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Model Inputs, Outputs, and Scripts associated with: “Spatial microbial respiration variations in the hyporheic zones within the Columbia River Basin”
This data package is associated with the publication “Spatial microbial respiration variations in the hyporheic zones within the Columbia River Basin” published in the Journal of Geophysical Research: Biogeosciences (Son et al. 2022) available at doi: 10.1029/2021JG006654. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes, which were used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) aerobic and anaerobic respiration at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions to compute respiration of the HZ for each National Hydrography Dataset (NHD) reach within the CRB. The reactions in HZs of each NHD reach include anaerobic respiration and two-step anaerobic respiration via denitrification. Our HZ respiration estimates are limited to the lotic (or flowing) stream/river systems, and do not account for the respiration process in water column. Note that the RCM only simulates the HZ’s contribution to the dissolved carbon dioxide (CO2) concentrations in the streams, and the CO2 emissions to the atmosphere are not modelled. The model computes at hourly timesteps because of the fast reaction rates. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values.This modeling framework successfully quantified HZ respiration components over multiple scales. It revealed key mechanisms driving the spatial variation of HZ aerobic and anaerobic respiration in reaches with varying hydrologic and substrate conditions. Thus, this modeling study offers a testing hypothesis in different river system (e.g., climate and biomes) for the HZ respiration processes, and can be used as a sampling design tool for large-scale HZ experimental studies.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.
Spatial Microbial Respiration Variations in the Hyporheic Zones Within the Columbia River Basin
Abstract While the hyporheic zone (HZ) accounts for a significant portion of whole stream CO 2 concentrations, HZ respiration modeling studies are lacking in quantifying their contributions to the total CO 2 at large watershed/basin scales. Quantifying the contribution of anaerobic respiration is also underappreciated. This study used a carbon‐nitrogen‐coupled river corridor model to quantify HZ aerobic and anaerobic respiration and determined the key factors controlling their spatial variability within the Columbia River Basin (CRB). The modeled respiration patterns showed high spatial variability. Among the nine sub‐basins composing the CRB, the Lower Columbia and the Willamette, which receive higher precipitation, had higher respiration. Medium‐sized rivers (fourth to sixth orders) produced the highest aerobic and anaerobic respiration among reaches of different sizes. At the basin scale, aerobic respiration is dominant, representing approximately 98.7% of the total respiration across the CRB. While most of the reaches were dominant with aerobic respiration, reaches in agricultural land showed a relatively higher anaerobic respiration (18%) ratio. A variable importance analysis showed that hyporheic exchange flux controlled most of the spatial variability of HZ respiration, dominating over other physical variables such as residence time, stream dissolved organic carbon (DOC), nitrate, and dissolved oxygen (DO). The influence of substrate concentration (DOC and DO) is larger in modeling anaerobic respiration than aerobic respiration. Future efforts will focus on improving the estimation of the HZ exchange flux and the implementation of spatially explicit parameterizations for the reactions of interest to reduce model uncertainty.
Maximum respiration rates in hyporheic zone sediments are primarily constrained by organic carbon concentration and secondarily by organic matter chemistry
Abstract. River corridors are fundamental components of the Earth system, and their biogeochemistry can be heavily influenced by processes in subsurface zones immediately below the riverbed, referred to as the hyporheic zone. Within the hyporheic zone, organic matter (OM) fuels microbial respiration, and OM chemistry heavily influences aerobic and anaerobic biogeochemical processes. The link between OM chemistry and respiration has been hypothesized to be mediated by OM molecular diversity, whereby respiration is predicted to decrease with increasing diversity. Here we test the specific prediction that aerobic respiration rates will decrease with increases in the number of unique organic molecules (i.e., OM molecular richness, as a measure of diversity). We use publicly available data across the United States from crowdsourced samples taken by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our continental-scale analyses rejected the hypothesis of a direct limitation of respiration by OM molecular richness. In turn, we found that organic carbon (OC) concentration imposes a primary constraint over hyporheic zone respiration, with additional potential influences of OM richness. We specifically observed respiration rates to decrease nonlinearly with the ratio of OM richness to OC concentration. This relationship took the form of a constraint space with respiration rates in most systems falling below the constraint boundary. A similar, but slightly weaker, constraint boundary was observed when relating respiration rate to the inverse of OC concentration. These results indicate that maximum respiration rates may be governed primarily by OC concentration, with secondary influences from OM richness. Our results also show that other variables often suppress respiration rates below the maximum associated with the richness-to-concentration ratio. An important focus of future research will identify physical (e.g., sediment grain size), chemical (e.g., nutrient concentrations), and/or biological (e.g., microbial biomass) factors that suppress hyporheic zone respiration below the constraint boundaries observed here.
Newly developed sugarbeet lines with altered postharvest respiration rates differ in transcription factor and glycolytic enzyme expression
Respiration is the principal cause for postharvest sucrose loss in sugarbeet (Beta vulgaris L.) roots. Although reductions in respiration rate could mitigate these losses, developing sugarbeet cultivars and storage procedures that reduce respiration are hindered by a lack of knowledge of the genetic and metabolic factors that control storage respiration rate. Research was conducted to identify genes and gene products that affect storage respiration rate by creating two sugarbeet lines that differ in respiration rate and characterizing gene expression differences between these lines. Sugarbeet lines F1056 and F1057, which differ by up to 42% in respiration rate, were created by divergent selection of a sugarbeet population using root respiration rate after 30 d in storage as the principal selection criterion. RNA sequencing identified 287 differentially expressed genes (DEGs) between these lines on the day of harvest and after 28-d storage. Of these DEGs, nine encoded transcription factors and five encoded enzymes involved in the respiratory pathway. Other DEGs contributed to a variety of biological and molecular functions based on gene ontology classifications. Of respiratory pathway DEGS, genes for NAD+- and NADP+-dependent forms of glyceraldehyde-3-phosphate dehydrogenase were of note due to their high upregulation in the high respiring line and for their established role in glycolysis, a pathway identified as a likely bottleneck in respiratory substrate production. Overall, lines F1056 and F1057 provide new tools for investigating genetic and physiological differences in storage respiration rate, and their DEGs identify candidates for genes affecting sugarbeet root respiration rate.
High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements
A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. Herein, we also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.
Dry heat sterilization as a method to recycle N95 respirator masks: The importance of fit
In times of crisis, including the current COVID-19 pandemic, the supply chain of filtering facepiece respirators, such as N95 respirators, are disrupted. To combat shortages of N95 respirators, many institutions were forced to decontaminate and reuse respirators. While several reports have evaluated the impact on filtration as a measurement of preservation of respirator function after decontamination, the equally important fact of maintaining proper fit to the users’ face has been understudied. In the current study, we demonstrate the complete inactivation of SARS-CoV-2 and preservation of fit test performance of N95 respirators following treatment with dry heat. We apply scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD) measurements, Raman spectroscopy, and contact angle measurements to analyze filter material changes as a consequence of different decontamination treatments. We further compared the integrity of the respirator after autoclaving versus dry heat treatment via quantitative fit testing and found that autoclaving, but not dry heat, causes the fit of the respirator onto the users face to fail, thereby rendering the decontaminated respirator unusable. Our findings highlight the importance to account for both efficacy of disinfection and mask fit when reprocessing respirators to for clinical redeployment.
Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"
This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.
Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA
Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p<0.05), seasonal variability was dominated by soil moisture and plant phenology (p<0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Lastly, we observed significant late-season CO 2 fluxes from the deep sub-soil (>165 cm) that support growing evidence for the importance of sub-soil processes in driving integrated respiration fluxes.
Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy
This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic charge of the filter was correlated with a reduction in capturing particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test and reduction in aerosol filtering efficiency. Key Words: N95 respirators, respirators, airborne transmission, pandemic prevention, COVID-19, gamma sterilization
Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration
The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.
Spatial Study 2022: Water Column, Sediment, and Total Ecosystem Respiration Rates across the Yakima River Basin, Washington, USA (v2)
This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin and is associated with the manuscript “Sediment-associated processes account for most of the spatial variation in ecosystem respiration in the Yakima River basin” submitted to Nature Communications Earth & Environment (Garayburu-Caruso et al., in review). The dataset provides ecosystem metabolism estimates generated from streamMetabolizer (Appling et al.; 2018) using data collected during the same five-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Additionally, it includes the scripts used for the analysis and producing the figures in the manuscript. The contents include streamMetabolizer inputs and outputs and additional relevant data needed to generate the main manuscript results. The data included are: total ecosystem respiration, water respiration, calculated sediment-associated respiration, gross primary production outputs from the river corridor model for the Yakima River Basin, median grain size (d50), depth, dissolved oxygen, water temperature, pressure, and annual oxygen consumption. The associated GitHub repository can be found at https://github.com/river-corridors-sfa/SSS_metabolism. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” Raw time series sensor data, total suspended solids, and depth data from SSS were published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566. A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Field metadata for the samples in this da This dataset is comprised of one main data folder with four subfolders. The main data folder contains of (1) file-level metadata; (2) data dictionary; (3) total/water column/sediment respiration; (4) gross primary production (GPP); (5) median grain size (d50); and (6) annual oxygen consumption. The “Figures” subfolder contains the figures used in the paper and all intermediate files (including geospatial files). The “Published_Data” contains a readme directing the user to download the public data to reproduce analyses and figures. The “Scripts” folder contains all scripts used in the analyses that were not part of running StreamMetabolizer. Lastly, the “Stream_Metabolizer” folder contains all files associated with running StreamMetabolizer including (1) model input files, (2) model output files, (3) processing scripts, (4) histogram plots of the outputs, and (5) an R project. All files are .csv, .pdf, .R, .Rmd, .Rproj, .html, .png, .txt, .qgz, .cpg, .dbf, .prj, .shp, .shp.ea.iso.xml, .shp.iso.xml, .shx, .sbn. ta package can be found at either link. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
Carbon availability, soil pH, and microbial allocation to nitrogen acquisition shape grassland heterotrophic respiration in response to a decade of nitrogen addition
Previous work has found that anthropogenic inputs of nitrogen (N) and phosphorus (P) impact heterotrophic respiration during soil organic matter decomposition in grasslands, a critical pathway through which carbon (C) is lost from soil to the atmosphere. While N addition typically reduces heterotrophic respiration, why the strength and direction of this N effect varies among sites is unclear. Here, to address this, we conducted a 339-day laboratory incubation to measure heterotrophic respiration from nine grasslands across North America that have received 10 years of factorial N and P fertilization. N addition reduced cumulative respiration most at sites with low pH, low microbial allocation towards N acquisition, and high soil C concentration and availability. However, N addition had neutral rather than positive effects on heterotrophic respiration in sites with high pH and decomposer allocation towards N acquisition. Across sites, a decade of N addition reduced heterotrophic respiration by ∼24 %, driven by reductions in microbial biomass. Heterotrophic respiration was less sensitive to P addition, despite its increasing microbial biomass. However, simultaneous N and P addition did ameliorate negative N effects. These results show that previously observed variation in the response of heterotrophic respiration to N addition can be explained by soil C availability and pH status, widely measured factors which can be used to predict how grassland C fluxes may change under continuing nutrient deposition.
Respiration is essential for aerobic growth of Zymomonas mobilis ZM4
ABSTRACT Zymomonas mobilis is an alpha-proteobacterium that is a promising platform for industrial scale production of biofuels due to its efficient ethanol fermentation and low biomass generation. Z. mobilis is aerotolerant and encodes a complete respiratory electron transport chain, but the benefit of respiration for growth in oxic conditions has never been confirmed, despite decades of research. Growth and ethanol production of wild-type Z. mobilis is poor in oxic conditions indicating that it does not benefit from oxidative phosphorylation. Additionally, in previous studies, aerobic growth improved significantly when respiratory genes were disrupted ( ndh ) or acquired point mutations ( cydA and cydB ), even if respiration was significantly reduced by these changes. Here, we obtained clean deletions of respiratory genes ndh and cydAB , individually and in combination, and showed, for the first time, that deletion of cydAB completely inhibited O 2 respiration and dramatically reduced growth in oxic conditions. Both respiration and aerobic growth were restored by expressing a heterologous, water-forming NADH oxidase, noxE . Oxygen can have many negative effects, including formation of reactive oxygen species (ROS) or directly inactivating oxygen sensitive enzymes. Our results suggest that the effect of molecular oxygen on enzymes had a greater negative impact on Z. mobilis than formation of ROS. This result shows that the main role of the electron transport chain in Z. mobilis is reducing the intracellular concentration of molecular oxygen, helping to explain why it is beneficial for Z. mobilis to use electron transport chain complexes that have little capacity to contribute to oxidative phosphorylation. IMPORTANCE A key to producing next-generation biofuels is to engineer microbes that efficiently convert non-food materials into drop-in fuels, and to engineer microbes effectively, we must understand their metabolism thoroughly. Zymomonas mobilis is a bacterium that is a promising candidate biofuel producer, but its metabolism remains poorly understood, especially its metabolism when exposed to oxygen. Although Z. mobilis respires with oxygen, its aerobic growth is poor, and disruption of genes related to respiration counterintuitively improves aerobic growth. This unusual result has sparked decades of research and debate regarding the function of respiration in Z. mobilis . Here, we used a new set of mutants to determine that respiration is essential for aerobic growth and likely protects the cells from damage caused by oxygen. We conclude that the respiratory pathway of Z. mobilis should not be deleted from chassis strains for industrial production because this would yield a strain that is intolerant of oxygen, which is more difficult to manage in industrial settings.
COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods
Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.
Evaluation of top-down and bottom-up global terrestrial respiration estimates and their mismatch with model simulations
Terrestrial respiration is one of the most poorly understood processes in the global carbon cycle, making respiration predictions uncertain. However, expanding observations and machine learning approaches have led to a proliferation of estimates. We compiled total ecosystem and heterotrophic respiration estimates derived from top-down atmospheric inversions and bottom-up upscaling of ecosystem observations and compared them with dynamic vegetation models (DGVM) simulations over the 1980-2020 period. Our analysis revealed a convergence in mean annual global total ecosystem respiration estimates between top-down 97.1 (± SD 6.8) PgC yr-1 and bottom-up 98.5 (+/-13.4) PgC yr-1, which were both significantly lower than the ensemble mean from DGVMs estimates 133.7 (±4.7) PgC yr-1. We also found similar temporal trends between top-down estimates with a mean of 0.075 (±0.05) PgC yr-2, and bottom-up estimates of 0.05 (±0.05) PgC yr-2, which were 5 to 7 times smaller than the ensemble mean trend of 0.34 PgC yr-2 simulated by DGVMs. Global heterotrophic respiration showed much less agreement, ranging from top-down estimates of 42.7 (±4.0) PgC yr-1 to bottom-up estimates of 51.5 (±4.0) PgC yr-1 and a significantly larger ensemble model mean estimate of 60.8 PgC yr-1 (±1.9). The temporal trends in observation-based bottom-up estimates of heterotrophic respiration of 0.03 PgC yr-2 were five times lower than the model ensemble mean 0.15 PgC yr-2. Large regional disagreements in heterotrophic respiration estimates and simulations were evident in tropical and boreal latitudes. Therefore, improved regional and heterotrophic respiration estimates are necessary to reduce uncertainties regarding the future vulnerability of soil carbon.
Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy
This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic capability of the filter reduced the efficiency of challenging particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test.
Soil Respiration Response to Simulated Precipitation Change Depends on Ecosystem Type and Study Duration
Soil respiration—the flow of biologically-generated CO 2 from the soil surface to the atmosphere—is a major component of global carbon cycling, but the long-term response of this flux to altered precipitation regimes remains uncertain, due to different responses of soil respiration in distinct ecosystems with varying degrees of water limitation. We conducted a meta-analysis to determine the role of soil characteristics (organic carbon stock and clay content), study duration, and ecosystem type (e.g., forest, grassland, etc) in modifying the response of soil respiration to precipitation change. In general, decreased precipitation (N = 128 effect size pairs) decreased soil respiration rates. In contrast, increased precipitation (N = 141) had a more variable and on-average weak positive effect on this flux; significantly increasing soil respiration only in desert ecosystems. The long-term response of ecosystems varied, with those that are less constrained by water-availability (e.g., forests) showing acclimation over time to altered precipitation. Soil organic carbon stock strongly modified the response of soil respiration to decreased precipitation (p < 0.0001), but only weakly modified the response to increased precipitation (p < 0.01). Our results suggest that ecosystems with limited soil water-holding capacity and strong inherent water-limitation will show long-term dynamic change in soil respiration regardless of the direction of precipitation change.