Emission Analysis Research Articles

A Comprehensive Analysis of Environmental Emissions from Trenchless CIPP and Excavation Technologies for Sanitary Sewers

The renewal of underground infrastructure is an emerging challenge for most municipalities in the United States. As compared to trenchless cured-in-place pipes (CIPPs), excavation technologies (ETs) have adverse impacts on the environment. Due to its lower ecological impact, trenchless technology is preferred in comparison to conventional pipe replacement. The selection of the most appropriate method depends on factors such as the existing sewer network, traffic disruption, soil conditions, and environmental safety. Recent concerns pertaining to environmental impact have increased the demand for reduced carbon footprints. The objectives of this paper are the following: (1) to present a comprehensive review on the achievements achieved over the years in understanding the factors influencing environmental emissions from the use of CIPP and ETs and (2) to analyze and compare the environmental emissions produced from CIPPs and ETs for 8-inch-, 10-inch-, and 12-inch-diameter pipes. Published papers from 1990 through 2024 have been included, which reported emissions from both alternatives. A comparison of total environmental emissions produced from both the processes is presented. The literature review and analysis suggest that higher emissions are a result of higher fuel consumption, material use, and input allocation. The emissions of pipeline renewal methods were evaluated using USEPA’s TRACI 2.1 methodology within SimaPro software. The analysis showed that CIPP renewal greatly reduced carbon emissions when compared with ET. CIPPs exhibited approximately 70% less ecological impact, 75% less impact on human health, and 60% less depletion of resources. CIPPs reduced carbon emissions by 78–100% in comparison to ETs. The recycling materials used in CIPPs potentially reduce the environmental impact by 10%, making them highly sustainable. The installation phase should therefore be carefully analyzed for factors like the pipe material and the pipes’ external diameter in view of achieving the greatest sustainability of these methods, as these characteristics affect emissions. It can be inferred that the comparison of the emissions of both alternatives is extremely vital for sustainable underground infrastructure development.

A comparative analysis and prediction of carbon emission in India using Machine learning models

<p style="line-height:150%;text-align:justify;text-indent:36.0pt;"><span style="background-color:yellow;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">Among the most significant issues that will impact and pose a major threat to our future are global warming and climate change. This study focuses on forecasting India's carbon emissions, specifically examining the role of agriculture and agri-related industries, using time-series data from 1990 to 2020. </span><span style="background-color:lime;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">To achieve this, a machine learning-based approach was proposed, utilizing five advanced models AdaBoost, XGB, Gradient Boosting, Random Forest, and Linear Regression. These models were selected for their ability to process large datasets, identify complex patterns, and provide precise predictions, offering a comprehensive framework for estimating carbon emissions. The approach aims to identify the most accurate predictive model for estimating carbon emissions, facilitating informed policy interventions and strategic planning. </span><span style="background-color:aqua;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">The Carbon Budget 2023 research highlights that India's carbon emissions increased by 8.2% in 2023, raising significant concerns for environmental sustainability. Using the proposed machine learning models, the study facilitates robust data prediction. Among the five models, XGB demonstrates superior performance with evaluation metrics such as a Mean Absolute Error (MAE) of 19843, Root Mean Square Error (RMSE) of 24022, Mean Squared Error (MSE) of 5770998, R² value of 0.9, and test accuracy of 99%. These findings establish XGB as the most reliable model for forecasting carbon emissions. Consequently, XGB emerges as a powerful tool for accurately estimating emissions and addressing the challenges posed by climate change.</span></p>

LiDAR Image-Based Earth Carbon Emission Analysis and Its Impact on Public Health: A Machine Learning Model

LiDAR Image-Based Earth Carbon Emission Analysis and Its Impact on Public Health: A Machine Learning Model

Evaluation of the effect of the water-energy nexus on the performance of the water-energy supply system.

In this study, the water-energy nexus is investigated throughout coupling the Water Evaluation and Planning (WEAP) and Low Emission Analysis Platform (LEAP) models under the climate change effects in the Marun basin, Iran. For this purpose, first, the climate change effects on water resources and consumption nodes are calculated under representative concentration pathway (RCP) scenarios from the fifth report of the International Panel on Climate Change (IPCC). Artificial neural network (ANN) is used to model river inflow and Cropwat model is used for agricultural water demand in future time (2015-2040). In the next step, water system is modeled in WEAP and energy system is modeled in LEAP. Then, three water-energy nexus scenarios include (1) calculating the actual hydropower generation capacity in LEAP model (W-E-N1), (2) water-energy nexus evaluation indexes in the WEAP model (W-E-N2), and (3) changes in the electricity consumption intensity in the LEAP model (W-E-N3) are modeled under RCP 2.6, RCP 4.5 and RCP 8.5 climate scenarios. The results of W-E-N1 scenario show that the actual hydropower generation capacity with water-energy nexus is reduced by 78%, 89%, and 91%, respectively, compared to no water-energy nexus under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios. The results of W-E-N2 show that the hydropower generation reliability index will be reduced by 19%, 31%, and 13%, respectively under RCP 2.6, RCP 4.5, and RCP 8.5 compared no water-energy nexus. The results of W-E-N3 scenario show that the electricity consumption intensity will increase considering the electricity required for water transferring to demand sites. The results of the present study show that the water-energy nexus will lead to more accurate results and, as a result, more realistic planning for water and energy resources.

Experimental investigation on the impact resistance and damage mechanism of carbon/glass unidirectional and woven hybrid laminates

AbstractThe mechanical properties of hybrid fiber composite laminates are affected by the hybrid ratio as well as the stacking sequences, to investigate the effect. In this paper, the effects of the hybrid ratio and stacking sequences on the impact resistance and residual compression properties of carbon/glass unidirectional and woven hybrid laminates are investigated. The use of acoustic emission technology enables the real‐time monitoring of the impact and compression damage processes of carbon/glass composite laminates with varying hybrid ratios and stacking sequences, thereby facilitating the elucidation of the underlying failure mechanisms and the evolution of the laminates. The findings indicated that laminates with symmetrical stacking demonstrated superior impact resistance, resilience, and residual compression properties compared to those with asymmetrical stacking when subjected to high‐energy impact. Sandwich stackings demonstrate superior energy absorption properties relative to alternate‐stacking laminates, although they exhibit diminished residual compression properties. The impact resistance of laminates with a high carbon content under a sandwich stacking sequence is poor. In the context of alternating symmetric stacking, the impact resistance of the laminate is not significantly influenced by the carbon content. The results of the acoustic emission analysis indicate that the damage is minimized when the stacking sequence is alternating symmetry and the impact side is a woven material.Highlights Enhancing impact resistance of composites through hybrid structure research. Report the optimal parameters of the effects on the impact resistance of the composite. The VMD method is used to determine the percentage of damage types in the laminate.

Diagnosis of pollutants in water utilizing analysis of laser-induced emission

Diagnosis of pollutants in water utilizing analysis of laser-induced emission

Evaluation of Carbon Emission Efficiency and Analysis of Influencing Factors of Chinese Oil and Gas Enterprises

Evaluation of Carbon Emission Efficiency and Analysis of Influencing Factors of Chinese Oil and Gas Enterprises

Cooling rate and turbulence in the intracluster medium of the cool-core cluster Abell 2667

We present a detailed analysis of the thermal X-ray emission from the intracluster medium in the cool-core galaxy cluster Abell 2667 at z=0.23. Our main goal is to detect low-temperature ($<2$ keV) X-ray emitting gas associated with a potential cooling flow connecting the hot intracluster medium reservoir to the cold gas phase responsible for star formation and supermassive black hole feeding. We combined new deep XMM-Newton EPIC and RGS data, along with archival Chandra data, and performed a spectral analysis of the emission from the core region. We find 1σ upper limits on the fraction of gas cooling equal to ∼ 40 rm M_ ⊙ yr^ and ∼ 50-60 rm M_ ⊙ yr^ in the temperature ranges of 0.5-1 keV and 1-2 keV, respectively. We do not identify OVII, FeXXI-FeXXII, and FeXVII recombination and resonant emission lines in our RGS spectra, implying that the fraction of gas cooling below 1 keV is limited to a few tens of solar masses per year at maximum. We do detect several lines (particularly SiXIV, MgXII, FeXXIII/FeXXIV, NeX, OVIIIα) from which we are able to estimate the turbulent broadening. We obtain a 1σ upper limit of ∼ 320 km/s, which is much higher than the one found in other cool-core clusters such as Abell 1835, suggesting the presence of some mechanisms that boost significant turbulence in the atmosphere of Abell 2667. Imaging analysis of Chandra data suggests the presence of a cold front possibly associated with sloshing or with intracluster medium cavities. However, current data do not allow us to clearly identify the dominant physical mechanism responsible for turbulence. These findings show that Abell 2667 is not different from other, low-redshift, cool-core clusters, with only upper limits on the mass deposition rate associated with possible isobaric cooling flows. Despite the lack of clear signatures of recent feedback events, the large upper limit on the turbulent velocity leaves room for significant heating of the intracluster medium, which may quench cooling in the cool core for an extended period, albeit also driving local intracluster medium fluctuations that will contribute to the next cycle of condensation rain.

Sustainable Development in Africa: A Comprehensive Analysis of GDP, CO2 Emissions, and Socio-Economic Factors

The fight against climate change is gaining momentum, with a growing focus on reducing carbon dioxide (CO2) emissions and mitigating environmental impacts. Africa, the continent most vulnerable to global warming, faces unique challenges in this context. This study examines the long-term association among CO2 emissions, economic growth, and different socio-economic factors in 36 African countries from 1990 to 2020. Employing the Pooled Mean Group (PMG) estimator with Autoregressive Distributed Lag (ARDL) model, along with U-test and Dumitrescu and Hurlin causality analyses, our study reveals substantial long-term connections amongst CO2 emissions and factors such as economic growth, trade openness, renewable energy consumption, urbanization, and population dynamics. The findings support the Environmental Kuznets Curve (EKC) hypothesis, indicating that CO2 emissions initially increase with GDP per capita growth but begin to decline after a turning point at approximately 10,614.75 USD. However, the evidence for this turning point remains weak, suggesting that most African countries have not yet achieved decoupling. Renewable energy consumption and urbanization are negatively associated with CO2 emissions, while trade openness and GDP per capita show positive correlations. Causality analysis reveals bidirectional relationships among most variables, except for population growth and CO2 emissions, which may involve other moderating factors. The findings highlight the urgent need for integrated policies that advance sustainable development by focusing on renewable energy adoption, sustainable urbanization, and green growth strategies. Policymakers should prioritize initiatives that harmonize economic growth with environmental sustainability, ensuring a lasting balance between development and ecological preservation across Africa.

Effect of tire pyrolysis oil and graphene oxide nanoparticles as an additive in the diesel engine.

The majority of industries throughout the world rely largely on fossil fuels as their primary energy source. However, these resources are finite and become scarcer by the day. Therefore, exploring alternative fuels and additives for diesel fuel is imperative to mitigate fuel consumption. A diesel engine powered with different blends of diesel fuel (DF), graphene oxide (GO) nanoparticles, and tire pyrolysis oil (TPO) was used in this investigation. The goal was to undertake in-depth evaluations of performance and emission characteristics. The findings of these investigations were compared to those obtained using neat diesel fuel as a baseline. In the study, diesel fuel blends containing various quantities of graphene nanoparticles (40, 80, and 120mg/L) and 10%, 20%, and 30% TPO were made. A single-cylinder, four-stroke, air-cooled spark-ignition engine was used for testing, with engine speed ranging from 1500 to 3500rpm with an increment of 500rpm. At 3000rpm, the engine running on DT10G80 blend fuel produced 10.46% greater torque than diesel fuel, and at 3500rpm, 11.7% more brake power. The BSFC of the DT10G80, which is 13.33% lower than that of diesel fuel, is the lowest at 2500rpm. When compared to diesel fuel, there are 33.3% and 19.8% fewer CO and NOx emissions, respectively, according to the emission analysis. Finally, combining nanoparticles, waste fuel tire pyrolysis oil, and diesel fuel improves engine power, decreases fuel consumption, and minimizes hazardous gas emissions and particulate matter.

A Comprehensive Approach to CO2 Emissions Analysis in High-Human-Development-Index Countries Using Statistical and Time Series Approaches

Reducing carbon dioxide (CO2) emissions is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emissions, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It is imperative to forecast CO2 emissions trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emissions. This paper presents an in-depth comparative study on the determinants of CO2 emissions in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emissions. Following this, the study leverages supervised and unsupervised time series approaches to further scrutinize and understand the factors influencing CO2 emissions. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a statistical time series forecasting model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised time series clustering approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emissions predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.

Influence of 1-Hexanol/Waste Plastic Oil Blends on Combustion, Performance, and Emission Characteristics of a Common Rail Direct Injection Diesel Engine.

Waste plastic oils (WPOs) can help address the global energy crisis caused by the rapid depletion of fossil fuels, global warming, and strict emission regulations. The present research delves into the intricate interplay of higher alcohol blends in the context of combustion, performance, and emission characteristics within a common rail direct injection engine. In this regard, 1-hexanol has been selected as the blending constituent for the WPO to tackle emission challenges while concurrently reducing dependence on conventional fuel, as it stands out for its enhanced fuel properties compared to lower alcohols. Simultaneously, the investigation took into account the influence of varying exhaust gas recirculation (EGR) rates. Experimental scrutiny centers on 10 to 30% by volume of 1-hexanol content with the WPO blends for the engine running at 2000 rpm at 20 to 80% of engine load. The inclusion of 1-hexanol led to a maximum cylinder pressure of 86.42 bar, a heat release rate of 117.43 J/degree, and a brake thermal efficiency of 42.1%. Brake-specific energy consumption values decreased with increasing hexanol concentration, demonstrating improved fuel economy. However, emission analysis revealed that HC emissions increased with the hexanol concentration, reaching a maximum of 45 PPM for the H30WPO70 blend at low loads. Similarly, CO emissions were highest for the H30WPO70 blend, with a peak value of 0.40%vol at 20% load. NOx emissions, on the other hand, decreased with increasing hexanol content and EGR rates with a reduction of 11.3 and 12.55% at 10 and 20% EGR respectively. Thus, the comprehensive investigation of 1-hexanol augmentation in waste plastic oil provides the intricate details of combustion processes and performance metrics with emission profiles, which entail the 1-hexanol integration as an effective means for characteristic improvement of automotive engines.

Methane Emissions in the ESG Framework at the World Level

Methane is a strong green gas that has higher GWP. Methane emissions, therefore, form one of the critical focuses within climate change mitigation policy. Indeed, the present study represents a very novel analysis of methane emission within the ESG framework by using the data across 193 countries within the period of 2011–2020. Methane reduction on account of ESG delivers prompt climate benefits and thereby preserves the core environment, social, and governance objectives. In spite of its importance, the role of methane remains thinly explored within ESG metrics. This study analyzes how factors like renewable energy use, effective governance, and socioeconomic settings influence the emission rate of the study subject, as many previous ESG studies are deficient in considering methane. By using econometric modeling, this research identifies that increasing methane emissions remain unabated with the improvement of ESG performances around the world, particularly within key agricultural and fossil fuel-based industrial sectors. Renewable energy cuts emissions, but energy importation simply transfers the burdens to exporting nations. It therefore involves effective governance and targeted internationational cooperation, as socioeconomic elements act differently in different developed and developing countries to drive various emission sources. These findings strongly call for balanced, targeted strategies to integrate actions of mitigation into ESG goals related to methane abatement.

Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites

Acoustic emission (AE) analysis was utilized to assess the cracking behavior of six lightweight self-consolidating concrete (LWSCC)–engineering cementitious composite (ECC) beams under flexural loading. Two control beams were fully cast with ECC containing either polyvinyl alcohol (PVA) fibers or steel fibers (SF). The remaining four beams were ECC-LWSCC composite beams, with the ECC layer containing PVA fibers or SF placed on either the tension or compression side. The results showed that the control beams had the highest ultimate load capacity, followed by beams repaired in tension, and then beams repaired in compression. PVA fibers exhibited higher performance compared to steel fibers at the first crack load, while steel fibers enhanced the beam’s performance at the ultimate load stage. During the flexural testing, AE parameters such as the number of hits, signal amplitude, and cumulative signal strength (CSS) were collected until failure. The analysis of these AE parameters was effective in detecting the first crack and evaluating cracking propagation in all beams. Changing the type of fibers (PVA and SF) in the ECC layer showed a significant effect on AE parameters. Moreover, adding a new ECC layer to an existing LWSCC layer resulted in variations in the signal amplitude. Finally, the flexural failure mode was confirmed with the aid of the rise time/maximum amplitude vs. average frequency analysis.

Comprehensive Analysis and Emission Reduction Pathway of GHG Emissions at a Wastewater Treatment Plant in Suzhou

Taking a sewage treatment plant in Suzhou City, Jiangsu Province, as an example, the greenhouse gas （GHG） emissions generated in the sewage treatment system were calculated using the carbon balance method and the emission factor method. The environmental impacts and economic aspects of different treatment units in wastewater treatment plants were analyzed using life cycle assessment, cost-benefit analysis, and data envelopment analysis models, and emission reduction pathways were proposed. The results indicated that the total GHG emissions （in terms of CO2） from a certain municipal wastewater treatment plant in Suzhou were 6 653.08 kg·（104 m3）-1, with direct and indirect GHG emissions accounting for 29.22% and 74%, respectively. The reuse of treated effluent achieved a reduction of 3.3% in emissions. The biological treatment phase and the sludge treatment phase were the main impact stages for GHG emissions at a certain wastewater treatment plant in Suzhou, where the high-power equipment, specifically the blowers used in the biological treatment phase, and the use of polymeric ferric sulfate agents in the sludge treatment phase were the primary factors contributing to GHG emissions. Life cycle assessment analysis revealed that electricity consumption, direct CO2 emissions, pollutant concentration in the effluent, and the use of chemical agents at wastewater treatment plants had negative impacts on global warming, atmospheric acidification, and eutrophication of water bodies. Calculations indicated that for every 10 000 m3 of wastewater treated, the sewage treatment plant achieved a net benefit of 13 630 RMB. However, from April to May 2023, the scale efficiency of the sewage treatment plant was less than 1. This indicates that during this period, the proportion of output increase was less than that of input increase, demonstrating an irrational structure of input-output. After June, through enhancing the overall operational load, advancing technical improvements, and management efforts, the optimization of scale efficiency was achieved. A sewage treatment plant in Suzhou could achieve the goal of being "green and low-carbon" by installing high-efficiency pumps and fans, utilizing solar photovoltaic and water source heat pump systems, and making process improvements.

Carbon Footprint Analysis of an Outpatient Dermatology Practice at an Academic Medical Center

There is growing awareness of the US health sector's substantial contribution to the country's greenhouse gas (GHG) emissions, exacerbating the health threats from climate change. Reducing health care's environmental impact requires understanding its carbon emissions, but there are few published audits of health systems and fewer comprehensive emissions analyses at the clinic or department level. To quantify the annual GHG emissions from a large outpatient dermatology practice, compare relative sources of emissions, and identify actionable targets. This quality improvement study involving a comprehensive carbon footprint analysis (scopes 1-3) of a large (nearly 30 000 visits/y), outpatient medical dermatology practice within the University of Pennsylvania's academic medical complex was conducted following the GHG Protocol Corporate and Corporate Value Chain reporting standards for fiscal year 2022 (ie, July 2021 through June 2022). Data were obtained through energy metering, manual audits, electronic medical records, and administrative data. Data were converted into metric tons of carbon dioxide equivalent (tCO2e), allowing comparison of global-warming potential of emitted GHGs. Primary outcomes were tCO2e by scope 1 (direct emissions), scope 2 (indirect, purchased energy), and scope 3 (indirect, upstream/downstream sources), as well as by individual categories of emission sources within each scope. Scope 3 contributed most to the clinic's carbon footprint, composing 165.5 tCO2e (51.1%), followed by scope 2 (149.9 tCO2e [46.3%]), and scope 1 (8.2 tCO2e [2.5%]). Within scope 3, the greatest contributor was overall purchased goods and services (120.3 tCO2e [72.7% of scope 3]), followed by patient travel to and from the clinic (14.2 tCO2e [8.6%]) and waste (13.1 tCO2e [7.9%]). Steam and chilled water were the largest contributors to scope 2. Clinic energy use intensity was 185.4 kBtu/sqft. In this quality improvement study, the composition of emissions at the clinic level reflects the importance of scope 3, paralleling the health sector overall. The lower-resource intensity of the clinic compared to the average energy requirements of the total clinical complex led to a relatively large contribution from scope 2. These findings support efforts to characterize high-yield emissions-reduction targets and allow for identification of actionable, clinic-level steps that may inform broader health system efforts.

Carbon Emission Analysis and Carbon Reduction Strategy of Small and Medium-scale Municipal Wastewater Treatment Plants in Cities and Towns

The carbon emissions of three typical processes （AAO, MSBR, and oxidation ditch） were systematically analyzed from the perspective of the whole wastewater treatment process based on the annual data of eleven urban small and medium-scale WWTPs in the year 2022, and the effects of different influent characteristics （TP, TN, BOD5, COD, influent volume, and COD/TN） on the carbon emissions were studied by using the partial least squares structural equation modeling （PLS-SEM） method. The results showed that indirect carbon emissions dominated the total carbon emissions of small and medium-scale WWTPs （69.5%）, and carbon emissions from electricity consumption were the largest source （43.1%）； the evaluated carbon emission intensity of the MSBR process [（0.363±0.007） kg·m-3] was lower than that of the AAO process [（0.439±0.099） kg·m-3] and oxidation ditch process [（0.396±0.025） kg·m-3], which had a superior level of low-carbon operation. The carbon intensity of the various components of small and medium-scale WWTPs was as follows： biological treatment （58.7%） > sludge dewatering+deodorisation （17.9%） > deep treatment （17.3%） > pre-treatment （6.1%）； the biological part accounted for 51.5%, 53.9%, and 73.1% of the total carbon intensity of the three processes, respectively. In addition, the influent COD concentration and COD/TN had significantly positive （path coefficients of 0.584 and 0.5） and negative （path coefficient of -0.416） impacts on the direct carbon emissions （N2O and CH4, respectively）, and the influent concentrations of TN, BOD5, and TP had significantly positive effects on the indirect carbon emissions （electricity consumption） and indirect carbon emissions （material consumption）, with path coefficients of 0.078, 0.537, and 0.5, respectively. Carbon reduction in small and medium-scale WWTPs can be carried out by reducing carbon intensity, decreasing carbon operation levels, and strengthening government management.

Correction: Flight Regimes and Environmental Impact: A Linear Regression Analysis of co2 Emissions for En-Route Air Traffic Operations

Correction: Flight Regimes and Environmental Impact: A Linear Regression Analysis of co2 Emissions for En-Route Air Traffic Operations

Combination of anaerobic digestion and sludge biochar for bioenergy conversion: Estimation and evaluation of energy production, CO2 emission, and cost analysis.

Waste-to-energy technologies involve the conversion of several wastes to useful energy forms like biogas and biochar, which include biological and thermochemical processes, as well as the combination of both systems. Assessing the economic and environmental impacts is an important step to integrate sustainability and economic viability at anaerobic digestion systems and its waste management. Energy production, CO2 emissions, cost analysis, and an overall process evaluation were conducted, relying on findings from both laboratory and pilot-scale experiments. The digestate generated during anaerobic digestion proved to be an effective approach for mitigating some CO2 emissions while managing sludge waste within the anaerobic digestion and CHP system. Incorporating biochar production and application in soil into the process led to a 3.5 % reduction in CO2 emissions, which contributed to a more sustainable form of energy production while offering the potential for the generation of carbon credits through a carbon-negative process. The employment of digestate biochar for energy production seems a feasible way to reduce the amount of residue to final disposal in landfill with a minimal reduction of profit per GWh and a slight increase in the CO2 emissions by 2.7 %.

Revised method for constructing acoustic vulnerability curves in trees

Abstract During drought, the formation of air bubbles known as embolisms in the water-conducting xylem reduces hydraulic conductivity, which can ultimately result in tree death. Accurately quantifying vulnerability to embolism formation is therefore essential for understanding tree hydraulics. Acoustic emission (AE) analysis offers a non-destructive method to monitor this process, yet the interpretation of captured signals remains debated. In this study, we introduce an improved methodology for constructing acoustic vulnerability curves (VCAE) that minimizes subjectivity and enhances the accuracy of assessing a tree’s vulnerability to drought stress. Our approach combines AE signal clustering with an objective method for pinpointing the endpoint (point of 100% embolism) based on the observed correlation between water potential at maximum AE activity and 50% loss of hydraulic conductivity. By applying a refined clustering algorithm to four temperate tree species (Platanus × acerifolia (Aiton) Willd., Betula pendula Roth, Quercus robur L. and Fagus sylvatica L.), we consistently identified natural frequency-based clusters that effectively separate embolism-related AEs from other signals. This focus on embolism-related AE activity allowed us to minimize the influence of non-embolism-related signals and identify the true VCAE endpoint. Our method, by reducing the subjectivity inherent in previous approaches, enhances the accuracy of VCAE construction, offering broader insights into tree hydraulics and expanding its applicability across different species and environmental conditions.