[Effects of Biochar on Growth and Pollutant Accumulation of Lettuce in Soil Co-contaminated with Tetracycline and Copper].

Through lettuce potting experiments, the effects of different types of biochar (apple branch, corn straw, and modified sorghum straw biochar with phosphoric acid modification) on lettuce growth under tetracycline (TC) and copper (Cu) co-pollution were investigated. The results showed that compared with those under CK, the addition of biochar treatment significantly increased the plant height, root length, shoot fresh weight, and root fresh weight of lettuce (P < 0.05). The addition of different biochars significantly increased the nitrate nitrogen, chlorophyll, and soluble protein content in lettuce physiological indicators to varying degrees, while also significantly decreasing the levels of malondialdehyde, proline content, and catalase activity. The effects of biochar on lettuce physiological indicators were consistent during both the seedling and mature stages. Compared with those in CK, the addition of biochar resulted in varying degrees of reduction in the TC and Cu contents of both the aboveground and underground parts of lettuce. The aboveground TC and Cu levels decreased by 2.49%-92.32% and 12.79%-36.47%, respectively. The underground TC and Cu levels decreased by 12.53%-55.64% and 22.41%-42.29%, respectively. Correlation analysis showed that nitrate nitrogen, chlorophyll, and soluble protein content of lettuce were negatively correlated with TC content, whereas malondialdehyde, proline content, and catalase activity were positively correlated with TC content. The resistance genes of lettuce were positively correlated with TC content (P < 0.05). In general, modified biochar was found to be more effective in improving lettuce growth quality and reducing pollutant accumulation compared to unmodified biochar, with modified sorghum straw biochar showing the best remediation effect.

Effect of seasonality and estuarine waters on the phytoplankton of the Guamá River (Belém, Amazon, Brazil).

This study aimed to analyze the application of the Phytoplankton Community Index-PCI and Functional Groups-FG in determining the water quality of the Guamá River (Pará, Amazônia, Brazil). Samplings occurred monthly for analyses of phytoplankton and physical and chemical parameters, for two years, at the station where water was collected for human supply consumption. Seasonality influenced electrical conductivity, total suspended solids, dissolved oxygen, transparency, winds, true color, and N-ammoniacal. The ebb tide showed high turbidity and suspended solids. The density varied seasonally with the highest values occurring in September and December (61.1 ind mL-1 and 60.2 ind mL-1, respectively). Chlorophyll-a was more elevated in December (21.0 ± 4.7 µg L-1) and chlorophyll-c higher in relation to clorophyll- b indicated the dominance of diatoms. Functional Group P prevailed in the study months. Through the PCI índex the waters of Guamá River varied from reasonable to excellent and the TSI ranged from oligo to mesotrophic. The use of Functional Groups proved to be a promising tool in the determination of water quality since it covered the most abundant species in the Environment, but the PCI is not adequate to characterize Amazonian white-waters rivers, which have diatoms as the leading dominant group.

Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia.

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops.

The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature.

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m3 across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.

Effects of ZnWO4 nanoparticles on growth, photosynthesis, and biochemical parameters of the green microalga Raphidocelis subcapitata.

Nanoparticles have applications in many sectors in the society. ZnWO4 nanoparticles (ZnWO4-NPs) have potential in the fabrication of sensors, lasers, and batteries, and in environmental remediation. Thus, these NPs may reach aquatic ecosystems. However, we still do not know their effects on aquatic biota and, to our knowledge, this is the first study that evaluates the toxicity of ZnWO4-NPs in a eukaryotic organism. We evaluated the toxicity of ZnWO4-NPs on the green microalga Raphidocelis subcapitata for 96 h, in terms of growth, cell parameters, photosynthesis, and biochemical analysis. Results show that most of Zn was presented in its particulate form, with low amounts of Zn2+, resulting in toxicity at higher levels. The growth was affected from 8.4 mg L-1, with 96h-IC50 of 23.34 mg L-1. The chlorophyll a (Chl a) content increased at 30.2 mg L-1, while the fluorescence of Chl a (FL3-H) decreased at 15.2 mg L-1. We observed increased ROS levels at 44.4 mg L-1. Regarding photosynthesis, the NPs affected the oxygen evolving complex (OEC) and the efficiency of the photosystem II at 22.9 mg L-1. At 44.4 mg L-1 the qP decreased, indicating closure of reaction centers, probably affecting carbon assimilation, which explains the decay of carbohydrates. There was a decrease of qN (non-regulated energy dissipation, not used in photosynthesis), NPQ (regulated energy dissipation) and Y(NPQ) (regulated energy dissipation via heat), indicating damage to the photoprotection system; and an increase in Y(NO), which is the non-regulated energy dissipation via heat and fluorescence. The results showed that ZnWO4-NPs can affect the growth and physiological and biochemical parameters of the chlorophycean R. subcapitata. Microalgae are the base of aquatic food chains, the toxicity of emerging contaminants on microalgae can affect entire ecosystems. Therefore, our study can provide some help for better protection of aquatic ecosystems.

Unraveling the spectral and biochemical response of mangroves to oil spills and biotic stressors.

Mangroves are prone to biotic and abiotic stressors of natural and anthropogenic origin, of which oil pollution is one of the most harmful. Yet the response of mangrove species to acute and chronic oil exposure, as well as to other stressors, remains barely documented. In this study, a non-destructive, non-invasive approach based on field spectroscopy is proposed to unravel these responses. The approach relies on tracking alterations in foliar traits (pigments, sugars, phenols, and specific leaf area) from reflectance data in the 400-2400 nm spectral range. Three mangrove species hit by two of the most notorious oil spills in Brazilian history (1983 and 2019) and various biotic stressors, including grazing, parasitism, and fungal disease, were investigated through field spectroscopy and machine learning. This study reveals strong intra- and interspecific variability of mangrove's spectral and biochemical responses to oil pollution. Trees undergoing acute exposure to oil showed stronger alterations of foliar traits than the chronically exposed ones. Alterations induced by biotic stressors such as parasitism, disease, and grazing were successfully discriminated from those of oil for all species based on Linear Discriminant Analysis (Overall Accuracy ≥76.40% and Kappa ≥0.70). Leaf chlorophyll, phenol, and starch contents were identified as the most relevant traits in stressor discrimination. The study highlights that oil spills affect mangroves uniquely, both acutely and chronically, threatening their global conservation.

Mechanisms of cadmium mitigation in tomato plants under orthophosphate and polyphosphate fertilization regimes.

Cadmium (Cd) is one of the most toxic elements in soil, affecting morphological, physiological, and biochemical processes in plants. Mineral plant nutrition was tested as an effective approach to mitigate Cd stress in several crop species. In this regard, the present study aimed to elucidate how different phosphorus (P) fertilization regimes can improve some bio-physiological processes in tomato plants exposed to Cd stress. In a hydroponic experiment, the impact of two phosphorus fertilizer forms (Polyphosphate (poly-P): condensed P-form with 100% polymerization rate and orthophosphate (ortho-P): from orthophosphoric acid) on the photosynthetic activity, plant growth, and nutrient uptake was assessed under three levels of Cd stress (0, 12, and 25 µM of CdCl2). The obtained results confirmed the negative effects of Cd stress on the chlorophyll content and the efficiency of the photosynthesis machinery. The application of poly-P fertilizer significantly improved the chlorophyll stability index (82%) under medium Cd stress (Cd12), as compared to the ortho-P form (55%). The analysis of the chlorophyll α fluorescence transient curve revealed that the amplitude of Cd effect on the different steps of electron transfer between PSII and PSI was significantly reduced under the poly-P fertilization regime compared to ortho-P, especially under Cd12. The evaluation of the RE0/RC parameter showed that the electron flux reducing end electron acceptors at the PSI acceptor side per reaction center was significantly improved in the poly-P treatment by 42% under Cd12 compared to the ortho-P treatment. Moreover, the use of poly-P fertilizer enhanced iron uptake and its stoichiometric homeostasis in the shoot tissue which maintained an adequate absorption of iron under Cd stress conditions. Findings from this study revealed for the first time that inorganic polyphosphate fertilizers can reduce Cd toxicity in tomato plants by enhancing photosynthesis activity, nutrient uptake, plant growth, and biomass accumulation despite the high level of cadmium accumulation in shoot tissues.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir.

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI's additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the -chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

Natural Albino Mutant of Daylily (Hemerocallis spp.) Reveals a Link between Drought Sensitivity and Photosynthetic Pigments Metabolism.

BACKGROUND: Mutant analysis remains one of the main genetic tools for characterising unclarified gene functions in plants, especially in non-model plants. Daylily (Hemerocallis spp.) is a popular perennial ornamental plant grown worldwide. Analysis of daylily mutants can enhance understanding of genes regulating the albino phenotype and improve the cultivar quality of daylily. METHODS: The natural albino mutant (Alb-⁣/-) was isolated by screening a self-pollinated progeny of daylily cultivar 'black-eyed stella'. Transmission electron microscopy was used in analysing the structure of plastids between mutant and wild-type seedlings. The content of chlorophyll, carotenoids and chlorophyll precursors in plants was measured by ultraviolet spectrophotometry. RNA sequencing and physiological measurements were performed to explore the association between drought tolerance and mutation. RESULTS: All the seedlings of the daylily albino mutants died spontaneously within fifteen days after germination when grown in soil. The carotenoid and chlorophyll content in the leaves of the mutant plants significantly decreased compared with those of the wild-type control. The mutant plants displayed stunted growth, and their leaves were white or light yellow in color. Abnormal plastids such as those showing endomembrane vesiculation and lacking stacking were discovered in the leaves of mutant plants. Furthermore, genetic analysis revealed that a single recessive nuclear gene mutation led to the albino trait, RNA sequencing and real-time quantitative PCR validation showed extensive differences in gene expression between the mutant plants and the wild-type control, and most of the genes related to chlorophyll metabolism were down-regulated, with foldchange ranging from 0.20-0.49. Additionally, the surviving homozygous plants (Alb+⁣/+), which do not contain this mutation, were also isolated by analysing the phenotype of their self-pollinated progeny. The net photosynthesis rate and light saturation point of Alb+⁣/+ were higher than those of heterozygous (Alb+⁣/-) plants. Additionally, the Alb+⁣/+ plants were more tolerant to drought conditions than the Alb+⁣/- plants, suggesting that a heterozygous Alb- mutation is sufficient to negatively affect photosynthetic efficiency and drought tolerance. CONCLUSIONS: The albino mutation negatively affects photosynthetic efficiency and drought tolerance, and homozygous mutation is required for the characteristic albino phenotype. This work highlights the link between albino mutation, photosynthetic pigment metabolism and drought sensitivity in daylily.

Study on the spatiotemporal distribution of algal blooms and its influencing factors in young reservoirs based on remote sensing interpretation.

Algal blooms caused by excessive proliferation of phytoplankton in young reservoirs have been frequently reported, seriously threatening the unstable aquatic ecosystem, water quality safety and public health. Thus, there is an urgent need to investigate the dynamics of phytoplankton in these young reservoirs, and many current studies on phytoplankton in young reservoirs are based on point monitoring information. This study used remote sensing interpretation to invert the chlorophyll-a concentration in 131 images of Zipingpu Reservoir from 2013 to 2021, and analyzed the spatiotemporal characteristics of algal blooms. Partial least squares-structural equation modeling was used to identify the environmental influencing factors of algal blooms. The results showed that the average chlorophyll-a concentration in the reservoir was 4.49 mg/m3, and the frequency of algal blooms was 28%. The maximum area of algal blooms shows a significant increase trend in the interannual (increase by 0.05%/yr in the proportion of water surface area), and the average blooms area shows a weaker increase trend (0.01%/yr). The prone period of algal bloom is from April to August every year. The solar duration and wind speed had significant direct positive effects on the maximum and average algal bloom area, which was the similar effects in different years and months (path coefficient exceeds 0.44). TP also has a significant direct positive effect on the average algal bloom area between different years (path coefficient of 0.30). The suitable meteorological factors level making the bloom-prone period from April to August, the prevailing westerly and southerly winds provide transport for the aggregation of phytoplankton and algal blooms outbreak in the northeastern waters. This study expand the monitoring frequency and spatial information of algal blooms, which provided a reference for young reservoir management and prevention of blooms.

Past environmental changes: using sedimentary photosynthetic pigments to enhance subtropical reservoir management.

The historical impacts of eutrophication processes were investigated in six subtropical reservoirs (São Paulo, Brazil) using a paleolimnological approach. We questioned whether the levels of pigment indicators of algal biomass could provide information about trophic increase and whether carotenoid pigments could offer additional insights. The following proxies were employed: organic matter, total phosphorus, total nitrogen, photosynthetic pigments (by high-performance liquid chromatography), sedimentation rates, and geochronology (by 210 Pb technique). Principal component analysis indicated a gradient of eutrophication. In eutrophic reservoirs (e.g., Rio Grande and Salto Grande), levels of lutein and zeaxanthin increased over time, suggesting growth of Chlorophyta and Cyanobacteria. These pigments were significantly associated with algal biomass, reflecting their participation in phytoplankton composition. In mesotrophic reservoirs, Broa and Itupararanga, increases and significative linear correlations (r > 0.70) between pigments and nutrients are mainly linked to agricultural and urban activities. In the oligotrophic reservoir Igaratá, lower pigment and nutrient levels reflected lesser human impact and good water quality. This study underscores eutrophication's complexity across subtropical reservoirs. Photosynthetic pigments associated with specific algal groups were informative, especially when correlated with nutrient data. The trophic increase, notably in the 1990s, may have been influenced by neoliberal policies. Integrated pigment and geochemical analysis offers a more precise understanding of eutrophication changes and their ties to human factors. Such research can aid environmental monitoring and sustainable policy development.

Assessing the trophic status of a tropical microtidal estuary applying TRIX and Random Forest - A combined approach.

The present study assessed the trophic status of a medium-sized microtidal estuary, Rushikulya, India using a combination of mutimetric trophic indices (TRIX, TRBIX) and a machine learning approach (Random Forest). A total of 108 samples were considered to build a predictive model for chlorophyll a (Chl a) and 17 response water variables by observing two annual periods (2021-2023) at six sampling sites. Mean values of TRIX (5.04 ± 0.72) and TRBIX (0.17 ± 0.08) reflected that the estuary has a moderate degree of eutrophication with 'good' water quality and 'biomass saturated'. However, the threshold of TRIX represents a transition state from 'moderate' to 'high' eutrophic. Random Forest model reflected that no apparent association between Chl a and water turbidity above 30 NTU and eutrophication in the estuary fluctuated mainly due to PO43--P along with electrical conductivity. Linear statistical correlations showed high correlation between Chl a and conductivity and a negative correlation between Chl a and dissolved oxygen, unlike PO43--P.

Non-traditional abiotic drivers explain variability of chlorophyll-a in a shallow estuarine embayment.

Understanding the factors influencing eutrophication, as represented by concentrations of chlorophyll-a (Chl-a), is needed to inform effective management and conservation strategies promoting ecological resilience. The objective of this study was to evaluate a unique combination of abiotic explanatory factors to describe Chl-a concentrations within the study estuary (North Biscayne Bay, Florida, USA). Multiple linear regression determined the strength and direction of influence of factors using data from 10 water quality monitoring stations. The analysis also considered time scales for evaluating cumulative effects of freshwater inflow and wind. Results show that dominant drivers of Chl-a were temperature, freshwater volume (whose cumulative effects were evaluated up to a 60-day time scale), and turbidity, which were statistically significant at 60, 60, and 70 % of the investigated stations, respectively. All drivers collectively accounted for 22 to 63 % of the variability of Chl-a measurements. Of the nine variables evaluated, nutrient concentrations (orthophosphate and ammonia) were not among the top three overall drivers. Despite nutrients historically being cited in the literature as the most significant factor, this study asserts that non-nutrient factors often govern Chl-a levels, necessitating a paradigm shift in management strategies to bolster estuarine resilience against climate change.

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.

Selection of tropical trees and shrubs for urban greening in coal mine complex: a case study of Singrauli, Madhya Pradesh.

An experimental investigation was conducted to determine the effectiveness of roadside trees for removing dust and the effects of dust load on the physiology and micromorphology of the foliage. The present study was conducted near an open coal mining complex situated in Singrauli, Madhya Pradesh, India, to assess the air pollution tolerance index (APTI), anticipated performance index (API), dust capturing capacity (DCC), and leaf morphology of trees and shrubs growing around the coal fields. Results showed that Azadirachta indica, Mangifera indica, Ficus religiosa, Ailanthus excelsa, and Ficus benghalensis were the most tolerant species towards air pollution (high APTI scores), while plants like Calotropis gigantea, Lantana camara, and Tectona grandis were proven to be bio-indicator species. Butea monosperma, Ficus benghalensis, Alstonia scholaris, and Terminalia arjuna were plant species with the highest DCC. Two-way ANOVA showed significant differences site-wise and season-wise in the biochemical parameters of APTI and a considerable difference site-wise with respect to dust capturing capacity. Correlation and regression analyses revealed a very high positive correlation between APTI and ascorbic acid value. The study recommends suitable plant species to manage rising air pollution in the coal mine and nearby areas apart from suggesting the development of a green belt.

Characterization of phytoplankton functional types in the western Bay of Bengal: HPLC- and CHEMTAX-based approach.

This study investigates phytoplankton functional group variations in the western Bay of Bengal (WBoB) during the Spring Intermonsoon. Samples were collected from four cross-shore transects: Mahanadi (MN), Vamsadhara (VD), Godavari (GD), and Krishna (KS). East India Coastal Current and warm gyre influenced the southern transects (KS, GD), VD was experiencing moderate upwelling and MN was characterized by low salinity and oligotrophic conditions due to freshwater input. In response to hydrography, phytoplankton biomass and functional types differed within and between the transects. Chlorophyll-a (Chl-a) was spatially high in VD and low in MN. The subsurface Chlorophyll-a maxima (SSCM) was prominent and shallow in the MN and VD, compared to the southern transects. Total diagnostic pigments concentration was high in VD, followed by GD, KS and MN. Phytoplankton functional groups and each groups contribution to Chl-a was calculated through CHEmical Taxonomy (CHEMTAX). Diatoms and cyanophytes were the dominant functional types in the surface layers. Progressive shift from diatoms in the nearshore region to cyanophytes in the offshore was observed. The low saline and low-nutrient conditions were conducible for the growth of cyanophytes, while nutrient-rich optimum light layer of SSCM and upper layer of VD were favorable for diatoms. Cryptophytes contribution to Chl-a was higher in southern transects compared to the north. Prymnesiophytes and prasinophytes were high in the subsurface and deep layers could be due to their adaptions to light and nutrients. The present study highlights the significance of physical processes associated hydrography in structuring the phytoplankton functional types.

Implementation of remote sensing algorithms to estimate TOC, Chl-a, and TDS in a tropical water body; Sanalona reservoir, Sinaloa, Mexico.

The present study implements a methodology to estimate water quality values using statistical tools and remote sensing techniques in a tropical water body Sanalona. Linear regression models developed by Box-Cox transformations and processed data from LANDSAT-8 imagery (bands) were used to estimate TOC, TDS, and Chl-a of the Sanalona reservoir from 2013 to 2020 at five sampling sites measured every 6 months. A band discriminant analysis was carried out to statistically fit and optimize the proposed algorithms. Coefficients of determination beyond 0.9 were obtained for these water quality parameters (r2TOC = 0.90, r2TDS = 0.95, and r2Chl-a = 0.96). A comparison between the estimated and observed water quality was carried out using different data for validation. The validation of the models showed favorable results with R2TOC = 0.8525, R2TDS = 0.8172, and R2Chl-a = 0.9256. The present study implemented, validated, and compared the results obtained by using an ordered and standardized methodology proposed for the estimation of TOC, TDS, and Chl-a values based on water quality parameters measured in the field and using satellite images.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India.

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

Significance of moss pretreatments in active biomonitoring surveys.

The present study examines the impact of pretreatment procedures on the metal concentrations in bags that are to be exposed. We examine Mn, Fe, Cu, Zn, Cd, and Pb amounts in Sphagnum fallax and Dicranum polysetum mosses using atomic absorption spectrometry. The concentration of Hg was also determined using a mercury analyzer. Two sample preparation ways were tested (with and without rinsing) and their influence was evaluated by determining the coefficient of variation (CV). Chlorophyll content was also determined in mosses collected from three habitats (deep woodland, forest road, and wood lot). The results indicate, that the concentration of elements deposited in mosses depends on the species and the habitat where they were collected (ANOVA, p < 0.001). Rinsing of mosses reduces the CV for Mn, Fe, Cu, and Zn and uniform the material prior to exposure (CV for the majority of metals <10%). Selected correlations were found for element concentrations with chlorophyll content. Photosynthetic activity of mosses decreased by about 80% during their one-month storage in the laboratory. Due to the varying concentration of metals in the collected samples, proper, and standardized preparation of mosses before exposure, they can be effectively used in active biomonitoring.

Compared to other biomonitoring work the novel approach is the simultaneous study of two moss species, the analysis of three different habitats and the tie-in of accumulated trace elements by mosses and their vitality by measuring chlorophyll content and photosynthetic activity.