• Energy Research
• Open Access close
• Restricted close
• JP close
• IN close
• UA close

Abstract. Regional land carbon budgets provide insights on the spatial distribution of the land uptake of atmospheric carbon dioxide, and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions, due to different definitions and component fluxes reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers, that connect CO2 uptake in one area with its release in another also requires better definition and protocols to reach harmonized regional budgets that can be summed up to the globe and compared with the atmospheric CO2 growth rate and inversion results. In this study, for the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims as an update of regional carbon budgets over the last two decades based on observations, for 10 regions covering the globe, with a better harmonization that the precursor project, we provide recommendations for using atmospheric inversions results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes and land use fluxes.

The performance of dual chambered mediator-less microbial fuel cell (MFC) operated under batch mode was evaluated under different operating temperatures, ranging between 20 and 55 °C, with step increase in temperature of 5 °C. Synthetic wastewater with sucrose as carbon source having chemical oxygen demand (COD) of 519–555 mg/L was used in the study. Temperature was a crucial factor in the performance of MFCs for both COD removal and electricity production. The MFC demonstrated highest COD removal efficiency of 84% and power density normalized to the anode surface area of 34.38 mW/m2 at operating temperature of 40 °C. Higher VSS to SS ratio was observed at the operating temperature between 35 and 45 °C. Under different operating temperatures the observed sludge yield was in the range of 0.05 to 0.14 g VSS/g COD removed. The maximum Coulombic and energy efficiencies were obtained at 40 °C, with values of 7.39 and 13.14%, respectively. Internal resistance of the MFC decreased with increase in operating temperature. Maximum internal resistance of 1,150 Ω was observed when the MFC was operated at 20 °C; whereas the minimum internal resistance (552 Ω) was observed at 55 °C.

In order to study the effect of burst temperature on the coolant flow channel restriction, burst tests of fuel bundles were performed. Each bundle consisted of 49 rods (7×7 rods), and bursts were conducted in flowing steam. Burst temperature was changed by changing the internal gas pressure in rods. After the burst, the ballooning behavior of each rod and the degree of coolant flow area restriction in the bundle were measured. Maximum swelling of rod occurs when the burst temperature is around α and α+β phase boundary, and this phenomenon is almost the same as that in single rod burst tests. Maximum coolant flow area restriction is also observed in this condition.

Les insectes nuisibles sont un problème très courant dans les jardins de thé où ils causent une perte énorme chaque année. L'étude a été menée pour mesurer le potentiel d'un piège à lumière LED solaire en tant que technologie de lutte antiparasitaire respectueuse de l'environnement dans le jardin de thé du Bangladesh. Les résultats ont montré que le plus grand nombre d'insectes des ordres des coléoptères (3526) et des hémiptères (557) était capturé sous le piège à lumière LED solaire alors qu'il était le plus faible chez les hyménoptères (47) et les orthoptères (3). Les résultats de l'étude ont également indiqué que sur une moyenne de 9 jours, le nombre total d'insectes capturés était de 725, 244, 146, 112 et 85 à 6-7, 7-8, 8-9, 9-10 et 22-11 heures, respectivement, alors qu'il était de 656, 1004, 566, 3970, 602, 489, 680, 620 et 3222 du 1er au 9e jour, respectivement. Par conséquent, le taux décroissant moyen avec le temps (horaire) a été calculé à 66,34, 40,16, 23,29 et 24,11 % par rapport à la 1ère heure (18h-19h). Le patron moyen de capture des insectes par rapport aux 1ers jours a été diminué ou vice-versa. De 18h00 à 11h00, l'intensité lumineuse initiale a été mesurée à 28 lux, ce qui a augmenté jusqu'à 245 lux à 20h00, puis a considérablement diminué. L'intensité lumineuse a une relation positive avec la capture des insectes. La plupart des insectes coléoptères et hémiptères ont été capturés sous faible intensité lumineuse en début de soirée (18h00). Par conséquent, l'étude a suggéré que le piège à lumière LED à base solaire pourrait avoir le potentiel de réduire un grand nombre de parasites du thé dans le jardin de thé. Las plagas de insectos son un problema muy común en el jardín de té, donde causan una gran pérdida cada año. El estudio se realizó para medir la potencialidad de una trampa de luz LED basada en energía solar como una tecnología de manejo de plagas ecológica en el jardín de té de Bangladesh. Los resultados mostraron que el mayor número de insectos de los órdenes Coleoptera (3526) y Hemiptera (557) se capturaron bajo la trampa de luz LED basada en energía solar, mientras que fue más bajo en Hymenoptera (47) y Orthoptera (3). Los resultados del estudio también indicaron que en un promedio de 9 días, el total de insectos capturados (número) fue de 725, 244, 146, 112 y 85 a las 6-7, 7-8, 8-9, 9-10 y 10-11 pm, respectivamente, mientras que fue de 656, 1004, 566, 3970, 602, 489, 680, 620 y 3222 en los días 1 a 9, respectivamente. Por lo tanto, la tasa media de disminución con el tiempo (por hora) se calculó en 66.34, 40.16, 23.29 y 24.11% en comparación con la 1ª hora (6-7 pm). El patrón medio capturado de los insectos en comparación con los primeros días disminuyó o viceversa. De 6:00 p. m. a 11:00 a. m., la intensidad de la luz inicial se midió a 28 lux, que se elevó hasta 245 lux a las 8:00 p. m., después de lo cual se redujo drásticamente. La intensidad de la luz tiene una relación positiva con la captura de insectos. La mayoría de los insectos coleópteros y hemípteros fueron capturados bajo baja intensidad de luz en la tarde (6.00 pm). Por lo tanto, el estudio sugirió que la trampa de luz LED basada en energía solar podría tener el potencial de reducir una gran cantidad de plagas de té en el jardín de té. Insect pest is a very common problem in tea garden where they caused a huge loss in each year. The study was conducted to measure the potentiality of a solar based LED light trap as an eco-friendly pest management technology in the tea garden of Bangladesh. The results showed that the highest number of insects from Coleoptera (3526) and Hemiptera (557) orders were captured under the solar based LED light trap while it was lowest in Hymenoptera (47) and Orthoptera (3). The study results also indicated that in an average of 9 days, total captured insects (number) were 725, 244, 146, 112, and 85 at 6-7, 7-8, 8-9, 9-10, and 10-11 pm, respectively while it was 656, 1004, 566, 3970, 602, 489, 680, 620 and 3222 at 1st to 9th days, respectively. Hence, the average decreasing rate with time (hourly) was computed at 66.34, 40.16, 23.29, and 24.11% compared to the 1st hour (6-7 pm). The average captured pattern of the insects compared to the 1st days was decreased or vice-versa. From 6.00 pm to 11.00 am, the initial light intensity was measured at 28 lux which raised up to 245 lux at 8.00 pm after that it down drastically. Light intensity has a positive relation with insect capture. Most of the Coleopteran and Hemipteran insects were captured under low light intensity in the early evening (6.00 pm). Therefore, the study suggested that the solar based LED light trap might have the potential to reduce a great number of tea pests in the tea garden. الآفات الحشرية هي مشكلة شائعة جدا في حديقة الشاي حيث تسببت في خسارة كبيرة في كل عام. أُجريت الدراسة لقياس إمكانات مصيدة إضاءة LED القائمة على الطاقة الشمسية كتقنية صديقة للبيئة لإدارة الآفات في حديقة الشاي في بنغلاديش. وأظهرت النتائج أن أكبر عدد من الحشرات من غمديات الأجنحة (3526) ونصف الأجنحة (557) تم التقاطها تحت فخ ضوء LED الشمسي بينما كان أدنى مستوى في غشاء البكارة (47) و Orthoptera (3). كما أشارت نتائج الدراسة إلى أنه في متوسط 9 أيام، بلغ إجمالي الحشرات التي تم اصطيادها (العدد) 725 و 244 و 146 و 112 و 85 في 6-7 و 7-8 و 8-9 و 9-10 و 10-11 مساءً، على التوالي، بينما كان 656 و 1004 و 566 و 3970 و 602 و 489 و 680 و 620 و 3222 في الأيام من الأول إلى التاسع على التوالي. وبالتالي، تم حساب متوسط معدل التناقص مع الوقت (بالساعة) عند 66.34 و 40.16 و 23.29 و 24.11 ٪ مقارنة بالساعة الأولى (6-7 مساءً). انخفض متوسط النمط الذي تم التقاطه من الحشرات مقارنة بالأيام الأولى أو العكس. من الساعة 6:00 مساءً إلى الساعة 11:00 صباحًا، تم قياس شدة الضوء الأولية عند 28 لوكس والتي ارتفعت إلى 245 لوكس في الساعة 8:00 مساءً بعد ذلك انخفضت بشكل كبير. شدة الضوء لها علاقة إيجابية مع التقاط الحشرات. تم التقاط معظم حشرات غمدية الأجنحة ونصف الأجنحة تحت شدة الإضاءة المنخفضة في وقت مبكر من المساء (6.00 مساءً). لذلك، أشارت الدراسة إلى أن مصيدة ضوء LED القائمة على الطاقة الشمسية قد يكون لها القدرة على تقليل عدد كبير من آفات الشاي في حديقة الشاي.

The 14 Pacific developing member countries (DMCs) of the Asian Development Bank (ADB) are exposed to a wide range of worsening climate-related hazards, such as tropical cyclones, floods, droughts, storm surges, and sea level rise. Climate change will affect all people living in Pacific DMCs. Extreme events can push governments into debt distress, businesses into insolvency, and individuals into extreme hardship. However, some will be affected more than others, as people experience different vulnerabilities and capacities to cope with a changing climate. Gender is one important factor affecting vulnerability.2 More frequent disasters and worsening slow-onset events, such as sea level rise, can amplify existing disparities between men and women. This tip sheet is intended for ADB project officers and government counterparts, specifically for Pacific island countries. It outlines steps to design ADB projects that seek to build resilience to current and future climate risks, while promoting gender equality through shared control of resources and decision-making. Such projects uphold two operational priorities under ADB’s Strategy 2030—Accelerating Progress in Gender Equality (OP2) and Tackling Climate Change and Building Climate and Disaster Resilience (OP3). Both of these priorities target 75% of committed operations by 2030. Recommendations focus primarily on projects that will help achieve Pillar 5 of OP2—strengthening women’s resilience to external shocks. By their very nature, these projects should have a gender equity theme (GEN) and may also be “Type 2” adaptation projects. Box 1 presents the proposed actions under Pillar 5 related to climate change.

The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MIROC.MIROC6' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

This file contains the raw dataset used in the manuscript "Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy" published in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659) Data has been acquired using Nion Swift (https://nionswift.readthedocs.io/en/stable/). Experimental details can be found in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659). The dataset has been analyzed using the following Python libraries: Numpy, Scipy, Hyperspy, Matplotlib EELS hyperspectral images have been aligned using the Hyperspy "align1D" method. Aligned EELS hyperspectral images are saved in files finished with "_Aligned.hspy": For the strong coupling experiments: Tip 1 is on hBN Tip 2 is on vacuum For each of the nanowires tips, a file with the fitted coefficients are available, as well as a plot of the data and the fitted curve. Datasets have been fitted with gaussian and/or lorentizan functions, as described in the published text. Any question can be forwarded to the corresponding authors of the published text. Other funding: 1) National Agency for Researchunder the program of future investment TEMPOS-CHROMATEM (reference no. ANR-10-EQPX-50); 2) Spanish MINECO (MAT2017-88492-R and SEV2015-0522); 3) the Catalan CERCA Program; 4) Fundació Privada Celle;