Frontiers in Environmental Research (FER) is an international peer-reviewed, open access journal.
FOREST FIRE MONITORING AND INTELLIGENT EARLY WARNING BASED ON MULTI-SOURCE PERCEPTION USING FIXED-WING UAVS
Forest fires are characterized by sudden occurrence, rapid spread, and wide-ranging destruction, posing serious threats to forest resource protection and ecological security management. To address the limitations of conventional monitoring methods, such as manual patrols, fixed video surveillance, and satellite remote sensing, in terms of insufficient coverage, limited real-time capability, and low early-warning efficiency in long-distance and large-area forest fire monitoring, this study proposes a forest fire monitoring and intelligent early warning framework based on multi-source perception using fixed-wing UAVs. In the proposed framework, a fixed-wing UAV serves as the airborne patrol platform and is equipped with sensing devices such as RGB cameras, thermal infrared cameras, and multispectral sensors. The UAV conducts large-area patrols over the target forest region along predefined flight routes and collects fire-related information from multiple dimensions, including smoke, flames, thermal anomalies, and vegetation conditions. Through integrated analysis of multi-source information, the system can preliminarily identify suspected fire points, potential fire-risk areas, and fire warning levels, and transmit the monitoring results to the ground control platform to support early fire detection and emergency response. The study indicates that the combination of the long-endurance and large-coverage capability of fixed-wing UAVs with multi-source perception technology can improve the efficiency, reliability, and intelligence of forest fire monitoring, showing practical value for forest fire prevention and ecological resource protection.
Copyright (c) 2026 KaiLe Yang; ZhuoYu Wang; ChenZhe Feng; XingKai Zhang; ZhiJian Li https://creativecommons.org/licenses/by/4.0
AGRICULTURAL ECOSYSTEMS BASED ON DYNAMIC SIMULATION (AEDSM) MODEL
This article combines mathematical modeling, simulation, and multi-objective evaluation methods to solve five key problems in agricultural ecosystems. Firstly, an Agricultural Ecosystem Dynamics Simulation (AEDSM) model was constructed based on the Lotka Volterra equation. By dividing the ecological process into four stages (forest ecological stage, conventional agricultural production stage, initial ecological restoration stage, and ecological restoration stage), it accurately depicts the evolution of the ecosystem. This modeling method enables us to understand the complex interactions between species and environmental factors that vary over time. Secondly, in order to analyze the impact of integrating two native species into the ecosystem, this article uses the established AEDSM model for simulation. By comparing the simulation results of introducing and not introducing these species, the changes in species interactions and population dynamics can be clearly observed, providing insights for biodiversity management. In order to explore the stability of ecosystems during herbicide removal, a comparative simulation scenario was established in the AEDSM model. Finally, in order to evaluate the impact of farmers adopting organic farming methods on the ecosystem, this paper developed a multi-objective evaluation model for organic agricultural ecosystems. This model integrates factors such as market incentives, technological support, and conversion costs. Using weighted scoring and scenario analysis methods, evaluate different organic agriculture scenarios from aspects such as crop yield, biodiversity, and economic costs. Sensitivity analysis is also applied to identify key parameters that can guide decision-making in organic agriculture practices.
Copyright (c) 2026 YuXiang Cui; YongZhuo An; JiaYu Fan; TianShuo Li https://creativecommons.org/licenses/by/4.0
URBAN INNOVATION AGGLOMERATION AND THE SYNERGISTIC EFFECTS OF POLLUTION AND CARBON REDUCTION
Urban innovation agglomeration plays an important role in promoting regional green transformation and achieving the synergistic governance of pollution reduction and carbon emission reduction. From the perspective of agglomeration economics, this paper investigates how urban innovation agglomeration enhances the synergistic efficiency of pollution and carbon reduction through three channels: source prevention, process control, and end-of-pipe interception. The results show that: there is a significant inverted U-shaped nonlinear relationship between urban innovation agglomeration and the synergistic efficiency of pollution and carbon reduction. This study verifies the nonlinear relationship between urban innovation agglomeration and the synergistic efficiency of pollution and carbon reduction, providing empirical evidence for identifying the appropriate range of innovation factor agglomeration and formulating regional green synergy policies.
Copyright (c) 2026 HongTao Li; ChenWei Zhang https://creativecommons.org/licenses/by/4.0
THE SPATIOTEMPORAL EVOLUTION OF LAND USE IN THE NANJING METROPOLITAN AREA AND DELIMITATION OF URBAN DEVELOPMENT BOUNDARIES BASED ON THE PLUS MODEL
This study focuses on the spatiotemporal dynamics of land use in the Nanjing Metropolitan Area and the scientific delimitation of its development boundaries. Based on land use observation data from 2000 to 2020, the study found that the Nanjing Metropolitan Area underwent rapid urban expansion over the two-decade period. The built-up area increased significantly from 2,318 square kilometers, reaching nearly double the 2000 level by 2020, while farmland—the land use type that shrank the most—decreased by a total of 4,171 square kilometers. By incorporating the PLUS model, this study identified the drivers of land expansion and conducted multi-scenario projections of land use patterns for 2025 and 2030. The results indicate that under natural evolutionary trends, construction land will continue to encroach upon farmland and ecological spaces. To curb the disorderly sprawl of urban areas, the study established a delineation approach centered on “ecological priority, farmland protection, and coordinated development.” Using GIS spatial analysis techniques and incorporating the rigid constraints of ecological and farmland red lines, the study preliminarily delineated the rigid urban development boundary for the Nanjing Metropolitan Area. The delineation results indicate that the total area of the rigid development boundary within the study area is 93,474.54 hectares, accounting for 65.45% of the total land area, primarily encompassing basic farmland protection zones and ecologically sensitive areas. The framework established in this study provides quantitative evidence and spatial constraint schemes for the optimization of territorial space and sustainable development at the metropolitan level.
Copyright (c) 2026 ChenXi Xu; YongYi Chen; JiaTong Yao https://creativecommons.org/licenses/by/4.0
INTELLIGENT EARLY-WARNING AND DECISION-MAKING FOR COALFIELD FIRE RISK BASED ON RANDOM FOREST: A CASE STUDY OF ABANDONED GOAFS IN NORTHERN CHINA
Coalfield fire risk identification is challenged by the strong concealment of goafs, the development of surface fissures, the unclear distribution of residual coal in abandoned workings, and uncertainties associated with multi-source detection data. Conventional approaches based on a single temperature threshold or expert empirical judgement are therefore insufficient for accurate early warning. To address field problems observed in an abandoned goaf of a coal mine in northern China, including temperature anomalies, abnormal CO concentrations at small-mine openings, smoke emissions from surface fissures, and the potential hydraulic or fracture connection between upper and lower goafs, this study develops an intelligent coalfield fire risk early-warning model based on the random forest algorithm. Thermal infrared imaging, isotopic radon measurements, and geological-goaf spatial information were used as the primary data sources. Surface temperature anomalies were extracted from UAV-based thermal infrared images, gas-migration signals along underground fracture pathways were obtained using alpha-cup radon measurements, and spatial-distance features to Jurassic abandoned-goaf boundaries and small-mine openings were quantified. Using expert interpretation results and field-observed anomaly points as labels, a random forest classification model was established to quantitatively classify coalfield fire risk levels in the study area. The results show that thermal anomaly features directly represent surface temperature responses, radon anomaly features effectively reveal concealed fractures and gas-migration pathways, and geological and goaf-related spatial features characterize the controlling conditions for fire development. Fusion of the three types of features substantially improves the stability and interpretability of coalfield fire risk identification. The proposed model provides a scientific basis for delineating temperature anomalies in abandoned goafs, optimizing verification borehole layout, zoning fire prevention and control measures, and ensuring safe longwall panel retreat.
Copyright (c) 2026 Rui Sun; ZiXue Wang https://creativecommons.org/licenses/by/4.0
LEACHING TOXICITY OF PHOSPHOGYPSUN-SLAG SUBGRADE MATERIALS
As a by-product of the phosphoric acid industry, phosphogypsum (PG) presents substantial environmental and health hazards due to residual fluorides, phosphorus compounds, and heavy metals, particularly during stockpiling or improper disposal. In this study, PG and slag were employed as primary precursors to synthesize subgrade materials, with a focus on evaluating their leaching toxicity characteristics via single-factor experimental designs. Leaching tests were conducted following the standard HJ/T 299-2007 to measure the concentrations of fluoride ion, phosphate ion, As, and Pb. Single-factor experiments were carried out to evaluate the effects of the water-to-binder ratio, hydrophobic agent dosage, and quicklime content. Results demonstrated that the optimal parameters achieved high immobilization efficiencies. Specifically, at a water-to-binder ratio of 0.32, the solidification rates for fluoride ion, phosphate ion, As, and Pb reached 98.53%, 99.12%, 76.37%, and 79.12%, respectively. Furthermore, a quicklime content of 12% and a hydrophobic agent dosage of 0.75% yielded the best comprehensive solidification performance. These findings provide crucial scientific evidence for the safe valorization of PG in subgrade engineering, contributing to the reduction of solid waste pollution and the sustainable development of the phosphorus chemical industry. It is recommended that admixture formulations be tailored to specific regional environmental conditions, accompanied by rigorous long-term monitoring of leaching toxicity.
Copyright (c) 2026 ZiLin Chen; Ling Tao; Duheng Xu; ZhiHao Wan; RunKe Tan; JiaWei Zhang; XiuRu Yin; Zheng Yao https://creativecommons.org/licenses/by/4.0
THE ENHANCEMENT OF BENZOQUINONE ON CR (VI) REDUCTION BY EXCESS SLUDGE
The excess sludge is rich in reducing substances, which can convert highly toxic hexavalent chromium (Cr(VI)) into less toxic trivalent chromium (Cr(III)). However, the process often suffers from low electron transfer efficiency. This study investigated the effect of the electron shuttle benzoquinone on the reduction of Cr(VI) by excess sludge. The results showed that, in a system containing 2 g/L of excess sludge and 5 mg/L of Cr(VI), after 30 minutes of reaction, the Cr(VI) removal efficiency increased from 43.2% to 76% as the benzoquinone concentration rose from 0 to 6 μM. However, when the benzoquinone concentration further increased to 10 μM, the removal efficiency decreased to 59.8%. Without benzoquinone, the Cr(VI) removal efficiencies within 30 minutes were 95.8%, 82.8%, 43.2%, 35.7%, and 23.6%, when the pH values were 3, 5, 7, 9, and 11, respectively. After adding benzoquinone, these values changed to 95.5% and 84.8% at pH 3 and 5, and increased to 76%, 54.2%, and 33.4% at pH 7, 9, and 11, respectively. These results indicate that benzoquinone can promote the reduction of Cr(VI) by excess sludge under neutral and alkaline conditions, while under acidic conditions or in the presence of bicarbonate, it has little effect on Cr(VI) reduction.
Copyright (c) 2026 JiaQi Cao; WanTing Tan; ZiHao Wang; YaJie Chen; LiJie Zhou; Rong Chen https://creativecommons.org/licenses/by/4.0
DISTRIBUTION AND STRUCTURE CHARACTERISTICS OF DISSOLVED ORGANIC MATTER IN SPARTINA ALTERNIFLORA WETLAND SOILS
The invasion of Spartina alterniflora has damaged coastal ecosystems in China. Current research focuses primarily on the total organic carbon stock and distribution in Spartina alterniflora wetlands, lacking a systematic exploration of the composition and characteristics of dissolved organic matter (DOM) in the soil. Therefore, Spartina alterniflora wetlands along China's coast were set as our research area. We determined the content and composition of the DOM in soils using UV-Vis absorption spectroscopy and three-dimensional fluorescence spectroscopy. The results show significant spatial differentiation in DOC content in Spartina alterniflora wetland soils across the study area. A positive correlation exists between CDOM and DOC content, indicating a synergistic effect between the formation of colored dissolved components and the release of organic carbon. DOM in Spartina alterniflora wetland soils is mainly composed of humic substances and protein-like substances, exhibiting high biological activity and a significant terrestrial-dominated characteristic. These findings provide a new perspective for assessing the carbon cycle effects of Spartina alterniflora invasion and offer a scientific basis for the ecological restoration and carbon sequestration management of coastal wetlands.
Copyright (c) 2026 RuPing Wu; KaiQi Li; YuXin Zheng; ShengJie Wu https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF NORMAL-PHASE CHROMATOGRAPHIC SEPARATION FOR QINGLONGYI AND PREDICTION OF ITS BIOACTIVITIES BASED ON PUBLIC CHEMICAL CONSTITUENT DATABASES
Qinglongyi is a natural medicinal resource with a defined origin and a chemically complex constituent profile, making it worthy of further investigation from the perspectives of chemical characterization and bioactivity-related research. In the present study, the reported chemical constituents of Qinglongyi were systematically compiled from publicly accessible chemical constituent databases, and a targeted normal-phase chromatographic separation strategy was designed and optimized based on their structural categories and physicochemical properties. On this basis, representative compounds were further subjected to bioactivity prediction in order to provide a reference for the subsequent screening of active constituents and the investigation of pharmacologically relevant material basis. The results showed that the reported constituents of Qinglongyi mainly included phenolics, flavonoids, quinones, terpenoids, and fatty acids, and evident differences in polarity distribution were observed among these compound classes, providing an important basis for the separation of its complex constituent system. The optimization results further indicated that petroleum ether-ethyl acetate-based mobile-phase systems afforded better component dispersion and separation performance, with an appropriate solvent ratio giving the best overall result. In addition, bioactivity prediction suggested that the representative compounds of Qinglongyi may be mainly associated with anti-inflammatory, antioxidant, antiproliferative, and antimicrobial activities. Overall, this study established an integrated research route combining constituent compilation, separation optimization, and bioactivity prediction for Qinglongyi, which may provide a theoretical basis and methodological reference for its subsequent isolation, functional screening, and material basis investigation.
Copyright (c) 2026 DaoShen Wang https://creativecommons.org/licenses/by/4.0
AN AI-DRIVEN HYBRID LSTM-RF FRAMEWORK FOR DECOUPLING ENVIRONMENTAL AND ANTHROPOGENIC IMPACTS ON AIR QUALITY
Evaluating atmospheric emission policies requires accurately separating anthropogenic activities from meteorological influences. Traditional statistical approaches often fail to capture the non-linear, high-dimensional dynamics of atmospheric environments. This research introduces a hybrid machine learning framework integrating an LSTM neural network and Random Forest meteorological normalization to decouple these overlapping effects. A dynamic inversion model was constructed using 2021 hourly observations of PM2.5, PM10, temperature, humidity, and wind speed from Jinan's Guodian station. The LSTM captured long-term temporal dependencies, achieving a coefficient of determination exceeding 0.80 across seasonal variations. To isolate meteorological impacts, the RF model employed a 14-day random resampling technique, calculating the conditional expectation of pollutant concentrations to marginalize weather randomness. Results revealed distinct seasonal contribution rates: meteorological factors contributed 47.36% to PM2.5 concentrations during severe December pollution events, driven by stagnant conditions and high humidity. Conversely, this contribution dropped to 24.87% in February, indicating primarily anthropogenic origins. Feature analysis identified humidity and temperature as the primary meteorological drivers of particulate accumulation. This framework provides an interpretable methodology to evaluate the actual efficacy of emission interventions under varying weather scenarios.
Copyright (c) 2026 HongYue Deng https://creativecommons.org/licenses/by/4.0
MOLECULAR STRUCTURE OF DISSOLVED ORGANIC MATTER IN MANGROVE SOILS
Mangrove ecosystems act a significant role in climate change adaptation and mitigation strategies. Large quantities of carbon are captured and sequestered in mangrove soils. Dissolved organic matter (DOM) in soils is known as the reactive carbon pool. It is a mixture of heterogeneous organics, involved in biochemical cycles. The molecular composition of DOM encapsulates the key information of carbon transformation and persistence in soils. However, there is still an unknown about the molecular compositions of DOM in mangrove soils. Here, we deciphered molecular constituents of DOM in mangrove soils from China by using the state-of-the-art tool, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Our study is the first to quantify and qualify soil DOM at molecular levels in mangrove wetlands. We found that DOM molecules in mangrove soils are combined with elements of C, H, O, N, S, and Cl. Soil DOM fates are revealed to be connected to multiple sources coupled with complex reactions in mangrove wetlands. We establish a comprehensive analytical framework of molecular diversity helpful to be extended to apply in other blue carbon ecosystems and provide novel insights into the DOM pool in mangrove wetlands.
Copyright (c) 2026 JiaCheng Lin; YaJie Xuan; ShengJie Wu https://creativecommons.org/licenses/by/4.0
EFFECTS OF MICROALGAE FERTILIZER BASED ON CHLORELLA VULGARIS HM-3 ON WHEAT GROWTH AND THE NUTRITIONAL QUALITY OF ITS GREEN JUICE
Wheat is an important food crop in China, exerting a profound impact on agriculture and food products. Microalgae, characterized by rapid growth, short life cycles, and high carbon fixation capacity, hold great potential for promoting crop growth and improving quality. This study aims to investigate the effects of substituting 70% of chemical fertilizer with heavy-ion mutagenized Chlorella vulgaris (HM-3) during the topdressing stage on wheat seedling growth and the nutritional quality of green juice. A pot experiment was conducted to compare the effects of full chemical fertilizer (T2), 70% substitution of chemical fertilizer with Chlorella vulgarisHM-3 (T1), and a no-fertilizer control (T0) on wheat seedlings at 30 and 60 days. The results showed that, compared to the T2 treatment, the T1 treatment (substituting 30% of chemical fertilizer with Chlorella vulgaris HM-3) increased root length, plant height, fresh weight, and leaf number by 81.28%, 13.34%, 53.84%, and 26.34%, respectively, at 30 days under a 30% fertilizer reduction. Total photosynthetic pigments, chlorophyll a, and chlorophyll b were significantly increased by 9.11%, 9.16%, and 5.13%, respectively. Soluble protein, amino acids, total phenols, and flavonoids increased by 40.57%, 14.77%, 12.27%, and 12.90%, respectively, while calcium and magnesium content accumulated by 6.14% and 8.77%. Previous studies have mostly focused on substituting base fertilizers for wheat seedlings or investigating the entire growth stage of wheat. This study specifically concentrates on topdressing and the wheat seedling stage.
Copyright (c) 2026 Run Wang; Xin Li; Hao Tan; ZhouLiang Tan; Liang Dong https://creativecommons.org/licenses/by/4.0
SPATIOTEMPORAL EVOLUTION OF CULTIVATED LAND PATTERN AND MULTIDIMENSIONAL DRIVING FORCE MECHANISM IN HEILONGJIANG PROVINCE
To deeply explore the dynamics of cultivated land use and its driving mechanism in Heilongjiang Province, a major grain-producing area in China, this study systematically reveals the spatiotemporal evolution characteristics of the cultivated land pattern in the study area over the past 20 years based on three phases of remote sensing of land use monitoring data (2000, 2010, and 2020) using methods such as land use transfer matrix and spatial analysis. On this basis, a driving force index system including natural factors (elevation, slope, precipitation, farmland production potential) and socioeconomic factors (population density, night-time light) was constructed. The Geographical Detector model was used to quantitatively examine explanatory capacity and interactive effects of each driving driver affecting the spatial heterogeneity of cultivated land changes in two time cross-sections (2000 and 2020). The research aims to clarify the evolution path of the cultivated land utilization pattern and its multidimensional driving force mechanism in Heilongjiang Province, providing a scientific basis for cultivated land protection, regional territorial space optimization, and the implementation of the national food security strategy.
Copyright (c) 2025 YiHui Chen; BoRui Li; XuSheng Zhao; BingYu Sun https://creativecommons.org/licenses/by/4.0
ENVIRONMENTAL RISKS AND ECOTOXICOLOGY OF ANTIBIOTIC POLLUTION IN WATER ENVIRONMENTS AND COMPREHENSIVE PREVENTION AND CONTROL STRATEGIES
The widespread detection of antibiotics in aquatic environments has become a global environmental issue, posing huge potential threats to ecosystems and human health. This paper systematically reviews research progress on antibiotic pollution in aquatic environments, focusing on its environmental behavior, ecotoxicological effects, and comprehensive prevention and control strategies. Firstly, an overview of the global and Chinese pollution status, major sources, and occurrence characteristics of antibiotics in aquatic environments is provided. Secondly, the migration and transformation laws, environmental fate, and ecological risk assessment methods of antibiotics in aquatic environments are explored in depth. Then, the ecotoxicological effects of antibiotics on aquatic organisms are elaborated in detail, including impacts on microbial community structures, the generation and spread of antibiotic resistance genes (ARGs), toxic effects on aquatic animals and plants, and potential risks to human health. Finally, comprehensive prevention and control strategies are proposed from four levels: source control, process interception, end-of-pipe treatment, and system management. Future research directions are also outlined to provide a scientific basis for the risk management of antibiotic pollution in aquatic environments.
Copyright (c) 2025 ZhiJiang Nan https://creativecommons.org/licenses/by/4.0
COMPARATIVE STUDY OF DIFFERENT WAVELET TRANSFORM METHODS FOR GNSS COORDINATE TIME SERIES ON THE QINGHAI–XIZANG PLATEAU
This study analyzes 1,611 days of GNSS data from the LHAZ IGS station on the Qinghai–Xizang Plateau (2018–2022). Daily coordinate time series were derived using GAMIT, followed by outlier removal and spline interpolation. Multi-resolution denoising was performed with DWT, CWT, and MODWT. Results show westward X displacement, minor Y fluctuations, and ~3 mm/year Z uplift with periodic variations. MODWT achieved the best denoising, outperforming DWT and CWT. MODWT is recommended for full 3D time series, while DWT suffices for horizontal-only applications. This study provides robust technical support for GNSS time series analysis on the Qinghai–Xizang Plateau.
Copyright (c) 2025 Wei Wu; JiWei Ma https://creativecommons.org/licenses/by/4.0
ECOSYSTEM SERVICES SUPPLY–DEMAND DYNAMICS AND MULTIDIMENSIONAL RELATIONSHIPS IN THE SHIYANG RIVER BASIN
Arid inland river basins are highly sensitive to climate change and anthropogenic disturbances, and persistent imbalances between the supply and demand of ecosystem services have become a critical constraint on regional sustainable development. Taking the Shiyang River Basin, a typical arid inland river basin in northwestern China, as the study area, this research selected four categories of ecosystem services—food provision, water yield, carbon storage, and windbreak and sand fixation. Using a parameter-calibrated InVEST model and GIS-based spatial analysis, we quantified ecosystem services supply and demand from 2000 to 2020, and evaluated supply–demand relationships across three dimensions: quantity matching, spatial matching, and trade-off/synergy assessment. The results show that: (1) From 2000 to 2020, the average supply of food provision and water yield exhibited an increasing trend, whereas carbon storage and windbreak and sand fixation showed fluctuating changes. High food supply was mainly concentrated in oasis areas, while the supply of water yield, carbon storage, and windbreak and sand fixation decreased from the southwest to the northeast; (2) During 2000–2020, the average demand for food provision and carbon storage continued to increase, while the average demand for water yield and windbreak and sand fixation gradually declined. High-demand areas for food provision, water yield, and carbon storage were primarily located in the main urban areas of the oasis, whereas demand for windbreak and sand fixation was mainly distributed in oasis–desert transition zones and desert regions; (3) In terms of quantity matching, food provision and carbon storage were predominantly surplus-oriented, water yield exhibited comparable proportions of surplus and deficit areas, and windbreak and sand fixation were dominated by deficit conditions. For all ecosystem services, low-low spatial matching and synergistic relationships were dominant, with synergy zones accounting for more than 50% of the basin.
Multidimensional supply–demand relationships
Copyright (c) 2025 ZeTao Chen https://creativecommons.org/licenses/by/4.0
RAINFALL PREDICTION FOR QINGMING FESTIVAL BASED ON ARIMA-LSTM MODEL
Requent rainfall during the Qingming Festival period has impacted public travel, cultural and tourism activities, as well as urban management. Based on nearly 20 years of meteorological data, this study develops an ARIMA-LSTM hybrid model to model and predict rainfall patterns in five representative cities: Xi’an, Turpan, Wuyuan, Hangzhou, and Wuhan. The results indicate that the model demonstrates strong fitting accuracy and stability, with an average R2 of 0.84196 and a prediction accuracy of 89.9%. After incorporating a real-time correction mechanism, the model’s responsiveness to abrupt weather changes improved, with error control enhanced by over 15%. This study provides data support and methodological reference for short-term meteorological services and public travel during the Qingming Festival.
Copyright (c) 2025 XinYing Song; YuXuan Zhao; SiYu Yang; Chao Luo; ZhenCheng Fu https://creativecommons.org/licenses/by/4.0
SPATIO TEMPORAL EVOLUTION AND INFLUENCING FACTORS OF WATER RESOURCE USE EFFICIENCY IN THE BEIJING–TIANJIN–HEBEI REGION: EVIDENCE FROM A SUPER EFFICIENCY SBM AND TOBIT MODEL
This study investigates the efficiency of water resource utilization in the Beijing–Tianjin–Hebei (BTH) region from 2013 to 2023. By employing a super-efficiency Slacks-Based Measure (SBM) model incorporating undesirable outputs, this research evaluated the spatial-temporal evolution of efficiency. Subsequently, a Tobit regression model was applied to analyze 13 determinants across natural, economic, social, and environmental dimensions. The results reveal that the region’s efficiency followed a fluctuating trajectory characterized by phases of stability, growth, decline, and leveling off, with a mean value of 0.870. Significant spatial heterogeneity was observed, with Beijing and Tianjin exhibiting higher efficiency levels than Hebei. The Tobit analysis identified positive correlations with per-capita water resources, annual precipitation, per-capita GDP, the share of the tertiary industry, and water intensity, reflecting the influence of resource endowment, economic structure, and management capacity. Conversely, factors such as per-capita water use, the share of the primary industry, and insufficient scientific and technological investment exerted negative impacts, suggesting constraints related to redundant consumption and structural imbalances. Based on these findings, we recommend implementing differentiated regional management strategies, establishing a market-oriented agricultural water rights trading system, and creating an integrated BTH platform for the collaborative innovation and diffusion of water-saving technologies. These measures aim to foster sustainable resource utilization and high-quality regional development.
Copyright (c) 2025 YuYang Liu https://creativecommons.org/licenses/by/4.0
THE ROLE OF MICROORGANISMS IN SOIL SALINIZATION REMEDIATION
Saline soil is one of the main forms of soil degradation, covering approximately 1 billion hectares globally. It is mainly found in Russia, the United States and China. Climate warming and improper irrigation practices have accelerated the salinization of the soil, posing a threat to crop productivity. In recent years, microorganisms, as an important part of the natural cycle, have been playing an increasingly significant role in the restoration of soil salinization. However, a comprehensive bibliometric analysis of this field has not yet been performed. Here CiteSpace, VOSviewer and the Bibliometrix R package were used to predict future trends in this field. The results showed numbers of published papers on the role of microorganisms in soil salinization remediation is increasing year by year. Functional microorganisms play a crucial role in restoring saline-alkali wetlands by regulating the expression of plant salt-tolerance genes and improving the soil microenvironment. Future research will focus on designing synthetic microbial communities, analyzing multi-omics interaction networks, and jointly enhancing the efficacy of ecological restoration and carbon sequestration, providing innovative solutions for the sustainable management of saline-alkali wetlands.
Copyright (c) 2025 XinRui Fan; DongXue Chen; Tong Gou; GanChenXi Chen; XuYue Zhang; HaiYan Xi; Jie Wei https://creativecommons.org/licenses/by/4.0
THE EFFECT OF OSPHF1 ON THE COMPOSITION AND STRUCTURE OF RHIZOSPHERE SOIL MICROORGANISMS IN RICE
Plant-microbe interactions represent a pivotal strategy for optimizing crop growth, and phosphorus (P) nutrition-related genes can mediate this process by regulating the composition of the rhizosphere microbial community. To investigate the effects of OsPHF1 ( Phosphate Transporter Traffic Facilitator 1 ) on the rhizosphere microbiota, we analyzed the differences in rhizobacterial community structures between OsPHF1 functional loss mutants ( Osphf1 ) and wild-type (WT) rice plants. High-throughput sequencing of 16S rRNA gene amplicons was performed to characterize the composition of the rhizosphere microbial community. The colonization abundances of Bacillus and Paenibacillus were quantified using the plate counting method, and pot experiments were conducted to verify the growth-promoting effects of Bacillus subtilis . The results show that the rhizobacterial community structure of Osphf1 mutants undergoes significant alterations, with distinct enrichment of taxa such as the phyla Pseudomonadota, Bacillota, Bacteroidota and Verrucomicrobiota. Within the phylum Bacillota, the abundances of the genera Bacillus and Paenibacillus in the rhizosphere of Osphf1 mutants are significantly higher than those in the rhizosphere of WT plants. The colonization amounts of Bacillus subtilis , Paenibacillus mucilaginosus and Paenibacillus polymyxa in the rhizosphere of Osphf1 mutants are all significantly increased. Inoculation with Bacillus subtilis significantly improves the aboveground fresh weight of rice plants, with an increase of 22.7% in WT plants and a more prominent increase of 31.6% in Osphf1 mutants. In conclusion, the OsPHF1 gene is involved in regulating the rhizosphere microbial community structure of rice, and specifically affects the rhizosphere enrichment and colonization of Bacillus species. These findings provide novel insights into the interaction mechanisms among plant nutrient-related genes, the microbiome and plant growth.
Copyright (c) 2026 Huang Wang; JinYu Zheng; Zhuang Xu; Lei Xu https://creativecommons.org/licenses/by/4.0
AN ECOSYSTEM-SERVICE EVALUATION METHOD OF A RIVER BASIN WITH RAPID CHANGES IN LAND USE WITHIN A SHORT TERM
In 2015, Zhangjiakou City was selected as the co-host city for the 2022 Winter Olympics. In order to improve ecosystem service functions, land use types in the region were changed greatly within the short term. Therefore, existing evaluation methods of the ecosystem service value (ESV) are not applicable to such an underdeveloped region. With the year of 2015 as the key time node, a dynamic process evaluation method of ESV was established for Zhangjiakou City based on classic equivalent factors in this study. The five economic coefficients of net primary productivity (NPP) coefficient, precipitation adjustment coefficient, soil conservation adjustment coefficient, habitat adjustment coefficient and accessibility adjustment coefficient were introduced into the classic method to correct the commonly Chinese equivalent factors to obtain the actual ESV. Firstly, the results obtained from the dynamic process evaluation method could clearly distinguish the changing trend of ecosystem service value (ESV) after the change of land use type. Secondly, the ESV increment calculated with the dynamic process evaluation method was more sensitive to land use change. The change in the forest area proportion was the most significant and reached 6.8%, and corresponding ESV increment was 14.89%. Thirdly, from the perspectives of 11 sub-categories of ecosystem services, the ESV of hydrological regulation was the largest and reached 30%. The ESVs of food production, raw material production, maintenance of nutrient cycle, water supply and aesthetic value supply were relatively small. From the sub-categories of enhancing ecosystem services, the ESV of each sub-category increased after changing land use type. Fourthly, after the introduction of five economic adjustment coefficients, the ESVs of the other four categories of ecosystem services, except the ESV of biodiversity, increased significantly after the change in the land use types. The results indicated that the dynamic evaluation method could better identify the sensitive factors affecting the value of regional ecosystem services.
Copyright (c) 2025 HeSheng Zhang; MengHao Li; ZiYao Xing; YuLing Zhang; Xin Zan; XiaoYu Jia https://creativecommons.org/licenses/by/4.0
SPATIOTEMPORAL VARIABILITY OF YELLOW RIVER WATER-SEDIMENT FLUXES: A HYBRID APPROACH USING CUBIC SPLINE INTERPOLATION AND MANN-KENDALL TEST
Studying the variation law of water and sediment fluxes in the Yellow River is of great significance for environmental protection, adaptation to climate change and improvement of the quality of life of residents in the Yellow River Basin. this paper established a cubic spline interpolation model to supplement the missing sediment content data. By using the known data and interpolation data for plotting, it was found that the interpolation effect was good. Subsequently, the total drainage volume and total sediment discharge volume from 2016 to 2021 were calculated respectively using the interpolation data. In order to further study the variability of water and sediment content, this paper adopts the Mann-Kendall non-parametric test method to analyze the variability of water and sediment flux, revealing its inherent law. Through specific data analysis, the important role of sand regulation and water control in ensuring the ecological health of the Yellow River has been further demonstrated.
Copyright (c) 2025 Yun Xiao https://creativecommons.org/licenses/by/4.0
RECONSTRUCTION OF SEVERAL LANDSLIDE DAMMED LAKES IN THE UPPER REACHES OF JINSHA RIVER AND ITS INFLUENCE ON RIVER PROFILE EASTERN XIZANG PLATEAU
The damming phenomenon caused by landslides is a significant disturbance factor in the evolution of mountainous rivers, influencing river morphology across multiple timescales. The temporal characteristics of this phenomenon are often analyzed by examining the relationship between dam locations and the river longitudinal profile within a specific area. In the Batang section of the upper Jinsha River, located within the Jinsha river suture zone, intense tectonic activity makes the region highly susceptible to geological hazards such as landslides. Through data collection, remote sensing interpretation, and field investigations, we identified nine landslide dams in this area. Systematic studies of these dams were conducted, including preliminary measurements of dam heights and volumes. By reconstructing dam elevations, we calculated the areas, volumes, and peak discharge flows of the associated dammed lakes. A detailed analysis of the Wangdalong landslide dam revealed that its maximum lake surface area reached 106.70 million m 2 , with a maximum volume of 14.57 billion m 3 . The peak discharge flow was calculated at 31.98×104 m 3 /s, and the dammed lake existed for a duration of approximately 1.09-1.76 ka. Additionally, a comparison of river geomorphological parameters indicated that the impact of a single dammed lake on the river's longitudinal profile is limited when its duration is relatively short. However, clusters of dammed barriers significantly affect the river steepness index, increasing the likelihood of forming knickpoints. These findings provide valuable insights into the role of landslide dams in shaping the geomorphological evolution of mountainous river systems.
Copyright (c) 2025 LiQin Zhou; Lu Cong https://creativecommons.org/licenses/by/4.0
QUANTITATIVE STUDY ON METHANE EMISSION IN LIAONING PROVINCE BASED ON GAUSSIAN PLUME MODEL
Methane, a critical greenhouse gas, significantly contributes to climate warming. This study quantifies methane emissions in Liaoning Province, China—a major industrial region—using TROPOMI satellite data and the Gaussian plume model. Filtered satellite observations and ERA5 meteorological parameters were integrated to retrieve emission rates and spatial patterns. Results indicate annual methane emissions of (720 ± 150) Gg yr -1 , with coal mining accounting for more than 60%. Seasonal analysis highlights elevated winter emissions linked to heating-related coal use. Satellite-derived emissions exceed EDGARv6.0 estimates by approximately 35%, suggesting underreported industrial sources, particularly mine ventilation leaks. The study demonstrates the effectiveness of combining remote sensing and dispersion modeling for regional methane monitoring. Key recommendations include enhancing emission inventories through high-resolution models and promoting methane recovery technologies in coal and steel sectors. These findings emphasize the need for targeted industrial emission controls to address climate and air quality challenges.
Copyright (c) 2025 YuChao Zhang https://creativecommons.org/licenses/by/4.0
GROUNDWATER LEVEL FITTING OF MONITORING WELLS IN THE BAODING REGION BASED ON LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORKS
This study applies a Long Short-Term Memory (LSTM) neural network to model daily groundwater level variations at four monitoring wells in the piedmont plain of Baoding City, Hebei Province, China. Using daily data from January 1, 2018 to August 31, 2019, the LSTM model is trained and tested under a one-step rolling prediction framework. The results demonstrate that the LSTM model accurately fits groundwater time series across various hydrogeological conditions, with testing-phase RMSE values ranging from 0.08 to 0.45 meters and R2 values exceeding 0.90. The model performs exceptionally well for both stable and fluctuating groundwater level conditions, capturing seasonal decline and recovery patterns without requiring explicit seasonal indicators. It also reveals the model’s ability to learn long-term trends and nonlinear dynamics inherent in groundwater systems. Despite its high short-term prediction accuracy, challenges remain regarding multi-step forecasting and responses to extreme events outside the training data. The study concludes that LSTM offers strong potential for groundwater simulation in data-limited environments and recommends further integration of hydro-meteorological variables for enhanced model robustness.
Copyright (c) 2025 Wei Guo https://creativecommons.org/licenses/by/4.0
CO-PYROLYSIS OF BIOMASS-BASED FURAN AND METHANOL TO LIGHT AROMATICS OVER MODIFIED HIERARCHICAL ZEOLITES
Light aromatics, including benzene, toluene, and xylene, are essential chemical feedstocks, traditionally produced via catalytic reforming of petroleum fractions. Due to the depletion of fossil resources, lignocellulose has attracted considerable interest as a renewable carbon source. Furans as key platform molecules can be catalyzed into aromatics over zeolites; however, their application is often limited by low selectivity and catalyst deactivation from coke. In this work, a co-pyrolysis approach using 2-methylfuran and methanol was developed to enhance aromatic production. Methanol served both as a heat-releasing agent during the aromatization process and as a hydrogen donor due to its high effective hydrogen-to-carbon ratio, promoting a synergistic effect with furan compounds, enhancing aromatization efficiency. Hierarchical HZSM-5 modified with Ga, Zn, Mo, and W were synthesized and characterized using various characterization techniques to correlate pore structure and acidity with product distribution. It is found that W-modified HZSM-5 can further improve the carbon yield of aromatics (70.62%), while other metal modifications resulted in decreased performance due to reduced acidity and pore blockage, accompanied by increased coke formation. This strategy provides new insight into the efficient catalytic conversion of biomass-derived platform molecules into high-value aromatics.
Copyright (c) 2025 Qing Xu; JunMing Liang; DongYan Zhang; ShengPeng Xia; AnQing Zheng; Kun Zhao; ZengLi Zhao https://creativecommons.org/licenses/by/4.0
CAN OPEN WEB DATA ASSESS URBAN FLOOD RISK? EVIDENCE FROM ZHENGZHOU
Under extreme rainfall scenarios, urban stormwater pipe networks are prone to saturation and failure, rendering flood risk assessment under such conditions of critical importance. This study established a flood risk assessment framework using GIS technology and open web data to evaluate 270 catchment subunits in Zhengzhou's main urban area. Results reveal an overall stepped spatial pattern of flood risks, characterized by lower risks in the southwest and higher risks in the northeast, with contiguous high-risk clusters identified in the city center. Spatial autocorrelation analysis confirms strong spatial dependence of flood risks: High-High clusters concentrate in the urban core and northeastern districts, while Low-Low clusters are distributed across the higher-elevation southwestern region. The study further conducts bivariate correlation analysis between zonal flood risks and influencing factors, culminating in the proposal of targeted mitigation strategies.
Copyright (c) 2025 Da Mao; Yan Xie; ZhiYu Yuan https://creativecommons.org/licenses/by/4.0
INTELLIGENT PREDICTION OF WATER QUALITY PARAMETERS BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION COUPLED WITH RANDOM FOREST MODEL
In recent years, the problem of water pollution has become increasingly serious, and traditional water quality monitoring methods are difficult to meet the demand for high precision. Constructing an efficient and reliable water quality prediction model is of great significance for governance decisions. To this end, this paper proposes a random forest (IPSO-RF) water quality prediction model optimized based on the improved particle swarm algorithm. Firstly, for the problems of traditional particle swarm algorithm (PSO), such as easy premature convergence and insufficient global search ability, an improved particle swarm algorithm (IPSO) with nonlinear iteration incorporating inertia weights is proposed, and its optimization performance is verified based on typical test functions. Secondly, the IPSO algorithm is combined with random forest (RF) to optimize the key hyperparameters of RF (e.g., the number of decision trees, the minimum number of samples for node splitting, etc.) in order to improve the prediction accuracy and generalization ability of the model. Simulation experiments were carried out based on the water quality monitoring data of a watershed, and the results showed that compared with RF and standard PSO-RF and other models, the IPSO-RF model showed lower MSE in the prediction of key indicators such as dissolved oxygen, phosphorus content of water body and ammonia nitrogen amount of water body, which verified its superiority in the prediction of water quality. This study not only provides new ideas for the application of intelligent optimization algorithms in the field of water environment, but also provides more accurate technical support for water quality monitoring and pollution prevention and control of environmental protection departments.
Copyright (c) 2025 JinYi Luo https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF RIPARIAN PLANT COMMUNITIES IN MOUNTAINOUS URBAN PARKS BASED ON THE CONCEPT OF REWILDING — A CASE STUDY OF CHONGQING URBAN AREA
This study examines the riparian zones of seven representative urban parks in Chongqing, exploring strategies for optimizing plant communities based on the concept of "rewilding." The findings indicate several issues in the current riparian vegetation, including a dominance of cultivated species (accounting for 80.57%), a simplistic community structure (with "tree + herb" combinations comprising 57.94%), and a scarcity of wild species (only 55 species, or 19.43%). These factors result in low biodiversity (average Shannon-Wiener index of 1.52), weak soil and water conservation capacity (soil erosion modulus of 312 t/(km2·a) in areas with a single structure), and landscape homogenization, all of which limit the ecological service functions of these areas. Rewilding refers to enhancing ecosystem self-regulation by reducing human intervention and actively introducing native species. Constraints to rewilding include excessive human interference, habitat fragmentation, invasive plant species, and outdated management practices.Based on this, a series of optimization strategies are proposed, including the selection of native wild plants, the development of multilayered plant communities, the implementation of tiered management and targeted control of invasive species, and the encouragement of public participation, aiming to provide scientific support and practical guidance for the ecological restoration of riparian zones in mountainous cities.
Copyright (c) 2025 KaiWei Du; ChengJi Shu; YuSong Li; ZiYi Jiang; YunDuo Kang https://creativecommons.org/licenses/by/4.0
SUITABILITY EVALUATION OF WATER INDEX UNDER EXTRACTION OF COMPLEX ENVIRONMENTAL WATER BODIES
With the development of remote sensing technology, remote sensing images are more and more widely used in water monitoring. This paper uses Landsat 8 remote sensing images as the data source, selects Poyang Lake, Taihu Lake and Dingnan County as the study area, and combines NDWI, MNDWI, EWI, SWI, TCW, MBWI, WI2015, AWEInsh and AWEIsh to extract water bodies and evaluate the accuracy of the study area. The results are as follows: 1. The overall extraction accuracy of TCW and MBWI is the highest. 2. For mountain shadow areas, AWEInsh and TCW have the best extraction effect. For shallow water area, TCW and MBWI have the b est extraction effect, and the spectral distinction between water and non water is greatly increased. For the eutrophication area of water body, the ten methods have high accuracy, and can distinguish the spectral characteristics of chlorophyll and non water body.
Copyright (c) 2025 ZhiHua Liu https://creativecommons.org/licenses/by/4.0