Environmental Monitoring and Assessment最新文献

{"title":"Temporal evolution of the performance evaluation of the laboratories at Spanish nuclear power plants in water samples","authors":"José Antonio Suárez-Navarro,&nbsp;Virginia Peyres,&nbsp;Ana Isabel Sánchez-Cabezudo,&nbsp;Nuria Navarro,&nbsp;Victor Manuel Expósito-Suárez,&nbsp;Jordi Español,&nbsp;José Manuel Arteaga,&nbsp;Manuel Brun,&nbsp;Rosaura Miret,&nbsp;Ana Llorente,&nbsp;Mercedes Ibañez,&nbsp;J. F. Benavente","doi":"10.1007/s10661-025-13851-8","DOIUrl":"10.1007/s10661-025-13851-8","url":null,"abstract":"<div><p>This study examined the performance evaluation and analysis of the laboratories of Spanish Nuclear Power Plants over the last 9 years of the 34 editions organized by CIEMAT. The participating nuclear power plants were Almaraz, Ascó I, Ascó II, Cofrentes, Santa María de Garoña, Trillo, and Vandellós II. The radionuclides analyzed were those necessary to ensure operational radiological protection and the control of liquid effluents from a nuclear power plant. The radionuclides are classified into beta emitters: ³H, ¹⁴C, and ⁹⁰Sr, and gamma emitters: ²⁴¹Am, ⁵⁷Co, ¹³⁷Cs, and ⁶⁰Co. The results of the z score showed that the performance in determining ³H was 100% satisfactory in most years, except in 2010 and 2014, where percentages of 83% and 87.5% were recorded, respectively. ⁹⁰Sr showed greater variability with a satisfaction range between 60 and 100%. Furthermore, the gamma emitters achieved satisfactory evaluations above 93% within the studied time range. The analysis of the distributions of the relative differences between the laboratory results and the reference values confirmed that the distributions were normal according to the Shapiro–Wilk test, with a percentage of outlier values below 5%. Additionally, linear regression analysis was used to evaluate the dispersion of the results relative to the reference values through the RSE parameter, being highest for ³H and ¹⁴C. The PomPlot graphs allowed for the visualization of the relationship between the obtained results and the reference values, indicating that, although high percentages of satisfactory evaluations were achieved, certain trends in the laboratory results must be considered to detect potential future deviations in the analytical methodologies.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-025-13851-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668172","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":"Microplastics in agricultural soils: sources, impacts on soil organisms, plants, and humans","authors":"Fatimo Ajoke Bello,&nbsp;Abidemi Bashiru Folorunsho,&nbsp;Rogers Wainkwa Chia,&nbsp;Jin-Yong Lee,&nbsp;Samuel Aanuoluwapo Fasusi","doi":"10.1007/s10661-025-13874-1","DOIUrl":"10.1007/s10661-025-13874-1","url":null,"abstract":"<div><p>Agricultural land has long been regarded as a resource for food production, but over time, the effects of climate change have reduced the ability of soil to produce food efficiently. Nowadays, farmers have moved from traditional to modern techniques of farming. Across the globe, plastic mulching has become widely used on farmlands. According to a few studies, the breakdown of plastic mulches releases microplastics (MPs) into the soil. Despite studies reporting the presence of MPs in soils, there are limited studies on the sources and impacts on soil organisms, plant growth, fruits, and human health. This study evaluated research articles collected from the Web of Science to assess the origin of MP in soil and crops and its effects on soil organisms, plants, and humans. It was observed that MPs come from different sources such as waste water, organic fertilizer, irrigation water, sewage, and sludge. Plastic mulching, which can spread across agricultural fields at varying depths, is the dominant source. Furthermore, it was observed that MPs alter crop quality, reduce the leaf count of wheat, and decrease the root length of crops such as maize, water spinach, black gram, and garden cress. MP can decrease the abundance of soil microarthropods and nematodes, damage the intestinal walls of earthworms, and reduce the feeding and excretion of snails. MP causes liver damage, inflammation, respiratory irritation, and immunological issues. Ultimately, these contaminants (MPs) can transfer and have been detected in fruits and vegetables, which pose adverse effects on human health.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668105","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":"Microplastic accumulation in respiratory and digestive systems of selected fish from Banten Bay, Indonesia","authors":"Sri Yuningsih Noor,&nbsp;Etty Riani,&nbsp;Sigid Hariyadi,&nbsp;Nurlisa Alias Butet,&nbsp;Muhammad Reza Cordova","doi":"10.1007/s10661-025-13933-7","DOIUrl":"10.1007/s10661-025-13933-7","url":null,"abstract":"<div><p>Microplastics pose a significant environmental threat as they can be consumed directly or indirectly by various marine organisms. This investigation explores the ecological hazards of microplastic pollution in marine life, with an emphasis on fish in Banten Bay, Indonesia. The sampling spots were in Banten Bay where traditional fishing gears known as “bubu” were used and nets. This study represents an inaugural examination of microplastic contamination in selected fish species including <i>Gambusia affinis</i><i>, </i><i>Stolephorus indicus</i><i>, </i><i>Scatophagus argus</i><i>, </i><i>Epinephelus coioides</i><i>, </i><i>Rastrelliger</i> sp<i>., Parapenaeopsis sculptilis,</i> and <i>Leiognathus</i> sp. As a result, microplastics were detected in each fish, with a notable increase in concentrations within the gills (7.85 ± 3.54 items/ind) as opposed to the digestive tract (4.95 ± 2.15 items/ind). Their presence indicated a significant difference (p &lt; 0.01), primarily observed as fragments and filaments. Nine distinct polymer types were identified with FT-IR in fish samples collected from Banten Bay, including polyethylene terephthalate, polyvinyl chloride, polyester, polyurethane, ethylene propylene, polypropylene, polyethylene, polystyrene and polyphenylene sulfide. Microplastics were primarily detected within the size range of 20 to 4510 µm. Microplastic contamination in fish causes detrimental implications on aquatic ecosystems and human health, since the particles exist in the food chain, altering the biological activities of fish, and potentially posing hazards to consumers. Considering the closeness of Banten Bay to Indonesia's capital and economic hub, immediate preventive actions are essential to safeguard human health and the ecosystem. Additional investigation into plastic degradation and waste management is crucial for comprehending the origins of contamination. The results establish a foundation for continuous monitoring of microplastic risks in Banten Bay and other swiftly evolving coastal ecosystems in Northeast Asia.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668197","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 impact assessment of stocking density in bamboo cage aquaculture: examining water quality dynamics, stress biomarkers, and ecosystem response in a subtropical forest water body of Northeast India","authors":"Chandan Debnath","doi":"10.1007/s10661-025-13902-0","DOIUrl":"10.1007/s10661-025-13902-0","url":null,"abstract":"<div><p>Forest-dependent communities in India, particularly in the Northeast region, face significant socio-economic challenges while possessing vast untapped aquatic resources. This study investigated the environmental sustainability of varying stocking densities (5–20 fish/m3) of Indian major carp <i>Catla catla</i> in eco-friendly bamboo cages within a forest water body in Tripura, Northeast India, intending to develop low-cost aquaculture solutions for forest dwellers. Water quality parameters, stress biomarkers, and ecosystem responses were monitored over 6 months. Results demonstrated optimal environmental and economic sustainability at 15 fish/m3, maintaining acceptable water quality (DO 5.6 ± 0.6 mg/L, ammonia-N 0.28 ± 0.04 mg/L) while maximizing production efficiency (survival 86.8 ± 2.1%, FCR 2.26 ± 0.14). Higher densities significantly increased environmental stress indicators, including elevated cortisol levels (26.5 ± 4.2 ng/mL) and reduced plankton diversity (Shannon–Wiener index 2.21 ± 0.15). Economic analysis revealed the highest profitability at 15 fish/m3 with a benefit–cost ratio of 1.83 ± 0.05 and net returns of Rs. 4311 ± 186 per cage, highlighting its commercial viability. The 15 fish/m3 treatment achieved optimal production (47.65 ± 1.86 kg/cage) while maintaining environmental parameters within acceptable limits. The study provides evidence-based guidelines for environmentally and economically sustainable cage aquaculture, offering viable livelihood opportunities for forest-dependent communities while preserving ecosystem integrity.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655286","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":"Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine","authors":"Olufemi Sunday Durowoju,&nbsp;Rotimi Oluseyi Obateru,&nbsp;Samuel Adelabu,&nbsp;Adeyemi Olusola","doi":"10.1007/s10661-025-13863-4","DOIUrl":"10.1007/s10661-025-13863-4","url":null,"abstract":"<div><p>Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitoring these dynamic changes is crucial for sustainable urban planning and management. This study leverages machine learning and Google Earth Engine to investigate urban dynamics and its interactions with biophysical conditions in the Kaduna River Basin (KRB), Nigeria. This study utilized a dataset of 192 points, initially extracted from Google Earth Engine, to analyze urban transitions between 1987 and 2020, incorporating biophysical and environmental variables such as population density, precipitation, and surface temperature. The dataset was processed in R using the CARET package, with missing data imputed via K-nearest neighbors (KNN), categorical variables transformed using One-Hot Encoding, and numerical variables rescaled for consistency. A tenfold cross-validation approach was used to train and validate machine learning models, including random forest, support vector machine, KNN, and multivariate adaptive regression splines, ensuring optimal model performance. Model evaluation metrics such as overall accuracy, kappa, F1 score, and area under the curve confirmed the reliability of the models in identifying the biophysical factors influencing urban changes. The findings revealed overall accuracy of 0.80, 0.73, 0.71, and 0.72 and kappa statistics of 0.60, 0.46, 0.42, and 0.45 for random forest (RF), multivariate adaptive regression splines, support vector machine, and KNN, respectively, with RF emerging as the most accurate model (80%) for predicting urban change patterns in KRB. Land cover changes reveal rapid urban expansion (154.81%), declining water bodies (− 95.79%), and vegetation growth (174%). Machine learning models identify population density and water stress index as key urban change drivers, with climate factors like temperature and precipitation playing crucial roles. The results of this study offer valuable insights into the processes driving urban transformation and present a robust methodology for monitoring and predicting future urban development. This study aids in the creation of strategies for sustainable urban growth and the mitigation of adverse environmental impacts.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-025-13863-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655204","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":"Influences of anaerobic treatment on chemical oxygen demand removal behavior of tannery wastewater","authors":"Venkatesh Rathinavelu,&nbsp;Viyat Varun Upadhyay,&nbsp;Rakesh Kumar,&nbsp;Vinayagam Mohanavel,&nbsp;Nagabhooshanam Nagarajan,&nbsp;Dhaval Rabadiya,&nbsp;Manzoore Elahi Mohammed Soudagar,&nbsp;Sami Al Obaid,&nbsp;Saleh Hussein Salmen","doi":"10.1007/s10661-025-13905-x","DOIUrl":"10.1007/s10661-025-13905-x","url":null,"abstract":"<div><p>Syngas are produced from wastewater through anaerobic reactions and thermochemical processes, using a catalyst that modifies the gas composition, reduces methane production, and achieves partial COD reduction. The current research is attempting to treat the tannery wastewater via an anaerobic process configured with 0.25, 0.3, and 0.35 volume units of granular activated carbon (GAC) with 10 nm size to minimize the concentration of chemical oxygen demand (COD) and improve the biological methane yield. During this anaerobic process, the up-flow anaerobic sludge blanket (UASB) reactor supports to enhance the bio-methane production and handle the high rate of organic load. Influences of GAC units and operating time (days) on COD and biological methane yield of an anaerobic system for tannery wastewater treatment are studied and measured in their value. The output results of COD and biological methane yield are compared, and it was spotted that the tannery water process with 0.3 volume units of GAC owns 87% of COD removal with biological methane yield of 66.3 mL/day (7.2 mL/g COD removed) and end of 30th day found 1939 mL.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655285","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":"Exploring the urban heat island phenomenon in a tropical medium-sized city: insights for sustainable urban development","authors":"Larissa Vieira Zezzo,&nbsp;Priscila Pereira Coltri,&nbsp;Vincent Dubreuil,&nbsp;Guilherme Almussa Leite Torres,&nbsp;Margarete Cristiane de Costa Trindade Amorim","doi":"10.1007/s10661-025-13890-1","DOIUrl":"10.1007/s10661-025-13890-1","url":null,"abstract":"<div><p>Urban Heat Islands (UHI) pose a significant challenge for tropical cities, especially as global temperatures continue to rise. Despite the prevalence of this problem, medium-sized cities in tropical regions remain underrepresented in UHI studies compared to large cities in temperate climates. This study aimed to characterize the UHI effect in a rapidly growing medium-sized city in a tropical region, which has experienced unplanned urban sprawl. Temperature data were collected using data loggers in ten distinct areas of the city. The results revealed a pronounced UHI effect, with higher intensities in the city center and lower intensities in areas with native vegetation. During the dry season, extreme UHI effects (&gt; 6 °C) were observed 10% of the time, with strong UHI effects (4 °C-6°C) present 50% of the month. In contrast, during the wet season, moderate UHI effects (2 °C-4°C) were recorded only 15% of the time. A strong correlation was identified between land use, particularly vegetation cover, and UHI intensity. This study not only sheds light on the dynamics of UHI in this specific tropical city, but also offers relevant insights for other mid-sized cities in similar tropical contexts around the world. The findings provide valuable knowledge for the development of adaptation and mitigation strategies in urban planning, which can be applied globally to cities facing rapid urbanization in hot climates. Furthermore, this research contributes to the advancement of UHI analysis methodologies that can be replicated in tropical and temperate cities, building on previous studies conducted in Brazil and other regions of the world.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668271","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":"Ambient exposure to nitrogen dioxide and lung function: a multi-metric approach","authors":"Carmel Raz-Maman,&nbsp;Nili Borochov-Greenberg,&nbsp;Rafael Y. Lefkowitz,&nbsp;Boris A. Portnov","doi":"10.1007/s10661-025-13871-4","DOIUrl":"10.1007/s10661-025-13871-4","url":null,"abstract":"<div><p>Most studies evaluating chronic ambient exposure to nitrogen dioxide (NO₂) have used averages as the exclusive exposure metric. However, this approach may lead to an underestimation of potential health effects. The objective of this study is to evaluate the association between ambient exposure to NO₂ assessed by various metrics, and lung function in a cohort of healthy male youth. A cross-sectional analysis of 5,462 subjects was conducted using multivariate linear regression. Exposure to NO₂ was assessed by spatial interpolation using Empirical Bayesian Kriging (EBK). Five different exposure metrics were evaluated over two years, including average concentration, the number and intensity of exceedances of the 24-h NO₂ World Health Organization air quality guideline (AQG), and the number and intensity of 1-h NO₂ peaks. Lung function indices, including percent predicted forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), forced expiratory flow between 25% and 75% of vital capacity (FEF_25-75), and FEV₁/FVC ratio, were assessed. The intensity of the 24-h AQG exceedances was associated with the largest reductions in FEV₁ (-0.82%, 95% CI: -1.61%, -0.03%) and FVC (-1.03%, 95% CI: -1.86%, -0.20%), while FEF_25-75 showed a significant decline only with the 1-h peak intensity metric (-2.78%, 95% CI: -5.02%, -0.54%). The study results support integrating diverse exposure metrics as part of NO₂ chronic exposure assessment, as these metrics may capture a wider range of potential health effects that could be underestimated or overlooked when relying only on average concentrations.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-025-13871-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655202","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":"Machine learning–based habitat mapping of the invasive Prosopis juliflora in Sharjah, UAE","authors":"Alya Almaazmi,&nbsp;Rami Al-Ruzouq,&nbsp;Abdallah Shanableh,&nbsp;Ali El-Keblawy,&nbsp;Ratiranjan Jena,&nbsp;Mohamed Barakat A. Gibril,&nbsp;Nezar Atalla Hammouri,&nbsp;Manar Abu Talib","doi":"10.1007/s10661-025-13876-z","DOIUrl":"10.1007/s10661-025-13876-z","url":null,"abstract":"<div><p><i>Prosopis juliflora</i>, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several attempts have been made to control this species in the United Arab Emirates (UAE) deserts but with little success. This study identifies and maps environmental variables influencing <i>P. juliflora</i> habitats using machine learning (ML); employs maximum entropy (MaxEnt) and statistical techniques to estimate its presence in Sharjah, UAE, home to one of its most intense populations; and conducts validation and sensitivity analysis. Eleven environmental variables representing geological, geomorphological, hydrological, eco-indicators, and climatological factors were selected to map the spread of the associated <i>P. juliflora</i> hazard. Variables were selected using collinearity and variance inflation factor (VIF) to eliminate bias, and ML techniques assigned weights based on overall accuracy (OA) and the Kappa coefficient before model implementation. Finally, a statistical comparison with MaxEnt was conducted to map <i>P. juliflora</i> habitats, classifying suitability as very high, high, low, and very low while estimating model accuracy. The results indicated that MaxEnt achieved a higher area under the curve (AUC 0.98) and more logical outcomes than statistical models (AUC 0.85) due to its superior handling of collinearity, complex environmental interactions, and capability of minimizing overfitting. The main findings show that the variable weights for MaxEnt and statistical models are primarily influenced by precipitation (27.0% and 18.18%), groundwater depth (14.9% and 26.8%), and total dissolved solids (TDS) (20.9% and 26.22%), respectively, indicating a shift in habitat distribution towards the eastern regions of the study area. Habitat mapping of <i>P. juliflora</i> is essential for local stakeholders and policymakers in decision-making regarding species conservation, sustainable land use, and climate adaptation. The findings conclude that ML offers a viable approach for habitat modeling of invasive species in similar arid regions worldwide.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655201","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 global survey on total arsenic concentrations in sharks","authors":"Oswaldo Luiz de Carvalho Maciel,&nbsp;Viviane Felix Moraes Lima,&nbsp;Grazielle Nascimento Silva,&nbsp;José Marcus de Oliveira Godoy,&nbsp;Wilson Machado,&nbsp;Rachel Ann Hauser-Davis","doi":"10.1007/s10661-025-13867-0","DOIUrl":"10.1007/s10661-025-13867-0","url":null,"abstract":"<div><p>Several shark species worldwide present high total arsenic (As) concentrations in muscle tissue, although to what extent has not yet been systematized. This study assessed total As concentrations in different shark species, as these animals comprise a significant fishery resource worldwide and are paramount for ecosystem equilibrium. A scientometric assessment retrieved 21 articles published between 1993 and 2023 reporting total As concentrations in muscle tissue of 27 shark species belonging to 16 different genera landed around the globe. Total As concentrations ranged from 0.14 to 152.20 mg kg⁻¹ wet weight and are not explained by mean length variability (23 to 297 cm). Toxic inorganic As contents usually range from 0.6 to 5%, in sharks, and organic, non-toxic, concentrations vary from 85 to 98% of total As contents, due to biologically-driven chemical speciation changes. Because of this, speciation assessments are paramount to adequately assess human health risks due to shark meat consumption.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668272","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}