Environmental Monitoring and Assessment最新文献

{"title":"Lead and cadmium contamination in soils: impacts and phytoremediation strategies using ornamental plants, nanoparticles, and organic growth regulators","authors":"Sumbal Ammara,&nbsp;Muhammad Tariq Rafiq,&nbsp;Basharat Ali,&nbsp;Rukhsanda Aziz,&nbsp;Maria Aziz","doi":"10.1007/s10661-026-15431-w","DOIUrl":"10.1007/s10661-026-15431-w","url":null,"abstract":"<div><p>The contamination of agricultural land with toxic chemicals, such as lead (Pb) and cadmium (Cd), has become a major global concern, negatively affecting the ecosystem, public health, and food safety. This review highlights the sources of Pb and Cd into the environment, current knowledge of the severity of Pb and Cd contamination in soil and vegetables, documents their phytotoxicity and human toxicity, and then assesses effective remediation strategies that include phytoremediation, foliar application of nanoparticles, and organic growth hormones. The current study found that the toxicity of Pb and Cd in soils and vegetables from different countries exceeded the WHO permissible limit. For the phytoremediation process, ornamental plants are selected due to their genetic and phenotypic characteristics, as well as their widespread use. Since the sisal plant (Agave sisalana) is a rapidly growing plant that produces a high quantity of biomass, its products never compete with the food chain. Hence, these characteristics make it a suitable choice for phytoremediation of Pb- and Cd-contaminated soil. Furthermore, the fiber derived from sisal’s leaves has the capacity to sequester these toxic metals straight from the contaminated soil. Nanoremediation involves the foliar application of zinc oxide nanoparticles, and moringa leaf extract has been proposed to reduce the uptake of Pb and Cd in plants. However, more research is needed to understand better how the individual and combined effects of these remediation techniques effectively treat Pb- and Cd-contaminated and co-contaminated soil.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829833","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":"Operational monitoring windows from cumulative rainfall to chlorophyll-a in subtropical reservoirs","authors":"Jingxin Wei,&nbsp;Peiliang Chen,&nbsp;Xingcheng Fu,&nbsp;Jie Ling,&nbsp;Bingqing Xiao,&nbsp;Xuedong Xie,&nbsp;Bozhu Huang,&nbsp;Jingyi Cen,&nbsp;Songhui Lu","doi":"10.1007/s10661-026-15418-7","DOIUrl":"10.1007/s10661-026-15418-7","url":null,"abstract":"<div><p>Extreme rainfall reshapes the underwater light field and nutrient regime, complicating the predictability of algal risk. We sampled 20 subtropical drinking-water reservoirs in Guangdong, China, in June and September 2024 and used a data-driven search to identify month-specific antecedent rainfall windows. The optimal antecedent windows showed strong seasonality, spanning 30 d in June but only 6 d in September, with contrasting cumulative rainfall thresholds (<i>k</i> = 26 d in June and <i>k</i> = 48 d in September). Then, we used piecewise SEM to disentangle the pathways of light attenuation coefficient (Kd) and nutrients (DIN, TP) influencing size-fractionated chlorophyll-a. Under the late-season window, rainfall consistently elevates turbidity and redistributes nutrients, yielding robust increases in pico/nano chlorophyll while effects on &gt; 20 μm remain weak—together providing an actionable 7–8-week lead time. Building on these patterns, we outline a joint monitoring bundle—windowed rainfall, Kd, and TN/TP—and convert windows into practical rules for sampling frequency and station placement in storm-prone reservoirs. The framework reduces window-selection bias and is readily transferable to operational early warning.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829834","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 hybrid machine learning approach to identify potential green cover area for bio–physical suitability mapping in the western semi–arid Rarh region of West Bengal, Purulia","authors":"Bikash Manna,&nbsp;Shweta Rani","doi":"10.1007/s10661-026-15404-z","DOIUrl":"10.1007/s10661-026-15404-z","url":null,"abstract":"<div><p>Forest cover restoration is urgently needed in a semi–arid district of West Bengal, where land degradation endangers environmental stability and community welfare. The present study introduces and validates a robust, data–driven framework using machine learning to isolate optimal sites for afforestation, aiming to enhance climate adaptability and create sustainable, forest–centric livelihood opportunities. The methodology is structured as a sequential, hybrid workflow. Initially, an unsupervised K–Means clustering algorithm was applied to a suite of eleven environmental variables derived from SRTM, Landsat, and national geospatial databases to perform an exploratory delineation of potential zones. This was followed by a meticulous training data generation were manually digitized through high–resolution visual validation on Google Earth Pro. This dataset then served as the basis for training two supervised algorithms: RF and XGBoost. A rigorous comparative evaluation confirmed the superior predictive power of the Random Forest model, which achieved an overall accuracy of 89.1% and Area Under the ROC Curve (AUC) of 0.9508. An interpretability analysis using SHAP further revealed that slope, soil moisture, and elevation were the most critical determinants of suitable area. The primary outcome is spatially explicit suitability map with 20.9% area of the district as potentially suitable for afforestation that serves as a decision–support tool, enabling policymakers and community stakeholders to implement strategic and effective afforestation programs in the study area.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829821","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":"Multiscale mining and reconstruction strategy for industrial water monitoring abnormal data","authors":"Feng Zhang,&nbsp;Qingyang Lu","doi":"10.1007/s10661-026-15410-1","DOIUrl":"10.1007/s10661-026-15410-1","url":null,"abstract":"<div><p>Improving the quality of water intake monitoring data is an urgent issue in current water management. The industrial water intake monitoring data obtained during the National Water Resources Monitoring Capacity Building Project promotion project was taken as a sample, and the common abnormal categories of water intake monitoring data were summarized, and the strategy of “rough screening–fine identification–reconstruction” was proposed. Considering the seasonal fluctuation law of water monitoring data, the multiscale industrial water monitoring abnormal data identification models were constructed based on segmented 3<i>σ</i> criterion, wavelet transform, and Fourier function. Moreover, the least squares support vector machine (LSSVM) model with adaptive inertia function and particle swarm optimization (PSO) was used to reconstruct the recovered anomaly data. The results indicate that the segmented 3<i>σ</i> criterion performs well for the rough processing of water intake monitoring data, identifying 26 data points that fall outside the corresponding threshold intervals. The Fourier function can effectively reduce the information loss associated with the wavelet transform, thereby improving the accuracy of abnormal data identification; based on verification feedback from monitoring users, 31 of the 38 detected abnormal points were confirmed as “demand-driven anomalies,” yielding an identification accuracy of 81.6%. Furthermore, the inertia function–particle swarm optimization LSSVM model meets the high-precision requirements for abnormal data reconstruction and recovery, and its reconstruction accuracy is higher than that of the LSSVM, the PSO-LSSVM, and the traditional curve fitting method. Specifically, the inertia function–particle swarm optimization LSSVM achieves an average fitting error of 0.0286, representing reductions of 46.2% and 44.4% compared with the LSSVM (0.0532) and PSO-LSSVM (0.0514), respectively; moreover, when compared with the ground-truth values obtained from verification feedback, the reconstruction error rate is below 5%. Overall, the proposed multiscale mining and reconstruction strategy for industrial water intake monitoring abnormal data can provide a valuable methodological reference for enhancing the decision support capability of data in the National Water Resources Monitoring Capacity Building Project.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829822","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":"Design and development of an optical setup to study light attenuation by suspended sediments in water","authors":"Prasanna Babanrao Khaire,&nbsp;Virendra N. Barai,&nbsp;Sachin B. Nandgude,&nbsp;Atul A. Atre,&nbsp;Mamta S. Patwardhan","doi":"10.1007/s10661-026-15426-7","DOIUrl":"10.1007/s10661-026-15426-7","url":null,"abstract":"<div><p>Suspended sediment concentration (SSC) and turbidity are two essential measurements used to evaluate water quality, as they reflect their impact on aquatic ecosystems. The conventional method requires manual intervention and is unable to promptly sense any environmental changes due to urban development, surface run-off, erosion, or climatic effects. Herein, an optical sensor is developed using light-dependent resistors (LDRs), which are non-linear devices used to measure light attenuation through turbidity in the liquid medium. It aims to create affordable sensors that can be expanded to monitor SSC levels using specific measurement techniques that require specialized equipment for precise calibration across different turbidity levels. LDRs were tested for resistance response at different path lengths (50–500 cm), SSC levels (0–7500 mg/L), and laser light wavelengths (532 and 650 nm). The study conducted baseline tests in air and water to measure light attenuation by assessing LDR performance at the center, top, and bottom locations. Four calibration models were developed and evaluated for their accuracy based on metrics such as <i>R</i>², RMSE, NRMSE, MAE, and the agreement index. Of these four models, the BiDoseResp model was found to be the best fitting, with <i>R</i>² and RMSE values of 0.98 and 150.05 mg/L, respectively. The system measures LDR data through its controlled calibration process. The testing of ionic compounds and matrices through dose–response curves establishes the verification of sensor performance. The study develops real-time SSC monitoring systems that enable sustainable water management and environmental protection in aquatic ecosystems.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828530","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":"An integrating approach to optimal characterization and quantification of mesoplastics in aquatic ecosystems: the SIJOUMI salt flat","authors":"Azza Ouni,&nbsp;Abdelaziz Sebei,&nbsp;Abdjlil Smida,&nbsp;Mohamed Maanan","doi":"10.1007/s10661-026-15380-4","DOIUrl":"10.1007/s10661-026-15380-4","url":null,"abstract":"<div><p>The first comprehensive assessment of mesoplastic (2–25 mm) pollution was conducted in the sediments of Sebkha Sijoumi, a crucial Tunisian ecosystem. Twenty surface sediment samples were collected and analyzed using an optimized rigorous protocol involving density separation and granulometric sieving. The optimized analytical protocol was a sequential, multi-stage process designed for maximal recovery and high-resolution characterization of mesoplastics. The recovered particles then underwent a rigorous, high-resolution granulometric analysis via a cascade of standardized sieves (2 to 25 mm), enabling precise particle size distribution profiling. The results revealed highly heterogeneous contamination, with concentrations ranging from 4 to 135 particles/100 g dry sediment. A severe pollution hotspot was identified (Sample S5), indicating intense point-source input, whereas the median concentration (11 particles/100 g) suggested a baseline level consistent with other Mediterranean transitional environments. Granulometric analysis showed a pronounced dominance of mid-sized fragments (4–10 mm), signaling an advanced stage of environmental aging and a “fragmentation bottleneck.” Morphologically, fragments were dominant, followed by films and fibers, with a complete absence of industrial pellets. Chromatically, white particles were overwhelmingly dominant (66%), indicating photodegraded consumer plastics. Source apportionment identified paints (52%) and packaging (34%) as the primary contributors, with paint particles identified as a significant and underestimated source. This study underscores Sebkha Sijoumi’s role as a reservoir for secondary mesoplastics generation and highlights the urgent need for targeted mitigation strategies focused on urban runoff and textile emissions. Analysis of the mesoplastics from Sebkha Sijoumi showed that they were primarily composed of acrylic resin and polyethylene (PE). These lightweight polymers degrade through photo-oxidation, releasing microplastics and toxic additives into sediments and water. This pollution is directly toxic to foundational species, while ingestion by birds and larger fauna causes internal damage and transfers pollutants up the food web, severely threatening the lagoon’s already fragile ecosystem.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828529","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":"Tracking PFAS in Miami-Dade, Florida groundwater: trends, hotspots, and regulatory implications from a multi-year monitoring study","authors":"Maria Guerra de Navarro,&nbsp;Richard Zurbaran,&nbsp;Carolina Cuchimaque Lugo,&nbsp;Natalia Quinete","doi":"10.1007/s10661-026-15419-6","DOIUrl":"10.1007/s10661-026-15419-6","url":null,"abstract":"<div><p>Per- and polyfluoroalkyl substances (PFAS) are persistent and mobile contaminants of growing concern in groundwater systems, yet multi-year assessments in large metropolitan areas remain limited. This study investigates PFAS occurrence, spatial distribution, and associated health risks in Miami-Dade County, Florida, using 1600 samples collected from monitoring wells, raw water, and point-of-exit (POE) locations between 2019 and 2023. PFOA and PFOS were detected in over 90% of samples, while 6:2 fluorotelomer sulfonate (6:2 FTS), perfluorohexanoic acid (PFHxA), and perfluoropentanoic acid (PFPeA) exhibited the highest concentrations. Spatial mapping identified contamination hotspots near airports and firefighting training facilities, with elevated 6:2 FTS levels suggesting aqueous film-forming foam (AFFF) as a primary source. PFAS profiles also indicated contributions from wastewater treatment, landfills, and industrial activities. Rainfall events were positively correlated with increased ∑PFAS concentrations, supporting infiltration-driven mobilization. All POE samples exceeded the newly established U.S. National Primary Drinking Water Regulations, frequently due to PFOA and PFOS concentrations, and some samples occasionally surpassed a hazard index of 1. Miami-Dade Water and Sewer Department (MDWASD) has initiated treatment pilot studies to address these exceedances. Cumulative risk assessments revealed elevated exposure levels across all service areas, underscoring the need for improved treatment technologies and regulatory oversight. This study represents a detailed longitudinal PFAS groundwater assessment conducted in a major U.S. urban area. The findings offer critical insights into contamination patterns, exposure risks, and the urgent need for mitigation strategies to protect public health and ensure regulatory compliance.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829952","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 novel Ni-MOF/rGO-TiO2 sensor for detecting polystyrene microplastics: toward advanced environmental monitoring","authors":"Hysil N Selvance,&nbsp;E. Manikandan","doi":"10.1007/s10661-026-15400-3","DOIUrl":"10.1007/s10661-026-15400-3","url":null,"abstract":"<div><p>Microplastics (MPs) have become a major environmental concern owing to their widespread occurrence in marine and aquatic environments and their potential to threaten human health. The development of rapid and precise methods for detecting MPs remains a major challenge despite increased research attention in the field. Herein, a nickel-based metal organic framework/ reduced graphene oxide-titanium dioxide modified glassy carbon electrode (Ni-MOF/rGO-TiO₂/GCE) was fabricated for the electrochemical detection of polystyrene microplastics (PS-MPs). The samples were characterized using XRD, UV–Vis, FTIR, Raman, and FESEM techniques. The sensor exhibited a linear response over the range from 0.001 to 0.01 mg/mL with a sensitivity of 231.15 mA·mg/mL·cm⁻², a limit of detection of 0.29 µg/mL, and a correlation coefficient of <i>R</i>² = 0.99. The calculated active surface area of the sensor was 0.62 cm², and the charge-transfer resistance (<i>R</i>_ct) was estimated to be 125.95 Ω. This demonstrates excellent conductivity. A fine reproducibility (1.78%) and repeatability (0.19%) were obtained for the sensor. Recovery studies conducted on real samples achieved up to 99% recovery within RSD below 0.6%, confirming its reliability for practical environmental water samples. Therefore, the proposed sensing mechanism of Ni-MOF/rGO-TiO₂/GCE acts as a promising, sensitive, and reusable platform for trace detection of PS-MPs in environmental monitoring applications, highlighting the role of conductive rGO, the catalytic activity of TiO₂ and the adsorption sites of Ni-MOF.</p><h3>Graphical abstract\u0000</h3>\u0000<div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829155","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 decade after the Fundão Dam collapse: key findings from studies in the Doce River basin and adjacent coastal and marine environments","authors":"Fabio Cavalca Bom,&nbsp;Fabiana de Matos Costa,&nbsp;Gisele Daiane Pinha,&nbsp;Manuela Santos Santana,&nbsp;Nelson de Almeida Gouveia,&nbsp;Beatrice Padovani Ferreira,&nbsp;Maikon Di Domenico,&nbsp;Nadson Ressye Simões,&nbsp;Alex Cardoso Bastos,&nbsp;Fabian Sá,&nbsp;Kyssyanne Samihra Santos Oliveira","doi":"10.1007/s10661-026-15356-4","DOIUrl":"10.1007/s10661-026-15356-4","url":null,"abstract":"<div><p>The Fundão tailings dam marks its 10th year of disaster in 2025, underscoring the need for a critical assessment of the studies related to the impacts on aquatic ecosystems caused by the largest disaster in mining history. In this context, the present study aimed to review articles that, throughout the years, have investigated changes in the Doce River basin, as well as in coastal and marine areas affected by this collapse. We applied a systematic analysis of the published literature, identifying the main results in relation to the methodologies applied. Therefore, the studies were characterized according to their methodological approaches, spatiotemporal efforts, environmental compartments, and topics of study evaluated. In total, 182 articles published between 2016 and July 2025 were considered. Considerable heterogeneity was observed regarding spatial and temporal coverage, ranging from point‐in‐time assessments to large-scale analyses. Most of the reviewed studies revealed both acute and chronic effects on water and sediment quality across all monitored environments, which in turn resulted in significant impacts on biodiversity. Some studies investigated all environments in an integrated manner, which made possible a deeper understanding of the connections between these systems, showing the importance of this type of approach. Together, the studies revealed the challenge of learning from disasters, especially regarding environmental issues. Furthermore, they encouraged a critical perspective for long-term monitoring planning and the next steps needed to address aquatic biota recovery. This synthesis underscores the critical need for long-term, integrated monitoring to effectively guide the recovery of these interconnected ecosystems.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-026-15356-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829748","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":"Climate-induced shifts and lengthening of growing seasons across Pakistan’s major vegetation types","authors":"Muhammad Shah Hanan,&nbsp;Sawaid Abbas","doi":"10.1007/s10661-026-15357-3","DOIUrl":"10.1007/s10661-026-15357-3","url":null,"abstract":"<div><p>Pakistan’s diverse ecology, ranging from arid to semi-arid zones and mountainous regions, makes vegetation phenology highly sensitive to climate variability. However, a long-term assessment of phenological shifts across its major ecological zones remains limited at national-scale. This study provides the first consistent nationwide analysis of changes in vegetation phenology metrics from 2001 to 2023, including the start of season (SOS), length of season (LOS), and end of season (EOS). The assessment focused on natural and semi-natural vegetation classes, excluding croplands, to understand climate-driven phenological responses. The MODIS phenology product was used to extract phenological indicators, while vegetation land cover types were derived from the MODIS land cover type product. The impact of climate variability on phenology was assessed using temperature and solar radiation data from the ECMWF ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5) and precipitation data from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). Temporal trends in phenological events and climatic variables were analyzed at seasonal and annual scales, with correlations assessed between each climatic driver and phenological parameters. Results indicated variation in SOS (May to August) and EOS (November to March), reflecting phenological differences across different vegetation types across the biomes. A significant trend of delayed SOS (0.10 days/year) and EOS (0.40 days/year) was observed. SOS onset for grassland and savannas were negatively correlated with temperature (−0.424 and −0.422), and consistently indicated negative association with solar radiation across all vegetation types, suggesting an earlier SOS with warmer temperature and bright sunny conditions. Among the divergent vegetation classes (mixed forests, open and close shrublands, and savannas and woody savannas), within-season precipitation showed positive correlations with EOS and LOS (<i>r</i> = 0.455–0.727), indicating moisture-constrained growing season length. Soil moisture exhibited stronger associations with EOS and LOS, particularly in mixed forests (EOS <i>r</i> = − 0.557, LOS: <i>r</i> = −0.71) with higher significance than with SOS, suggesting a greater role in regulating senescence and growing season duration than green-up onset. These asymmetric phenological responses to different climate controls suggest the complex influence of climatic conditions at different stages of vegetation growth, varying across vegetation types and landscapes. The reliance on the MODIS phenology product may introduce uncertainties, suggesting further validation and independent phenology observation across the landscape.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829670","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}