Applied Water Science最新文献

{"title":"Dual functional zeolite/hydroxyapatite nanocomposite for highly effective removal of malachite green dye and cadmium from wastewater","authors":"Mohamed A. Hassan,&nbsp;Rowaida M. Swify,&nbsp;Nada M. Rizk,&nbsp;Menna T. M. Mohammed,&nbsp;Ganat A. Ragab,&nbsp;Talal B. Almeelbi","doi":"10.1007/s13201-026-02867-1","DOIUrl":"10.1007/s13201-026-02867-1","url":null,"abstract":"<div><p>Water scarcity and contamination demand efficient wastewater treatment technologies that are both effective and sustainable. In this study, a waste‑derived zeolite/hydroxyapatite nanocomposite (W‑ZEO/HAP‑NC) was synthesized from rice‑husk‑derived silica and aluminum‑can waste and evaluated for its versatile, dual-functional capacity to remove both Cd(II) and malachite green (MG) from water. XRD, SEM–EDX, FTIR, zeta potential and BET analyses confirmed a porous W‑ZEO/HAP‑NC framework enriched with hydroxyl, phosphate, and silicate groups that supply abundant adsorption sites. Batch experiments showed rapid, pH‑dependent uptake, with equilibrium and kinetic data best described by the Freundlich and pseudo‑second‑order models. Under optimized conditions, W‑ZEO/HAP‑NC achieved Langmuir capacities of 666.7 mg g^− 1 for Cd(II) and 400.0 mg g^− 1 for MG, driven by electrostatic attraction, ion exchange, surface complexation, π–π interactions, and hydrogen bonding. The composite retained 85–95% of its initial efficiency after four adsorption–desorption cycles, underscoring its potential as a high‑capacity, regenerable adsorbent for wastewater purification and waste valorization.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02867-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The concentration of environmental estrogens in surface water in China: a systematic review, meta-analysis and health risk assessments","authors":"Minhao Wang,&nbsp;Hongran Ding,&nbsp;Ting Tong,&nbsp;Lei Han","doi":"10.1007/s13201-026-02907-w","DOIUrl":"10.1007/s13201-026-02907-w","url":null,"abstract":"<div><p>Environmental estrogens (EEs) have been recognized as emerging organic pollutants in surface waters, posing significant ecological and human health risks. Following the PRISMA guidelines, we conducted a systematic review and meta-analysis of EEs (estrone (E1), 17β-estradiol (E2), estriol (E3) and 17α-ethinylestradiol (EE2)) in Chinese surface water, synthesizing English and Chinese literature. The meta-analysis estimated the concentrations (ng/L) of each EE in water as follows: E1 = 4.48 (95% CI: 2.15, 6.81), E2 = 5.76 (95% CI: 1.34, 10.17), E3 = 7.72 (95% CI: 2.88, 12.57), and EE2 = 6.11 (95% CI: 1.44, 10.78). Spatial patterns showed higher levels in eastern and central China, while studies conducted post-2020 reported lower concentrations, which corresponded to reduced discharges during the COVID-19 lockdown. Age-grouped health risk assessment indicated that current levels of EEs in surface waters have not yet exceeded the lower limit threshold for potential health risks. However, the potential risks to sensitive groups such as children cannot be overlooked. The substantial heterogeneity across studies reflects variations in geographical location, seasonality, and methodology. This study provides the most comprehensive assessment of EEs in Chinese surface water, highlighting their widespread occurrence, potential risks to vulnerable populations, and evidence gaps in western and rural areas. These findings provide a scientific basis for strengthening water quality standards, prioritizing pollutant management, and guiding future monitoring efforts in China.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02907-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surfactant-assisted hydrothermal synthesis of silver tungstate with controlled phase composition and morphology for enhanced electrochemical hydrogen storage","authors":"Samira Shahbazi,&nbsp;Farideh Sedighi,&nbsp;Mohsen Behpour","doi":"10.1007/s13201-026-02885-z","DOIUrl":"10.1007/s13201-026-02885-z","url":null,"abstract":"<div><p>In this study, Ag₂WO₄ structures were synthesized via a hydrothermal method using four different surfactants: polyethylene glycol (PEG), polyvinylpyrrolidone (PVP), polyvinyl alcohol (PVA), and sodium dodecylbenzene sulfonate (SDBS). The effects of surfactant type on the phase purity, morphology, particle size, and electrochemical hydrogen storage capacity of the synthesized samples were systematically investigated. The structural analyses confirmed that the use of different surfactants led to distinct morphologies and variations in phase composition, ranging from pure to mixed phases. Electrochemical measurements after 15 charge–discharge cycles in alkaline electrolyte demonstrated that the sample prepared with polyvinylpyrrolidone (PVP) exhibited the highest hydrogen storage capacity, reaching approximately 1200 mAh g^− 1. The findings indicate that the choice of surfactant during synthesis significantly influences the structural and electrochemical properties of Ag₂WO₄, suggesting its potential as a promising material for hydrogen storage applications.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02885-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing deep learning performance in hydrological forecasting: the role of population-based optimization and block bootstrapping","authors":"Adnan Mazraeh,&nbsp;Meysam Bagherifar,&nbsp;Saeid Shabanlou","doi":"10.1007/s13201-026-02886-y","DOIUrl":"10.1007/s13201-026-02886-y","url":null,"abstract":"<div><p>Accurate river flow simulation presents a fundamental challenge in water resource management, particularly given the complexities introduced by climate variability. While Long Short-Term Memory (LSTM) networks are powerful tools for modeling such temporal sequences, their performance is highly dependent on the precise tuning of hyperparameters and they are prone to overfitting. To address these limitations, this study proposes a novel hybrid framework that integrates the Block Bootstrap Aggregation (BBA) algorithm and population-based optimization algorithms (POAs) with an LSTM network. The objective of the proposed BBA-POA-LSTM model is to enhance the accuracy and stability of predictions under diverse climatic conditions. Within this framework, five metaheuristic algorithms—Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO)—are employed to optimize the critical hyperparameters of the LSTM, replacing traditional trial-and-error methods. Simultaneously, the BBA technique was utilized to generate synthetic datasets for model training, thereby mitigating overfitting and quantifying modeling uncertainty. The framework was rigorously evaluated using twenty years of hydro-climatic data (2004–2024) from five distinct hydrometric stations in Iran.The results demonstrate the superiority of the optimized hybrid models over the base trial-and-error LSTM model. Specifically, the WOA-LSTM model performed best for datasets DS1 and DS3, HHO-LSTM for DS2, GWO-LSTM for DS4, and GOA-LSTM for DS5. On average, these optimized models reduced the Root Mean Square Error (RMSE) by approximately 52 to 55% compared to the base LSTM model. Furthermore, the top-performing models achieved Nash–Sutcliffe Efficiency (NSE) index values exceeding 0.99 during the testing phase, indicating very high accuracy and an almost perfect fit between observed and simulated river flow values. A critical finding of this study is that no single optimization algorithm outperformed the others across all stations, underscoring the importance of selecting an optimizer tailored to the specific characteristics of the watershed. Ultimately, this research provides not only a precise mathematical model but also a practical decision-support tool for water resource managers, enabling them to conduct strategic planning with greater confidence in the face of climatic fluctuations.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02886-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Enhanced polysulfone membranes modified with TiO₂-Mn₃O₄ nanocomposites for high-efficiency methyl orange removal: a sustainable approach for wastewater treatment","authors":"Amira M. Shawky,&nbsp;Doaa F. Ahmed,&nbsp;Aya Abd El Aziz Elfiky,&nbsp;Hany M. Youssef,&nbsp;Mahmoud F. Mubarak","doi":"10.1007/s13201-026-02898-8","DOIUrl":"10.1007/s13201-026-02898-8","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02898-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Copper removal through microbially induced carbonate precipitation (MICP): performance, mineralogy and process limitations for wastewater treatment","authors":"Carla Duarte-Nass,&nbsp;Katherina Rebolledo,&nbsp;Sebastián Aravena,&nbsp;Gustavo Ciudad,&nbsp;David Jeison,&nbsp;Álvaro Torres-Aravena","doi":"10.1007/s13201-026-02905-y","DOIUrl":"10.1007/s13201-026-02905-y","url":null,"abstract":"<div><p>Heavy metal contamination by wastewater industrial discharged into waterbodies, poses a persistent environmental challenge. In this study, we explored the application of microbially induced carbonate precipitation (MICP) as a sustainable and low-impact strategy for copper removal from synthetic wastewater using <i>Sporosarcina pasteurii</i>. The influence of key operational parameters—including bacterial presence, calcium addition (12.5 mM), and pH variation (6–7)—on copper precipitation was systematically evaluated. Under all tested conditions, copper removal remained within a range of 50–60%, and ANOVA analysis revealed that none of the variables significantly enhanced removal efficiency. Characterization of the precipitates by SEM–EDX and FTIR confirmed the formation of malachite (Cu₂CO₃(OH)₂) as the dominant copper mineral phase, with no evidence of co-precipitation with calcite. These findings indicate that copper carbonate formation occurs independently of calcium ions, and that while microbial urea hydrolysis facilitates carbonate generation, it does not directly influence copper precipitation efficiency. This study contributes novel insights into the mechanistic limitations of MICP for copper removal and highlights the need for exploring more resilient microbial strains or modified operational strategies to enhance metal sequestration performance. The results support the potential for MICP-based technologies in wastewater treatment systems, while also identifying critical factors that constrain its efficiency when applied to copper-contaminated effluents.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02905-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-pyrolyzed sawdust–polypropylene biochar as a sustainable adsorbent for heavy-metal removal in wastewater","authors":"Torpong Kreetachat,&nbsp;Saksit Imman,&nbsp;Nopparat Suriyachai,&nbsp;Suphalerk Khaowdang,&nbsp;Aitsara Chanthakhot,&nbsp;Athicha Janthakhot,&nbsp;Surachai Wongcharee,&nbsp;Worachate Sangsida,&nbsp;Sukanya Hongthong,&nbsp;Kowit Suwannahong","doi":"10.1007/s13201-026-02908-9","DOIUrl":"10.1007/s13201-026-02908-9","url":null,"abstract":"<div><p>Co-pyrolysis of lignocellulosic sawdust and non-recyclable polypropylene waste provides a biorefinery-driven pathway for producing high-performance biochar while simultaneously valorizing forestry residues and plastic waste. Mixed sawdust-PP feedstocks were thermochemically converted at 300, 400, and 500 °C using a semi-continuous LPG-fired reactor designed for rural Thai communities. Physicochemical characterization revealed a clear temperature-dependent structural evolution from cellulose-rich matrices to carbon-dense, partially graphitic frameworks with enhanced aromaticity and pore accessibility, while the BET surface area varied in the low range (~ 102–166 m² g⁻¹) due to plastic-induced pore masking. The surface pH_pzc shifted toward near-neutral values (~ 6.0–6.2) with increasing temperature, enhancing electrostatic interactions with Cu²⁺. Copper adsorption performance increased markedly with temperature, with maximum capacities of 85.4 mg g⁻¹ (300 °C), 111.7 mg g⁻¹ (400 °C), and 126.1 mg g⁻¹ (500 °C). Kinetic analysis showed that fractal-like and general-order models (R² &gt; 0.99) best described the adsorption behavior, confirming heterogeneous and diffusion-influenced chemisorption. The Redlich–Peterson and Liu isotherms provided the best equilibrium fits, indicating adsorption on energetically heterogeneous surfaces. Thermodynamic parameters verified that the adsorption process was spontaneous, endothermic, and entropy driven. The SPB produced at 500 °C retained over 73% of its initial removal efficiency after five regeneration cycles, demonstrating strong operational stability. Overall, these results establish co-pyrolyzed sawdust–PP biochar as a cost-effective, activation-free adsorbent aligned with circular bioeconomy and biorefinery strategies, supporting decentralized wastewater treatment and contributing to SDGs related to clean water, responsible production, and climate action.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02908-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust operation of Haditha dam under deep uncertainty: a surrogate-based stochastic optimization approach","authors":"Abu Baker Ahmad Najm,&nbsp;Safaa Ahmed,&nbsp;Badir S. Alsaeed","doi":"10.1007/s13201-026-02891-1","DOIUrl":"10.1007/s13201-026-02891-1","url":null,"abstract":"<div><p>Strategic management of multipurpose dams under deep hydrological uncertainty is particularly challenging in data-scarce, climate-vulnerable basins. This paper develops and applies a Surrogate-Based Stochastic Optimization (S-BSO) framework to the Haditha Dam on the Euphrates River in western Iraq. The framework integrates four stages: (1) stochastic inflow generation using an ARMA(1,2) model calibrated on the 1991–2011 record, yielding 1,000 synthetic 50 year scenarios; (2) a physics based monthly reservoir simulator with proactive flood control and explicit separation of flood risk and overtopping metrics; (3) an Artificial Neural Network (ANN) surrogate with four outputs, trained under a tempered weighted loss function and evaluated via Group K-Fold cross validation; and (4) Mult objective optimization using NSGA-II with 13 constraints, including a hard dam-safety cap on overtopping, followed by physics based revalidation of all candidates. The current operational policy proved to be a demanding baseline, delivering a simulator-validated median hydropower of 1,591 GWh/yr with essentially perfect E-Flow reliability and zero median flood risk. The optimization identified 49 validated Pareto-optimal policies, from which three representative options are presented as a decision menu: a Balanced policy delivering + 2.0% median hydropower (+ 31 GWh/yr) with minimal degradation in safety metrics; a Hydro Priority policy delivering + 4.2% (+ 66 GWh/yr) at a small quantified increase in extreme case flood risk; and a conservative Flood Safe reference policy. A systematic surrogate fidelity analysis across all 100 candidates quantified the ANN bias (+ 100 GWh/yr for hydropower), confirming that the surrogate reliably ranks policies while the simulator provides authoritative final estimates.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02891-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics and source analysis of nitrate pollution of karst groundwater in the Xiaoli area of the lower Yellow River Basin","authors":"Qinghai Deng,&nbsp;Xiao Wu,&nbsp;Wenqiang Zhang,&nbsp;Changsuo Li,&nbsp;Liping Zhang,&nbsp;Yue Teng,&nbsp;Yunfeng Zhang,&nbsp;Genlin Li","doi":"10.1007/s13201-026-02904-z","DOIUrl":"10.1007/s13201-026-02904-z","url":null,"abstract":"<div><p>Groundwater nitrate pollution poses a critical threat to karst water resources in the Xiaoli area of Jinan. Based on the water chemical data of 23 samples, this study employed integrated techniques including hydrochemical analysis, reverse hydrogeochemical modeling, and the absolute principal component score-multivariate linear regression (APCS-MLR) model to reveal pollution characteristics and sources. The research found that groundwater chemistry type was mainly HCO₃-Ca·Mg type, with its evolution primarily controlled by rock weathering and overlaid by human activities. At the same time, reverse hydrogeochemical modeling demonstrated the influence of biogeochemical processes (nitrification). The nitrate exceedance rate was 48% and the pollution was spatially distributed in a northeast-southwest direction, mainly concentrated in the runoff area and discharge area, with particularly severe contamination near towns. Source apportionment identified agricultural activities such as fertilization and irrigation as the primary contributors to nitrate pollution, accounting for 62.35%, followed by the discharge of domestic sewage and feces, accounting for 20.20%. To ensure groundwater safety in the study area, strict control of agricultural fertilizer application is essential, alongside monitoring the discharge of domestic sewage and feces, and conducting regular groundwater quality monitoring. These findings provide a basis for groundwater nitrate pollution prevention and control in the study area and other similar regions.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02904-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviving the Wular Lake: tracking its decline with remote sensing and NDWI analysis","authors":"Imran Ahmad,&nbsp;Amnah Aldohan,&nbsp;Mithas Ahmad Dar,&nbsp;Nazih Y. Rebouh,&nbsp;Youssef M. Youssef","doi":"10.1007/s13201-026-02897-9","DOIUrl":"10.1007/s13201-026-02897-9","url":null,"abstract":"<div><p>Wular Lake, one of Asia’s largest freshwater bodies, has undergone significant shrinkage over the past four decades, threatening its ecological stability and local livelihoods. This study examines the lake’s decline using Normalized Difference Water Index (NDWI) and Support Vector Machine (SVM) classification, analyzing satellite imagery from 1985, 2000, 2015, and 2024. Findings indicate a reduction in surface area from 90.57 km² in 1985 to 65.33 km² in 2024, primarily due to climate change, urbanization, and excessive water extraction. Satellite altimetry data between 2016 and 2025 reveal seasonal fluctuations in water levels, reinforcing concerns about long-term hydrological instability. These results emphasize the urgent need for integrated water resource management to mitigate environmental degradation. Remote sensing offers a powerful tool for tracking hydrological changes, urging immediate conservation efforts to restore Wular Lake’s ecological balance and sustain communities dependent on its water resources.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02897-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}