Search Results (1,042)

Micro- and nanoplastics (MPs/NPs) are increasingly recognized as persistent contaminants in agricultural systems, where repeated inputs from mulch films, biosolids, composts, irrigation water, and atmospheric deposition create sustained exposure pathways for crops. Although various studies report effects on crop growth and physiology, mechanistic interpretation remains limited because outcomes vary widely across experiments and are often discussed without appropriate attention to exposure context, particle properties, and evidentiary strength. This review advances an agroecosystem-centered, evidence-aware framework for interpreting how MPs/NPs influence crops from environmental entry to plant phenotype. We argue that crop responses cannot be inferred from polymer identity alone, but must be interpreted through the interacting effects of particle size, morphology, surface chemistry, weathering state, aggregation behavior, co-contaminant associations, and exposure matrix. Within this framework, crop responses are organized along a mechanistic chain linking environmental entry and plant contact, interface behavior at root and leaf surfaces, conditional barrier crossing and transport, ROS-centered stress signaling with hormonal and ionic regulation, and downstream effects on germination, root function, photosynthesis, biomass, productivity, and quality-related traits. Particular emphasis is placed on distinguishing surface association, supported internalization, and supported systemic translocation, because these categories carry distinct implications for edible-tissue occurrence, crop quality, and food-system relevance. Current evidence suggests that the soil–plant–food pathway is plausible and increasingly supported, but its interpretation remains constrained by uneven analytical rigor and limited field realism. Future progress will require realistic agricultural exposure designs, stronger polymer-specific confirmation, and closer integration of mechanistic evidence with agronomic and food-system endpoints. Full article

The urban–rural gradient of surface ozone concentration is closely associated with urban scale and has been widely reported in megacities globally. However, in the Sichuan Basin of southwestern China, a paradoxical asymmetric pattern between the ozone gradient and the physical urban footprint has emerged. By integrating multi-source satellite observations (e.g., TROPOMI), reanalysis data (ERA5-Land), and a concentric-ring spatial gradient analysis, we quantify a dipole-like urban surface ozone trap pattern in two megacities (Chengdu and Chongqing) from 2013 to 2019. We found that the urban–rural ozone gradients in Chongqing were substantially steeper than those in Chengdu, despite Chongqing’s smaller physical urban footprint. Specifically, in winter, the maximum daily average 8 h ozone level in the urban core drops to 27.5 μg m⁻³ in Chongqing and 47.9 μg m⁻³ in Chengdu, with outward radial increasing rates of 6.49% and 1.88% per 10 km, respectively. Conversely, the absolute nitrogen dioxide level in Chengdu is higher, highlighting an asymmetric titration behavior between the two cities. Regarding the chemical regime, analysis of the ratio (β) of nitrogen dioxide to formaldehyde reveals that Chongqing’s core operates under a more severe VOC-limited environment (β is 2.53 and radial gradient is −6.77% per 10 km) compared to Chengdu (β is 2.43 and gradient is −5.34% per 10 km). Furthermore, vertical cross-section analyses indicate that Chongqing’s deep-valley topography induces severe boundary layer compression and aerodynamic stagnation. Thus, rather than acting independently, these localized meteorological constraints function as crucial physical modulators that trap precursor emissions and exacerbate the non-linear chemical titration. This study elucidates how synergistic interactions between basin topography, physical urban footprints, and atmospheric chemistry shape localized ozone traps, providing a referable perspective for assessing complex urban atmospheric environments. Full article

The interplay between the environmental exposome and the cancer genome remains a critical gap in precision oncology. While somatic mutational signatures—genomic fossils imprinted by exposures such as ultraviolet radiation; tobacco smoke; and industrial pollutants—are well characterised for their etiological significance; their functional impact on therapeutic efficacy remains largely unexplored. We hypothesised that these environmental genomic scars induce distinct pharmacogenomic vulnerabilities and resistance mechanisms that vary by geographical exposure patterns. This study employs two complementary analytical frameworks. First, a linear regression-based pharmacogenomic screen across four datasets (GDSC1, GDSC2, CTRP, CCLE; 1001 cell lines, 31 cancer types) identified 608 statistically significant (p < 0.01) mutational signature–drug interactions, revealing that UV-associated signature SBS7a is associated with broad-spectrum therapeutic resistance, including to BRAF inhibitors (PLX-4720, p < 10⁻⁴), while pollution-driven oxidative stress (SBS18) is associated with sensitivity to p38 MAPK inhibition (VX-702, r = −0.45, p < 10⁻⁹). Second, an XGBoost predictive model trained exclusively on 33,679 GDSC2 records using a 1265-feature matrix integrating 40 SBS signatures, drug chemistry descriptors, proteomic features, and two satellite-derived environmental variables (NASA PM_2.5 and UV)—achieved R² = 0.7973 on a 20% holdout set (grouped cross-validation R² = 0.7296). SHAP analysis revealed that satellite-derived PM_2.5 (Zone_PM25) ranked 7th of 1265 features, exceeding all 40 individual SBS mutational signatures. Synthesising these findings with satellite-derived atmospheric data, we constructed an exploratory spatially interpolated risk surface spanning 122 nations, generating the hypothesis that uniform drug efficacy assumptions may not apply globally. These findings suggest that a patient’s environmental exposure history may constitute a measurable pharmacogenomic variable. This exploratory framework warrants validation in independent datasets and with individual-level geographic data before clinical application. Full article

In this study, we present a machine-learning approach—a land use random forest (LURF) model—to produce daily PM₁₀ concentration maps at a 1 km resolution across the Campania region for the year 2022. The model combines daily measurements from 13 ARPA Campania monitoring stations with a wide set of spatial and atmospheric information. The predictors include population, land cover, road network, ERA5 meteorological data, satellite aerosol observations from MODIS, output from the CHIMERE chemistry transport model, and a flag identifying days affected by Saharan dust transport. The model is trained and validated using a station-based cross-validation scheme that accounts for spatial correlation between sites. Under this scheme, the LURF reproduces observed concentrations with substantially smaller errors than the raw CHIMERE output (RMSE of 11.0 vs. 23.6 $\mathsf{\mu }$g m⁻³). CHIMERE concentrations and ERA5 meteorology emerge as the most informative predictors, while the dust flag specifically improves the representation of episodic high-PM₁₀ events. The resulting 1-km maps reveal clear urban–rural contrasts. They identify pollution hotspots in the Naples metropolitan area and along major motorways that are not visible in coarser model outputs. Probabilistic exceedance maps further show that meeting the future 2030 EU limit value of 20 $\mathsf{\mu }$g m⁻³ will be challenging across much of the metropolitan area. Overall, the proposed framework provides a low-cost, practical tool for high-resolution PM₁₀ exposure assessment, supporting epidemiological studies, environmental justice analyses, and air quality management in regions with complex terrain and limited monitoring coverage. Full article

The Antarctic stratospheric ozone plays a crucial role in the polar climate system and is strongly influenced by energetic particle precipitation. Among these processes, medium-energy electron (MEE) precipitation enhances the production of odd nitrogen (NOx) in the polar mesosphere and stratosphere, thereby driving ozone depletion through catalytic reactions. However, quantifying its atmospheric impact remains challenging, largely because the spatial and temporal variability of MEE is poorly constrained, and most current global chemistry–climate models lack a realistic MEE forcing. This study employs the Whole Atmosphere Community Climate Model coupled with Sodankylä Ion Chemistry (WACCM–SIC) to investigate the influence of MEE precipitation during 2013–2014, when moderate geomagnetic storms were more frequent in the winter of 2013. A control simulation (Case1) and two sensitivity experiments (Case 2 and Case 3) were conducted to isolate MEE-driven effects. Model-simulated NO_x (NO + NO₂) and ozone concentrations agree well with satellite observations, indicating that WACCM–SIC captures the key photochemical and dynamical processes. The results further suggest that the direct impact of MEE precipitation on the middle and lower atmosphere during winter is relatively weak. Nevertheless, MEE-generated NO_x can be efficiently transported downward within the polar vortex, reaching altitudes below 15 km. In these regions, MEE-related NO_x enhancement can reach up to 5%, with values during the winter of 2013 approximately twice those in 2014. Sensitivity experiments further reveal that enhanced NO_x leads to pronounced ozone depletion in the lower stratosphere, with ozone losses reaching up to 25%. A clear negative relationship between NO_x and ozone is therefore evident, highlighting the importance of accurately representing MEE precipitation in chemistry–climate models. Full article

This paper presents a theoretical engineering feasibility analysis of the RK-X photocatalytic process for In Situ Resource Utilisation (ISRU) on Mars. Experimental validation under simulated Martian conditions is the essential next step before any mission deployment claim can be made. The RK-X process converts the two most abundant Martian resources, atmospheric carbon dioxide (CO₂) and subsurface water ice (H₂O), into formic acid (HCOOH) and oxygen (O₂) through a fulvic acid-based photocatalytic cycle validated at the industrial scale in Hungary. A reference module processing 10 tonnes of CO₂ per Earth year yields 10.459 tonnes of formic acid and 3.636 tonnes of oxygen, sufficient to sustain a six-person crew for approximately two Earth years with a 198% safety margin over nominal respiratory demand. The economic analysis indicates that importing equivalent oxygen from Earth costs $1.82–$3.64 million per year; equivalent energy storage (Li-ion) costs $30.5–$61 million for one-time use. Formic acid stores 15.25 MWh of energy in ambient-stable liquid form at a round-trip efficiency of 68.64% without cryogenic infrastructure. A photovoltaic array of 55.37 m² provides the primary energy source; a kilowatt-class nuclear fission reactor constitutes the strategic opportunity for continuous, dust-storm-immune operation with free thermal co-generation. Three critical research gaps have been identified requiring laboratory validation before Mars deployment: (i) catalyst performance at the Martian CO₂ partial pressure (p(CO₂) < 10 mbar, T = 15 °C); (ii) water ice and dry ice extraction at an operational scale; and (iii) integrated closed-loop system demonstration. Built on Earth-proven chemistry with identified, addressable development pathways, the RK-X process theoretically resolves the problems of oxygen supply, seasonal energy storage, water management, and cryogenic infrastructure within a single closed-loop chemical cycle. Full article

During wet deposition, particulate matter and gaseous species in the atmosphere are ultimately transported to the Earth’s surface via precipitation and subsequently incorporated into terrestrial ecosystems. Therefore, investigating the fluxes, chemical compositions, and source apportionment of regional wet deposition is of great scientific importance. An analysis of the concentrations, deposition fluxes, spatiotemporal variations, and source apportionment of water-soluble ions in wet deposition can further enhance our understanding of the water-soluble ion characteristics, atmospheric pollution profiles, and potential ecosystem impacts of wet deposition in the Yangtze River Delta and Pearl River Delta regions. Coastal cities in China are most developed regions, and also areas suffering from severe air pollution. This study investigates the chemical characteristics, sources and wet deposition fluxes of water-soluble inorganic ions in precipitation in two typical coastal urban agglomerations of China: Ningbo in the Yangtze River Delta and Guangzhou in the Pearl River Delta. Precipitation samples were collected and analyzed to determine the concentrations of major ions. The results revealed distinct ionic compositions between the two regions. In Ningbo, NO₃⁻ and SO₄²⁻ were the predominant ions accounting for 16.98% to 23.22% of the total, reflecting the influence of anthropogenic emissions from fossil fuel combustion and mobile sources with the NO₃⁻/SO₄²⁻ ratio of 0.90 and 0.70. In Guangzhou, precipitation was characterized by high contributions of SO₄²⁻, NO₃⁻, NH₄⁺, and Ca²⁺, accounting for 17.22% to 23.29% of the total, indicating a mixed influence of industrial emissions, agricultural activities, and construction dust with the NO₃⁻/SO₄²⁻ ratio of 0.92 and 0.87. A clear inverse relationship between rainfall amount and ion concentration was observed at all sites (p < 0.05), demonstrating a significant dilution effect. Seasonality played a crucial role in deposition fluxes. In Ningbo, fluxes peaked during summer from 4667 to 5156 mg·m⁻², while in Guangzhou, distinct dry and rainy season patterns influenced the scavenging efficiency of different ion species. Urban sites exhibited enhanced scavenging of crustal and anthropogenic ions (e.g., Ca²⁺, NH₄⁺) during the rainy season, whereas the coastal site showed elevated fluxes of marine-derived ions (Na⁺, Cl⁻, Mg²⁺, SO₄²⁻) during the same period. The observed trends in ion fluxes suggest a gradual improvement in regional air quality over the study period. These findings elucidate the complex interactions between anthropogenic activities, natural sources, and meteorological factors in shaping the wet deposition chemistry in coastal urban environments, providing essential data for developing regional deposition models and assessing the ecological impacts of atmospheric pollution. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. Full article

Ammonia (NH₃) is an important alkaline gas and a key precursor to secondary inorganic aerosol. In the Fen River valley, coking plants are concentrated due to transportation advantages, while NH₃ emissions from coking processes have received limited attention despite their potential importance. In this study, atmospheric NH₃ was sampled by OGAWA samplers in a typical coal coking industrial park in Taiyuan during autumn and winter of 2024–2025, and its nitrogen isotopic composition was used for source apportionment. The results showed that the NH₃ concentration in the industrial park was 27.4 ± 3.8 μg m⁻³, significantly higher than that in the urban area (9.3 ± 4.2 μg m⁻³) and higher than winter levels reported for North China cities. The δ¹⁵N-NH₃ was −29.7 ± 1.6‰ and increased to −14.7 ± 1.6‰ after correcting for passive sampling bias. Source apportionment further indicated that NH₃ in the industrial park was dominated by non-agricultural sources (80.7%), with ammonia slip as the largest contributor (34.2 ± 20.1%), followed by coal combustion (25.8 ± 16.5%), traffic emissions (20.7 ± 11.6%) and agricultural sources (19.3 ± 11.6%). Therefore, some measures should be taken to reduce the NH₃ emissions from ammonia slip and traffic during autumn and winter. Full article

The demand for materials that can operate reliably in extreme environments, including rocket nozzles, re-entry heat shields, sharp leading edges, high-velocity impact, and high-temperature energy systems, continue to drive advances in thermal–structural materials. Carbon/Carbon composites remain a leading baseline because of their low density, high-temperature mechanical retention in inert atmospheres, and excellent thermal-shock tolerance. However, long-term durability is constrained by rapid oxidation in air at elevated temperatures, limited fracture toughness and elastic modulus in many architectures, and high manufacturing cost driven by multi-cycle densification and stringent quality assurance. Consequently, contemporary strategies increasingly rely on modifying Carbon/Carbon composites with ultra-high-temperature ceramics and adopting accelerated or simplified manufacturing routes. This review synthesizes recent progress in the design, manufacture, and application of high-performance modified Carbon/Carbon composite systems for extreme aerospace environments, emphasizing composition/architecture selection, oxidation, and ablation protection, toughening concepts, and cost-aware densification. Because extreme environments performance is governed by coupled aerothermal loading, gas–surface chemistry, internal transport, recession, and thermomechanical response, the review also consolidates the multiscale modeling and software toolchains increasingly used to size thermal-protection systems, interpret experiments, and guide down-selection. Key challenges and future directions are further discussed for reusable materials and validated performances beyond ~2000 °C. Full article

Reliable and timely estimation of air pollution exposure at high spatial and temporal resolution remains challenging in complex urban environments, where pollutant concentrations vary due to traffic emissions, urban morphology, and meteorological conditions. This study presents a physics-informed machine learning framework for near-real-time estimation of NO₂ concentrations at fine spatial scales. The approach combines a limited set of steady-state computational fluid dynamics (CFD) simulations with operational meteorological and air-quality data. CFD simulations under specific wind directions are first used to characterize site-specific dispersion patterns. These outputs are then scaled using hourly meteorological observations to generate physics-based concentration descriptors. A machine learning predictor, implemented using Random Forest and Extreme Gradient Boosting, is trained to refine these estimates by incorporating additional environmental and observational features. The method is applied to a 1 km × 1 km urban district in Antwerp, Belgium, within the FAIRMODE intercomparison framework. Validation against measurements from 105 passive samples collected over one month shows substantial improvement compared to standalone dispersion modeling, with coefficients of determination up to R² = 0.965 and reduced bias across locations. These findings demonstrate that integrating physical modeling with machine learning enables accurate and computationally efficient high-resolution exposure assessment in urban settings. Full article

The scalable and sustainable production of graphene remains a significant challenge due to the high cost, complex processing, and environmental impact associated with fossil-derived graphite precursors. In this work, we report a biorenewable pathway for producing graphitic carbon from industrial hemp biomass, yielding a plant-derived material called CleanGraphene. This approach provides a renewable and potentially scalable alternative to petroleum- and coal-based graphene production while maintaining competitive structural and electrical performance. CleanGraphene samples are systematically characterized using X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA) to evaluate crystallographic order, layer stacking, defect density, surface chemistry, and thermal stability. The results show that optimized CleanGraphene materials consist of multilayer graphene-like platelets with compact interlayer spacing (d(002) ≈ 3.36–3.37 Å), extended crystallite coherence lengths (L_c up to ~75 nm), large in-plane sp² domains (L_a exceeding ~200 nm), and relatively low defect densities, indicating well-developed graphitic ordering. Electrical conductivity measurements using a binder-free pelletization method and four-point probe analysis demonstrate that the highest quality CleanGraphene samples achieve conductivities of (8.4–8.6) × 10⁴ S m⁻¹, surpassing leading commercial graphene benchmarks measured under identical conditions. Structure–property correlations confirm that electrical performance is governed primarily by crystallite coherence, defect density, and interlayer stacking order, while surface oxygen content plays a secondary role within an ordered graphitic framework. All CleanGraphene samples exhibit excellent thermal stability, retaining more than 95% mass up to ~800–900 °C under an inert atmosphere. Collectively, these findings establish quantitative quality benchmarks for hemp-derived graphene and demonstrate that biomass-based graphene can achieve electrical and thermal performance comparable to, and in some cases exceeding, conventional commercial products. This work highlights industrial hemp as a promising renewable precursor for the scalable production of high-performance graphitic nanomaterials for electrically and thermally conductive composite applications. Full article

Cross-reactions between peroxy radicals (RO₂) derived from different volatile organic compound (VOC) precursors have been proposed as an important pathway during atmospheric oxidation. However, direct molecular evidence has been limited. In this study, α-pinene and fully deuterated toluene (d8-toluene) were oxidized separately and as a mixture in a potential aerosol mass (PAM) flow reactor, and the resulting secondary organic aerosol (SOA) was characterized by a high-resolution mass spectrometer (ESI FT-ICR-MS). A constrained chemical mass balance (CMB) model attributed 82.9% of the mixed-SOA signal to single-precursor sources (66.5% α-pinene, 16.4% d8-toluene), leaving a 17.1% signal-based residual fraction unexplained by linear mixing. Among 2450 residual molecular formulas exclusive to the mixed-SOA, 1858 were identified as cross-reaction candidates, with carbon, oxygen, and double bond equivalents (DBE) distributions consistent with RO₂-RO₂ cross-reactions between toluene- and α-pinene-derived fragments. We also identified representative monomer-dimer pairs, where one monomer corresponded to a known α-pinene oxidation product, while the other matched a primary oxidation product of d8-toluene oxidation based on the Master Chemical Mechanism (MCM), providing strong molecular-level evidence for RO₂-RO₂ cross-reactions. Our findings demonstrate that the mixed VOCs generate unique SOA products that extend beyond simple additive chemistry, with implications for SOA yield parameterizations and chemical transport models. Full article

This exploratory study examines how planetary climate regulation is addressed in 39 Compulsory Secondary Education and Bachillerato textbooks used in Spain, focusing on three key regulating factors, global ocean circulation, atmospheric circulation, and the greenhouse effect, and their integration into a coherent, interrelated model. Textbooks from Biology and Geology, Physics and Chemistry, Scientific Culture, and Earth and Environmental Sciences, published by three anonymised Spanish publishers, were analysed using two complementary instruments—a global presence grid and an analytical grid—examining explanation type, presentation format, didactic resources, and activities associated with each submodel. The results reveal a fragmented and largely disconnected treatment of the three factors across educational stages, with limited explicit articulation of their interrelationships. This fragmentation restricts students’ ability to understand the functioning of each factor, recognise their systemic interdependencies, and appreciate the role of human activity in climate regulation. Full article