Meten van broeikasgassen in het landschap

Verschillende meetinstrumenten en technieken worden ingezet om de dynamiek van deze uitwisseling in ruimte en tijd in beeld te krijgen. De metingen moeten vertellen wat de netto broeikasgasbalans van het landschap is en hoe deze zal reageren op een veranderend klimaa

Agricultural peatlands: towards a greenhouse gas sink - a synthesis of a Dutch landscape study

It is generally known that managed, drained peatlands act as carbon (C) sources. In this study we examined how mitigation through the reduction of the intensity of land management and through rewetting may affect the greenhouse gas (GHG) emission and the C balance of intensively managed, drained, agricultural peatlands. Carbon and GHG balances were determined for three peatlands in the western part of the Netherlands from 2005 to 2008 by considering spatial and temporal variability of emissions (CO2, CH4 and N2O). One area (Oukoop) is an intensively managed grass-on-peatland area, including a dairy farm, with the ground water level at an average annual depth of 0.55 (±0.37) m below the soil surface. The second area (Stein) is an extensively managed grass-on-peatland area, formerly intensively managed, with a dynamic ground water level at an average annual depth of 0.45 (±0.35) m below the soil surface. The third area is a (since 1998) rewetted former agricultural peatland (Horstermeer), close to Oukoop and Stein, with the average annual ground water level at a depth of 0.2 (±0.20) m below the soil surface. During the measurement campaigns we found that both agriculturally managed sites acted as C and GHG sources and the rewetted former agricultural peatland acted as a C and GHG sink. The ecosystem (fields and ditches) total GHG balance, including CO2, CH4 and N2O, amounted to 3.9 (±0.4), 1.3 (±0.5) and -1.7 (±1.8) g CO2-eq m-2 d-1 for Oukoop, Stein and Horstermeer, respectively. Adding the farm-based emissions to Oukoop and Stein resulted in a total GHG emission of 8.3 (±1.0) and 6.6 (±1.3) g CO2-eq m-2 d-1, respectively. For Horstermeer the GHG balance remained the same since no farm-based emissions exist. Considering the C balance (uncertainty range 40–60%), the total C release in Oukoop and Stein is 5270 and 6258 kg C ha-1 yr-1, respectively (including ecosystem and management fluxes), and the total C uptake in Horstermeer is 3538 kg C ha-1 yr-1. Water bodies contributed significantly to the terrestrial GHG balance because of a high release of CH4. Overall, this study suggests that managed peatlands are large sources of GHGs and C, but, if appropriate measures are taken, they can be turned back into GHG and C sinks within 15 years of abandonment and rewetting. The shift from an intensively managed grass-on-peat area (Oukoop) to an extensively managed one (Stein) reduced the GHG emissions mainly because N2O emission and farm-based CH4 emissions decreased

Folate receptor-alpha targeted near-infrared fluorescence imaging in high-risk endometrial cancer patients: a tissue microarray and clinical feasibility study

Surgical oncolog

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n=142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤1.7 Gy for Dmean, ≤2.9 Gy for D2cc, and ≤13% for V5Gy and V10Gy, were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, our novel organ dose reconstruction method is not only accurate, but also efficient, as the setup of a surrogate is no longer needed

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Eddy covariance observations of methane and nitrous oxide emissions: Towards more accurate estimates from ecosystems

About 30% of the increased greenhouse gas (GHG) emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are related to land use changes and agricultural activities. In order to select effective measures, knowledge is required about GHG emissions from these ecosystems and how these emissions are influenced by management and meteorological conditions. Accurate emission values are therefore needed for all three GHGs to compile the full GHG balance. However, the current annual estimates of CH4 and N2O emissions from ecosystems have significant uncertainties, even larger than 50%. The present study showed that an advanced technique, micrometeorological eddy covariance flux technique, could obtain more accurate estimates with uncertainties even smaller than 10%. The current regional and global trace gas flux estimates of CH4 and N2O are possibly seriously underestimated due to incorrect measurement procedures. Accurate measurements of both gases are really important since they could even contribute for more than two-third to the total GHG emission. For example: the total GHG emission of a dairy farm site was estimated at 16.10^3 kg ha-1 yr-1 in CO2-equivalents from which 25% and 45% was contributed by CH4 and N2O, respectively. About 60% of the CH4 emission was emitted by ditches and their bordering edges. These emissions are not yet included in the national inventory reports. We recommend including these emissions in coming reports.Multi-Scale Physics, Research Group Clouds, Climate and Air QualityApplied Science