Earth and Environmental Sciences

Abstract. The processes of tree water uptake in karst environments are poorly understood. One of the main challenges to improved understanding is the complex interaction between soil water and bedrock water, especially in systems characterized by structurally heterogeneous rock fractures. While some studies have highlighted the potential importance of fractured bedrock as a water source for plants, few have quantitatively assessed how fracture characteristics regulate water storage, residence times, and plant water uptake across seasons. Here, we combine stable isotope tracing, a Bayesian mixing model (MixSIAR), and hydrometric monitoring to quantify the contributions and mean residence times (MRT) of soil and rock water accessed by trees as a function of fracture properties. We use a four-compartment sampling framework that distinguishes between soil water (mobile and bulk) and rock water (fracture and infilled fracture). Our results show that fracture characteristics exert a primary control on seasonal tree water uptake patterns. During the peak growing season, mobile soil water (mean MRT =88 d) dominates uptake (mean contribution 41 %), whereas in late growing season, trees increasingly rely on bulk soil water (mean MRT =95 d, mean contribution 55 %). During the transition from the dry to the wet season, reliance on rock water increased in fracture-rich areas. In the reactivation stage, trees exhibited a mean rock-water contribution of 69 % (mean MRT =117 d), and in the subsequent early growing season, large trees derived up to 85 % of their water from rock, primarily from soil-filled fractures with apertures >10 mm that act as seasonal storage reservoirs with prolonged residence times (MRT =84–303 d). Trees preferentially access short-MRT sources under wet conditions and shift to longer-MRT pools during dry periods, demonstrating that seasonal water-use strategies are strongly regulated by fracture-controlled storage and connectivity. This study demonstrates that fracture characteristics play a central role in regulating rock water storage and tree water uptake in karst systems, providing new insights into vegetation resilience in structurally complex landscapes and implications for water resource management under changing climatic conditions.

Abstract Indian Space Research Organization (ISRO) recently launched the meteorological satellite Microsat‐2B (M2B), which provides high‐resolution atmospheric moisture radiance observations, crucial for improving the predictability of numerical weather prediction (NWP) models. This study evaluates the impact of assimilating M2B radiances into the NCMRWF Global Forecast System (NGFS‐T1534) using the GSI‐3DVar assimilation system. Spectral and transmittance coefficients, crucial for radiance assimilation, were generated for the first time in India using CRTM to enable the assimilation of M2B radiances into the NGFS model. An Observing System Experiment (OSE) was conducted during the 2023 Indian summer monsoon season (May–August) to compare analyses with and without M2B radiance assimilation. The results demonstrate that assimilation of M2B radiances considerably reduced temperature and wind errors and improved mid‐tropospheric moisture, particularly over data‐sparse regions. These findings underscore the significance of M2B radiances in improving global NWP model performance in the tropics.

Tropical North Atlantic (TNA) warming typically favors tropical cyclone (TC) genesis over the North Atlantic but suppresses TC formation over the Northwest Pacific during boreal summer. The TNA anomaly patterns can be classified into an eastern coastal and a western warm-pool type, but their respective impacts remain unclear. Here, we find a pronounced difference in the impact between the two TNA flavors. The warm-pool TNA warming suppresses Northwest Pacific TC genesis through a remote dynamical control, while the coastal warming promotes North Atlantic TC genesis via a local thermodynamic control. High-resolution modeling reveals that, compared with the canonical TNA warming, the warm-pool TNA warming suppresses Northwest Pacific TC genesis by 65.2%, while the coastal warming enhances North Atlantic TC genesis by 60.1%. Under greenhouse warming, increased coastal TNA warming is projected to intensify North Atlantic TC activity. Therefore, distinguishing TNA flavors is critical for improving seasonal prediction and future projections of cross-basin TC activity.

The ocean carbon cycle spans multiple scales and reservoirs, challenging efforts to build a coherent picture and fostering misconceptions or fragmented narratives in science and public discourse. Common examples include the belief that the biological processes control the ocean’s carbon sink (i.e., fraction of human CO 2 emissions absorbed by ocean), that restoring coastal ecosystems is highly effective at mitigating climate change, or that whales substantially contribute to carbon sequestration. We provide a comprehensive review of living and nonliving ocean carbon stocks and fluxes—from plankton to mangroves, whales, fish, and plastics—and an integrated perspective on global ocean carbon cycling, disentangling well-supported insights from misconceptions. This synthesis reaffirms the ocean’s key role as a physics- and chemistry-driven carbon sink, while clarifying the limited contribution of coastal and open-ocean ecosystems to carbon sequestration and climate mitigation. We caution against frameworks that justify marine conservation through climate mitigation—a narrative useful to draw attention, but not always robust and unnecessary, since marine biodiversity is worth preserving regardless of its impact on carbon.

• A large-scale, high spatial resolution remote sensing farmland dataset (HSRRSFPD) covering various terrains was constructed. • A multi-scale Gaussian edge refinement dual-branch network (MEGRDNet) was proposed. • Refined farmland parcel mapping was conducted in complex terrains. Existing methods in farmland extraction tasks still face limitations due to significant scale differences among farmland parcels and insufficient boundary perception capabilities, which undoubtedly hinders the promotion and application of large-scale and detailed farmland parcel mapping. To address these technical challenges of fine-grained farmland extraction in complex terrain areas, this study constructed a high spatial resolution remote sensing farmland parcel dataset (HSRRSFPD) covering multiple geomorphic regions (plains, hills, and mountains). HSRRSFPD includes 8,441 image chips with sizes of 256 x 256 pixels, and its total coverage area exceeds 550.00 km 2 . Based on HSRRSFPD, a novel multi-scale edge Gaussian refinement dual-branch network (MEGRDNet) was proposed for refined farmland parcel mapping. MEGRDNet integrated a multi-scale spatial (MSS) branch to address the challenges posed by the diversity of spatial scales of ground objects. Additionally, an edge Gaussian refinement (EGR) branch was developed to enhance the continuity and integrity of farmland boundaries. Compared with existing state-of-the-art algorithms, MEGRDNet achieved maximum improvements of 20.03% in IOU and 10.45% in F1 score. It significantly enhanced the accurate representation of multi-scale farmland boundaries under different terrains. Furthermore, the synergistic effect of the MSS and EGR branches improved MEGRDNet’s IOU performance by 21.93% compared to the Baseline model. In practical applications, a main grain-producing region (Sichuan Province) in China was used for fine-grained farmland parcel mapping at a 1 m spatial resolution. The results were highly consistent with the local government statistical data (GSD). Compared with existing farmland products, MEGRDNet provided finer boundary information, higher spatial resolution, and more accurate spatial representation. The findings and methods of this study are of great significance for land resource optimization, crop monitoring, and food security assurance in complex terrain regions of China or globally.

Abstract. Spring snowmelt is a critical period for dissolved organic carbon (DOC) export from northern boreal peatlands, yet the spatiotemporal dynamics of this process remain poorly understood. To reveal the spatial patterns, we used a novel combination of high-resolution Unmanned Aircraft System (UAS) snow depth mapping, topographic wetness index, and high-frequency stream monitoring during the peak snowmelt in 2024. The study took place in a small 6 ha fen in northeastern Finland. Our results show that DOC leaching is triggered after widespread snow cover depletion, likely due to thawing of surficial peat layers and increased connectivity in the peatland. High-resolution UAS snow surveys captured the progression of snowmelt from drier, south-facing slopes and forested areas toward wetter fen areas, with the expansion of snow-free areas in high-wetness zones initiating hydrological connectivity and pathways for DOC transport. Event-based hysteresis and flushing analyses enabled by high-frequency stream monitoring revealed transitions from deeper to more surficial flow paths towards the final peak melt. The integration of high-resolution spatial and temporal datasets enabled the detailed identification of DOC transport mechanisms during the snowmelt period. These findings underscore the sensitivity of peatland carbon dynamics to late winter processes and snow conditions, highlighting their potential vulnerability to future shifts in climate.

Per- and polyfluoroalkyl substances (PFASs), comprising over 10,000 persistent chemicals, are prevalent in aquatic ecosystems and threaten ecological health. Fish no-observed-effect concentrations (NOECs) are a key ecological safety indicator; however, NOECs remain undefined for most PFASs. Here, we develop a machine learning model to predict mortality-based NOECs for 10,863 PFAS in Danio rerio using compiled toxicity data and mechanistically meaningful molecular structural descriptors. The model achieved robust predictive performance (Rtest2 = 0.7096), with predicted NOECs ranging from 0.239 (0.131–1.129) to 163.452 (77.207–341.332) mg/L, highlighting a wide toxicity variation that suggests further structural investigation would be beneficial. Analysis reveals a “U-shaped” toxicity chain-length trend, with higher toxicity observed for C8–C12 PFAS and polar functional groups (e.g., sulfonates and carboxylates). We further extrapolate these toxicity profiles to 12 additional fish species through trait-based inference and found that warm water and omnivorous species are more sensitive, reflecting trait-mediated differences in bioaccumulation potential and metabolic vulnerability. Structure–species interactions jointly shape toxicity patterns across species. Together, these results extend the mechanistic understanding of the toxicological responses of fish to PFASs. Integrating chemical structures with ecological characteristics enables high-throughput toxicity predictions using limited data, provides mechanistic insights into PFAS toxicity, and establishes a transferable framework for ecological risk screening and prioritization.

Abstract This study proposes a modeling framework based on piecewise constant regression with a Periodic Autoregressive (PAR) remainder. Model complexity is controlled using Rissanen’s Minimum Description Length (MDL) criterion, while Fourier-based compression reduces dimensionality without compromising likelihood, improving computational efficiency. A multi-island Genetic Algorithm (GA) jointly estimates changepoint (CP) configurations and seasonal order. Monte Carlo (MC) experiments under Dobrogea conditions show reliable detection of all three change points when shifts exceed 1.5 innovation standard deviations. Applied to Medgidia precipitation (1965–2019), the method identifies an early shift while retaining a parsimonious AR(1) structure. By separating true climatic shifts from noise, the framework provides a robust basis for water resource assessment and infrastructure planning.

Fine particulate matter (PM2.5) represents a pervasive global environmental hazard linked to adverse respiratory and systemic health outcomes, yet sensitive and noninvasive early biomarkers for real-world exposure remain poorly defined. In this 35 day panel study conducted in Shijiazhuang, China, we recruited 30 healthy young adult males to characterize proteomic and metabolomic alterations in exhaled breath condensate (EBC) under ambient PM2.5 exposure. The average personal PM2.5 concentration was 73.08 μg/m3, with distinct high- and low-exposure periods of 131.33 and 40.46 μg/m3, respectively. High PM2.5 exposure was associated with mild lung function decline, increased systemic inflammation, and elevated oxidative DNA damage. Proteomic profiling identified consistent downregulation of six key proteins in EBC, including PPIA, ENO1, EEF1A1, HSPA8, GAPDH, and RPSA. Metabolomic analysis further revealed disrupted energy metabolism and lipid homeostasis closely tied to inflammatory and oxidative stress responses. Notably, leucine, ornithine, and phenylalanine displayed consistent dose–response trends across EBC, plasma, and urine, supporting their promise as cross-compartment biomarkers. Our results validate EBC as a minimally invasive matrix to detect both local pulmonary and systemic perturbations induced by ambient PM2.5 exposure.

Equatorial waves are key drivers of intraseasonal variability in the tropics, exerting a strong influence on convection and rainfall over northern South America. This variability arises from a broad spectrum of quasi-periodic and aperiodic modes operating on synoptic to global scales. Although each wave exhibits distinct dynamics and seasonality, isolating the individual effects of these waves on precipitation remains challenging. Here, we employ a local index to estimate precipitation anomalies associated with the enhancing and inhibiting phases of multiple convectively coupled equatorial waves (CCEWs): easterly waves (EW), Kelvin waves (KW), mixed Rossby–gravity waves (MRG), the Madden–Julian Oscillation (MJO), eastward inertio-gravity waves (EIG), and equatorial Rossby waves (ER). Our results are broadly consistent with previous studies for northern South America and with local indices applied elsewhere, but provide novel evidence of the effects of EIG and ER waves, which had not been previously documented in the region. Positive (negative) precipitation anomalies are systematically associated with enhancing (inhibiting) phases, and their spatial distribution highlights particularly affected areas, including the Andes, the Pacific coast, and the eastern plains. This work presents advances in understanding of the precipitation anomalies during the transit of tropical waves, which is crucial for improving rainfall forecasting systems in the region.

Abstract The Toarcian Oceanic Anoxic Event (T‐OAE, ∼183 Ma) was characterized by globally enhanced organic‐carbon burial and a negative carbon‐isotope excursion (N‐CIE). However, the role of marine productivity at this time, and its spatiotemporal variability, is unclear. We present the first carbonate barium‐isotope (δ 138 Ba carb ) records across the T‐OAE from a shallow‐water platform (Nianduo, SE Tethys) and a pelagic basin (Dogna, Alpine‐Mediterranean Tethys) to reconstruct productivity dynamics. Both sites show positive δ 138 Ba carb shifts at the N‐CIE onset, indicating supra‐regional productivity enhancement. At Nianduo, δ 138 Ba carb rises through the onset, consistent with increased export production driven by weathering‐derived nutrient inputs. At Dogna, δ 138 Ba carb declines within the N‐CIE onset due to reduction‐driven barite dissolution, followed by a rise during the N‐CIE recovery. The Dogna data suggest protracted elevation of pelagic productivity supported by nutrient upwelling and aeolian fertilization. As such, pelagic basins may have acted as an important carbon sink regulating the T‐OAE carbon cycle.

Environmental exposure to toxic chemicals has long been suspected an important factor contributing to autism spectrum disorder (ASD) in children. Our study developed a suspect screening strategy to broaden the understanding of neurotoxicant exposure in children with ASD (n = 307) and healthy controls (n = 461) and the association between ASD and mixed chemical exposure. Suspect screening of urine samples from the study population identified a total of 94 neurotoxicants designated as confidence level 1, with additional 16 and 34 compounds designated as confidence level 2 and 3, respectively. Among identified level 1 compounds, 48 had a detection frequency >70% in the study population, covering plasticizers, polycyclic aromatic hydrocarbons, insecticides, flame retardants, ultraviolet filters, antimicrobial agents and synthetic antioxidants. The results reveal a complexity of exposure spectrum in ASD children. Conditional logistic regression analyses with level 1 compounds revealed significant associations between ASD and increasing urinary levels of 28 neurotoxicants. Mixed exposure analysis revealed a strong association between combined neurotoxicant exposure and the ASD diagnosis. Among the diversity of neurotoxicants, 1,3-diphenylguanidine (DPG), diphenyl phosphate (DPP) and mono (2-ethyl-5-carboxypentyl) phthalate (mECPP) were identified as the key substances contributing to the exposure-ASD associations. Collectively, our work reported a complex neurotoxicant exposure spectrum in ASD children and associations between neurotoxicant exposure and ASD. The findings highlight the complexity of neurotoxicant exposure in children and the importance of exploring environmental factors of ASD.

Abstract We report the detection of a traveling ionospheric disturbance (TID) captured by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) Active Ionospheric Sounding (AIS) instrument aboard the Mars Express spacecraft at 150–250 km altitude. TID manifests as quasi‐periodic modulations in the ionosphere, and was observed by MARSIS‐AIS as deviations in electron density relative to background levels and undulations in the ionospheric altitude. The analysis suggests that this TID exhibited multi‐cycle wave train characteristics, corresponding to horizontal wavelengths of ∼200 km. Our investigation demonstrates that this TID is generated by the propagation of atmospheric gravity waves (GWs), which is confirmed by an ionospheric simulation for the conditions of the observations. This investigation not only marks the first tracking of Martian GWs using MARSIS but also allows us to investigate the two‐dimensional (2D) vertical propagation of these waves from the lower atmosphere to the ionosphere.

Introduction Dengue remains a significant global health threat, with the majority of cases concentrated in high-burden countries (HBCs) where climatic factors shape transmission. This systematic review and meta-analysis synthesize evidence on the associations between key climatic variables (temperature, relative humidity, rainfall, and wind speed) and dengue incidence in these settings. Methods A systematic search of PubMed and Scopus was conducted up to June 27, 2025, following the PRISMA guidelines. Non-general-population studies, and other non-primary sources were excluded. Effect estimates were extracted from 45 eligible studies for meta-analysis, and pooled relative risks (RRs) per unit increase in each climatic variable were calculated using a random-effects meta-analysis, with subgroup analyses to explore heterogeneity. Evidence quality and strength were appraised using the Navigation Guide systematic review framework. Results A total of 1,279 records were screened, and 140 studies met the inclusion criteria, of which 45 studies were eligible for meta-analysis, predominantly from tropical countries such as Brazil, India, and Indonesia. More than 80% of eligible studies showed low risk of bias in confounding, exposure and outcome assessment. Pooled estimates indicated a 16% increase in dengue incidence per 1 °C rise in temperature (RR = 1.16; 95% confidence interval [CI]: 1.08–1.25), a 5% increase per unit rise in relative humidity (RR = 1.05; 95% CI: 1.00–1.11), and a positive association for rainfall (RR = 1.00; 95% CI: 0.98–1.03). Wind speed demonstrated a negative association (RR = 0.91; 95% CI: 0.70–1.17) from three available studies. Overall, the strength of evidence was rated as “limited” for temperature, humidity, and rainfall, and “inadequate” for wind speed. Discussion Although significant associations between key climatic factors and dengue were observed, considerable spatio-temporal heterogeneity persists in the evidence. The interdependent nature of the climatic variables complicating the independent effects to individual climatic variables. Therefore, well-designed research using finer-scale climatic and dengue datasets from HBCs is essential to improve understanding of climate-driven dengue dynamics. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251132403 , identifier PROSPERO (CRD420251132403).

As industrial parks face increasing pressure to balance economic development with environmental sustainability, optimizing emission strategies becomes critical for achieving sustainable development goals. In this study, a pollutant dispersion module is coupled with the WRF-FDDA-LES (Weather Research and Forecasting four-dimensional data assimilation and large-eddy simulation) to establish a multiscale air quality model for the Pengzhou Industrial Park, Sichuan, China, hereafter referred to as PZ-LESTD. Using PZ-LESTD, the study conducts refined large-eddy simulations of pollutant dispersion from elevated sources in the industrial park on 23 August 2022. The capability of the model in simulating large-scale weather conditions and pollutant transport, together with its performance in refined-grid LES of elevated emission dispersion, is evaluated. Sensitivity experiments with different pollutant emission heights are also carried out. The results demonstrate that the model can satisfactorily reproduce large-scale meteorological variables and pollutant distributions over China and achieve high accuracy in the refined LES simulations. Analysis of the simulated dispersion processes of elevated sources indicates that the current elevated emission strategy in the Pengzhou Industrial Park is effective in mitigating the impact of industrial exhaust on surface air quality in the park and surrounding areas. Sensitivity tests of emission heights reveal that source heights of 20 m to 50 m can significantly reduce impacts on nearby ambient air quality, whereas increasing the source height from 50 m to 160 m results in only minor differences in surface-level pollution, although higher emission sources lead to greater horizontal transport of pollutants. This study provides scientific evidence for sustainable industrial planning and emission management strategies, supporting the transition towards environmentally sustainable industrial parks. The findings contribute to evidence-based policymaking for air pollution prevention and control, facilitating the achievement of sustainable development goals through optimized industrial emission layouts and green industrial transformation.

Ferrate (Fe(VI)) is a promising oxidant for removing trace organic contaminants (TrOCs) in water treatment, yet the reaction kinetics of its short-lived intermediates, Fe(V) and Fe(IV), remain insufficiently resolved. A dual-probe kinetic framework employing methyl phenyl sulfoxide (PMSO) and diphenyl sulfoxide (Ph2SO) was established to independently resolve the reactivity of these coexisting species. The second-order rate constants of Fe(VI), Fe(V), and Fe(IV) with the probes were first determined, revealing that buffer composition exerted a pronounced influence on Fe(V) reactivity, whereas effects on Fe(VI) and Fe(IV) were substantially weaker. Using the dual-probe approach, the kinetic parameters, including time-integrated exposures (CT values), rate constants, and relative oxidation contributions, of high-valent iron species were determined in the oxidation of two representative TrOCs, sulfamethoxazole (SMX) and carbamazepine (CBZ). Fe(V) was the dominant oxidant in borate buffer, while phosphate complexation markedly suppressed its reactivity, increasing the relative contribution of Fe(VI); Fe(IV) contributed minimally in both systems. This study provides mechanistic insight into the distinct roles of high-valent iron species and establishes a methodologically transferable kinetic framework for resolving Fe(V) and Fe(IV) reactivity under controlled aqueous conditions.

Pyrolysis is a promising thermal treatment technique for removing per- and polyfluoroalkyl substances (PFASs) from biosolids. In this study, biosolids and pyrolysis-derived byproducts from a full-scale plant were characterized using liquid chromatography–tandem mass spectrometry and high-resolution mass spectrometry (LC-MS/MS, LC-HRMS), combustion ion chromatography (CIC), and fluorine-19 nuclear magnetic resonance spectroscopy (19F-NMR) to detect and identify fluorinated organic compounds, including PFASs. CIC-based extractable organic fluorine (EOF) analysis showed only a 12% reduction in EOF in biochar after pyrolysis. In contrast, 19F-NMR revealed an 85% reduction in PFAS-specific −CF3 groups and an 81% reduction in total organic fluorine (TOF) in biochar relative to biosolids. Targeted LC-MS/MS identified nine PFASs in biosolids, with perfluorobutanesulfonic acid (PFBS, 0.056 nmol/g), 6:2 fluorotelomersulfonic acid (6:2 FTS, 0.018 nmol/g), and 8:2 fluorotelomersulfonic acid (8:2 FTS, 0.012 nmol/g) as the three most abundant. Only five PFASs were detected in biochar, with the shorter chain, perfluorobutanoic acid (PFBA) and PFBS being the most abundant. These elevated concentrations in biochar (PFBA: 0.121 nmol/g; PFBS: 0.162 nmol/g) compared to biosolids (PFBA: 0.036 nmol/g; PFBS: 0.056 nmol/g) suggest transformation of long-chain PFASs into shorter chain analogues during pyrolysis. Trifluoroacetic acid (TFA) was enriched in biochar, further supporting the accumulation of short-chain products formed through chain-shortening reactions.