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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

The influence mechanism of water level operation on algal blooms in canyon reservoirs and bloom prevention.

The water level operation of reservoirs affects the spatiotemporal patterns of water quality, light-heat, hydrodynamics and phytoplankton, which have implications for algal bloom prevention. However, the theoretical analysis and practical applications of related research are limited. Based on prototype observations and numerical modeling, data on algae, water level operation and environmental factors in the Zipingpu Reservoir from April and September in 2015 to 2017 and 2020 to 2022 were collected. An in-depth analysis of the causal mechanisms between algal blooms and water level operation was performed, and prevention strategies with practical application assessments were developed. Water level operation control in the reservoir from April to September can be divided into five stages (falling-rising-oscillating-falling-rising), with algal blooms occurring only in the second stage. The rising water level with inflow into the middle layers shapes a closed-loop circulation in the surface waters. This distributes the nutrients that were trapped in the surface layer during the first stage, helping algae avoid to phosphorus limitation and thrive in the closed loop circulation, leading to algal blooms (chlorophyll-a exceeding 10 mg/m3). There is a significant positive correlation (p < 0.05) between algal blooms and the rapid rise in water levels in the second stage, occurring within a span of three days. To contain the algal bloom, a water level operation limit of rising waters on the third day after a two-day consecutive rise in water level was examined. This was found to be effective after its practical application to the case reservoir in 2022, with chlorophyll-a concentrations consistently below 10 mg/m3. This study unveils the mechanisms through which water level operation affects algal blooms and presents a successful case of bloom prevention. Furthermore, it serves as a valuable reference for the management of canyon reservoirs.

Ecological changes in subtidal macrobenthic communities of the Taean coast following the Hebei Spirit oil spill: A 10-year longitudinal study.

We examined long-term response (2008-2017) of the macrobenthos to the Hebei Spirit oil spill that occurred around the Taean coast, Korea, in December 2007. Oil concentrations were below the Korea/US environmental standards as of January 2008. Organic matter, chlorophyll-a, and zooplankton abundance dominated by Noctiluca scintillans were higher after the spill. Macrobenthic diversity recovered to pre-incident (2007) level in 2011. Biomass exceeded that level in 2011 and the increase prolonged for 5 years. Cross-correlation and regression analyses showed that chlorophyll-a at year t and zooplankton abundance at t-2 had a significant relationship with macrobenthic biomass at t (p < 0.05 for both), suggesting the transfer of increased organic matter (transformed from crude oil within the pelagic ecosystem) into the benthic ecosystem. Coastal wetlands around the incident area, vulnerable to oil pollution and slowly remobilizing accumulated oil, seemed to affect pelagic ecosystem processes and the unexpectedly increased and sustained biomass.

Water quality seasonal variation assessment of the Gongji and Yaksa streams, Chuncheon, South Korea.

Gongji Stream flows into Lake Uiam, a potable water source for the capital region of Chuncheon, South Korea. Algal blooms often occur downstream of the Gongji stream in combination with drastic flow rate variations. Downstream water quality may also be affected by Yaksa stream. Yaksa stream joins Gongji stream before it reaches Uiam Lake, which is a drinking water source for the city. Limited data exists on the Yaksa stream water quality. Therefore, water quality parameters (pH, electrical conductivity (EC), biological oxygen demand (BOD), total nitrogen (T-N), total phosphorous (T-P), chlorophyll-a (Chl-a), total coliforms, and Escherichia coli (E. coli) concentration) were sampled from Gongji (at sites GJ1 and GJ2) and Yaksa (at sites YS1 and YS2) streams from May to September, 2022. The results revealed the overall water quality of both streams was good (BOD = 0.27-3.66 mg/L; TP = 0.003-0.074 mg/L), except on August 3. On August 3, the concentrations of BOD, TP, total coliforms, and E. coli were elevated, with the highest concentrations in samples from GJ2. The recent heavy rainfall potentially caused sewage inflows near GJ2. The correlation analysis revealed positive linear relationships in the 1-day cumulative precipitation with BOD (r = 0.503), total coliforms (r = 0.547), and TP (r = 0.814). The Yaksa stream may be an Anabaena sp. source, which contaminated samples from YS1, YS2, and GJ2, but not at GJ1 (upstream of the tributary).

Improved eutrophication model with flow velocity-influence function and application for algal bloom control in a reservoir in East China.

Improving hydrodynamic conditions is considered an effective method for facilitating the eutrophication management. However, the effect of hydrodynamic conditions on algal growth has rarely been quantified. In this work, a eutrophication model was developed and flow velocity was introduced into the algae growth kinetic formula to simulate the dynamics of algae growth in a drinking water source reservoir in East China. Based on the previous research and model calibration, the flow velocity-influence function f(v) and its parameters were determined. Accordingly, the optimal flow velocity for the dominant algae growth and critical flow velocity for algal growth inhibition were presented to be 0.055 m/s and 0.200 m/s for the study reservoir. Modeled results considering f(v) agreed with better with observations and reproduced the algal overgrowth process more accurately. The spatial-temporal differences in chlorophyll a (Chl a) concentration distribution during the algal proliferation period were analyzed on the basis of simulation results, which corroborated the significant influence of flow velocity on algal growth. The established model was applied to investigate the effect of improvement in hydrodynamic conditions on algal bloom control in the reservoir, and the scenario simulation of the additional sluice was conducted. Results showed that the additional sluice operation inhibited algal overgrowth effectively, resulting in an average decrease of 24.8%, 3.3%, 43.0%, and 37.5% in modeled Chl a concentration upstream north, upstream south, midstream and downstream, respectively. The established model might serve as a practical tool for eutrophication management in the study reservoir and other water bodies with similar hydrological characteristics and geographical features.

Chemodynamics of Polycyclic Aromatic Hydrocarbons and Their Alkylated and Nitrated Derivatives in the Yellow Sea and East China Sea.

The occurrence of continuously released polycyclic aromatic hydrocarbons (PAHs) in marginal seas is regulated by hydrological and biogeochemical processes; however, scarce knowledge is about their derivatives in marine environments. In this study, the dissolved and particulate PAHs and their alkylated/nitrated derivatives (A-PAHs/N-PAHs) in surface seawater of the southwestern Yellow Sea (YS) and northwestern East China Sea (ECS) during September 2022 were comprehensively discussed. Results confirm higher levels of Σ26PAHs (9.3-70 ng/L) and Σ43A-PAHs (13-76 ng/L) than Σ20N-PAHs (0.80-6.6 ng/L). The spatial heterogeneity of contaminants was regulated by substantial riverine runoff and ocean currents. Lagrangian Coherent Structure analysis further revealed the existence of a transport barrier at the shelf break of the southwestern YS where contaminants hardly crossed and tended to accumulate. The relationship between dissolved compounds and chlorophyll a indicated both biodegradation and the biological pump contributed to the depletion of PAHs and A-PAHs from surface seawater while the biological pump was the major driver for N-PAHs, despite their complicated water-particle partition behavior due to variations in physicochemical properties in the presence of nitro groups. Source identification demonstrated that pyrogenic and petrogenic sources dominated the YS and ECS, respectively, while photochemical transformations appeared more active in the YS.

Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data.

Monitoring the concentration of pigments like chlorophyll (Chl) in water-bodies is a key task to contribute to their conservation. However, with the existing sensor technology, measurement in real-time and with enough frequency to ensure proper risk management is not completely feasible. In this work, with the concept of data-driven soft-sensing, three hydrophysical features are used together with three meteorological ones to estimate the concentration of Chl in two tributaries of the River Thames. Data driven models, specifically neural networks, are used with three learning approaches: individual, centralized and federated. Data reduction scenarios are proposed in order to analyze the performance of each approach when less data is available. The best results in the training are usually obtained with the individual approach. However, the federated learning provides better generalization ability. It was also observed that in most of the cases the results of the federated learning approach improve those of the centralized one.