Chemical Model Research Articles

Fully coupled hydro-mechanical–chemical continuum modeling of fluid percolation through rock salt

Fully coupled hydro-mechanical–chemical continuum modeling of fluid percolation through rock salt

Natural Compounds for Alzheimer's Prevention and Treatment: Integrating Selformer-Based Computational Screening with Experimental Validation

BackgroundThis study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical language model, with advanced deep learning techniques to predict natural compounds (NCs) with potential in Alzheimer's disease (AD) treatment. The NCs were identified based on activity related to seven AD-specific genes, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), and presenilin-1 (PSEN1). MethodsWe implemented a computational pipeline using SELFormer and deep learning techniques, conducted optimal clustering and quantitative structure-activity relationship (QSAR) analyses, and performed a uniform manifold approximation and projection (UMAP) to categorize compounds based on bioactivity levels. Molecular docking analysis was carried out on selected compounds. To validate the computational predictions, we conducted in vitro studies using nerve growth factor (NGF)-differentiated PC12 cells. Finally, we mapped the relationships between food sources containing the identified compounds and their target proteins. ResultsOptimal clustering analysis revealed five distinct groups of NCs, while QSAR analysis highlighted variations in molecular properties across clusters. The UMAP projection identified 17 highly active NCs (pIC50>7). Molecular docking analysis showed that cowanin, β-caryophyllene, and L-citronellol demonstrated decreased binding energy across target proteins. In vitro studies confirmed significant biological activities of these compounds, including increased cell viability, decreased AChE activity, reduced lipid peroxidation and tumor necrosis factor (TNF)-α mRNA expression, and increased brain-derived neurotrophic factor (BDNF) mRNA expression compared to the control. The study also identified natural sources of these compounds, such as anatidae, mangosteen, and celery, providing insights into potential dietary interventions. ConclusionThis integrated computational and experimental approach offers a promising framework for identifying potential NCs for AD treatment. The results contribute to exploring effective therapeutic strategies against AD.

Transition metal complexes of N-furfuryl-N′-benzoylthiourea: A study of synthesis, antibacterial activities, quantum chemical calculations and molecular modeling simulations

Transition metal complexes of N-furfuryl-N′-benzoylthiourea: A study of synthesis, antibacterial activities, quantum chemical calculations and molecular modeling simulations

Understanding key interactions between NOx and C2-C5 alkanes and alkenes: The ab initio kinetics and influences of H-atom abstractions by NO2

This study aims to reveal the important role and the respective rate rules of H-atom abstractions by NO2 for better understanding NOX/hydrocarbon interactions. To this end, H-atom abstractions from C2-C5 alkanes and alkenes (15 species) by NO2, leading to the formation of three HNO2 isomers (trans-HONO, HNO2, and cis-HONO) and their respective products (45 reactions), are first characterized through quantum chemistry computation, where electronic structures, single point energies, C-H bond dissociation energies and 1-D hindered rotor potentials are determined at DLPNO-CCSD(T)/cc-pVDZ//M06–2X/6−311++g(d,p). The rate coefficients for all studied reactions, over a temperature range from 298.15 to 2000 K, are computed using transition state theory with the Master Equation System Solver program. Comprehensive analysis of branching ratios elucidates the diversity and similarities between different species, HNO2 isomers, and abstraction sites, from which accurate rate rules are determined. With the rate rules, the rate coefficients at various reaction sites on heavier hydrocarbons (e.g., > C5) can be reliably estimated by analogy. Incorporating the updated rate parameters into a detailed chemical kinetic model reveals the significant influences of this type of reaction on model prediction results, where the simulated ignition delay times are either prolonged or reduced, depending on the original rate parameters presented in the selected model. Sensitivity and flux analysis further highlight the critical role of this type of reaction in affecting system reactivity and reaction pathways, emphasizing the need for adequately representing these kinetics in existing chemistry models. This can now be sufficiently achieved for alkanes and alkenes based on the results from this study.

A modular transfer learning approach for complex chemical process network modeling

A modular transfer learning approach for complex chemical process network modeling

Modelling chemical clocks. Theoretical evidences of the space and time evolution of [s/α] in the Galactic disc with Gaia-ESO survey

Chemical clocks based on s-process element/α element ratios are widely used to estimate the ages of Galactic stellar populations. However, the s/α versus age relations are not universal, varying with metallicity, location in the Galactic disc, and specific s-process elements. Moreover, current Galactic chemical evolution models struggle to reproduce the observed s/α increase at young ages, particularly for Ba. Our aim is to provide chemical evolution models for different regions of the Milky Way (MW) disc in order to identify the conditions required to reproduce the observed s/H s/Fe and s/α versus age relations. We adopted a detailed multi-zone chemical evolution model for the MW including state-of-the-art nucleosynthesis prescriptions for neutron-capture elements. The s-process elements were synthesised in asymptotic giant branch (AGB) stars and rotating massive stars, while r-process elements originate from neutron star mergers and magneto-rotational supernovae. Starting from a baseline model that successfully reproduces a wide range of neutron-capture element abundance patterns, we explored variations in gas infall/star formation history scenarios, AGB yield dependencies on progenitor stars, and rotational velocity distributions for massive stars. We compared the results of our model with the open clusters dataset from the sixth data release of the Gaia -ESO survey. A three-infall scenario for disc formation aligns better with the observed trends. The models capture the rise of s/α with age in the outer regions but fail towards the inner regions, with larger discrepancies for second s-process peak elements. Specifically, Ba production in the last 3 Gyr of chemical evolution would need to increase by slightly more than half to match the observations. The s-process contribution from low-mass ( 1.1 M_⊙) AGB stars helps reconcile predictions with data but it requires a too-strong increase that is not predicted by current nucleosynthesis calculations, even with a potential i-process contribution. Variations in the metallicity dependence of AGB yields either worsen the agreement or show inconsistent effects across elements, while distributions of massive star rotational velocities with lower velocity at high metallicities fail to improve results due to balanced effects on different elements. The predictions of our model confirm, as expected, that there is no single relationship s/α versus age and that it varies along the MW disc. However, the current prescriptions for neutron-capture element yields are not able to fully capture the complexity of evolution, particularly in the inner disc.

Redistribution of ices between grain populations in protostellar envelopes. Only the coldest grains get ices

Matter that falls onto a protoplanetary disk (PPD) from a protostellar envelope is heated before it cools again. This induces sublimation and subsequent re-adsorption of ices that accumulated during the prestellar phase. We explore the fate of ices on multiple-sized dust grains in a parcel of infalling matter. A comprehensive kinetic chemical model using five grain-size bins with different temperatures was applied for an infalling parcel. The parcel was heated to 150,K and then cooled over a total timescale of 20,kyr. Effects on ice loss and re-accumulation by the changed gas density, the maximum temperature, the irradiation intensity, the size-dependent grain temperature trend, and the distribution of the ice mass among the grain-size bins were investigated. A massive selective redistribution of ices exclusively onto the surface of the coldest grain-size bin occurs in all models. The redistribution starts already during the heating stage, where ices that are sublimated from warmer grains re-adsorb onto colder grains before complete sublimation. During the cooling stage, the sublimated molecules re-freeze again onto the coldest grains. In the case of full sublimation, this re-adsorption is delayed and occurs at lower temperatures because a bare grain surface has lower molecular desorption energies in our model. Most protostellar envelope grains enter the PPD ice poor (bare). Ices are carried by a single coldest grain-size bin, here representing 12,% of the total grain surface area. This bare ice-grain dualism can affect the rate of the grain coagulation. The ice components are stratified on the grains according to their sublimation temperatures.

Molecular Modeling on Duplexes with Threose-Based TNA and TPhoNA Reveals Structural Basis for Different Hybridization Affinity toward Complementary Natural Nucleic Acids.

Synthetic nucleic acids, also defined as xenobiotic nucleic acids (XNAs), opened an avenue to address the limitations of nucleic acid therapeutics and the development of alternative carriers for genetic information in biotechnological applications. Two related XNA systems of high interest are the α-l-threose nucleic acid (TNA) and (3'-2') phosphonomethyl threosyl nucleic acid (tPhoNA), where TNAs show potential in antisense applications, whereas tPhoNAs are investigated for their predisposition toward orthogonal genetic systems. We present predictions on helical models of TNA and tPhoNA chemistry in homoduplexes and in complex with native ribose chemistries. A stretched right-handed helical structure with a sugar puckering preference for the 4'3'T (C3'- endo/C4'- exo) and O4'1'T (C1'- endo/O4'- exo) is found for the in silico model of dsTNA, while for the in silico model of dstPhoNA a B-type structure is found with a sugar puckering preference for O4'1'T (C1'- endo/O4'- exo). Simulations with complementary DNA and RNA provided insight into the distinct pairing capabilities of TNA and tPhoNA.

Impact of introducing electric vehicles on ground-level O3 and PM2.5 in the Greater Tokyo Area: yearly trends and the importance of changes in the urban heat island effect

Abstract. Battery electric vehicles (BEVs) are considered a solution for global warming and air pollution, and several countries have announced they will shift to BEVs in the 2030s. Even though previous studies have shown the effects of reducing vehicular emissions on the formation of tropospheric ozone (O3), no studies have evaluated the effect of decreasing anthropogenic heat, which is expected to mitigate urban heat island (UHI) effect, on air quality issues. We used a numerical weather prediction to estimate changes in the UHI effect in the Greater Tokyo Area (GTA) of Japan by introducing BEVs. The results indicated that the introduction of BEVs would lead to a maximum local temperature decrease of 0.25 °C in the GTA. The effects of introducing BEVs on O3 and fine particulate matter (PM2.5) were estimated using a regional chemical transport model. The results indicated that mitigating the UHI effect would lead to a reduction in ground-level O3 formation. This is due to the increased NO titration effect caused by the lowered planetary boundary layer height and due to the degradation of photochemistry related to O3 formation caused by a decrease in temperature and biogenic volatile organic compounds (BVOCs). The mitigation of UHI would result in enhanced particle coagulation, with an increase in ground-level PM2.5. Furthermore, a decrease in BVOC emissions would result in increased PM2.5 owing to enhancement of the OH + SO2 reaction. A total of 175 and 77 annual premature deaths would be prevented from changes in O3 and PM2.5, respectively.

Spectroscopically Resolved Partial Phase Curve of the Rapid Heating and Cooling of the Highly Eccentric Hot Jupiter HAT-P-2b with WFC3

Abstract The extreme environments of transiting close-in exoplanets in highly eccentric orbits are ideal for testing exoclimate physics. Spectroscopically resolved phase curves not only allow for the characterization of their thermal response to irradiation changes but also unveil phase-dependent atmospheric chemistry and dynamics. We observed a partial phase curve of the highly eccentric close-in giant planet HAT-P-2b (e = 0.51, M = 9M Jup) with the Wide Field Camera 3 aboard the Hubble Space Telescope. Using these data, we updated the planet's orbital parameters and radius and retrieved high-frequency pulsations consistent with the planet-induced pulsations reported in Spitzer data. We found that the peak in planetary flux occurred at 6.7 ± 0.6 hr after periastron, with heating and cooling timescales of 9 . 0 − 2.1 + 3.5 hr and 3 . 6 − 0.6 + 0.7 hr, respectively. We compare the light curve to various 1D and 3D forward models, varying the planet's chemical composition. The strong contrast in flux increase and decrease timescales before and after periapse indicates an opacity term that emerges during the planet's heating phase, potentially due to more H− than expected from chemical equilibrium models. The phase-resolved spectra are largely featureless, which we interpret as indicative of an inhomogeneous dayside. However, we identified an anomalously high flux in the spectroscopic bin coinciding with the hydrogen Paβ line, and that is likely connected to the planet's orbit. We interpret this as due to shock heating of the upper atmosphere given the short timescale involved, or evidence for other star−planet interactions.

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints.

Constraining the Acetone Photolysis Quantum Yield: Current Insights and Atmospheric Chemistry Implications

AbstractAcetone photolysis is a potentially important source of hydroperoxyl and hydroxyl radicals (HOx) to the upper troposphere. The extent to which acetone photolysis is a significant source of HOx in the upper troposphere is unclear in part because of scarce measurements of the acetone photolysis quantum yield (Φacetone) in the actinic region (i.e., λ > 300 nm). Past measurements of the Φacetone have derived temperature‐ and pressure‐dependent parameterizations that lead to significantly different conclusions about the importance of acetone to HOx formation in the upper troposphere. Here, we focus on previously published data to derive the recommended Φacetone for atmospheric chemical modeling. Using Stern‐Volmer analyses, we determine temperature‐ and pressure‐dependent Φacetone using updated measurements of fundamental acetone photolysis parameters. We also use simulations of the Φacetone produced from recent photophysical modeling to derive temperature‐ and pressure‐dependent parameterizations of the Φacetone. In contrast to the current Φacetone parameterization used in atmospheric chemical modeling, our parameterization reflects a predicted nonzero Φacetone in the wavelength region above 320 nm. Despite the increased Φacetone values in the higher wavelength region of the acetone photolysis action spectrum, the modeled effect on HOx production is not significantly different from HOx produced using the current recommended Φacetone parameterization. Uncertainties in the acetone photolysis mechanism remain; thus, more direct temperature‐ and pressure‐dependent measurements of Φacetone are warranted.

Modified particle swarm optimisation to determine the kinetic parameters of 2-chlorophenol oxidation in supercritical water

ABSTRACT An analytical chemical kinetics model can investigate the impact of concentrations, pressures, temperatures, and catalysts on the rates of reactions. It serves as the foundation for process design, effectiveness, and control. Nevertheless, the chemical kinetics model presents numerous dynamic characteristics that pose challenges in terms of prediction based on experimental empirical evidence. The present work employed three particle swarm optimisation (PSO) algorithms in order to determine the optimal parameters of the kinetic model. We wanted to reduce root mean squared errors. The operators’ efficacy is shown by numerical tests on benchmark functions and comparison to the fundamental GWO and ABC operators. The computational findings demonstrate that m-PSO shows a least RMSE value of 0.043 in comparison to other models and also significantly enhances both accuracy and convergence rate compared to other described methods. The model's improved search capabilities are shown by the kinetic parameter estimate findings utilising supercritical water oxidation experimental data.

Potential and costs required for methane removal to compete with BECCS as a mitigation option

Abstract Methane is the second most important anthropogenic greenhouse gas (GHG) causing warming after carbon dioxide, and the emission reductions potentials are known to be limited due to the difficulty of abating agricultural methane. We explore in this study the emerging option of atmospheric methane removal (MR) that could complement carbon dioxide removal (CDR) in mitigation pathways. MR is technologically very challenging and potentially very expensive, so the main question is at which cost per ton of methane removed is MR more cost effective than CDR. To address this question, we use an intertemporal optimization climate-GHG-energy model to evaluate the MR cost and removal potential thresholds that would allow us to meet a given climate target with the same or a lower abatement cost and allowing for equal or higher gross CO2 emissions than if CDR through bioenergy with carbon capture and storage were an option. We also compare the effects of MR and CDR on the cost-effective mitigation pathways achieving four different climate targets. Using the ACC2-GET integrated carbon cycle, atmospheric chemistry, climate and energy system model, we consider a generic MR technology characterized by a given unit cost and a maximal removal potential. We show that to totally replace bioenergy based CDR with MR, the MR potential should reach at least 180–290 MtCH4 per year, i.e. between 50% and 90% of current anthropogenic methane emissions, with maximum unit cost between 11 000 and 69 000 $/tCH4, depending on the climate target. Finally, we found that replacing CDR by MR reshapes the intergenerational distribution of climate mitigation efforts by delaying further the mitigation burden.

Significant Impact of a Daytime Halogen Oxidant on Coastal Air Quality.

Chlorine radicals (Cl·) are highly reactive and affect the fate of air pollutants. Several field studies in China have revealed elevated levels of daytime molecular chlorine (Cl2), which, upon photolysis, release substantial amounts of Cl· but are poorly represented in current chemical transport models. Here, we implemented a parametrization for the formation of daytime Cl2 through the photodissociation of particulate nitrate in acidic environments into a regional model and assessed its impact on coastal air quality during autumn in South China. The model could reproduce over 70% of the high Cl2 level measured at a coastal site, revealing a discernible presence of Cl2 and released Cl· in coastal and adjacent areas. Abundant Cl2 alters the oxidative capacity of the atmosphere, consequently increasing O3 (6-12%) and PM2.5 (10-16%) concentrations in high-NOx areas and reducing O3 (3%) concentration in low-NOx areas. Accounting for chlorine chemistry shifts the O3 - precursor relationships from VOC limited to mixed or NOx -limited regimes, enhancing the benefits of NOx emission reduction in mitigating O3 pollution. Our findings suggest that tightening emission control for two acidic pollutants, NOx and SO2, would alleviate reactive Cl· production and its adverse impact on air quality.

Three-dimensional CFD simulation of co-gasification of biomass and plastic wastes (SRF) in a bubbling fluidized bed with detailed kinetic chemical model

In this study, a three-dimensional computational fluid dynamics (CFD) model using multiphase particle-in-cell (MP-PIC) method with large eddy simulation (LES) was developed to investigate the characteristics of the co-gasification of densified wood pellets (WP) and rice husk pellets (RHP) with SRF in a bubbling fluidized bed reactor. ER reduction from 0.30 to 0.10 resulted in a decrease in specific gas yield per total feed from 1.20 g/gtotal to 0.96 g/gtotal and from 1.20 g/gtotal to 0.67 g/gtotal during SRF/WP and SRF/RHP co-gasification, respectively. Reducing ER during SRF/WP co-gasification allows to increase C2–C3 HCs yield reaching 0.33 g/gsrf (0.15 g/gtotal) at ER = 0.15, 0.556 kg/h SRF, and 0.693 kg/h WP. Carbon conversion efficiency (CCE) and cold gas efficiency (CGE) at the same conditions reduced to 83% and 74%, respectively. Co-gasification of 0.667 kg/h SRF and 2.040 kg/h RHP allowed to reach 0.28 g/gsrf (0.07 g/gtotal) C2–C3 HCs yield, but drastically reducing CCE and CGE to 71% and 46%, respectively. SRF/WP co-gasification is suggested for low cost C2–C3 HCs recovery from SRF with coproduction of syngas retaining 48% to 57% of initial LHV, meaning that SRF/RHP co-gasification is more suitable for syngas production.

A systematic review suggests extension and redefinition of a food-deception pollination syndrome involving anautogenous flies.

The currently recognized diversity of pollination strategies requires pollination syndromes to be updated. Described a decade ago, kleptomyiophily is a deceptive pollination system in which plants exploit the nutrient-seeking behavior of females of kleptoparasitic flies (Chloropidae and Milichiidae) by olfactorily mimicking their insect host. Such a pollination system was already hypothesized for pollination by biting midges (Ceratopogonidae) but has never been formalized. This review aims to explore the extent of pollination interactions deceiving anautogenous flies, especially by considering pollination by biting midges and kleptomyiophily as two facets of a common pollination syndrome: insect-host mimicry. As attraction of these flies seems to rely on insect-mimicking volatile organic compounds (VOCs), we propose an overview of the floral odours emitted by these plant species. We compiled bibliographic data on plant species pollinated by Chloropidae, Ceratopogonidae and Milichiidae, to list plant species that could be involved in insect-host mimicry pollination strategies. Then, we reanalyzed data from the literature on floral VOCs of 18 of these plant species distributed among four plant families and compared them with related plant species performing brood-site mimicry, the pollination syndrome closest to insect-host mimicry. We show that at least 97 plant species from seven families are mainly pollinated by Chloropidae, Ceratopogonidae and Milichiidae, with almost exclusively females found in flowers. Deception of anautogenous flies has been shown for only four plant species but has been supposed for a total of 28 others. Comparison of floral VOCs shows significant differences between insect-host mimicry and brood-site mimicry in terms of chemical composition, diversity and models mimicked. Despite fragmentary knowledge about the biology of the Diptera involved in insect-host mimicry, our results show similarities in floral odours and the putative mimicked resource between kleptomyiophilous plants and those pollinated by biting-midges, emphasizing a broader, unique, pollination syndrome.

Protocol to generate dual-target compounds usingatransformer chemical language model.

Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST- and corresponding DT-CPD data for specific protein pairs and evaluating the model's performance on hold-out test sets. For complete details on the use and execution of this protocol, please refer to Srinivasan and Bajorath.1.

Increased nerve density adversely affects outcome in colorectal cancer and denervation suppresses tumor growth

BackgroundThe colon and rectum are highly innervated, with neural components within the tumor microenvironment playing a significant role in colorectal cancer (CRC) progression. While perineural invasion (PNI) is associated with poor prognosis in CRC, the impact of nerve density and diameter on tumor behavior remains unclear. This study aims to evaluate the prognostic value of nerve characteristics in CRC and to verify the impact of nerves on tumor growth.MethodsTissue samples from 129 CRC patients were stained with immunofluorescent markers NF-L and S100B to detect nerves. Nerve diameter and density were measured and normalized. Kaplan-Meier survival analysis and Cox regression models were used to identify prognostic factors. Prognostic models were established using receiver operating characteristic (ROC) curve analysis to assess the predictive value of neural factors. A murine chemical denervation model was employed to disrupt sympathetic nerves using 6-hydroxydopamine, inhibit muscarinic receptor 3 with darifenacin, and ablate sensory neurons with capsaicin.ResultsThe total nerve density was 0.72 ± 0.59/mm², with intratumoral (0.42 ± 0.40/mm²) being significantly lower than extratumoral regions (1.00 ± 0.75/mm²). The average nerve diameter was 28.14 ± 6.04 μm, with no significant difference between intratumoral (28.2 ± 7.65 μm) and extratumoral regions (27.86 ± 6.72 μm). PNI was observed in 65 patients (50.4%). PNI and high normalized nerve density (NND) were associated with shorter overall survival and disease-free survival in CRC patients, with PNI identified as an independent prognostic factor. Patients with PNI exhibit higher NND. Incorporating PNI and NND into ROC curve analysis improved the sensitivity and specificity of survival predictions. In the murine model, chemical denervation of sympathetic, parasympathetic, and sensory nerves significantly reduced rectal tumor volume.ConclusionsPNI and NND are critical factors influencing CRC patient survival and enhance the accuracy of survival prediction models. Moreover, chemical denervation effectively inhibits rectal tumor growth in vivo, highlighting the potential of neural targeting as a therapeutic strategy in CRC.

Twenty years of studying the origins and fate of atmospheric mercury over the Mediterranean Sea.

This study provides a review of 13 oceanographic campaigns between 2000 and 2017 to measure Hg in the Mediterranean, highlighting major findings from measurement and modelling activities during the Med-Oceanor program. The initial campaigns showed that high concentrations of RGM could be found far from industrial source regions and the observed daily variation in concentration, with peaks at midday and lower concentrations during darkness gave the first indications that photochemically mediated oxidation reactions were producing RGM in the MBL. Later atmospheric chemistry modelling studies showed the feasibility of Hg oxidation by bromine containing oxidants, which are released as a result of the acidification of sea salt aerosols in the Marine Boundary Layer (MBL). Spatial and seasonal variations of DGM were observed at different depths in the water column, with average DGM concentrations higher in the West and East Mediterranean deep and intermediate waters, than in overlaying Atlantic waters. DGM in water profiles typically increased with depth, together with nutrients and decreasing oxygen, indicating a possible bacterial and/or geogenic origin. Using measured DGM and meteorological variable values Hg fluxes can be calculated. When these are included in regional and global models they suggest that the net Hg evasion from the Mediterranean is around 30Mgyr-1. Repetition of these campaigns should be an extremely high priority, both to continue to monitor changes in atmospheric and aquatic Hg species concentrations, but also to perform RGM detection methodology intercomparisons so that the historical data is adequately contextualised and may be used to evaluate temporal trends.