Direct elemental analysis of plant oils by inductively coupled plasma mass spectrometry: Simple sample dilution combined with oxygen introduction into the plasma.

Assessment of trace metal concentrations in plant oils has been considered a crucial quality control marker for potential health risks, oil flavour, and oxidative stability. A straightforward inductively coupled plasma mass spectrometry (ICP-MS) methodology was developed and validated through introduction of argon:oxygen gas mixture into plasma, allowing for a direct elemental analysis of organic matrices. This approach offers the advantage of a simple one-step preparation of plant oil samples with negligible contamination risks. The complete solubilization of the oil matrix enables the determination of total metal content from a single test tube with low dilution factor of 5. The modified plasma conditions resulted in the development of a robust and accurate ICP-MS method providing limits of detection at sub ng·g-1 levels. The ICP-MS method allowed the determination of trace levels of Ba, Cd, Cu, Fe, Mn, Pb, Sn, V, and Zn in olive, sunflower and rapeseed oils.

Estuarine hydrodynamic processes driving the molecular changes of terrestrial dissolved organic nitrogen: From mixing to biological modification.

Estuaries receive substantial amounts of terrestrial dissolved organic nitrogen (tDON), which will be transported from the freshwater to the oceanic terminus through vigorous exchange processes. However, the intricate migration and transformation dynamics of tDON during this transportation, particularly at a molecular level, remain constrained. To address this knowledge gap, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used for the analysis of DON molecular composition in the Pearl River Estuary (PRE), a river-dominated estuarine system influenced by intensified anthropogenic activities in southern China. The results showed a pronounced spatial-temporal variation in DON concentration in the study area. At the molecular level, tDON exhibited reduced unsaturation and aromaticity, coupled with an elevated abundance of DON compounds containing one­nitrogen atom (1 N-DON, 53.17 %) and compounds containing carbon, hydrogen, oxygen, nitrogen, and sulfur (CHONS) (27.46 %). It was evident that lignin was depleted while more oxygenated tannin compounds were generated in the freshwater-seawater mixing zone. This transformation is attributed to heightened biological activities, likely influenced by the priming effect of terrestrial nutrient inputs. In summer, the prevailing plume combined with biological activities in the strong mixing area and outer estuary increased the abundance of 3 N-DON molecules and a concurrent rise in the abundance of DON compounds containing only carbon, hydrogen, oxygen, and nitrogen (CHON), DON compounds containing carbon, hydrogen, oxygen, nitrogen, sulfur, and phosphorus (CHONSP), and CHONS. This trend also underscores the expanding role of marine plankton and microbes in the utilization of DON compounds containing carbon, hydrogen, oxygen, nitrogen, and phosphorus (CHONP). These findings provide details of tDON transformation processes at the molecular level in a river-dominated estuary and underline the estuarine hydrodynamics involved in transporting and altering DON within the estuary.

Characteristics of microbial community structure and influencing factors of Yangcheng Lake and rivers entering Yangcheng Lake during the wet season.

Yangcheng Lake, a typical fishery lake in the middle and lower reaches of the Yangtze River, is threatened by eutrophication. As the main performers of biogeochemical cycles, microorganisms affect the ecological stability of the lake. To study the structural characteristics of the microbial community in Yangcheng Lake and rivers entering Yangcheng Lake and the response relationship with environmental factors, the microbial community was categorized based on the contour of Yangcheng Lake, the major rivers entering Yangcheng Lake, and the pollution sources. The distribution characteristics of seven physicochemical indices were analyzed, including total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), water temperature (WT), pH, dissolved oxygen (DO), and ratio of total nitrogen to total phosphorus (TN/TP). Characterization of microbial community structure based on 16S rRNA high-flux sequencing technology and ANOSIM analysis were used to explore the differences in the relative abundance of microorganisms at each sampling point in the lake and rivers, and redundancy analysis (RDA) was used to analyze the relationship between the microbial community and physicochemical factors. The results showed that the dominant phyla, genera of microorganisms, and the total number of OTUs in the lake and rivers were similar. The dominant phyla included Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia; the dominant genera included the hgcI clade, CL500-29 marine group, Microcystis PCC-7914, Chloroplast_norank, Clade III_norank, and Flavobacterium. ANOSIM analyses revealed that the microbial community of Yangcheng Lake exhibited an association with geographical space, while the microbial community in the rivers that was linked to the type of pollution source. Redundancy analysis (RDA) indicated that dissolved oxygen (DO), total nitrogen (TN), and pH were significantly correlated with the dominant phyla in Yangcheng Lake (p < 0.05), while total nitrogen (TN), water temperature(WT), and the ratio of total nitrogen to total phosphorus (TN/TP) were significantly related with the dominant genera in Yangcheng Lake (p < 0.05). Total nitrogen (TN) was also significantly linked to the dominant phyla and genera of the tributaries (p < 0.05). Despite the structural similarities in microbial communities between Yangcheng Lake and its inflowing rivers, environmental factors demonstrated significant associations with these communities, providing crucial data support for pollution prevention and the ecological restoration of Yangcheng Lake.

Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications.

The Pearl River (PR) is China's second-largest river, playing a crucial role in regulating and supplying water in the southeast. However, for the last decade, the PR has been experiencing water quality deterioration due to population growth, rapid economic development, and diverse human activities, particularly in its delta areas. This study analyzed the characteristics and evolution of eight water quality variables, including pH values (pH), water temperature (WT), dissolved oxygen (DO), five-day biochemical oxygen demand (BOD5), permanganate index (PI), total phosphorus (TP), ammonia nitrogen (NH3N), and fluoride (F-), which were monitored monthly at 16 water quality monitoring stations from January 2009 to August 2019. Overall, annual average BOD5 and F- concentrations met Class I water quality standards, while TP and NH3N conformed to lower standards. The cluster results showed noticeable differences for parameter grouping (DO-organic parameters-nutrient and solutes), seasonal variation (wet and dry), and water quality status (contaminated-remediating-fine). The Water Quality Index (WQI) ranged from 8.3 ("very poor") to 91.7 ("excellent") in the entire basin from 2009 to 2019, and NH3N-DO based WQImins were identified using the All-Subsets Linear Regression method. The fitting results of the Generalized Additive Models displayed that the deviance explained by natural factors ranged from 37.2% to 61.3%, while the socioeconomic explanation exceeded 70%. The WQImin component evolution (2003-2019) of Shenzhen River estuary, the most important part of the PR estuary, agreed with key parameter variations in heterogeneous clusters in the entire basin. Moreover, Shenzhen's water quality remediation applications indicated that reasonable-efficient-powerful efforts and support from governments could accelerate recovery. For the management departments, consistent measures should be strictly enforced, and elaborate regionalized management based on clusters could be attempted to maintain excellent water quality.

Uncovering the Ediacaran phosphorus cycle.

Phosphorus is a limiting nutrient that is thought to control oceanic oxygen levels to a large extent1-3. A possible increase in marine phosphorus concentrations during the Ediacaran Period (about 635-539 million years ago) has been proposed as a driver for increasing oxygen levels4-6. However, little is known about the nature and evolution of phosphorus cycling during this time4. Here we use carbonate-associated phosphate (CAP) from six globally distributed sections to reconstruct oceanic phosphorus concentrations during a large negative carbon-isotope excursion-the Shuram excursion (SE)-which co-occurred with global oceanic oxygenation7-9. Our data suggest pulsed increases in oceanic phosphorus concentrations during the falling and rising limbs of the SE. Using a quantitative biogeochemical model, we propose that this observation could be explained by carbon dioxide and phosphorus release from marine organic-matter oxidation primarily by sulfate, with further phosphorus release from carbon-dioxide-driven weathering on land. Collectively, this may have resulted in elevated organic-pyrite burial and ocean oxygenation. Our CAP data also seem to suggest equivalent oceanic phosphorus concentrations under maximum and minimum extents of ocean anoxia across the SE. This observation may reflect decoupled phosphorus and ocean anoxia cycles, as opposed to their coupled nature in the modern ocean. Our findings point to external stimuli such as sulfate weathering rather than internal oceanic phosphorus-oxygen cycling alone as a possible control on oceanic oxygenation in the Ediacaran. In turn, this may help explain the prolonged rise of atmospheric oxygen levels.

Optimizing heterotrophic nitrification process: The significance of demand-driven aeration and organic matter concentration.

Heterotrophic nitrification and aerobic denitrification (HNAD) sludge were successfully acclimated. The effects of organics and dissolved oxygen (DO) on nitrogen and phosphorus removal by the HNAD sludge were investigated. The nitrogen can be heterotrophically nitrified and denitrified in the sludge at a DO of 6 mg/L. The TOC/N (total organic carbon to nitrogen) ratio of 3 was found to result in removal efficiencies of over 88% for nitrogen and 99% for phosphorus. The use of demand-driven aeration with a TOC/N ratio of 1.7 improved nitrogen and phosphorus removal from 35.68% and 48.17% to 68% and 93%, respectively. The kinetics analysis generated an empirical formula, Ammonia oxidation rate = 0.08917·(TOC·Ammonia)0.329·Biomass0.342. The nitrogen, carbon, glycogen, and poly-ß-hydroxybutyric acid (PHB) metabolism pathways of HNAD sludge were constructed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The findings suggest that heterotrophic nitrification precedes aerobic denitrification, glycogen synthesis, and PHB synthesis.

Proposal for a new customization process for a data-based water quality index using a random forest approach.

As the water quality index (WQI) represents water quality, it is crucial to customize the WQI for a specific purpose. In this study, to better represent water quality data using WQI, a random forest (RF) approach was used to derive the parameter weight and calculate the WQI according to the watershed and its use. Eight parameters (water temperature, dissolved oxygen, pH, electrical conductivity, suspended solids, total nitrogen, total phosphorus, and total organic carbon) were evaluated using a total of 220,103 data points collected from 900 monitoring sites throughout South Korea between 2011 and 2020. The estimation of parameter weights, key elements in developing the WQI model, was performed through the variable importance estimation method that can be derived from the RF model. The parameter weights were derived based on various spatiotemporal datasets, and it was confirmed that the spatiotemporal differences in weights according to data characteristics represented the regional and seasonal water quality characteristics. Consequently, a customized WQI representing water quality characteristics could be calculated using data-based weights, and it is expected that a data-based customized WQI could be developed to better match the previous WQI to the purpose and target source.

Assessing chlorophyll-a and water quality dynamics in arid-zone temporary pan systems along a disturbance gradient.

Temporary pans are susceptible to various anthropogenic effects such as pollution, resource extraction, and land use intensification. However, given their small endorheic nature, they are almost entirely influenced by activities close to their internally drained catchments. Human-mediated nutrient enrichment within the pans can lead to eutrophication, resulting in increased primary productivity and decreased associated alpha diversity. The Khakhea-Bray Transboundary Aquifer region and the pan systems that characterise the area are understudied area with no records available of the biodiversity therein. Additionally, the pans are a major water source for the people in these areas. This study assessed differences in nutrients (i.e., ammonium, phosphates) and their effect on chlorophyll-a (chl-a) concentrations in pans along a disturbance gradient in the Khakhea-Bray Transboundary Aquifer region, South Africa. Physicochemical variables, nutrients, and chl-a were measured from 33 pans representing variable anthropogenic exposure during the cool-dry season in May 2022. Five environmental variables (i.e., temperature, pH, dissolved oxygen, ammonium, and phosphates) showed significant differences between the undisturbed and disturbed pans. The disturbed pans generally had elevated pH, ammonium, phosphates and dissolved oxygen compared to the undisturbed pans. A strong positive relationship was observed between chl-a and temperature, pH, dissolved oxygen, phosphates and ammonium. Chlorophyll-a concentration increased as surface area, and the distance from kraals, buildings and latrines decreased. Anthropogenic activities were found to have an overall effect on the pan water quality within the Khakhea-Bray Transboundary Aquifer region. Therefore, continuous monitoring strategies should be established to better understand the nutrient dynamics through time and the effect that this may have on productivity and diversity in these small endorheic systems.

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants.

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers - a classical PI and a non-integer (fractional)-order PI - with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

Auto-floating oxygenic microalgal-bacterial granular sludge.

As an emerging green wastewater treatment technology, the microalgal-bacterial granular sludge (MBGS) process has attracted increasing interest under the current situation of global climate change. However, little information is available for its performance in treating municipal wastewater under outdoor conditions. Thus, this study evaluated the behaviors of MBGS for treating simulated and real municipal wastewater under natural diel conditions. The results showed that a significant accumulation of oxygen bubbles during daily operation led to the auto-floating of the conventional settable MBGS. The removal of organics was relatively stable during day-night cycles, while the removals of total nitrogen and total phosphorus were dependent on the saturated oxygen concentration over 10 mg/L in MBGS system. Furthermore, oxygen bubbles generated by photosynthesis of microalgae (Scenedesmaceae and Cyanobacteria) due to microalgae phototaxis were found to be attached onto the surface of granules, causing the auto-flotation of MBGS. The formation process of the auto-floating oxygenic MBGS was clarified and further analysis suggested that the non-aerated settable MBGS would be able to auto-float at an average outdoor light intensity of 140 µ mol/m2/s. Overall, the auto-floating oxygenic MBGS process was demonstrated to be feasible for real municipal wastewater treatment, even under rainy and cloudy days, advancing the knowledge and adding theoretical basis for its further applications.

Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China.

As the most water shortage and water polluted area in China, the water quality prediction is of utmost needed and important in Haihe River Basin for its water resource management. The long short-term memory (LSTM) has been a widely used tool for water quality forecast in recent years. The performance and adaptability of LSTM for water quality prediction of different indicators needs to be discussed before it adopted in a specific basin. However, literature contains very few studies on the comparative analysis of the various prediction accuracy of different water quality indicators and the causes, especially in Haihe River Basin. In this study, LSTM was employed to predict biochemical oxygen demand (BOD), permanganate index (CODMn), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), hydrogen ion concentration (pH), and chemical oxygen demand digested by potassium dichromate (CODCr). According to results under 24 different input conditions, it is demonstrated that LSTMs present better predicting on BOD, CODMn, CODCr, and TP (median Nash-Sutcliffe efficiency reaching 0.766, 0.835, 0.837, and 0.711, respectively) than NH3-N, DO, and pH (median Nash-Sutcliffe efficiency of 0.638, 0.625, and 0.229, respectively). Besides, the performance of LSTM to predict water quality is linearly related to the maximum value of temporal autocorrelation and cross-correlation coefficients of water quality indicators calculated by maximal information coefficient with the coefficients of determination of 0.79 to approximately 0.80. This study would provide new knowledge and support for the practical application and improvement of the LSTM in water quality prediction.

Dominant phytoplankton groups as the major source of polyunsaturated fatty acids for hilsa (Tenualosa ilisha) in the Meghna estuary Bangladesh.

The tropical estuarine ecosystem is fascinating for studying the dynamics of water quality and phytoplankton diversity due to its frequently changing hydrological conditions. Most importantly, phytoplankton is the main supplier of ω3 polyunsaturated fatty acids (PUFA) in the coastal food web for fish as they could not synthesize PUFA. This study evaluated seasonal variations of water quality parameters in the Meghna River estuary (MRE), explored how phytoplankton diversity changes according to hydro-chemical parameters, and identified the major phytoplankton groups as the main source of PUFA for hilsa fish. Ten water quality indicators including temperature, dissolved oxygen, pH, salinity, dissolved inorganic nitrogen (DIN = nitrate, nitrite, ammonia) and phosphorus, dissolved silica and chlorophyll-a were evaluated. In addition, phytoplankton diversity was assessed in the water and hilsa fish gut. Principal component analysis (PCA) was used to analyze the spatio-temporal changes in the water quality conditions, and the driving factors in the MRE. Four main components were extracted and explained 75.4% variability of water quality parameters. The most relevant driving factors were dissolved oxygen, salinity, temperature, and DIN (nitrate, nitrite and ammonia). These variabilities in physicochemical parameters and dissolved inorganic nutrients caused seasonal variations in two major groups of phytoplankton. Peak abundance of Chlorophyta (green algae) occurred in water in nutrient-rich environments (nitrogen and phosphorus) during the wet (36%) season, while Bacillariophyta (diatoms) were dominant during the dry (32%) season that depleted dissolved silica. Thus, the decrease of green algae and the increase of diatoms in the dry season indicated the potential link to seasonal changes of hydro-chemical parameters. The green algae (53.7%) were the dominant phytoplankton group in the hilsa gut content followed by diatoms (22.6%) and both are contributing as the major source of PUFAs for hilsa fish according to the electivity index as they contain the highest amounts of PUFAs (60 and 28% respectively).

Uncertainty evaluation and performance assessment of water quality model for mariculture management.

Ecosystem based water quality models are important tools for prognostic site assessment and evaluation of ecosystem-performance of marine fish farms. We present the development and application of a comprehensive Fish Culture Zone Water Quality Model using continuous bi-weekly field data over a six-year period (2012-2017). The model simulates five interacting subsystems: phytoplankton, phosphorus and nitrogen cycles, and the dissolved oxygen (DO) and particulate organic carbon balance. The application of the model to two fish culture zones in Hong Kong shows the model captures the trends of nutrient and DO variation and the performance in quantitative prediction of algal biomass is challenging. The effect of errors in the specification of primary model inputs are evaluated using dimensionless sensitivity coefficients based on First Order Variance Analysis reveals the relative importance of fish stock (loading), physical size (volume), tidal flushing rate and boundary conditions in the prediction of key water quality variables.

Extraction and characterization of phenolic compounds and their potential antioxidant activities.

For thousands of years, plant has been widely applied in the medical area and is an important part of human diet. A high content of nutrients could be found in all kinds of plants, and the most outstanding group of nutrients that attracts scientists' attention is the high level of phenolic compounds. Due to the relationship between high phenolic compound content and high antioxidant capacity, plant extracts are expected to become a potential treatment for oxidation stress diseases including diabetes and cancer. However, according to the instability of phenolic compounds to light and oxygen, there are certain difficulties in the extraction of such compounds. But after many years of development, the extraction technology of phenolic compounds has been quite stable, and the only problem is how to obtain high-quality extracts with high efficiency. To further enhance the value of plant extracts, concentration and separation methods are often applied, and when detailed analysis is required, characterization methods including HPLC and LC/GC-MS will be applied to evaluate the number and type of phenolic compounds. A series of antioxidant assays are widely performed in numerous studies to test the antioxidant capacity of the plant extracts, which is also an important basis for evaluating value of extracts. This paper intends to provide a view of a variety of methods used in plants' phenolic compound extraction, separation, and characterization. Furthermore, this review presents the advantages and disadvantages of techniques involved in phenolic compound research and provides selected representative bibliographic examples.

Predicting in-stream water quality constituents at the watershed scale using machine learning.

Predicting in-stream water quality is necessary to support the decision-making process of protecting healthy waterbodies and restoring impaired ones. Data-driven modeling is an efficient technique that can be used to support such efforts. Our objective was to determine if in-stream concentrations of contaminants, nutrients-total phosphorus (TP) and total nitrogen (TN) -total suspended solids (TSS), dissolved oxygen (DO), and fecal coliform bacteria (FC) can be predicted satisfactorily using machine learning (ML) algorithms based on publicly available datasets. To achieve this objective, we evaluated four modeling scenarios, differing in terms of the required inputs (i.e., publicly available datasets (e.g., land-use/land cover)), antecedent conditions, and additional in-stream water quality observations (e.g., pH and turbidity). We implemented five ML algorithms-Support Vector Machines, Random Forest (RF), eXtreme Gradient Boost (XGB), ensemble RF-XGB, and Artificial Neural Network (ANN) -and demonstrated our modeling framework in an inland stream-Bullfrog Creek, located near Tampa, Florida. The results showed that, while including additional water quality drivers improved overall model performance for all target constituents, TP, TN, DO, and TSS could still be predicted satisfactorily using only publicly available datasets (Nash-Sutcliffe efficiency [NSE] > 0.75 and percent bias [PBIAS] < 10%), whereas FC could not (NSE < 0.49 and PBIAS >25%). Additionally, antecedent conditions slightly improved predictions and reduced the predictive uncertainty, particularly when paired with other water quality observations (6.9% increase in NSE for FC, and 2.7% for TP, TN, DO, and TSS). Also, comparable model performances of all water quality constituents in wet and dry seasons suggest minimal season-dependence of the predictions (<4% difference in NSE and < 10% difference in PBIAS). Our developed modeling framework is generic and can serve as a complementary tool for monitoring and predicting in-stream water quality constituents.

Nutrient and organic pollutants removal in synthetic wastewater by Pseudomonas aeruginosa and Chryseobacterium sp./biofilter systems.

Nutrient and organic pollution raise serious problems for aquatic ecosystems through the accumulation of organic carbon, the reduction of light penetration, and the loss of submerged aquatic vegetation. The over-enrichment of water with nitrogen and phosphorus leads to an imbalance in nutrient ratios, creating favorable conditions for toxic algal blooms, formation of oxygen-depleted water, etc. Thus, developing new technological solutions to reduce their amount is imperative. The present study investigates the capacity of Pseudomonas aeruginosa and Chryseobacterium sp. bacterial strains to form biofilm on solid support (biofilter), both individually and in tandem, using various analytical techniques. Also, the biofilm/biofilter systems' efficiency in removing nutrients such as nitrate, nitrite, ammonium, and phosphate ions from municipal wastewaters is assessed. The results showed a reduction of nutrient pollution of up to 91%, 98%, 55%, and 71% for nitrite, nitrate, ammonium, and phosphate ions. A reduction of about 78% of COD was also observed. The results were obtained in the absence of an additional aeration process, thus having a great potential for reducing total costs of wastewater treatment and developing ecological systems for wastewater management.

Analysis of the eutrophication in a wetland using a data-driven model.

Eutrophication is a major problem in the international Anzali wetland (northern Iran). The present research initially aimed to determine the trophic state index (TSI) in ten sampling sites in the main parts of the Anzali wetland (western, eastern, central, and Siahkeshim parts). After determining the TSI in the wetland, a data-driven method (classification tree model with a J48 algorithm) was implemented to predict the trophic condition in the wetland based on a set of water quality and physical-structural variables. One hundred twenty samples related to chlorophyll-a (the model's output) and environmental variables (the model's inputs) were measured monthly during 1-year study period (2017-2018). Based on the TSI calculation, the western, Siahkeshim, eastern, and central parts of the wetland are classified as eutrophic, super-eutrophic, hyper-eutrophic, and hyper-eutrophic, respectively. When all environmental variables were introduced to the model (with five-time randomization effort, pruning confidence factor = 0.01, and seven-fold cross-validation), eight variables (bicarbonate, pH, water temperature, electric conductivity, dissolved oxygen, total phosphorus, water depth, and water turbidity) were predicted by the model. The model predicted that an increase in total phosphate, water turbidity, and electric conductivity concentration may contribute to the hyper-eutrophic state of the wetland. In contrast, the hyper-eutrophic of the wetland is associated with a decrease in water depth, dissolved oxygen, and pH concentration. According to ANOVA test, the trophic condition in the wetland can be affected by spatial and temporal patterns. Anthropogenic pressures such as the influx of chemicals particularly the nutrients (phosphorus and nitrogen) are the main cause of water enrichment (eutrophication problem) in main parts of the Anzali wetland ecosystem.

Enhanced biodegradation of oil-contaminated soil oil in shale gas exploitation by biochar immobilization.

The enhanced biodegradation of oil-contaminated soil by fixing microorganisms with corn cob biochar was investigated. It was found that the components of oil in the test soil were mainly straight-chain alkanes and branched alkanes. When using corn cob biochar as a carrier to immobilize microorganisms, the best particle size of corn cob biochar as an immobilization carrier was 0.08 mm, and the best immobilization time was 18 h. SEM analysis confirmed that the microorganisms were immobilized on the corn cob biochar. Immobilized microorganisms exhibited high biodegradability under stress to high concentrations of petroleum pollutants, heavy metals, and organic pollutants. Infrared spectroscopy analysis showed that oxygen-containing groups such as hydroxyl, carboxyl, and methoxy on the surface of biochar were involved in the complexation of heavy metals. The mechanism of immobilization promoted microbial degradation of oil contamination was explained by gas chromatography mass. First, alkanes and aromatics were adsorbed by corn cob biochar and passed to immobilized microorganisms to promote their degradation. Their bioavailability increased, especially for aromatics. Second, biochar provided a more suitable environment for microorganisms to degrade. Third, the conversion of ketones to acids was accelerated during the biodegradation of alkanes, and the biodegradation of alkanes was accelerated by immobilization. The biodegradable efficiency of oil by immobilized microorganisms in soil was 70.10% within 60 days, 28.80% higher than that of free microorganisms. The degradation of immobilized microorganisms was highly correlated with the activities of catalase, urease, and polyphenol oxidase.

Comprehensive analysis of the migration and transformation of nutrients between sediment and overlying water in complex habitat systems.

Under the influence of environmental change, disturbance and other external conditions, sediments release internal nutrients to the overlying water and become a contamination source in the lake. Complex habitat systems provide a unique opportunity for determining the influences of environmental changes in lakes. In this study, Baiyangdian Lake (BYDL) was divided into different habitat systems (connected water areas, river courses, reed fields, lotus ponds, fishponds, farmland, and thorps) based on the influence of natural and artificial activities. The physical and chemical properties of overlying water and sediment in different habitat systems were investigated. In addition, statistical analytical methods were used to analyze the relationship between sediment characteristics and overlying water parameters in different habitat systems. The results showed that nitrogen and phosphorus in the overlying water could accumulate in the sediments, while disturbance was one of the main factors affecting the release of nutrients from sediments. Disturbance promoted the suspension of sediments and increased the oxygen content, thereby facilitating the internal release of nutrients. However, there were also some differences in the process of internal release of nutrients between the habitat systems. Nitrogen in the overlying water was closely related to the source of organic matter (r > 0.950), especially in the ponds (including lotus ponds, reed fields, and fishponds), and phosphorus was mainly influenced by turbidity (r > 0.870). In the river course (p = 0.198, n = 26), the disturbance and increase in pH promoted the internal release of nutrients from the sediments (contributions of 35.2 % and 25.1 %, respectively). In the ponds, the aquatic macrophytes reduced the release of nitrogen and phosphorus in sediments. Overall, this study provides more information on the migration and transformation of nutrients between sediment and overlying water in lakes with multiple habitats.

Peptide-Calcium Chelate from Antler (Cervus elaphus) Bone Enhances Calcium Absorption in Intestinal Caco-2 Cells and D-gal-Induced Aging Mouse Model.

Antler bone calcium (AB-Ca) and bioactive peptides (ABPs) were extracted from antler bones (Cervus elaphus) to maximize their value. In this study, 0.14 g calcium was obtained from 1 g antler bone. The peptide-calcium chelate rate was 53.68 ± 1.80%, and the Gly, Pro, and Glu in ABPs were identified to donate most to the increased calcium affinity through the mass spectrometry. Fourier transform infrared spectroscopy showed that calcium predominantly interacted with amino nitrogen atoms and carboxyl oxygen atoms, thereby generating a peptide-calcium chelate. The peptide-calcium chelates were characterized using scanning electron microscopy. A Caco-2 cell monolayer model showed that ABPs significantly increased calcium transport. Furthermore, the D-gal-induced aging mouse model indicated that the ABPs + AB-Ca group showed higher Ca and PINP levels, lower P, ALP, and CTX-1content in serum, and considerably higher tibia index and tibia calcium content. Results showed that ABPs + AB-Ca increased bone formation and inhibited bone resorption, thereby providing calcium supplements for ameliorating senile osteoporosis (SOP).