Lower Greenhouse Gas Emissions Research Articles

Understanding the Dairy Sector in Slovenia: A Modeling Approach for Policy Evaluation and Decision Support

This study investigates the dairy sector in Slovenia, focusing on farm heterogeneity, efficiency in resource utilization, and policy implementations. Through a modeling approach, we explore the differences among dairy farms, considering factors such as herd size, farm management, natural conditions, and production potential. Based on 32 typical dairy farms, representing the entire dairy sector, composed of 6400 dairy farms, the analysis was performed using the farm model (SiTFarm). We emphasize the importance of accurate assessments, given the variability of policy impacts across farm types. While medium-to-large, specialized farms dominate milk production, smaller farms, particularly in less favored areas, hold social and environmental importance despite facing competitive challenges. Addressing environmental sustainability could involve promoting practices that improve milk yield and include grazing, as this tends to lower greenhouse gas emissions per kilogram of milk (−5%). Dairy farms contribute about one-third of the generated revenue in Slovene agriculture, of which a good half goes to farms located in less favored areas. They manage a good quarter of permanent grassland in Slovenia, and it is certainly the sector that can achieve the highest return on these areas. In 75% of the farms, the gross margin is higher than 1756 EUR/ha and using best practices they exceed 3400 EUR/ha. The model results indicate that the average hourly rate on dairy farms during the observed period falls within the range of EUR 7.3 to 17.4 of gross margin for most farms, with the top-performing ones exceeding 24 EUR/h. However, due to the significant reliance on budgetary payments (on average, 58% of the gross margin), the implementation of the common agricultural policy strategic plan generally leads to a deterioration in the economic indicators of dairy farms. This impact is particularly pronounced on medium-sized and larger farms, increasing the effect on income due to the substantial fixed costs involved. Our findings underscore the interplay between policy interventions, farm characteristics, and sectoral outcomes, offering valuable insights for policy-makers and stakeholders.

Assessing the Environmental Impact of Biobased Exterior Insulation Panel: A Focus on Carbon Uptake and Embodied Emissions

There are millions of older buildings in the colder climate regions of the world where envelope upgrades are needed to improve the indoor quality of buildings, reduce energy costs, and lower greenhouse gas (GHG) emissions. This study assessed the global warming potential (GWP) with and without accounting for CO2 uptake in trees (biogenic carbon) for 20- and 100-year timespans of an exterior insulation panel. The panels consisted of six different materials with three-ply cross-laminated timber (CLT) as its main component. The net GWP100-Uptake impact when explicitly accounting for biogenic CO2 uptake over a 100-year time period was 7.2 kgCO2-eq/m2 which was 92.7% lower than if it was not included (GWP100-Fossil of 98.7 kgCO2-eq/m2). Using a 20-year GWP increased the impact of the GWP fossil by 21.7% and the CO2 uptake scenario by 298%. The major contributor was the energy used for manufacturing panel’s materials (53%), with embodied carbon in bio-products primarily responsible for offsetting emissions. The findings will be helpful for policymakers in setting net-zero carbon emission goals for embodied and operational impacts of building materials.

Evaluating the economic and environmental benefits of deploying a national-scale, thermo-chemical plastic waste upcycling infrastructure in the United States

Emerging chemical technologies can upcycle plastic waste by producing high-value polymers and other products. In this work, we study the economic and environmental benefits of deploying an upcycling infrastructure in the continental United States for producing low-density polyethylene (LDPE) and polypropylene (PP) from post-consumer plastic waste. Our analysis is based on a computational framework that integrates techno-economic analysis, life-cycle assessment, and value chain optimization. Our results demonstrate that the infrastructure could generate a market of nearly 20 billion USD per year and that this market is robust to various externalities. Our analysis also indicates that the infrastructure can achieve a plastic-to-plastic degree of circularity of 34% relative to residential plastic waste production, and leads to significant environmental benefits over alternative waste disposal methods, including 69%–75% lower greenhouse gas emissions than waste-to-energy systems and 38 million tonnes of avoided landfill waste per year.

Forest dynamics in LCA: Integrating carbon fluxes from forest management systems into the life cycle assessment of a building

The urgent issue of climate change has sparked increasing interest in using wood to reduce buildings' greenhouse gas emissions (GHGe). While attributional life cycle assessment (LCA) methods are commonly employed to estimate GHGe from buildings, they lack a temporal distribution of carbon fluxes from biogenic materials, overlooking forest management impacts on emissions and sequestration. Consequently, we investigated the integration of forest and building systems, examining emissions associated with three different forest management scenarios at stand and landscape levels. Our findings suggest a 6 % to 81 % lower GHGe for the building using this study's approach compared to the static methodology recommended by the European Standard EN16485 in a 50 year perspective. However, the accumulated impact over the building's lifetime remains similar. Hence, both methods incentivize building designers’ to use wood to lower GHGe, although the dynamic integration postpones benefits from the forests' carbon sequestration to later stages of the building's lifetime.

Guidelines for efficient nitrogen preservation in sewage sludge-based fertilizers

This study explores sustainable methods to mitigate nitrogen (N) loss in agriculture amid rising food demands and limited arable land. It examines sewage sludge (SS) as an alternative to synthetic N fertilizers. SS is rich in nitrogen (4.21 ± 0.42 %) and phosphorus (3.60 ± 0.72 %), making it suitable for nutrient recovery and soil enhancement. Unfavorable sludge management methods result in the loss of 950,000 tons of nitrogen, meeting almost 10 % of the EU's nitrogen fertilization demand. This research evaluates SS treatment methods, including chemical conversion, thermal treatment, and biological composting, focusing on nitrogen conservation efficiency. Results show nitrogen loss during hydrolysis is minimized at pH 4 to 8 but increases significantly as ammonia (NH3) at pH 9 to 11, ranging from 4.2 % to 9 %. Neutralizing the hydrolysate is crucial; using solid KOH resulted in 13.5 % nitrogen loss, 11 times more than using slightly alkaline ash (1.22 %). Adding ash during drying reduced nitrogen emissions by 30 % compared to traditional drying at 105 °C. Improving the C/N ratio with food residues reduced nitrogen losses by 46.3 % during composting. These findings highlight the importance of pH control in chemical processes and temperature regulation in thermal treatments. Adding residues from other processes, such as biomass combustion waste, enhances SS processing conditions. Understanding nitrogen retention mechanisms is crucial for the environmental sustainability of SS usage. Efficient nitrogen retention strategies improve the fertilization value of SS and reduce its environmental footprint by lowering greenhouse gas emissions, particularly ammonia. Reducing nitrogen loss during SS treatment significantly lowers ammonia emissions, a major contributor to greenhouse gas emissions. These results help determine optimal methods for managing and processing SS to minimize emissions and increase agricultural usability.

Development of comprehensive models for precise prognostics of ship fuel consumption

This study incorporates two unique machine learning algorithms, Huber regression and Light Gradient Boosting Machines (LGBM), for estimating ship consumption of fuel. These methods are employed to create forecasting models for ship fuel consumption during journeys, which is especially useful when interacting with non-linear data. The study then analyzes and evaluates the prediction accuracy of these two approaches compared to a baseline model generated using linear regression. The results of the investigation show that both methods establish extremely accurate predictions while handling non-linear data quickly. However, the Huber-based model outperforms the LGBM in terms of prediction accuracy, with an R-squared value of 0.979 versus 0.917 for the LGBM. In addition, the Huber-based model has a diminished prediction error, with an RMSE of 2.278, compared to the LGBM model's RMSE of 4.55. The graphical methods of the violin plot and Taylor’s diagram further established the superiority of Huber ML. These findings imply that Huber regression could be a suitable option for estimating in-route ship fuel usage in real time. As a consequence, this study emphasises the potential benefits of machine learning for accurately predicting ship fuel consumption, providing encouraging possibilities to optimise fuel usage while lowering greenhouse gas emissions.

Fast as a plane, clean as a train? Prospective life cycle assessment of a hyperloop system

Today, options to reduce the climate impacts of high-speed passenger transport over hundreds of kilometres are limited to using low-carbon synthetic fuels in aviation and high-speed trains. In the future, alternatives like battery electric airplanes might be available. Further, vehicles operating in near-vacuum tubes, so-called “hyperloop systems”, could represent an alternative. Our first-of-its-kind environmental life cycle assessment (LCA), considering its construction, operation, and end-of-life, shows that such a hyperloop system is energy-efficient and can exhibit very low greenhouse gas emissions (<8 g CO2/pkm) if low-carbon sources provide electricity for its operation and relatively high occupation rates can be realised. The environmental performance of a hyperloop system can be regarded as very similar to that of a train offering the same transport service. Compared to air travel, environmental burdens can be substantially reduced (<5% impact on climate change compared to conventional aircraft). This fundamental finding holds despite uncertainties regarding technical properties and design choices, which reflect the current development status of the hyperloop.

A Review of Biogas Production from Small-Scale Anaerobic Digestion Plants

One of the most serious problems facing the whole world today is global warming. Their lease of greenhouse gases is exacerbating the effects of global warming. Reduced greenhouse gas emissions and searching for alternative energy sources are becoming increasingly crucial. This study aims to review that one of the effective methods for lowering greenhouse gas emissions is the creation of biogas from agricultural waste using anaerobic digester plants. A lab-scale 5-liter batch fermenter was incubated at room temperature, specifically mesophilic (35°C). Biogas is a clean, reasonably priced, and sustainable energy source produced by the anaerobic fermentation of waste and organic waste. Mixing sludge and waste to react with each other during biogas production is essential. Factors affecting biogas, such as loading rate, retention time, operating ambient temperature, pH, mixing, etc., are also discussed. Asia is a region where the generation of this form of renewable energy is widespread, particularly in nations like China and India. The generation of biogas never has any adverse environmental effects, but it also yields environmentally safe byproducts. Agricultural waste is a large and anthropogenic source of methane in the atmosphere. It can be converted into nutrient-rich fertilizer. Agricultural waste, food waste, animal or human manure, and other organic waste are all converted into energy (in the form of biogas or electricity) using anaerobic digesters. Another advantage of biological fermentation is that it leaves behind a high-quality organic fertilizer

Steam &amp; Power Dashboard

Abstract— The development of the Steam and Power Dashboard represents a significant advancement in energy management practices for industrial plants. By integrating data from steam and power systems, the dashboard offers real-time monitoring and analysis capabilities, allowing plant operators to identify inefficiencies and make informed decisions to optimise energy consumption. The research study delves into the design and implementation of the dashboard, highlighting its user-friendly interface and its ability to provide actionable insights for improving plant performance. Furthermore, the impact of the Steam and Power Dashboard on energy efficiency, cost savings, and greenhouse gas emissions is a key focus of the research. The findings demonstrate that the dashboard is effective in enabling plant operators to make data-driven decisions that lead to significant improvements in energy management practices. These improvements not only result in cost savings for the plant but also contribute to reducing the plant’s environmental impact by lowering greenhouse gas emissions. Overall, this research contributes to the advancement of sustainable energy strategies in industrial settings by showcasing the benefits of utilising innovative technologies, such as the Steam and Power Dashboard, to improve energy management practices and drive towards a more sustainable future. IndexTerms – – Python, SQL, EDA

Green Lean Six Sigma in the food industry: a systematic literature review

PurposeThe current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy processors to ascertain the current state of the literature and aid future research direction.Design/methodology/approachUtilising a systematic literature review (SLR), the paper addresses various terms and different written forms in the literature. The study characterises the current deployment of GLSS in the food industry and explains the reported benefits of this approach.FindingsGLSS, a concept that has yet to be fully explored in the food industry, as in other sectors, holds significant potential to enhance the food industry’s sustainability practices. The dairy sector, a subsector of the food industry known for its high greenhouse gas emissions, is a prime candidate for the application of GLSS. In instances where it has been applied, GLSS has demonstrated its effectiveness in improving sustainability, reducing waste, lowering greenhouse gas emissions and minimising water usage. However, the specific tools used and the model for GLSS implementation are areas that require further study, as they have the potential to revolutionise food industry operations and reduce their environmental impacts.Practical implicationsBenchmarking of this research by the food industry sector and by academics can aid understanding of the practical application of GLSS tools and aid implementation of these practices to evolve the dairy processing sector in the next decade as sustainability champions in the sector.Originality/valueThis study extensively analyses GLSS in the food industry, with a particular focus on dairy processors.

Exploring the performance of biodiesel-hydrogen blends with diverse nanoparticles in diesel engine: A hybrid machine learning K-means clustering approach with weighted performance metrics

Biodiesel, an eco-friendly fuel with lower greenhouse gas emissions, is a crucial alternative to traditional fossil fuels. Adding hydrogen and nanoparticles to biodiesel enhances diesel engine performance, reduces emissions, and improves fuel efficiency through better combustion and fuel atomization. In this study, various nanoparticles were experimented, and their performance, emission, and acoustic outcomes were systematically evaluated, resulting in a comprehensive ranking and clustering. The k-means clustering-Entropy-TOPSIS method is applied to weigh performance outcomes, rank the nanoparticles, and create clusters among them among best and worst, considering multiple criteria's in this study. This study further divides diverse nanoparticles into clusters, employing a hybrid machine learning k-means clustering method. The CO parameter carries the highest weight (57%), followed by UBHC (22%), owing to their significant variation induced by the addition of nanoparticles, surpassing the variability observed in other parameters. Manganese oxide nanoparticles exhibited superior performance across various critical parameters among all nanoparticles with a minimum centroidal distance of 25.97. Moreover, enrichment with hydrogen gas, combined with nanoparticles, significantly enhanced diesel engine performance, manifesting in an approximate 8 % increase in Brake Thermal Efficiency (BTE) and 23 % reduction in CO emissions. Identifying the optimal nanoparticles is crucial for enhancing biodiesel properties, and the paramount importance of hydrogen mixing lies in its potential to significantly improve combustion efficiency, emissions, and overall diesel engine performance.

Economic, Societal, and Environmental Impacts of Available Energy Sources: A Review

The impacts that the available energy sources have had on society, the environment, and the economy have become a focus of attention in recent years, generating polarization of opinions. Understanding these impacts is crucial for rational evaluation and the development of strategies for economic growth and energy security. This review examines such impacts of the main energy resources currently exploited or in development, including fossil fuels, geothermal, biomass, solar, hydropower, hydrogen, nuclear, ocean, and wind energies on society through analysis and comparison. It is essential to consider how high energy demand influences energy prices, the workforce, and the environment and to assess the advantages and disadvantages of each energy source. One significant finding from this review is that the levelized cost of energy (LCOE) may vary substantially depending on the energy source used and show substantial ranges for different applications of the same energy source. Nuclear energy has the lowest LCOE range whereas ocean energy has the highest LCOE range among the nine energy sources considered. Fossil fuels were found to have the most substantial societal impacts, which involved on the positive side providing by far the largest number of jobs and highest tax revenues. However, on the negative side, fossil fuels, biomass, and nuclear energy sources pose the most significant health threats and social well-being impacts on communities and societies compared to other energy sources. On the other hand, solar, ocean and wind energy pose the lowest risk in terms of health and safety, with solar and wind also currently providing a substantial number of jobs worldwide. Regarding environmental consequences, fossil fuels generate the highest greenhouse gas (GHG) emissions and have the highest adverse impacts on ecosystems. In contrast, nuclear, ocean, solar and wind energies have the lowest GHG emissions and low to moderate impacts on ecosystems. Biomass, geothermal and hydropower energy sources have moderate to high ecosystem impacts compared to the other energy sources. Hydropower facilities require the most materials (mainly concrete) to build per unit of energy generated, followed by wind and solar energy, which require substantial steel and concrete per unit of energy generated. The lack of substantial materials recycling causes associated with solar and wind energy sources. All the energies that use thermal power generation process consume substantial quantities of water for cooling. The analysis and comparisons provided in this review identified that there is an urgent need to transition away from large-carbon-footprint processes, particularly fossil fuels without carbon capture, and to reduce the consumption of construction materials without recycling, as occurs in many of the existing solar and wind energy plants. This transition can be facilitated by seeking alternative and more widely accessible materials with lower carbon footprints during manufacturing and construction. Implementing such strategies can help mitigate climate change and have a positive impact on community well-being and economic growth.

A Review of Energy-Efficient Technologies and Decarbonating Solutions for Process Heat in the Food Industry

Heat is involved in many processes in the food industry: drying, dissolving, centrifugation, extraction, cleaning, washing, and cooling. Heat generation encompasses nearly all processes. This review first presents two representative case studies in order to identify which processes rely on the major energy consumption and greenhouse gas (GHG) emissions. Energy-saving and decarbonating potential solutions are explored through a thorough review of technologies employed in refrigeration, heat generation, waste heat recovery, and thermal energy storage. Information from industrial plants is collected to show their performance under real conditions. The replacement of high-GWP (global warming potential) refrigerants by natural fluids in the refrigeration sector acts to lower GHG emissions. Being the greatest consumers, the heat generation technologies are compared using the levelized cost of heat (LCOH). This analysis shows that absorption heat transformers and high-temperature heat pumps are the most interesting technologies from the economic and decarbonation points of view, while waste heat recovery technologies present the shortest payback periods. In all sectors, energy efficiency improvements on components, storage technologies, polygeneration systems, the concept of smart industry, and the penetration of renewable energy sources appear as valuable pathways.

Energy efficiency and greenhouse gas emission reduction potential of solar PV and biomass-based systems for a food processing plant

The industrial sector is one of the most energy-intensive sectors, with a share of nearly 38 % (156 EJ) of all energy used globally in 2020. Historical energy demand and consumption data from a poultry processing plant have been analyzed to lower greenhouse gas emissions (GHG) and reliance on traditional energy sources. The study includes energy, economic, and emissions analyses for (a) solar photovoltaic (PV) systems, (b) biomass fuels, and (c) coal-fired with wood. Fixed, single, and two-axis tracking solar PV systems that are company-owned and funded by banks are considered. The historical energy consumption data of the plant shows that 52.36 % of energy demand is met by electrical energy and 47.63 % by thermal energy. The yearly electricity demand is 9,938 MWh, costs $1,192,560, and emits 5117.6 tCO2. Thermal energy demand is based on an annual use of 898 tonnes of coal and 71.6 tonnes of liquefied petroleum gas (LPG), which costs $ 143,681 and emits 2977.7 tonnes of carbon dioxide (tCO2). Feasibility of 1.3 MW, fixed axis company-owned, solar PV plant shows an annual electricity production of 1876 MWh, GHG emission reduction of 965.9 tCO2 with a payback period of 3.4 years. Biomass system delivers yearly fuel cost savings of $25,724, reducing 2784 tCO2 with a 1.9-year payback period. Co-firing biomass offers a 3.58 % fuel cost saving and 557 tCO2 with a 0.93-year payback period. It concluded that the wood fuel feedstock biomass option is most suitable renewable energy resource for such large-scale poultry facilities.

Comparison of Bio-Coke and Traditional Coke Production with Regard to the Technological Aspects and Carbon Footprint Considerations

In a world facing the challenges of climate change, it is imperative to prioritize the search for sustainable technical solutions. This study focuses on evaluating the environmental impact of using bio-coke compared to traditional metallurgical coke, employing Life Cycle Assessment (LCA) as the evaluation tool. Bio-coke, produced from a blend of coking coals enriched with biomass, offers greater environmental potential than traditional coke due to a reduced share of non-renewable raw materials. The steel and coking industries are significant contributors to carbon dioxide emissions. LCA provides a comprehensive assessment of the environmental impact of bio-based additives, considering raw material deliveries, the coking process, application in metallurgy, and product end-of-life disposal. The analysis results indicate that the use of biomass additives leads to lower greenhouse gas emissions compared to coke production without bio-additives. Given the urgency of addressing global warming and the increasing demand for sustainable energy sources, this study’s findings can advocate for bio-coke as a more environmentally friendly alternative to traditional coke in the steel industry.

Chemical composition, energy content, and nutrient digestibility of rye grain in broiler chickens

The development of livestock farming is always associated with the search for new dietary components that increase feed efficiency or reduce feed costs. The maximum use of regional, conventional feed in the feeding of farm animals is quite relevant today. Furthermore, certain feeds, the production of which is accompanied by low greenhouse gas emissions, help reduce the carbon footprint of the diet as a whole. The purpose of this study was to determine the nutritional value of rye grain of the modern hybrid KWS Tayo and the digestibility of nutrients in poultry. The chemical composition of grain, digestibility of nutrients in broiler chickens, and total energy content were investigated. The chemical composition of feed, compound feed, and litter was determined according to the Wende method. Nutrient digestibility studies were conducted on broiler chickens of the ROSS-308 cross. For the physiological experiment, 5 chickens aged 22 days were selected. The experiment was carried out using a complex method, by replacing part of the basic diet. The experiment consisted of 2 parts. In the first experiment, the nutrient digestibility of the main diet was determined – compound feed containing 5% rye grain; in the second, the main diet with an additional 5% rye grain was used. The calculation was made on the assumption that the digestibility of nutrients in the main diet was the same in both experiments. The compound feed was balanced according to the recommendations of the cross developer. The energy content of rye grain was calculated in J of metabolisable energy by the regression equation, based on the data on the content of digestible protein, fat, fibre, and NFES determined in the physiological experiment. The average dry matter content of the rye samples was 87.4%. The content of the main nutrients in the grain of natural moisture was as follows: crude protein – 8.87±0.484%; crude fat – 1.63±0.105%; NFES – 73.95±0.494%, and crude ash – 0.53±0.109%. The nutrient digestibility of rye grain was as follows: crude protein – 76%; crude fat – 82%, crude fibre – 8%; NFES – 81% and organic matter – 78%. The total energy value of rye grain was 12.420±0.2573 MJ/kg of metabolic energy for poultry. Nutritionists can use the nutritional and nutrient digestibility data of rye grain to develop practical recipes for poultry compound feed

Policies for aquifer thermal energy storage: international comparison, barriers and recommendations

AbstractAquifer thermal energy storage (ATES) represents a promising solution for heating and cooling, offering lower greenhouse gas emissions and primary energy consumption than conventional technologies. Despite these benefits and the widespread availability of suitable aquifers, ATES has yet to see widespread utilisation, with uptake highly concentrated in select countries (Netherlands, Belgium, Sweden and Denmark). Beyond technical and hydrogeological feasibility, appropriate national policies are paramount in driving ATES deployment. This study provides an international comparison of ATES policies, highlighting best practices and revealing where measures are missing. It sources insights from a survey of experts across academia, industry and governmental bodies in 30 countries, complemented by semi-structured expert interviews. The study reveals significant differences in the existence and strength of supportive policy environments between countries with different ATES market maturity. A mere 33% of all survey respondents stated that there are policies designed to support ATES utilisation in their respective countries, while the existence of laws and regulations governing ATES was confirmed by 56% of the respondents. The interviews provide details on creating supportive environments (e.g. through facilitators like pre-existing groundwater technology use and building energy efficiency standards) and further barriers to ATES deployment. Ten recommendations for ATES policies are derived to address the following areas: legislative and regulatory issues, raising public awareness, ATES’ role in local energy transitions, and social engagement. This work aims to steer global policy towards better harnessing the potential of ATES to decarbonise buildings. Graphical abstract

Changing the order and ratio of substrate filling reduced CH4 and N2O emissions from the aerated constructed wetlands

Constructed wetlands (CWs) have been used to enhance pollutant removal by filling several types of material as substrates. However, research on substrate filling order remains still limited, particularly regarding the effects of greenhouse gas (GHG) emissions. In this study, six CWs were constructed using zeolite and ferric‑carbon micro-electrolysis (Fe-C) fillers to evaluate the effect of changing the filling order and ratio on pollutant removal, GHGs emissions, and associated microbial structure. The results showed that the order of substrate filling significantly impacted pollutant removal performance on CWs. Specifically, CWs filled with zeolite in the top layer exhibited superior NH4+-N removal compared to those filled in the lower layer. Moreover, the highest NH4+-N removal (95.0 % ± 1.9 %) was observed in CWs with a zeolite to Fe-C volume ratio of 8:2 (CWZe-1). Moreover, zeolite-filled at the top had lower GHGs emissions, with the lowest CH4 (0.22 ± 0.10 mg m−2 h−1) and N2O (167.03 ± 61.40 μg m−2 h−1) fluxes in the CWZe-1. In addition, it is worth noting that N2O is the major contributor to integrated global warming potential (GWP) in the six CWs, accounting for 81.7 %–90.8 %. The upper layer of CWs filled with zeolite exhibited higher abundances of nirK, nirS and nosZ genes. The order in which the substrate was filled affected the microbial community structure and the upper layer of CWs filled with zeolite had higher relative abundance of nitrifying genera (Nitrobacter, Nitrosomonas) and denitrifying genera (Zoogloea, Denitratisoma). Additionally, N2O emission was reduced by approximately 41.2 %–64.4 % when the location of the aeration of the CWs was changed from the bottom to the middle. This study showed that both the order of filling the substrate and the aeration position significantly affected the GHGs emissions from CWs, and that CWs had lower GHGs emissions when zeolites were filled in the upper layer and the aeration position was in the middle.

Solar Operated Car Enabled with AI

The convergence of renewable energy and artificial intelligence (AI) has spurred a transportation revolution, exemplified by the rise of solar-powered cars equipped with advanced AI capabilities. This abstract digs into the integration of solar energy and AI technology in autos, examining their synergistic potential to transform the automotive industry and pave the way for a more sustainable future.The paper presents the concept of solar-powered cars and discusses their importance in reducing environmental concerns connected with regular fossil-fuelled vehicles. Solar energy, captured by solar panels built into the vehicle's construction, provides a clean and abundant power source, minimizing reliance on non-renewable resources and lowering greenhouse gas emissions. Furthermore, advances in solar panel efficiency and energy storage technology have increased the practicality of solar-powered vehicles for widespread use.. Furthermore, AI-powered autonomous driving capabilities improve safety by reducing human error and allowing for proactive collision avoidance methods. The paper highlights how AI-enabled solar-powered vehicles have the potential to completely change the automotive sector and hasten the shift to a sustainable transportation network

Cardiovascular Imaging, Climate Change, and Environmental Sustainability.

Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.