Environmental Monitoring and Assessment最新文献

{"title":"Monitoring the hyporheic zone: a global review and strategic directions for improvement","authors":"Daniel da Silva Andrade,&nbsp;Tiziana Di Lorenzo,&nbsp;Silvia Vendruscolo Milesi,&nbsp;Luiz Ubiratan Hepp,&nbsp;Riccardo Mugnai","doi":"10.1007/s10661-026-15291-4","DOIUrl":"10.1007/s10661-026-15291-4","url":null,"abstract":"<div><p>The hyporheic zone (HZ) is a crucial interface in riverbeds that links surface and subterranean waters, supporting a unique ecological community. While recent reviews have explored theoretical and ecological aspects of HZ, this study focuses on evaluating the advances in monitoring the quality and conservation status. We conducted a global systematic review, screening 16,135 articles and retaining a final data pool of 102 peer-reviewed studies published between 1989 and 2024. We address four main areas: (i) spatial and temporal distribution, (ii) monitoring activities, (iii) methodologies employed, and (iv) the impact of legislation. Despite advances in technical knowledge, HZ monitoring efforts remain quantitatively limited. Geographically, research is highly skewed, with Europe (51.0%), Asia (21.6%), and North America (19.6%) dominating the field, while other regions are severely underrepresented. Taxonomically, studies predominantly focus on the entire invertebrate community (35.5%) or microorganisms (30.3%), whereas efficient trait-based approaches remain scarce (6.6%). Methodologically, sampling is largely confined to shallow depths (up to − 50 cm). Furthermore, the enactment of environmental legislation showed a localized, rather than global, stimulus on monitoring outputs. This study emphasizes the urgent need for standardized methodologies, integrated surface–groundwater approaches, and targeted efforts to ensure effective water resource management. Finally, we propose a strategic roadmap with short-, medium-, and long-term actions to guide these improvements.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-026-15291-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738486","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":"Linking biophysical indices with land surface temperature to assess ecological stress in the Nagri River bank in the Malda District, West Bengal, India","authors":"Kabita Lepcha,&nbsp;Tandra Roy","doi":"10.1007/s10661-026-15279-0","DOIUrl":"10.1007/s10661-026-15279-0","url":null,"abstract":"<div><p>Understanding how land use and land cover (LULC) changes influence land surface temperature (LST) is essential for evaluating ecological vulnerability, particularly in riparian regions that support rural communities. This study investigates LULC dynamics and their impacts on vegetation and surface conditions along the Nagri River in Malda district, West Bengal, India, from 2001 to 2022. Landsat imagery was analyzed using a random forest classifier within Google Earth Engine, and key biophysical indices (NDVI, NDWI, SAVI, and BSI) were derived alongside LST measurements. The results reveal extensive conversion of vegetation and water bodies into agriculture and settlements, with bare land increasing by 10.4% over the study period. NDVI values declined from 0.66 to 0.44, while NDWI decreased from 0.45 to 0.11, reflecting vegetation depletion and declining soil moisture. Statistical analysis showed strong negative correlations between LST and vegetation and water indices, and a positive association with BSI, indicating that bare soil surfaces intensify thermal stress. Hotspot and Environmental Stress Zonation (ESZ) analyses further identified the riverbank as the most ecologically stressed area, with high-stress zones expanding significantly in recent years. Future projections using the MOLUSCE model suggest additional declines in vegetation and water bodies by 2050, with a validation accuracy of 74%. Overall, the study underscores rising ecological stress in riparian systems and highlights the urgent need for sustainable land use management strategies applicable to riverbank ecosystems globally.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147721283","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":"Machine learning insights into land surface temperature variability and prediction: a spatiotemporal approach with feature importance and uncertainty analysis","authors":"Ali Rezaee,&nbsp;Mohammad Reza Goodarzi,&nbsp;Seyed Mohammad Alavizadeh,&nbsp;Mojtaba Goldani","doi":"10.1007/s10661-026-15205-4","DOIUrl":"10.1007/s10661-026-15205-4","url":null,"abstract":"<div><p>Land surface temperature (LST) is a critical parameter in climate studies, influenced by meteorological and anthropogenic factors. This study integrates multiple satellite-derived products including MODIS land surface temperature (MOD11A1) and NDVI (MOD13A3) products with CHIRPS precipitation, TerraClimate meteorological variables, and Sentinel-2 land cover data, applying machine learning techniques to develop a comprehensive spatiotemporal framework for LST prediction in Khuzestan Province. Three regression models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR), were employed to predict LST using predictor variables including minimum and maximum air temperature, precipitation, evapotranspiration, soil moisture, vegetation index (NDVI), Digital Elevation Model (DEM), and land use/land cover (LULC) classes from 2001 to 2023. Feature importance analysis was conducted using model-derived importance scores and SHapley Additive exPlanations (SHAP) to quantify the contribution of each predictor. The analysis revealed that vegetation index (NDVI), air temperature, and elevation were the most influential variables governing LST variability. Among LULC-related variables, water cover showed moderate importance, whereas other land cover types had relatively minor effects. Quantile Regression Forest (QRF) was further used to quantify prediction uncertainty. Results demonstrated that RF and XGBoost significantly outperformed MLR, achieving RMSE = 1.5 and <i>R</i>² = 0.92 compared to RMSE = 5.5 and <i>R</i>² = 0.76 for MLR. Temporal predictions with XGBoost yielded RMSE values ranging from 1.4 to 6.6 and <i>R</i>² from 0.68 to 0.98, highlighting its robustness for long-term modeling. Uncertainty analysis revealed that 55% of predictions fell within ± 2°C, 35% within ± 2 to ± 5°C, and 10% within ± 5 to ± 8°C, indicating reliable and interpretable results. This study underscores the effectiveness of RF and XGBoost in capturing complex LST dynamics and the value of SHAP analysis for identifying key drivers. Integrating uncertainty quantification enhances prediction reliability and provides a robust framework for climate research. The proposed approach is adaptable to other regions and offers a valuable tool for the monitoring of land-atmosphere interactions.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727879","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":"Air quality assessment of Haryana state: evidence from 24 cities’ data with emphasis on stubble burning period","authors":"Rahul Langyan,&nbsp;Sunil Gulia,&nbsp;Azad Yadav,&nbsp;Rajesh Dhankhar,&nbsp;S. K. Goyal","doi":"10.1007/s10661-026-15326-w","DOIUrl":"10.1007/s10661-026-15326-w","url":null,"abstract":"<div><p>Haryana state, positioned between Punjab and Delhi, is uniquely exposed to both local agricultural residue burning emissions and transboundary pollution transported by prevailing north-westerly winds during the October–November period. Stubble burning smoke carried by wind only reaches Delhi, if any, after crossing the atmosphere above the central part of Haryana. Most of the past studies discussed the air quality issues of Delhi and correlated it with stubble burning in Punjab, but not for Haryana state in detail. Considering this, using validated/calibrated 24 CAAQMS data, the present study analyzes the air quality status of Haryana state during this post-monsoon season for 3 years (2020–2023). The study considered both particulate matter (PM_2.5 and PM₁₀) and gaseous pollutants (NO₂, SO₂ and CO). The findings reveal high levels of PM_2.5 and PM₁₀ in the whole state, exceeding specified standards during the post-monsoon season. The increment was highest in the Northwest part of Haryana state (area near stubble burnings along Punjab state). The stations aligned with the dominant downwind paths recorded markedly higher PM_2.5 and PM₁₀ levels by 32% and 24%, respectively. The gaseous air pollutants also showed an increment comparatively less. The findings stress the need for region-specific, seasonal policies, including in situ residue management, stricter burning bans, and expanded monitoring. Coordination with Punjab and Delhi NCR under airshed management is crucial. The study offers strong evidence for targeted air quality strategies in Haryana.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727893","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":"Machine learning for land use change analysis in environmental protection areas","authors":"Mayra Vannessa Lizcano Toledo,&nbsp;Johnnatan Rodrigues de Oliveira,&nbsp;Luis Armando De Oro Arenas,&nbsp;Leopoldo André Dutra Lusquinho Filho,&nbsp;Arthur Pereira dos Santos,&nbsp;Raphael de Vicq Ferreira da Costa,&nbsp;Roberto Wagner Lourenço,&nbsp;Darllan Collins da Cunha e Silva","doi":"10.1007/s10661-026-15280-7","DOIUrl":"10.1007/s10661-026-15280-7","url":null,"abstract":"<div><p>Anthropogenic land use and land cover change (LULCC), combined with ongoing climate variability, poses significant challenges to environmental protection areas (EPAs) by altering ecosystem structure, degrading vegetation integrity, and disrupting local climate regulation. Despite their importance, traditional LULCC approaches often fail to incorporate dynamic environmental drivers, limiting their capacity to represent complex landscape-climate interactions. This study investigates the environmental dynamics of the Guaraqueçaba Environmental Protection Area and evaluates the landscape’s potential for automated classification and prediction of impacts associated with land use change. A multitemporal dataset spanning 15 years (2009–2023) was analyzed, comprising an original set of approximately 30.2 million records of annual time series of precipitation, maximum and minimum temperatures, evapotranspiration, global solar radiation, relative humidity, wind speed, land use and land cover information, and the Normalized Difference Vegetation Index (NDVI). To address class imbalance, a balanced subset of 3.6 million records was used for modeling. Predictive models were developed using multiple linear regression (MLR), <i>k</i>-nearest neighbors (KNN), and random forest (RF), with performance assessed under both imbalanced and balanced data conditions using accuracy, <span>(R^{2})</span>, precision, recall, and F1-score metrics. The results indicate pronounced local climate changes, including increasing temperatures in anthropogenically modified areas and altered humidity patterns associated with vegetation loss. Among the evaluated models, RF exhibited the highest predictive performance, achieving accuracies of up to 96% and an R<span>(^{2})</span> of 88.6%, effectively capturing the nonlinear interactions between LULCC, climate variables, and vegetation dynamics. Precipitation and NDVI emerged as the most influential drivers of LULCC processes. These findings demonstrate the effectiveness of machine learning approaches for identifying environmental degradation trajectories in protected areas and provide a robust framework to support targeted mitigation strategies and policy development applicable to other EPAs facing increasing anthropogenic pressure and climate variability.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-026-15280-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727939","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":"Satellite-based algae estimation in reservoirs integrating basin-reservoir modeling","authors":"Mehran Ghodrati,&nbsp;Alireza B. Dariane","doi":"10.1007/s10661-026-15347-5","DOIUrl":"10.1007/s10661-026-15347-5","url":null,"abstract":"<div><p>This study employs a coupled basin–reservoir modeling system along with satellite imagery to assess nitrate and phosphate levels in the Mamloo basin and reservoir, located east of Tehran, Iran. The SWAT model is utilized to simulate streamflow and nutrient dynamics within the basin, while the CE-Qual-W2 model focuses on reservoir processes. Calibration and validation against data from five hydrometric stations demonstrate a strong correlation between simulated and observed streamflow at the basin outlet, achieving Nash–Sutcliffe Efficiency (NSE) values of 0.89 for the period 2005–2014 and 0.88 for 2015–2018. The simulated mean daily concentrations of nitrate (9.6 mg/L) and phosphate (0.19 mg/L) closely align with observed values of 9.1 mg/L and 0.21 mg/L, respectively. The reservoir model has been calibrated for bathymetry, water levels, temperature, dissolved oxygen, and nutrient concentrations. Innovatively, satellite images and Artificial Neural Networks (ANN) were employed to extract chlorophyll a and algal data, resulting in an NSE of 0.9 during the test period for the ANN. Sensitivity analysis reveals that nitrate loads primarily originate from point sources and fertilizers, while phosphate levels are significantly influenced by soil content and pollutants. These findings indicate that while the reservoir effectively reduces nitrate levels, a comprehensive basin-wide approach is crucial for effective water quality management.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715456","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":"Net effect of urbanization on vegetation dynamics in city surrounding zones in China’s arid regions: spatiotemporal patterns and driving mechanisms","authors":"Junnan Gan,&nbsp;Hongzhan Sun,&nbsp;Ashraf Dewan,&nbsp;Haoyan Zhang,&nbsp;Xiaoyan Cao,&nbsp;Yaowen Xie","doi":"10.1007/s10661-026-15318-w","DOIUrl":"10.1007/s10661-026-15318-w","url":null,"abstract":"<div><p>Rapid urbanization profoundly reshapes vegetation dynamics in arid regions, yet its ecological effects—characterized by the coexistence of “greening” and “degradation”—exhibit complex spatiotemporal heterogeneity. In response, this study develops an integrated framework that couples high-resolution Global Artificial Impervious Area (GAIA) data with a dynamic urban-rural gradient to precisely capture the evolution of urbanization footprints across 40 major cities in China’s arid regions from 2000 to 2020. Central to our methodology is the Urban Background Difference (UBD) index, which isolates the net anthropogenic signal of urbanization by removing natural background fluctuations driven by climate variability. Results demonstrate that vegetation greenness follows a robust “inverted U-shaped” spatial pattern along the urban-rural gradient, with peak greening occurring in peri-urban transition zones rather than urban cores. A significant geographical divergence is observed: oasis cities west of the Zhangye-Xining line show a stable “Green Island” effect (e.g., Kashi), while eastern steppe cities often face a “Replacement Deficit” due to the encroachment of high-quality natural grasslands (e.g., Hulunbuir). Temporally, a policy-driven inflection point in UBD emerged around 2012, reflecting the macro-impact of national ecological zoning and the “Main Function Zone Plan.” Mechanistic interpretation via XGBoost-SHAP highlights that the initial ecological matrix (desert percentage) is the primary determinant of greening potential (39.78% contribution), while socioeconomic investment and compact urban morphology act as “amplifiers” that decouple vegetation growth from natural precipitation constraints. This study elucidates the “Constraint-Release” mechanism of urban vegetation in water-scarce environments, providing a scientific basis for adaptive urban planning and ecological restoration strategies in global arid lands.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715517","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":"Multi-season mobile monitoring of intra-urban heat and pollution gradients in a rapidly urbanizing coastal Indian city","authors":"Syed Zaki Ahmed,&nbsp;Shanmuganathan Jayakumar","doi":"10.1007/s10661-026-15283-4","DOIUrl":"10.1007/s10661-026-15283-4","url":null,"abstract":"<div><p>This study presents a spatially explicit, seasonally resolved analysis of the intra-urban thermal heterogeneity in Chennai, a rapidly urbanizing tropical megacity along India’s southeast coast. Leveraging mobile environmental surveys across 81 georeferenced sites spanning six land-use zones, data on temperature, humidity, PM_2.5, PM₁₀, CO₂, and formaldehyde were collected during nighttime in summer and winter seasons. Thermal comfort was assessed using the thermal humidity index (THI), while spatial variability was visualized using GIS-based heat maps and inverse distance weighting (IDW) interpolation. Results revealed a pronounced summer intra-urban thermal contrast, with air temperatures in urban cores exceeding 32.5 °C compared to 31 °C or lower in vegetated suburban zones. In winter, central hotspots remained elevated at ~ 28.9 °C relative to peripheral regions (~ 25–26 °C). PM_2.5 concentrations were significantly higher in summer (<i>p</i> = 0.00082), reflecting enhanced photochemical activity and dust resuspension under drier conditions. CO₂ showed a moderate positive correlation with temperature (<i>R</i>² = 0.096, <i>p</i> = 0.0052), suggesting a potential climate–pollution feedback linked to anthropogenic heat emissions and increased energy demand. Analysis of thermal comfort revealed that 63% of surveyed sites were in the “torrid” discomfort category during summer, while the remaining 37% were “very hot.” Even in winter, 98% of sites were classified as “hot,” indicating persistent nocturnal thermal stress across the city. PCA indicated that temperature and pollution gradients jointly shaped the spatial clustering of intra-urban thermal hotspots, particularly in industrial and commercial zones. The study emphasizes the compounded impact of heat and pollution in shaping Chennai’s urban microclimates and highlights the need for climate-sensitive planning, urban greening, and adaptive infrastructure for tropical coastal Indian cities.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715442","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":"Assessment of gamma-emitting radionuclides in the total Kuwaiti diet","authors":"Aishah Alboloushi,&nbsp;Anfal Ismaeel,&nbsp;Omar Alboloushi","doi":"10.1007/s10661-026-15345-7","DOIUrl":"10.1007/s10661-026-15345-7","url":null,"abstract":"<div><p>Activity concentrations of gamma-emitting radionuclides were determined in commonly consumed Kuwaiti foods and beverages using calibrated high-purity germanium (HPGe) detectors. The total annual activity intakes of ⁴⁰K, ²¹⁰Pb, ²²⁴Ra, ²²⁶Ra, and ²²⁸Ra were estimated at 103,481 ± 4,889, 243 ± 55, 9.6 ± 0.7, 21.9 ± 9.0, and 13.4 ± 1.5 Bq y⁻¹, respectively. ⁴⁰K was detected in all samples, with the highest activity concentration in coffee (640 ± 30 Bq kg⁻¹) and the lowest in grains (64.0 ± 3.3 Bq kg⁻¹). In contrast, ²¹⁰Pb and radium isotopes were detected only in coffee and tea, while ¹³⁷Cs and ¹³⁴Cs were below detection limits. The total annual effective ingestion dose was estimated at 823 ± 71 µSv, of which 642 ± 30 µSv (about 78%) was attributed to ⁴⁰K, while radionuclides from the uranium and thorium series contributed about 22%. Although the ⁴⁰K contribution exceeds the UNSCEAR reference value, it does not pose a health risk due to physiological potassium regulation. However, uranium and thorium series radionuclides remain radiologically significant due to their long-term accumulation and radiotoxicity, highlighting the need for continued monitoring, particularly in frequently consumed beverages.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715434","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":"Changes in serum lactate and creatine kinase levels in free-living Geoffroy’s (Phrynops geoffroanus) side-necked turtle captured using funnel traps","authors":"Nicole Wirschke de Azevedo,&nbsp;Rafael Martins Valadão,&nbsp;Ana Paula Gomes Lustosa,&nbsp;Patrick Luiz Bola Gonsales,&nbsp;Ana Letícia Rodrigues Marques,&nbsp;Marina Marangoni,&nbsp;Ademar Francisco Fagundes Meznerovvicz,&nbsp;Andriel Gustavo Felichak,&nbsp;Paulo Henrique Braz","doi":"10.1007/s10661-026-15332-y","DOIUrl":"10.1007/s10661-026-15332-y","url":null,"abstract":"<div><p>Capture techniques are widely employed in wildlife monitoring programs, yet confinement and handling may induce transient physiological alterations that complicate interpretation of biochemical parameters. This study evaluated temporal changes in serum lactate and creatine kinase (CK) concentrations in free-living <i>Phrynops geoffroanus</i> captured using funnel traps in southern Brazil. Ten adult turtles (5 males, 5 females) were sampled at two time points: immediately after transport from capture sites (T0) and 24 h later (T24). Paired analyses indicated a significant decrease in lactate concentrations from T0 (90.6 ± 44.0 mg/dL) to T24 (38.2 ± 20.8 mg/dL; p = 0.0044), consistent with recovery from acute anaerobic activation. In contrast, CK activity increased significantly from T0 (2,227.8 ± 2,776.9 U/L) to T24 (8,131.0 ± 5,306.7 U/L; p = 0.0012), suggesting delayed enzymatic release associated with muscular strain. These divergent temporal patterns reflect distinct physiological mechanisms and biomarker kinetics following capture. The results highlight the importance of considering sampling latency when interpreting post-capture biochemical values in ecological studies. Funnel traps proved operationally effective, but confinement duration should be carefully managed to minimize physiological stress. Incorporating temporal context into capture protocols can improve the accuracy of physiological assessments in environmental monitoring programs.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13090290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715503","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}