Volatile Organic Compound Metabolism on Early Earth.

Biogenic volatile organic compounds (VOCs) constitute a significant portion of gas-phase metabolites in modern ecosystems and have unique roles in moderating atmospheric oxidative capacity, solar radiation balance, and aerosol formation. It has been theorized that VOCs may account for observed geological and evolutionary phenomena during the Archaean, but the direct contribution of biology to early non-methane VOC cycling remains unexplored. Here, we provide an assessment of all potential VOCs metabolized by the last universal common ancestor (LUCA). We identify enzyme functions linked to LUCA orthologous protein groups across eight literature sources and estimate the volatility of all associated substrates to identify ancient volatile metabolites. We hone in on volatile metabolites with confirmed modern emissions that exist in conserved metabolic pathways and produce a curated list of the most likely LUCA VOCs. We introduce volatile organic metabolites associated with early life and discuss their potential influence on early carbon cycling and atmospheric chemistry.

Termite gas emissions select for hydrogenotrophic microbial communities in termite mounds.

Organoheterotrophs are the dominant bacteria in most soils worldwide. While many of these bacteria can subsist on atmospheric hydrogen (H2), levels of this gas are generally insufficient to sustain hydrogenotrophic growth. In contrast, bacteria residing within soil-derived termite mounds are exposed to high fluxes of H2 due to fermentative production within termite guts. Here, we show through community, metagenomic, and biogeochemical profiling that termite emissions select for a community dominated by diverse hydrogenotrophic Actinobacteriota and Dormibacterota. Based on metagenomic short reads and derived genomes, uptake hydrogenase and chemosynthetic RuBisCO genes were significantly enriched in mounds compared to surrounding soils. In situ and ex situ measurements confirmed that high- and low-affinity H2-oxidizing bacteria were highly active in the mounds, such that they efficiently consumed all termite-derived H2 emissions and served as net sinks of atmospheric H2 Concordant findings were observed across the mounds of three different Australian termite species, with termite activity strongly predicting H2 oxidation rates (R2 = 0.82). Cell-specific power calculations confirmed the potential for hydrogenotrophic growth in the mounds with most termite activity. In contrast, while methane is produced at similar rates to H2 by termites, mounds contained few methanotrophs and were net sources of methane. Altogether, these findings provide further evidence of a highly responsive terrestrial sink for H2 but not methane and suggest H2 availability shapes composition and activity of microbial communities. They also reveal a unique arthropod-bacteria interaction dependent on H2 transfer between host-associated and free-living microbial communities.

Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils.

Numerous diverse microorganisms reside in the cold desert soils of continental Antarctica, though we lack a holistic understanding of the metabolic processes that sustain them. Here, we profile the composition, capabilities, and activities of the microbial communities in 16 physicochemically diverse mountainous and glacial soils. We assembled 451 metagenome-assembled genomes from 18 microbial phyla and inferred through Bayesian divergence analysis that the dominant lineages present are likely native to Antarctica. In support of earlier findings, metagenomic analysis revealed that the most abundant and prevalent microorganisms are metabolically versatile aerobes that use atmospheric hydrogen to support aerobic respiration and sometimes carbon fixation. Surprisingly, however, hydrogen oxidation in this region was catalyzed primarily by a phylogenetically and structurally distinct enzyme, the group 1l [NiFe]-hydrogenase, encoded by nine bacterial phyla. Through gas chromatography, we provide evidence that both Antarctic soil communities and an axenic Bacteroidota isolate (Hymenobacter roseosalivarius) oxidize atmospheric hydrogen using this enzyme. Based on ex situ rates at environmentally representative temperatures, hydrogen oxidation is theoretically sufficient for soil communities to meet energy requirements and, through metabolic water production, sustain hydration. Diverse carbon monoxide oxidizers and abundant methanotrophs were also active in the soils. We also recovered genomes of microorganisms capable of oxidizing edaphic inorganic nitrogen, sulfur, and iron compounds and harvesting solar energy via microbial rhodopsins and conventional photosystems. Obligately symbiotic bacteria, including Patescibacteria, Chlamydiae, and predatory Bdellovibrionota, were also present. We conclude that microbial diversity in Antarctic soils reflects the coexistence of metabolically flexible mixotrophs with metabolically constrained specialists.

Reductions in NO2 burden over north equatorial Africa from decline in biomass burning in spite of growing fossil fuel use, 2005 to 2017.

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NOx: nitrogen dioxide (NO2) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning-another important NOx source-has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO2 vertical column densities (VCDs) and burned area to identify NO2 trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs-home to hundreds of millions of people-mean annual tropospheric NO2 VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO2 VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa's biomass burning regions, raising mean GDP density (USD⋅km-2) above its lowest levels is associated with lower NO2 VCDs during the dry season, suggesting that economic development mitigates net NO2 emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa's northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization.

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO2) concentration daily.

Deep Learning for Gas Sensing via Infrared Spectroscopy.

Deep learning methods, a powerful form of artificial intelligence, have been applied in a number of spectroscopy and gas sensing applications. However, the speciation of multi-component gas mixtures from infrared (IR) absorption spectra using deep learning remains to be explored. Here, we propose a one-dimensional deep convolutional neural network gas classification model for the identification of small molecules of interest based on IR absorption spectra in flexible user-defined frequency ranges. The molecules considered include ten that are of interest in the atmosphere or in industrial and environmental processes: water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, nitric oxide, sulfur dioxide, nitrogen dioxide, and ammonia. A simulated dataset of IR absorption spectra for mixtures of these molecules diluted in air was generated and used to train a deep learning model. The model was tested against simulated spectra containing noise and was found to provide speciation predictions with accuracy from 82 to 97%. The internal operation of the model was investigated using class activation maps that illustrate how the model prioritizes spectral information for classification. Finally, the model was demonstrated for the prediction of speciation for two synthetic experimental mixture spectra. The proposed model and the dataset generation strategies are generalized and can be implemented for other gases, different frequency ranges, and spectroscopy types. The multi-component speciation method developed herein is the first application of a convolutional neural network model, trained on HITRAN-based simulations, for spectral identification.

Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions.

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.

Polar amplification of Pliocene climate by elevated trace gas radiative forcing.

Warm periods in Earth's history offer opportunities to understand the dynamics of the Earth system under conditions that are similar to those expected in the near future. The Middle Pliocene warm period (MPWP), from 3.3 to 3.0 My B.P, is the most recent time when atmospheric CO2 levels were as high as today. However, climate model simulations of the Pliocene underestimate high-latitude warming that has been reconstructed from fossil pollen samples and other geological archives. One possible reason for this is that enhanced non-CO2 trace gas radiative forcing during the Pliocene, including from methane (CH4), has not been included in modeling. We use a suite of terrestrial biogeochemistry models forced with MPWP climate model simulations from four different climate models to produce a comprehensive reconstruction of the MPWP CH4 cycle, including uncertainty. We simulate an atmospheric CH4 mixing ratio of 1,000 to 1,200 ppbv, which in combination with estimates of radiative forcing from N2O and O3, contributes a non-CO2 radiative forcing of 0.9 [Formula: see text] (range 0.6 to 1.1), which is 43% (range 36 to 56%) of the CO2 radiative forcing used in MPWP climate simulations. This additional forcing would cause a global surface temperature increase of 0.6 to 1.0 °C, with amplified changes at high latitudes, improving agreement with geological evidence of Middle Pliocene climate. We conclude that natural trace gas feedbacks are critical for interpreting climate warmth during the Pliocene and potentially many other warm phases of the Cenezoic. These results also imply that using Pliocene CO2 and temperature reconstructions alone may lead to overestimates of the fast or Charney climate sensitivity.

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose.

Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy.

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.