Chemical Model Research Articles

Interannual variability of summertime formaldehyde (HCHO) vertical column density and its main drivers at northern high latitudes

Abstract. The northern high latitudes (50–90° N, mostly including boreal-forest and tundra ecosystems) have been undergoing rapid climate and ecological changes over recent decades, leading to significant variations in volatile organic compounds (VOC) emissions from biogenic and biomass burning sources. Formaldehyde (HCHO) is an indicator of VOC emissions, but the interannual variability of HCHO and its main drivers over the region remains unclear. In this study, we use the GEOS-Chem chemical transport model and satellite retrievals from the Ozone Monitoring Instrument (OMI) and the Ozone Mapping and Profiler Suite (OMPS) to examine the interannual variability of HCHO vertical column density (VCD) during the summer seasons spanning from 2005 to 2019. Our results show that, in 2005–2019 summers, wildfires contributed 75 %–90 % of the interannual variability of HCHO VCD over Siberia, Alaska and northern Canada, while biogenic emissions and background methane oxidation account for ∼ 90 % of HCHO interannual variability over eastern Europe. We find that monthly solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2), an efficient proxy for plant photosynthesis, shows a good linear relationship (R= 0.6–0.7) with the modeled biogenic HCHO column (dVCDBio,GC) in eastern Europe, Siberia, Alaska and northern Canada, indicating the coupling between SIF and biogenic VOC emissions over the four domains on a monthly scale. In Alaska, Siberia and northern Canada, SIF and dVCDBio,GC both show relatively lower interannual variabilities (SIF: CV = 1 %–9 %, dVCDBio,GC: CV = 1 %–2 %; note that CV stands for coefficient of variation) in comparison to wildfire-induced HCHO (CV = 8 %–13 %), suggesting that the high interannual variabilities of OMI HCHO VCD (CV = 10 %–16 %) in these domains are likely driven by wildfires instead of biogenic emissions.

A systematic study of carbon clustering dynamics for explosives

ABSTRACT The carbon clustering mechanism of insensitive explosive is investigated by molecular dynamics. We find that the free carbon gas aggregates to form flexible chain, and the chain changes to graphite-phase particle. A set of thermodynamic properties for carbon clusters are calculated, and five clustering mechanisms are investigated, including the nucleation, deposition, sublimation, diffusion and phase transition effects. We use chemical reaction model to describe the free carbon aggregation, use diffusion/impaction model to describe the deposition/growing process, use heterogeneous equilibrium condition to evaluate the sublimation rate, use Shaw's model to describe the Smoluchowski coagulation and use Arrhenius model to describe the phase transition rate. Based on the microscopic properties, the detonation wave of explosive is simulated by finite difference method. A set of detonation properties are calculated, and the meso-structure of slow reaction zone is obtained.

The Design of a Parameterization Scheme for 137Cs Based on the WRF-Chem Model and Its Application in Simulating the Fukushima Nuclear Accident

Based on the Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem) atmospheric chemistry model, a parameterization scheme for the radioactive isotope caesium (137Cs), considering processes such as advection, turbulent diffusion, dry deposition, and wet deposition, was constructed, enabling the spatial distribution simulation of the concentration and deposition of 137Cs. The experimental simulation studies were carried out during the high emission period of the Fukushima nuclear accident (from 11 to 17 March 2011). Two sets of comparison experiments, with or without deposition, were designed, the effects of wind field and precipitation on the spatial transport and ground deposition of 137Cs were analyzed, and the influence of wind field and precipitation on 137Cs vertical transport was analyzed in detail. The results indicate that the model can accurately simulate the meteorological and 137Cs variables. On 15 March, 137Cs dispersed towards the Kanto Plain in Japan under the influence of northeastern winds. In comparison to the experiment without deposition, the concentration of 137Cs in the Fukushima area decreased by approximately 286 Bq·m−3 in the deposition experiment. Under the influence of updrafts in the non-deposition experiment, a 137Cs cloud spread upward to a maximum height of 6 km, whereas in the deposition experiment, the highest dispersion of the 137Cs cloud only reach a height of 4 km. Affected by the wind field, dry deposition is mainly distributed in Fukushima, the Kanto Plain, and their eastern ocean areas, with a maximum dry deposition of 5004.5 kBq·m−2. Wet deposition is mainly influenced by the wind field and precipitation, distributed in the surrounding areas of Fukushima, with a maximum wet deposition of 725.3 kBq·m−2. The single-station test results from the deposition experiment were better than those for the non-deposition experiment: the percentage deviations of the Tokyo, Chiba, Maebashi, and Naraha stations decreased by 61%, 69%, 46%, and 51%, respectively, and the percentage root mean square error decreased by 46%, 25%, 38%, and 48%, respectively.

CON-quest

Context. Nuclear regions of ultraluminous and luminous infrared galaxies (U/LIRGs) are powered by starbursts and/or active galactic nuclei (AGNs). These regions are often obscured by extremely high columns of gas and dust. Molecular lines in the submillimeter windows have the potential to determine the physical conditions of these compact obscured nuclei (CONs). Aims. We aim to reveal the distributions of HCN and HCO+ emission in local U/LIRGs and investigate whether and how they are related to galaxy properties. Methods. Using the Atacama Large Millimeter/submillimeter Array (ALMA), we have conducted sensitive observations of the HCN J = 3−2 and HCO+J = 3−2 lines toward 23 U/LIRGs in the local Universe (z < 0.07) with a spatial resolution of ∼0.3″ (∼50−400 pc). Results. We detected both HCN and HCO+ in 21 galaxies, only HCN in one galaxy, and neither in one galaxy. The global HCN/HCO+ line ratios, averaged over scales of ∼0.5−4 kpc, range from 0.4 to 2.3, with an unweighted mean of 1.1. These line ratios appear to have no systematic trend with bolometric AGN luminosity or star formation rate. The line ratio varies with position and velocity within each galaxy, with an average interquartile range of 0.38 on a spaxel-by-spaxel basis. In eight out of ten galaxies known to have outflows and/or inflows, we found spatially and kinematically symmetric structures of high line ratios. These structures appear as a collimated bicone in two galaxies and as a thin spherical shell in six galaxies. Conclusions. Non-LTE analysis suggests that the high HCN/HCO+ line ratio in outflows is predominantly influenced by the abundance ratio. Chemical model calculations indicate that the enhancement of HCN abundance in outflows is likely due to high-temperature chemistry triggered by shock heating. These results imply that the HCN/HCO+ line ratio can aid in identifying the outflow geometry when the shock velocity of the outflows is sufficiently high to heat the gas.

Pylattica: a package for prototyping lattice models in chemistry and materials science

pylattica: a package for prototyping lattice models in chemistry and materials science

Corrigendum: Coupling plasma physics and chemistry in the PIC model of electric propulsion: application to an air-breathing, low-power Hall thruster

Corrigendum: Coupling plasma physics and chemistry in the PIC model of electric propulsion: application to an air-breathing, low-power Hall thruster

Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling

Abstract. Ground-level ozone (O3) pollution is a persistent environmental concern, even in regions that have made efforts to reduce emissions. This study focuses on the state of Arizona, which has experienced elevated O3 concentrations over past decades and contains two non-attainment areas as designated by the U.S. Environmental Protection Agency. Using the Weather Research and Forecasting with Chemistry (WRF-Chem) model, we examine O3 levels in the semi-arid and arid regions of Arizona. Our analysis focuses on the month of June between 2017 and 2021, a period characterized by high O3 levels before the onset of the North American Monsoon (NAM). Our evaluation of the WRF-Chem model against surface Air Quality System (AQS) observations reveals that the model adeptly captures the diurnal variation of hourly O3 levels and the episodes of O3 exceedance through the maximum daily 8 h average (MDA8) O3 concentrations. However, the model tends to overestimate surface NO2 concentrations, particularly during nighttime hours. Among the three cities studied, Phoenix (PHX) and Tucson (TUS) exhibit a negative bias in both hourly and MDA8 O3 levels, while Yuma demonstrates a relatively large positive bias. The simulated mean hourly and MDA8 O3 concentrations in Phoenix are 44.6 and 64.7 parts per billion (ppb), respectively, compared to observed values of 47.5 and 65.7 ppb, resulting in mean negative biases of −2.9 and −1.0 ppb, respectively. Furthermore, the analysis of the simulated ratio of formaldehyde (HCHO) to NO2 (HCHO/NO2; FNR), reveals interesting insights of the sensitivity of O3 to its precursors. In Phoenix, the FNR varies from a VOC (volatile organic compound)-limited regime in the most populated areas to a transition between VOC-limited and NOx-limited regimes throughout the metro area, with an average FNR of 1.15. In conclusion, this study sheds light on the persistent challenge of ground-level O3 pollution in semi-arid and arid regions, using the state of Arizona as a case study.

Numerical Simulation and Consequence Analysis of Full-Scale Jet Fires for Pipelines Transporting Pure Hydrogen or Hydrogen Blended with Natural Gas

The use of existing natural gas pipelines for the transport of hydrogen/natural gas mixtures can achieve large-scale, long-distance and low-cost hydrogen transportation. A jet fire induced by the leakage of high-pressure pure hydrogen and hydrogen-blended natural gas pipelines may pose a severe threat to life and property. Based on the Abel–Nobel equation of state and a notional nozzle model, an equivalent pipe leakage model is established to simulate high-pressure pipeline gas leakage jet fire accidents. Large-scale high-pressure hydrogen and natural gas/hydrogen mixture jet fires are simulated, showing the jet impingement process and obtaining an accurate and effective simulation framework. This framework is validated by comparing the simulated and experimental measured results of flame height, flame appearance and thermal radiation. Several combustion models are compared, and the simulated data show that the non-premixed chemical equilibrium combustion model is superior to other combustion models. The influence of the pipe pressure and the hydrogen blending ratio on the consequences of natural gas/hydrogen mixture pipeline leakage jet fire accidents is explored. It is found that when the hydrogen blending ratio is lower than 22%, the increase in the hydrogen blending ratio has little effect on the decrease in the thermal radiation hazard distance.

Photodissociation and induced chemical asymmetries on ultra-hot gas giants

Context. Recent observations have resulted in the detection of chemical gradients on ultra-hot gas giants. Notwithstanding their high temperature, chemical reactions in ultra-hot atmospheres may occur in disequilibrium, due to vigorous day-night circulation and intense UV radiation from their stellar hosts. Aims. The goal of this work is to explore whether photochemistry is affecting the composition of ultra-hot giant planets, and if it can introduce horizontal chemical gradients. In particular, we focus on hydrogen cyanide (HCN) on WASP-76 b, as it is a photochemically active molecule with a reported detection on only one side of this planet. Methods. We used a pseudo-2D chemical kinetics code to model the chemical composition of WASP-76 b along its equator. Our approach improved on chemical equilibrium models by computing vertical mixing, horizontal advection, and photochemistry. Results. We find that the production of HCN is initiated through the thermal and photochemical dissociation of CO and N2 on the day side of WASP-76 b. The resulting radicals are subsequently transported to the night side via the equatorial jet stream, where they recombine into different molecules. This process results in an HCN gradient with a maximal abundance on the planet’s morning limb. We verified that photochemical dissociation is a necessary condition for this mechanism, as thermal dissociation alone proves insufficient. Other species produced via night-side disequilibrium chemistry are SO2 and S2. Conclusions. Our model acts as a proof of concept for chemical gradients on ultra-hot exoplanets. We demonstrate that even ultra-hot planets can exhibit disequilibrium chemistry and recommend that future studies do not neglect photochemistry in their analyses of ultra-hot planets.

Finite-rate chemistry Favre-Averaged Navier-Stokes based simulation of a non-premixed SynGas/Air flame

Abstract The present paper is devoted to the study of the influence of chemical mechanisms, turbulence models and gas radiative properties models on the characteristics of a turbulent diffusion CO/H2/N2 -air flame, i.e., the so-called syngas flame in a Favre-averaged Navier-Stokes (FANS) environment. For this purpose, a transient FANS solver for combustion is used. The simulations are carried out using three distinct turbulence models, i.e., the standard k-ε (SKE), the renormalization group (RNG) k-ε, and the Shear Stress Transport (SST) models. The turbulence-chemistry interaction is modeled using the Partially Stired Reaction (PaSR) model. The chemical mechanisms used in the present study are: (i) a compact skeletal C2 mechanism, (ii) a mechanism developped by Frassoldati-Faravelli-Ranzi (FFR) containing 14 species and 33 reactions and (iii) the optimised syngas mechanism by Varga. Radiation heat transfer is handled by the P-1 method. In addition, the performances of two gas radiative properties models, i.e., the grey mean gas and the weighted-sum-of-gray-gases (WSGG) models, are assessed in radiative heat transfer modeling of the syngas flame. The predicted results reveal that the combination of the RNG turbulence model and the C2 skeletal mechanism shows the best agreement with measurements. The WSGG model used predicts results with the same level accuracy as the grey gas model in modeling of the syngas flame.

Aβ Oligomer Dissociation Is Catalyzed by Fibril Surfaces.

Oligomeric assemblies consisting of only a few protein subunits are key species in the cytotoxicity of neurodegenerative disorders, such as Alzheimer's and Parkinson's diseases. Their lifetime in solution and abundance, governed by the balance of their sources and sinks, are thus important determinants of disease. While significant advances have been made in elucidating the processes that govern oligomer production, the mechanisms behind their dissociation are still poorly understood. Here, we use chemical kinetic modeling to determine the fate of oligomers formed in vitro and discuss the implications for their abundance in vivo. We discover that oligomeric species formed predominantly on fibril surfaces, a broad class which includes the bulk of oligomers formed by the key Alzheimer's disease-associated Aβ peptides, also dissociate overwhelmingly on fibril surfaces, not in solution as had previously been assumed. We monitor this "secondary nucleation in reverse" by measuring the dissociation of Aβ42 oligomers in the presence and absence of fibrils via two distinct experimental methods. Our findings imply that drugs that bind fibril surfaces to inhibit oligomer formation may also inhibit their dissociation, with important implications for rational design of therapeutic strategies for Alzheimer's and other amyloid diseases.

Public Health Impacts of Ambient Particulate Matter Pollution in Libya from 1990 to 2019: An Analysis of the 2019 Global Burden of Disease (GBD) Study

Air pollution is recognized as a critical global health risk, yet there has been no comprehensive assessment of its impact on public health in Libya until now. This study evaluates the burden of disease associated with ambient particulate matter (PM2.5) in Libya, drawing on data from the Global Burden of Disease Study 2019. By integrating satellite-based estimates, chemical transport models, and ground-level measurements, PM2.5 exposure and its effects on mortality and disability-adjusted life years (DALYs) across the different sexes and all age groups from 1990 to 2019 are estimated. Our findings reveal that the annual population-weighted mean PM2.5 concentration in Libya was 38.6 μg/m3 in 2019, marking a 3% increase since 1990. In the same year, PM2.5 was responsible for approximately 3368 deaths, accounting for 11% of all annual deaths in the country. Moreover, a total of 107,207 DALYs were attributable to PM2.5, with ischemic heart disease being the leading cause, representing 46% of these DALYs. The analysis also highlights a significant burden of years of life lost (YLLs) at 89,113 and years lived with disability (YLDs) at 18,094, due to PM2.5. Given the substantial health risks associated with air pollution, particularly from ambient particulate matter, Libyan authorities must implement effective policies aimed at reducing air pollution to enhance healthcare outcomes and preventive services.

Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Abstract. Accurate estimation of global winds is crucial for various scientific and practical applications, such as global chemical transport modeling and numerical weather prediction. One valuable source of wind measurements is atmospheric motion vectors (AMVs), which play a vital role in the global observing system and numerical weather prediction models. However, errors in AMV retrievals need to be addressed before their assimilation into data assimilation systems, as they can affect the accuracy of outputs. An assessment of the bias and uncertainty in passive-sensor AMVs can be done by comparing them with information from independent sources such as active-sensor winds. In this paper, we examine the benefit and performance of a colocation scheme using independent and sparse lidar wind observations as a dependent variable in a supervised machine learning model. We demonstrate the feasibility and performance of this approach in an observing system simulation experiment (OSSE) framework, with reference geophysical state data obtained from high-resolution Weather Research and Forecasting (WRF) model simulations of three different weather events. Lidar wind data are typically available in only one direction, and our study demonstrates that this single component of wind in high-precision active-sensor data can be leveraged (via a machine learning algorithm to model the conditional mean) to reduce the bias in the passive-sensor winds. Further, this active-sensor wind information can be leveraged through an algorithm that models the conditional quantiles to produce stable estimates of the prediction intervals, which are helpful in the design and application of error analysis, such as quality filters.

Numerical investigation of thermochemical non-equilibrium effects in Mach 10 scramjet nozzle

Abstract High-temperature non-equilibrium effects are prominent in scramjet nozzle flows at high Mach numbers. Hence, the thermochemical non-equilibrium gas model incorporating the vibrational relaxation process of molecules in the hydrocarbon-air reaction is developed to numerically simulate the flow of a hydrocarbon fuel scramjet nozzle at Mach 10. Besides, the results computed by the models of the thermally perfect gas, chemically non-equilibrium gas, and thermally non-equilibrium chemically frozen gas are applied for comparative studies. Results indicate that chemical non-equilibrium effects are more significant for the flow-field structure and parameters compared to thermal non-equilibrium effects. Meanwhile, vibrational relaxation and chemical reactions interact in the flow-field. The heat released from the chemical reactions in the flow-field of the thermochemical non-equilibrium gas model makes the thermal non-equilibrium effects weaker compared to the thermally non-equilibrium chemically frozen gas model; the chemical reactions in the thermochemical non-equilibrium gas model are more intense than in the chemically non-equilibrium gas model. Due to the slow relaxation of vibrational energy, the thermal non-equilibrium models predicted nozzle thrust lower than the thermal equilibrium models by approximately 1.11% to 1.33%; when considering the chemical reactions, the chemical non-equilibrium models predicted nozzle thrust higher than the chemical frozen models by approximately 7.30% to 7.54%. Hence, the structural design and performance study of the high Mach numbers scramjet nozzle must consider thermochemical non-equilibrium effects.

Microscopic Chemical Reaction Mechanism and Kinetic Model of Al/PTFE

Microscopic Chemical Reaction Mechanism and Kinetic Model of Al/PTFE

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications.Scientific contributionThe approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Chemical evolution of the Galactic bulge with different stellar populations

The metallicity distribution function (MDF) of the Galactic bulge is characterized by a multi-peak shape, with a metal-poor peak centered at $ Fe/H sim-0.3 $ dex and a metal-rich peak centered at $ Fe/H sim+0.3 $ dex. The bimodality of the MDF is also reflected in the alpha /Fe versus Fe/H abundance ratios, suggesting the presence of different stellar populations in the bulge. In this work we aim to reproduce the observed MDF of the Galactic bulge by testing a scenario in which the metal-poor component of the bulge is formed by stars formed in situ, during a strong burst of star formation, while the metal-rich population is formed by stars created in situ during a second burst of star formation and/or stars accreted from the innermost part of the Galactic disk as an effect of a growing bar. We adopted a chemical evolution model that is able to follow the evolution of several chemical species with detailed nucleosynthesis prescriptions. In particular, because of the importance of the production of Fe in constraining the MDF, close attention is paid to the production of this element in both Type Ia supernovae and massive stars. In particular, we included yields from rotating massive stars with different rotational velocity prescriptions. Our model also takes the infall and outflow of gas into account, as well as the effect of stellar migration. Results are compared to sim 13000 stars from the SDSS/APOGEE survey that belong to the region located at a Galactocentric distance $ R_ GC kpc We successfully reproduce the observed double-peak shape of the bulge MDF as well as the abundance trends of the alpha elements relative to Fe by assuming both (i) a multi-burst star formation history with a quenching of the first burst of $ 10^2\ Myr $ and (ii) migration of stars from the innermost part of the Milky Way disk, as an effect of a growing bar. According to our results, the fraction of the stellar mass of the bulge-bar that belongs to the inner disk is $ sim40<!PCT!> $. In terms of the nucleosynthesis, we conclude that models that assume either no rotation for massive stars or a distribution of rotational velocities that favors slow rotation at high metallicities best reproduce the observed MDF as well as the alpha /Fe and the Ce/Fe versus Fe/H abundance patterns.

Atomistic Details of Methyl Linoleate Pyrolysis: Direct Molecular Dynamics Simulation of Converting Biodiesel to Petroleum Products

Dependence on petroleum and petrochemical products is unsustainable; it is both a finite resource and an environmental hazard. Biodiesel has many attractive qualities, including a sustainable feedstock; however, it has its complications. The pyrolysis (a process already in common use in the petroleum industry) of biodiesel has demonstrated the formation of smaller hydrocarbons comprising many petrochemical products but experiments suffer from difficulty quantifying the myriad reaction pathways followed and products formed. A computational simulation of pyrolysis using “ab initio molecular dynamics” offers atomic-level detail of the reaction pathways and products formed. Herein, the most prevalent fatty-acid ester (methyl linoleate) from the most prevalent feedstock for biodiesel in the United States (soybean oil) is studied. Temperature acceleration within the atom-centered density matrix propagation formalism (Car–Parrinello) utilizing the D3-M06-2X/6-31+G(d,p) model chemistry is used to compose an ensemble of trajectories. The results are grounded in comparison to experimental studies through agreement in the following: (1) the extent of reactivity (40% in the experimental and 36.1% in this work), (2) the homology of hydrocarbon products formed (wt % of C6–C10 products), and (3) the CO/CO2 product ratio. Deoxygenation pathways are critically analyzed (as the presence of oxygen in biodiesel represents a disadvantage in its current use). Within this ensemble, deoxygenation was found to proceed through two subclasses: (1) spontaneous deoxygenation, following one of four possible pathways; or (2) induced deoxygenation, following one of three possible pathways.

Air quality and public health effects of dairy digesters in California

The dairy industry in California emits large amounts of methane (CH4) that contributes significantly to the state's overall Greenhouse Gas (GHG) budget. Reducing CH4 emissions has become a key priority for dairy farms in support of California's GHG reduction goals. Anaerobic digesters designed to capture CH4 from animal manure present a practical option for reducing CH4 emissions, but a comprehensive evaluation of the local air quality impacts of this technology has not been previously undertaken. The simplest digester configurations decrease local emissions of volatile organic compounds (VOCs) but increase emissions of oxides of nitrogen (NOx), potentially changing local air quality. Here, we evaluate the air quality implications of widespread digester adoption in the year 2050 across the San Joaquin Valley (SJV) in central California, which is home to the highest concentration of dairy farms in the state. Changes to concentrations of air pollutants including ozone (O3), airborne particulate matter with diameter smaller than 2.5 μm (PM2.5), and various PM2.5 chemical components are predicted using the UCD/CIT chemical transport model at 4 km resolution. Dairy digester adoption is evaluated within two regional energy scenarios, including a business as usual (BAU) scenario and an 80% greenhouse gas reduction (GHGAi) scenario, to consider potential changes to the chemical regime that governs formation of secondary air pollution. Concentrations are evaluated across 32 randomly selected weeks over a 10-year period from the year 2046–2055 to establish a long-term average impact in the presence of El Niño Southern Oscillation (ENSO) variability. Regional weather patterns are downscaled from Global Climate Model simulations under the RCP8.5 global scenario. The results indicate that baseline dairy emissions make minor contributions to air pollutant concentrations in 2050. Under a worst-case scenario for digester adoption, PM2.5 concentrations would increase by 0.06 μg/m3 (current standard = 9 μg/m3), and maximum daily 8-h average (MDA8) O3 would change by −1.0 ppb to +0.2 ppb depending on the surrounding regional energy scenario (current standard = 70 ppb). A health impact analysis shows that the widespread use of dairy digesters would result in fewer than 0.1 additional deaths per 100,000 people due to changing air pollution. For comparison, this level of mortality change is more than 100 times smaller than the risk posed by seasonal flu. Further, Environmental Justice analysis indicates that the implementation of digesters will not influence the exposure disparities among different racial groups in either the SJV or the surrounding San Francisco Bay & Sacramento area. These findings suggest that dairy digesters can be widely adopted in central California to reduce GHG emissions with minimal effect on regional air quality and public health.

Quantum Chemical Modeling of Reactions of the Catalytic Transformation of Alkanes

Quantum Chemical Modeling of Reactions of the Catalytic Transformation of Alkanes