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We have recently argued that marine interfacial surface tension must have a distinctive biogeography because it is mediated by fresh surfactant macromolecules released locally through the food web. Here we begin the process of quantification for associated climate flux implications. A low dimensionality (planar) equation of state is invoked at the global scale as our main analysis tool. For the reader’s convenience, fundamental surfactant physical chemistry principles are reviewed first, as they pertain to tangential forces that may alter oceanic eddy, ripple, and bubble fields. A model Prandtl (neutral) wind stress regime is defined for demonstration purposes. It is given the usual dependence on roughness, but then in turn on the tension reduction quantity known as surface pressure. This captures the main net influences of biology and detrital organics on global microlayer physics. Based on well-established surrogate species, tangent pressures are related to distributed ecodynamics as reflected by the current marine systems science knowledge base. Reductions to momentum and related heat-vapor exchange plus gas and salt transfer are estimated and placed on a coarse biogeographic grid. High primary production situations appear to strongly control all types of transfer, whether seasonally or regionally. Classic chemical oceanographic data on boundary state composition and behaviors are well reproduced, and there is a high degree of consistency with conventional micrometeorological wisdom. But although our initial best guesses are quite revealing, coordinated laboratory and field experiments will be required to confirm the broad hypotheses even partially. We note that if the concepts have large scale validity, they are super-Gaian. Biological control over key planetary climate-transfer modes may be accomplished through just a single rapidly renewed organic monolayer.

Clouds exhibit a wide range of vertical morphologies that are regulated by distinct atmospheric dynamics and thermodynamics and are related to a diversity of microphysical properties and radiative effects. In this study, the new CERES-CloudSat-CALIPSO-MODIS (CCCM) RelD1 dataset is used to investigate the morphology and spatial distribution of different cloud vertical structure (CVS) types during 2007–2010. The combined active and passive satellites provide a more precise CVS than those only based on passive imagers or microwave radiometers. We group the clouds into 12 CVS classes based on how they are located or overlapping in three standard atmospheric layers with pressure thresholds of 440 and 680 hPa. For each of the 12 CVS types, the global average cloud radiative effects (CREs) at the top of the atmosphere, within the atmosphere and at the surface, as well as the cloud heating rate (CHR) profiles are examined. The observations are subsequently used to evaluate the variations in total, high-, middle- and low-level cloud fractions in CMIP6 models. The “historical” experiment during 1850–2014 and two scenarios (ssp245 and ssp585) during 2015–2100 are analyzed. The observational results show a substantial difference in the spatial pattern among different CVS types, with the greatest contrast between high and low clouds. Single-layer cloud fraction is almost 4 times larger on average than multi-layer cloud fraction, with significant geographic differences associated with clearly distinguishable regimes, showing that overlapping clouds are regionally confined. The global average CREs reveal that four types of CVSs warm the planet, while eight of them cool it. The longwave component drives the net CHR profile, and the CHR profiles of multi-layer clouds are more curved and intricate than those of single-layer clouds, resulting in complex thermal stratifications. According to the long-term analysis from CMIP6, the projected total cloud fraction decreases faster over land than over the ocean. The high clouds over the ocean increase significantly, but other types of clouds over land and the ocean continue to decrease, helping to offset the decrease in oceanic total cloud fraction. Moreover, it is concluded that the spatial pattern of CVS types may not be significantly altered by climate change, and only the cloud fraction is influenced. Our findings suggest that long-term observed CVS should be emphasized in the future to better understand CVS responses to anthropogenic forcing and climate change.

For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice–ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.

In this work, we chiefly deal with the uptake and the capture of CO 2 gas by the B 6 N 6 monolayer via periodic density functional theory. Based on the results, the unit cell of the B 6 N 6 sheet can take up nine CO 2 molecules with a CO 2 capture capacity of 73 wt%. Average adsorption energy for the 1CO 2 /B 6 N 6 , 2CO 2 /B 6 N 6 , 3CO 2 /B 6 N 6 , 4CO 2 /B 6 N 6 , 5CO 2 /B 6 N 6, and 9CO 2 /B 6 N 6 complexes are − 3.163, − 1.649, − 1.191, − 0.972, − 0.834, and − 0.574 eV/CO 2 , respectively. Mulliken charge values on the CO 2 gas show that the B 6 N 6 nanosheet has transferred charge to the gases. The results indicate that a pristine B 6 N 6 sheet, due to the stronger adsorption of CO 2 over the surface compared to other gases, can selectively capture CO 2 instead of N 2 , H 2 , and CH 4 gases in the air. The structural and thermal stability of the pristine B 6 N 6 substrate and 9CO 2 /B 6 N 6 complex at 300 K has been proven by utilizing molecular dynamics analysis. The electron density difference has also been used to investigate the physical or chemical interaction between CO 2 molecules and the substrate. The present study results introduce pristine B 6 N 6 substrate as an investible substrate for highly efficient CO 2 capture and separation.

This review explores the development, technological foundations, and applications of biosensor technologies across various fields, such as medicine for disease diagnosis and monitoring, and the food industry. However, the primary focus is on their use in detecting contaminants and pathogens, as well as in environmental monitoring for water quality assessment. The review classifies different types of biosensors based on their bioreceptor and transducer, highlighting how they are specifically designed for the detection of emerging contaminants (ECs) and pathogens in water. Key innovations in this technology are critically examined, including advanced techniques such as systematic evolution of ligands by exponential enrichment (SELEX), molecularly imprinted polymers (MIPs), and self-assembled monolayers (SAMs), which enable the fabrication of sensors with improved sensitivity and selectivity. Additionally, the integration of microfluidic systems into biosensors is analyzed, demonstrating significant enhancements in performance and detection speed. Through these advancements, this work emphasizes the fundamental role of biosensors as key tools for safeguarding public health and preserving environmental integrity.

Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

The present work reports the results of a systematic study on the evolution of the morphological properties of porous carbons derived from coffee waste using a one-pot potassium-hydroxide-assisted process at temperatures in the range of 400–900 °C. Raw materials and obtained carbons were studied by TG, DTG, SEM and nitrogen adsorption porosimetry. The decomposition temperature ranges for hemicellulose, cellulose and lignin as the main component of the feedstock have been established. It is shown that the proposed method for the thermochemical treatment of coffee waste makes it possible to obtain activated carbon with a controllable pore size distribution and a high specific surface area (up to 1050 m2/g). A comparative study of the evolution of the distribution of pore size, pore area and pore volume has been carried out based on the BJH and NL-DFT (slit-like pores approximation) methods. The fractal dimension of the obtained carbons has been calculated by Frenkel–Halsey–Hill method for single-layer and multilayer adsorptions.

This work covers a lifecycle assessment of monolayer and multilayer films to quantify the environmental impacts of changing the management of plastic film waste. This lifecycle assessment offers the possibility of quantifying the environmental impacts of processes along the lifecycle of monolayer and multilayer films and mapping deviating impacts due to changed process parameters. Based on the status quo, the changes in global warming potential and abiotic fossil resource depletion were calculated in different scenarios. The changes included collecting, sorting, and recycling mono- and multilayer films. The “Functional Unit” under consideration comprised 1000 kg of plastic film waste, generated as post-consumer waste in Austria and captured in the lightweight packaging collection system. The results showed the reduction of environmental impacts over product lifecycles by improving waste management and creating a circular economy. Recycling all plastic film reduced global warming potential by 90% and abiotic fossil resource consumption by 93%. The necessary optimisation steps to meet the politically required recycling rates by 2025 and 2030 could be estimated, and the caused environmental impacts are presented. This work shows the need for increased collection, recycling, and significant improvement in the sorting of films to minimise global warming potential and resource consumption.

The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar and fixed-location observations were collected in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October in 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, and wind and temperature profiles were measured using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analysed. The results show that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, whereas they were distributed homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory reveals different sources of particles for the three types. Particles concentrating near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There were two mechanisms affecting the distribution of particulate matter in the upper and lower boundary layers. Based on this observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter is proposed.

The ionospheric delays and satellite differential code biases (DCBs) act as the significant error sources in the global navigation satellite system (GNSS) positioning, navigation and timing (PNT) services, and are still challenging to estimate correctly. In this study, the ionospheric vertical total electron content (VTEC) and satellite DCBs are estimated by a refining single-frequency precise point positioning (SFPPP) method based on the multi-layer ionosphere mapping function (MF), as well as the dual-frequency methods, including the carrier-to-code leveling (CCL) and dual-frequency PPP (DFPPP). The solutions isolate the ionospheric VTEC values from the slant ionospheric delay with the generalized trigonometric series function (GTSF) and precisely estimate the satellite DCB with a zero-mean condition. The SFPPP-derived VTEC estimates are validated and evaluated by comparing with the International GNSS Service (IGS) products and using ionosphere-corrected (IC) SFPPP in both static and kinematic scenarios. Using the 74 experimental stations collected from the multi-GNSS experiment (MGEX) network from January to March 2020, the results show that the VTEC estimation precision by applying the multi-layer MF is improved for the SFPPP approach. The positioning performances of the static and kinematic BDS IC SFPPP with the ionospheric correction derived from the multi-layer MF SFPPP are better when compared to the single-layer MF. The estimated BDS DCB with the SFPPP is stable and of high accuracy. The SFPPP approach with multi-layer MF is demonstrated as a promising and reliable method to retrieve the VTEC and satellite DCB with the low-cost property for the GNSS users.