4-dichlorophenyl)-1,1-dimethylurea-treated samples. The hierarchy of characteristic times of the processes considered in the model makes it possible to reduce the model to a system of three ordinary differential equations. The analytic solution of the reduced three-state model is expressed as a sum of two exponential functions, and it exactly reproduces the solution of the complete system within the time range from microseconds to hundreds of milliseconds. The combination of several such models for reaction centers with different properties made it possible to use it as an instrument to study PSII heterogeneity. PSII heterogeneity was studied for Chlamydomonas at different intensities of actinic light, for Scenedesmus under short-term heating, and for Chlorella grown in nitrate-enriched and nitrate-depleted media.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas.

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity.

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %-25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %-27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %-7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91-0.93, RMSE = 314.2-376.6 g C m-2 yr-1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Prediction of chlorophyll a and risk assessment of water blooms in Poyang Lake based on a machine learning method.

Four different methods were used to identify the important factors influencing chlorophyll-a (Chl-a) content: correlation analysis (CC-NMI), principal component analysis (PCA), decision tree (DT), and random forest recursive feature elimination (RF-RFE). Considering the relationship between Chl-a and its active and passive factors, we established machine learning combination models based on multiple linear regression (MLR), multi-layer perceptron (MLP), and support vector regression (SVR) to predict Chl-a content for Poyang Lake, China. Then, the predictive effects of different combination models were compared and evaluated from multiple perspectives. Considering the actual needs for eutrophication prevention and control, the concept of risk probability was then introduced to assess the risk degree of risk associated with water blooms in Poyang Lake. The results indicated that the mean R2 for the Chl-a predictions using the MLR, MLP, and SVR models was 0.21, 0.61, and 0.75, respectively. Consequently, the SVR model demonstrated higher precision and more accurate predictions. Compared to other methods, integrating the SVR model with the RF-RFE method significantly improved the prediction accuracy, with the R2 increasing to 0.94. For Poyang Lake, 8.8% of random samples indicated a low risk level with a water bloom probability of 21.1%-36.5%; one sample indicated a medium risk level with a risk probability of 45.5%. The research results offer valuable insights for predicting eutrophication and conducting risk assessments for Poyang Lake. They also provide reliable scientific support for making decisions about eutrophication in lakes and reservoirs. Therefore, the results hold significant theoretical importance, practical value, and potential for widespread application.

Assessment of the oxidative stress intensity and the integrity of cell membranes under the manganese nanoparticles toxicity in wheat seedlings.

A response to manganese nanoparticles was studied in seedlings of two wheat cultivars and a model system of plant cell membranes. Nanoparticles at concentrations of 125 and 250 mg/ml were applied foliar. The application of NPs enhanced the content of Mn in plant cells, indicating its penetration through the leaf surface. The stressful effect in the plant cells was estimated based on changes in the activity of antioxidant enzymes, content of chlorophylls and starch. MnNPs evoked no significant changes in the leaf morphology, however, an increase in enzyme activity, starch accumulation, and a decrease in chlorophyll synthesis indicated the stress occurrence. Moreover, a rise in the electrokinetic potential of the chloroplast membrane surface and the reconstruction of their hydrophobic parts toward an increase in fatty acid saturation was found.

Microbial desalination cell treated spent geothermal brine as a nutrient medium in hydroponic lettuce cultivation: Health risk assessment.

The scarcity and contamination of freshwater resources are extremely critical issues today, and the expansion of water reuse has been considered as an option to decrease its impact. Therefore, the reuse of microbial desalination (MDC)-treated spent geothermal brine for agricultural purposes arises as a good solution to prevent water contamination and provide sustainable water usage. In this study, the potential of treated spent geothermal water from MDC system as a nutrient solution for the hydroponic cultivation of lettuce was evaluated. The effects of different water samples (Hoagland solution (R1) as a control, MDC-treated water (R2), 1:1, v/v mixture of MDC-treated water and Hoagland solution (R3), 4:1, v/v mixture of MDC-treated water and Hoagland solution (R4), and tap water (R5)) on lettuce growth were considered. The application of R3 and R4 samples for hydroponic lettuce cultivation was promising since the lettuce plants uptake sufficient nutrients for their growth and productivity with low toxic metal concentrations. In addition, the chlorophyll-a, chlorophyll-b, and carotene contents of lettuce were in the range of 1.045-2.391 mg/g, 0.761-1.986 mg/g, and 0.296-0.423 mg/g in different water samples, respectively. The content of chlorophyll-a was highest in R1 (2.391 mg/g), followed by R3 (2.371 mg/g). Furthermore, the health risk assessment of heavy metal accumulations in the lettuce plants cultivated in the various water samples was determined. Results showed that heavy metal exposure via lettuce consumption is unlikely to suffer noticeable adverse health problems with values below the permissible limit value.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal.

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Toxic effects of lanthanum(III) on photosynthetic performance of rice seedlings: Combined chlorophyll fluorescence, chloroplast structure and thylakoid membrane protein assessment.

Rare earth elements (REEs) are emerging as an anticipated pollution in the environment due to their active use in many areas. However, the effects of REEs on the photosynthesis of rice have not been thoroughly explored. Therefore, this study emphasizes how high levels of La(III) affect the thylakoid membrane of rice seedlings, thereby inhibiting photosynthesis and growth. Here, we reported that rice plants treated with La(III) exhibited an increase in La accumulation in the leaves, accompanied by a decrease in chlorophyll content and photosynthetic capacity. La(III) exposure decreased Mg content in leaves, but possibly increased other nutrients including Cu, Mn, and Zn through systemic endocytosis. K-band and L-band appeared in the fluorescence OJIP transients, indicating La(III) stress destroyed the donor and receptor sides of photosystem II (PSII). Numerous reaction centers (RC/CSm) were inactivated by La(III) treatment, which resulted in a reduction in electron transport capacity (decreased ETo/RC and ETo/CSm) and an increase in the dissipation of the excess excitation energy by heat (increased DIo/RC and DIo/CSm). The BN-PAGE analysis of thylakoid membrane protein complexes showed that La(III) induced the degradation of supercomplexes, PSII core, LHCII, PSI core, LHCI, and F1-ATPase binding Cyt b6f complex. Collectively, this study revealed that La(III) causes significant degradation of thylakoid membrane proteins, thereby promoting the decomposition of photosynthetic complexes, ultimately destroying the chloroplast structure and reducing the photosynthetic performance of rice seedlings.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

A novel low-cost plug-and-play multi-spectral LED based fluorometer, with application to chlorophyll detection.

In this paper a novel low-cost multi-spectral optical fluorometer is presented and evaluated. The device uses a range of LEDs in the blue and violet regions of the electromagnetic spectrum and a mini-spectrometer to detect the emitted fluorescence in the UV to IR spectrum region. Custom built electronics and software were designed to control the system and the components were housed in bespoke 3D printed parts. A number of known fluorophores were tested to determine the capabilities of the fluorometer. Application of the device is demonstrated for the detection of chlorophyll a (Chl a) from laboratory grown algae and from environmental samples while analytical performance is established using both in vivo and extracted Chl a fluorescence and by comparison with a benchtop fluorometer.

A comprehensive satellite-based assessment across the Pacific Arctic Distributed Biological Observatory shows widespread late-season sea surface warming and sea ice declines with significant influences on primary productivity.

Massive declines in sea ice cover and widespread warming seawaters across the Pacific Arctic region over the past several decades have resulted in profound shifts in marine ecosystems that have cascaded throughout all trophic levels. The Distributed Biological Observatory (DBO) provides sampling infrastructure for a latitudinal gradient of biological "hotspot" regions across the Pacific Arctic region, with eight sites spanning the northern Bering, Chukchi, and Beaufort Seas. The purpose of this study is two-fold: (a) to provide an assessment of satellite-based environmental variables for the eight DBO sites (including sea surface temperature (SST), sea ice concentration, annual sea ice persistence and the timing of sea ice breakup/formation, chlorophyll-a concentrations, primary productivity, and photosynthetically available radiation (PAR)) as well as their trends across the 2003-2020 time period; and (b) to assess the importance of sea ice presence/open water for influencing primary productivity across the region and for the eight DBO sites in particular. While we observe significant trends in SST, sea ice, and chlorophyll-a/primary productivity throughout the year, the most significant and synoptic trends for the DBO sites have been those during late summer and autumn (warming SST during October/November, later shifts in the timing of sea ice formation, and increases in chlorophyll-a/primary productivity during August/September). Those DBO sites where significant increases in annual primary productivity over the 2003-2020 time period have been observed include DBO1 in the Bering Sea (37.7 g C/m2/year/decade), DBO3 in the Chukchi Sea (48.0 g C/m2/year/decade), and DBO8 in the Beaufort Sea (38.8 g C/m2/year/decade). The length of the open water season explains the variance of annual primary productivity most strongly for sites DBO3 (74%), DBO4 in the Chukchi Sea (79%), and DBO6 in the Beaufort Sea (78%), with DBO3 influenced most strongly with each day of additional increased open water (3.8 g C/m2/year per day). These synoptic satellite-based observations across the suite of DBO sites will provide the legacy groundwork necessary to track additional and inevitable future physical and biological change across the region in response to ongoing climate warming.

A comparative analysis of growth kinetics, image analysis, and biofuel potential of different algal strains.

Due to the global population growth and economic development, energy demand has increased worldwide. Countries take steps to improve their alternative and renewable energy sources. Algae is one of the alternative energy sources and can be used to produce renewable biofuel. In this study, nondestructive, practical, and rapid image processing techniques were applied to determine the algal growth kinetics and biomass potential of four algal strains, including C. minutum, Chlorella sorokiniana, C. vulgaris, and S. obliquus. Laboratory experiments were conducted to determine different aspects of biomass and chlorophyll production of those algal strains. Suitable non-linear growth models, including Logistic, modified Logistic, Gompertz, and modified Gompertz models, were employed to determine the growth pattern of algae. Moreover, the methane potential of harvested biomass was calculated. The algal strains were incubated for 18 days, and the growth kinetics were determined. After the incubation, the biomass was harvested and assessed for its chemical oxygen demand content and biomethane potential. Among the tested strains, C. sorokiniana was the best in biomass productivity (111.97 ± 0.9 mg L-1d-1). The calculated vegetation indices, namely; colorimetric difference, color index vegetation, vegetative, excess green, excess green minus excess red, combination, and brown index values showed a significant correlation with biomass and chlorophyll content. Among the tested growth models, the modified Gompertz shows the best growth pattern. Further, the estimated theoretical CH4 yield was highest for C. minutum (0.98 mL g-1) compared to other tested strains. The present findings suggest that image analysis can be used as an alternative method to study the growth kinetics and biomass production potential of different algae during cultivation in wastewater.

Simulation and Economic Analysis of the Biotechnological Potential of Biomass Production from a Microalgal Consortium.

The biomass of microalgae and the compounds that can be obtained from their processing are of great interest for various economic sectors. Chlorophyll from green microalgae has biotechnological applications of great potential in different industrial areas such as food, animal feed, pharmaceuticals, cosmetics, and agriculture. In this paper, the experimental, technical and economic performance of biomass production from a microalgal consortium (Scenedesmus sp., Chlorella sp., Schroderia sp., Spirulina sp., Pediastrum sp., and Chlamydomonas sp.) was investigated in three cultivation systems (phototrophic, heterotrophic and mixotrophic) in combination with the extraction of chlorophyll (a and b) on a large scale using simulation; 1 ha was established as the area for cultivation. In the laboratory-scale experimental stage, biomass and chlorophyll concentrations were determined for 12 days. In the simulation stage, two retention times in the photobioreactor were considered, which generated six case studies for the culture stage. Subsequently, a simulation proposal for the chlorophyll extraction process was evaluated. The highest microalgae biomass concentration was 2.06 g/L in heterotrophic culture, followed by mixotrophic (1.98 g/L). Phototrophic and mixotrophic cultures showed the highest chlorophyll concentrations of 20.5 µg/mL and 13.5 µg/mL, respectively. The simulation shows that higher biomass and chlorophyll production is attained when using the mixotrophic culture with 72 h of retention that we considered to evaluate chlorophyll production (a and b). The operating cost of the entire process is very high; the cultivation stage has the highest operating cost (78%), mainly due to the high energy consumption of the photobioreactors.