Earth

Mangroves are highly productive ecosystems due to their great capacity to store carbon, but they are also vulnerable to human activities and adverse environmental conditions. Their conservation is often constrained by the opportunity costs of shifting from traditional economic activities to blue carbon projects. This work analyzes the prices of the different carbon markets in ecosystems, compares them with the benefits obtained from other productive activities, and evaluates the viability of implementing carbon projects in mangroves. An exhaustive literature search is conducted to assess the carbon prices of ecosystems worldwide. The opportunity costs of mangrove carbon capture projects in Mexico are estimated from site-specific and regionally relevant economic data; additionally, broader national benchmarks are presented for contextual comparison but are not interpreted as direct opportunity costs where they do not represent realistic land-use alternatives for the mangrove sites analyzed. The price of carbon ranges from 4 to 86 USD per Mg CO₂e. The most studied natural ecosystems are forests. The highest gross annual profit (GAP) from carbon sales is observed in Tabasco and Campeche. GAP with mangrove wood harvesting ranges from 628.0 USD ha⁻¹ year⁻¹ to 3917.7 USD ha⁻¹ year⁻¹. The highest GAP for crops is obtained for white corn in the state of Hidalgo. GAP of the economic activity of livestock ranges from 3167.59 USD ha⁻¹ year⁻¹ to 3365.71 USD ha⁻¹ year⁻¹. The blue carbon projects are competitive with other productive activities at relatively high prices (86 USD per Mg CO₂e). In Tabasco, under certain high-price and high-sequestration scenarios, blue carbon projects can be competitive with local agricultural activities; however, this competitiveness is highly conditional on carbon price, sequestration rates, and local opportunity costs, and therefore cannot be generalized to all mangrove owners without site-specific appraisal. Fair carbon prices are required to make mangrove conservation projects attractive to producers. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent synthetic chemicals of global concern. While most research has focused on their occurrence and toxicity, far less attention has been paid to their impacts on the global carbon cycle. This review synthesizes current evidence on how PFASs influence carbon cycling across soils, aquatic systems, and the atmosphere. In soils, PFASs alter organic carbon inputs by affecting plant biomass and root exudates and shift microbial community composition and enzyme activities, thereby modulating organic matter decomposition. In aquatic ecosystems, PFASs biologically impair carbon sequestration by inhibiting plankton, and abiotically interact with extracellular polymeric substances to prolong the cycling of dissolved organic carbon. The atmosphere acts as a key mediator as follows: thermal treatment of PFASs generates perfluorocarbons, potent greenhouse gases that exacerbate global warming and further disturb carbon cycling. Despite clear disruptive effects, major knowledge gaps remain. Future research should use quantitative structure–property relationship modeling to assess PFAS alternatives (e.g., PFHxS), and employ advanced molecular tracking (e.g., isotopic labeling, NanoSIMS) and machine learning to unravel nonlinear PFAS–carbon dynamics. Improved detection technologies are needed to identify greenhouse gas byproducts from PFAS thermal treatment. Ultimately, deploying high-resolution flux observation networks and integrating PFAS dynamics into Earth system models and carbon-accounting frameworks are critical for predicting carbon–climate feedback and supporting global carbon neutrality goals. Full article

The abundant rainfall and rugged topography characteristic of southern Zacatecas promoted soil leaching. This differentiation in soil physicochemical properties, driven by leaching, results in higher-altitude areas having soils with high sand and aluminum (Al³⁺) content. The influence of altitude and soil units on acidity levels and the quantity of amendments required for the study region (the municipality of Momax) was determined. Samples were collected from sixty-one agricultural sites, based on pH data reported by INEGI in 1979 and variance estimated from an interpolated map. Principal component analysis was used to divide the samples into four contrasting groups. Group 4 exhibited the highest values for clay, organic matter, exchangeable cations, and cation exchange capacity (p < 0.05). Group 1 and 2 served as a transition zone; Group 3 showed the lowest pH values (mean of 4.7) (p < 0.05) and the highest levels of sand (65.5%), aluminum (0.83 cmol kg-1), and hydrogen (0.09 cmol kg⁻¹) (p < 0.05). Given that 80% of the study area contains exchangeable aluminum, it is necessary to implement a future technological intervention plan, incorporating this study’s recommendations, as well as, the possibility of reducing cost by applying less CO₃ amendment rates within a range from 0 to 1.76 t ha⁻¹. Full article

Agricultural drought threatens rainfed agriculture in northeast Thailand, where variable monsoon rainfall, limited irrigation access, and extensive cropland increase vulnerability. This study developed a Google Earth Engine-based Agricultural Drought Risk Index (ADRI) for the Chi River Basin using six benchmark years (2000, 2005, 2010, 2015, 2020, and 2025). CHIRPS precipitation, MODIS-derived vegetation health, ERA5-Land soil moisture, irrigation accessibility, and agricultural land exposure were normalized and integrated by weighted linear combination. The analysis quantified risk-class areas, irrigated–rainfed contrasts, persistent hotspots, weight sensitivity, and spatial agreement with the official Land Development Department recurring-drought map. Moderate risk dominated most years, but high-risk area expanded to 60.7% in 2015, coincident with severe rainfall deficits during the 2015–2016 El Niño event. Conditions improved in 2020 and 2025 as rainfall, vegetation health, and soil moisture recovered. Rainfed areas consistently had higher ADRI values than irrigated areas, and persistent hotspots were concentrated in southeastern and downstream agricultural zones. The principal spatial and temporal patterns remained stable under ±10% weight perturbations. External validation identified ADRI > 2.90 as the optimal threshold, with raster-level precision, recall, and F1 of 0.779, 0.884, and 0.828, respectively; the 998-point sample produced an F1 of 0.832. ADRI therefore provides a practical basin-scale screening framework for drought monitoring, adaptation prioritization, and agricultural water-management planning. Full article

Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts. Full article

Sandy beaches in the Central Region of Veracruz (CRV) face constant anthropogenic pressure from port and urban activities. This study aimed to evaluate total hydrocarbon (TH) concentrations in the intertidal interstitial water of five beaches in the CRV, analyzing their variability by depth (15 and 30 cm) and seasonality (northerly winds, dry, and rainy seasons). TH determination was performed using gas chromatography (GC-FID), following the NMX-AA-117-SCFI-2001 and NOM-138-SEMARNAT/SSA1-2012 standards. Results showed concentrations ranging from 0.86 to 6.53 µg L⁻¹. Significant spatial differences were identified (p < 0.05); Antepuerto beach presented the highest levels due to its proximity to the port, while Farallón showed the lowest concentrations, confirming its role as a reference site. No significant variations were detected by depth or season (p > 0.05), indicating temporal stability associated with continuous anthropogenic inputs. Although levels comply with Mexican regulations, the continuous presence of TH represents a potential risk to benthic biota and the integrity of the Veracruz Reef System (SAV). This study provides a critical baseline for strengthening coastal ecosystem management strategies in the Gulf of Mexico. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

Mediterranean coastal regions are increasingly affected by hydrometeorological hazards associated with high-impact weather events, including cyclones. Between 18 and 21 January 2026, the intense extratropical cyclone Harry affected Sicily, Sardinia, and Calabria, producing severe weather conditions including heavy precipitation, strong winds, and extreme wave activity. This study investigates both the meteo-marine characteristics of the event and its associated damage in Calabria, where the cyclone triggered multiple hazards (wave storms, landslides, flooding, and strong winds). Meteo-marine forcing was characterized using integrated rainfall data, wave parameters, and wind data. In situ observations, radar-derived precipitation estimates, satellite measurements, and model-based reanalysis products were combined to provide a comprehensive evaluation of the event. A georeferenced database of 195 damage records was compiled and classified according to the EU Floods Directive (2007/60/EC), allowing spatial analyses within a GIS framework. Although the cyclone produced exceptional rainfall totals, locally exceeding 580 mm in 90 h, the distribution of impacts reveals the predominance of coastal processes. Wave storm-related damage accounted for 68% of all recorded impacts, mainly affecting transportation and communication infrastructures, tourism facilities, and population. The prevalence of coastal damage appears to be linked not only to the intensity of marine forcing but also to its persistence which locally exceeded the maximum climatological persistence, suggesting that event duration plays a critical role in determining impact severity. Geomorphological analyses indicate that short-term coastal vulnerability is influenced not only by long-term shoreline evolution but also by local topographic characteristics and exposure to marine forcing. These findings contribute to improving risk assessment and mitigation strategies for Mediterranean coastal regions under a changing climate. Full article

Climate change and glacial retreat in the tropical Andes—as evidenced by environmental changes in the Carihuairazo volcano area and human-induced alterations to the páramo—reveal complex environmental dynamics that require an integrated understanding. This study evaluates edaphic variation along a periglacial–agricultural spatial gradient, relating soil properties to sustainable management strategies. Using a methodological approach that includes multi-criteria spatial delineation, altitude-stratified sampling, and multivariate modeling via HJ-Biplot, the physical, chemical, and biological properties were analyzed, with a focus on basal microbial respiration. The data show that periglacial soils exhibit geochemical–mineral control and high basal respiration, potentially influenced by moisture peaks, despite their low organic matter content. In contrast, lowland andisols exhibit biological–structural control conditioned by organic matter accumulation, reflecting distinct conditions associated with agricultural management and land use. It is concluded that understanding these spatial edaphic patterns and their vulnerability to human intervention is essential for designing sustainable management and conservation frameworks that mitigate the impact of climate change on high-mountain ecosystems. Full article

Over the past two decades, the notion of landscape observatories has gained prominence as a strategic tool for monitoring, documenting and interpreting landscape change. These entities combine scientific research, policy advice and public engagement, aiming to bridge the gap between territorial knowledge and decision-making. This paper presents a systematic review of the scientific literature on landscape observatories, complemented by an original comparative database of 188 landscape observatories and related initiatives worldwide. Bibliographic searches were conducted in Web of Science, Scopus and ProQuest, using the term “Landscape Observatory” and related expressions. The quantitative analysis reveals a steady growth of publications since the early 2000s, with a marked concentration in Europe, particularly France, Italy, and Spain, where the implementation of the European Landscape Convention fostered institutionalisation. Regional-scale observatories dominate the dataset, while national examples illustrate standardised approaches to landscape monitoring. Methodological diversity is evident, as most initiatives rely on GIS and remote sensing, while others emphasise photographic documentation or participatory perception studies. Despite this richness, the review identifies persistent gaps in impact evaluation, long-term institutional sustainability and methodological integration across physical and social dimensions of the landscape. Landscape observatories are thus positioned as promising but still evolving instruments for multi-scalar governance, capable of connecting observation, policy and collective awareness. To consolidate their role, future efforts should focus on harmonising indicators, ensuring continuity beyond political cycles, and promoting interdisciplinary and participatory frameworks that capture both the material and experiential facets of landscape transformation. Full article

Understanding how land-use change affects habitat fragmentation and connectivity is essential for assessing landscape degradation and conservation effectiveness in protected areas globally. It is particularly acute in tropical protected areas where anthropogenic pressures are intensifying. This study investigated long-term landscape dynamics, forest fragmentation, and structural connectivity in the Bena Mulumbu Hunting Domain, a Category VI protected area located in the Miombo woodland region of southeastern Democratic Republic of the Congo. Landsat imagery acquired in 1995, 2005, 2015, and 2025 was classified using the Random Forest algorithm into six land-cover classes (Miombo woodland, savanna, agricultural land, mining areas, built-up/bare land, and water bodies) to quantify land-cover changes over 30 years. Landscape composition was assessed using the percentage of landscape (PLAND), Shannon diversity metrics, and transition analyses. At the same time, fragmentation and structural connectivity of Miombo woodland were evaluated along an edge-to-core gradient (0–2 km, 2–4 km, 4–6 km, and >6 km) using landscape metrics. Results showed that savanna remained the dominant land-cover type throughout the study period. However, the landscape underwent progressive reorganization characterized by recurrent transitions among Miombo woodland, savanna, and agricultural land, leading to increased spatial heterogeneity. Fragmentation analyses revealed significant spatial differences in total core area among zones (Kruskal–Wallis: H = 8.12, p = 0.044); however, after normalization by zone area, no consistent edge-to-core gradient was observed for core habitat proportion, indicating that raw differences primarily reflect zone size rather than a systematic ecological gradient. Despite increasing fragmentation, structural connectivity remained high across the hunting domain. The CONNECT index increased significantly from the edge toward the core zone (p = 0.003), highlighting better-connected forest networks in interior sectors. These findings suggest that the Bena Mulumbu Hunting Domain is experiencing an intermediate stage of landscape transformation, where forest fragmentation is evident but has not yet resulted in widespread connectivity loss. Maintaining existing forest cores and connectivity corridors should therefore be prioritized to prevent further degradation of ecological integrity. These findings challenge the assumption that landscape degradation in protected tropical Miombo woodlands necessarily follows a simple edge-to-core gradient. Full article

The overexploitation of groundwater resources is a significant concern due to the potential risks associated with a decline in freshwater availability. Future planning and policymaking should consider long-term groundwater availability and urban expansion patterns to understand urban growth. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the land surface temperature (LST) and vegetation change using Normalized Difference Vegetation Index NDVI analysis have been performed to find the spatial spread of urbanization and its impact on surface temperature in the area. For groundwater assessment, the Gravity Recovery and Climate Experiment (GRACE) data have been used, while for land cover, NDVI, and LST assessment, Landsat data have been used. The GRACE-based groundwater storage (GWS) anomaly has been correlated with Global Precipitation Measurement (GPM) data. An annual groundwater storage decline of ~7.01 mm/year was identified. Groundwater and land-cover changes were evaluated at five-year intervals from 1990 to 2025. The urban expansion from 838 to 1470 km² coverage shows the rapid expansion and its impact on vegetation and groundwater recharge in the area. The results demonstrate a rapid increase in the urban area, which affected the vegetation and increased the surface temperature in the area. Urban expansion reduced vegetation cover and infiltration, contributing to elevated land surface temperature and groundwater depletion. This study focused on integrating the groundwater impacts due to other environmental variables, i.e., temperature increase and vegetation decrease. The temporal increase in urban expansion decreases the infiltration rate, which impacts the groundwater storage and depletion, as shown by the linear trend. These findings underscore the urgent need for effective groundwater management and vegetation management policies and integrated urban planning strategies to ensure the long-term sustainability of freshwater resources. Full article

Non-grain production of cultivated land has increasingly affected regional food security and the stability of agricultural ecosystems. In traditional rice-producing regions, changes associated with non-rice cultivation, fallow land, rice-fishery integrated farming, and intensive agricultural management are reshaping the spatial structure of paddy landscapes. To identify the long-term spatiotemporal evolution of paddy systems, this study investigated Northern Jiangxi, China, using Landsat surface reflectance imagery from 2000, 2005, 2010, 2015, and 2020 on the Google Earth Engine (GEE) platform. The Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used to construct a phenology-based Flooding Frequency (FF) indicator. Based on the annual frequency with which pixels satisfied the condition LSWI > EVI, cultivated land was classified into three categories: non-flooded cropland, standard rice paddy, and high-frequency flooded cropland. In this study, non-flooded cropland was used as an indicator of potential non-rice cultivation rather than as direct evidence of confirmed non-grain production. Landscape metrics, transition matrices, gravity center migration, standard deviation ellipses, and geographically weighted regression (GWR) were then used to examine paddy landscape dynamics, fragmentation patterns, and county-level spatial associations with socioeconomic factors. The results suggest that the paddy system in Northern Jiangxi experienced marked stage-based fluctuations between 2000 and 2020. Standard rice paddy recovered during 2005–2010, whereas non-flooded cropland expanded considerably during 2010–2015, accompanied by intensified paddy landscape fragmentation. Non-flooded cropland was mainly distributed around urban fringes, transport corridors, and some hilly margins. Standard rice paddy was concentrated in traditional grain-producing areas, including the Poyang Lake Plain and the Gan-Fu Plain. High-frequency flooded cropland was primarily located in low-lying lake areas, where its dynamics were likely associated with rice-fishery integrated farming, continuous irrigation, and hydrological fluctuations. Landscape metrics showed that the largest patch index and mean patch size of standard rice paddy declined after 2010, indicating reduced spatial continuity of core paddy fields. The GWR analysis provided auxiliary evidence that total population, per capita gross domestic product (GDP), and urbanization rate were spatially associated with changes in non-flooded cropland at the county level; however, the results should be interpreted as exploratory associations rather than causal mechanisms. Overall, paddy landscape change in Northern Jiangxi was expressed not only through changes in cultivated land area, but also through the reorganization of paddy function, spatial continuity, and land-use intensity. Future cropland protection should therefore move beyond area-based control toward integrated management of quantity, quality, function, and spatial configuration. Future research should further verify these findings using dynamic cropland boundaries, higher-resolution imagery, and more detailed socioeconomic data. Full article

Nature-based solutions (NbS) are increasingly recognized as important strategies for advancing agricultural sustainability and supporting the United Nations Sustainable Development Goals (SDGs). Much research has highlighted the biophysical potential of NbS; however, the literature remains fragmented regarding their socio-economic and governance implications. This study synthesizes existing research, identifies major knowledge structures, and develops a forward-looking research agenda. We analyzed 1198 academic articles using Structural Topic Modeling (STM) to identify ten latent topics. These topics were subsequently mapped onto the adapted Agro-Ecotopia model based on two dimensions: Ecosystem State & Control and Governance Logic & Goal Alignment. This approach established an Agro-NbS analytical framework and identified four exploratory scenarios: Green Regulation, Engineered Ecotopia, Vulnerable Wilderness, and Grassroots Resilience. These scenarios characterize diverse pathways of agricultural NbS development, highlighting potential spatial trade-offs and emerging system vulnerabilities associated with social equity. Furthermore, this study extends traditional biophysical assessments by emphasizing the importance of polycentric governance and Traditional Ecological Knowledge (TEK) in addressing complex climate challenges. The findings contribute to the understanding of agricultural Socio-Ecological Systems (SESs) and provide insights for policymakers seeking to promote a more equitable transition toward sustainable agriculture. Full article

Crop monitoring is an important aspect of agricultural management, as it provides insights into cultivated area, crop health, growth patterns, and yields potential. Mapping cultivated areas and identifying crop types was historically conducted through field surveys and manual mapping, which are time-consuming and labor-intensive. Remote sensing classification has transformed large-scale land cover mapping, including crop identification. This work aims to: (1) compare the performance of Dynamic Time Warping (DTW) and two machine learning families (artificial neural networks and decision trees) for crop classification using Sentinel-2 data; (2) assess whether reflectance data, spectral indices, or both yield better classification results; and (3) evaluate the effect of hyperparameters on model performance. Among the DTW variants evaluated, dynamic time warping without a time constraint performed the best, with an overall accuracy of 0.921 using the combination of both reflectance and spectral indices. Most machine learning methods outperformed DTW. Although the convolutional neural network reached the highest single accuracy (0.948), the transformer was selected as the best model overall (accuracy of 0.944), as it combined a comparable accuracy with the lowest sensitivity to hyperparameter variations, making it a reliable option when testing machine learning architectures applied to crop mapping. This work also provides insights for model architecture development based on an exhaustive hyperparameter search for the machine learning models. Full article

Wildfires pose increasing environmental and socio-economic risks, particularly in climate-sensitive and tropical regions, necessitating reliable satellite-based monitoring and predictive frameworks. This study presents a comprehensive survey of satellite-derived wildfire indicators and spatiotemporal modeling approaches, covering their historical development, current methodologies, and emerging research directions. We review major active fire and hotspot datasets derived from MODIS, VIIRS, and related platforms, along with key environmental drivers such as vegetation indices, meteorological variables, and land-surface with a specific case study for the Indonesian region. Modeling approaches are synthesized from classical statistical regression and time-series analysis to contemporary machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models. The analysis highlights the transition toward multi-source data integration and spatiotemporal deep learning frameworks capable of capturing complex wildfire dynamics. Finally, we identify future research challenges, including hybrid physical–AI modeling, uncertainty quantification, and scalable real-time wildfire intelligence systems. This survey provides a structured reference for researchers and practitioners seeking to advance satellite-based wildfire monitoring and prediction. Full article

The objective of this study was to propose a simple, low-cost in-situ remediation method for NO₃⁻-N that effectively utilizes natural denitrification processes. We verified the inflow of surrounding groundwater containing high concentrations of NO₃⁻-N when groundwater at the denitrification depth was pumped, as well as the denitrification effect at that depth, under two pumping flow-rate conditions (low and high) at a site where denitrification had been confirmed. The results suggest that pumping groundwater at the denitrification depth enables the inflow of surrounding groundwater, thereby enabling denitrification of high-concentration NO₃⁻-N in the surrounding groundwater under oxidizing conditions. The denitrification amounts were 72 mg-N/h for the high-flow Pumped Denitrification Test (PDT) and 3.3 mg-N/h for the low-flow PDT. Additionally, the nitrate removal efficiency of the high-flow PDT was higher than the results obtained in previous studies at the same site and season under natural groundwater flow. It was also comparable to that of artificially created denitrification environments at other sites when assuming conditions with high NO₃⁻-N concentrations in shallow groundwater. This study demonstrated that the pumping of reductive groundwater transports high concentrations of NO₃⁻-N along with the surrounding groundwater, and that denitrification occurs without impairing denitrification capacity. Full article

Long-term wildfire susceptibility mapping represents a significant component of disaster prevention and the protection of human communities, public health, and local ecosystems. In this study, a wildfire inventory was developed through multi-sensor fusion of satellite data (MODIS and VIIRS), comprising 153,305 fire events across China for the period 2001–2024. In addition to historical incidents, 14 predictive variables were processed, representing geomorphological, climatological, hydrological, vegetative, and anthropogenic conditions. This study evaluates long-term spatial wildfire susceptibility based on long-term mean environmental and climatic conditions. Methodologically, the research applies six models from machine learning (ML), deep learning (DL), and transformer-based approaches: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Fourier Multi-Layer Perceptron (F-MLP), Kolmogorov–Arnold Network (KAN), and Feature Tokenizer (FT) Transformer. The results were integrated into an ensemble susceptibility map with a spatial resolution of 500 m using Geographic Information Systems (GIS), indicating that 7.4% of China’s territory is classified as having a very high wildfire susceptibility. In addition to the national-scale assessment, a local differentiation was conducted across 34 province-level divisions, revealing that Fujian Province (86.8%) and the Guangxi Zhuang Autonomous Region (82.9%) had the largest shares of areas classified as high and very high wildfire susceptibility. Performance evaluation under spatial block-based validation demonstrated that the Random Forest model achieved the highest predictive power, with an area under the curve (AUC) of 87.8%, followed by XGBoost (87.3%) and Fourier MLP (86.6%). Based on the combined SHAP (Shapley additive explanations) analysis of all applied models, soil moisture, elevation, and terrain slope were identified as the most influential factors affecting wildfire occurrence in China. Overall, the findings contribute to more effective wildfire prevention and risk management strategies at both the local and national levels. Full article

Grassland degradation (GD) varies markedly across space. Identifying potential drivers at the county level enables precise grassland conservation and supports a win–win between economic development and ecological protection. However, most existing studies adopt a single, region-wide lens and lack county-level analyses. Focusing on Ordos, we conduct a county-level assessment and rank potential driver groups using partial-order theory. The results indicated the following: (1) from 2000 to 2020, a total of 6.9% (6026 km²) of grassland was restored, while approximately 5.0% (4372 km²) underwent degradation, with grassland recovery outpacing degradation; (2) urbanisation and economic development were identified as the leading drivers in five counties, followed by human activities and climate (three), and livelihood development (one); and (3) Ordos should adopt county-level differentiated management strategies: controlling urban and industrial expansion in urbanisation and economic-development-dominated counties, regulating grazing and land-use activities in human-activity-dominated counties, implementing dynamic grazing bans and drought preparedness in climate-dominated counties, promoting livelihood diversification in livelihood-development-dominated counties, and applying priority-based integrated governance in multi-driver counties. Full article