Clustering evaluation of water quality for various classes of in-flow rivers in connected brackish lakes.

Brackish waters and estuaries at the lower reaches of rivers accumulate organic matter and nutrients from various sources in the watershed. Sufficient light and shallow water depth stimulate phytoplankton growth, resulting in a more diversified ecosystem with higher trophic levels. For effective watershed management, it is crucial to characterize the water quality of all rivers, including small and medium-sized ones. Our field survey assessed water quality parameters in 26 inflow rivers surrounding Lakes Shinji and Nakaumi, two consolidated brackish lakes in Japan. The parameters included water temperature, salinity, chlorophyll-a, and nutrients. The study used hierarchical clustering. The Silhouette Index was used to assess clustering outcomes and identify any difficulties in dispersion across clusters. The 26 rivers surrounding Lakes Shinji and Nakaumi were classified into six groups based on their water quality characteristics. This classification distinguishes itself from earlier subjective methods that relied on geographical factors. The new approach identifies a need for improved management of river water quality. The results of the cluster analysis provide valuable insights for future management initiatives. It is important to consider these findings alongside established watershed criteria.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Atlas of phytoplankton phenology indices in selected Eastern Mediterranean marine ecosystems.

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series.

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Both external and internal factors induce heterogeneity in senescing leaves of deciduous trees.

Autumn senescence is characterised by spatial and temporal heterogeneity. We show that senescing birch (Betula spp.) leaves had lower PSII activity (probed by the F V /F M chlorophyll a fluorescence parameter) in late autumn than in early autumn. We confirmed that PSII repair slows down with decreasing temperature, while rates of photodamage and recovery, measured under laboratory conditions at 20°C, were similar in these leaves. We propose that low temperatures during late autumn hinder repair and lead to accumulation of non-functional PSII units in senescing leaves. Fluorescence imaging of birch revealed that chlorophyll preferentially disappeared from inter-veinal leaf areas. These areas showed no recovery capacity and low non-photochemical quenching while green veinal areas of senescing leaves resembled green leaves. However, green and yellow leaf areas showed similar values of photochemical quenching. Analyses of thylakoids isolated from maple (Acer platanoides ) leaves showed that red, senescing leaves contained high amounts of carotenoids and α-tocopherol, and our calculations suggest that α-tocopherol was synthesised during autumn. Thylakoids isolated from red maple leaves produced little singlet oxygen, probably due to the high antioxidant content. However, the rate of PSII photodamage did not decrease. The data show that the heterogeneity of senescing leaves must be taken into account to fully understand autumn senescence.

Shining Light into a "Black Box": Essential Rationale Underlying Multiphase Flash Methodology.

The world we live in is very fragile. Sustainable food production is increasingly under intense pressure due to changing environmental conditions on many levels. Understanding the complexities of how to optimize food production under increasingly deleterious environmental conditions is dependent upon accurate and detailed analyses of plant productivity from the molecular-to-the-remote scales. One method that can link many of these scales has been around for decades, namely, pulse amplitude modulation (PAM) chlorophyll a fluorescence. This technique is used to measure an assortment of important parameters based on chlorophyll a fluorescence. One of the parameters measured by this method is termed the steady state maximum fluorescence yield ( Φ Fm ' ). This parameter, while extremely informative when used to quantify an assortment of processes of intense scientific interest, is nonetheless subject to intrinsic underestimation. A clever approach has evolved over several decades to more accurately estimate Φ Fm ' . The underlying rationale of the methodology requires a thorough and nuanced explanation, which is lacking in the literature. Herein, we systematically develop the essential rationale for accurately measuring Φ Fm ' based on the latest evolution of this approach, called multiphase flash (MPF) methodology.

Temporal drivers of tryptophan-like fluorescent dissolved organic matter along a river continuum.

Tryptophan-like fluorescence (TLF) is used to indicate anthropogenic inputs of dissolved organic matter (DOM), typically from wastewater, in rivers. We hypothesised that other sources of DOM, such as groundwater and planktonic microbial biomass can also be important drivers of riverine TLF dynamics. We sampled 19 contrasting sites of the River Thames, UK, and its tributaries. Multivariate mixed linear models were developed for each site using 15 months of weekly water quality observations and with predictor variables selected according to the statistical significance of their linear relationship with TLF following a stepwise procedure. The variables considered for inclusion in the models were potassium (wastewater indicator), nitrate (groundwater indicator), chlorophyll-a (phytoplankton biomass), and Total bacterial Cells Counts (TCC) by flow cytometry. The wastewater indicator was included in the model of TLF at 89 % of sites. Groundwater was included in 53 % of models, particularly those with higher baseflow indices (0.50-0.86). At these sites, groundwater acted as a negative control on TLF, diluting other potential sources. Additionally, TCC was included positively in the models of six (32 %) sites. The models on the Thames itself using TCC were more rural sites with lower sewage inputs. Phytoplankton biomass (Chlorophyll-a) was only used in two (11 %) site models, despite the seasonal phytoplankton blooms. It is also notable that, the wastewater indicator did not always have the strongest evidence for inclusion in the models. For example, there was stronger evidence for the inclusion of groundwater and TCC than wastewater in 32 % and 5 % of catchments, respectively. Our study underscores the complex interplay of wastewater, groundwater, and planktonic microbes, driving riverine TLF dynamics, with their influence determined by site characteristics.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

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.

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.

Occurrence, source apportionment, and ecological risk of legacy and emerging per- and poly-fluoroalkyl substances (PFASs) in the Dahei river basin of a typical arid region in China.

Per- and poly-fluoroalkyl substances (PFASs) are artificial chemicals with broad commercial and industrial applications. Many studies about PFASs have been conducted in densely industrial and populated regions. However, fewer studies have focused on the PFASs' status in a typical arid region. Here, we investigated 30 legacy and emerging PFASs in surface water from the mainstream and tributaries of the Dahei River. Our results revealed that total PFASs concentrations (∑30PFASs) in water ranged from 3.13 to 289.1 ng/L (mean: 25.40 ng/L). Perfluorooctanoic acid (PFOA) had the highest mean concentration of 2.44 ng/L with a 100% detection frequency (DF), followed by perfluorohexanoic acid (PFHxA) (mean concentration: 1.34 ng/L, DF: 59.26%). Also, perfluorohexane sulfonate (DF: 44.44%), perfluorobutane sulfonate (DF: 88.89%), and perfluorooctane sulfonate (PFOS) (DF: 92.59%) had mean concentrations of 12.94, 2.00, and 1.05 ng/L, respectively. Source apportionment through ratio analysis and principal component analysis-multiple linear regression analysis showed that treated or untreated sewage, aqueous film-forming foam, degradation of precursors, and fluoropolymer production were the primary sources. The PFOS alternatives were more prevalent than those of PFOA. Conductivity, total phosphorus, and chlorophyll a positively correlated with Σ30PFASs and total perfluoroalkane sulfonates concentrations. Furthermore, ecological risk assessment showed that more attention should be paid to perfluorooctadecanoic acid, perfluorohexadecanoic acid, perfluorooctane sulfonate, perfluorohexane sulfonate, and (6:2 and 6:2/8:2) polyfluoroalkyl phosphate mono- and di-esters. The mass load of PFASs to the Yellow River was 1.28 kg/year due to the low annual runoff in the Dahei River in the arid region. This study provides baseline data for PFASs in the Dahei River that can aid in the development of effective management strategies for controlling PFASs pollution in typical arid regions in China.

Anoxia begets anoxia: A positive feedback to the deoxygenation of temperate lakes.

Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep-water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (i.e., very low DO) during a given summer begets increasingly severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth, and subsequent phytoplankton decomposition can fuel heterotrophic respiration, resulting in increased spatial extent and duration of anoxia. However, while the individual relationships in this feedback are well established, to our knowledge, there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the mechanisms necessary to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed anoxia begets anoxia feedback. Lakes in the dataset span a broad range of surface area (1-126,909 ha), maximum depth (6-370 m), and morphometry, with a median time-series duration of 30 years at each lake. Using linear mixed models, we found support for each of the positive feedback relationships between anoxia, phosphorus concentrations, chlorophyll a concentrations, and oxygen demand across the 656-lake dataset. Likewise, we found further support for these relationships by analyzing time-series data from individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake-specific characteristics: For example, we found that surface phosphorus concentrations were more positively associated with chlorophyll a in high-phosphorus lakes, and oxygen demand had a stronger influence on the extent of anoxia in deep lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.

A method based on improved ant colony algorithm feature selection combined with GWO-SVR model for predicting chlorophyll-a concentration in Wuliangsu Lake.

Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional methods. In this paper, using Sentinel-2 remote sensing images as the data source combined with measured data, taking Wuliangsu Lake as the study area, a new intelligent algorithm is proposed for prediction of Chl-a concentration, which uses the adaptive ant colony exhaustive optimization algorithm (A-ACEO) for feature selection and the gray wolf optimization algorithm (GWO) to optimize support vector regression (SVR) to achieve Chl-a concentration prediction. The ant colony optimization algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GWO-SVR model is built by optimizing SVR using GWO with the selected feature bands as input and comparing it with the traditional SVR model. The results show that the usage of feature bands selected by the presented A-ACEO algorithm as inputs can effectively reduce complexity and improve the prediction performance of the model, under the condition of the same model, which can provide valuable references for monitoring the Chl-a concentration in Wuliangsu Lake.

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.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine.

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

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.

Water extract of Fagopyrum dibotrys (D. Don) Hara straw increases selenium accumulation in peach seedlings under selenium-contaminated soil.

To promote the selenium (Se) uptakes in fruit trees under Se-contaminated soil, the effects of water extract of Fagopyrum dibotrys (D. Don) Hara straw on the Se accumulation in peach seedlings under selenium-contaminated soil were studied. The results showed that the root biomass, chlorophyll content, activities of antioxidant enzymes, and soluble protein content of peach seedlings were increased by the F. dibotrys straw extract. The different forms of Se (total Se, inorganic Se, and organic Se) were also increased in peach seedlings following treatment with the F. dibotrys straw extract. The highest total shoot Se content was treated by the 300-fold dilution of F. dibotrys straw, which was 30.87% higher than the control. The F. dibotrys straw extract also increased the activities of adenosine triphosphate sulfurase (ATPS), and adenosine 5'-phosphosulfate reductase (APR) in peach seedlings, but decreased the activity of serine acetyltransferase (SAT). Additionally, correlation and grey relational analyses revealed that chlorophyll a content, APR activity, and root biomass were closely associated with the total shoot Se content. Overall, this study shows that the water extract of F. dibotrys straw can promote Se uptake in peach seedlings, and 300-fold dilution is the most suitable concentration.

The water extract of Fagopyrum dibotrys (D. Don) Hara straw promoted the selenium (Se) uptake in peach seedlings under selenium-contaminated soil. The concentration of F. dibotrys straw extract showed a quadratic polynomial regression relationship with the total root and shoot Se. Furthermore, chlorophyll a content, APR activity, and root biomass were closely associated with the total shoot Se. This study shows that water extract of F. dibotrys straw can promote Se uptake in peach seedlings, and 300-fold dilution is the most suitable concentration.

Metal accumulation and genetic adaptation of Oryza sativa to Cadmiun and Chromium heavy metal stress: A hydroponic and RAPD analyses.

This research was performed to assess the influence of Cd and Cr metals on growth, pigments, antioxidant, and genomic stability of Oryza sativa indica and Oryza sativa japonica were investigated under hydroponic conditions. The results revealed that significant metal influence on test crop growth, pigment content, metal stress balancing antioxidant activity in a dose dependent manner. Since, while at elevated (500 ppm) concentration of Cd as well as Cr metals the pigment (total chlorophyll, chlorophyll a, b and carotenoids) level was reduced than control; however antioxidant activity (total antioxidant, H2O2, and NO) was considerably improved as protective mechanisms to combat the metal toxicity and support the plant growth. Furthermore, the test crops under typical hydroponic medium (loaded with Cd and Cr as 200, 300, 400, and 500 ppm) growth conditions, effectively absorb the metals from medium and accumulated in the root and least quantity was translocated to the shoot of this test crops. Furthermore, typical RAPD analysis with 10 universal primers demonstrated that the genomic DNA of the test crops was adaptable to develop metal resistance and ensure crop growth under increased concentrations (500 ppm) of tested heavy metals. These findings suggest that these edible crops have the ability to accumulate Cd along with Cr metals, and additionally that their genetic systems have the ability to adapt to metal-stressed environments.

Dynamics and geochemical responses of dissolved metals (Mn and Cu) in a subtropical estuary, China.

The research delved into the occurrence and dynamics of dissolved metals, specifically manganese (Mn) and copper (Cu), within the Jiulong River Estuary, South China, a medium-sized subtropical estuary. Our findings unveiled a nuanced seasonal and spatial variability of dissolved metals throughout the entire estuarine system. Notably, dissolved Mn concentrations peaked (~ 3.5 µM) in the upper estuary, diminishing sharply along the salinity gradient, with a modest rise in the middle estuary and outer Xiamen Bay. In the upper estuary, heightened concentrations of dissolved Mn occurred in spring due to augmented terrestrial particle inputs, followed by suboxically reductive releases; conversely, concentrations were low in summer, attributed to dilution from increased freshwater discharges and particle scavenging. In contrast, dissolved Cu exhibited differently, with elevated concentrations (29.2-37.5 nM) in the upper and middle estuaries, driven by reductive dissolution of Mn particles and chloride-induced ion exchanges, respectively. Concurrently, heightened inputs of nutrients and metals correlated with elevated phytoplankton productivity (indicated by chlorophyll a) in the upper and outer estuary regions. Our analysis underscored the sensitivity of dissolved metals to environmental parameters, including temperature, pH, and dissolved oxygen. The integration of compiled historical data underscored the dynamic nature of dissolved metals, particularly Cu, in response to geochemical processes.The elevated ion levels indicated intensified ion releases from particles and sediments, attributable to increased anthropogenic perturbation and climatic changes (e. g. ocean warming).