Environmental Monitoring and Assessment最新文献

{"title":"Microplastics in solar salt: baseline contamination assessment from Goa, India.","authors":"Shritesh Mhapsekar, Niyati G Kalangutkar, Nitesh Joshi","doi":"10.1007/s10661-025-14288-9","DOIUrl":"https://doi.org/10.1007/s10661-025-14288-9","url":null,"abstract":"<p><p>Microplastics (MPs) in food pose potential health risks, yet their occurrence in solar salt remains underexplored. However, limited research exists on MP contamination in natural solar salt, particularly in estuarine environments. This study addresses this gap by assessing MP contamination in salt harvested from solar salt pans downstream of the Mandovi estuary, Goa, India. Eight salt samples were collected from the saltpan and analysed using density separation, filtration, and FTIR spectroscopy. MPs were detected in all samples, with concentrations ranging from 64.00 ± 1.89 to 106.67 ± 10.37 particles/100 g (mean: 84.17 ± 14.47 particles/100 g). The majority of MPs were within the 0.1-0.3 mm size range (49.21%), predominantly fibres (90.40%), and colourless (64.30%). Polyethylene (29.2%), polyester (20.8%), and polypropylene (16.7%) were the dominant polymer types. The pollution load index (1.16) and polymer risk index (PRI > 1000) indicated a high ecological hazard (Level V). Estimated annual MP intake from salt consumption was 2,457.8 particles per person. These findings provide critical evidence of MP contamination in natural solar salt and underscore the need for targeted mitigation strategies to minimize human exposure.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"820"},"PeriodicalIF":2.9,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525880","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":"Distribution characteristics of microplastics and potentially toxic elements as co-contaminants in groundwater in mid-Brahmaputra Valley, northeastern India.","authors":"Ankita Saha, Kundil Kumar Saikia, Sumi Handique","doi":"10.1007/s10661-025-14266-1","DOIUrl":"https://doi.org/10.1007/s10661-025-14266-1","url":null,"abstract":"<p><p>Groundwater is a vital source of freshwater, and its contamination by microplastics (MPs) and potentially toxic elements (PTEs) is a growing concern. This study explores the occurrence and associated ecological and health risks of MP-PTE co-contamination in groundwater in the mid-Brahmaputra Valley, India. A total of 169 MPs, derived from polyamides, polyacetonitrile, polyethylene, polymethacrylates, polypropylene and polyvinylchlorides, were identified in 21 samples. Polypropylene MPs (41%) were the most abundant while polymethacrylate MPs (8%) were found to be the most hazardous, both ranking in hazard category V. The samples also contained elevated levels of Fe, Mn and Pb, with 26% of samples exceeding permissible limits. Spectroscopic analysis confirmed PTE adsorption onto MP surfaces suggesting a synergistic contamination mechanism. Risk assessment based on incremental lifetime cancer risk showed that 33% of samples posed potential risks to children and none for adults. Non-carcinogenic risks via oral intake were observed in 76% of samples for children and 24% for adults. Sites were categorized into four pollution-based clusters using hierarchical clustering. The PTE index rated 70% of samples as excellent, 25% as poor to very poor and 5% as unsuitable for drinking. These findings underscore the importance of integrated pollution control, reduced plastic usage and improved waste management strategies. Further research is needed to explore the long-term fate of MPs and their role in contaminant transportation under varying environmental conditions.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"818"},"PeriodicalIF":2.9,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525876","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":"Evaluation of spatio-temporal variation in fish fauna within the Point Calimere Marine Key Biodiversity Area, India.","authors":"R Abinaya, A Kanishkar, M K Sajeevan","doi":"10.1007/s10661-025-14210-3","DOIUrl":"https://doi.org/10.1007/s10661-025-14210-3","url":null,"abstract":"<p><p>Biodiversity loss is expanding at an unprecedented rate, owing to habitat degradation, overexploitation, pollution, and climate change. Key Biodiversity Areas (KBAs) play an essential role in conservation and spatial planning to reduce the detrimental effects of biodiversity loss. The present study examined the spatio-temporal variations in fish diversity within selected zones of the Point Calimere Wildlife Sanctuary Key Biodiversity Area (PCWSKBA) from January 2023 to December 2024. In this study, 255 species from 39 orders and 85 families were identified. Escualosa thoracata (Valenciennes, 1847) was the most abundant fish species, comprising 7% of the total number of fish collected, followed by Eleutheronema tetradactylum (Shaw, 1804) (4%) and Mugil cephalus Linnaeus, 1758 (3.8%). The diversity indices revealed that Kodiyakarai Wetland (KW) had the highest species richness and abundance. Simultaneously, the SC recorded lower diversity due to anthropogenic pressure and environmental constraints. The post-monsoon season had the greatest seasonal diversity, which corresponded to the input of nutrients from monsoon rain. The K-dominance curve indicated that the samples from 2023 exhibited a higher concentration of dominance than those from 2024. Redundancy analysis (RDA) revealed that dissolved oxygen, salinity, and phosphate nutrient levels significantly influenced fish distribution across various zones. This study suggests that increasing phosphate and organic matter levels may enhance productivity by promoting algal growth and by providing a food source for fishes. However, excessive nutrient enrichment can lead to eutrophication, depletion of oxygen levels, and promotion of opportunistic species that ultimately disrupt the ecosystem balance. Therefore, it is essential to implement measures to reduce nutrient runoff and organic pollution to prevent oxygen depletion and protect the biodiversity and ecological health of the PCWSKBA. Sustainable land-use practices and enhanced water quality monitoring can help mitigate nutrient overload while preserving the productivity and diversity of aquatic ecosystems.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"819"},"PeriodicalIF":2.9,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525878","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":"Homeostasis shift threshold in the relationship between grassland ecosystem quality and ecosystem services: a case study of the Agro-Pastoral Ecotone in Northern China.","authors":"Qinhui Zhou, Lixuan Cheng, Jian Zhou","doi":"10.1007/s10661-025-14278-x","DOIUrl":"https://doi.org/10.1007/s10661-025-14278-x","url":null,"abstract":"<p><p>Identifying homeostasis shift thresholds of the relationship between ecosystem quality (EQ) and ecosystem service (ES) is important for managing ecosystems to achieve sustainable development, especially for dryland ecosystems. The study was carried out on EQ-ES homeostasis shift thresholds in the grassland of Agro-Pastoral Ecotone in Northern China (APENC) to provide the scientific basis for ecological restoration. Homeostasis shift thresholds in the relationship between EQ and key ESs (WEP, wind erosion prevention; SC, soil conservation; WR, water retention) were identified by constraint lines. EQ-ES homeostasis shift drivers were quantitatively classified using GeoDetector. Thresholds-based ecological restoration areas were identified applying local spatial autocorrelation. The results showed that EQ-ES exhibited nonlinear homeostasis shifts, mostly manifested as the single-peak constraint from promotion of homeostasis to inhibition of homeostasis. Homeostasis shifts range for EQ-WEP, EQ-SC and EQ-WR was 200-400 g C·m^-2, 400-600 g C·m^-2, and 200-300 g C·m^-2 of NPP, respectively. NDVI was the dominant factor influencing EQ-ES homeostasis shifts. Meanwhile, the regional aggregation of EQ-ES homeostasis stages was obvious, and ecological restoration should be site-specific. The key to ecological restoration in the Inner Mongolian Plateau and the northern Loess Plateau lies in the selection of suitable biological species to establish the stable vegetation system. In the southern Loess Plateau, it is recommended to control NPP in 200-300 g C·m^-2. In Horqin Sand Area, it should continue implementing ecological projects to achieve NPP in 200-400 g C·m^-2.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"817"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511390","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":"A methodology for establishing historical wetland habitat change in Irish freshwater pearl mussel catchments.","authors":"Barry G Walls, Evelyn A Moorkens, Jeremy J Piggott","doi":"10.1007/s10661-025-14206-z","DOIUrl":"10.1007/s10661-025-14206-z","url":null,"abstract":"<p><p>Since the 1700s, global wetlands have declined by 3.4 million km². Wetland quality loss is a key driver of freshwater pearl mussel (FPM) population decline in peaty catchments. GIS techniques were used to determine wetland cover in 1834 and 2023, in eight peaty FPM catchments in Ireland, based on historical Ordnance Survey mapping (1834) and Irish National Land Cover (NLC) mapping (2023). Historical catchment wetland change (1834-2023) ranged between a net loss of 7.03 to 29.96%. Wetland coverage in 2023 varied between 53.85 and 83.53%. In 1834, that coverage ranged from 59.72 to 89.85%. The Hydromusindex (HDi) was developed to evaluate catchment-scaled historical wetland change, which for each of the catchments studied was a net loss. This damage index was based on the ratio of the 2023 catchment wetland coverage in proportion to the 1834 baseline scenario, and the catchment proportional coverage of each wetland cover type weighted by their potential contribution towards water storage; higher HDi values indicate increased damage. The HDi values of eight studied catchments ranged from 21 to 46. The HDi can be used to assess catchment restoration design and to rank FPM catchments for restoration, under the Nature Restoration Law. Measurable targets for catchment-scaled restoration have been produced. The sum of weighted wetland scores, in contrast to predefined reference values, can be used for rapid damage evaluation. An estimated 41.06% of Ireland's plantation forestry is located on peatlands and peat soils, thereby highlighting considerable opportunities in terms of meeting the EU Nature Restoration Law's targets.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"815"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511389","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":"Leveraging machine learning for monitoring afforestation in mining areas: evaluating Tata Steel's restoration efforts in Noamundi, India.","authors":"Wang Xiuqing, Saied Pirasteh, Hishmi Jamil Husain, Bhavesh Chauhan, Vidhya Lakshmi Sivakumar, Mahdieh Shirmohammadi, Davood Mafi-Gholami","doi":"10.1007/s10661-025-14294-x","DOIUrl":"https://doi.org/10.1007/s10661-025-14294-x","url":null,"abstract":"<p><p>Mining activities have long been associated with significant environmental impacts, including deforestation, habitat degradation, and biodiversity loss, necessitating targeted strategies like afforestation to mitigate ecological damage. Tata Steel's afforestation initiative near its Noamundi iron ore mining site in Jharkhand, India, spanning 165.5 hectares with over 1.1 million saplings planted, is a critical case study for evaluating such restoration efforts. However, assessing the success of these initiatives requires robust, scalable methods to monitor land use changes over time, a challenge compounded by the need for accurate, cost-effective tools to validate ecological recovery and support environmental governance frameworks. This study introduces a novel approach by integrating multiple machine learning (ML) algorithms, classification and regression tree (CART), random forest, minimum distance, gradient tree boost, and Naive Bayes, with multi-temporal, multi-resolution satellite imagery (Landsat, Sentinel-2A, PlanetScope) on Google Earth Engine (GEE) to analyze land use dynamics in 1987, 2016, and 2022. In a novel application to such contexts, high-resolution PlanetScope data (3 m) and drone imagery were leveraged to validate classification accuracy using an 80:20 training-testing data split. The comparison of ML methods across varying spatial resolutions and temporal scales provides a methodological advancement for monitoring afforestation in mining landscapes, emphasizing reproducibility and precision. Results identified CART and Naive Bayes classifier classifiers as the most accurate (83% accuracy with PlanetScope 2022 data), effectively mapping afforestation progress and land use changes. These findings highlight the utility of ML-driven remote sensing in offering spatially explicit, cost-effective monitoring of restoration initiatives, directly supporting Environmental, Social, and Governance (ESG) reporting by enhancing transparency in ecological management.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"816"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511391","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":"Broadscale reconnaissance of coral reefs from citizen science and deep learning.","authors":"Christopher L Lawson, Kathryn M Chartrand, Chris M Roelfsema, Aruna Kolluru, Peter J Mumby","doi":"10.1007/s10661-025-14261-6","DOIUrl":"10.1007/s10661-025-14261-6","url":null,"abstract":"<p><p>Coral reef managers require various forms of data. While monitoring is typically the preserve of scientists, there is an increasing need to collect larger scale, up-to-date data to prioritise limited conservation resources. Citizen science combined with novel technology may achieve data collection at the required scale, but the accuracy and feasibility of new tools must be assessed. Here, we show that a citizen science program that collects large field-of-view benthic images and analyses them using a combination of deep learning and online citizen scientists can produce accurate benthic cover estimates of key coral groups. The deep learning and citizen scientist analysis methods had different but complementary strengths depending on coral category. When the best performing analysis method was used for each category in all images, mean estimates from 8086 images of percent benthic cover of branching Acropora, plating Acropora, and massive-form coral were ~ 99% accurate compared to expert assessment, and > 95% accurate at all coral cover ranges tested. Site-level accuracy of 95% was attainable with 18-80 images. Power analyses showed that up to 114 images per site were needed to detect a 10% absolute difference in coral cover per category (power = 0.8). However, estimates of 'all other coral' as a single category achieved 95% accuracy at only 60% of sites and for images with 10-30% coral cover. Overall, emerging technology and citizen science present an attainable tool for collecting inexpensive, widespread data that can complement higher resolution survey programs or be an accessible tool for locations with limited scientific or conservation resources.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"814"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144504411","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":"Correction to: Soil functioning and interrelations with hydrophysical attributes and organic and microstructural fractions in soils under land uses in the Brazilian Semiarid.","authors":"Joaquim Emanuel Fernandes Gondim, Jeane Cruz Portela, Eulene Francisco da Silva, Paulo Jardel Mota, Wandson Mendes Vieira, Weverton Andrade Cunha, Davison Victor de Oliveira Gomes, Bruno Caio Chaves Fernandes, Maria Laiane do Nascimento Silva, Matias de Souza Dantas, Paula Romyne de Morais Cavalcante Neitzke, Luiz Fernando de Sousa Antunes","doi":"10.1007/s10661-025-14338-2","DOIUrl":"https://doi.org/10.1007/s10661-025-14338-2","url":null,"abstract":"","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"813"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144504412","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":"A comparative study of fully automatic and semi-automatic methods for oil spill detection using Sentinel-1 data.","authors":"Muhammad Iqbal Habibie, Hariyanto, Robby Arifandri, Zulfa Qonita, Pronika Kricella, Muh Hisyam Khoirudin, Noor Muhammad Ridha Fuadi, Nurul Shabrina, Nanda Itohasi Gutami, Siti Sadiah, Dewi Kartikasari, Muh Mulyadi Agus Widodo, Waluyo, Farid Arif Binaruno, Kunto Ismoyo","doi":"10.1007/s10661-025-14222-z","DOIUrl":"https://doi.org/10.1007/s10661-025-14222-z","url":null,"abstract":"<p><p>The oil spill detection and assessment study conducted in the Banten Province of Indonesia involves the application of Sentinel-1 satellite data and machine learning tools in the year 2024. Synthetic Aperture Radar (SAR) data were used with VV polarization to observe the surface characteristics, using an oil spill threshold of - 25 dB to differentiate clean water from the oil spill based on low backscatter intensity. After desiring image processing and binary masking applications on the data that improve visibility of the oil spill-affected zones, vectorization was conducted for integration into geographic information systems (GIS). A temporal analysis indicated high variability across the spill sizes with an extreme peak on May 16 (79.686 km²) and July 3 (41.593 km²), which are likely dictated by the weather and oceanographic conditions plus the ship traffic of that time. Wind pattern analysis via ERA5 reanalysis data presented more insight into spill dispersion dynamics. Three machine learning classifiers were applied toward oil spill detection, namely Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Performance metrics indicate the ANN outperformed in discriminative ability (AUC = 0.92), while RF was highly accurate (99.01%) and precise (99.02%). This clearly demonstrates the viability of using an integrated approach of remote sensing, advanced image processing, and supervised learning for environmental monitoring and provides important information for minimizing ecological impacts and optimizing disaster response plans for maritime areas. Such an integrated scheme calls for advanced technology to combat ecological threats in maritime areas and provides crucial evidence toward ongoing interventions to protect and manage marine ecosystems and the associated local communities.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"808"},"PeriodicalIF":2.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493330","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":"Trends of leading pollutant in a highly polluted global city: processes involved.","authors":"Latha Radhadevi, Murthy Bandaru, Yesobu Yarragunta, Gufran Beig, Aditi Rathod, Siddhartha Singh","doi":"10.1007/s10661-025-14243-8","DOIUrl":"https://doi.org/10.1007/s10661-025-14243-8","url":null,"abstract":"<p><p>The impact of air pollution mitigation policies needs to be studied by evaluating long-term trends of lead pollutant to determine air quality index, the particulate matter (PM). A decade of SAFAR (System of Air quality and weather Forecasting And Research) observations revealed that the trend of particulate matter (PM) with size < 2.5 µm (PM_2.5) and size < 10 µm (PM₁₀), respectively, in a highly polluted global city, Delhi, shows a reduction of - 3.12 ± 0.52 µg/m³/year (- 4.68 ± 0.84 µg/m³/year) or overall, 28.8% (25.2%) reduction between 2011 and 2022 due to the implementation of eco-friendly technologies and strict industrial regulation despite doubling of number of vehicles. Seasonal negative trends during post-monsoon of PM_2.5 (- 4.64 ± 2.68 µg/m³/year) and PM₁₀ (- 8.64 ± 2.68 µg/m³/year) are significantly higher than that in other seasons. PM_2.5 and PM₁₀ show a relatively higher negative trend during winter (- 2.94 ± 1.08 µg/m³/year) and pre-monsoon (- 4.86 ± 2.07 µg/m³/year), respectively. The influence of dust storms, fire counts, and annual rainy days on PM trends is discussed. The contribution of meteorology to the trend is estimated using the WRF-Chem simulation of PM_2.5 for October when maximum stubble burning occurs in Haryana and Punjab regions and gets transported to Delhi by upwind flow. The model is run for the post-monsoon month (October) with the meteorological initial conditions of 2018, 2015, and 2011 while keeping the emissions of 2018 with identical model configuration and found that meteorology contributes 9.8%, while the observed decline in PM_2.5 is 28.8% during 2011-2022. The study identifies the governmental control measures at various levels and green initiatives as the significant contributors to air quality improvement during 2011-2022.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 7","pages":"812"},"PeriodicalIF":2.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493336","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}