Emission Index Research Articles

Multi-objective Optimal Power Flow and Emission Index Based Firefly Algorithm

The economic operation of electric energy generating systems is one of predominant problems in energy systems. In this work one evolutionary optimization method, based on the meta-inference behavior called the Firefly Algorithm (FFA) is applied to solve such as the multipurpose optimum power flow (OPF) and emission index (EI) problems. Our main goal is to improve the objective function necessary to achieve the best balance between production and its energy consumption, which is presented as a non-linear function, taking into account some constraints of equality and inequality. The goal is to reduce the total cost of generations, active losses, and emission index.The FFA approach was examined and tested on a standard IEEE-30 bus system. The validations of obtained results were compared with some well-known and recently published references. The efficiency and credibility of the proposed method has been proven by the obtained results.

Accurate Dust Temperature and Star Formation Rate in the Most Luminous z > 6 Quasar in the Hyperluminous Quasars at the Epoch of Reionization (HYPERION) Sample

We present ALMA Band 9 continuum observation of the ultraluminous quasi-stellar object (QSO) SDSS J0100+2802 providing a ∼10σ detection at ∼670 GHz. SDSS J0100+2802 is the brightest QSO with the most massive supermassive black hole (SMBH) known at z > 6, and we study its dust spectral energy distribution in order to determine the dust properties and the star formation rate (SFR) of its host galaxy. We obtain the most accurate estimate so far of the temperature, mass, and emissivity index of the dust, which are T dust = 48.4 ± 2.3 K, M dust = (2.29 ± 0.83) × 107 M ⊙, and β = 2.63 ± 0.23, respectively. This allows us to measure the SFR with the smallest statistical error for this QSO, SFR = 265 ± 32 M ⊙yr−1. Our results enable us to evaluate the relative growth of the SMBH and host galaxy of J0100+2802. We find that the SMBH is dominating the process of black-hole galaxy growth in this QSO at z = 6.327, when the universe was 865 Myr old. Such unprecedented constraints on the host-galaxy SFR and dust temperature can only be obtained through high-frequency observations and highlight the importance of ALMA Band 9 to obtain a robust overview of the buildup of the first quasars’ host galaxies at z > 6.

The Relationship Between Digital Performance and Production of Greenhouse Gas Emissions in EU Countries: Correlation Analysis and ANOVA Method

Agricultural activities produce the significant amounts of greenhouse gas emission. The importance of an ever-changing climate means that digital technologies and their environmental impact are more frequently discussed in the context of the 5th Industrial Revolution. It is important to minimize environmental threats and reduce production waste on the way to a sustainable path. The main scientific aim of the paper is to examine, based on correlation analysis and ANOVA method, the relationship between two variables, digital performance of individual EU countries expressed by the indicator Digital Economy and Society Index and production of Greenhouse Gas Emissions, specifically how digital technologies affect the environment and how to transform digital technologies to supporting the European Green Deal and accelerate sustainable growth. The reasons are that digital technologies can play an important role in reducing greenhouse gas emissions. According to the results, it has been proven that exists a positive correlation between two variables regarding as a weak correlation between DESI and GHG emissions. Analysis of variance indicates the highly significant differences between variables. Countries with the higher DESI index produce more Greenhouse gas emissions as well but in a weak manner.

Technology, Urbanization and Natural Gas Supply Matter for Carbon Neutrality: A New Evidence of Environmental Sustainability under the Prism of COP26

The rising temperature and environmental deterioration have been observed as crucial agendas for the globe. Therefore, economies worldwide have tried to minimize growing temperatures up to 1.5 0C. In the recent decade, a meeting (COP26) by the UN has been held in Paris to fight for environmental sustainability. However, the energy sector can play a significant role in determining the best solution. Thus, natural gas has attained remarkable attention from policymakers. Accordingly, the current study mainly focuses on the natural gas supply for environmental sustainability under COP26. Consequently, we consider the economic complexity index, urbanization, technology, and natural gas supply determinants of carbon emissions in the top 15 natural gas supplier economies from 2000 to 2019. However, Augmented Mean Group (AMG) and Cross-Sectional ARDL estimators are employed to inspect the long-term connection among selected variables. Similarly, summarized results validate the inverted U-shaped EKC curve for selected economies. Similarly, urbanization significantly contributes to environmental deterioration. Besides, technology and natural gas supply (NGS) are inversely associated with carbon emissions and contribute to ecological sustainability. Besides the moderate role of NGS on ECI, urbanization and technology significantly contribute to environmental quality. Several necessary policies are proposed in this study to achieve the target level of environmental sustainability.

3D radiative transfer modelling and virial analysis of starless cores in the B10 region of the Taurus molecular cloud

ABSTRACT Low-mass stars like our Sun begin their evolution within cold (10 K) and dense (∼105 cm−3) cores of gas and dust. The physical structure of starless cores is best probed by thermal emission of dust grains. We present a high-resolution dust continuum study of the starless cores in the B10 region of the Taurus Molecular Cloud. New observations at 1.2 and 2.0 mm (12 and 18 arcsec resolution) with the NIKA2 instrument on the IRAM 30m have probed the inner regions of 14 low-mass starless cores. We perform sophisticated 3D radiative transfer modelling for each of these cores through the radiative transfer framework pandora, which utilizes RADMC-3D. Model best-fits constrain each cores’ central density, density slope, aspect ratio, opacity, and interstellar radiation field strength. These ‘typical’ cores in B10 span central densities from 5 × 104 to 1 × 106 cm−3, with a mean value of 2.6 × 105 cm−3. We find the dust opacity laws assumed in the 3D modelling, as well as the estimates from Herschel, have dust emissivity indices, β’s, on the lower end of the distribution constrained directly from the NIKA2 maps, which averages to β = 2.01 ± 0.48. From our 3D density structures and archival NH3 data, we perform a self-consistent virial analysis to assess each core’s stability. Ignoring magnetic field contributions, we find nine out of the 14 cores (64 per cent) are either in virial equilibrium or are bound by gravity and external pressure. To push the bounded cores back to equilibrium, an effective magnetic field difference of only ∼15 $\mu$G is needed.

Thermoeconomic appraisal of a novel power and hydrogen cogeneration plant with integration of biomass and geothermal energies

The present research aims at development and design of a new high-efficiency power/hydrogen co-production framework running by biomass/geothermal renewable resources. In this regard, to attain global energy transition goals based on green hydrogen utilization of renewable resources instead of conventional fossil fuel-based routes for hydrogen production is followed. The proposed system structure consists mainly of a gas turbine coupled to a geothermal assisted Rankine unit which extracts the gas turbine wasted heat to run a water electrlyzer for H2 production. To illustrate a comprehensive performance evaluation, technical (thermodynamic), environmental and economic aspects are considered and assessed. Eight performance indices are evaluated including: power and hydrogen productions, thermal and exergetic efficiencies, environmental damage and emission index, LCOP and overall system cost. In addition, a bi-objective optimization is implemented with respect to efficiency and product cost. Results show that, the cogeneration framework under optimum condition, operates with exergetic efficiency of 42.37% and levelizedproductcostof68.52$/MWh, whose emitted CO2 is 0.7443kg/kWh. Also, compared to basic design point conditions, it is found out that optimization leads to performance enhancements by 7.5%, 9.0% and 7.7% for the three mentioned indicators, respectively.

Investigating asymmetric impacts of total factor energy efficiency on carbon emissions in India.

As many complex energy relations are not linear and have diminishing returns, assuming a symmetric (linear) effect of energy efficiency (ENEF) on carbon emissions (CAE) has limited our understanding of the emission-ENEF nexus. This research, therefore, initiallyestimates total factor energy efficiency by applying a stochastic frontier technique using sample panels for India encompassing the period from 2000 to 2014. Further, a nonlinear panel autoregressive distributed lag modelling framework is utilised in order to investigate the asymmetric (nonlinear) long- and short-run impacts of ENEF on CAE. The findings demonstrated that ENEF has asymmetric long- and short-run impacts on CAE in India. Based on the outcomes, numerous crucial implications are discussed with a particular reference to developing economies like India.

Ecological sustainability and high-quality development of the Yellow River Delta in China based on the improved ecological footprint model

Aiming at the traditional ecological footprint model, the improved ecological footprint of the carbon footprint effectively makes up for the singularity of the ecological footprint's consideration of carbon emissions, and plays an important role in promoting high-quality development and ecological sustainability. This paper selects 2015, 2018 and 2020 as important time points for the study, corrects the ecological footprint parameter factors based on net primary productivity (NPP), measures the ecological footprint after the improvement of the carbon footprint, studies the spatial and temporal variation in the ecological footprint at the 100-m grid scale with the support of IPCC greenhouse gas inventory analysis, and analyzes the current ecological conservation status of the Yellow River Delta. Additionally, in the context of a low carbon economy, the decoupling index of carbon emissions and GDP is extended to the evaluation and analysis of high-quality development. The study showed that (1) the ecological footprint of the Yellow River Delta has increased year by year, from 0.721 hm2·person− 1 to 0.758 hm2·person− 1, an average annual increase of 2.9%; the ecological carrying capacity has decreased from 0.40 hm2·person− 1 to 0.31 hm2·person− 1, an overall decrease of 28.59%. (2) The overall ecological deficit of the Yellow River Delta grid is lightly overloaded, with most of the ecological surplus occurring in the northern and eastern parts of the study area and a few moderate and heavy overloads in the center of the core area where there is a lot of built-up land and the area is small and easy to gather. (3) Based on the low-carbon economy analysis, 2015, 2017 and 2020 reach absolute decoupling and are in the ideal scenario. However, in the rest of the years, carbon emissions and economic development are still in a large contradiction, and decoupling has fluctuated and varied greatly in the last six years. The effective combination of ecological footprint and low carbon economy analysis provides an important theoretical basis for improving ecological conservation and achieving high-quality development.

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft.

As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fifty-one mixed flow turbofan engines (MFTE) with different bypass ratio, overall pressure ratio and fuel flow are modeled with multi-regression (MR) method. The obtained models are subjected to metaheuristic approaches involving genetic algorithm (GA) and simulated annealing (SA) so as to decrease error of the models. According to MR findings, rated thrust of MFTEs is estimated with 1.4877 of minimum square error (MSE) whereas GA and SA make it lower as 1.3404 and 1.2524, respectively. On the other hand, NOx emission index of MFTEs is predicted with relatively low coefficient of determination (R2) as 0.8620. However, its accuracy is enhanced to 0.8633 (with GA) and 0.8655 (with SA). Finally, exergy efficiency of MFTEs is estimated the highest model correctness with GA. Namely, R2 of the model is computed as 0.9280 with GA and 0.9277 with SA. Without applying these methods, its R2 is obtained as 0.9263 with MR. When considering these outcomes, thanks to modeling and optimization methods, prediction of performance and emission indexes of mixed flow turbofan engines could be performed with lower error values. It is thought that the study helps in prediction of environmental effect regarding turbofan engines that are utilized at busy airports.

The spillover effects of uncertainty and globalization on environmental quality in India: Evidence from combined cointegration test and augmented ARDL model

Environmental quality and climate change have become hot topics among academics in all scientific fields in recent decades due to their impact on human health and economic development. Hence, this paper investigates the key factors of carbon dioxide emissions in India from 1970–2020 through the Bayer-Hanck test and Augmented ARDL framework on an augmented STIRPAT model, introducing uncertainty and globalization. We employ a set of unit-root tests and a combination of cointegration techniques (DOLS and FMOLS), which permit us to estimate the long-run and short-run relationships. Empirical findings confirmed that the series is I(1) series and there is the existence of a long-run relationship between our variables using three cointegration tests, meaning that the variables have the same behavior in the long run term. The findings revealed that India has an inverse U shape of the Environmental Kuznets curve (EKC) due to the positive association between GDP per capita and CO2 emissions until reaching a threshold, after which the link becomes inverse due to the negative impact of GDP square on CO2 emissions. Furthermore, the findings demonstrated a positive influence of uncertainty and a negative impact of globalization on long-term environmental degradation. Besides, energy consumption and population density are positively associated with CO2 emissions in the long and short run. We advocate for policies that promote more trade openness by entering new markets and cooperating with new trading partners.

Airborne Brake Wear Emissions from a Battery Electric Vehicle

Although traffic exhaust emissions in Europe have been drastically reduced, airborne particle emissions caused by brakes and tires are still increasing with the number of vehicles. The measurement of non-exhaust emissions is an emerging technological challenge. We present a custom measurement setup to investigate the brake- and tire-wear emissions of an in-use battery electric vehicle. A separate brake housing and HEPA ventilation enabled airborne brake wear emissions to be measured under realistic conditions without external influences. The emission tests on a chassis dynamometer included particle number concentrations and particle size distribution for diameters of 4 nm to 10 μm. Emission indices were determined for three driving cycles: WLTC Class 3b, WLTC Brake Part 10, and a real driving cycle. Further investigations focused on emission control through regenerative braking and brake coating. Driving with regenerative braking reduced emissions by up to 89.9%, which related to the concentration of particles in the ultrafine/fine size range. Hard-metal brake coating led to a further significant reduction in emissions of up to 78.9%. The results point the way to future RDE measurement of non-exhaust emissions and show the potential of regenerative braking and brake coating to reduce airborne brake wear emissions.

Multicomponent Activity Cycles Using Hilbert–Huang Analysis

The temporal analysis of stellar activity evolution is usually dominated by a complex trade-off between model complexity and interpretability, often by neglecting the nonstationary nature of the process. Recent studies appear to indicate that the presence of multiple coexisting cycles in a single star is more common than previously thought. The correct identification of physically meaningful cyclic components in spectroscopic time series is therefore a crucial task, which cannot overlook local behaviors. Here we propose a decomposition technique that adaptively recovers amplitude- and frequency-varying components. We present our results for the solar activity as measured both by the sunspot number and the K-line emission index, and we consistently recover the Schwabe and Gleissberg cycles as well as the Gnevyshev–Ohl pattern probably related to the Hale cycle. We also recover the known 8 yr cycle for 61 Cygni A, in addition to evidence of a three-cycles-long pattern reminiscent of the Gnevyshev–Ohl rule. This is particularly interesting as we cannot discard the possibility of a relationship between the measured field polarity reversals and this Hale-like periodicity.

Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes

To predict aircraft emissions from their own features has become more important as the usage field of aviation engines is extended to different sectors for different purposes. In the present study, thanks to by-pass ratio, pressure ratio and rated thrust of the one hundred sixty Mixed-Flow Turbofan (MFT) engines, NOx and CO emission indices as well as fuel flow for take-off and climb-out phases are predicted by using conventional Long-Short Term Memory (LSTM) at initial stage. After that, to improve the obtained estimations, hybrid Convolutional Neural Network (CNN-LSTM) model is employed to these engine dataset. The differences between two methods are presented by several types of error for each parameter. When considering the correlations, the relation between three variables and CO emission index is opposite whereas it is positively for NOx index. Moreover, NOx emission index of the MFT engines is predicted with 0.8166 of R2 by LSTM whereas its R2 increases to 0.8991 by means of hybrid CNN-LSTM approach. Amongst the models, the fuel flow is predicted the highest more than 0.95 of R2 whereas hybrid model makes it predictable with the higher accuracy, which has more than 0.99 of R2 for both phases. Moreover, these enhancements are observed in predicting of the other parameters regarding MFTs. It is thought that the proposed CNN-LSTM model could help in estimation of important parameters of gas turbine engines with higher level of accuracy.

Impact of Design Constraints on Noise and Emissions of Derivative Supersonic Engines

The propulsion systems used in commercial supersonic transport (SST) aircraft, such as the Concorde, have used repurposed engines or derivative engines based on cores from existing donor engines rather than purpose-designed clean-sheet engines. A similar approach is currently being adopted in the development of new SSTs. Turbomachinery components and cooling mass flow rates in derivative engines are sized by the design cycle of the donor engine and constrain the design of the derivative engine cycle. Here, we identify the constraints imposed by the donor engines and quantify their impact on the specific fuel consumption (SFC), certification noise, and (oxides of nitrogen) emissions index [EI()] relative to purpose-designed clean-sheet engines. We design and optimize a clean-sheet and derivative engine for a notional 55 metric ton SST proposed by NASA. A clean-sheet engine optimized for SFC results in an approximately 4.5% reduction in SFC, an approximately 2.5-fold increase in EI(), and a 1.2 EPNdB increase in certification noise relative to the derivative engine. Applying a constraint on EI() to the clean-sheet engine results in an approximately 0.5% reduction in SFC relative to the derivative engine. The work provides a quantitative comparison of clean-sheet purpose-built engines and derivative engines from an environmental perspective that can inform policy makers as they develop updated environmental standards for civil supersonic aircraft.

Study on the environmental effects of two-way foreign direct investment in China’s service industry

A mathematical model was built in this paper to divide the environmental effects of Foreign Direct Investment (FDI) in the service sector in terms of scale effect, structural effect and technical effect. Also, inbound FDI (IFDI) and outbound FDI (OFDI) were put under the same research framework, where the effects of two-way FDI on the emissions of SO2 and chemical oxygen demand (COD) were empirically tested with the 2004-2016 panel data of China’s service sector. The results show that IFDI in the service sector has significantly reduced China’s pollution emissions and improved environmental quality. This is mainly due to the positive technical and structural effects. OFDI in the service sector exhibits different responses to different pollution emission indexes. When there are both IFDI and OFDI in the service sector, SO2 emissions are suppressed to a certain extent, whereas COD emissions are not effectively controlled. In addition, the increased service output scale, per-capita capital stock and R&D input also have varying effects on environmental pollution in China.

Geothermal energy and carbon emissions nexus in leading geothermal-consuming nations: Evidence from nonparametric analysis

The pursuit of decarbonization has created an awareness globally in relation to geothermal energy, either for electricity generation or direct usage, since fuel combustion is not needed. Premised on this intriguing detail, this present research scrutinizes the impact of geothermal energy on CO2 emissions in the top seven geothermal energy-consuming nations. Using the quarterly dataset of geothermal and CO2 emissions stretching over the period between 1990 and 2019. The quantile-on-quantile (QQ) approach, which considers the conventional quantile analysis and nonparametric, is utilized in this research to provide more accurate and robust estimations. Furthermore, the QQ approach differentiates the impacts of geothermal energy on CO2 emissions into three quantiles: upper, medium, and lower. Additionally, the nonparametric causality-in-quantiles is used to evaluate the causation in quantiles between geothermal and CO2 emissions. From the outcomes of the QQ approach, we discovered that in several quantiles, geothermal energy mitigates environmental deterioration, wherein CO2 emission is reduced in Italy, Mexico, and New Zealand. However, geothermal energy boosts CO2 emissions in India, the USA, Turkey, and the Philippines. Furthermore, the nonparametric causality-in-quantiles discovered that geothermal energy predicts CO2 emissions in all nations.

An Econometric Analysis of Economic Development and CO2 Emissions in India

Purpose: The purpose of this article is to investigate the impact of economic development on the environment. Using time series data from 1991 to 2019, GDP per capita and CO2 emissions are utilised as indicators of economic development and environmental degradation, respectively.
 Design/Methodology/Approach: PP and ADF unit root tests verify variable stationarity. Econometric study using Johansen cointegration test and vector error correction model (VECM) and residual stability model.
 Findings: Study revealed that long-run association among Carbon dioxide emissions, gross domestic product per capita, and industrial Value added. Carbon dioxide emissions increased with the rised in the Gross domestic product per capita. GDP per capita was outcomes to be unfavorably related to Carbon dioxide emissions in India.
 Research Limitations/Implications: This study was suggest that environmental degradation can be reduced at the cost of economic development. Energy-efficient technologies should be encouraged to enhance the domestic product with the help of the industrial sector by improving environmentally friendly technologies from advanced economies. This study suggests the need for environmental policies to reduce emissions during periods of economic growth.
 Originality/Value: This paper used new econometric methods to study the relationship between economic development and environmental degradation in India year 1991 to 2019.

Power Consumption and Carbon Emission Equivalent for Virtualized Resources – An Analysis

The International Energy Agency (IEA) revealed that the worldwide energy-related carbon dioxide (CO2) situation has hit a historic high of 33.1 Giga tonnes (Gt) of CO2. 85% of the rise in emissions was due to China, India, and the United States. The increase in emissions in India was 4.8%, or 105 Mega tonnes (Mt) of CO2, with the increase in emissions being evenly distributed across the transportation and industrial sectors, according to Beloglazov et al (2011). Environmental contamination brought on by carbon emissions is harmful to the environment. As a result, there is an urgent need for the IT sectors to develop effective and efficient technology to eliminate such carbon emissions. The primary focus is on lowering carbon emissions due to widespread awareness of the issue.

Consistent emission correction factors applicable to novel energy carriers and conversion concepts

Volume correction factors have been an important aspect when reporting pollutant emissions from gaseous combustion for many years. Their application has become standard procedure in such a way that their applicability is seldom questioned. However, their emergence dates back to a time when power generation primarily relied on conventional fuels such as coal and gas. The advent of alternative fuels and new combustion concepts reveals the classical correction formula prescribed by many current legal regulations to produce results that can be misleading when comparing different mixtures with regard to their emission behavior. For this reason, this work derives a more generalized correction formula that extends the applicability to any fuel–oxidizer combination and is based on the same principles as conventional volume correction. The impact of the choice of correction formula is then exemplified by comparing results for various fuels for a selection of numerical and experimental examples. They consist of a simple equilibrium calculation, as well as experimental data from a premixed swirl combustor and a pulse detonation combustor. The results show that conventional correction terms can lead to misleading perceptions of emission performance when comparing fuels. In contrast, the extended correction terms derived in this work allow for a comparison of emission behavior on a common basis that is independent of effects introduced by fuel and oxidizer stoichiometry, in a manner similar to alternative fuel-mass based metrics such as emission index. This is not restricted to the investigated examples but can be applied to all areas where oxygen correction is common. It is suggested that future emission regulations should incorporate the issues discussed in this work to ensure unbiased comparison of emission values across a wide range of combustion applications.

The Impact of Innovation Technology on Carbon Emissions in India

Numerous studies have examined the determinants of CO2 emission. However, earlier research has neglected to analyse the emission of CO2 resulting from the exchange of innovation technology. The present work contributes to the research stream by examining the relationship between innovation technology and environmental degradation in India from 1980 to 2021 during the time period of study. On the basis of the Auto Regressive Distributive Lag (ARDL) model and the Vector Error Correction (VECM) Granger Causality approach, the main finding is that innovation technologies are the primary contributors to India’s long-term CO2 emission. Short-term causality emerges from one-way causation between innovation technology and CO2 emissions. Additionally, a long-term feedback hypothesis between energy usage and CO2 emissions is rejected. Numerous critical tests are conducted to assure the stability of the model and the dependability of policy-relevant conclusions. This study proposes that the Indian government should invest more in research and development to increase its innovative technological power, which would be beneficial for environmental protection.