Chemical Model Research Articles

Formation of Products of Incomplete Destruction (PID) from the Thermal Oxidative Decomposition of Perfluorooctanoic Acid (PFOA): Measurement, Modeling, and Reaction Pathways.

The thermal decomposition of perfluorooctanoic acid (PFOA) under oxidative conditions was investigated using air (O2) and N2O as oxidants over temperatures ranging from 400 to 1000 °C in an α-alumina reactor. In the presence of air, PFOA was found to decompose into perfluorohept-1-ene (C7F14) and perfluoroheptanoyl fluoride (C7F14O) in addition to HF, CO, and CO2. At temperatures above 800 °C, both C7F14 and C7F14O were no longer detected. A comprehensive analysis of the reaction mechanisms through quantum chemical analysis and kinetic modeling in combination with experimental observations was utilized to identify key reaction pathways. Quantum chemical analysis led to the conclusion that oxygen atoms are crucial in decomposing perfluoroalk-1-enes, especially the stable perfluorohept-1-ene (C7F14). Under oxidative conditions, it was found that significant quantities of C2F6 and CF4 were formed. Further quantum chemical analysis suggests that the O atoms facilitate the formation of volatile fluorinated compounds (VFCs) such as tetrafluoromethane (CF4) and hexafluoroethane (C2F6), particularly at higher temperatures. By elucidating these key reactions, an improved understanding of the potential formation products of incomplete combustion (PICs) or products of incomplete destruction (PIDs) is made.

Mechanistic Insight into the Thermal "Blueing" of Cyanine Dyes.

In recent work to develop cyanine dyes with especially large Stokes shifts, we encountered a "blueing" reaction, in which the heptamethine cyanine dye Cy7 (IUPAC: 1,3,3-trimethyl-2-((1E,3E,5E)-7-((E)-1,3,3-trimethylindolin-2-ylidene)hepta-1,3,5-trien-1-yl)-3H-indol-1-ium) undergoes shortening in two-carbon steps to form the pentamethine (Cy5) and trimethine (Cy3) analogs. Each step blue-shifts the resulting absorbance wavelength by ca. 100 nm. Though photochemical and oxidative chain-shortening reactions had been noted previously, it is simple heating alone or with amine bases that effects this unexpected net C2H2 excision. Explicit acetylene loss would be too endothermic to merit consideration. Our mechanistic studies using 2H labeling, mass spectrometric and NMR spectroscopic analyses, and quantum chemical modeling point instead to electrocyclic closure and aromatization of the heptamethine chain in Cy7 forming Fischer's base FB (1,3,3-trimethyl-2-methyleneindoline), a reactive carbon nucleophile that initiates chain shortening of the cyanine dyes by attack on their polymethine backbones. The byproduct is the cationic indolium species TMP (IUPAC: 1,3,3 trimethyl-2-phenyl indolium).

Accurately Predicting Spatiotemporal Variations of Near-Surface Nitrous Acid (HONO) Based on a Deep Learning Approach.

Gaseous nitrous acid (HONO) is identified as a critical precursor of hydroxyl radicals (OH), influencing atmospheric oxidation capacity and the formation of secondary pollutants. However, large uncertainties persist regarding its formation and elimination mechanisms, impeding accurate simulation of HONO levels using chemical models. In this study, a deep neural network (DNN) model was established based on routine air quality data (O3, NO2, CO, and PM2.5) and meteorological parameters (temperature, relative humidity, solar zenith angle, and season) collected from four typical megacity clusters in China. The model exhibited robust performance on both the train sets [slope = 1.0, r2 = 0.94, root mean squared error (RMSE) = 0.29 ppbv] and two independent test sets (slope = 1.0, r2 = 0.79, and RMSE = 0.39 ppbv), demonstrated excellent capability in reproducing the spatiotemporal variations of HONO, and outperformed an observation-constrained box model incorporated with newly proposed HONO formation mechanisms. Nitrogen dioxide (NO2) was identified as the most impactful features for HONO prediction using the SHapely Additive exPlanation (SHAP) approach, highlighting the importance of NO2 conversion in HONO formation. The DNN model was further employed to predict the future change of HONO levels in different NOx abatement scenarios, which is expected to decrease 27-44% in summer as the result of 30-50% NOx reduction. These results suggest a dual effect brought by abatement of NOx emissions, leading to not only reduction of O3 and nitrate precursors but also decrease in HONO levels and hence primary radical production rates (PROx). In summary, this study demonstrates the feasibility of using deep learning approach to predict HONO concentrations, offering a promising supplement to traditional chemical models. Additionally, stringent NOx abatement would be beneficial for collaborative alleviation of O3 and secondary PM2.5.

Evaluation of modelled versus observed non-methane volatile organic compounds at European Monitoring and Evaluation Programme sites in Europe

Abstract. Atmospheric volatile organic compounds (VOCs) constitute a wide range of species, acting as precursors to ozone and aerosol formation. Atmospheric chemistry and transport models (CTMs) are crucial to understanding the emissions, distribution, and impacts of VOCs. Given the uncertainties in VOC emissions, lack of evaluation studies, and recent changes in emissions, this work adapts the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West (EMEP MSC-W) CTM to evaluate emission inventories in Europe. Here we undertake the first intensive model–measurement comparison of VOCs in 2 decades. The modelled surface concentrations are evaluated both spatially and temporally, using measurements from the regular EMEP monitoring network in 2018 and 2019, as well as a 2022 campaign. To achieve this, we utilised the UK National Atmospheric Emissions Inventory to derive explicit emission profiles for individual species and employed a tracer method to produce pure concentrations that are directly comparable to observations. The degree to which the modelled and measured VOCs agree varies depending on the specific species. The model successfully captures the overall spatial and temporal variations of major alkanes (e.g. ethane, n-butane) and unsaturated species (e.g. ethene, benzene) but less so for propane, i-butane, and ethyne. This discrepancy underscores potential issues in the boundary conditions for the latter species and in their primary emissions from, in particular, the solvent and road transport sectors. Specifically, potential missing propane emissions and issues with its boundary conditions are highlighted by large model underestimations and smaller propane-to-ethane ratios compared to the measurement. Meanwhile, both the model and measurements show strong linear correlations among butane isomers and among pentane isomers, indicating common sources for these pairs of isomers. However, modelled ratios of i-butane to n-butane and i-pentane to n-pentane are approximately one-third of the measured ratios, which is largely driven by significant emissions of n-butane and n-pentane from the solvent sector. This suggests issues with the speciation profile of the solvent sector, underrepresented contributions from transport and fuel evaporation sectors in current inventories, or both. Furthermore, the modelled ethene-to-ethyne and benzene-to-ethyne ratios differ significantly from measured ratios. The different model performance strongly points to shortcomings in the spatial and temporal patterns and magnitudes of ethyne emissions, especially during winter. For OVOCs, the modelled and measured concentrations of methanal and methylglyoxal show a good agreement, despite a moderate underestimation by the model in summer. This discrepancy could be attributed to an underestimation of contributions from biogenic sources or possibly a model overestimation of their photolytic loss in summer. However, the insufficiency of suitable measurements limits the evaluation of other OVOCs. Finally, model simulations employing the CAMS inventory show slightly better agreements with measurements than those using the Centre on Emission Inventories and Projections (CEIP) inventory. This enhancement is likely due to the CAMS inventory's detailed segmentation of the road transport sector, including its associated sub-sector-specific emission profiles. Given this improvement, alongside the previously mentioned concerns about the model's biased estimations of various VOC ratios, future efforts should focus on a more detailed breakdown of dominant emission sectors (e.g. solvents) and the refinement of their speciation profiles to improve model accuracy.

Climate and air quality impact of using ammonia as an alternative shipping fuel

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO x , NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO x , NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NO x , low NH3 emissions) vs pure NH3 (low NO x , very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16 900 mortalities from PM2.5 and 16 200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr−1) from pure NH3 engines could lead to 668 100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current emission control areas leads to smaller improvements in PM2.5-related mortalities (22 100 avoided mortalities for NH3–H2 and 623 900 additional mortalities for pure NH3 annually), while extending both Tier III NO x standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH3–H2 and 1200 additional mortalities for pure NH3 annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

A Potential Surface Warming Regime for Volcanic Super‐Eruptions Through Stratospheric Water Vapor Increases

AbstractLarge volcanic eruptions are known to influence the climate through a variety of mechanisms including aerosol‐forced cooling and warming via emitted CO2. The January 2022 Hunga shallow underwater eruption caused an increase in stratospheric water vapor, and demonstrated how the associated positive radiative forcing can be an important component of an eruption's climate forcing. We present interactive stratospheric aerosol model simulations of super‐volcanic eruptions with a range of SO2 emissions that can produce climate warming through feedback effects produced by a large igneous province (or “flood basalt”) mid‐latitude super‐eruption using Goddard Earth Observing System Chemistry Climate Model climate model simulations. The model experiments suggest total SO2 emissions ≳4,000 Tg/4 Gt generate a multi‐year period of sustained aerosol absorptive local‐heating of the upper troposphere and lower stratosphere and hence produce net climate warming after strong initial cooling. The eruptions produce stratospheric water vapor increases of factors of 8–600. The initiation of these feedbacks within the simulations suggest they could occur for individual stratovolcano eruptions of the scale of the Toba or Tambora eruptions. We note the sensitivity of our results to volcanic sulfate aerosol microphysics and model chemistry.

Ultrasound-assisted efficient and convenient method of extracting valuable metals (Ni, Co, and Cd) from waste Ni–Cd batteries using DL-malic acid

Recycling waste Ni–Cd batteries has received much attention recently because of the serious environmental pollution they cause and to avoid the dissipation of valuable metals. Despite significant research, it is still difficult to efficiently recycle valuable and hazardous metals from waste Ni–Cd batteries in an economical and environmentally friendly manner. This study employed a novel process utilizing ultrasound-assisted leaching to recover Ni, Cd, and Co from waste nickel-cadmium (Ni–Cd) batteries. Organic DL-malic acid served as the leaching agent and H2O2 was employed as an oxidizing agent. The effects of various factors on the recovery efficiency of Ni, Cd, and Co, such as leaching temperature, time, DL-malic acid concentration, pulp density, H2O2 concentration, and ultrasound frequency, were also examined. To predict the chemical compounds present before and after the recycling experiments, the solid residues from the metal extraction were analyzed using XRD, XPS, FE-SEM, and EDS element mapping. Concurrently, ICP-OES was utilized to determine the metal content in the leachate. Under optimized conditions of 90 °C, 90 min, 2M DL-malic acid, 160 mL/g pulp density, and 20% ultrasound frequency, over 83% of Ni, 94% of Cd, and 98% of Co were effectively leached from the waste Ni–Cd battery powder. The leaching kinetics of Ni, Cd, and Co followed the surface chemical reaction control model. The activation energies (Ea) for Ni, Cd, and Co leaching were 21.34, 20.47, and 18.38 kJ/mol, respectively. The findings suggest that ultrasound-assisted leaching is an efficient, cost-effective, environmentally friendly, and sustainable alternative for extracting precious and hazardous metals from waste Ni–Cd batteries. Additionally, it reduces industrial chemical usage and enhances waste management sustainability.

Uncertainty quantification and data consistency analysis for the development of hydrogen and syngas oxidation model

The exponential growth of contemporary chemical kinetics data coupled with the potential inherent high uncertainty poses significant challenges for developing chemical kinetic models. To obtain a high-quality dataset for the optimization of H2 and syngas oxidation model, this study developed a framework for data consistency analysis and uncertainty analysis of chemical kinetic data. The framework combines simulations based on solving conservation equations, data-driven metaheuristic algorithms, and statistical analysis. Over 2000 experimental data points, including ignition delay times measured in shock tubes, laminar flame speeds, and concentration profiles measured in perfectly stirred reactors and plug flow reactors, were collected and used to construct the experimental dataset. Seventeen parameters of a detailed H2 and syngas oxidation model were selected to build the reaction rate constant (RRC) dataset. The initially estimated experimental uncertainties and RRC uncertainty factors were updated by slack variables in the iterative algorithm. Some high-uncertainty data were identified and excluded from the optimization dataset. The final experimental dataset achieves data consistency, and the feasible parameter set of RRC shows good prediction capability. The experimental data with high uncertainties were highlighted, and the uncertainty sources were analyzed.

Measurements of Ignition Delay Time of Gas-to-Liquid (GTL) Fuel Blends

The ignition delay time (IDT) is a fundamental combustion parameter of any fuel, which has a direct impact on its combustion and exhaust emissions in diesel engines. In this research, a thorough experimental study was conducted to investigate the ignition delay of Gas-to-Liquid (GTL) fuel and its 50%-50% blends with diesel under a wide range of operating conditions using a conventional shock tube apparatus. Simultaneously, a comprehensive chemical kinetic model, based on the well-established POLIMI mechanism, was developed to simulate the complex combustion chemistry of diesel, GTL, and their blends. Several loading pressures in the driver section ranging from 6 to 14 bar that results in reflected pressure of 8-16 bar, and equivalence ratios ranging from 0.5 to 1.3 as well as different initial temperatures have been examined. Calibration of the shock tube indicated that a driver gas tailoring technique was necessary to be used, which was conducted by adding a small ratio of either argon or nitrogen to helium to get an extended test time by 32 % with a good rapture pressure. IDT measurements showed that GTL has the shorter ignition delay time than that of diesel fuel for all conditions. The findings show that IDT measurements for GTL fuel has a shorter ignition delay time than that of diesel fuel for all conditions. Under stoichiometric conditions (ɸ=1.0), IDT of the blended fuel was about 3 ms, very close to that of GTL at 975 K. However, at a temperature above 1000 K, the IDT of the three fuels were almost identical and ranging between 1 and 2 ms. At a temperature of 1100 K, the IDT of the blended fuel was 5.5 ms, almost a middle value between those of the pure diesel, 0.522 ms, and GTL fuel, 0.495 ms. The shortest IDT of the blended fuel was about 0.4 ms with the highest value of the reflected pressure of 14 bar, while the longest IDT was around 10 ms with the rich mixture at ɸ = 1.3. The study can add to the existing literature illustrating the behavior of GTL- diesel blend can affect IDT.

PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models

SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn’t require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.

Computational studies on a selection of phosphite esters as antioxidants for polymeric materials.

Phosphite esters, a class of organo-phosphorus compounds, are widely used as non-discolouring antioxidants in many polymeric products. Apart from normal radical scavenging, they prevent the splitting of hydroperoxides (ROOH), one of the initial products of autoxidation, from forming extremely reactive free radicals such as alkoxy (RO.) and hydroxy (.OH) radicals. The inherent molecular properties of antioxidants and the chemistry of their action are essential for researchers working in this field of science. Four organo-phosphorous compounds well-known for their antioxidant activity are selected here for theoretical analysis: Tri(m-methylphenyl) phosphite (m-TMPP), Tri(4-methyl-2,6-di-tert-butylphenyl) phosphite (TMdtBPP), Tri(allylphenyl) phosphite (TAPP) and Tri(mercaptobenzothiazoyl) thiophosphate (TMBTTP). The antioxidant activity exhibited by these compounds is theoretically verified, and the results are consistent with the available experimental data. Such theoretical predictions offer advantages in scientific research, particularly when researchers need to select certain molecules as antioxidants for experiments from a pool of molecular systems. The chemical computations presented in this report are done in Gaussian 16 program package. The procedure of density functional theory (DFT) with the model chemistry B3LYP/6-31G(d,p) is used to generate computational data. Global reactivity indices, thermochemical data, Fukui functions, molecular electrostatic potential and NMR spectra are computed for the chosen molecular systems from their optimized geometries.

Does the Asian summer monsoon play a role in the stratospheric aerosol budget of the Arctic?

Abstract. The Asian summer monsoon has a strong convectional component with which aerosols are able to be lifted up into the lower stratosphere. Due to usually long lifetimes and long-range transport aerosols remain there much longer than in the troposphere and are also able to be advected around the globe. Our aim of this study is a synergy between simulations by Chemical Lagrangian Model of the Stratosphere (CLaMS) and KARL (Koldewey Aerosol Raman Lidar) at AWIPEV, Ny-Ålesund in the Arctic, by comparing CLaMS results with exemplary days of lidar measurements as well as analyzing the stratospheric aerosol background. We use global three-dimensional Lagrangian transport simulations including surface origin tracers as well as back trajectories to identify source regions of the aerosol particles measured over Ny-Ålesund. We analyzed lidar data for the year 2021 and found the stratosphere generally clear, without obvious aerosol layers from volcanic eruptions or biomass burnings. Still an obvious annual cycle of the backscatter coefficient with higher values in late summer to autumn and lower values in late winter has been found. Results from CLaMS model simulations indicate that from late summer to early autumn filaments with high fractions of air which originate in South Asia – one of the most polluted regions in the world – reach the Arctic at altitudes between 360 and 380 K potential temperature. We found a coinciding measurement between the overpass of such a filament and lidar observations, and we estimated that backscatter and depolarization increased by roughly 15 % during this event compared to the background aerosol concentration. Hence we demonstrate that the Asian summer monsoon is a weak but measurable source for Arctic stratospheric aerosol in late summer to early autumn.

Synoptic Analysis and WRF‐Chem Model Simulation of Dust Events in the Southwestern United States

AbstractDust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March‐May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large‐scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High‐resolution Weather Research and Forecasting coupled with Chemistry model (WRF‐Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo‐based drag‐partition was used to explore land surface‐atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub‐tropical jet stream interaction was a common upper‐level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast‐directed pressure gradient upstream and over the source areas resulted in strong near‐surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid‐tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF‐Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust‐on‐snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo‐based drag partition provides a basis for additional dust‐on‐snow simulations to assess the hydrologic impact in the Southwest US.

Chemical evolution models: the role of type Ia supernovae in the α-elements over Iron relative abundances and their variations in time and space

Abstract The role of type Ia supernovae (SN Ia), mainly the Delay Time Distributions (DTDs) determined by the binary systems, and the yields of elements created by different explosion mechanisms, are studied by using the MulChem chemical evolution model applied to our Galaxy. We explored 15 DTDs and 12 tables of elemental yields produced by different SN Ia explosion mechanisms, doing a total of 180 models. Chemical abundances for α-elements (O, Mg, Si, Ca) and Fe derived from these models are compared with recent solar region observational data of α-elements over Fe relative abundances, [X/Fe], as a function of [Fe/H] and age. A multidimensional maximum likelihood analysis shows that 52 models are able to fit all these datasets simultaneously, considering the 1σ confidence level. The combination of STROLG1 DTD and LN20181 SN Ia yields provides the best fit. The exponential model with very prompt events is a possible DTD, but a combination of several channels is more probable. The SN Ia yields that include MCh or Near MCh correspond to 39 (75%) of the 52 best models. Regarding the DTD, 31 (60%) of the 52 most probable models correspond to the SD scenario, while the remaining 21 (40%) are based on the DD scenario. Our results also show that the relatively large dispersion of the observational data may be explained by the stellar migration from other radial regions, and/or perhaps a combination of DTDs and explosion channels.

Study of combustion flow field in a scramjet engine under inflow oscillation variations based on OpenFOAM

In this study, a compressible supersonic numerical model was established based on the open-source computational fluid dynamics software OpenFOAM. The supersonic air inflow was set in sinusoidal variation and gas hydrogen was employed as fuel for the DLR engine numerical simulation. The SST k-ω model and a 9-species 27-reaction hydrogen/oxygen chemical reaction model was employed as the turbulence and reaction mechanical kinetics models respectively. The effect of continuous variable inflow on the combustion performance for the scramjet engine was analyzed and the results showed that the combustion flame inside the scramjet engine exhibited significant partitioning phenomena around the combustion stabilization zones under periodic continuous variable inflow conditions. It was found that intense combustion zones were formed at both upstream and downstream flow as the induction of intersecting shock waves. When the flame was stabilized in the engine, the initiation position of the intense combustion zone at the upstream was transferred to the distance of twice the strut length, while the initiation position of the intense combustion zone at the downstream was anchored at a distance of about five times the strut length.

Impact of Molecular Chlorine Production from Aerosol Iron Photochemistry on Atmospheric Oxidative Capacity in North China.

Elevated levels of atmospheric molecular chlorine (Cl2) have been observed during the daytime in recent field studies in China but could not be explained by the current chlorine chemistry mechanisms in models. Here, we propose a Cl2 formation mechanism initiated by aerosol iron photochemistry to explain daytime Cl2 formation. We implement this mechanism into the GEOS-Chem chemical transport model and investigate its impacts on the atmospheric composition in wintertime North China where high levels of Cl2 as well as aerosol chloride and iron were observed. The new mechanism accounts for more than 90% of surface air Cl2 production in North China and consequently increases the surface air Cl2 abundances by an order of magnitude, improving the model's agreement with observed Cl2. The presence of high Cl2 significantly alters the oxidative capacity of the atmosphere, with a factor of 20-40 increase in the chlorine radical concentration and a 20-40% increase in the hydroxyl radical concentration in regions with high aerosol chloride and iron loadings. This results in an increase in surface air ozone by about 10%. This new Cl2 formation mechanism will improve the model simulation capability for reactive chlorine abundances in the regions with high emissions of chlorine and iron.

MECHANISM OF DEPROTONATION OF THE AMINO GROUP OF GLUTAMATE UPON BINDING TO N-ACETYLGLUTAMATE SYNTHASE

Gcn5-related N-acetyltransferases catalyze the transfer of an acetyl group to a primary amino group of a wide class of substrates. Protonation of the amino group upon binding to the enzyme is necessary to activate the nucleophilic attack on the substrate. The process of glutamate binding to N-acetylglutamate synthase is considered using molecular modeling and quantum chemistry methods. It has been shown that deprotonation of the primary amino group of glutamate occurs upon binding to the active site of the enzyme with the participation of the side chain of the aspartate residue.

The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars

AbstractOzone simulated in Mars Global Climate Models (MGCMs) is used to assess the underlying chemistry occurring in the atmosphere. Currently, ozone total column abundance (TCA) is under‐predicted in MGCMs by up to 120%, implying missing or inaccurate chemistry in models. Heterogeneous reactions of hydroxyl radicals (HOX) have been offered as an explanation for some of this bias, because they cause ozone to increase at locations where it's currently under‐predicted. We use four simulations to compare modeled ozone TCA with observations from the UVIS spectrometer aboard the ExoMars Trace Gas Orbiter to improve the representation of heterogeneous processes and their impact on ozone. We use a gas‐phase only run, a dissociative scheme, an adsorbed HOX retention scheme, and a hybrid scheme that combines the dissociative mechanism with the retention of HOX on water ice. We find retention of HOX is dependent on water ice sublimation, and ozone abundance increases when water ice persists for longer periods (1–20 sols). Over time, the loss of HOX causes a depletion in H2O2 concentration (HOX reservoir), and thus allows ozone concentration to increase. When adsorbed HOX are desorbed and dissociate into other by‐products, HOX are not immediately available to destroy ozone. This results in larger ozone concentrations than if desorbed HOX are released directly back into their gaseous states. When using the hybrid scheme, ozone TCA is increased up to 50% where the ozone deficit is greatest, demonstrating the best agreement with observations, and implying that HOX radicals are both retained when adsorbed and dissociate.

The impact of shipping on the air quality in European port cities with a detailed analysis for Rotterdam

Air quality in cities with large maritime ports is considerably impacted by emissions from shipping activity which is of a growing relevance due to an increasing relative contribution. To explore the extent of shipping emissions to ambient air quality, simulations with the chemical transport model LOTOS-EUROS (LOng Term Ozone Simulation – EURopean Operational Smog model) were performed for the year 2018 at an approximate 1x1 km resolution for six European cities with large ports, i.e., Rotterdam, Antwerp, Hamburg, Amsterdam, Le Havre, and London. It was found that depending on the investigated city, 6.5% to 62% of the nitrogen dioxide (NO2) concentration in the city centres is attributable to shipping activities. This corresponds to contributions of 1.8 to 11.5 μg/m3 to the ambient air NO2 concentrations. The average NO2 contribution of shipping in these six cities was 28% (7.1 μg/m3). The largest relative contribution was found for Le Havre where 62% (10.8 μg/m3) of the annual average NO2 concentration was caused by shipping emissions. The largest absolute contribution is found for the city centre of Hamburg with 11.5 μg/m3 (41%). The lowest absolute and relative contribution (respectively 1.8 μg/m3 and 6.5%) are found for London, also having the smallest port in terms of tonnage throughput, which is one of the influential factors that determine emission totals, investigated in this study. For the other investigated pollutants, i.e., PM2.5, PM10 and SO2, contributions from shipping were less pronounced with average contribution for all cities of 10% (1.2 μg/m3)7% (1.5 μg/m3) and 4% (0.16 μg/m3) respectively. To assess the effect of model choices on these results, this study also looked into the choice of simulation resolution and relations between meteorological parameters and NO2 concentrations. Following simulations with varying chemical transport model resolutions (1x1 km to 24x24 km), it is found that a decrease in ambient air pollutant concentrations away from localized emission sources is more pronounced at higher (1x1 km) model resolutions and source contributions are influenced more significantly than total concentrations. Considering meteorology, generally low wind speeds (1-2 m/s) lead to high NO2 concentration in city centres. For the cities where the port is much closer to the city centre (e.g., London, Le Havre, Hamburg and Antwerp) the absolute NO2 concentrations as well as the contributions from shipping emissions become highest for windless conditions. The high concentrations (>60 μg/m3 NO2) only occur when wind speeds fall below 6 m/s.

Investigating Sulfur Chemistry in the HD163296 Disk

Sulfur chemistry in the formation process of low-mass stars and planets remains poorly understood. The protoplanetary disks are the birthplace of planets and its distinctive environment provides an intriguing platform for investigating models of sulfur chemistry. We analyzed the Atacama Large Millimeter/submillimeter Array observations of CS 7–6 transitions in the HD 163296 disk and performed astrochemical modeling to explore its sulfur chemistry. We simulated the distribution of sulfur-containing molecules and compared it with observationally deduced fractional column densities. We have found that the simulated column density of CS is consistent with the observationally deduced fractional column densities, while the simulated column density of C2S is lower than the observationally deduced upper limits on column densities. This results indicate that we have a good understanding of the chemical properties of CS and C2S in the disk. We also investigated the influence of the C/O ratio on sulfur-containing molecules and found that the column densities of SO, SO2, and H2S near the central star are dependent on the C/O ratio. Additionally, we found that the N[CS]/N[SO] ratio can serve as a promising indicator of the disk’s C/O ratio in HD 163296. Overall, the disk of HD 163296 provides a favorable environment for the detection of sulfur-containing molecules.