Search Results (22)

Neglecting ecosystem services has impeded sustainable urban–rural development, particularly in terms of the efficient flow of water supply services between urban and rural areas. This study focuses on the Fenhe River Basin, evaluating water supply and demand at the sub-basin, as well as county levels. Using the InVEST model to analyze basin-level geographic, meteorological, hydrological, and socio-economic data, the study reveals significant spatial and temporal mismatches between water supply and demand from 2010 to 2020. Through the calculated ecosystem services supply and demand ratio (0.3731 in 2010, −0.1555 in 2015, and −0.1063 in 2020), it is found although both supply and demand increased over the period, persistent deficits emerged, with water supply concentrated in upstream areas and demand primarily in downstream regions. The improved network connectivity by 2020, supported by water-saving policies and technological advancements, partially alleviated earlier imbalances. This research contributes a multi-scale framework to analyze ecosystem service flows and compensation mechanisms across grid, sub-basin, and county scales. Overall, the study underscores that research into ecological compensation plays a crucial role in enabling efficient resource flow, enhancing governance systems, and fostering an ecologically friendly urban–rural development model. Full article

Oculatellaceae is a family of cyanobacteria with orange spots in the apical cells and has a wide distribution in various living environments. The species of this family are widely distributed but relatively few in number. In order to enrich our knowledge of the species diversity of cyanobacteria in China, and further achieve the monophyletic development of modern cyanobacteria classification systems, we studied two algal strains, designated as SXACC0114 and SXACC0117, isolated from China and subjected to taxonomic studies using a multiphase approach. The colony of the strain SXACC0114 is bright blue-green in color and does not form a biofilm. The trichomes are yellow-green to bright blue-green. For the strain SXACC0117, no false branching is observed. It has wider filaments and more distinct sheaths, and lacks swollen cells. Based on 16S rRNA gene phylogenetic analysis, the results showed that these two algal strains clustered in Albertania and Tildeniella evolutionary branches, respectively, with high bootstrap support. In addition, the secondary structures, which are constructed based on the internal transcription spacer (ITS) of 16S-23S rRNA, exhibit differences, and the algal strain has unique D1-D1ʹ, Box-B, and V3 helix structures. These results support the establishment of two new species, described as Albertania yunnanense sp. nov. and Tildeniella yunnanense sp. nov. The discovery of these new species provides a scientific basis for the development and utilization of algae. Full article

Analysing the patterns and impacts of land-use changes in the production–living–ecological space (PLES) of the Fenhe River Basin (FRB 39,721 km²), China, is necessary to support sustainable development. Based on remote sensing images from 1990 to 2020, we aimed to analyse the PLES land-use changes. Industrial production and living spaces continuously encroached on the agricultural production and ecological spaces between 1990 and 2022 owing to industrialisation and urbanisation, and the ecological land area decreased by 699.21 km², while the industrial production land area increased by 521.32 km². We used the soil and water assessment tool (SWAT) model to quantitatively analyse the impact of PLES changes on runoff in the FRB. With the continuous expansion of production and living spaces, the extensive use of concrete in cities has led to ground hardening, making it difficult for precipitation to infiltrate, with surface runoff increasing by 0.3 mm annually. The reduction in ecological space has led to a reduction in forests and grasslands, weakening the water-holding capacity of the watershed and affecting groundwater storage. This study provides a scientific basis for watershed management and the integrated development of PLES. Full article

Affected by point and non-point source pollution, the Fenhe River Basin faces significant environmental challenges. This study aimed to analyze the distribution characteristics and influencing factors of antibiotics in the water and sediments of the Fenhe River Basin. Samples were collected from 23 sites within the basin, and 26 antibiotics from five different classes were detected and analyzed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The water–sediment partition coefficient (Kp) was calculated, and spatial analysis was conducted using geographic information system (GIS) technology. The results showed that 25 antibiotics were detected in the water, with concentrations ranging from 130 to 1615 ng/L, and 17 antibiotics were detected in the sediments, with concentrations ranging from 121 to 426 μg/kg. For quinolones (QNs), except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, with considerable point source pollution from industries and non-point source pollution from developed agriculture. The distribution of Kp is closely associated with risk. This study found that the Kp values of the antibiotics were influenced by various factors such as temperature, water flow, and the physicochemical properties of sediments. Correlation analysis revealed significant relationships between Kp and parameters such as river width, water depth, water quality (total nitrogen, total phosphorus, and chemical oxygen demand), and sediment pH and clay content. Full article

Bioremediation is a promising technique owing to its effectiveness, low cost, and environmental friendliness. Previous studies have focused on the degradation efficiency of polycyclic aromatic hydrocarbons (PAHs) in soil and water. However, the expression of PAH-catabolic genes in organisms involved in the degradation process has been rarely and unsystematically reported. In this study, a PAH-degrading strain—Pseudomonas aeruginosa (PQ249631)—was successfully isolated from coking-contaminated soil and used for PAH degradation in soil and water. Furthermore, the degradation of PAHs (naphthalene, fluorene, phenanthrene, anthracene, and pyrene) was investigated in single, binary, and mixture systems to explore the interaction of substrates. The results showed that when naphthalene was used as a cometabolite carbon source, the removal rates of fluorene, phenanthrene, anthracene, and pyrene increased from 14.33%, 17.25%, 6.61%, and 4.47% to 72.08%, 100.00%, 15.63%, and 6.63%, respectively. In a PAH mixture, the degradation rate of each PAH was higher when naphthalene, rather than glucose, was used as the cometabolite carbon source. Transcriptome analysis revealed significant differential expression of PAH-catabolic genes and ATP-binding cassette transporter-related genes under naphthalene stress. The enhanced degradation of PAHs could be attributed to the augmentation of the PAH metabolic pathway and membrane transportation, facilitating the transfer of PAHs to bacteria. These findings underscore the effectiveness of P. aeruginosa as a PAH degrader and provide molecular insights into enhancing PAH degradation. Full article

In plants, RopGEF-mediated ROP signaling is pivotal in cellular signaling pathways, including apical growth, pollen germination and perception, intercellular recognition, as well as in responses to biotic and abiotic stresses. In this study, we retrieved a total of 37 RopGEF members from three C₄ Crops, of which 11 are from millet, 11 from sorghum, and 15 from maize. Based on their phylogenetic relationships and structural characteristics, all RopGEF members are classified into four subfamilies. The qRT-PCR technique was utilized to evaluate the expression profiles of 11 SiRopGEFs across different tissues in foxtail millet. The findings indicated that the majority of the SiRopGEFs exhibited higher expression levels in leaves as opposed to roots and stems. The levels of expression of SiRopGEF genes were examined in response to abiotic stress and plant hormones. SiRopGEF1, SiRopGEF5, SiRopGEF6, and SiRopGEF8 showed significant induction under abiotic stresses such as salt, cold, and heat. On the other hand, SiRopGEF1, SiRopGEF2, and SiRopGEF7 were consistently upregulated, while SiRopGEF3, SiRopGEF4, SiRopGEF6, SiRopGEF9, and SiRopGEF10 were downregulated upon exposure to abscisic acid (ABA), ethylene (ET), salicylic acid (SA), and gibberellic acid (GA₃) hormones. The alterations in the expression patterns of RopGEF members imply their potential functions in plant growth and development, abiotic stress response, and hormone signal transduction. These discoveries suggest that the RopGEF genes may function as a potential genetic marker to facilitate future studies in elucidating the functional characteristics of RopGEFs. Full article

Hydrological modeling is a crucial tool in hydrology and water resource management for analyzing runoff evolution patterns. In this study, the process-driven soil and water assessment tool (SWAT) model and data-driven machine learning techniques (XGBoost, random forest, LSTM, BILSTM, and GRU) were employed to simulate runoff at monthly and daily intervals in the Fenhe River basin, situated in the middle reaches of the Yellow River, respectively. The SWAT model demonstrated effective performance in simulating runoff at various scales, with the coefficient of determination (R²) exceeding 0.80 and the Nash–Sutcliffe efficiency (NSE) surpassing 0.79. Sensitivity analysis reveals varying degrees of sensitivity among the model parameters. Furthermore, the deep learning techniques (LSTM, BILSTM, and GRU) exhibited superior simulation generalization capabilities compared to the SWAT model across various scales. Additionally, the generalization abilities of traditional machine learning techniques (XGBoost and random forest) were comparable to the SWAT model. This indicates that deep learning techniques demonstrate remarkable stability and generalization capabilities across various scales. This analysis was motivated by the use of external continuous time series data as input and the application of deep learning techniques to internal mechanisms. Moreover, an integrated modeling approach was used to enhance simulation accuracy by combining the SWAT model with machine learning techniques. The results indicate that the integrated modeling approach improves simulation performance across various scales compared to the single-model approach. This research is significant for improving the efficiency of water resource utilization and management in the Fenhe River basin. Full article

The carbon density of subalpine-alpine grasslands (SGs) is significantly vital to sustaining the carbon cycle in global terrestrial ecosystems. However, on the Loess Plateau of China, it remains unclear how the geographical environment and plant functional groups affect the spatial variation pattern of plant carbon density in these grasslands. Here, nine typical SGs distributed in the eastern Loess Plateau with elevations ranging from 1720 to 3045 m were investigated. The biomass indices from grassland plants of different functional groups were investigated using plot surveys. The Kriging interpolation method was used to explore the spatial variation pattern of plant carbon density along geographical gradients. We found that (1) the total plant carbon density of SGs was 2676.825 g C/m² on the eastern plateau, with 37.07%, 37.50%, and 25.43% contributed by the northern, central, and southern areas, respectively. Above- (666.338 g C/m²) and belowground (2010.488 g C/m²) carbon density accounted for 24.9% and 75.11% of the total, respectively. (2) At the horizontal scale, the plant carbon density in the northern SGs was high in the northwest and low in the southeast; in the central SGs, it was low in the northwest and high in the southeast; and in the southern SGs, it was high in the southwest and low in the northeast. At the vertical scale, plant carbon density in all SGs decreased with increasing altitude. (3) The carbon density of grasses, forbs, and sedges was 247.419 g C/m², 26.073 g C/m², and 23.471 g C/m², respectively. With increased latitude, the carbon density of all functional groups (grasses, forbs, and sedges) decreased; the carbon density of forbs and grasses increased with increased longitude, while that of sedges decreased; and with increased altitude, the carbon density of all functional groups increased. In conclusion, the spatial variation pattern of plant carbon density in the SGs was not only influenced by the geographical environment but also by the plant functional groups at the horizontal and vertical scales on the eastern Loess Plateau of China. Full article

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China’s monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China’s average GPP increased significantly, by 9.77 g C m⁻² yr⁻¹ (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China’s GPP growth. China’s afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m⁻² yr⁻¹. This research provides a theoretical foundation for the long-term management of China’s terrestrial ecosystems and helps develop adaptive ecological restoration tactics. Full article

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions. Full article

Chloramphenicol antibiotics (CAs) are broad-spectrum antibiotics which are widely used in the prevention and treatment of infectious diseases in livestock and poultry breeding. However, overused CAs can enter the watershed and eventually enter the sediment. Antibiotics in sediment can cause secondary pollution through disturbance and suspension. In this study, taking the Fenhe River Basin as the research area, the risk of CAs in sediment were assessed by collecting sediment samples. The results showed that CAs were detected in all sediment samples of the Fenhe River Basin. The mean concentration of CAs was 79.1 μg/kg, and the concentration of thiamphenicol (THI) was dominant, which was up to 58.3 μg/kg. Temporally, there are great differences in different seasons; the concentration of CAs was higher in winter than that in summer, up to 4.79–174 times. Spatially, the mean concentration of CAs in midstream was 83.5 μg/kg, which was higher than that in the upstream and downstream. The concentration of CAs in tributaries were generally higher than that in the main stream, and the mean concentration of tributaries was 1.1 times that of the main stream. CAs in S2 (Lanhe River) was the most prominent among all sample sites; the concentration of CAs was 190.8 μg/kg. The risk threshold of CAs in the sediment was calculated using the Equilibrium Partitioning approach (EqP), based on the distribution coefficient (Kp) and the predicted no-effect concentration (PNEC) in the water, and the values were 0.091–1.44 mg/kg. Based on the risk threshold, the ecological risk of the CAs in sediment was assessed using risk quotients (RQ). The results showed that the Chloramphenicol (CHL) was the most prominent in the Fenhe River Basin, and the proportion of medium-risk areas reached 21.7%, while all the other areas showed low risk. Secondly, the proportion of medium-risk areas was 17.4% for THI, and all the other areas showed low risk. The risk for Florfenicol (FF) was least among all CAs, and the proportion of low-risk areas was only 8.7%, while all the other areas were of insignificant risk. Full article

Water pollution is a serious problem in China and abroad. Revealing the source types and their spatio-temporal characteristics is the premise of effective watershed management and pollution prevention. Since the national control unit can better match the administrative division, it was useful for the manager to control water pollution. Taking the Fenhe River Basin as the research area, a SWAT model based on the national control unit was established in this study to reveal the current situation of water quantity and quality. Then, in combination with the differential evolution algorithm, the dynamic water environment capacities of each control unit were further discussed. The results showed that the flow upstream was lower, only 7.62–8.40 m³/s, but flow in the midstream and downstream increased to 17.58 m³/s and 18.32 m³/s. Additionally, the flow in tributaries was generally lower than that in the main stream, the flow in unit 6 and unit 11 were only 0.23 m³/s and 0.62 m³/s. The water quality upstream could meet the water quality requirements of drinking water sources, but the pollution in the midstream was the most serious after passing through Taiyuan City, the concentration of NH₃-N and TP reached to 6.75 mg/L and 0.41 mg/L. The results of water environmental capacity showed that the residual capacity of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the main stream were positive, indicating that the Fenhe River Basin can accommodate the current pollution load in general, but there was an obvious difference in different months of the year. Especially in the wet season, the non-point source (NPS) pollution problem in the midstream and downstream was more prominent, resulting in a high-capacity consumption rate. It showed that in Taiyuan, Jinzhong, and Linfen Yuncheng in Shanxi Province, should be wary of non-point source pollution. In addition, the water environmental capacity of different units also varied greatly. The capacity consumption of the Taiyuan Section in the midstream was the highest, which mainly occurred in the wet season. The negative values of the residual capacity of NH₃-N and TP reached the highest, −131.3 tons/month and −12.1 tons/month. Moreover, the capacity consumption downstream also reached 21–40% of the whole year in the wet season. In addition to the impact of NPS pollution in the wet season, due to the impact of point source pollution, units 8, 9, and 10 downstream had high negative residual capacity in the dry season, especially in January and February. The construction of a SWAT model based on control units and the further analysis of dynamic water environment capacity could provide technical support for Fenhe River Basin management to realize accurate pollution control. Full article

River water is the most important water source that people can use. Since the 20th century, human influence on river courses has become increasingly serious. The quantitative analysis of water quality is even more difficult. According to the characteristics of Fenhe water chemistry, pollution time and pollution control factors, the contribution rate of people in the polluted water body is not clear. Therefore, this paper aims to use AI identification methods and technologies to study water pollution and provenance tracing. The combination of major elements, trace elements and stable isotopes was used to study the chemical characteristics, water quality status, and sources of pollution of the Fenhe water in the Fenhe area. Because the water contains a large number of pollution sources, it is difficult to find the source using traditional methods. Using correlation analysis, principal component analysis, multi-factor regression analysis, trend analysis and other methods, the macroelements and trace elements in the water body of the Fenhe River were analyzed. The boron sources in the Fenhe river were qualitatively and quantitatively analyzed using mass spectrometry equilibrium equation. Using the boron isotope value of the river, it showed a spatial variation of upstream (+5.1‰) < middlestream (+8.6‰) < downstream (+9.5‰) in dry season, and showed a spatial variation of upstream (+6.1‰) < downstream (+7.2‰) < middlestream (+9.0‰) in the wet season. The contribution of silicate to B is calculated by subtracting the contribution of other resources from the comprehensive contribution rate. It is found that the contribution of silicate is about 38.8%, 22% in dry season and 49.2%, 17% in wet season. The research results have provided a reliable scientific basis for the protection of water resources and pollution control in the Fenhe River Basin. Therefore, the above research confirms the role of AI identification method in the process of boron isotope geochemistry and provenance tracing of water pollution in river basins. Full article

Climate variation and underlying surface dynamics have caused a significant change in the trend of evapotranspiration (ET) in the Yellow River Basin (YRB) over the last two decades. Combined with the measured rainfall, runoff and gravity recovery and climate experiment (GRACE) product, five global ET products were firstly merged using a linear weighting method. Linear slope, “two-step” multiple regression, partial differential, and residual methods were then employed to explore the quantitative impacts of precipitation (PCPN), temperature (Temp), sunshine duration (SD), vapor pressure deficit (VPD), wind speed (WS), leaf area index (LAI), and the residual factors (e.g., microtopography changes, irrigation, etc.) on the ET trend in the YRB. The results show that: (1) The ET estimates were improved by merging five global ET products using the linear weighting method. The sensitivities of climatic factors and LAI on the ET trend can be separately calculated using proposed “two-step” statistical regression method; (2) the overall ET trend in the entire study area during 2000–2018 was 3.82 mm/yr, and the highest ET trend was observed in the Toudaoguai-Longmen subregion. ET trend was dominantly driven by vegetation greening, with an impact of 2.47 mm/yr and a relative impact rate of 51.16%. The results indicated that the relative impact rate of the residual factors (e.g., microtopography, irrigation, etc.) on the ET trend is up to 28.17%. The PCPN and VPD had increasing roles on the ET trend, with impacts of 0.45 mm/yr and 0.05 mm/yr, respectively, whereas the Temp, SD, and WS had decreasing impacts of –0.19 mm/yr, –0.15 mm/yr, and –0.17 mm/yr, respectively. (3) The spatial pattern of impact of specific influencing factor on the ET trend was determined by the spatial pattern of change trend slope of this factor and sensitivity of ET to this factor. ET trends of the source area and the Qingtongxia–Toudaoguai were dominated by the climatic factors, while the residual factors dominated the ET trend in the Tangnaihai–Qingtongxia area. The vegetation restoration was the dominant factor causing the increase in the ET in the middle reaches of the YRB, and the impact rates of the LAI were ranked as follows: Yanhe Rive > Wudinghe River > Fenhe River > Jinghe River > Beiluohe River > Qinhe River > Kuyehe River > Yiluohe River. Full article

The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel method for analyzing the dynamics of landscape patterns. First, patch density (PD), largest patch index (LPI), landscape shape index (LSI), and contiguity index (CI) were used to identify the types of forest spatial patterns. The frequent sequential pattern mining method was used to detect the frequent subsequences from the time series of landscape pattern types from 1991 to 2020 and further evaluate the forest landscape stability of the Fenhe River Basin in China. The results show that different frequent sequence patterns have conspicuous spatial and temporal differences, which describe the evolution processes and stability changes during a certain period of forest evolution and play an important role in the analysis of forest dynamics. The proportion of the disturbed regions to the total forest area exhibited a downward trend. The long-term evolution pattern indicates that there are many evolution processes and trends in the forest at the same time, showing an aggregation distribution law. Compared with 2016, the forest landscape has become complete in 2020, and the overall stability of the Fenhe River Basin has improved. This study can provide scientific support to land managers and policy implementers and offer a new perspective for studying forest landscape pattern changes and evaluating landscape stability. Full article