Estimates Of Carbon Dioxide Emissions Research Articles

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.

Recommendations on visit duration and sample number requirements for an automated head chamber system.

Automated head chamber systems (AHCS; GreenFeed, C-Lock Inc., Rapid City, SD) increasingly are being used for measuring the gas flux of unrestrained cattle. There are a wide range of recommendations for what constitutes a "good" visit (i.e., duration) to an AHCS and how many visits are required for the AHCS to quantify gas fluxes accurately and precisely. Accordingly, the purpose of this experiment was to investigate the effects of visit duration thresholds and the subsequent effects of these thresholds on the number of visits needed to provide adequate estimates of carbon dioxide (CO2) and methane (CH4) emissions, and oxygen (O2) consumption by beef cattle. This analysis utilized data from three previously published experiments with grazing beef steers and one experiment with finishing beef steers, with 103 steers total. When comparing all available visits, there was excellent agreement [Lin's concordance correlation coefficient (CCC) ≥ 0.96] between visits ≥ 3min in duration and those ≥ 2min for the three gases in all four experiments. When data from all four experiments were pooled, there was excellent agreement between visits ≥ 3min and those ≥ 2min and ≥ 1min for all gases (CCC ≥ 0.96). These results suggest that estimates from visits ≥ 2min are like those from visits ≥ 3min. Next, we investigated if including visits ≥ 2min or ≥ 1min would increase the minimal number of visits required to provide excellent agreement with the "gold-standard" (mean of all visits ≥ 3min). For this, we used only one of the experiments and randomly selected visits per animal ranging from n = 5 to 60, in increments of 5. The sole experiment was used because all animals had more than 60 visits. We then assessed the agreement between the "gold-standard" (mean of all visits ≥ 3min [144 ± 55.01 visits per steer]) estimates of CO2, O2, and CH4. The minimum number of visits required to achieve excellent agreement (CCC ≥ 0.90) to the "gold-standard" estimate for all gases was 30 visits ≥ 3min in duration, or 40 visits ≥ 2min in duration. Visits ≥ 1min in duration did not achieve excellent agreement, even when 60 were used. Based on these results, we recommend excluding visits < 3min in duration with 30 minimum visit records per animal. However, if researchers choose to implement a 2-min visit duration threshold then 40 visit records are needed per animal.

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. &#x0D; 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.

Additive and Subtractive Manufacturing of Inconel 718 Components - Estimation of Time, Costs and Carbon Dioxide Emission – Case Study

Additive and Subtractive Manufacturing of Inconel 718 Components - Estimation of Time, Costs and Carbon Dioxide Emission – Case Study

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

Estimation of local pollutant and carbon dioxide emissions of road and rail transportation in the Philippines

The 2016 baseline road and rail transportation pollutant emissions in the Philippines using a bottom-up approach are estimated at 9.212 million tons and 377 tons, respectively, while CO2 emissions are estimated at 97.6 million tons and 62,248 tons, respectively. Road transportation activity is estimated to be 231.53 billion vehicle-kilometers, where 58% is from motorcycles. Top pollutants are CO and VOC from motorcycles, tricycles and cars. Motorcycles contribute the largest share of CO2 emissions. NOₓ and CO are major pollutants from rail transportation. Based on these results and the assessment of selected transport projects, the government may develop policies on major emitters of criteria pollutants and carbon dioxide, aside from improvement of public transportation. Furthermore, the study is limited in using average vehicle emission factors which need updating.

Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor

Under the United Nations Framework Convention on Climate Change, Annex 1 countries must report annual carbon dioxide (CO2) emissions from peatlands drained for extraction. However, the Tier 1 emission factor (EF) provided in the IPCC 2014 Wetland Supplement is based mainly on warm season data from a limited number of sites. Here we evaluate the current IPCC EF and revise it with newly published data. The updated EF is 2.46 ± 0.25 t C ha−1 yr−1, a 12% reduction and a threefold decrease in the confidence interval compared to the current IPCC (2014) EF. We generate a Tier 3 EF, 1.4 ± 0.25 t C ha−1 yr−1 for a typical extraction site in eastern Canada using numerical CoupModel that explicitly considers seasonality and interannual climatic variability, and suggest how to account for seasonality for the previously published EFs. This reduction has implications for comparing alternatives to peat-based growing substrates, the assessment of offsets, and possible punitive carbon taxes or cap-and-trade schemes.

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.

Estimation of methane emissions from reservoirs for hydroelectric generation in Costa Rica

Greenhouse gas emissions are related to non-renewable sources. For this reason, the methodological guide for the estimation of methane and carbon dioxide emissions in flooded lands was published in 2006 by the Intergovernmental Panel on Climate Change. Since 2016, several studies have been carried out in temperate and tropical zones reservoirs. Costa Rica is a Central American country known for its large hydroelectric resources and its highly renewable electricity generation matrix. This work represents the first study for 11 of 24 hydroelectric plants managed by the Costa Rican Electricity Institute. Methane emissions, energy density and emission factors for electricity generation are determined. Furthermore, a static mathematical model is used to determine these factors with little input data. It is estimated that the greatest contribution to methane emissions corresponds to the Arenal reservoir, which has the largest surface area and the lowest energy density.

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.

Estimates of daily oxygen consumption, carbon dioxide and methane emissions, and heat production for beef and dairy cattle using spot gas sampling.

A simulation study was conducted to examine accuracy of estimating daily O2 consumption, CO2 and CH4 emissions, and heat production (HP) using a spot sampling technique and to determine optimal spot sampling frequency (FQ). Data were obtained from 3 experiments where daily O2 consumption, emissions of CO2 and CH4, and HP were measured using indirect calorimetry (respiration chamber or headbox system). Experiment 1 used 8 beef heifers (ad libitum feeding; gaseous exchanges measured every 30 min over 3 d in respiration chambers); Experiment 2 used 56 lactating Holstein-Friesian cows (restricted feeding; gaseous exchanges measured every 12 min over 3 d in respiration chambers); Experiment 3 used 12 lactating Jersey cows (ad libitum feeding; gaseous exchanges measured every hour for 1 d using headbox style chambers). Within experiment, averages of all measurements (FQALL) and averages of measurements selected at time points with 12, 8, 6, or 4 spot sampling FQ (i.e., sampling every 2, 3, 4, and 6 h in a 24-h cycle, respectively; FQ12, FQ8, FQ6, and FQ4, respectively) were compared. Within study a mixed model was used to compare gaseous exchanges and HP among FQALL, FQ12, FQ8, FQ6, and FQ4, and an interaction of dietary treatment by FQ was examined. A regression model was used to evaluate accuracy of spot sampling within study [i.e., FQALL (observed) vs. FQ12, FQ8, FQ6, or FQ4 (estimated)]. No interaction of diet by FQ was observed for any variables except for CH4 production in experiment 1. No FQ effect was observed for gaseous exchanges and HP except in experiment 2 where CO2 production was less (5,411 vs. 5,563 L/d) for FQ4 compared with FQALL, FQ12, and FQ8. A regression analysis between FQALL and each FQ within study showed that slopes and intercepts became farther from 1 and 0, respectively, for almost all variables as FQ decreased. Most variables for FQ12 and FQ8 had root mean square prediction error (RMSPE) less than 10% of the mean and concordance correlation coefficient (CCC) greater than 0.80, and RMSPE increased and CCC decreased as FQ decreased. When a regression analysis was conducted with combined data from the 3 experiments (mixed model with study as a random effect), results agreed with those from the analysis for the individual studies. Prediction errors increased and CCC decreased as FQ decreased. Generally, all the estimates from FQ12, FQ8, FQ6, and FQ4 had RMSPE less than 10% of the means and CCC greater than 0.90 except for FQ6 and FQ4 for O2 consumption and CH4 production. In conclusion, the spot sampling simulation with 3 indirect calorimetry experiments indicated that FQ of at least 8 samples (every 3 h in a 24-h cycle) was required to estimate daily O2 consumption, CO2 and CH4 production, and HP and to detect changes in those in response to dietary treatments. This sampling FQ may be considered when using techniques that measure spot gas exchanges such as the GreenFeed and face mask systems.

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process.

Estimation and forecasting of carbon dioxide emissions from coal-fired thermal power plants in Ukraine

Purpose. Development and verification of a method for calculating and forecasting CO2 emissions from coal combustion at thermal power plants based on proximate analysis data. Calculation of gross and specific CO2 emissions per unit of output energy and mass of coal consumed at Ukrainian thermal power plants (TPPs). Methodology. Methods of mathematical statistics were used for processing the data of ultimate and proximate analysis of 170samples of A, L, G, and LFG coal ranks with low heat value on operating state in the range of 17.2 to 31.0 MJ/kg and ash content on dry state (Ad) in the range of 3.8 to 38.0% to determine relationships between carbon emission factors (kc), calorific value, and ash content. Findings. The values of emission factors and gross CO2 emissions for mixtures of coals of grades A and L, G and LFG at Ukrainian TPPs in 20172021 were calculated. For 2021, the average value of for coals of grades G and LFG was 94,128 g/GJ, and for coals of grades A and L it was 104,987 g/GJ. Gross CO2 emissions at Ukrainian TPPs have been in the range of 3849million tons in recent years, and their annual reduction is due to a decrease in energy production and fuel consumption at TPPs, primarily of grades A and L. Originality. Empirical dependencies kc for steam coal of different ranks are determined in the form of The coefficients a, b, and c are determined for grades A, L, G, and LFG and their mixtures. The relationship between the carbon content in coal and the low heat value for coal is linear: where K is a coefficient depending on the coal grade. The values of K are determined for coal of grades A, L, G, and LFG. Practical value. Verification of the created method shows that the calculation error is less than 1.0%. This is in line with the requirements of the Monitoring Procedure and Directive 2003/87/EC. In 2021, the specific CO2 emission per unit of output energy at TPPs in Ukraine was 1,084 g/kWh for all ranks of steam coal. The values of specific CO2 emissions per unit mass of consumed coal were 1.94 t/t for coals of all grades, 1.91 t/t for grades G and LFG, and 2.21 t/t for grades A and L. The official annual reports of the Ministry of Energy of Ukraine contain information on the amount of produced electricity, consumed coal, and forecast balances of electricity production at TPPs, therefore, the values of specific emissions established by us are convenient to use for estimating and forecasting carbon dioxide emissions.

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.

Industrial production of recycled cement: energy consumption and carbon dioxide emission estimation.

The urge to reduce greenhouse gas emissions, in particular carbon dioxide, is a global problem, not only in spatial terms but also in terms of the scope of activities and sectors involved. Nevertheless, some sectors/industries are more critical due to their overall contribution to the problem, which is the case of the Portland cement industry. The present research estimates the energy consumption and carbon emissions associated with a novel process for producing cement by recycling used concrete and mortars. The novel process assessed resorts to the magnetic separation of the cement paste from the aggregates, followed by the thermal reactivation of the cement paste. Comparing the recycled cement production with the clinker production, higher energy consumption (over 9000MJ/t compared with roughly 4000MJ/t for Portland cement) and lower carbon dioxide emissions (average 730kg CO2/t compared with more than 800kg CO2/t for Portland cement) were estimated. However, the potential benefits in an industrial application are potentially much higher with the optimization of the production process. In particular, improvements in the washing and drying of the material prior to the magnetic separation will be critical since most of the energy is consumed in the process of drying.

Carbon Dioxide Neutralization Across the Global Supply Chain

The increased impacts of climatic change and global warming has led many organizations to adopt green initiatives in several areas of their business processes. Many multinational companies are moving towards reduction of carbon emission across its various operations. Carbon neutrality is the process where steps are taken to achieve net zero carbon dioxide emissions. This article proposes measures to achieve carbon neutrality across the supply chain globally. As part of its sustainability initiative, organizations have decided to reduce carbon consumption across their plants. This calls for estimation of carbon dioxide emissions and reducing the carbon footprint in the entire supply chain process. It also involves gauging greenhouse CO2 emissions during the transportation process for all TMC regions and transportation models used by various companies. The main calculations include total CO2 emissions, CO2 emissions per ton of goods transported, CO2 emissions per transport Km. These calculations are done based on factors such as full truck load, less truck load, sea mode of transportation, and air mode of transportation. An analysis is performed on the resulting calculation figures for different modes of transportation such as road, air, and sea. The analysis shows that there is an increase in overall CO2 for air mode of transportation. The least increase in overall Co2 is sea mode of transportation. Through this analysis, it helps the company to take better decisions regarding the mode of transportation that they need to adopt to achieve carbon neutrality.

Seasonal and diel variability of CO2 emissions from a semiarid hard-water reservoir

Reservoirs represent a key component of the global carbon cycle. However, estimates of carbon dioxide (CO2) emissions from reservoirs remain poorly constrained due to the absence of spatially and temporally resolved measurements. We performed high-resolution monitoring of CO2 emissions (FCO2) in a semiarid hard-water reservoir to examine its seasonal and diel variability. Our results suggest that dissolved inorganic carbon input plays a central role in sustaining the surface water CO2 partial pressure (pCO2), which varies from 1076 to 4587 μatm. Although the reservoir is moderately to highly productive throughout the year, it is a net CO2 source with FCO2 values in the range of 308–1753 mg C m−2 d–1. This high CO2 efflux indicates that productive waters are not necessarily CO2 sinks. Both pCO2 and FCO2 exhibit clear seasonal and diel patterns. Surface water pCO2 is highest in March and presents a consistent diurnal/nocturnal pattern with the daytime pCO2 6–13% lower than the nighttime pCO2. High CO2 efflux is observed during the ice-thaw period, indicating the release of CO2 that was accumulated during the winter. CO2 effluxes are typically higher during the nighttime driven by aquatic metabolism, but episodic weather events (e.g., rainfall and strong winds) can significantly enhance CO2 emissions and even reverse the diel pattern. Our study also shows that using only daytime measurements to estimate daily CO2 emissions would underestimate it by 9–25%. Hence, future global assessments should incorporate CO2 emissions from hard-water reservoirs and account for their seasonal and diel variability.

A Parametric Study to Assess Lightweight Aggregate Concrete for Future Sustainable Construction of Reinforced Concrete Beams

Lightweight aggregate concrete (LWC) is an attractive alternative to conventional concrete in building construction. It leads to lighter self-weight in beams and floor slabs and thus might have a positive impact on reinforcing steel consumption, also reducing the loads withstood by columns and foundations. However, LWC may increase cement consumption to maintain the required concrete compressive strength. This study presents compact equations for the design of reinforced LWC beams and subsequently applies them to a parametric analysis programmed in MATLAB. The aim of this analysis is to obtain an estimation of the equivalent carbon dioxide emissions associated with steel and cement consumption if using LWC instead of conventional concrete. The analysis involves more than 3 million beams simulating real scenarios by varying different design parameters, such as mix design, concrete strength, span length and applied loads and verifying both Ultimate and Serviceability Limit States. Whereas LWC of density equal or below 1600 kg/m3 does not seem to be feasible when trying adequately control cement content, the study shows that LWC with densities of 1800 and 2000 kg/m3 would not have a negative impact on the carbon dioxide emissions and would adequately comply with the various design restrictions.

Different responses of soil respiration to environmental factors across forest stages in a Southeast Asian forest.

Soil respiration (SR) in forests contributes significant carbon dioxide emissions from terrestrial ecosystems and is highly sensitive to environmental changes, including soil temperature, soil moisture, microbial community, surface litter, and vegetation type. Indeed, a small change in SR may have large impacts on the global carbon balance, further influencing feedbacks to climate change. Thus, detailed characterization of SR responses to changes in environmental conditions is needed to accurately estimate carbon dioxide emissions from forest ecosystems. However, data for such analyses are still limited, especially in tropical forests of Southeast Asia where various stages of forest succession exist due to previous land‐use changes. In this study, we measured SR and some environmental factors including soil temperature (ST), soil moisture (SM), and organic matter content (OM) in three successional tropical forests in both wet and dry periods. We also analyzed the relationships between SR and these environmental variables. Results showed that SR was higher in the wet period and in older forests. Although no response of SR to ST was found in younger forest stages, SR of the old‐growth forest significantly responded to ST, plausibly due to the nonuniform forest structure, including gaps, that resulted in a wide range of ST. Across forest stages, SM was the limiting factor for SR in the wet period, whereas SR significantly varied with OM in the dry period. Overall, our results indicated that the responses of SR to environmental factors varied temporally and across forest succession. Nevertheless, these findings are still preliminary and call for detailed investigations on SR and its variations with environmental factors in Southeast Asian tropical forests where patches of successional stages dominate.