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International audience; Wetlands are the largest natural source of methane (CH4) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4, but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model—iPEACE—reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (

Community pharmacies are required to play a role in maintaining the health of local residents. Since September 2015, a national policy in Japan has allowed saliva tests to be used for oral health check-ups at community pharmacies. In the present study, we aimed to reveal dentists' perceptions and expectations for oral health check-ups provided at community pharmacies. A questionnaire survey was administered to dentists at 1,000 randomly selected dental clinics in Tokyo, Japan; 257 responses (25.7%) were included in the analysis and 85.2% of respondents approved of oral health check-ups at community pharmacies. Most respondents who approved of oral health check-ups expected that community pharmacists would recommend that local residents visit a dental clinic (85.8%) and provide a report to dentists (60.3%) after the check-up. Furthermore, 79.0% of respondents desired collaboration with community pharmacies. These findings suggested the potential for oral health check-ups to facilitate collaboration between community pharmacies and dental clinics aimed at maintaining and improving oral health of local residents.

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large-lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy-covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17±11days, and lagged air and soil temperature by median values of 8±16 and 5±15days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

High-latitude warming has stimulated CO2 and CH4 emissions from permafrost peatland. This study evaluated growing season CH4 and CO2 emissions from a forest floor of a lowland black spruce forest on permafrost in interior Alaska using automated-closed chambers, anaerobic incubation of peat soils and next-generation sequencing. The CH4 emissions from May to October were 723 ± 432 mg C m−2 and 829 ± 628 mg C m−2 from the two Carex wet plots, 124 ± 76 mg C m−2 from the Sphagnum moss plot, and 11 mg C m−2 from the lichen plot (± denotes standard deviation of different years). The CH4 emissions in the peak season showed diurnal variations with a single peak around noon or double peaks depending on the plots. The CH4 emissions were an order of magnitude lower than those for other northern wetlands, possibly because deep, cold peats regulated CH4 production and oxidation at the dry aerobic surface layer. Low water table positions strongly inhibited CH4 emissions in 2018, suggesting the importance of dissolved CH4 pool at the bottom of the active layer. The future trajectory of CH4 emissions could substantially change with water conditions, deep soil temperatures, and sedge compositions.

The spatiotemporal variations in spring onset were evaluated based on a satellite-derived enhanced vegetation index (EVI), land surface temperature, and snow cover from 2000 to 2021 in interior Alaska. Spring onset was determined as a budburst or seasonal green-up of vegetation based on EVI. The analysis showed that snow disappearance date was the key driver of spatiotemporal variations in the spring onset date. These dates for snow disappearance and spring onset varied with elevation, showing importance of elevation to explain the spatial heterogeneity in spring onset. The magnitudes in the heterogeneity in spring onset varied year to year, where the heterogeneity increased in warm spring owing to an increased heterogeneity in snow disappearance. This phenomenon was associated with slower snowmelt under lower shortwave radiation earlier in the season. These results suggest that future warming could increase the heterogeneity in spring onset in this region. Spring onset was further modeled with the growing degree-day model using the Bayesian optimization, which showed that model performance did not deteriorate with coarse-resolution inputs. The results indicate that coarse-resolution models, such as Earth system models, are a reliable tool for predicting spring onset in this region.

Maintaining good oral health is important because oral diseases are related to systemic diseases, and community pharmacies play a key role in maintaining the health of local residents. This study aimed to examine the effects of oral health check-ups and information provision at community pharmacies on oral health-associated behaviors as well as patient satisfaction. We conducted oral health check-ups and provided information about oral health self-care to 84 patients at a community pharmacy, and then asked them to complete a questionnaire survey. One month later, we sent them a follow-up questionnaire and received responses from 66.7% (56/84) of the participants. The large majority were satisfied with the salivary test (95.2%) and the information (96.4%) we provided. Most of the participants (89.3%) indicated that they wanted to use the oral health check-up service again in the future. Compared with baseline, the ratio of participants restricting their intake of sugar-rich foods and drinks significantly increased 1 month later (p = 0.021). About 60% of those who had not undergone a regular dental examination at baseline reported newly visiting or planning to visit a dental clinic. The results revealed high satisfaction with the oral health check-up and information about oral self-care they received at the community pharmacy. The results suggested that oral health check-ups had the potential to change both oral self-care habits and dental consultation behavior. Our findings indicate that community pharmacies can contribute to the maintenance and promotion of oral health by providing oral health check-ups to local residents.

To compare the impact of surface–atmosphere exchanges from rural and urban areas, fully vegetated areas (e.g. deciduous trees, evergreen trees and grass) commonly found adjacent to cities need to be modelled. Here we provide a general workflow to derive parameters for SUEWS (Surface Urban Energy and Water Balance Scheme), including those associated with vegetation phenology (via leaf area index, LAI), heat storage and surface conductance. As expected, attribution analysis of bias in SUEWS-modelled QE finds that surface conductance (gs) plays the dominant role; hence there is a need for more estimates of surface conductance parameters. The workflow is applied at 38 FLUXNET sites. The derived parameters vary between sites with the same plant functional type (PFT), demonstrating the challenge of using a single set of parameters for a PFT. SUEWS skill at simulating monthly and hourly latent heat flux (QE) is examined using the site-specific derived parameters, with the default NOAH surface conductance parameters (Chen et al., 1996). Overall evaluation for 2 years has similar metrics for both configurations: median hit rate between 0.6 and 0.7, median mean absolute error less than 25 W m−2, and median mean bias error ∼ 5 W m−2. Performance differences are more evident at monthly and hourly scales, with larger mean bias error (monthly: ∼ 40 W m−2; hourly ∼ 30 W m−2) results using the NOAH-surface conductance parameters, suggesting that they should be used with caution. Assessment of sites with contrasting QE performance demonstrates how critical capturing the LAI dynamics is to the SUEWS prediction skills of gs and QE. Generally gs is poorest in cooler periods (more pronounced at night, when underestimated by ∼ 3 mm s−1). Given the global LAI data availability and the workflow provided in this study, any site to be simulated should benefit.

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

We present the first data describing molecular evidence of planktonic coenzyme factor 430 (hereafter referred to as F430), a key signature of methanogenic archaea, obtained from a water column sample in a freshwater lake in Japan. We report concentrations of native F430 ranging from 6.8–35 × 102 femto mol g-wet−1 and 8.5–86 × 102 femto mol g-wet−1 in the water column and core-top sediment, respectively. Among these profiles, the epimer F430 of the deactivated chemical formula was very low overall in both planktonic and benthic environments. Small subunit (SSU) rRNA gene sequencing revealed that the planktonic microbial community was clearly different from that of the benthic sediment. Planktonic cyanobacteria (e.g., Microcystis) were predominant in the water column, whereas a wide variety of bacteria and archaea, including methanogens (e.g., Methanobacterium, Methanoregula, Methanothrix, Methanosarcina, and Methanocella), were observed in the core-top sediment. These results suggest that the planktonic cyanobacterial community may be rapidly degraded at the water–sediment interface driven by activated benthic microbial heterotrophic processes. Graphic abstract

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET). ISSN:0168-1923 ISSN:1873-2240

Background In adults, high-dose remifentanil during surgery has been reported to increase postoperative opioid consumption, but this has not been well documented in children. Multimodal analgesia is recommended in the perioperative period for adolescent idiopathic scoliosis (AIS), but no report has examined opioid consumption under epidural analgesia, which is one of the most common types of analgesia. Aims To investigate the association between intraoperative remifentanil dosage and postoperative opioid consumption in AIS in the setting of combined epidural analgesia for postoperative multimodal analgesia. Methods In this retrospective cohort study, patients aged 10-18 years who underwent surgery for scoliosis and epidural analgesia for postoperative pain between July 2012 and April 2019 were included. The primary endpoint was the association between intraoperative cumulative weight-adjusted remifentanil dosage and logarithmic transformation of cumulative weight-adjusted fentanyl consumption in the intensive care unit (ICU). Nonopioid analgesics were investigated as secondary endpoints. An epidural catheter was inserted by the surgeon intraoperatively, and a local anesthetic was administered at the end of the surgery. Multivariate linear regression analysis with adjustment for confounders was performed for all analyses. Results In total, 142 patients were included, and the median intraoperative remifentanil dosage for all patients was 0.27 (interquartile range, 0.24-0.34) µg/kg/min. No association was observed between cumulative weight-adjusted intraoperative dosage of remifentanil and fentanyl, even after adjusting for potential confounders (slope = -1.25; 95% confidence interval [CI], -4.35 to 1.85; P = 0.43). No association was observed between nonopioid analgesic use and intraoperative remifentanil dosage. Conclusion No association was noted between remifentanil dosage during surgery for AIS and postoperative opioid consumption with epidural analgesia. However, this study has limitations due to its retrospective design; thus, further prospective studies are warranted.