Environmental Monitoring and Assessment最新文献

{"title":"Application of machine learning models for zooplankton abundance prediction in ponds of Southeastern Coastal Regions in Bangladesh.","authors":"Jayanta Kumar Basak, Sanjay Saha Sonet, Rahnumazzaman Rumman, Bhola Paudel, Sumaya Akter, Tapash Kumar Sarkar, Mohammed Mafizul Islam","doi":"10.1007/s10661-025-14382-y","DOIUrl":"https://doi.org/10.1007/s10661-025-14382-y","url":null,"abstract":"<p><p>Zooplankton abundance prediction in surface water bodies is crucial because they reflect ecosystem health and have role in aquatic food webs and nutrient cycling. This study examined the applicability of machine learning algorithms to estimate zooplankton abundance in ponds using water quality parameters. Data on zooplankton abundance and water quality parameters were collected monthly. Multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), multi-layer perceptron (MLP), and extreme gradient boosting (XGB), were used to observe the relation between zooplankton abundance and the water quality indicators i.e., ammonia (NH₃), dissolved oxygen (DO), biochemical oxygen demand (BOD), electrical conductivity (EC), carbon dioxide (CO₂), pH, phosphate (PO₄), total dissolved solids (TDS), water hardness (WH), water salinity (WS), and water temperature (WT). The findings revealed that the MLP model, outperformed other models during the training and testing stages, achieving the highest accuracy during training (R² > 0.98) as well as in testing (R² > 0.88), with increases in R² by 20.27%, 2.49%, 1.99%, and 0.92% in training, and 23.32%, 3.40%, 12.79%, and 2.23% in testing compared to the MLR, RFR, SVR, and XGB models, respectively. MLP and MLR were the most stable models, while SVR was the least stable. Sensitivity analysis indicated WT as the most influential parameter for predicting zooplankton abundance, followed by DO, BOD, PO₄, WS, pH, NH₃, CO₂, EC, WH, and TDS. The findings have substantial implications for environmental and fisheries management, providing a valuable tool for monitoring aquatic ecosystems.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"893"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoAI-based soil erosion risk assessment in the Brahmaputra River Basin: a synergistic approach using RUSLE and advanced machine learning.","authors":"Toushif Jaman, Shashank Bhaskar, Victor Saikhom, Rekha Bharali Gogoi, K K Sarma, S P Aggarwal","doi":"10.1007/s10661-025-14314-w","DOIUrl":"https://doi.org/10.1007/s10661-025-14314-w","url":null,"abstract":"<p><p>Soil erosion is a critical environmental issue in the Brahmaputra River Basin, threatening agricultural productivity, water resources, and ecological balance. This study employs the revised universal soil loss equation (RUSLE) alongside remote sensing, geographic information systems (GIS), and advanced machine learning models like random forest (RF) and gradient boosting (GB) to analyze soil erosion patterns from 2005 to 2024. The analysis revealed that average annual soil loss increased from 15.8 tons/ha/year in 2005 to 25.4 tons/ha/year in 2024, marking a 60.76% rise over two decades. Peak erosion rates were observed in 2020, with localized hotspots recording up to 32,130 tons/ha/year. Spatial analysis from 2005 to 2024 indicated substantial variability, with soil loss values ranging from - 7.024 to 9034 tons/ha in 2005. Topographic influence, quantified using the LS factor, revealed that 47.2% of the basin area has slopes steeper than 16°, significantly contributing to elevated erosion risk. The rainfall erosivity (R-factor) fluctuated throughout the period, peaking at 2305.73 MJ mm/ha h year in 2015 but declining to 799.21 MJ mm/ha h year by 2024, indicating a temporal shift in rainfall patterns. Vegetation cover improvements during this time reduced the mean C-factor from 0.52 to 0.34, though 13.8% of the basin (approximately 3.05 million ha) still falls under high to very high erosion risk zones. RF model predictions achieved an R² of 0.915 and RMSE of 4.82, while GB attained an R² of 0.952 with RMSE of 3.97, indicating superior predictive performance. These findings underscore the urgent need for targeted soil conservation measures, afforestation programs, and sustainable watershed management. The integration of AI-driven modeling with remote sensing and GIS provides a robust framework for long-term soil erosion monitoring, enabling informed decision-making for climate adaptation in the region.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"901"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling water quality in the brazilian semiarid region using remote sensing: support for water management.","authors":"Ester Milena Dos Santos, Jocimar Coutinho Rodrigues Junior, Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Débora Natália Oliveira de Almeida, Sylvana Melo Dos Santos, Leidjane Maria Maciel de Oliveira, Anderson Luiz Ribeiro de Paiva","doi":"10.1007/s10661-025-14368-w","DOIUrl":"https://doi.org/10.1007/s10661-025-14368-w","url":null,"abstract":"<p><p>Water management in semi-arid regions faces challenges due to water scarcity and the need for continuous quality monitoring. This study evaluates the use of remote sensing to analyze a reservoir's water quality status in Brazil's semi-arid region to support its management. Data from the Landsat-8 (Operational Land Imager) and Sentinel-2 (MultiSpectral Instrument) satellites were used to correlate spectral bands with water quality parameters such as Chlorophyll-a and Total Phosphorus. Using the Stepwise method, multiple regression models were developed to predict these parameters. Landsat-8 achieved determination coefficients (R²) of 0.81 for Chl-a and 0.72 for TP, outperforming Sentinel-2. Spectral analysis indicated that the higher signal-to-noise ratio of Landsat-8 in visible and near-infrared wavelengths contributed to the quality of the predictive models. Additionally, the assessment of land use along the reservoir margins revealed that the reduction of pasture areas is associated with the stability of TP levels. The trophic classification of the reservoir remained in an ultra-oligotrophic state during the analyzed period; however, seasonal episodes of TP increase exceeding established environmental limits were observed. These results highlight the need for continuous monitoring integrated with land use data. Expanding the collected database and adopting advanced methodologies, such as machine learning and hyperspectral remote sensing, is recommended to improve estimation accuracy. This study provides evidence supporting water management policies and environmental conservation in the Brazilian semi-arid region.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"891"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring the dynamics of irrigated parcels and impacts on phreatic water quality in the Mostaganem Plateau (northwestern Algeria): an integrated analysis using remote sensing and field data for 2010 and 2020.","authors":"Yamina Benkesmia, Nadjla Bentekhici","doi":"10.1007/s10661-025-14325-7","DOIUrl":"https://doi.org/10.1007/s10661-025-14325-7","url":null,"abstract":"<p><p>Since the early 2000s, Algeria has implemented several agricultural policies to expand its irrigated areas and enhance its national food security. While these efforts have significantly increased irrigated land, they have raised concerns about groundwater sustainability. The study examines the impact of changes in irrigated parcels (IPs) between 2010 and 2020 on groundwater quality on the Mostaganem Plateau, an area of intense agricultural activity. Using Landsat satellite data and a range of spectral indices, including the Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), and Chlorophyll Index Green (CIG), we have mapped the IPs and tracked their changes over time. The results based on a SAVI 0.38 threshold showed a net increase of 690 hectares. This result was validated by a machine learning method based on a support vector machine (SVM) algorithm with a high accuracy of 98% compared to the index method. Groundwater quality analyses revealed higher nitrate concentrations (up to 50 mg/L), while the salinity decreased significantly from 3650 to 1265. This improvement is attributed to more sustainable practices, such as drip irrigation and composting, along with improved rainfall conditions observed since 2005, as indicated by the Standardized Precipitation Index (SPI). This study underscores the value of integrating remote sensing and field data to monitor agricultural expansion and inform sustainable groundwater management in semi-arid regions.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"898"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the impact of multiple background species on the reactivity of GSH@Fe₃O₄ employing RSM-CCD design tool for the sorption of a model anionic dye.","authors":"Monalisha Behera, Sonamuni Murmu, Nisha Kumari, Prashant Kumar Jaiswal, Ritu Singh","doi":"10.1007/s10661-025-14287-w","DOIUrl":"https://doi.org/10.1007/s10661-025-14287-w","url":null,"abstract":"<p><p>The major setback during wastewater treatment is the competition from several background ions and organic acids, hindering adsorbent-pollutant interactions and reducing removal efficiency. To address this issue, it is essential to find a promising methodology that can optimize ion effects, predict adsorbents' best removal efficiency, and determine significant influencing ions. Thus, the present study employed RSM-CCD design-based methodology with five input variables (HCO₃^-, SO₄^2-, NO₃^-, Cl^-, and humic acid) to simulate and optimize various wastewater environments, so as to investigate the reactivity of GSH@Fe₃O₄ NPs for phenol red (PR) dye. The quadratic model was found to be in best agreement with the experimental data, with an R² of 0.98. The other statistical tests and diagnostic plots also confirmed that the above model is highly reliable, accurate and provides good precision. The highest removal in a multicomponent system was predicted to be 83.09% when all the dominating species were kept at optimal points. Moreover, the experimental data fitted well for pseudo-second-order reaction, elucidating chemisorption as the major adsorption mechanism. When tested with real wastewater matrices, the PR dye removal followed the order: tap water > paper and pulp wastewater > sewage > textile wastewater. The findings of this study can be very instrumental in strategizing the wastewater conditions and enhancing removal efficiency in treatment plants while contributing towards real-world applications and sustainable wastewater treatment practices.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"890"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oxidative stress and antioxidant biomarker responses in fish exposed to heavy metals: a review.","authors":"Yu Zeng, Zhixin Song, Gangfu Song, Shuo Li, Haosen Sun, Chao Zhang, Guoting Li","doi":"10.1007/s10661-025-14376-w","DOIUrl":"10.1007/s10661-025-14376-w","url":null,"abstract":"<p><p>Heavy metal pollution represents a serious threat to aquatic environments and human health. In aquatic ecosystems, heavy metals can lead to toxicity in fish by inducing oxidative stress, which disrupts the balance between reactive oxygen species and the body's antioxidant defense system. This review summarizes how heavy metal exposure triggers oxidative stress in fish and highlights the role of key antioxidant enzymes. Many members of the antioxidant defense system, including metallothionein, superoxide dismutase, catalase, glutathione, glutathione transferase, glutathione reductase, and glutathione peroxidase, work together to mitigate the harmful effects of heavy metal exposure on fish health. Additionally, this review systematically synthesizes and analyzes the literature published over the past 16 years regarding the levels of eight crucial antioxidant biomarkers in various fish tissues. Through a critical examination of the accumulated data, this study elucidates the temporal patterns and dose-response relationships of antioxidant responses in fish populations exposed to environmentally relevant concentrations of heavy metals. While responses may vary depending on factors such as exposure duration, metal type, and fish species, antioxidant levels generally increase in response to both acute and chronic exposure. Understanding how fish respond to heavy metal contamination, as well as the patterns of variation in antioxidant indices within their bodies, provide a foundation on which fish can be used as biological indicators to assess the ecological status of heavy metal contaminants.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"892"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution of soil organic carbon in an Irish salt marsh (Rogerstown Estuary).","authors":"Juliet Rounce, Iris Möller, Andrew J Manning","doi":"10.1007/s10661-025-14250-9","DOIUrl":"10.1007/s10661-025-14250-9","url":null,"abstract":"<p><p>Salt marshes are globally widespread, found on low-lying coastal shores, and are highly effective at long-term carbon storage; thus, they are vital for climate change impact mitigation. Accurate carbon stock estimation requires an understanding of local-scale spatial variability of carbon storage and the facilitating processes. Few studies investigate the cumulative impact of controlling factors on within-site carbon distribution. This study utilises 60 cores from a salt marsh in Turvey Nature Reserve (Rogerstown Estuary), on the Irish east coast, to investigate spatial variability in soil organic carbon (SOC) content, alongside bio-sedimentary, and environmental factors. Mean carbon density (CD) was 11.1 ± 4.2 kg m^-3 at 10-cm depth, ranging from 5.2 to 22 kg m^-3 (423% increase) across the marsh. We recommend that to obtain measurements across the full range of the site, for small sample sizes (n < 20), random sampling should be used (mean difference between the site-wide CD and 'subsample CD' ranged from 0.04 (n = 10) to 0.29 (n = 5) kg m^-3) and marsh edge clustering should be avoided. These results provide the first ever systematic record of local-scale (within ~ 800 m²) SOC and CD variability within an Irish east coast salt marsh and the variation of known influencing factors (including sedimentary and environmental). We also present the first study to systematically provide guidance on capturing marsh-wide SOC and CD most effectively based on limited sampling. The outputs help constrain uncertainties around scaled-up carbon accumulation estimates for regional, national and international inventories.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"899"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Long‑term monitoring of mercury in Vermont's forest soils: trends over 20 years in near-surface horizons.","authors":"Donald S Ross, James B Shanley, Angelica T Quintana, Thomas R Villars, Joshua M Halman, Scott W Bailey, Jennifer A Bower","doi":"10.1007/s10661-025-14306-w","DOIUrl":"https://doi.org/10.1007/s10661-025-14306-w","url":null,"abstract":"<p><p>Mercury is a soil pollutant of widespread concern, usually derived from airborne deposition. A long-term forest soil monitoring program began in 2002 at five sites (elevation 590-1140 m) in Vermont, USA. Total mercury (THg) and soil organic carbon (SOC) were assessed in the uppermost humified soil layer, either an Oa or A horizon. Sampling occurred every 5 years from ten subplots at each site. Tree species ranged from deciduous, mixed deciduous/conifer, to all conifer. After five samplings, the site means for THg ranged from 167 to 447 µg kg^-1 and increased linearly with elevation (R² = 0.78). One site had a significant temporal increase in THg of 3.9 µg kg^-1 yr^-1 (p = 0.02). Overall SOC site means ranged from 133 to 434 g kg^-1. One site showed a significant temporal increase in SOC (p = 0.01). The mean ratio of THg:SOC was similar at four of the sites (1318-1518 µg kg^-1) but much lower (648 µg kg^-1) at the site with the highest SOC and the temporal increase in THg. Within individual subplots, THg increased up to a maximum SOC concentration of ~ 320 g kg^-1 and usually decreased above that threshold. Although both wet and dry mercury deposition in the northeast USA have declined, no evidence of declining soil THg concentration was found, likely due to strong retention by SOC. Continued monitoring is essential considering ongoing changes in deposition and future changes in the source-sink balance of mercury.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"895"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal distribution and driving forces of ecological fragility in Chengde's transitional region from plateau to plain, China.","authors":"Mei Dong, Qingyu Zhang, Qingning Wang, Chunhao Jing, Han Luo","doi":"10.1007/s10661-025-14321-x","DOIUrl":"https://doi.org/10.1007/s10661-025-14321-x","url":null,"abstract":"<p><p>Assessing the spatial distribution and drivers of ecological vulnerability in the Chengde Plateau-to-Plains Transition Area is essential for effective ecological management and conservation strategies in this ecologically sensitive region. This study investigated the factors influencing ecological fragility across three counties: Longhua County, Fengning Manchu Autonomous County, and Weichang Manchu and Mongolian Autonomous County. Using the sensitivity-resilience-pressure (SRP) model as the analytical framework, we developed a comprehensive ecological fragility assessment system incorporating ten key indicators. The Ecological Fragility Index (EVI) for the period 2000-2020 was calculated using spatial principal component analysis, with results classified into five fragility levels and visualized spatially through ArcGIS software. Additionally, the Comprehensive Ecological Fragility Index (CEVI) was employed to quantify overall ecological fragility patterns. The geographic detector model was utilized to examine how individual factors and their interactions influenced the spatial distribution of ecological fragility. The results indicated that severe and moderate ecological fragility predominated throughout the study region from 2000 to 2020. Areas characterized by mild and slight fragility were primarily distributed in northern Weichang Manchu and Mongolian Autonomous County, while extremely and severely vulnerable areas were concentrated in southern Fengning Manchu Autonomous County and southern Longhua County. Land use analysis revealed that forestland exhibited predominantly low ecological fragility, whereas construction land demonstrated extremely high fragility levels. The spatial variation of ecological fragility in this region was primarily driven by mean annual temperature, altitude, and soil erosion severity. Notably, factor interactions exerted greater influence on fragility patterns than individual factors alone, providing valuable insights for environmental restoration and management strategies in plateau-to-plain transition zones.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"897"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental risk assessment of heavy metal air pollution in Serbian spruce (Picea omorika) ecosystems.","authors":"Jelena Beloica, Snežana Obradović, Milan Medarević, Nevena Čule, Stefan Miletić, Predrag Miljković","doi":"10.1007/s10661-025-14355-1","DOIUrl":"10.1007/s10661-025-14355-1","url":null,"abstract":"<p><p>This paper aims to analyze the patterns and dynamics of long-term atmospheric pollution (deposition of Pb, Cd, and Hg) and its impact on Picea omorika (Pančić) Purk (Serbian spruce) forests in Tara National Park. Due to changing ecological conditions, these relict-endemic forests have experienced significant habitat loss and fragmentation of their last remaining refugia. In this study, we analyze heavy metal deposition patterns and dynamics and the content of these metals in the Serbian spruce forest (soil and biomass). The heavy metal content was analyzed and compared with ICPF (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests) data across Serbia, revealing the level of environmental risk these ecosystems face from air pollution in comparison to other forest ecosystems in the country. Four Serbian spruce forest clusters stand out compared to others, with the highest long-term heavy metal deposition and elevated levels of lead, cadmium, and mercury detected in both soil and biomass at these sites. These findings may serve as a guide for identifying priority locations for future monitoring, facilitating the implementation of the Critical Loads concept. It also highlights the need for revision of forest management practices in protected areas and the implementation of buffer zones.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 8","pages":"900"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}