Estimating Carbon Dioxide Research Articles

Carbon Emission Status and Regional Differences of China: High-Resolution Estimation of Spatially Explicit Carbon Emissions at the Prefecture Level

There are disagreements regarding the accuracy of estimation and spatial distribution of carbon emissions in China. It is of great significance to estimate a more detailed carbon emission inventory for China and analyze the carbon emission characteristics of different regions. This study comprehensively estimated carbon dioxide and methane emissions (and their spatial distributions) across eight carbon-emitting sectors in 360 prefecture-level cities in China in 2020. The results indicated that total carbon emissions in China amounted to 146.00 × 108 t, with carbon dioxide and methane accounting for 95.87% and 4.13%, respectively. The industrial sector was the main source of carbon emissions, accounting for 75.42% of the total. The North China Plain, the Northeast Plain, and the Sichuan Basin were identified as the carbon emission hotspot areas with the most intensive carbon emission densities. Among the clustered four carbon emission zones based on carbon emission density and economic carbon intensity, the High Carbon Emission Density and High Economic Carbon Intensity zones accounted for 41.73% of total carbon emissions. To achieve carbon neutrality, it is essential to devise emission reduction strategies for specific areas by thoroughly considering spatially explicit variation at the prefecture level, with a focus on primary carbon-emitting cities and sectors.

The Effectiveness of Green Open Spaces in Absorbing Carbon Dioxide Emissions in the Area of PT. Pelabuhan Indonesia (Persero) Regional 3 Subregional Jawa

Introduction: Green spaces serve as various ecological purposes, most notably the removal of pollutants. Although PT. Pelabuhan Indonesia (Persero) Regional 3 Subregional Jawa was estimated to have quite significant carbon dioxide (CO2) emissions, the company had provided green open space in multiple locations. Calculating the effectiveness of green open spaces in absorbing CO2 emissions in the area of Pelindo Subregional Jawa was the aim of this study. Methods: The primary data used was observation, and secondary data were gathered through document and literature studies. The dependent variable was the calculation of carbon dioxide emissions, while the independent variable was the effectiveness of green open spaces (land cover types and green open spaces area). The IPCC 2006 equations were used for data analysis. Results and Discussion: The estimated carbon dioxide emissions at Pelindo Subregional Jawa came from the following sources: 1,945.51 tons from electricity consumption; 1,845.97 tons from ships; 1,102.30 tons from operational, employee, and passenger vehicles; and 4.49 tons from genset usage. That amounted to 4,898.27 tons per year overall. Based on the calculation, it was found that the green open space could absorb 4,434.94 tons of CO2 per hectare annually. Conclusion: The existing green open spaces in the area of Pelindo Subregional Jawa were not enough to absorb CO2 emissions, as CO2 emissions still left a residual of 463.33 tons/year.

Estimating Carbon Dioxide Solubility in Brine Using Mixed Effects Random Forest Based on Genetic Algorithm: Implications for Carbon Dioxide Sequestration in Saline Aquifers

Summary Accurate prediction of carbon dioxide (CO2) solubility in brine is crucial for the success of carbon capture and storage (CCS) by means of geological formations like aquifers. This study investigates the effectiveness of a novel genetic algorithm-mixed effects random forest (GA-MERF) model for estimating CO2 solubility in brine. The model’s performance is compared with established methods like the group method of data handling (GMDH), backpropagation neural networks (BPNN), and traditional thermodynamic models. The GA-MERF model utilizes experimental data collected from literature, encompassing key factors influencing CO2 solubility: temperature (T), pressure (P), and salinity. These data are used to train and validate the model’s ability to predict CO2 solubility values. The results demonstrate the superiority of GA-MERF compared to the other models. Notably, GA-MERF achieves a high coefficient of determination (R) of 0.9994 in unseen data, indicating a strong correlation between estimated and actual CO2 solubility values. Furthermore, the model exhibits exceptionally low error metrics, with a root mean squared error (RMSE) of 2×10-8 and a mean absolute error (MAE) of 1.8×10-11, signifying outstanding accuracy in estimating CO2 solubility in brine. Beyond its high accuracy, GA-MERF offers an additional benefit—reduced computational time compared to the other models investigated, with 65 seconds. This efficiency makes GA-MERF a particularly attractive tool for real-world applications where rapid and reliable CO2 solubility predictions are critical. In conclusion, this study presents GA-MERF as a powerful and efficient model for predicting CO2 solubility in brine. Its superior performance compared to existing methods and previous literature highlights its potential as a valuable tool for researchers and engineers working on CCS projects utilizing aquifer storage. The high accuracy, low error rates, and reduced computational time make GA-MERF a promising candidate for advancing the development of effective and efficient CCS technologies.

Accuracy and Interpretation of Transcutaneous Carbon Dioxide Monitoring in Critically Ill Children.

Transcutaneous carbon dioxide (Tc co2 ) monitoring can noninvasively assess ventilation by estimating carbon dioxide ( CO2 ) levels in the blood. We aimed to evaluate the accuracy of Tc co2 monitoring in critically ill children by comparing it to the partial pressure of arterial carbon dioxide (Pa co2 ). In addition, we sought to determine the variation between Tc co2 and Pa co2 acceptable to clinicians to modify patient care and to determine which patient-level factors may affect the accuracy of Tc co2 measurements. Retrospective observational cohort study. Single, quaternary care PICU from July 1, 2012, to August 1, 2020. Included participants were admitted to the PICU and received noninvasive ventilation support (i.e., continuous or bilevel positive airway pressure), conventional mechanical ventilation, or high-frequency oscillatory or percussive ventilation with Tc co2 measurements obtained within 15 minutes of Pa co2 measurement. None. Three thousand four hundred seven paired arterial blood gas and Tc co2 measurements were obtained from 264 patients. Bland-Altman analysis revealed a bias of -4.4 mm Hg (95% CI, -27 to 18.3 mm Hg) for Tc co2 levels against Pa co2 levels on the first measurement pair for each patient, which fell within the acceptable range of ±5 mm Hg stated by surveyed clinicians, albeit with wide limits of agreement. The sensitivity and specificity of Tc co2 to diagnose hypercarbia were 93% and 71%, respectively. Vasoactive-Infusion Score (VIS), age, and self-identified Black/African American race confounded the relationship between Tc co2 with Pa co2 but percent fluid overload, weight-for-age, probe location, and severity of illness were not significantly associated with Tc co2 accuracy. Tc co2 monitoring may be a useful adjunct to monitor ventilation in children with respiratory failure, but providers must be aware of the limitations to its accuracy.

Spatiotemporal heterogeneity of lake carbon dioxide flux leads to substantial uncertainties in regional upscaling estimates

Limited field samplings result in significant uncertainties in regional and global estimates of lake carbon dioxide (CO2) emissions. However, quantitative analysis of uncertainty in regional lake CO2 emission estimates remains unclear. In this study, we utilized satellite data to estimate carbon dioxide flux from 113 eastern China lakes, revealing substantial spatial and temporal variations in flux, averaging 18.07 ± 81.83 mg m−2 d−1. Additionally, satellite-estimated total CO2 effluxes indicated previous upscaling studies had overestimated total CO2 effluxes from these studied lakes by approximately 3–11 times, primarily due to substantial variations in lake CO2 fluxes. Insufficient sampling resolution resulted in considerable uncertainty in upscaling estimations. Temporal variations in carbon dioxide contributed greater upscaling uncertainties than spatial variations in carbon dioxide. To capture the dynamics of lake CO2, increasing the number of sampling points and events is necessary as lake size decreases and trophic state increases. Finally, we propose a prediction for the optimal sampling resolution based on lake area and trophic state, recommending an average of 5 points per lake and bi-monthly sampling as the ideal resolution for similar shallow eutrophic lakes. This approach has been validated as effective in lakes across North America and Europe. We believe that future global-scale lake carbon budget estimates would benefit from field observations conducted at more reasonable sampling points and frequency.

A numerical approach to exergy-based sustainability and environmental assessments of solar energy-powered district cooling systems using actual operational data

The demand for cooling in buildings has been increasing at a higher rate than heating, and more energy is expected to meet this demand. Solar energy can be vital in fulfilling this energy requirement based on its unique renewable energy features. The solar thermal powered absorption cooling (STAC) and solar electrical assisted vapor compression cooling (SEVC) systems are assessed in this study by conducting the conventional and advanced exergy analyses and environmental assessment. Determining the unavoidable part of exergy destruction, as in this study, provides a unique convenience in design problems where the thermodynamic performances of distinct systems are compared. Under current technological conditions, removing the thermodynamically optimized parameters of the designed systems from the minima-maxima dichotomy and rationally evaluating the avoidable part of exergy destruction will protect the researcher from the arbitrariness of the design. The obtained results based on conventional exergy analysis in a component manner showed that priority should be given to solar technologies due to their lowest exergy efficiencies (0.16 for a photovoltaic (PV) and 0.19 for a collector) and sustainability indices (1.20 for the PV and 1.24 for the collector). Advanced exergy analysis results revealed that the exergy destruction significantly originated from the unavoidable part of the total exergy destruction of the components for the solar technologies (93.02 % for the collector and 96.41 % for the PV), cooling (92.12 % for the absorption and 98.42 % for the vapor compression), and overall system (99.92 % for the SEVC and 99.99 % for the STAC). The initial estimated carbon dioxide emissions from the STAC were 0.28 kg CO2-eq, attributed to pump power consumption. However, these emissions varied dynamically for the SEVC, ranging from 0 (when the solar PV field meets the total power) to 5.58 kg CO2-eq (when radiation is not available), depending on the power-consuming components (compressor and pumps).

Strength, carbon emissions, and sorptivity behavior of cement paste and mortar containing thermally activated clay

The detrimental impact of anthropogenic gases such as carbon dioxide, methane, and nitrous oxide has become a pressing concern across various industries. In this study, thermally activated clay (TAC) was investigated as a substitute for Portland cement in proportions ranging from 10 to 40 wt%. Compressive strength and efficiency factors were employed to ascertain the optimal mortar mixture. Both the control mortar and the optimal blend underwent capillary water sorptivity tests. Fourier Transformed Infrared spectroscopy (FT-IR) was utilized to gain insight into the hydration process and pozzolanic reaction within the mortar mixtures. Furthermore, the study assessed the impact of the optimized blended mortar on greenhouse gas emissions through estimated carbon dioxide equivalent values. Results revealed that incorporating 30% TAC attained the maximum strength of approximately 33, 36, and 48 MPa at 3, 7, and 28 days respectively compared with the other mortars. The mortar containing 30% TAC enhances both compressive strength and efficiency factors, demonstrating effective pozzolanic activity and minimal compromise on performance. Notably, the inclusion of 30% TAC also led to reduced water absorption rates, indicating improved durability-related performance. Additionally, the study uncovered the superior performance of the 30% TAC mix in terms of efficiency factors and carbon savings. FTIR analysis provided novel insights into the hydration and pozzolanic reactions induced by TAC, resulting in the formation of compounds contributing to enhanced compressive strength over time. This research advocates for the substitution of Portland cement with TAC as a low-carbon binder alternative to traditional Portland cement binders.

Estimation Of Turkey's Carbon Dioxide Emission with Machine Learning

Carbon dioxide emissions are an important factor in the increase of greenhouse gases in the atmosphere and climate change. Controlling and reducing carbon dioxide emissions plays an important role in combating global warming and climate change. Various national and international efforts are being carried out to reduce greenhouse gas emissions and switch to sustainable energy sources. For this reason, estimating carbon dioxide emissions in the coming years is important for determining the measures to be taken. 
 In this study, Turkey's carbon dioxide emissions are successfully estimated using two different machine learning models. The success of the study was evaluated using three different statistical measures: R2, MSE and MAE. The R2 of decision trees was 89.4%, MSE was 0.013 and MAE was 0.011; the R2 of artificial neural networks was 92.7%, MSE was 0.009 and MAE was 0.006. When we compare the two models, it is seen that ANN is more successful than decision trees and predicts with less error.

Assessment of carbon emissions due to landscape fires in Ukraine during war in 2022

The russian military aggression and the related socio-economic and environmental consequences have significantly affected the climate and production of ecosystem services through damage to forests, ecosystems, landscape fires and emissions of gases into the atmosphere. The study aims to estimate carbon dioxide emissions due to landscape fires in Ukraine during the year 2022. The OroraTech wildfire monitoring technology was used to detect fires, while perimeters of burned areas were delineated with Sentinel 2 time series. The Copernicus Dynamic Land Cover map was used to extract burned land covers. Emissions were calculated based on the intensity of fires (dNBR) with the share of burned biomass in different types of land cover. Biomass models were selected considering the dominant tree species within a specific region and the species structure of the sown areas of croplands. The volume of biomass losses was estimated as a result of fires of different severities. It was estimated that during in 2022, landscape fires burned 749.5 thousand hectares thereof: croplands – 419.1 thousand hectares; other natural vegetation – 273.8 thousand hectares; conifer forests – 31.1 thousand hectares; other forests – 25.5 thousand hectares. The impact of the war on landscape fires is confirmed by the large proportion of fires in the 60-kilometre buffer zone along the frontline – 68.9% of the total area of fire. Among all fires, 42.5% of fires occurred in the occupied territory. Total CO2 emissions from all types of landscape fires reached 5.20 million tons and other greenhouse gases – 0.28 million tons. It is the first detailed mapping of landscape fires with an analysis of each polygon for the whole territory of Ukraine. The results provide important information for assessing the loss of ecosystem services and estimating carbon dioxide emissions as well as for confirming the impact of hostilities on landscape fires

Picture This: A Deep Learning Model for Operational Real Estate Emissions

We present a deep learning model estimating carbon dioxide equivalent (CO2e) emissions in the real estate sector. The model, which utilizes convolutional neural networks (CNNs) and image classification techniques, is designed to estimate CO2e emissions based on publicly available images of buildings and their corresponding emissions. Our findings show that the model has the ability to provide reasonably accurate estimations of CO2e emissions using images as the sole input. Notably, incorporating primary energy sources as additional input further improves the accuracy up to 75%. The creation of such a model is particularly important in the fight against climate change, as it allows for transparency and fast identification of buildings, contributing significantly to CO2e emissions in the building sector. Currently, information on emission intensity in the real estate sector is scarce, with only a few countries collecting and providing the required data. Our model can help reduce this gap and provide valuable insights into the carbon footprint of the real estate sector.

A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Abstract. In peatlands drained for forestry, the soil carbon (C) or carbon dioxide (CO2) balance is affected by both (i) higher heterotrophic CO2-C release from faster decomposing soil organic matter (SOM) and (ii) higher plant litter C input from more vigorously growing forests. This balance and other greenhouse gas (GHG) sinks and sources in managed lands are annually reported by national GHG inventories to the United Nations Climate Change Convention. In this paper, we present a revised, fully dynamic method for reporting the CO2 balance of drained peatland forest soils in Finland. Our method can follow temporal changes in tree biomass growth, tree harvesting and climatic parameters, and it is built on empirical regression models of SOM decomposition and litter input in drained peatland forests. All major components of aboveground and belowground litter input from ground vegetation as well as live trees and trees that died naturally are included, supplemented by newly acquired turnover rates of woody plant fine roots. Annual litter input from harvesting residues is calculated using national statistics of logging and energy use of trees. Leaching, which also exports dissolved C from drained peatlands, is not included. The results are reported as time series from 1990–2021 following the practice in the GHG inventory. Our revised method produces an increasing trend of annual emissions from 0.2 to 2.1 t CO2 ha−1 yr−1 for the period 1990–2021 in Finland (equal to a trend from 1.4 to 7.9 Mt CO2 yr−1 for the entire 4.3 Mha of drained peatland forests), with a statistically significant difference between the years 1990 and 2021. Across the period 1990–2021, annual emissions are on average 1.5 t CO2 ha−1 yr−1 (3.4 Mt CO2 yr−1 for 2.2 Mha area) in warmer southern Finland and −0.14 t CO2 ha−1 yr−1 (−0.3 Mt CO2 yr−1 for 2.1 Mha area) in cooler northern Finland. When combined with data on the CO2 sink created by the growing tree stock, in 2021 the drained peatland forest ecosystems were a source of 1.0 t CO2 ha−1 yr−1 (2.3 Mt CO2 yr−1) in southern Finland and a sink of 1.2 t CO2 ha−1 yr−1 (2.5 Mt CO2 yr−1) in northern Finland. We compare these results to those produced by the semi-dynamic method used earlier in the Finnish GHG inventory and discuss the strengths and vulnerabilities of the new revised method in comparison to more static emission factors.

1258 Audit of Environmental Costs for Bariatric Face to Face Clinics: A Single Centre Study – Interim Findings

Abstract Aim The NHS has committed to delivering a net zero national health system by 2045. Patient travel accounts for approximately 20% of carbon footprint amongst resource use within outpatient appointments. The increased acceptability of digitalized healthcare during the COVID-19 pandemic has helped in reducing this. The study aims to look at the carbon cost of in-person bariatric clinics over a two-month period. Method A prospective review of all in-person bariatric clinics in a single high-volume bariatric center was carried out from October 2022 to December 2022. Patient’s postal codes were used to estimate mileage and duration of travel to the hospital via Google Maps. The Centre for Sustainable Healthcare Patient Travel Emission Calculator was used to estimate carbon dioxide emissions of different travel modes. Calculations were adjusted for the number of people attending with the patient and mode of travel. Results Between October and December 2022, there were a total of 48 in-person bariatric clinics. Of which, 87.5% traveled by car and 4.2% by bus or taxi. The total distance traveled was 2727 km, equivalent to 58.5 hours of travelling time. The total CO2 emissions and estimated travel-related costs were 277.4 kg and £534.98. Travels by car had the highest impact on travel costs and carbon emissions, followed by taxi and bus. Conclusions Significant cost and emission reduction could be potentially achieved by remote consultation rather than in-person clinics. Data collection is ongoing with the aim of collecting information from at least 100 patients.

Carbon dioxide emissions through land use change, fire, and oxidative peat decomposition in Borneo

Borneo has accumulated an abundance of woody carbon in its forests and peat. However, agricultural land conversion accompanied by plantation development, dead wood burning, and peat drying from drainage are major challenges to climate change mitigation. This study aimed to develop a method of estimating carbon dioxide (CO2) emissions from land use change, forest and peat fires, and oxidative peat decomposition, and CO2 uptake from biomass growth across Borneo using remote sensing data from 2001 to 2016. Although CO2 uptake by biomass growth in vast forests has shown a significant increasing trend, an annual net release of 461.10 ± 436.51 (average ± 1 standard deviation) Tg CO2 year−1 was observed. The estimated emissions were predominantly characterized by land use changes from 2001 to 2003, with the highest emissions in 2001. Land use change was evaluated from annual land use maps with an accuracy of 92.0 ± 1.0% (average ± 1 standard deviation). Forest and peat fires contributed higher emissions in 2002, 2006, 2009, 2014, and 2015 compared to other years and were strongly correlated with the Southern Oscillation Indexes. These results suggest that more CO2 may have been released into the atmosphere than previously thought.

Coordinating the electric vehicle transition and electricity grid decarbonization in the U.S. is not essential to achieving substantial long-term carbon dioxide emissions reductions

How quickly the US can decarbonize light-duty vehicle (LDV) transportation depends on the rates of change of electric vehicle (EV) sales, stock turnover, and grid decarbonization. We build a stock turnover model to assess how sensitive achieving 2050 LDV decarbonization targets is to these rates. We estimate carbon dioxide (CO2) reductions of 70%–85% by 2050, including emissions from vehicles and upstream electricity generation, provided that new vehicle sales transition to 100% EVs and substantial grid decarbonization are accomplished by 2050. This result is robust to continuation of long-term trends of increasing vehicle longevity, and to whether the timing of EV sales growth and grid decarbonization are coordinated. If the two key goals are met, the annual contribution of EV electricity use to CO2 emissions will be small over the entire period.

Numerical Simulations of Carbon Dioxide Storage Efficiency in Heterogeneous Reservoir Models

The U.S. Department of Energy’s National Energy Technology Laboratory (DOE-NETL) has been developing methods and tools (the online Carbon Dioxide Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN) tool) to estimate carbon dioxide (CO2) storage potential in subsurface reservoirs. The CO2 storage efficiency terms are input in the tool to calculate storage potential in targeted reservoirs. In this effort, two CO2 storage efficiency terms were evaluated: volumetric displacement ( E V ) and microscopic displacement ( E d ). The first term deals with efficiency of CO2 propagation into an accessible reservoir volume, while the second term evaluates effectiveness of native fluid displacement with CO2. The interpreted well logs and core sample measurements were applied to create the heterogeneous reservoir models including geostatistical realizations of porosity and intrinsic permeability fields. Supercritical CO2 was injected over the course of 30 years into brine-saturated reservoir models for clastics, limestone, and dolomite lithologies and deltaic fluvial, aeolian, shallow marine, and reef depositional environments by means of varying reservoir parameters and injection scenarios. The reservoir models providing vertically heterogeneous petrophysical properties and designated as “layered reservoir models” (with homogeneous parameters along each layer of the model) were not determined to be a transition between the homogeneous and heterogeneous models in respect to storage efficiency. Another finding shows that high-efficiency factors do not necessarily mean increased CO2 storage; they rather indicate that the available volume and pore space are more fully utilized. The CO2 storage efficiency factors were evaluated dynamically at the select time points using P 10 ‐ P 50 ‐ P 90 percentiles. The results of this study show that the P 10 ‐ P 90 distribution for volumetric efficiency is wider when compared to the microscopic efficiency. It was found that where dominant buoyancy forces drive the plume to the top of a target formation, the volumetric efficiency is low. Tighter sandstone and carbonate formations show prevalence of capillary forces and better utilization of reservoir volume.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

​An Engineering Approach to Estimating Carbon Dioxide Consumption and Recovery Potential in the Brewery​

​An Engineering Approach to Estimating Carbon Dioxide Consumption and Recovery Potential in the Brewery​

Estimating Carbon Dioxide Emissions and Direct Power Consumption of Linear Accelerator–Based External Beam Radiation Therapy

Climate change is one of the direst health threats that humanity faces. We aim to estimate the carbon dioxide (CO2) emissions associated with the energy usage from linear accelerator (LINAC)-based external beam radiation therapy (EBRT) for the most common cancer diagnoses. We identified patients with the 4 most common cancer types treated with curative-intent EBRT. Beam-on time for each fraction was extracted from the treatment planning system and averaged over each site and treatment modality. The power was multiplied by the beam-on time in hours to yield kilowatt hours (kWh). Using the US Environmental Protection Agency Greenhouse Gas Equivalencies calculator, we converted the kWh into estimates of CO2-equivalent emissions for the average US power grid. Idle time of the LINAC was estimated via Varian Medical Systems. A total of 10 patients were included for each of the following modalities: conventionally fractionated for prostate cancer (28 fractions [fx]), prostate stereotactic body radiation therapy (SBRT) (5 fx), 15- and 5-fx regimens for early-stage breast cancer, 3- and 5-fx SBRT regimens for early-stage lung cancer, conventional EBRT (30 fx) for locally advanced lung cancer, and short- (5 fx) and long-course (25-28 fx) for rectal cancer. The modality with the lowest and highest carbon emissions per course, on average, was prostate SBRT (2.18 kg CO2; interquartile range, 1.92-2.30) and conventional treatment for prostate cancer (17.34 kg CO2; interquartile range, 10.26-23.79), respectively. This corresponds to CO2-equivalent emissions of driving an average of 5.4 miles and 41.2 miles in a standard vehicle, respectively. "Standby" mode for a LINAC TrueBeam and Clinac IX uses 112 kWh and 64.8 kWh per day, respectively. We have estimated CO2 emissions arising from direct energy usage of a LINAC for 4 common cancers treated with EBRT. "Standby" mode of a LINAC uses the most energy per day. Comprehensive studies are warranted to minimize the environmental effects of health and cancer care.

Variabilities of the carbon storage of mangroves in Gili Meno Lake, North Lombok District, Indonesia

Abstract. Hilyana S, Rahman FA. 2022. Variabilities of the carbon storage of mangroves in Gili Meno Lake, North Lombok District, Indonesia. Biodiversitas 23: 5862-5868. Mangrove is one of the coastal vegetation that can act as carbon mitigation (carbon sink and carbon storage). This study aims to determine the potential for carbon sinks and storage in the leaves and roots of each type of mangrove found in Gili Meno lake, North Lombok, Indonesia. The research includes the identification of species and sampling (leaves and roots) of mangroves in the research quadrant. The organic carbon content of mangrove leaves and roots was tested using the Wakley and Black method. The results showed that there were 5 (five) types of mangroves in Gili Meno lake, namely: Avicennia marina, Lumnitzera racemosa, Bruguiera cylindrica, Rhizophora apiculata, and Excoecaria agallocha. The highest leaf tissue carbon content value was R. apiculata at 45.85%C or equivalent to 3.19 g.C, while in roots, A. marina was 50.06%C, equivalent to 4.49 g.C. In addition, the potential carbon stock in the leaves of the entire mangrove ecosystem in an area of 3 ha is 762.81 tons.C±199.257 and at the roots is 659.76 tons.C±394.848, while the largest potential carbon stock in leaf organs is the type of mangrove R. apiculata, which is 318.91 tons.C.ha-1. and at the root is the type of mangrove A. marina, amounting to 448.54 tons.C.ha-1. The estimated carbon dioxide uptake by the Gili Meno mangrove leaves is in the range of 130.36 g.CO2-168.27 g.CO2 or with an average of 154.34 g.CO2±14.376, while the species with the highest carbon dioxide absorption capacity is R. apiculata (268.27 g.CO2) and the lowest in the species of L. racemosa (130.36 g.CO2).

Estimation of greenhouse gas emission flux from agricultural lands of Khuzestan province in Iran.

Greenhouse gas emissions and their effects on global warming are one of the serious challenges of developed and developing countries. Investigating the amount of greenhouse gas emissions of different countries makes it possible to determine the share of countries in the production of greenhouse gases. The purpose of this study is to use DAYCENT and DNDC models to estimate the emission rate of methane, nitrous oxide, and carbon dioxide greenhouse gases as well as to estimate the global warming potential in Khuzestan agricultural lands in Iran. For this purpose, the gas sampling was done in rice, wheat, and sugarcane fields using a static chamber, and then the concentration of methane, nitrous oxide, and carbon dioxide was determined by using gas chromatography. In the following, DAYCENT and DNDC models were used to estimate gas emissions and the global warming potential of these gases was estimated. Finally, TOPSIS method was used to prioritize gas emissions. In order to evaluate the modeling accuracy, the statistical indicators of maximum error, root mean square error, determination coefficient, model efficiency, and residual mass coefficient were used. According to the results, the highest measured gas flux was obtained for rice fields at Baghmalek and the lowest for sugarcane in Abadan. The results of DAYCENT model estimation showed that the highest emissions were obtained for methane gas and rice cultivation, and lowest gas emissions were obtained for sugarcane cultivation. The results of DNDC model estimation also showed that the highest flux was determined for nitrous oxide gas in rice cultivation. The results of the estimation of global warming potential also showed that it was the highest in sugarcane cultivation (Shushtar station) and the DAYCENT model, and the lowest was also in wheat cultivation and the DNDC model. The statistical results of the estimation of DAYCENT and DNDC models showed that the DAYCENT model in sugarcane cultivation (Shushtar station) was the most accurate in estimating carbon dioxide gas, and the lowest accuracy was related to the DNDC model and sugarcane cultivation (Shushtar station) in estimating nitrous oxide gas. According to the results of agricultural activities in Khuzestan province, they have made a major contribution to the production of greenhouse gases, which, or the lack of attention to this issue, will have an effect on the future climate of this region.