Many atmospherically relevant multiphase reactive systems exhibit size-dependent kinetics in laboratory studies, with apparent reaction rates increasing with decreasing droplet size, suggesting an important role for interfacial processes. Here, we present CHAI (CHemistry of Aerosol Interfaces), a physicochemical modeling framework that describes these reactive systems using an additive resistance approach, considering the various mass transfer and reaction processes taking place simultaneously in the gas phase, droplet bulk, and at the droplet surface, and their relative time scales. We demonstrate its applicability to modeling and developing parametrizations for several inorganic and organic oxidation processes in microdroplets and aerosols, including S-(IV) to S-(VI) conversion, through simulation of experimental data. CHAI is also used to reconcile single-droplet observations with chamber and flow tube experiments and is extended to parametrization of these processes for representation in large-scale atmospheric models. Our results show that neglecting the size dependence of aerosol reaction processes can lead to inaccuracies in atmospheric chemistry modeling, motivating the CHAI approach for modeling these multiphase reactive systems and further experimental studies.
Each melt season, a "Dark Zone" develops on the southwestern margin of the ice sheet of Kalaallit Nunaat, reducing surface albedo and enhancing surface melt rates. While several processes are known to locally influence albedo, their respective contributions to the regional-scale darkening remain unknown. Here, we combine laboratory measurements, in situ observations, and physical and probabilistic modeling to quantify the albedo-reducing mechanisms across the Dark Zone. Our results indicate that the darkening is primarily driven by a combination of cryoconite material accumulation and densification of the ice column, followed by the development of microalgal blooms, while mineral dust particles do not directly contribute to the darkening. Our study provides an empirically constrained physical albedo model to represent bare ice darkening processes at play in the Dark Zone and beyond, which can guide modeling efforts to include surface darkening in ice sheet mass loss predictions.
Building local surrogates to accelerate stationary point searches on potential energy surfaces span decades of effort. Done correctly, surrogates can reduce the number of expensive electronic structure evaluations by factors of several, and in favorable regimes by roughly an order of magnitude, while preserving the accuracy of the underlying theory; the gain depends on oracle cost, search distance, and the availability of analytical forces. We present a unified Bayesian optimization view of minimization, single-point saddle searches, and double-ended path searches: all three share one six-step surrogate loop and differ only in the inner optimization target and the acquisition criterion. The framework uses Gaussian process regression with derivative observations, inverse-distance kernels, and active learning, and we develop optional extensions for production use, including farthest-point sampling with the Earth Mover's Distance, MAP regularization, an adaptive trust radius, and random Fourier features for scaling. An accompanying pedagogical Rust code demonstrates that all three applications use the same Bayesian optimization loop, bridging the gap between theoretical formulation and practical execution.
Over the last several decades, investigations of Earth's subsurface and other extremely low-biomass systems have refined our understanding of the environmental limits of life, driven by methodological advances that permit agnostic life detection of biology and their respective physical biosignatures and chemical biomarkers. These advances enable mission concepts centered on microbiological processes that facilitate identification of both active life and preserved biosignatures through measurements of metabolism and associated biochemical markers that, on Mars, are more likely to be retained below the surface. Terrestrially, although biological processes can exert a significant influence on Earth's crust, the presence of habitable conditions does not necessarily imply the existence of cellular life. The Viking missions constituted the first direct life-detection experiments on Mars but produced equivocal outcomes, prompting subsequent exploration strategies to emphasize surface habitability rather than direct biological testing. Leveraging progress in subsurface microbiology and planetary exploration, we contend that Mars missions are now poised to shift toward direct tests for extant microbial activity in the subsurface, with metabolic processes serving as a broadly applicable indicator of life.
The Azores are characterized by intense volcanic activity, creating unique environments such as fumarole sites, where geothermal gases and high temperatures drive distinct chemical and biological processes. To investigate small-scale heterogeneity within such a site, six visually distinct samples were collected within a 30 cm radius at an active fumarole on São Miguel Island. The samples were analyzed for elemental and mineralogical composition, bacterial lipid biomarkers (PLFAs), and microbial community structure using a novel DNA separation technique to specifically target the living microbiome. Despite mineralogical similarities across all samples-predominantly composed of alunite, alkali-feldspar, and quartz-significant microbial heterogeneity was observed. Both PLFA and bacterial iDNA analyses revealed distinct microbial communities associated with specific conditions indicated by the specific colors: red and brown samples were dominated by Proteobacteria and Actinobacteriota, yellow and green by Thermoplasmatota and Actinobacteriota, and white and gray by Crenarchaeota. Interestingly, the gray samples exhibited a broader microbial composition, sharing some taxa with all other samples. These striking color variations are likely driven by differences in both specific mineral composition and microbial pigmentation, reflecting localized biogeochemical processes. Our findings demonstrate that extreme microbial heterogeneity can occur over remarkably small spatial scales within fumarolic systems, underscoring the complex interplay between chemical and biological factors in these dynamic volcanic habitats.
Snow exerts intricate influences on alpine ecosystems, and winter snow process is undergoing drastic change with climate warming. Yet the impacts of winter snow on vegetation growth (hereafter, snow effects) and their underlying mechanisms remain uncertain, as snow effects operate through multiple pathways. Based on remote sensing-derived snow and vegetation products, we examine the snow effects on vegetation growth in spring and summer across the Tibetan Plateau through partial correlation analysis. We then quantify the distinct pathways through which snow influences vegetation growth in spring and summer using pixel-wised piecewise-Structural Equation Modeling, and further identify the transitions of dominant ecological processes underlying these effects. We report a marked seasonal transition of snow effects on vegetation growth, with negative-to-positive shifts observed in 25% of the Tibetan Plateau from spring to summer. The negative snow effects in spring are mainly attributable to the snowmelt-date (SMD)-induced phenological pathway, through which earlier snowmelt advances spring phenology and thereby influences vegetation growth. In contrast, snow effects in summer are predominantly positive due to the reversal of snow-induced phenological effects from spring and the carryover of snow-driven soil moisture effects into summer, which promotes vegetation growth. Finally, we found that most of the dynamic global vegetation models (DGVMs) used in this study have limited ability to reproduce this seasonal transition in snow effects. These findings highlight the critical seasonal shifts in ecological processes that underpin snow effects on ecosystems, providing valuable insights into improving ecosystem models.
The use of cover crops (CCs) is increasingly promoted to diversify cropping systems and advance agricultural sustainability. Yet, CC adoption can involve context-dependent trade-offs, including resource competition and elevated greenhouse gas (GHG) emissions. In this opinion article, we propose enhanced rock weathering (ERW) as a complementary strategy to improve biogeochemical synchrony within CC systems. By synthesizing emerging evidence, we show how CC-ERW interactions can synergistically enhance carbon sequestration, nutrient cycling, GHG mitigation, and soil food-web functioning, mainly via root-driven weathering processes and soil feedbacks. We further outline opportunities for application across diverse agroecosystems and highlight key challenges for scaling, including weathering thresholds, potential metal risks, and governance constraints. Overall, harnessing the CC-ERW nexus offers a promising pathway toward climate-resilient and multifunctional agriculture.
The extreme and fragile environments of high-altitude proglacial lakes shape unique microbial communities and metabolic networks, serving as active interfaces in the biogeochemical cycles of carbon (C), nitrogen (N), and sulfur (S). However, the metabolic processes underlying microbially driven biogeochemical cycling in these lakes remain poorly understood. In this study, by integrating field investigations and isotopic analyses across multiple seasons, we observed geochemical and genomic evidence consistent with significant microbial methane (CH4) oxidation in the surface sediments of cryo-oligotrophic proglacial lakes in the Nyainqentanglha Range on the Tibetan Plateau. Metagenome-assembled genomes (MAGs) analysis revealed that Methylobacter was the dominant methanotroph in surface sediments, possessing complete pathways for aerobic CH4 oxidation, partial denitrification (nitrate → nitrous oxide), and sulfide oxidation (sulfide → elemental S), suggesting its genetic capacity to potentially participate in C, N, and S transformations. The co-occurring Nitrospira (Palsa-1315) was identified as a key player in the N cycle through complete ammonia oxidation (comammox, ammonia → nitrate), while Rhodoferax and Thiobacillus were considered important contributors via heterotrophic and autotrophic denitrification (nitrate → dinitrogen), respectively. Additionally, Thiobacillus may be the key genus involved in the S cycle through S/sulfide oxidation (S0/sulfide → sulfate). Overall, this study reveals the key microbial taxa involved in CH4, N, and S cycling and highlights the potential importance of methanotrophy in rapidly expanding proglacial ecosystems amid ongoing climate warming.
Fulvic acid (FA), a highly reactive and soluble fraction of dissolved organic matter in cultivated soils, facilitates the formation of stable colloids through complexation with iron (Fe), thereby significantly modulating the environmental mobility of arsenic (As). However, the migration behavior of As associated with FA-Fe colloids in porous media remains insufficiently characterized, particularly regarding the integration of coupled migration processes with quantitative modeling. This study investigated colloid-mediated As(III) migration in saturated porous media using column experiments and a time-fractional advection-dispersion equation (fADE). Increasing FA concentration enhanced As mobility, as evidenced by elevated breakthrough ratios and an increase in the fractional order α from 0.475 to 0.881, signifying the attenuation of memory effects and a transition toward Fickian migration. Conversely, elevated Fe concentrations promoted colloidal aggregation and suppressed As migration, with α decreasing to 0.437, capturing non-Fickian behavior associated with particle retention and deposition. Mechanistically, FA stabilizes FA-Fe colloids through electrostatic repulsion and steric hinderance while competing for adsorption sites, whereas Fe induces aggregation and enhances pore-scale interception, leading to As sequestration via inner-sphere complexation with Fe-OH groups. Under alkaline conditions, surface charge effects strengthened electrostatic repulsion and promoted migration, while elevated ionic strength compressed the electrical double layer, facilitated deposition. These results demonstrate that As migration is governed by the coupling between colloidal stability and interfacial interactions, which is effectively quantified by fADE. These findings provide a theoretical framework for understanding As mobility in subsurface environments and offer critical insights for groundwater remediation strategies involving colloid-facilitated migration.
Space weathering causes physical and spectral changes on the surfaces of airless bodies. However, our understanding of how space weathering operates in the presence of volatile ices is in its early stages. Electron irradiation of ice-coated surfaces is expected in astrophysical environments including the early solar system, volatile ice-rich permanently shadowed regions of the Moon and Mercury, and other airless bodies like asteroids. A recent study suggests that anomalous oxygen isotope exchange occurs between water-ice and underlying surfaces when exposed to electron irradiation at extremely low temperatures (10 K). To delve deeper into the physical processes underlying isotopic exchange, we employ nanoscale atomic force microscopy-based infrared (AFM-IR) spectroscopy to identify Si-O bond formation resulting from the electron irradiation of H2O ice coated silicon targets. Experimental variables include electron energy, amount and timing of water-ice deposition, and surface area exposed to the electron beam. AFM-IR point spectra, surface topography and IR absorption mapping reveal that the degree of surface oxidation is dependent upon experimental conditions. Scanning electron microscopy and (scanning) transmission electron microscope imaging confirm the formation of thicker SiO x in regions of enhanced interaction between electron irradiation, water-ice, and the silicon substrate. In summary, we find that electron irradiation with energies as low as 1 keV/electron can break the chemical bonds of refractory solids like Si under these simulated cold astrophysical conditions. These results suggest that cosmic rays may play a more significant role than previously thought in the chemical evolution of dust grains in cold astrophysical and protoplanetary environments.
Mass loss from debris-covered glaciers in High Mountain Asia drives the expansion of small supraglacial lakes (SGLs) and changes the occurrence patterns of hazardous processes including glacial lake outburst floods. Here, we analyse surface area changes of small SGLs ( > 0.045×104 m2) in the Sagarmatha region, Eastern Himalaya, integrating data with daily resolution from 2016 to 2024. The results show SGLs expanded at a mean rate of 5.90 ± 1.49×104 m2·yr⁻¹. Starting from October 2022, SGLs in the study area have exhibited anomalous expansion, with the total lake area reached its maximum during this period, 67.35% higher than during the second peak (2020). The seasonal fluctuations of SGLs are synchronous with short-term variations in global temperature. By mid-century, SGL expansion in High Mountain Asia is expected to continue. Given their rapid response to climate change, small SGLs could support the development of early-warning frameworks for hydrological pulses and extreme climate events.
In recent decades, the monitoring of volcanoes has been revolutionized by the launch of Earth-observing satellites and advances in thermal infrared remote sensing. These developments have revealed a wide range of thermal responses of volcanic surfaces to subsurface processes, even demonstrating that eruptions are often preceded by measurable thermal anomalies. This recognition highlights the need for robust tools to systematically detect and track such anomalies, making full use of existing satellite datasets and maximizing the value of current instruments in orbit. To address this challenge, we present the Subtle Surface Thermal Anomalies Recognizer (SSTAR), a versatile and user-friendly application designed to analyze diffuse thermal anomalies, i.e., subtle thermal unrest (~ 1 K) across large areas (several km2). SSTAR leverages data from NASA's Terra and Aqua satellites, which host the Moderate Resolution Imaging Spectroradiometers (MODIS), and builds upon a robust statistical framework. By processing pixel-level data, SSTAR tracks the temporal evolution of diffuse thermal anomalies at specific target sites and maps their spatiotemporal distribution across extended areas. Key features include filtering tools that distinguish between long-term (years) and short-term (weeks) anomalies, as well as uncertainty quantification using bootstrapping. The application is standalone, features an interactive interface for streamlined analysis, and is accessible to newcomers to satellite-based thermal remote sensing. At the same time, specialized users can customize the underlying scripts for other specific research needs. As a demonstration, we apply SSTAR to Shishaldin volcano (Alaska), revealing the emergence of significant thermal anomalies around the summit crater and flanks prior to eruptions. We envision SSTAR as a valuable resource for studying subtle thermal unrest at active volcanoes and hydrothermal systems, where the detection of faint and spatially coherent anomalies may help identify subsurface fluid pathways. Its flexible design enables integration with additional satellite datasets, positioning SSTAR as a forward-looking tool for advancing space-based volcanic thermal monitoring. Building on this capability, daily updated diffuse thermal anomalies are provided for target volcanoes through an open web platform hosted at Geosciences Barcelona-CSIC (https://sstar.geo3bcn.csic.es/), to support surveillance agencies and expert committees in alert-level assessments. The online version contains supplementary material available at 10.1186/s40623-026-02497-6.
Current understanding of the role of ocean variability in air-sea exchange is constrained to large and mesoscale dynamics. Oceanic fronts and filaments with horizontal spatial scales of order 0.1 to 10 km-denoted submesoscale-are challenging to observe due to their fast-evolving flow and small spatiotemporal scales of variability. Observations investigating the air-sea fluxes at the submesoscale have shown substantial fluxes of heat, moisture, and momentum, affecting the structure of the overlying atmosphere. Here, modulations of the turbulent atmospheric boundary layer driven by ocean temperature anomalies are investigated using submesoscale-resolving ship and airborne measurements, providing in situ evidence of the atmospheric response to ocean submesoscale temperature variability. Observations suggest near-surface turbulent mixing driven by strong air-sea fluxes of heat and momentum, modifying the vertical structure of the planetary boundary layer. Linear regression coefficients between wind speed and sea surface temperature anomalies reveal a response similar in magnitude to that seen at larger scales, with an integrated change of 0.23 m s-1 °C-1, but occurring over smaller length-scales, implying sharper gradients. Lagged correlations and scaling analysis imply a combined influence of horizontal advection and vertical turbulent mixing of momentum in the atmosphere, previously only described by numerical simulations. Observed cross-frontal wind divergences over the lower 200 m suggest coherent circulations with vertical velocities of order 1 cm s-1. These observations confirm the rapid adjustment of the marine boundary layer to submesoscale ocean temperature variability and the importance of submesoscale-driven air-sea fluxes in changing the properties of the lower atmosphere, processes not resolved in most forecasting and prediction models.
As Earth's principal reservoir of organic carbon and microbial biomass, the deep subsurface hosts microorganisms capable of mobilizing this once-sequestered carbon. Contrary to standard assumptions of eukaryotic scarcity, this study documents abundant fungal communities, ranging from 4.2 × 103 to 6.8 × 103 fungal cells mL-1, across a methane-producing organic-rich shale 247-556 meters below the surface. Although fungal:bacterial cell ratios ranged from 1:7028 to 1:713, application of biomass conversion factors developed for oceanic systems yielded a median fungal:bacterial biomass ratio of 1:4.7. 16S rRNA gene amplicons revealed bacterial and archaeal communities mirroring those found in well-characterized extremophilic, carbon-degrading environments, while sequencing of 18S rRNA gene and ITS rRNA spacer amplicons collectively identified a eukaryotic hotspot with 689 fungal OTUs across six phyla. The dominant fungal classes, Agaricomycetes and Dothideomycetes, are well-established degraders of recalcitrant carbon compounds at the surface, suggesting they may similarly contribute to organic matter degradation and ecosystem maintenance in the subsurface. Cultivation and isolation efforts yielded 205 fungal strains, including 13 candidate novel taxa, underscoring the deep subsurface as an underexplored eukaryotic habitat. Stable carbon isotopes indicate methane is predominantly generated via microbial conversion of the fossil carbon, while water isotopes suggest in situ geochemical conditions have been relatively stable since the Late Pleistocene, with subglacial recharge as a plausible mechanism for microbial introduction. Collectively, these findings suggest that fungi are underrecognized contributors to organic matter transformation and functional diversity in the deep biosphere, revealing a critical gap in our understanding of deep subsurface ecosystem processes.
Although low-permeability oil reservoirs boast abundant resources, oil recovery remains relatively low due to the limitations of current water flooding development technology in oilfields. To address the current challenges of low-permeability oil reservoirs, nano-SiO2 particle aqueous solutions, instead of conventional water injection, have been applied to these reservoirs, which can achieve promising results. Nevertheless, due to the simple surface structure of nano-SiO2 particles, the unsaturated hydroxyl groups on their surfaces tend to undergo electrostatic attraction with cations in formation water, leading to particle aggregation and flocculation, ultimately compromising their stability. Therefore, studying the interaction between nano-SiO2 particles and cations in saline solutions is of great significance for providing guidance on the application of nano-SiO2 particles in low-permeability oilfields. In light of this, this paper employs molecular dynamics simulations and quantum chemical methods to investigate the processes of interactions between nano-SiO2 particles and cations from a microscopic perspective. The results indicate that the interaction zone between monovalent cations and nanoparticles lies approximately 0.2 nm to 0.3 nm away from the particle surface. In comparison, the interaction zone between divalent cations and nanoparticles extends roughly from 0.3 nm to 0.4 nm from the particle surface. The range and depth of influence of divalent cations are more pronounced. No covalent or ionic bonds are formed between monovalent cations and nanoparticles. However, divalent cations can form ionic bonds with nanoparticles, thereby altering their structural configuration. Among these interactions, electrostatic forces represent the dominant interaction force responsible for changing the configuration of nano-SiO2 particles, whereas van der Waals forces and hydrogen bonding forces are merely weak interactions. Moreover, as the valence state of the cation increases from monovalent to divalent, the cation forms new ionic bonds with the nano-SiO2 particles, significantly modifying their structural configuration and further undermining their stability. The findings of this study can improve our understanding of the existing state of nano-SiO2 particles in formation water, which can help to improve the application effect of nano-SiO2 particles in low-permeability oil fields.
The adsorption performance and mechanisms of calcium phosphate are highly dependent on its crystallinity, yet precise control over crystallinity remains challenging due to the instability of low-crystalline phases. Inspired by the Mg2+-aspartic acid synergy in crustacean molting, we developed a bio-inspired strategy using polyacrylic acid and Mg2+ to finely regulate calcium phosphate crystallinity. Based on this strategy, three sodium alginate (SA) composite hydrogels were fabricated for Eu3+ and Tb3+ recovery, including SA/HAp (crystalline hydroxyapatite), SA/Half (semi-crystalline), and SA/ACP (amorphous). The crystallinity of calcium phosphate strongly influenced adsorption performance, with SA/ACP showing the highest capacities: 401.61 mg/g for Eu3+ and 471.68 mg/g for Tb3+, 79 and 57% higher than those of SA/HAp, respectively. All composite hydrogels followed pseudo‑second‑order kinetics and the Langmuir model, while adsorption affinity and intraparticle diffusion rates increased as crystallinity decreased. Lower crystallinity also increased the specific surface area, mesopore volume, and solubility of calcium phosphate, thereby facilitating ion transport and active-site accessibility. Thermodynamic analysis indicated spontaneous, endothermic, and entropy-increasing processes, with SA/ACP showing the most significant temperature response. Mechanism studies demonstrated that adsorption involves surface complexation, dissolution-precipitation, and ion exchange, with the contribution of each pathway varying with crystallinity. ACP underwent nearly complete transformation to phosphates of REEs through dissolution-precipitation and ion exchange, while crystalline hydroxyapatite showed a limited surface reaction dominated by surface complexation. SA/ACP also exhibited excellent fluorescence detection for Eu3+ and Tb3+ in acidic phosphor leachates. Fixed‑bed column tests demonstrated SA/ACP's practical potential, with removal efficiencies exceeding 90% throughout the treatment of approximately 2500 (Eu3+) and 2850 mL (Tb3+) of simulated wastewater. This bio‑inspired approach provides fundamental insights into structure-property relationships in calcium phosphate‑based adsorbents.
Urban air pollution in cold environments poses a significant public health risk. However, the physicochemical processes determining the concentrations of primary and secondary pollutants remain poorly understood. This is due to fundamentally different conditions compared to warmer environments, such as extremely shallow polluted surface layers (PSLs) and low ultraviolet radiation. We apply an observation-driven chemical transport model to a multiday persistent PSL event during the 2022 Alaskan Layered Pollution and Chemical Analysis (ALPACA) experiment in Fairbanks, AK, USA. The simulations account for pollutant emissions, multiphase chemical kinetics, and turbulent and advective exchange of PSL air with the clean background atmosphere. This exchange is continuous, occurs on a time scale of 30 min to 3.5 h, and is essential for an accurate representation of the PSL composition. We find that measured diurnal cycles of particulate nitrate reflect the interplay between photochemical nitric acid formation and the loss of nitrate aerosol from of the PSL through mixing with clean background air. The continuous removal and replenishment of PSL aerosol counteracts self-acidification and prevents coagulation with acidic primary sulfate aerosol, thus sustaining sulfate and hydromethanesulfonate (HMS) formation throughout the pollution event. Sensitivity calculations show that within the coupled chemistry and transport system of a shallow PSL, reductions of nitrogen oxide emissions can change the multiphase oxidation regime and thereby even increase the levels of secondary nitrate and sulfate in the PSL.
Intensive pesticide use can enhance crop yields while posing risks to non-target organisms across ecological compartments. A watershed-scale, process-based framework that dynamically links crop-specific applications of multiple pesticides (including transformation products) with their fate in soil and water is still lacking. This study introduces the MARINA-Pesticides model, that simulates monthly, sub-basin-scale transport of 30 pesticides and three transformation products from 12 crops to river networks, while accounting for their degradation, partitioning, and transport processes in both croplands and rivers. We apply the MARINA-Pesticides model to the Three Gorges Reservoir Area (TGRA) to quantify pesticide residue concentrations in soil and river and to assess associated ecological risks. In 2020, an estimated 903 tonnes of pesticides were applied to croplands across the TGRA, with 3% exported to aquatic systems, 10% remaining in soils and 87% degraded. Among 30 pesticides, chlorpyrifos, imidacloprid, and carbendazim posed ecological risks in the soil. In total, 11.2 tonnes of parent pesticides and 2 tonnes of three transformation products were exported into rivers, predominantly via surface runoff (91%), with the remainder via soil erosion (9%). Riverine pesticide concentrations peaked during the summer season. In summer, chlorpyrifos, imidacloprid, and fenpropathrin posed a high risk to riverine ecosystems. Our conclusions advance the understanding of the multi-pesticide fate and timing in diverse agricultural watersheds, supporting guidance for soil and water-resource protection.
This study investigates atmospheric microplastic (MP) exchange between marine and terrestrial compartments and associated deposition patterns at Bushehr Port, Persian Gulf. We combined field sampling of the sea-surface microlayer (SML), bulk seawater, sea foam, deposited particles, and suspended airborne particles with FLEXPART Lagrangian dispersion modelling and exploratory Elastic Net regression to evaluate MP sources, transport pathways, and meteorological controls. The simulations indicate a pronounced seasonal contrast in atmospheric MP transport and suggest that land-based sources collectively represented the largest modelled contribution to the atmospheric MP burden. Within the FLEXPART inventory, textile-related microfibres were the largest modelled source category for suspended MPs (∼61%); for deposited MPs, the estimated microfibre contribution (∼32%) was comparable to bare-soil resuspension (∼31%), while sea spray contributed ∼11% to both fractions. Elastic Net regression repeatedly retained air pressure as a positive predictor and the Lifted Index as a negative predictor; however, these associations are interpreted as exploratory because of the limited number of independent sampling intervals. Sea foam and SML samples were enriched in MPs relative to bulk seawater, although the enrichment pattern varied with wave period and tidal-current conditions. The large difference between field-derived net deposition velocities (Vd) and theoretical terminal velocities (Vt) indicates that turbulence, resuspension, environmental mixing, and particle-shape assumptions substantially affect the apparent removal of atmospheric MPs. Overall, the results suggest that MP cycling at this semi-enclosed coastal margin is influenced by coupled land-based emissions, marine surface processes, and atmospheric dynamics, highlighting the need for mitigation strategies that consider both local terrestrial inputs and air-sea exchange.
Understanding landfill-related groundwater contamination is challenging in heterogeneous hydrogeological settings characterized by sparse monitoring networks and limited subsurface information, constraints common to many environmental and earth-science systems. Based on our previous investigations, we present an interpretive framework that synthesizes machine learning (ML), hydrogeochemistry, and subsurface geoelectrical imaging to characterize leachate plume dynamics and surface methane emissions. We examine two Canadian case studies: a closed, nonengineered municipal landfill and an industrial bark dump near an Indigenous community. At both sites, direct-current electrical resistivity (ER) and induced polarization (IP) surveys were conducted, and the resulting data were inverted to produce 2D and pseudo-3D tomographic images of leachate-related anomalies. In the municipal landfill, a supervised Adaptive Neuro-Fuzzy Inference System employed geoelectrical proxies of waste stabilization to infer landfill-scale methane-emission trends, with localized misfits attributed to near-surface biogas attenuation and metallic-waste-related high-IP responses. Gaussian Mixture Modeling (GMM) and stacked pollution index mapping of hydrogeochemical data delineated contamination zones used to validate low-ER anomalies at well-screening depth. For the industrial landfill, hydrogeochemical facies were identified from water-quality parameters using unsupervised ML methods (GMM, Hierarchical Agglomerative Clustering, and Self-Organizing Maps + K-means), supporting the interpretation of ER and IP anomalies associated with plume migration, mixing interfaces, and matrix-controlled attenuation. These studies illustrate a methodological framework in which ML links geophysical proxies and environmental processes. By integrating geophysical and hydrogeochemical information and evaluating cross-domain consistency using multiple ML methods, this framework reduces interpretive ambiguity and strengthens process-based conceptual models in data-limited settings.