• Energy Research

L'oxyde nitreux (N2O) est un puissant gaz à effet de serre et le principal moteur de l'appauvrissement de l'ozone stratosphérique. Étant donné que les sols sont la plus grande source de N2O, il est essentiel de prévoir la réponse des sols aux changements climatiques ou à l'utilisation des terres pour comprendre et gérer le N2O. Ici, nous constatons que le flux de N2O peut être prédit par des modèles intégrant la concentration en nitrates du sol (NO3-), la teneur en eau et la température à l'aide d'une enquête de terrain mondiale sur les émissions de N2O et les facteurs déterminants potentiels dans un large éventail de sols organiques. Les émissions de N2O augmentent avec le NO3- et suivent une distribution en forme de cloche avec la teneur en eau. La combinaison des deux fonctions explique 72 % des émissions de N2O de tous les sols organiques. Au-dessus de 5 mg de NO3--N kg-1, le drainage des sols humides ou l'irrigation des sols bien drainés augmente les émissions de N2O de plusieurs ordres de grandeur. Comme la température du sol ainsi que le NO3- expliquent 69 % des émissions de N2O, les zones humides tropicales devraient être une priorité pour la gestion du N2O. El óxido nitroso (N2O) es un potente gas de efecto invernadero y el principal impulsor del agotamiento del ozono estratosférico. Dado que los suelos son la mayor fuente de N2O, predecir la respuesta del suelo a los cambios en el clima o el uso de la tierra es fundamental para comprender y gestionar el N2O. Aquí encontramos que el flujo de N2O se puede predecir mediante modelos que incorporan la concentración de nitrato en el suelo (NO3-), el contenido de agua y la temperatura utilizando un estudio de campo global de las emisiones de N2O y los posibles factores impulsores en una amplia gama de suelos orgánicos. Las emisiones de N2O aumentan con el NO3- y siguen una distribución en forma de campana con contenido de agua. La combinación de las dos funciones explica el 72% de las emisiones de N2O de todos los suelos orgánicos. Por encima de 5 mg de NO3--N kg-1, el drenaje de suelos húmedos o el riego de suelos bien drenados aumenta la emisión de N2O en órdenes de magnitud. Como la temperatura del suelo junto con el NO3- explica el 69% de las emisiones de N2O, los humedales tropicales deben ser una prioridad para la gestión del N2O. أكسيد النيتروز (N2O) هو غاز دفيئة قوي والمحرك الرئيسي لاستنفاد الأوزون في الستراتوسفير. نظرًا لأن التربة هي أكبر مصدر لأكسيد النيتروز، فإن التنبؤ باستجابة التربة للتغيرات في المناخ أو استخدام الأراضي أمر أساسي لفهم وإدارة أكسيد النيتروز. هنا نجد أنه يمكن التنبؤ بتدفق أكسيد النيتروز من خلال نماذج تتضمن تركيز نترات التربة (NO3 -)، ومحتوى الماء ودرجة الحرارة باستخدام مسح ميداني عالمي لانبعاثات أكسيد النيتروز وعوامل القيادة المحتملة عبر مجموعة واسعة من التربة العضوية. تزداد انبعاثات أكسيد النيتروز مع NO3 - وتتبع توزيعًا على شكل جرس مع محتوى الماء. يفسر الجمع بين الوظيفتين 72 ٪ من انبعاثات أكسيد النيتروز من جميع التربة العضوية. تزيد انبعاثات أكسيد النيتروز التي تزيد عن 5 ملغ من أكسيد النيتروز - N kg -1، إما عن طريق تصريف التربة الرطبة أو ري التربة جيدة التصريف، من انبعاثات أكسيد النيتروز حسب الحجم. نظرًا لأن درجة حرارة التربة جنبًا إلى جنب مع NO3 - تفسر 69 ٪ من انبعاثات أكسيد النيتروز، يجب أن تكون الأراضي الرطبة الاستوائية أولوية لإدارة أكسيد النيتروز. Nitrous oxide (N2O) is a powerful greenhouse gas and the main driver of stratospheric ozone depletion. Since soils are the largest source of N2O, predicting soil response to changes in climate or land use is central to understanding and managing N2O. Here we find that N2O flux can be predicted by models incorporating soil nitrate concentration (NO3-), water content and temperature using a global field survey of N2O emissions and potential driving factors across a wide range of organic soils. N2O emissions increase with NO3- and follow a bell-shaped distribution with water content. Combining the two functions explains 72% of N2O emission from all organic soils. Above 5 mg NO3--N kg-1, either draining wet soils or irrigating well-drained soils increases N2O emission by orders of magnitude. As soil temperature together with NO3- explains 69% of N2O emission, tropical wetlands should be a priority for N2O management.

AbstractAmazonian swamp forests remove large amounts of carbon dioxide (CO2) but produce methane (CH4). Both are important greenhouse gases (GHG). Drought and cultivation cut the CH4 emissions but may release CO2. Varying oxygen content in nitrogen-rich soil produces nitrous oxide (N2O), which is the third most important GHG. Despite the potentially tremendous changes, GHG emissions from wetland soils under different land uses and environmental conditions have rarely been compared in the Amazon. We measured environmental characteristics, and CO2, CH4 and N2O emissions from the soil surface with manual opaque chambers in three sites near Iquitos, Peru from September 2019 to March 2020: a pristine peat swamp forest, a young forest and a slash-and-burn manioc field. The manioc field showed moderate soil respiration and N2O emission. The peat swamp forests under slight water table drawdown emitted large amounts of CO2 and CH4. A heavy post-drought shower created a hot moment of N2O in the pristine swamp forest, likely produced by nitrifiers. All in all, even small changes in soil moisture can create hot moments of GHG emissions from Amazonian wetland soils, and should therefore be carefully monitored.

The aim of this article is to evaluate and compare common cattail (Typha latifolia) biomass production and annual accumulation of nitrogen, phosphorus, carbon, and heavy metals (Cd, Cu, Pb, Zn) in phytomass in 3 treatment wetland systems in Estonia. The biomass samples (roots/rhizomes, shoots with leaves, and spadixes) and litter were collected from 1 x 1 m plots--15 plots in Tänassilma seminatural wetland, 15 plots in Põltsamaa constructed wetland, and 10 plots in Häädemeeste constructed wetland. The highest average total cattail phytomass was 2.54 kg DW m(-2) in Häädemeeste. In Tänassilma and Põltsamaa this value was 2.3 and 2.11 kg DW m(-2), respectively. The average total aboveground biomass production and roots/rhizomes phytomass was not significantly different in three studied wetland systems. We have found significantly less spadixes and litter in Tänassilma than in Põltsamaa and Häädemeeste. In Põltsamaa, the N and P content in all plant fractions were higher than in other test areas. The Cd concentration in all samples (shoots, spadixes, litter) varied from < 0.01 to < 0.02 mg/kg. The average concentration of Zn in litter varied from 12.2 mg kg(-1) in Häädemeeste to 12.6 mg kg(-1) in Tänassilma and 13.3 mg kg(-1) in Põltsamaa. There has been found a significantly higher average contents of Cu (39.3 mg kg(-1)), Pb (30.4 mg kg(-1)), and Zn (412.3 mg kg(-1)) in Tänassilma than those in Häädemeeste or Põltsamaa: Cu-11.6 and 15.9, Pb--2.3 and 3.3, and Zn--57.5 and 73.2 mg kg(-1), respectively. The highest heavy metal retention (303.2 mg Pb m(-2), 29.4 mg Zn m(-)2, 22.9 mg Cu m(-2), and 0.35 mg Cd m(-2)) was observed in root and rhizome samples from the Tänassilma wetland.

AbstractRiparian forests are known as hot spots of nitrogen cycling in landscapes. Climate warming speeds up the cycle. Here we present results from a multi-annual high temporal-frequency study of soil, stem, and ecosystem (eddy covariance) fluxes of N2O from a typical riparian forest in Europe. Hot moments (extreme events of N2O emission) lasted a quarter of the study period but contributed more than half of soil fluxes. We demonstrate that high soil emissions of N2O do not escape the ecosystem but are processed in the canopy. Rapid water content change across intermediate soil moisture was a major determinant of elevated soil emissions in spring. The freeze-thaw period is another hot moment. However, according to the eddy covariance measurements, the riparian forest is a modest source of N2O. We propose photochemical reactions and dissolution in canopy-space water as reduction mechanisms.

1 Study site and set-upThe studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964).The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone.The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011).The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst.2 Soil flux measurementsSoil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer.Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations.After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period.3 Stem flux measurementsThe tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011).4 Soil and stem flux calculationFluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995).Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values.To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020).5 Eddy covariance instrumentationEddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios).6 Eddy-covariance flux calculation and data quality controlThe fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004).Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004).Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis.Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage.While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient.To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018).MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis.7 Ancillary measurementsAir temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data.Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).

The global goal to mitigate climate change (CC) is to achieve net zero greenhouse gas emissions (GHGE) by 2050; the European Union (EU) aim is to cut GHGE at least by 55% already by 2030. These ambition targets require new GHGE mitigation measures across all land use sectors (LULUCF), where wetlands, as carbon (C) rich ecosystem, can effectively contribute to climate targets, biodiversity, and water-related ecosystem services. Natural peatlands accumulate C effectively due to water-logged conditions. However, they can turn into high GHG sources if they are drained, therefore there is still need to enhance knowledge regarding how and/or how much C is sequestered or released by peatlands after their restoration, as well as the socioeconomic effects.&#8220;ALFAwetlands - Restoration for the future&#8221; (www.alfawetlands.eu) is a Horizon Europe funded project (2022-2026), which is coordinated by Luke and carried out at local to EU levels with 15 partners across Europe. It&#8217;s main goal, in short, is to mitigate CC while supporting biodiversity and ecosystem services (BES) and being socially just and rewarding. This includes, e.g., increasing the knowledge about C storage and release in peatlands, specifically after restoration. While, in terms of C fluxes, focussing on peatlands, the project scope is larger and includes additionally floodplains, coastal wetlands and few artificial wetlands. ALFAwetlands will develop and indicate management alternatives for wetlands including such that have been or will be restored during this project. Measures under this project are not restricted to ecological restoration but include rehabilitation and re-vegetation action to improve ecosystem conditions (e.g., peatland forest: continuous-cover-forestry, cultivated peatlands: paludiculture). Studies are conducted in 9 Living Labs (LL&#8217;s) including 30 sites, which are located in wetlands in different parts of Europe (north-south gradient). At the local level, LL&#8217;s support and integrate interdisciplinary and multi-actor research on ecological, environmental, economic, and social issues. Experimental data from local sites are scaled-up and will be utilized e.g., by models to gain and understanding the potential impacts of upscaled wetland restoration measures. To achieve ALFAwetlands goals, 5 research workpackages are being implemented, namely: 1)improve geospatial knowledge base of wetlands, 2)co-create socially fair and rewarding pathways for wetland restoration, 3)estimate effects of restoration on GHGE and BES, with the data achieved from field experiments, 4)develop policy relevant scenarios for CC and BES, and 5)study societal impacts of wetland restoration. The project will also encourage stakeholders to utilise outputs and support their active participation in wetland management.

The decomposition of fresh crop residues added to soil for agricultural purposes is complex. This is due to different factors that influence the decomposition process. In field conditions, the incorporation of crop residues into soil does not always have a positive effect on aggregate stability. The aim of this study was to investigate the decomposition effects of residues from two different cover crops (Brassica napus var. oleifera and Secale cereale) and one main crop (wheat straw) on soil aggregate stability. A 105-day incubation experiment was conducted in which crop residues were mixed with sandy loam soil at a rate of 6 g C kg−1 of soil. During the incubation, there were five water additions. The decomposition effects of organic matter on soil conditions during incubation were evaluated by determining the soil functional groups; carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) emissions; soil microbial biomass carbon (MBC); and water-stable aggregates (WSA). The functional groups of the plant residues and the soil were analyzed using Fourier transform infrared spectroscopy (FTIR) and a double exponential model was used to estimate the decomposition rates. The results show that the decomposition rate of fresh organic materials was correlated with the soil functional groups and the C/N ratio. Oilseed rape and rye, with lower C/N ratios than wheat straw residues, had faster decomposition rates and higher CO2 and N2O emissions than wheat straw. The CO2 and N2O flush at the start of the experiment corresponded to a decrease of soil aggregate stability (from Day 3 to Day 10 for CO2 and from Day 19 to Day 28 for N2O emissions), which was linked to higher decomposition rates of the labile fraction. The lower decomposition rates contributed to higher remaining C (carbon) and higher soil aggregate stability. The results also show that changes in the soil functional groups due to crop residue incorporation did not significantly influence aggregate stability. Soil moisture (SM) negatively influenced the aggregate stability and greenhouse gas emissions (GHG) in all treatments (oilseed rape, rye, wheat straw, and control). Irrespective of the water addition procedure, rye and wheat straw residues had a positive effect on water-stable aggregates more frequently than oilseed rape during the incubation period. The results presented here may contribute to a better understanding of decomposition processes after the incorporation of fresh crop residues from cover crops. A future field study investigating the influence of incorporation rates of different crop residues on soil aggregate stability would be of great interest.