Tracking polycyclic aromatic hydrocarbons (PAHs) across Palermo with the invasive grass Cenchrus setaceus (syn. Pennisetum setaceum).

Separating fluorine from phosphate wastes residual via reactive roasting: Fluorine emission pattern and phosphorus polymerization mechanism

We demonstrate a tailorable topological multimode nanolaser that supports simultaneous lasing in modes belonging to different topological classes and exhibiting no mutual phase correlation. Arrays of gold nanoparticles (NPs) with varying diameters were fabricated and embedded in a fluorescent dye gain medium, enabling the systematic investigation of the emergence and interplay between topologically trivial dipolar and topologically nontrivial quasi-bound-state-in-the-continuum (qBIC) modes. Angle- and wavelength-resolved measurements reveal single- and dual-mode lasing behavior, with far-field emission patterns and threshold characteristics strongly dependent on nanoparticle size. Spatial and temporal coherence characterization using interferometry shows mode-dependent coherence distributions across both the source plane and the far-field. We show that the relative contributions of the lasing modes can be tuned by adjusting the nanoparticle geometry, providing insights into the interplay between topologically distinct photonic modes and coherent light emission. Cross-correlation frequency-resolved optical gating measurements further resolve the temporal dynamics of the topologically trivial and qBIC modes, showing that these modes, having orthogonal polarizations at the sample plane, are mutually incoherent. This is in stark contrast to our previous results on topologically trivial superlattice modes, where shared excited-state molecular populations led to mode locking. The absence of mutual phase correlations is advantageous for applications requiring minimal crosstalk between lasing channels, such as multiplexed communication and multichannel sensing.

High-throughput monitoring of organic pollutants in aquatic environments remains a formidable challenge due to heterogeneous emission sources, irregular emission patterns, and the scarcity of analytical standards. The diffusive gradients in thin-films (DGT) technique provides time-integrated and representative sampling. High-resolution mass spectrometry-based nontargeted analysis (NTA) enables broad-spectrum chemical identification. However, the application of DGT-based NTA (DGT-NTA) is hindered by the lack of pollutant-specific parameters required for concentration calculations, including diffusion coefficients (D) and ionization efficiencies (IE). This study proposes a machine learning (ML)-assisted framework that enabled semiquantitative analysis in DGT-NTA workflows by predicting D and IE, thereby reducing the dependence on compound-specific standards in the routine quantification step. When applied to municipal wastewater effluents, the framework enabled semiquantitative estimation of 85 identified pollutants. Targeted validation demonstrated that the mean prediction error was 1.78-fold for compounds within the applicability domains of the constructed ML models for D and IE. An additional ecological risk assessment was conducted using predicted concentrations and aquatic toxicity data, thereby prioritizing 8 potentially high-risk pollutants. This framework provides a promising avenue for high-throughput monitoring of emerging pollutants in aquatic environments.

Variations in GHG fluxes in small- and medium-sized water bodies in different climate zones.

Nondestructively Integrated Graphene Infrared Emitter with Angle-Programmable Emission Patterns for Flexible Array Displays

Volatile aromatic hydrocarbons, including benzene, toluene, ethylbenzene, and xylenes (BTEX), are widely used in paint manufacturing and can create occupational health hazards because of their toxicity and high volatility. This study assessed personal and exposure levels of BTEX compounds among workers in small- and medium-sized paint factories in Jeddah, Saudi Arabia. Air samples were collected using charcoal tubes and analyzed by gas chromatography with a flame ionization detector (GC-FID). Statistical analyses, including correlation and principal component analysis (PCA), were used to identify emission patterns and likely exposure sources. Benzene was not detected in any samples, whereas toluene, ethylbenzene, and xylenes were consistently present and frequently exceeded occupational exposure limits set by ACGIH and OSHA. The highest mean concentrations were found in mixing, grinding, and packaging operations, suggesting inadequate ventilation and direct solvent handling. Strong correlations between ethylbenzene and xylenes indicated shared emission sources from mixed solvent formulations. PCA showed that exposure variability was mainly process-related, with distinct components linked to blending and hardener-related activities. Chronic exposure to these levels may cause neurological, hepatic, and hematological effects, especially in workers with limited protection. The study highlights the need for engineering controls, worker training, and substitution with low-VOC or water-based products.

Fluorescence intensity variation has long served as a primary readout for monitoring biological events. However, single-fluorophore signals arising from distinct molecular events often exhibit similar intensity profiles, making further classification challenging using conventional methods. In this study, we introduce smDeepFLUOR, a deep learning-based framework that resolves seemingly homogeneous spatiotemporal fluorescence signals by uncovering latent features imperceptible to conventional analyses. By leveraging a three-dimensional convolutional neural network trained on image sequences captured over 7 × 7 × 10 voxel windows, smDeepFLUOR reliably distinguishes specific from nonspecific protein binding, even across different experimental days, with an accuracy of up to 97%. Remarkably, smDeepFLUOR also captures real-time DNA synthesis kinetics by identifying subtle changes in the spatial distance between the fluorophore and the 3' end of nascent DNA, a feature undetectable by traditional methods. These classifications were achieved without incorporating explicit physical rules or engineered features, implying the presence of intrinsic, previously unrecognized differences in emission patterns. This approach significantly extends the analytical capabilities of single-molecule fluorescence imaging and opens new avenues for minimally labeled and label-free protein activities.

A comprehensive understanding of land use carbon metabolism characteristics from the production-living-ecological space (PLES) perspective is crucial for formulating carbon reduction strategies. As the core economic zone of northern China, the Beijing-Tianjin-Hebei (BTH) region faces severe carbon emission pressures due to rapid urbanization and intensive land use transformation. However, focusing solely on carbon metabolism calculation without considering future changes and optimization effects may prevent achieving carbon emission reduction targets. This study assessed carbon emissions and sequestration based on different land use types in PLES, constructed a multi-objective carbon reduction scenario utilizing the Dinamica-EGO model, nondominated sorting genetic algorithm II, and entropy weight-TOPSIS model, and simulated 2035 carbon reduction characteristics by coupling PLES changes. Taking the BTH region as a case study, a methodological framework and corresponding models were established. The results show that from 2000 to 2020, the total carbon emissions in the BTH region increased significantly, presenting a spatial pattern of high emissions in the southeast and low emissions in the northwest. In contrast, the overall carbon sequestration capacity showed a decreasing trend, with stronger capacity in the northwest and weaker capacity in the southeast. The multi-variable 2035 carbon emission reduction prediction model achieved an accuracy of 82.24%. The 2035 carbon reduction plan developed based on this framework outperformed the original land use plan: economic benefits, emission reduction efficiency, spatial compactness, and accessibility are projected to increase by 15.8%, 7.9%, 2.5%, and 8.3%, respectively, while carbon emissions are expected to decrease by 19.04%. The proposed PLES-based framework for carbon metabolism measurement and emission reduction simulation exhibits good applicability in regional spatial emission reduction. These findings contribute to exploring regional carbon dynamics and provide references for governments to formulate carbon reduction policies.

This study proposes an integrated multi-model coupling framework for ex-ante carbon assessment in new urban district planning. The framework combines investment allocation, land-use carbon estimation, and traffic emission models within a unified analytical environment to simulate spatial and economic feedbacks that drive urban carbon emissions. Compared with conventional carbon assessment approaches, which often evaluate land use and transport as independent modules, the proposed framework introduces a rule-based coupling mechanism that dynamically links investment intensity, spatial configuration, and carbon output through iterative feedback. Each sub-model exchanges parameters via a shared data layer, enabling recursive interactions among economic input, land-use change, and mobility patterns. The framework advances urban carbon modeling through three key innovations. First, it establishes an investment-carbon elasticity mechanism that quantifies how capital concentration influences spatial emission patterns. Second, it formulates a rule-based symbolic coupling algorithm that enables cross-model parameter updating during iteration. Third, it develops a feedback-controlled carbon evaluation process that transforms traditional PSS from descriptive visualization tools into predictive decision-support frameworks. Applied to a representative new urban district, the framework demonstrates effectiveness in identifying low-carbon planning strategies under varying investment-intensity scenarios. By integrating economic, spatial, and environmental dimensions into a unified analytical logic, this research provides a scalable foundation for quantitative decision-making in sustainable urban transitions and contributes a theoretical model applicable to broader regional and national carbon-neutral planning frameworks.

This study presents a comprehensive spatiotemporal assessment of key ambient air pollutants (PM2.5, PM10, SO2, NO2, NH3, O3, and CO) across seven urban and semi-urban districts of the Uttar Pradesh National Capital Region (UP-NCR), India, during 2019-2023. Despite an overall decline of approximately 23% in particulate matter concentrations over the study period, annual mean PM2.5 (88 ± 15µg/m3) and PM10 (185 ± 28µg/m3) levels consistently exceeded the National Ambient Air Quality Standards. Land use and land cover (LULC) analysis revealed a 7.06% expansion in built-up areas, reflecting rapid urbanization and its influence on local emission patterns. This urban growth was associated with persistent NO2 enrichment, particularly in Noida, where concentrations increased by 22%. Pronounced seasonal variability was observed, with PM2.5 concentrations peaking during the post-monsoon season particularly in Noida (200 ± 102µg/m3) and Ghaziabad (178 ± 99µg/m3), identified as the regional pollution hotspot. Meteorological analysis revealed strong seasonal influences on pollutant concentrations. Relative humidity exhibited positive correlations with particulate matter during winter (r ≈ 0.44-0.59), reflecting hygroscopic growth and stagnant atmospheric conditions, but strong negative correlations during the monsoon (r ≈ -0.72 to -0.95) due to efficient wet scavenging. Bivariate polar plot analysis identified stagnation-driven pollutant accumulation in densely urbanized districts and wind-induced resuspension of agricultural and crustal dust in peripheral regions. HYSPLIT backward-trajectory clustering further demonstrated substantial contributions from long-range transport originating from western and northwestern source regions during pollution episodes. These findings highlight pronounced spatial heterogeneity and seasonal dynamics in air quality, emphasizing the need for region-specific, airshed-based mitigation strategies across rapidly urbanizing peri-urban corridors.

Field monitoring and analysis of landfill gas emissions in a large-scale landfill in Hangzhou, China.

Health risks from PM2.5-bound heavy metals from religious burning activities on the plateau.

Compound-specific effects of phthalate esters on nitrogen cycling and N2O emissions in paddy soils under contrasting moisture regimes.

Treated sewage discharge mitigates net greenhouse gas emissions from polluted urban rivers.

In the context of escalating global climate change and China's commitment to its dual carbon goals, this study investigates the spatiotemporal dynamics of carbon balance across 296 prefecture-level and higher cities in mainland China from 2001 to 2022, using emission data from the EDGAR database. By integrating ecosystem carbon sequestration models with economic contribution coefficients (ECC), ecological support coefficients (ESC), and China's main functional zoning framework, we systematically analyze regional carbon sources and sinks and propose a spatially explicit optimization strategy. Results reveal a distinct "higher in the north than in the south, higher in the east than in the west" emission pattern, driven by economic agglomeration and energy structure, with industrial clusters such as Beijing-Tianjin-Hebei and the Yangtze River Delta acting as high-emission hotspots; notably, the Chengdu-Chongqing region exhibited a sharp emissions surge in 2022 due to accelerated industrialization. In contrast, carbon sequestration capacity forms a "northeast-southwest high axis," enhanced by ecological restoration in regions like the Yellow River Basin but diminished along the eastern coast due to urban expansion, thereby exacerbating regional carbon imbalance. Based on ECC and ESC, we classify areas into four dynamically adjusted carbon balance zones: (1) low-carbon maintenance zones (e.g., Beijing-Tianjin-Hebei, Chengdu-Chongqing), characterized by strong economic output and carbon sink potential; (2) economic development zones (e.g., Central Plains, Shandong Peninsula), reliant on high-carbon industries yet underpinned by relatively sound ecological foundations; (3) carbon sink development zones (middle reaches of the Yangtze River), combining significant economic contributions with fragile ecosystems; and (4) comprehensive optimization zones (e.g., Hohhot-Baotou-Ordos-Yulin, Hexi Corridor), facing dual economic and ecological pressures. These are further refined into 16 sub-zones aligned with national functional zoning to enable targeted policy implementation. We recommend zone-specific strategies: consolidating low-carbon technologies and ecological advantages in maintenance zones; accelerating industrial decarbonization and energy efficiency in development zones; strengthening ecological restoration and green industry cultivation in sink zones; and fostering coordinated socio-ecological development in optimization zones through policy incentives, interregional collaboration, and market mechanisms. Ultimately, achieving nationwide carbon balance hinges on two core pathways: optimized land-use planning and the implementation of differentiated, spatially tailored policies that account for local socioeconomic and ecological contexts.

The recycling of green crop residues is promoted for enhancing soil organic carbon sequestration and health, but can also stimulate emissions of nitrous oxide (N₂O) and ammonia (NH₃). Soil tillage can lead to different residue distributions which affect contact with decomposers and local gas and solute diffusion. This study aims to quantify N₂O, NH₃, and carbon dioxide emissions from N-rich red clover residue subjected to three different distributions in the soil-surface, layered, and mixed-across three contrasting agricultural soils. The incubations were performed under laboratory conditions for 50days at 15°C, with a water-filled pore space of 60%. Gas fluxes as well as soil ammonium and nitrate contents were measured. Results showed that N₂O emissions were strongly influenced by soil type and residue placement, with sandy loam producing the highest cumulative fluxes, particularly in the mixed (17.4kg N ha⁻1) and layered (11.5kg N ha⁻1) treatments. NH₃ volatilization occurred almost exclusively from surface residues, peaking at 10.2kg N ha⁻1 in sandy loam soil. Emission for N2O were significantly higher than previously reported for residues. These findings highlight that both residue placement and soil properties critically determine gaseous emission patterns.

Contrasting spatial patterns of mercury and nitrogen in Mediterranean moss biomonitors: a multi-proxy survey across Tuscany (Italy).

Deep-blue phosphorescent OLEDs (Ph-OLEDs) with high efficiency and stability are essential for advanced display technologies, ensuring sharp image quality and enhanced visibility. In this work, we report a novel class of asymmetric [3 + 2 + 1] coordinated iridium(III) complexes incorporate strongly electron-withdrawing trifluoromethyl (–CF3) and fluorine (–F) modified N-heterocyclic carbene ligands. This strategic molecular design enables efficient deep-blue emission. Among these complexes, the CF3-substituted Ir(III) complex (CF3-2) exhibits pronounced charge-transfer (CT) characteristics and a significantly enhanced radiative decay rate ({k}_{r} = 1.28 ×10⁶ s-1), enabling rapid and efficient phosphorescence at 443 nm. Devices employing CF3-2 demonstrated exceptional maximum external quantum efficiency (EQEmax) of up to 29.0%, with emission centered at 443 nm and Commission Internationale de L’Éclairage (CIE) coordinates of (0.147, 0.089), fulfilling National Television System Committee (NTSC) blue standards for high-quality displays. Meanwhile, devices employing CF3-1 reached an EQEmax of 24.6% with a maximum luminance of 6542 cd m−2 and CIEx,y of (0.152,0.126), demonstrating high color purity and efficiency. A control device fabricated without sensitization using CF3-1 further confirms its intrinsic material stability by exhibiting a remarkable operational lifetime of LT50 of 3875 h at L = 100 cd m−2 with CIEx,y of (0.132,0.131). Furthermore, hyper-OLEDs were developed using these complexes as phosphorescent sensitizers. The hyper-OLED incorporating CF3-1 with the TADF emitter v-DABNA achieved an impressive device lifetime of LT50 = 2127 h at 100 cd m−2. In parallel, the CF3-2-sensitized hyper-OLED using DOB2-DABNA-A achieved a deep-blue emission with CIE coordinates of (0.146, 0.067) and a lifetime of LT50 = 373 h under the same luminance, representing a significant advancement in the practical stability of deep-blue OLEDs. Notably, we demonstrate the successful integration of these deep-blue Ph-OLEDs with OLED-on-TFT microdisplay technology, achieving a pixel resolution of 94 PPI (270 × 270 μm) with programmable emission patterns. This innovative molecular coordination design strategy provides valuable insights into ligand engineering and exciton management, opening new pathways toward high-efficiency, long-lifetime deep-blue OLEDs for next-generation microdisplay and display technologies.

The emergence of the United States as the leading global producer of oil and gas has driven increased interest in the greenhouse gas emissions from US energy supply chains. Methane emissions are a major portion of these greenhouse gas emissions, and the spatial and temporal patterns of methane emissions from oil and gas sources are complex. A wide variety of measurement and modeling approaches for estimating methane emissions from US oil and gas supply chains have emerged over the last decade, and this review summarizes their current status and prospects for improvement. Although no single measurement method or modeling approach will be successful in accurately characterizing all emissions, the integration of multi-scale measurement and modeling approaches can provide accurate and comprehensive estimates of emissions.