Excess Emission Research Articles

Research progress of novel bio-denitrification technology in deep wastewater treatment.

Excessive nitrogen emissions are a major contributor to water pollution, posing a threat not only to the environment but also to human health. Therefore, achieving deep denitrification of wastewater is of significant importance. Traditional biological denitrification methods have some drawbacks, including long processing times, substantial land requirements, high energy consumption, and high investment and operational costs. In contrast, the novel bio-denitrification technology reduces the traditional processing time and lowers operational and maintenance costs while improving denitrification efficiency. This technology falls within the category of environmentally friendly, low-energy deep denitrification methods. This paper introduces several innovative bio-denitrification technologies and their combinations, conducts a comparative analysis of their denitrification efficiency across various wastewater types, and concludes by outlining the future prospects for the development of these novel bio-denitrification technologies.

Excess Emission Sources Identification via Sparse Monitoring Networks

Abnormality detection and identification is of great concern for complex processes monitoring. In chemical industrial parks (CIPs), the primary task is to identify abnormal (excess) sources when unexpected excess emissions occur. However, it is an ill-posed problem to detect, especially to identify them in dense industrial areas where a vast number of emission sources are concentrated in limited space, challenging the relatively sparse wireless sensor networks (WSNs). Meanwhile, barely detecting the existence rather than identifying excess sources can hardly meet the requirements of fine management in CIPs. In this paper, a QR decomposition-based method for excess sources identification (QR-ESI) has been proposed. By introducing equivalent sources (ES) as a substitute for real sources (RS), the semi-independent relation between ES and RS has been developed. By monitoring ES, excess sources can be inferred progressively through the iteration of logical judgments. The performance was evaluated with simulated data and then validated and compared with a state-of-the-art method in a case study in a real-world chemical industrial park.

Effect of hydraulic regime on sulfur-packed bed performance: Denitrification and disproportionation

Sulfur-packed beds (SPBs) have been increasingly incorporated into constructed wetland systems to overcome limitations in achieving satisfactory nitrate removal efficiency. However, the underlying impact of hydraulic regimes on SPB performance remains understudied. This study investigated the performance of a pilot-scale SPB, encompassing sulfur autotrophic denitrification (SAD) and sulfur disproportionation (SDP) processes, under various horizontal flow (HF) and vertical flow (VF) regimes. The HF regime exhibited superior SAD efficiency, achieving 3.1–4.4 mg-N/L of nitrate removal compared to 0.9–2.8 mg-N/L under VF regimes. However, greater sulfide production of 3.8–5.6 mg/L was observed, in contrast to only 1.5–2.3 mg/L under VF regimes when SDP occurred. Employing current computational fluid dynamics simulations could predict general regimes but lacked precision in detailing sulfur layer dynamics. In contrast, determining the spatial distribution of SAD substrates and SDP products offered a viable solution, revealing stagnate, short-circuit, and back flows. Moreover, the feasibility of an aeration approach to reduce sulfide emissions below 0.5 mg/L in case of accidental SDP occurrence was confirmed. This study offers a method for assessing detailed hydraulic regimes within SPBs. Additionally, it provides guidance on optimizing the packing of sulfur-based materials when implementing SPBs in constructed wetland systems and presents a strategy for mitigating excessive sulfide emissions.

The complex impacts of economic growth pressure on carbon emission intensity: an empirical evidence from city data in China.

Excessive carbon emissions are the major challenge to global sustainable development. In the context of the coronavirus pandemic, pressure on global economic growth is gradually rising, threatening established carbon reduction targets. However, the relationship between economic growth pressures and carbon emission intensity has yet to be clearly discussed. Thus, this study quantitatively discusses the impacts of economic growth pressures from central (EGPN) and provincial (EGPP) governments on city carbon intensity. The study is based on data from China's city panels from 2005 to 2019. This study finds that (1) there is a U-shaped correlation between economic growth pressure and a city's carbon emission intensity, whether the economic growth pressure comes from the central government or the provincial government; (2) carbon emission intensity is more sensitive to economic growth pressure from the provincial government than it is to economic growth pressure from the central government. The findings of this study will help enhance the understanding of the relationship between economic growth pressure and carbon emission intensity, and can also provide a reference for global sustainable development that balances economic growth and environmental protection.

Observations of Magnetospheric Solar Wind Charge Exchange

The study of solar wind charge exchange (SWCX) emission is vital to both the X-ray astrophysics and heliophysics communities. SWCX emission contaminates all astrophysical observations in X-rays regardless of the direction. Ignoring this contribution to X-ray spectra can lead to erroneous conclusions regarding the astrophysical plasmas along the line of sight owing to the similar spectral distributions of SWCX and several common types of more distant astrophysical plasmas. Since its discovery, the literature has distinguished between diffuse SWCX emission resulting from solar wind–neutral interactions within Earth’s magnetosphere, called magnetospheric SWCX, and similar interactions occurring more generally throughout the heliosphere, called heliospheric SWCX. Here we build on previous work validating a modeling method for the heliospheric SWCX contribution in X-ray spectra obtained with a medium-resolution CubeSat instrument named HaloSat at low ecliptic latitudes. We now apply this model to a specially designed set of extended observations with the same instrument and successfully separate the spectral contributions of the astrophysical background and the heliospheric SWCX from the remaining contributions. Specifically, we find significant excess emission for four observations in the O vii emission line not explained by other sources, possibly indicative of magnetospheric SWCX. We discuss these results in comparison with simulation results publicly available through the Community Coordinated Modeling Center. We also report an absorbed high-temperature component in 2 of the 12 fields of view analyzed.

Dynamic speed harmonization for mixed traffic flow on the freeway using deep reinforcement learning

AbstractIn the vicinity of weaving areas, freeway congestion is nearly unavoidable due to their negative effects on the continuous freeway mainline flow. The adverse impacts include increased collision risks, extended travel time, and excessive emissions and fuel consumption. Dynamic Speed Harmonization (DSH) has the potential to dampen traffic oscillation during congestion. However, its effectiveness is typically limited by the low compliance rates of drivers and delays in information access. The integration of Connected and Automated Vehicles (CAVs) into intelligent transportation systems aims to enhance various measures of effectiveness. This research investigates the effects of DSH in mixed traffic flow involving human‐driven vehicles and CAVs on the freeway. A deep reinforcement learning‐based strategy is developed to better understand how CAVs can improve operational performance. A holistic evaluation is conducted to quantify the impacts under different penetration rates of CAVs in multiple simulated scenarios. The results reveal that the proposed method can enhance mobility and achieve co‐benefits with safety, and environmental sustainability could be improved under higher penetration rates. Spatiotemporal features of bottleneck speed demonstrate that DSH powered by CAVs can smooth speed variations for partial areas. Sensitivity analysis of headways indicates that high‐level CAVs can further improve performance.

Life cycle assessment of greenhouse gas emission from the dairy production system - review.

Climate change is altering ecological systems and poses a serious threat to human life. Climate change also seriously influences on livestock production by interfering with growth, reproduction, and production. Livestock, on the other hand, is blamed for being a significant contributor to climate change, emitting 8.1 gigatonnes of CO2-eq per year and accounting for two-thirds of global ammonia emissions. Methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) are three major greenhouse gases (GHG) that are primarily produced by enteric fermentation, feed production, diet management, and total product output. Ruminants account for three-quarters of total CO2-equivalent (CO2-eq) emissions from the livestock sector. The global dairy sector alone emits 4.0% of global anthropogenic GHG emissions. Hence, dairy farming needs to engage in environmental impact assessment. Public concern for a sustainable and environmentally friendly farming system is growing, resulting in the significant importance of food-based life cycle assessment (LCA). Over the last decade, LCA has been used in agriculture to assess total GHG emissions associated with products such as milk and manure. It includes the production of farm inputs, farm emissions, milk processing, transportation, consumer use, and waste. LCA studies on milk production would assist us in identifying the specific production processes/areas that contribute to excessive greenhouse gas emissions when producing milk and recommending appropriate mitigation strategies to be implemented for a clean, green, and resilient environment.

In-situ oxidation of Sn catalysts for long-term electrochemical CO2 reduction to formate

Electrochemical carbon dioxide (CO2) reduction (ECR) into valuable products such as formate is a compelling approach to managing excessive CO2 emissions. Tin (Sn)-based catalysts have been used for selective CO2 reduction to formate. However, they are often unstable for long-term ECR operation, especially at high reaction rates. In this work, we report highly selective Sn-based catalysts for CO2 reduction to formate at high current densities. The catalysts were prepared by depositing a layer of Sn nanoparticles on the surface of sputtered Ag on a polymer membrane. The catalysts exhibited over 90% Faradaic efficiency toward formate in the current density range of 50–300 mA/cm2 and were stable for over 20 h at a current density of 100 mA/cm2. Characterization of catalysts after ECR reaction reveals significant changes in catalyst morphology during ECR. To extend ECR operation time, we developed an in-situ catalyst regeneration strategy in which the catalysts are periodically oxidized during the reduction reaction. Using this approach, the operation time of Sn-catalysts was extended to more than 150 h with a selectivity over 90%, at the current density of 100 mA/cm2.

Research on Optimal Scheduling of Multi-Energy Microgrid Based on Stackelberg Game

In recent years, rapid industrialization has driven higher energy demand, depleting fossil-fuel reserves and causing excessive emissions. China’s “dual carbon” strategy aims to balance development and sustainability. This study optimizes microgrid efficiency with a tiered carbon-priced economy. A Stackelberg game establishes microgrid-user equilibrium, solved iteratively with a multi-population algorithm (MPGA). Comparative analysis can be obtained without considering demand response scenarios, and the optimization cost of microgrid operation considering price-based demand response scenarios was reduced by 5%; that is 668.95 yuan. In addition, the cost of electricity purchase was decreased by 23.8%, or 778.6 yuan. The model promotes user-driven energy use, elevating economic and system benefits, and therefore, the scheduling expectation of “peak shaving and valley filling” is effectively realized.

Indoor Environmental Quality (IEQ) and Sustainable Development Goals (SDGs): Technological Advances, Impacts and Challenges in the Management of Healthy and Sustainable Environments

The growing concern for sustainability is evident, given the importance of guaranteeing resources for the next generations, especially in the face of increasing energy consumption in buildings. Regardless of the context, people seek comfort, which makes investigating Indoor Environmental Quality crucial. This covers aspects such as indoor air, temperature, noise and lighting, positively impacting quality of life, reducing stress, saving energy and promoting health, well-being and productivity. A literature review was conducted using the Scopus and PubMed databases to analyze technological advances and challenges in managing healthy and sustainable environments, focusing on the relationship between Indoor Environmental Quality and the Sustainable Development Goals. Initially, 855 articles were identified, of which 123 were selected based on established criteria. Three research questions (RQs) were formulated, leading to the following conclusions. (i) The assessment of sustainability in buildings is crucial, encompassing economic, social and environmental aspects. Furthermore, the COVID-19 pandemic has underscored the importance of adapting energy strategies, thereby contributing to the achievement of the Sustainable Development Goals through the utilization of advanced technologies that promote healthy and efficient environments. (ii) Evaluations have evolved, ranging from energy savings to human well-being and mental health, including disease prevention strategies. (iii) Challenges in managing the promotion of Indoor Environmental Quality include excessive resource consumption, emissions and economic–environmental balance.

Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises

In the steel industry, the imbalance between fluctuating oxygen demand and stable supply generally results in excessive oxygen emissions and power waste. Independent optimal scheduling of oxygen distribution (OD) or steelmaking-continuous casting (SCC) has limited ability to handle this issue. To address this challenge, we propose an integrated scheduling model that allows for flexible interaction between OD and SCC. We first develop an SCC scheduling model that applies to machine sharing of duplex and conventional smelting, and derive an OD scheduling model. A critical demand interface is further designed to connect the scheduling models of SCC and OD, resulting in a mixed integer linear programming-based integrated scheduling model. Existing research overlooks the probability information of demand uncertainty posed by unpredictable interferences, which limits the ability to handle uncertain demands. To address this limitation, we embed demand probability distribution via a data-driven ambiguity set and upgrade the deterministic integrated scheduling model via two-stage distributionally robust optimization. Our model was tested with real data and resulted in a 15% reduction in total costs compared to independent scheduling of OD and SCC. Additionally, it effectively reduced uncertainty risk to 8.3% with low conservativeness, demonstrating superior performance compared to recent methods.

Mesoporous α‑Fe2O3 nanostructures decorated with perovskite Cs3Bi2Br9 quantum dots as an S-scheme photocatalyst for efficient charge transfer and CO2 conversion

In view of the greenhouse effect caused by excessive CO2 emission in the atmosphere, the efficient capture and conversion of CO2 to chemical feedstock is imminent. Herein, a novel Cs3Bi2Br9/α‑Fe2O3 (CBB/α‑Fe2O3) composite was constructed by decorating lead-free perovskite Cs3Bi2Br9 quantum dots (QDs) on the pores of mesoporous α‑Fe2O3 nanostructures. The developed CBB/α‑Fe2O3 composite possesses bifunctional advantages for both the efficient capture and conversion of CO2, with a CO production yield of 84.12 μmol g−1h−1 for the optimized sample. Experimental results and theoretical predictions indicate that the electrons transfer via an S-scheme route in the CBB/α‑Fe2O3 composite, which not only boosts the charge separation but also retains the strong reduction ability of Cs3Bi2Br9. By combination of in-situ diffuse reflectance infrared Fourier transform spectra (DRIFTS) with theoretical calculations, the mechanism of the photocatalytic CO2 reduction over the CBB/α‑Fe2O3 composite was explored in depth. This study opens a new avenue for the exploitation of photocatalysts with high CO2 capture ability and conversion efficiency.

Br-doped Cu nanoparticle formed by in situ restructuring for highly efficient electrochemical reduction of CO2 to formate

Electrochemical conversion of CO2 into chemical feedstock, such as an energy-dense liquid product (formate), is desirable to address the excessive emission of greenhouse gases and store energy. Cu-based catalysts exhibit great advantages in electrochemical CO2 reduction reaction (eCO2RR) due to their low cost and high abundance, but suffer from low selectivity of formate. In this work, a facile one-pot approach is developed to synthesize CuBr nanoparticle (CuBr NP) that can conduct in situ dynamic restructuring during eCO2RR to generate Br-doped Cu NP. The in situ-formed Br-doped Cu NP can afford up to 91.6% Faradaic efficiency (FE) for formate production with a partial current density of 15.1 mA·cm−2 at −0.94 V vs. reversible hydrogen electrode (RHE) in an H-type cell. Moreover, Br-doped Cu NP can deliver excellent long-term stability for up to 25 h. The first-principles density functional theory (DFT) calculations show that the doped Br can regulate the electronic structure of Cu active sites to optimize the adsorption of the HCOO* intermediate, greatly hindering the formation of CO and H2. This work provides a strategy for electronic modulation of metal active site and suggests new opportunities in high selectivity for electrocatalytic reduction of CO2 to formate over Cu-based catalysts.

Morphology Effects on Electro- and Photo-Catalytic Properties of Zinc Oxide Nanostructures.

Environmental problems are among the most pressing issues in the modern world, including the shortage of clean drinking water partially caused by contamination from various industries and the excessive emission of CO2 primarily from the massive use of fossil fuels. Consequently, it is crucial to develop inexpensive, effective, and environmentally friendly methods for wastewater treatment and CO2 reduction, turning them into useful feedstocks. This study explores a unique method that addresses both challenges by utilizing ZnO, which is recognized as one of the most active semiconductors for photocatalysis, as well as a cost-effective electrocatalyst for the CO2 reduction reaction (CO2RR). Specifically, we investigate the influence of the morphology of various ZnO nanostructures synthesized via different low-cost routes on their photocatalytic properties for degrading the rhodamine-B dye (RhB) and on their electrocatalytic performance for the CO2RR. Our results show that the ZnO lamella morphology achieves the best performance compared to the nanorod and nanoparticle structures. This outcome is likely attributed to the lamella's higher aspect ratio, which plays a critical role in determining the structural, optical, and electrical properties of ZnO.

Amine-Functionalized Amyloid Aerogels for CO2 Capture.

Climate change caused by excessive CO2 emissions constitutes an increasingly dire threat to human life. Reducing CO2 emissions alone may not be sufficient to address this issue, so that the development of emerging adsorbents for the direct capture of CO2 from the air becomes essential. Here, we apply amyloid fibrils derived from different food proteins as the solid adsorbent support and develop aminosilane-modified amyloid fibril-templated aerogels for CO2 capture applications. The results indicate that the CO2 sorption properties of the aerogels depend on the mixing ratio of aminosilane featuring different amine groups and the type of amyloid fibril used. Notably, amine-functionalized β-lactoglobulin (BLG) fibril-templated aerogels show the highest CO2 adsorption capacity of 51.52 mg (1.17 mmol) CO2 /g at 1 bar CO2 and 25.5 mg (0.58 mmol) CO2 /g at 400 ppm; similarly, the CO2 adsorption capacity of chitosan-BLG fibril hybrid aerogels is superior to that of pure chitosan. This study provides a proof-of-concept design for an amyloid fibril-templated hybrid material facilitating applications of protein-based adsorbents for CO2 capture, including direct air capture.

Poisoning of automotive methane oxidation catalysts by silicon compounds

Biomethane production offers a viable option to meet the growing demands of renewable energy sources in the transportation sector. However, biomethane contains trace amounts of impurities, such as siloxanes, that can potentially hinder the methane oxidation catalyst performance and lead to excessive greenhouse gas emissions. To investigate this potential problem, the commercial methane oxidation catalysts were poisoned in a laboratory setup with two common siloxanes found in biomethane and with their combustion product, silica. The performances of the clean and poisoned methane oxidation catalysts were measured under realistic lean-burn conditions, after which the catalyst structural changes were determined using XRD, BET and CO chemisorption. The results revealed two different possible poisoning mechanisms. First, both tested siloxanes poisoned the catalysts by migrating selectively on top of the active metal sites and became combusted to form amorphous silica. This caused the methane oxidation performance to drop rapidly with relatively small amounts of siloxane used. In contrast, a significantly higher dose of silica was required to produce a similar poisoning effect because its poisoning mechanism is based on nonselective deposition and physical blocking of the catalyst surface.

A Review of Recovering Lithium and Cobalt from Spent LiCoO2 Lithium‐Ion Batteries Cathode

AbstractTo address the issue of excess carbon emissions, China has outlined peaking the carbon emissions and achieving neutrality of carbon goals. New energy storage devices thus have been popularly developed in the past decade. Lithium‐ion batteries(LIBs) have emerged as the most prevalent devices in our daily routines. In the coming decades a huge amount of LIBs will be retired. Spent LIBs are endowed with valuable metal elements that are potentially recyclable. This paper is intended to review the recent studies on the recovery of lithium(Li) and cobalt(Co) from spent LiCoO2 batteries. This paper pays much attention to the hydrometallurgical, pyrometallurgical approaches to recycle lithium and cobalt from spent LIBs including leaching, reducing, and extracting by various inorganic or organic agents in hydrometallurgical methods and different kinds of reductants in pyrometallurgical methods. Last but not least, electrochemical methods, mechanochemistry and biometallurgy are focused for reviewing emerging, low‐cost, environmentally friendly methods. Hopefully, this review can inspire to further studies on recycling lithium and cobalt from spent LiCoO2 LIBs.

Unraveling the impact of Cu-doping in lead free halide perovskites for markedly enhancing photocatalytic CO2 reduction performance

Excessive CO2 emissions from the consumption of fossil fuels contribute to both global warming and the energy crisis. To address these pressing issues, it is essential to capture and convert CO2 into chemical feedstock using renewable energy such as solar energy. Solar-powered photocatalytic fuel production is widely acknowledged as a viable solution for a sustainable energy future. However, the development of high-performance photocatalysts remains a significant challenge to overcome in this endeavor. Recently, lead-free perovskites have emerged as a promising category of materials for CO2 photoreduction, thanks to their remarkable optoelectronic properties, cost-effective solution processing, and non-toxic nature. Nonetheless, their inherent photocatalytic CO2 reduction activity remains low due to the insufficient separation of photogenerated charges and the lack of active sites. Here, we demonstrate that Cu doping in lead-free double perovskites can lead to markedly enhanced CO2 reduction performance. The interstitial Cu dopants modulate the fermi level and introduce the active energy sites near the conduction band, which significantly enhances light absorption and separation of charge carriers. The Cu-Cs2AgBiCl6 shows the activity of 88 and 48 µmol/gcat for CH4 and CO products which are both higher than those of the pristine Cs2AgBiCl6 (27 and 10 µmol/gcat). The total yield of Cu-Cs2AgBiCl6 perovskite for CO2 photoreduction is superior to those reported under similar conditions. Transient absorption results show that hot carrier relaxation is slower and carrier decay lifetime is extended in Cu-Cs2AgBiCl6, which can enhance the charge migration efficiency and boost the CO2 reduction performance. These findings provide valuable insight into the mechanism of CO2 reduction in Cu-doped Cs2AgBiCl6 perovskite crystals and suggest that Cu-doping is a promising strategy for developing highly efficient and selective CO2 reduction photocatalysts.

Prediction of energy-related carbon emission intensity in China, America, India, Russia, and Japan using a novel self-adaptive grey generalized Verhulst model

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development.

Embedding nickel diselenide in carbon derived from biomass and its electrocatalytic activity for hydrogen evolution reaction

Electrolysis of water can generate green hydrogen as a sustainable energy resource, which mitigates the impacts of climate change caused by burning fossil fuels and excessive carbon emissions. Cheap and abundant transition metal compounds are emerging as promising candidates to replace noble metal catalysts. Yet their poor conductivity is a critical challenge to obtain a high activity. In this study, we present a facile approach to construct the nanocomposite of NiSe2 embedded on multi-element (N, S) doped carbon derived from biomass. The synthesized electrocatalyst (NiSe2@C) exhibited an active hydrogen evolution electrocatalyst with a low overpotential of 206 mV to obtain a current density of 10 mA cm−2 and a small Tafel slope of 59 mV dec−1. Electrochemical analysis ascribed the high catalytic performance to the synergistic effect of the NiSe2 nanoparticles with large active sites and the superior conductive carbon framework, boosting the charge transfer for hydrogen generation. The findings may lead to new opportunities for enhancing hydrogen generation from electrochemical water splitting using earth-abundant catalysts.