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Abstract Most cancer cells reprogram their glucose metabolic pathway from oxidative phosphorylation to aerobic glycolysis for energy production. By reducing enzyme activity of pyruvate kinase M2 (PKM2), cancer cells attain a greater fraction of glycolytic metabolites for macromolecule synthesis needed for rapid proliferation. Here we demonstrate that hydrogen sulfide (H2S) destabilizes the PKM2 tetramer into monomer/dimer through sulfhydration at cysteines, notably at C326, leading to reduced PKM2 enzyme activity and increased PKM2-mediated transcriptional activation. Blocking PKM2 sulfhydration at C326 through amino acid mutation stabilizes the PKM2 tetramer and crystal structure further revealing the tetramer organization of PKM2-C326S. The PKM2-C326S mutant in cancer cells rewires glucose metabolism to mitochondrial respiration, significantly inhibiting tumor growth. In this work, we demonstrate that PKM2 sulfhydration by H2S inactivates PKM2 activity to promote tumorigenesis and inhibiting this process could be a potential therapeutic approach for targeting cancer metabolism.

Currently, steel is mainly alloyed by adding ferrochrome alloys in the production of chromium-containing steels. However, the production of ferrochrome consumes a lot of electricity and produces a large number of additional products that are extremely harmful to the environment. For the consideration of the above problems, the experiment studied the reduction behavior of Cr2O3 using carbon powder as the reductant under different ambient pressures, different reductant dosages, and different initial oxygen contents in the molten steel. The results show that CO bubbles can be formed by decreasing the air pressure and increasing the amount of reductant, thus the reduction rate and reduction efficiency of Cr2O3 can be increased. It was found that the ambient air pressure mainly affected the reduction rate of Cr2O3, and the fastest reduction rate of Cr2O3 was found in vacuum environment. The reductant dosage has great influence on the reduction rate, reduction efficiency, density and size of inclusions of Cr2O3. Addition of carbon powder with a carbon-to-oxygen molar ratio of 2 will significantly improve the reduction rate of Cr2O3. The yield of [Cr] in steel can reach more than 95 %. In addition, the density of inclusions in steel is stabilized below 0.2/mm−2. The addition of carbon powder with nc:no ≤ 1.0 significantly reduces the reduction rate of Cr2O3 due to the decrease of carbon concentration in the steel. The density of inclusions in steel also increased significantly up to 210.4/mm−2. The initial oxygen content in the molten steel had little effect on the reduction of Cr2O3. However, the reduction rate of Cr2O3 is lower in steel with high oxygen concentration than in steel with low oxygen concentration. Under the optimum reduction conditions-nc:no = 2.0, initial oxygen content of 3 × 10−4, and ambient air pressure of 50 Pa. The yield of [Cr] in steel can reach 96.85 %, the total oxygen content can be reduced to 1.6 × 10−5, the density of inclusions is 0.04/mm−2, and the size of inclusions is below 1.8 μm.

This study investigated the drought trends, frequency, and duration in three drought-prone cities in Northwest China under various Shared Socioeconomic Pathways (SSPs) climate scenarios from 2015 to 2099. Utilizing a two-step downscaling method, we generated high-resolution (1 km) temperature and precipitation projections for the cities using multiple global climate model (GCM) data. The Standardized Precipitation Evapotranspiration Index (SPEI) was then calculated to assess drought conditions. Our results revealed that under the influence of global climate change, the 12-month SPEI exhibited a significant increasing trend for all three cities, indicating a shift towards a more humid climate. The influence of climate change on drought trends was more pronounced at higher latitudes. Additionally, the occurrence and duration of severe droughts (3-month SPEI

Abstract The fish immune system is a topic or subject that offers a unique understanding of defensive system evolution in vertebrate heredity. While gut microbiota plays several roles in fish: well-being, promoting health and growth, resistance to bacterial invasion, regulation of energy absorption, and lipid metabolism. However, studies on fish gut microbiota face practical challenges due to the large number of fish varieties, fluctuating environmental conditions, and differences in feeding habits. This study was carried out to evaluate the impacts of supplemented three autochthonous strains, Bacillus sp. RCS1, Pantoea agglomerans RCS2, and Bacillus cereus RCS3 mixture diet on cobia fish (Rachycentron canadum). Also, chromatography, mass spectrometry and high throughput sequencing were combined to explore composition and metabolite profile of gut microbiota in juvenile cobia fed with supplemented diet. In the trial group, juvenile cobia received diets supplemented with 1 × 1012 CFU mL−1 autochthonous strains for ten weeks and a control diet without supplementation. Juvenile cobia receiving diets supplementation exhibited significantly improved growth than those without additives (control). Haematological indices, such as red blood cells, white blood cells, corpuscular haemoglobin concentration, mean corpuscular volume, haemoglobin, and mean corpuscular haemoglobin, were higher in the supplemented group. Similarly, digestive enzymes (trypsin, lipase, amylase, pepsin and cellulose, activities) activities were higher in supplemented diet with an indigenous isolates mixture. Serum biochemical parameters albumin, globulin, and total protein were significantly higher, while triglyceride, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and cholesterol showed no significant difference. On the other hand, glucose was significantly (P

Bearing steel is subjected to complex alternating stress conditions for a long time which requires excellent service properties such as high hardness, high wear resistance, high elastic limit, and high contact fatigue strength. Therefore, during bearing steel production, it is necessary to strictly control the process and improve the purity of steel to ensure high precision, long service life, and high reliability of bearings. China has made considerable progress in the production technology of high-quality bearing steel, and some enterprises can produce world-class bearing steel. However, the stability of bearing steel still requires improvement. Currently, the aluminum deoxidation process is mainly used to produce bearing steel at home and abroad. Through aluminum deoxidation and the production of high-alkalinity slag, the oxygen content in liquid steel can be rapidly reduced. The total oxygen mass fraction in high-quality bearing steel can be controlled below 5×10−6. However, fatigue failure caused by occasional Ds-type inclusions still occurs. Concurrently, other problems such as blockage of small billet continuous casting nozzle and difficulty in stable control of ultralow total oxygen and titanium content also occur. To circumvent the aforementioned problems, this study proposed a nonaluminum deoxidation process by adding silicon–manganese alloy for pre-deoxidation during converter tapping, adding silicon deoxidizer to the ladle furnace (LF) slag surface for diffusion deoxidation, and Ruhrstahl‒Heraeus (RH) vacuum deep deoxidation to ensure that the total oxygen mass fraction of the molten steel was approximately 8×10−6, to produce bearing steel. While ensuring the low aluminum and low titanium contents of liquid steel, low-alkalinity slag is used to change the type of inclusions and control the plasticity of inclusions to effectively solve the problem of liquid steel fluidity. The fatigue life of bearing steels by two kinds of processes was evaluated using the ultrasonic fatigue testing machine, the effects of different types of inclusions on fatigue performance were verified, the fatigue fracture mechanism of bearing steels by different processes was analyzed, and the critical size of inclusions causing fatigue cracks was predicted. The application of the aforementioned key technologies plays a guiding role in the large-scale production of nonaluminum deoxidized high-quality bearing steel. However, its quality still lags behind the most advanced production level of bearing steel worldwide, including the poor desulfurization effect caused by the use of low-basicity slag in the refining process, which needs to be further investigated.

The steel industry is an important embodiment of national productivity and contributes to the development of the national economy and defense construction as a material foundation. Recently, China’s crude steel production ranked first in the world and in 2020, it exceeded 1 billion tons for the first time, reaching 1.065 billion tons. However, the steel industry is also a major energy consumer and polluter. In the current national coordination to do a good job of “carbon peak” and “carbon-neutral” background, the traditional steelmaking process urgently needs to be transformed into intelligent and green. Recently, as an important branch of machine learning, with artificial neural networks as the basic architecture, deep learning, a nonlinear modeling algorithm that can extract features from data and realize knowledge learning, has been applied in various industrial fields. The steelmaking process is an extremely complex industrial scenario with many influencing factors and high-security requirements. It is also an area where deep learning has not been applied on a large scale yet. Accordingly, in this study, the principles and types of deep learning were compared, and the development history and research status of deep learning in the steelmaking process with domestic and foreign application examples were summarized. The application of deep learning to the steelmaking process mainly has the advantages of simple feature extraction, strong generalization ability, and high model plasticity, but it also faces the challenges of high data dependency, difficult preprocessing, and verification of production safety. In the future, with the application of high-precision sensors, popularization of the Internet of Things, iteration of computing hardware, and innovation of algorithms, deep learning models can be effectively applied to more scenarios in steelmaking, which will promote the intelligent development of the metallurgical industry.

Mycelia products enhance edible mushrooms in alignment with future sustainability trends. To meet forthcoming market demands, the morphology of mycelial pellets was optimized for direct consumption. Among ten commercial edible mushrooms in Taiwan, Pleurotus sp. was selected for its rapid growth and was identified via an internal transcribed spacer sequence. A combination of Plackett-Burman design and Taguchi’s L9(34) orthogonal table revealed the optimal formula as potato dextrose broth (2.4%), olive oil (2%), calcium carbonate (0.5%), yeast extract (0.75%), and soy flour (0.5%). This led to a biomass increase to 19.9 ± 1.1 g/L, resulting in a 2.17-fold yield increase. To refine morphology, image processing by ImageJ quantified spherical characteristics. The addition of 0.2 to 1.0% Tween 80 enhanced pellet compaction by over 50%. Dilution of the medium improved uniformity (0.85) and conversion rate (42%), yielding mycelial pellets with 2.10 ± 0.52 mm diameters and a yield of 15.1 ± 0.6 g/L. These findings provide an alternative evaluation and application of edible mycelial pellets as future food.

The steel industry is a major energy consumer in China. As an effective measure for energy saving, cost and emission reduction, and higher efficiency among enterprises, ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality. In the steelmaking process, the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties. The chemical composition of molten steel is mainly adjusted using ferroalloys. With the development of ferroalloy smelting technology, ferroalloys of various types are developed. These ferroalloys show major gaps in cost performance and composition. Before ferroalloy addition, it is essential to determine an appropriate and cost-effective type and its amount for cost-saving purposes. However, the traditional method of offering a manually determined amount cannot meet the above requirement. Therefore, it is necessary to explore an intelligent ferroalloy addition method without human intervention. Based on the K-means clustering algorithm, this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process. The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process. Using this model, an intelligent control system for alloy reduction was developed. The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming. This system was implemented in a steelmaking plant, in which the accuracy and practicability of this model were verified by running it online. This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition, which are frequently seen in traditional calculations with a manual experience. The ferroalloy dosing scheme is also optimized, and the alloying cost of steelmaking is reduced. The total cost of adding ferroalloys of various types is reduced by 5.95% to 14.74%, with an average reduction of 11.72%.

Evapotranspiration (ET) is a key component of the water and carbon cycles. Because it cannot be observed directly on large regional scales at present, many satellite-based ET datasets have been widely used for different purposes. However, their use has been limited at regional and field scales because of their coarse spatial and temporal resolution. In this study, the Bayesian model averaging (BMA) method was used to simulate daily ET values with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2019. Validation performed with the in-situ observations showed that the BMA ET values had higher accuracy (R2 = 0.69, RMSE = 1.15 mm/day) than those found with individual models. The BMA ET values also had higher accuracy and more credibility based on a water balance equation validation in the HRB. We used interpolated meteorological datasets, reanalysis net radiation products, and remote-sensing datasets to drive the BMA ET model. The mean annual BMA ET in the HRB from 2000 to 2019 was about 601.8 mm/year. The monthly change characteristics of this BMA ET product reflected water consumption characteristics and irrigation regularity of the winter wheat and summer maize rotation system. The BMA ET in the HRB showed a significant increasing trend from 2000 to 2019 of 3.39 mm/y2. The BMA ET values showed a strong positive trend at 80 % of HRB areas. The increasing trend of the BMA ET values was mainly attributed to an increase in the leaf area index. The high spatiotemporal resolution product showed variations in ET trends more accurately than the coarse resolution products. This high spatiotemporal product has great potential for applications in studies of regional microclimate change, interactions between human activities and climate change, drought disaster monitoring, agricultural policy making, and water security.

Given the accelerating depletion of iron ore resources, there is growing concern within the steel industry regarding the availability of high-phosphorus iron ore. However, it is important to note that the utilization of high-phosphorus iron ore may result in elevated phosphorus content and notable fluctuations in molten iron, thereby imposing additional challenges on the dephosphorization process in steelmaking. The most urgent issue in the process of converter steelmaking is how to achieve efficient dephosphorization. In this study, the influence of various factors on the logarithm of the phosphorus balance distribution ratio (lgLp), the logarithm of the P2O5 activity coefficient (lgγP2O5), and the logarithm of the phosphorus capacity (lgCp) were examined through thermodynamic calculations. The impact of each factor on dephosphorization was analyzed, and the optimal conditions for the dephosphorization stage of the converter were determined. Furthermore, the influence of basicity and FetO content on the form of phosphorus in the slag was analyzed using FactSage 7.2 software, and the precipitation rules of the slag phases were explored. The thermodynamic calculation results indicated that increasing the basicity of the dephosphorization slag was beneficial for dephosphorization, but it should be maintained below 3. The best dephosphorization effect was achieved when the FetO content was around 20%. The reaction temperature during the dephosphorization stage should be kept low, as the dephosphorization efficiency decreased sharply with the increasing temperature. In dephosphorization slag, Ca3(PO4)2 usually formed a solid solution with Ca2SiO4, so the form of phosphorus in the slag was mainly determined by the precipitation form and content of Ca2SiO4. The phases in the dephosphorization slag mainly consisted of a phosphorus-rich phase, an iron-rich phase, and a matrix phase. The results of scanning electron microscopy and X-ray diffraction analyses were consistent with the thermodynamic calculation results.

The sensitivity of hydrological processes to the changed environment is of great concern. The integrated impacts of climate change and urbanization in the future have been assessed in a watershed in Northwest China through a multimodel approach based on the combined application of Generalized Watershed Loading Functions, the Long Ashton Research Station Weather Generator, and the Land Change Modeler. The results showed that both climate change and urbanization would lead to more watershed streamflow, and their combination would have synergistic effects on additional increases. In addition, there would be different seasonal distributions of streamflow with a greater proportion of runoff. These study results are helpful in supporting projects and/or decision-making processes for managers by providing more insights into the regional hydrological changes affected by climate change and urbanization. The proposed methodology of the combined multimodel approach may be applicable in other areas with similar conditions. HIGHLIGHTS A multimodel linkage approach was proposed for future hydrological estimations.; The individual and combined effects of climate change and urbanization were investigated.; Both individual effects would increase streamflow and change its seasonal distribution.; The combined effect had a synergistic mechanism for the additive increase of streamflow.; There would be higher streamflow with more runoff and an earlier peak of snowmelt in future spring.;

Bearing steel is a significant material for producing basic components in many industrial sectors, such as automotive, high-speed trains, and aerospace. In most cases, the bearings belong to the safety-relevant parts of these structures, so that extensive quality control measures are applied to ensure the entire assembly’s reliability and safety. In the last century, the precise instruments’ bearings were almost dependent on imports, which cost a large fortune. In recent years, the production technique and the production quality of domestic bearing steels keep improving, and the cleanliness keeps increasing. The qualities of some superior domestic bearing steels have become close to foreign bearing steels with high quality. However, the stability still needs to be improved, especially in the aspects of calcium aluminate inclusions and the titanium content in steels. To understand the gaps between domestic and foreign bearing steels, the differences in metallurgical properties of high-fatigue-life bearing steels, such as the main impurity elements and the characteristics of main inclusions, were compared in this paper. Fatigue properties of bearing steels and factors that cause fatigue fracture under various metallurgical properties were also compared. The cleanliness status of domestic bearing steels and the gap with foreign high-quality bearing steels were summarized. There are two types of controlling strategies of the cleanliness for bearing steels: 1) controlling the total oxygen content strictly to get an extremely low total oxygen content; 2) controlling the size and type of oxide inclusions with a relatively low total oxygen content. On this basis, in order to further improve the efficiency of domestic steel bearings, the development of the smelting technology and the integration control system for domestic steel bearings were analyzed and sorted out. Besides, the development direction of further improving the quality and fatigue life of domestic bearing steel was discussed.

Cobia (Rachycentron canadum) is an important cultured marine fish species in southern China. It is characterized by fast growth. Due to the effects of climate change on water oxygen levels and seawater temperatures, the understanding of the influence of environmental changes on cobia culture has become important. In this study, to explore the stress and adaptability of cobia under hypoxia-reoxygenation conditions, the fish were exposed to an oxygen-deficient environment with dissolved oxygen (DO) level of 2.64 ± 0.25 mg/L. Serum liver function target, lipid metabolism-related enzymes and the serum antioxidant capability were detected in cobia under acute hypoxic stress after which fish returned to normal DO level (6.34 ± 0.15 mg/L) at 8, 24 and 48 h. Conventional paraffin sectioning and hematoxylin and eosin (H-E) staining were used to observe gill and liver tissues of cobia by electron microscopy. After hypoxic stress, the activities of alkaline phosphatase (ALP), total protein (TP), albumin (ALB), triglyceride (TG), total cholesterol (TC) and superoxide dismutase (SOD) in cobia decreased. Under acute hypoxic stress, the gill patches of the fish were contracted and slightly curved. Liver disorder and vacuoles appeared between liver tissues. After reoxygenation, SOD and catalase (CAT) were recovered to the level of the control group with time, and all peaks appeared. The gill and liver of fish were also recovered with time. The results showed that hypoxic stress could cause oxidative damage to cobia.

Wood and metal are the two most common materials, both with distinct advantages and disadvantages. Through the metallization process, the inherent defects of the two materials can be avoided, so that the composite material exhibits excellent structural properties and anisotropy. In this study, the pine wood was pretreated with dilute ammonia (5%), and then the low melting point Sn–Bi alloy was filled into the pine fiber without lignin at 180 °C and 3000 Pa. The density of metallic wood is about 3 times that of pine, while the impact toughness can be up to 2 times that of pine. By building the capillary buddle model, the filling mechanism of molten metal in pine wood was analyzed, and it was clear that high vacuum, high temperature, large porosity and large tracheid diameter are the conditions for rapid penetration. The conductive anisotropy was found on metalized wood, making metalized wood available in different places with requirements for electromagnetic shielding and antistatic.

Abstract Soil microorganisms are considered to be important indicators of soil fertility and soil quality. Most previous studies have focused solely on surface soil, but there were numerous active cells in deeper soil layers. However, studies regarding microbial communities in deeper soil layers were not comprehensive and sufficient. In this study, phylogenetic molecular ecological networks (pMENs) based on the 16S rRNA Miseq sequencing technique were applied to study the response of soil microbial communities to depth gradients and the changes of key genera along 3 meter depth gradients (0–0.2 m, 0.2–0.4 m 0.4–0.6 m, 0.6–0.8 m, 0.8–1.0 m, 1.0–1.3 m, 1.3–1.6 m, 1.6–2.0 m, 2.0–2.5 m, and 2.5–3.0 m). The results showed that the modularity of microbial communities was consistently high in all soil layers and each layer was similar, which indicated that microbial communities were more resistant to depth changes. The pMENs further demonstrated that microbial community interactions were stable as the depth increased and they cooperated well to adapt to changes in different soil gradients. This was evidenced by similar positive links, average degree, and average clustering coefficient. In addition, key genera were obtained by analyzing module hubs in the pMENs. There may be at least one dominant genus in each layer that adapted to and resisted changes in the soil environment. It seems microbial communities demonstrate a stable and strong adaptability to depth gradients in farmland soils.

Groundwater is a key component for water resources in Sanjiang and Songnen Plain, an important agriculture basement in China. The quality and the renewability of irrigation groundwater are essential for the stock raising and agricultural production. Shallow groundwater was sampled and analyzed for various variables. The salinity sodium concentration and bicarbonate hazard, were examined with regard to the United States Department of Agriculture (USDA) irrigation water standards. The concentration of chlorofluorocarbons (CFCs) was determined to analyze the age of groundwater. Most groundwater samples labeled as excellent to good for irrigation with low salinity hazard or medium salinity hazard. Four groundwater samples were good and suspected for irrigation with high salinity hazard. Generally groundwater in Sanjiang Plain was younger than the groundwater in Songnen Plain. Meanwhile, groundwater nearby river is younger than the groundwater further away inside the watershed. The mean age of groundwater in Sanjiang Plain is in average of 44.1, 47.9 and 32.8 years by CFC-11 (CCl3F), CFC-12 (CCl2F2) and CFC-113 (C2Cl3F3), respectively. The mean ages of groundwater in Songnen Plain is in average of 46.1, 53.4, and 40.7 years by CFC-11, CFC-12 and CFC-113, respectively. Thus, groundwater nearby rivers could be directly exploited as irrigation water. Partial groundwater has to be processed to lower the salt concentration rather than directly utilized as irrigation water in Songnen Plain. Both water quality and renewability should be put in mind for sustainable agricultural development and water resources management.

Bisphenol A (BPA) is a typical endocrine-disrupting chemical. The removal of BPA has raised much concerns in recent years. This paper examined the adsorption behavior of BPA to biochars and the different effects of cationic, anionic, and nonionic surfactants. The results indicated that peanut shell biochars prepared at 300°C (BC300), 500°C (BC500), and 700°C (BC700) showed strong adsorption affinity for BPA, and the adsorption affinity of biochars increased with the increase of pyrolysis temperature. The range of log Kd values was 2.83∼3.71, 2.91∼4.57, and 3.24∼5.50 for BC300, BC500, and BC700, respectively. Both the type of surfactants and the properties of biochars could affect the adsorption behavior of BPA. Cetyltrimethyl ammonium bromide (CTAB) showed negligible effect on the adsorption of BPA on BC300, and the inhibition effect of CTAB was stronger with the increase of biochar pyrolysis temperature. Tween 20 and sodium dodecyl benzene sulfonate (SDBS) showed stronger inhibition effect than CTAB, especially on BC300. This is likely because the inhibition effect caused by competition of CTAB may be counterbalanced by the enhancement caused by the partitioning effect by adsorbed CTAB and the bridge effect between the –NH4+ group of CTAB and the phenol group on BPA/O-functional groups of biochars, whereas Tween 20 and SDBS do not have this bridge effect advantage. This study could provide insightful information for the application of biochars in removal of BPA.

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

The eutrophication issue in the Yangtze Basin was considered, and the phosphorus loads from its tributary, the Modaoxi River, were estimated. The phosphorus flux and source apportionment of the Modaoxi River watershed were modeled and quantified, and their changes with respect to future projected climate scenarios were simulated with multiple model applications. The Regional Nutrient Management (ReNuMa) model based on Generalized Watershed Loading Functions (GWLF) was employed as a tool to model the hydrochemical processes of the watershed and thereby estimate the monthly streamflow and the phosphorus flux as well as its source apportionment. The Long Ashton Research Station Weather Generator (LARS-WG) was used to predict future daily weather data through the statistical downscaling of the general circulation model (GCM) outputs based on projected climate scenarios. The synthetic time series of daily precipitation and temperatures generated by LARS-WG were further used as input data for ReNuMa to estimate the responses of the watershed hydrochemical processes to future changed climate conditions. The results showed that both models could be successfully applied and that the future wetter and warmer climate trends would have generally positive impacts on the watershed phosphorus yields, with greater contributions coming from runoff. These results could provide valuable support for local water environmental management.

One of the most important water quality problems affecting lakes and reservoirs is eutrophication, which is caused by multiple physical and chemical factors. As a representative index of eutrophication, the concentration of chlorophyll-a has always been a key indicator monitored by environmental managers. The most influential factors on chlorophyll-a may be dependent on the different water quality patterns in lakes. In this study, data collected from 27 lakes in different provinces of China during 2009–2011 were analyzed. The self-organizing map (SOM) was first applied on the datasets and the lakes were classified into four clusters according to 24 water quality parameters. Comparison amongst the clusters revealed that Cluster I was the least polluted and at the lowest trophic level, while Cluster IV was the most polluted and at the highest trophic level. The genetic algorithm optimized back-propagation neural network (GA-BPNN) was applied to each lake cluster to select the most influential input variables for chlorophyll-a. The results of the four clusters showed that the performance of GA-BPNN was satisfied with nearly half of the input variables selected from the predictor pool. The selected factors varied for the lakes in different clusters, which indicates that the control for eutrophication should be separate for lakes in different provinces of one country.

Over the past six decades, coastal wetlands in China have experienced rapid and extensive agricultural reclamation. In the context of saline conditions, long-term effect of cultivation after reclamation on soil chemical properties has not been well understood. We studied this issue using a case of approximately 60-years cultivation of a coastal saline marsh in Bohai Rim, northern China. The results showed that long-term reclamation significantly decreased soil organic carbon (SOC) (-42.2%) and total nitrogen (TN) (-25.8%) at surface layer (0-30 cm) as well as their stratification ratios (SRs) (0-5 cm:50-70 cm and 5-10 cm:50-70 cm). However, there was no significant change in total phosphorus (TP) as well as its SRs under cultivation. Cultivation markedly reduced ratios of SOC to TN, SOC to TP and TN to TP at surface layer (0-30 cm) and their SRs (0-5 cm:50-70 cm). After cultivation, electrical conductivity and salinity significantly decreased by 60.1% and 55.3% at 0-100 cm layer, respectively, suggesting a great desalinization. In contrast, soil pH at 20-70 cm horizons notably increased as an effect of reclamation. Cultivation also changed compositions of cations at 0-10 cm layer and anions at 5-100 cm layer, mainly decreasing the proportion of Na+, Cl- and SO4(2-). Furthermore, cultivation significantly reduced the sodium adsorption ratio and exchangeable sodium percentage in plow-layer (0-20 cm) but not residual sodium carbonate, suggesting a reduction in sodium harm.