Emission Area Research Articles

Spatial and temporal analysis of influential factors on motor vehicle carbon monoxide emissions in China considering emissions trading scheme.

The number of cars is increasing every year and the environmental aspects of transport are becoming a hot topic. The spatial and temporal patterns of motor vehicle carbon monoxide (CO) emissions are still unclear due to the unbalanced economic development and heterogeneous geographic conditions of China. With the objective of realizing a reduction in motor vehicle CO emissions, his study explores the transport carbon emission reduction pathways of China from motor vehicle CO emission. Firstly, the entropy method is adopted to comprehensively evaluate the CO emissions from motor vehicles in each province; secondly, the development of a Geographically and Temporally Weighted Regression (GTWR) model facilitates the examination of the spatiotemporal dynamics pertaining to the influencing factors of motor vehicle CO emissions within each province.; finally, the characteristics of motor vehicle CO emissions in ETS pilot areas and non-ETS pilot areas are compared. The results show that: (1) After the completion of the six ETS pilot areas in 2011, the CO emission from motor vehicles is reduced by 18% compared with 2010.(2)The entropy method shows that the largest CO emissions from motor vehicles are from Beijing, Shanghai, Guangdong and other provinces with high economic levels.(3) The results of the GTWR model show that the positive effects of economic level, population size, road mileage intensity and motor vehicle intensity on motor vehicle CO emissions are decreasing year by year. The negative effect of metro line intensity on CO emission decreases year by year. This study can help decision makers to identify the high emission areas, understand the influencing factors, and formulate emission reduction measures to achieve the purpose of carbon emission reduction in transport.

Analysis of Agricultural Carbon Emissions and Carbon Sinks in the Yellow River Basin Based on LMDI and Tapio Decoupling Models

In addition to creating economic value, crops also serve important ecological functions. Especially their carbon sink function, which plays a key role in mitigating climate change. In this study, the LMDI and the Tapio model were innovatively combined to quantitatively evaluate the carbon emissions and sinks in the Yellow River basin (YRB). It shows that the average annual growth rate of the YRB was −0.1344% during 2002–2020. Carbon emissions show a negative trend due to the transformation and upgrading of agriculture from traditional to modern and the implementation of policies related to China’s agricultural benefits. Agricultural production efficiency is a major factor in inhibiting agricultural carbon emissions, reducing carbon emissions by an average of approximately 8.07 million tons per year. High-carbon emission and high-sink areas in agriculture are mainly concentrated in the southeast of the YRB, where livestock and poultry farming is the principal source of carbon emissions, with rice, wheat, and corn being the principal contributors to the carbon sink. Moreover, there are significant differences in the carbon sink capacity of crops in the YRB. Optimizing crop selection and area distribution can enhance the carbon sink capacity in different regions, contributing to more effective carbon emission control. This study combines agricultural carbon emissions with the carbon sequestration capacity of crops, providing data support and a theoretical basis for the policy formulation and planning of low-carbon agriculture in China. It is of great significance for promoting sustainable agricultural development and mitigating climate change.

Nonlinear and Negative Effective Diffusivity of Interlayer Excitons in Moiré-Free Heterobilayers.

Interlayer exciton diffusion is studied in atomically reconstructed MoSe_{2}/WSe_{2} heterobilayers with suppressed disorder. Local atomic registry is confirmed by characteristic optical absorption, circularly polarized photoluminescence, and g-factor measurements. Using transient microscopy we observe propagation properties of interlayer excitons that are independent from trapping at moiré- or disorder-induced local potentials. Confirmed by characteristic temperature dependence for free particles, linear diffusion coefficients of interlayer excitons at liquid helium temperature and low excitation densities are almost 1000 times higher than in previous observations. We further show that exciton-exciton repulsion and annihilation contribute nearly equally to nonlinear propagation by disentangling the two processes in the experiment and simulations. Finally, we demonstrate effective shrinking of the light emission area over time across several hundreds of picoseconds at the transition from exciton- to the plasma-dominated regimes. Supported by microscopic calculations for band gap renormalization to identify the Mott threshold, this indicates transient crossing between rapidly expanding, short-lived electron-hole plasma and slower, long-lived exciton populations.

How to perceive and map the synergy between CO2 and air pollutants: Observation, measurement, and validation from a case study of China

Cities occupy a central position in addressing climate change and promoting sustainable regional development. Synergistic control of urban gas emissions at the city level is one of the main issues typically explored. The confounding effect and the interactions between the urban indicators of population and area have been ignored in previous studies. In this study, we examined the spatial distribution characteristics and synergy between greenhouse gases (CO2) and air pollutants (SO2 and NOX) using spatial population and gas emission data. By upgrading the city clustering algorithm (CCA), we established a method for defining active areas of gas emissions (spatial element-coupled clustering, SECC) and identified active areas of gas emissions in China. In this study, we created a research framework that can simultaneously consider the effects of population and area, as well as the possible interactions between these indicators in active areas. The superlinear scaling relationship between the above three gases was revealed at the active zone level, and the existence of synergy between the emission patterns of the three gases was confirmed. Via further model application, we measured the synergistic efficiency of the three gases. It was found that for every 1% increase in SO2 and NOX in an active zone, CO2 increases by 0.86%. In this study, we explored a new perspective and approach to explain the synergy between greenhouse gases and air pollutants. This is essential to promote national competition among cities to achieve synergistic control of CO2 and local air pollutants.

Challenges to sustainable production: a case study of machining process

Nowadays, a very important aspect of industry is the realization of sustainable production (SP) goals in three areas: economic, environmental and social. SP in machining requires production to be carried out in line with Sustainability Developments Goals (SDGs). The SDGs are adopted by the United Nations, and this is a set of global goals to support the implementation of actions to achieve a sustainable tomorrow, e.g. carbon footprint reduction, circular economy improvement, and social responsibility. Although the SDGs are developed nationally, their implementation requires sub-national and local action. In terms of the machining process, the following challenges can be defined in the context of improving the level of SP: waste management, improving the efficiency of energy consumption, reducing pollution in the production hall, improving safety and health of work, complexity of SP, etc. This article includes a detailed discussion of problems in the area of air emissions and pollution in production halls and employee health problems based on a case study of machining process. The research experiments were carried out in laboratory conditions for the turning process and the level of pollutants during the realization of this process thanks to the use of Industry 4.0 technology, namely the Internet of Things (IoT) was monitored. The received results made it possible to define challenges for production managers in the context of increasing the level of SP in machining processes in the environmental aspect.

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty.

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0-1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency.

Congestion and Pollutant Emission Analysis of Urban Road Networks Based on Floating Vehicle Data

Global warming caused by greenhouse gas (GHG) is receiving increasingly attention from all over the world, and urban transportation is a significant source of greenhouse gas and pollutant emission. However, the research on traffic state of urban road networks (URNs) based on sparse floating vehicle data (FVD) is insufficient. Therefore, we mainly utilize big data techniques to explore the congestion and pollutant emission of URN with FVD. Firstly, the location of vehicles is identified and matched with the URN. We then grid the FVD and city maps to more accurately identify areas of congestion and emission in later section. Following this, we use the congestion index and K-means clustering algorithm to evaluate the traffic state over time, pollutant emission is calculated based on emission calculation standards and carbon emission is estimated by using the fuel consumption-speed model. The results indicate that congestion and emission are very severe during peak hours (e.g., 8:00 a.m.), particularly in some transportation hub areas, such as high-speed rail stations. During off-peak hours (e.g., 11:00 p.m.), congestion and emission are relatively lower. The negative correlation between congestion index and emission is also revealed. This study provides some practical approaches to more accurately estimate the overall urban traffic state by using sparse traffic data, and may offer support to urban traffic managers in managing traffic congestion and pollutant emissions.

A Global Spatial–Temporal Energy Poverty Assessment and Social Impacts Analysis

Energy poverty (EP) has emerged as a major challenge to achieving sustainable development goals, and its significance in social development has increased over time. This paper aims to analyze the spatial autocorrelation between EP and social factors on a global scale. Utilizing the panel data of 116 countries from 2012 to 2019, the Bivariate local Moran index, a representative spatial econometrics tool, has been employed to examine temporal changes and spatial differences of transboundary synergy and tradeoff relations between EP and social factors. The results indicate that EP has synergy relationships with social factors, including life expectancy at birth, access to immunization, CO2 emission, and forest area, and tradeoff relationship with social factors, such as infant mortality rate, prevalence of undernourishment, forest rents, and gender inequality. Significant spatial differences have been observed that clusters of high‐income countries, particularly those in the Global North, tend to have better energy access and are surrounded by areas with favorable social conditions, and clusters of lower‐income countries, especially those in South Africa and Southeast Asia, have lower energy access and are surrounded by areas with more severe social conditions. The robustness analysis has been conducted to verify the reliability of the results. The spatial imbalance of findings offers robust evidence by emphasizing the importance of key areas, such as Southeast Asia and South Africa, that should be prioritized to take essential policy measures to address the EP and social issues.

Nitrous Oxide Generation, Influencing Factors and Emission Reduction in Anaerobic-Anoxic-Oxic Process Wastewater Treatment Plant

To achieve the reduction of emissions and control of non-CO2 greenhouse gases in municipal wastewater treatment plants, this study conducted a one-year monitoring of N2O in the A2O process of a large municipal wastewater treatment plant in Beijing. N2O released from the water surface of the A2O process was collected by using a gas flux chamber, and the key influencing factors affecting N2O were analysed using statistics. The results showed that N2O was not easy to be released under non-aerated conditions, and the aerobic zone was its main emission area. Temperature leads to high N2O emissions in winter and low in summer. Correlation analyses showed that NO2-N is one of the most critical factors affecting N2O production, and its accumulation level directly affects the amount of N2O produced in the system. DO also has a great influence on the production of N2O, and the amount of N2O produced decreases with the increase of the DO concentration, and the effect on the enzyme activity also makes the pH have a significant effect on the production of N2O.

Electromagnetic–thermal field coupling in tissue ablation: The EAES design and optimization

The coupling control of multi-physics fields has become a focal issue in the engineering domain, especially for achieving a more precise simulation and prediction of physical phenomena. This approach plays a crucial role in enhancing design efficiency and optimizing material selection. Focusing primarily on the controllability between microwave and temperature fields, the Emission Area Extension Structure (EAES) is a novel structure in response to the demand for high circularity in microwave ablation. Finite element analysis was employed to examine the distribution characteristics of electromagnetic and temperature fields in liver tissue under varying power and time conditions. Comparative analyses were conducted to evaluate the impact and performance of the EAES vs traditional structures in ablation. Incorporating the EAES into conventional ablation needles allows for a more concentrated distribution of the electromagnetic field without altering the needle diameter. In the simulation of tissue ablation, the circularity of ablation increased by 10%, while water-cooling efficiency increased by 11%. The introduction of EAES, while maintaining its minimally invasive characteristics, effectively enhanced the controllability of conformal ablation. Furthermore, a mathematical model was developed to serve as a theoretical basis for selecting optimal preoperative parameters, incorporating conditions such as ablation time, power, range, and circularity. Overall, by conducting a multi-physics field coupling analysis on microwave ablation needles and proposing the EAES solution, this study aims to enhance the predictability, controllability, and safety of conformal ablation.

Carbon Emission and Annual Income in the Britain in 2019: An Intra-regional Spatial Analysis

This study conducts a comprehensive spatial analysis of carbon emissions and annual income in Great Britain in 2019 using data from the Office for National Statistics (ONS) and the Annual Survey of Hours and Earnings (ASHE). Using spatial autocorrelation techniques, especially Local Morans I, I identify significant spatial clusters, where Low - Low carbon emission areas are located in Manchester, West Yorkshire, Dorset and Wiltshire. In contrast, High-High clusters occur in Moray, the Midlands, South Yorkshire and Lancashire, highlighting areas where carbon emissions are spatially correlated. In addition, this research uses geographically weighted regression (GWR) analysis to identify any existing relationship between annual income and carbon emissions with accounting of geography. The results show a consistent trend in the strength of this relationship, decreasing from northwest to southeast across the Great Britain. The highest coefficients are concentrated in eastern regions such as the South East, London and the East of England. This research aims to contribute to the understanding of spatial heterogeneity of the local environmental economics by informing relevant stakeholders and politicians about which regions are most in demand of targeted interventions. By identifying vulnerable areas, this study provides valuable insights into the UK's pursuit of sustainable development and low-carbon economy with the global movement towards green economy.

(Invited) Die to Wafer Direct Bonding: Overview of Processes for Optoelectronic and 3D at CEA

Direct wafer bonding of wafers is nowadays well established. However, direct bonding of dies to wafers can result in innovative devices for optoelectronic or 3D applications (Memory, High Performance Computing...). The paper will focus on specific fundamental mechanisms involved in direct bonding, III/V die-to-wafer bonding and copper hybrid bonding. Die-To-Wafer (DTW) direct hybrid bonding, using copper/oxide mixed interfaces [1] is foreseen by major microelectronics companies as essential for future memory or HPC stacks and as a support to extend Moore’s Law. It indeed increases functionalities per surface unit by optimizing space in three directions: different dies embedding specific technologies coming from different wafers can closely be assembled in one place[2] [3]. However, there are specific integration challenges such as keeping delicate surfaces clean from any degradation or particle contamination after dicing and during die handling and stacking. We describe here important progresses concerning dies preparation as well as alignment with a SET NEO HB die bonder. DTW bonding with 5µm interconnection pitch was demonstrated by CEA with an alignment of 1µm[4]. The capillary approach used during die self-alignment on wafer will notably be discussed [5]. Integrated transmitters incorporating lasers and modulators on silicon are of prime importance for communications. They are at the same time most challenging to manufacture due to a need for hybrid III-V integration [6]. In order to use III-V materials in low cost silicon platforms, direct bonding is of great interest due to difficulties encountered when growing III-V hetero-epitaxial layers directly onto silicon (presence then of anti-phase boundaries, dislocations and so on). Photonic demonstration devices are often processed using as a template a III-V stack on a III-V wafer that is transferred thanks to direct bonding onto a silicon wafer [7]. The potential low cost model of silicon photonics is however based on making full use of the surface of a 200 or 300 mm diameter SOI photonic wafer. III-V wafer direct bonding is thus not suitable for two reasons. First, the maximum diameter available for III-V wafers is up to now limited to 150 mm, while Si-based wafers are nowadays typically 300 mm in diameter. Second, the III-V stack is needed only in the emitter and receiver areas that cover only a very small fraction of the overall device area. Therefore, most of the blanket III-V stack is wasted when removing, thanks to patterning, materials in unwanted area. To overcome this wafer diameter mismatch and limit the loss of a very expensive starting material, a collective die direct bonding seems therefore well adapted. The idea is to collectively bond III-V chips only where there are needed and over the full photonic SOI surface. Until now, only a few teams focused on such an approach. Collective die bonding with an adhesive carrier wafer has however already been demonstrated [8]. At CEA, after exploring a bonding process based on a silicon die wafer holder [9], we describe here an alternative solution using a tape on a dicing frame (see figure 1). As before, throughput is favoured over die placement accuracy. This is well adapted as the transferred layer can be resized by photolithography and etching after the die substrate removal. Furthermore, this approach with a “flexible” tape presents the great advantage of being extremely tolerant regarding die thicknesses variations and enables the use of multiple die wafer sources. We will describe such DTW bonding processes with InP materials. 3 x 3 mm² dies will be used as standard die size for bonding performance evaluation.[1]L. Sanchez et al, ECTC, pp.1960-1964[2] A. Bond et al, ECTC 2022, pp.168-176[3] A. Jouve et al, ECTC 2021, pp.225-234[4] E. Bourjot et al, ECTC 2021, pp 470-475[5]J. Berthier et al, Journal of Applied Physics, 2010, 108, 054905[6] B. Szelag et al, IEEE Journal of Selected Topics in Quantum Electronics, 2019, 25, 8201210[7] J. Durel at al, IEEE IEDM 2016[8] L. Xianshu et al, Journal of Selected Topics in Quantum Electronics, 2016, 22, 8200612[9] L. Sanchez, ECS transactions, 2018, 86, 223-231 Figure 1

Prediction of the Concentration and Source Contributions of PM2.5 and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System

The SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km2) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban area of Patras, Greece, for four months (July 2021, September 2021, December 2021, and March 2022), covering all seasons. In this work, we assess the system’s ability to forecast PM2.5 levels and the major gas-phase pollutants during periods with different meteorological conditions and local emissions, but also in areas of the city with different characteristics (urban, suburban, and background sites). We take advantage of this SmartAQ application to also quantify the main sources of the pollutants at each site. During the summertime, PM2.5 model performance was excellent (Fbias < 15%, Ferror < 30%) for all sites both in the city center and suburbs. For the city center, the model reproduced well (MB = −0.9 μg m−3, ME = 2.5 μg m−3) the overall measured PM2.5 behavior and the high nighttime peaks due to cooking activity, as well as the transported PM pollution in the suburbs. During the fall, the SmartAQ PM2.5 performance was good (Fbias < 42%, Ferror < 45%) for the city center and the suburban core, while it was average (Fbias < 50%, Ferror < 54%, MB, ME < 3.3 μg m−3) for the suburbs because the model overpredicted the long-range transport of pollution. For wintertime, the system reproduced well (MB = −2 μg m−3, ME = 6.5 μg m−3) the PM2.5 concentration in the high-biomass-burning emission area with an excellent model performance (Fbias = −4%, Ferror = 33%) and reproduced well (MB < 1.1 μg m−3, ME < 3 μg m−3) the background PM2.5 levels. SmartAQ reproduced well the PM2.5 concentrations in the urban and suburban core during the spring (Fbias < 40%, Ferror < 50%, MB < 8.5 μg m−3, ME < 10 μg m−3), while it tended to slightly overestimate the regional pollution. The main local source of fine PM during summer and autumn was cooking, but most of the PM was transported to the city. Residential biomass burning was the dominant particle source of pollution during winter and early spring. For gas-phase pollutants, the system reproduced well the daily nitrogen oxides (NOx) concentrations during the summertime. Predicted NOx concentrations during the winter were consistent with measurements at night but underestimated the observations during the rest of the day. SmartAQ achieved the US EPA modeling goals for hourly O3 concentrations indicating good model performance.

Visual characterization of coal gas desorption using infrared radiation

Gas monitoring is an important means of preventing gas disasters in coal mines. Correct gas monitoring methods can be used to locate abnormal areas of gas emission on the surface of coal bodies. Therefore, an infrared radiation (IR) testing system of coal gas adsorption and desorption is established in the present work, and the IR-based measurement of coal gas desorption is conducted. The corresponding relationship between amount of gas desorption and the infrared radiation temperature (IRT) difference in coal gas desorption process is studied. The general formula of amount of gas desorption of coal based on the average infrared radiation temperature (AIRT) difference is established, and the visual characterization of amount of gas desorption of coal based on IRT difference is realized. The results show that the process of coal gas desorption is a cooling process, and AIRT difference, in the process of coal gas desorption shows a downward trend. There is a linear relationship between the amount of gas desorption of coal and the AIRT difference. The gas desorption curve of coal based on the AIRT difference is consistent with the measured curve. The cloud diagram of coal gas desorption under different adsorption equilibrium pressures can better reflect the variation characteristics of gas desorption in the process of coal gas desorption, and quantitatively characterize the distribution of gas desorption in different regions of the coal. The research results will help to accurately grasp the regional distribution characteristics of coal gas emissions and provide theoretical support for accurate monitoring and control of gas disaster risk at the mining face.

Geographical approach analysis of the impact of air pollution on newborn intrauterine growth and cord blood DNA damage in Mexico City

Abstract Background Few epidemiologic studies have focused on the specific source of ambient air pollution and adverse health effects in early life. Here, we investigated whether air pollutants from different emission sources were associated with decreased birth anthropometry parameters and increased DNA adduct formation in mother-child pairs residing in the Mexico City Metropolitan Area (MCMA). Methods This cross-sectional study included 190 pregnant women recruited during their last trimester of pregnancy from two hospitals at MCMA, and a Modeling Emissions Inventory (MEI) to calculate exposure to ambient air pollutants from different emissions sources (area, point, mobile, and natural) for two geographical buffers 250 and 750 m radii around the participants households. Results Contaminants were positively correlated with umbilical cord blood (UCB) adducts, but not with maternal blood (MB) adducts. PM10 emissions (area and point sources, overall emissions), PM2.5 (point sources), volatile organic compounds (VOC), total organic compounds (TOC) from point sources were positively correlated with UCB adducts. Air pollutants emitted from natural sources were correlated with a decrease in MB and UCB adducts. PM10 and PM2.5 were correlated (p < 0.05) with a decrease in birth weight (BW), birth length (BL) and gestational age at term (GA). In multivariate analyses adjusted for potential confounders, PM10 was associated with an increase in UCB adducts. PM10 and PM2.5 from overall emissions were associated with a decrease in BW, BL and GA at term. Impact Results suggested higher susceptibility of newborns compared to mothers to damage related to ambient air pollution. PMs are associated with birth anthropometry parameters and DNA damage in adjusted models, highlighting the need for more strict regulation of PM emissions.

Spatiotemporal characteristics and zonal analysis of carbon balance in county-level regions: case study of Jiangsu Province

In China, county-level regions play a pivotal role in achieving dual carbon goals. In this study, we analyzed the spatiotemporal characteristics of carbon balance coefficients for counties in Jiangsu Province from 2000 to 2021. The logarithmic mean Divisia index decomposition method explored the factors influencing county-level carbon balance. The findings indicated that CO2 emissions in Jiangsu Province increased from 192 million tons in 2001 to 637 million tons in 2011 and exhibited a small fluctuation from 2012 to 2021. A “higher in the south, lower in the north” distribution pattern emerged, with high CO2 emission areas concentrated in the economically developed south. Vegetative carbon sequestration increased from 142 million tons in 2000 to 171 million tons in 2021 with a “higher boundary and lower at the center” distribution pattern. High carbon sequestration areas were mainly in regions with abundant natural resources and vegetation. The number of counties experiencing carbon imbalance rose from 63 in 2000 to 95 in 2021, with a spatial distribution similar to high carbon emissions. Economic development and urbanization accelerated carbon imbalance, whereas technological advancement and reduced regional carbon fixation pressure effectively alleviated it. Pursuing of dual carbon goals can be supported by reducing emissions and increasing carbon sinks.

Unraveling the factors influencing CO2 emissions from hydroelectric reservoirs in karst and non-karst regions: A comparative analysis

Carbon migration, transformation, and emissions as CO2 in reservoir and lake systems have been extensively studied. However, uncertainties persist regarding carbon cycling variations in both karst and non-karst regions within large thermal stratified river-reservoir systems. To address this knowledge gap, we combined measurements of water chemistry, isotopic compositions (δ13C), partial pressure of CO2 (pCO2), and CO2 fluxes (FCO2) to elucidate the differences, control mechanisms, and environmental effects of various carbon biogeochemical processes in the Hongjiadu reservoir (HJD, karst reservoir) and Shangyoujiang reservoir (SYJ, non-karst reservoir), Yangtze River basin, China. Our results demonstrate that key biogeochemical processes are associated with CO2 production and emissions. The stronger biological carbon pump (BCP) effect in the lentic area of the HJD reservoir limited CO2 emission (0.9 ± 5.0 mmol m−2 d−1) compared to the SYJ reservoir (50.9 ± 33.4 mmol m−2 d−1), leading to low dissolved inorganic carbon (DIC) and high saturation state of carbonate minerals in the lentic epilimnion. Although the released water from both reservoirs acted as hotspots for CO2 emissions, the results of higher Revelle factor (RF) in the HJD reservoir than SYJ reservoir suggest the greater buffer capacity and lower sensitivity of OC degradation changes in DIC. Despite the shorter hydraulic retention time (HRT) of SYJ reservoir (120 d) compared to HJD reservoir (368 d), FCO2 was 3.5 times higher than that of the HJD reservoir (652.9 mmol m−2 d−1). Moreover, upon analyzing selected reservoirs worldwide, it becomes apparent that karst reservoirs demonstrate a reduced potential for CO2 emissions. Our results indicate that the impact of varying geologic settings should be considered to minimize errors in regional and global CO2 emission estimates.

Evaluation of Detection and Efficacy of Decontamination of Respiratory Pathogens Including SARS-CoV-2 on Basic Military Trainee Gas Masks.

Basic military trainee (BMT) gas mask training poses a potential mechanism of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) transmission. After training, gas masks are decontaminated. Insufficient decontamination can lead to viral transmission in the next training class. To our knowledge, the decontamination process has not been validated for efficacy in removing respiratory pathogens such as SARS-CoV-2. Inactivated strains of SARS-CoV-2, influenza A and B, and Bordetella pertussis were separately inoculated onto gas masks in the emitter area (n = 5). Pathogen detection in swabs collected from gas masks was performed by real-time polymerase chain reaction (RT-PCR) using the BioFire® RP2.1 panel and Biomeme Franklin system. For decontamination efficacy experiments, pathogens were inoculated onto gas masks, and contaminated areas were swabbed before and after decontamination, with detection using both PCR platforms. Lastly, 65 gas masks were swabbed after gas mask training, and again after the trainees and guardians decontaminated the masks, to identify the presence of any respiratory pathogen exhaled onto the gas masks. All four pathogens were detected by both PCR platforms. The BioFire® FilmArray® was more sensitive than the Biomeme platform. Decontamination resulted in undetectable levels of all three viruses. B. pertussis was detected on one mask after decontamination. Experiments with live B. pertussis validated that decontamination eliminated all viable bacteria from gas masks. For BMT sampling, all masks were negative for SARS-CoV-2. One mask tested positive for coronavirus 229E. Once decontaminated, all masks tested negative. BMT gas masks can be monitored for the presence of respiratory pathogens using RT-PCR. The decontamination process removed all viable respiratory pathogens tested from the gas masks. This study demonstrates that RT-PCR can be used to conduct pathogen surveillance on BMT gas masks after training and that the current decontamination process is effective to eliminate respiratory viruses including SARS-CoV-2.

Relationship Between Carbon Emissions and Economic Development of Industrial Parks Based on Decoupling Index

Industrial parks are the main carriers of industrial activities and are also key areas for carbon emissions. To deeply explore the decoupling state of carbon emissions and economic development of industrial parks and the driving forces, Zhengzhou Economic Development Zone were taken as example, based on the energy consumption data of industrial enterprises above a designated size from 2011 to 2020, the IPCC carbon emission accounting method, Tapio decoupling model, and logarithmic mean Divisia index decomposition method were used to analyze the characteristics of carbon emissions from energy consumption in the park, the relationship between carbon emissions and economic development, and the driving factors of decoupling. The results showed that:① in terms of carbon emission characteristics, the carbon emissions of energy consumption in Zhengzhou Economic Development Zone were mainly indirect carbon emissions, and the total carbon emissions showed a trend of rapid growth in the early stage, slowing down in the medium term, and negative growth in the later stage. The carbon emission intensity was decreasing annually. ② From the perspective of decoupling, the decoupling index between total carbon emissions and economic development in Zhengzhou Economic Development Zone from 2011 to 2016 was 1.021, which was in a state of growth linkage, and the decoupling index decreased to 0.089 from 2016 to 2020, turning into a weak decoupling state. ③ From the analysis of driving factors, from 2011 to 2016, four factors, namely carbon emission coefficient, energy efficiency, industrial structure, and economic level, all had a restraining effect on the decoupling of carbon emissions in Zhengzhou Economic Development Zone, and from 2016 to 2020, they all turned into promotion except for the economic level. This study showed that among the factors for the decoupling of carbon emissions in the Zhengzhou Economic Development Zone, the economic level played a major inhibitory role, and energy efficiency played a major role in promoting it. The results of this study can provide a reference for the industry-city integrated industrial park represented by Zhengzhou Economic Development Zone to formulate corresponding carbon emission reduction policies and achieve the carbon peaking and carbon neutrality goals.

Measurement of risk spillover effect based on EV-Copula method

AbstractBased on the extreme value theory, copula function, and conditional value at risk (Abbreviated as CoVaR) model, an extreme value copula CoVaR (EV-Copula CoVaR) model is established. In application, the risk spillover effect of the carbon trading market on the stock market of China is investigated. Firstly, using the index synthesis method, the carbon trading price index is synthesized through the price data of the test area of carbon emission, then the risk spillover effect of the carbon market is measured by the EV-Copula CoVaR, and the dynamic risk spillover ΔCoVaR of the carbon market to each stock market is investigated. Finally, the downside ΔCoVaR under different significance levels is measured, and the relationship between the self-risk and spillover risk of the carbon market is explored, the largest risk spillover effect to the stock market is the electricity market. The smaller the significance level, the greater the carbon market self-risk, and the greater the risk spillover of the carbon market to the stock market, which shows that there is a positive correlation between them.