工程机械不同工况下PM_(2.5)排放及其碳质组分特征

我国工程机械排放控制起步较晚.为研究实际工况下工程机械的PM2.5排放特性及其碳质组分构成,采用便携式颗粒物稀释采样系统,对3台工程机械(2台挖掘机和1台装载机)在不同典型工况(行驶、作业和怠速)下的PM2.5及其碳质组分〔OC(有机碳)和EC(元素碳)〕的现场排放特征进行了测试.结果表明:沃尔沃挖掘机、山河智能挖掘机的PM2.5排放因子(基于燃油)分别为1.85~3.26和1.56~2.62 g/kg,厦工装载机的PM2.5排放因子为0.98~1.48 g/kg.不同工况对PM2.5排放因子影响较大,怠速工况下PM2.5排放因子是行驶工况下的1.49~1.76倍.工程机械排放的PM2.5中,碳质组分是最主要的成分,其质量分数高达71.0%~84.5%.其中,w(OC)为44.6%~72.0%,在怠速工况下最高;w(EC)则为8.6%~30.9%,在行驶工况下较高.测试工程机械的PM2.5排放水平较高,因此应尽快加强工程机械排放的污染防治.     

三峡库区主要森林植被类型土壤有机碳贮量研究

根据全国森林资源清查资料,按主要优势树种和分布面积将三峡库区主要森林植被划分为马尾松针叶林、栎类混交林、灌木林等11种主要森林植被类型。基于196个土壤剖面数据,分析了11种主要森林植被类型下土壤有机碳含量、碳密度大小和分配特征。研究发现,三峡库区主要森林植被类型下土壤有机碳含量和碳密度均存在较大差异,二者总体上都随土层加深而降低。11种主要森林植被类型中以杉木针叶林土壤有机碳密度最大,达16.0 kg/m2,温性松林下土壤碳密度最小,仅为7.9 kg/m2。不同植被类型下土壤有机碳贮量在土层中的分配比例也不同,以灌木林和柏木林土壤碳贮量在土层间的差异最大。11种主要森林植被类型土壤平均厚度为56.3～98.5 cm,其中杉木针叶林土壤最厚,达98.5 cm,灌丛土壤最薄,平均厚度仅56.3 cm。三峡库区11种主要森林植被类型总面积为3 313 251 hm2,土壤总有机碳贮量为 366.36 t,其中0～10、10～20、20～40和＞40 cm土层分别占22.90%、18.36%、28.33%和30.41%。     

Toward sustainable use of natural resources: Nexus between resource rents,affluence, energy intensity and carbon emissions in developing and transition economies

Sustainable use of natural resources would entail ensuring that derived economic benefits today do not undermine the welfare of generations to come. On this basis, this study examines the nexus between natural resource rents and carbon dioxide (CO₂) emissions disaggregated into production and consumption-based (i.e., trade-adjusted) CO₂ emissions for a selected panel of 45 developing and transition economies over the period 1995–2017. The empirical model also incorporates the impacts of population, affluence, and energy intensity. The results show that affluence increases production-based CO₂ emissions by 1.407%, with the EKC's predicted inverted U-shaped curve only explaining consumption-based CO₂ emissions. Economic reliance on natural resource rents and energy intensification contribute 0.022% and 0.766%, respectively, to CO₂ emissions embedded in territorial production inventories and 0.035% and 0.583%, respectively, to CO₂ emissions embedded in consumption inventories. The bootstrap non-causality test shows that historical data on each variable has significant predictive power for future CO₂ emissions from both sources. The historical information about natural resource rents has significant predictive power over the future levels of affluence and energy intensity. Clearly, the results show that the environmental impact of natural resource rents is stronger when CO₂ emissions are adjusted for trade and varies among the countries, with Bangladesh, Guinea, India, Malaysia, Mexico, Nigeria, Pakistan, Saudi Arabia, Vietnam, and Zimbabwe among the most affected countries. Overall, this study provides motivation for policies to keep the use of natural resources within sustainable limits.     

A discussion to promote global sustainability techniques for motivating plastic waste to minimize carbon footprints

This paper emphasizes the significant challenges facing the sustainable environment, including managing and handling plastic waste and reducing carbon footprints. To tackle these challenges, it is essential to identify people's awareness levels of waste handling techniques and their pro-environmental behaviors. The study focuses on Guwahati, one of the most important cities in Northeast India, which generates increasing plastic waste daily. The paper aims to identify the factors contributing to the reduction of carbon footprints resulting from plastic waste management activities. The data collected from 1326 respondents was analyzed using factor analysis, and the reliability of the dataset was confirmed using Cronbach's alpha (0.84 for the awareness level of waste management techniques and 0.780 for the prevalent mode of plastic waste management techniques). KMO (0.796), Bartlett's test of sphericity (p < 0.001), and determinant score (0.019) were used to assess the data adequacy and factorability of the dataset, and the results were found to be satisfactory. Principal component analysis, exploratory factor analysis, and varimax orthogonal rotation method were used to identify high-loaded factors by reducing the number of variables. The results showed that two highly loaded components, namely awareness level of waste management techniques (AWMT) and prevalent mode of plastic waste management techniques (PWMT), explained 27.53% and 24.34% of the total variance, respectively, with eigenvalues of 3.35 and 2.88. The regression model confirmed the statistical significance of these factors (p < 0.001) and their relationship with the dependent variable, greenhouse reduction (GHGR). The study proposes that minimizing carbon footprints in the environment can be achieved by focusing on a limited number of controllable factors such as AWMT and PWMT. This study provides valuable insights to the authorities in controlling waste generation and achieving a pollution-free environment.     

中国铜产业体系演化的碳中和实现机制研究

本文研究了我国铜产业采选、冶炼、加工和再生等环节的演化趋势,构建了铜产业直接碳排放-能源间接排放-其他间接排放相结合的多层级碳排放核算模型,分析了我国铜产业各环节碳排放演变规律及碳中和实现机制。结果表明：①1980-2020年我国铜精矿、精炼铜、铜加工材、再生铜产品分别增长了6.6倍、 25.1倍、 88.5倍、 37.1倍,受碳中和目标实现对电力需求量增加的影响,预计2060年铜资源消费总量将较2020年提升62.3%,铜资源社会存量将达到3.9亿t,再生铜将于2030年超过原生铜成为主导资源类型。②2020年铜产业的碳排放总量达到2968.2万tCO₂e,其中,采选环节的吨铜碳排放量最高,达到了3.4tCO₂e,是第二位冶炼的2.3倍;冶炼环节的碳排放总量最大,达到铜产业的39.3%;再生环节的降碳效果突出,相较原生采选冶炼环节减少1251.2万tCO₂e;进出口贸易则进一步降低了该产业43.7%的碳排放总量。③预计2060年铜产业碳排放总量将达到1499.8万tCO₂e,通过促进国际贸易、循环经济、技术创新及环境市场建设等举措,可大幅降低产业的碳排放总量,其中国际贸易及循环经济情景的碳减排效果在2030年前均较显著、随后逐渐下降,技术创新及环境市场建设是该产业碳中和目标实现的根本,在2060年的减排潜力分别达到535.9万t及607.9万t。为了更好地促进该产业可持续发展及“双碳”目标实现,建议依托国内国际双循环格局合理调控铜产业结构,秉持全生命周期理念加快构建铜产业绿色供应链,紧随碳中和发展趋势促进资源循环减污降碳协同增效。     

碳标签对低碳购买意愿的作用路径研究——基于亲社会行为调节的中介效应

降低消费阶段的碳排放量对实现国家“双碳”战略目标具有重要意义。在此背景下,本研究从碳标签出发,引入“亲社会行为”这一变量,提出研究假设并构建了消费者低碳购买意愿的模型,利用调查问卷和结构方程模型进行数据分析,探究碳标签对消费者低碳购买意愿的影响,并分析亲社会行为在其中的调节作用。研究结果显示,碳标签认知、碳标签宣传均直接影响消费者的低碳购买意愿,碳标签公信力对低碳购买意愿没有直接影响;感知价值和购买态度在碳标签相关因素与低碳购买意愿之间起到完全或部分中介作用;亲社会行为在感知价值和购买态度对低碳购买意愿产生中介效应的链路上具有调节作用。基于研究结果,本文从提高消费者碳标签的认知水平、增强碳标签的宣传效果、提升碳标签的公信力、倡导亲社会的低碳行为等方面提出对策建议,以期通过政府、企业和消费者的多方努力,促进消费者积极参与低碳消费,实现消费领域的低碳变革。     

湛江电力公司氮氧化物减排措施

介绍了湛江电力公司4×300 MW燃煤发电机组减排氮氧化物实施的技术。     

交通领域减污降碳协同控制研究回顾及展望

交通运输行业是二氧化碳和空气污染物排放的主要贡献者之一,开展交通领域的减污降碳协同控制政策是实现“双碳”目标和空气污染防治的迫切需要。本研究追溯了交通领域减污降碳协同控制的起源及发展,回顾了交通领域协同减污降碳的主要路径,具体包括交通基础信息协同、交通排放清单编制协同、交通减排目标设定与减排情景分析协同、交通减排技术及措施工具箱协同,以及交通减排保障措施协同。然后,从研究范围、研究方法、协同度评价体系构建、污染相关健康协同效应、研究区域等方面对交通领域减污降碳协同控制的研究实践及特点进行了分析,并相应提出了对未来研究的展望。     

电流密度对BDD电极电化学矿化吲哚的影响与机制

掺硼金刚石膜(BDD)电极电化学氧化法是去除难降解有机污染物的有效手段.与总有机碳(TOC)等的测定相比,气态中间产物的逸出量能够更直观有效地反映有机物的矿化程度与去除效果.本研究以吲哚为代表性污染物,通过对比不同电流密度(10、20和30 m A·cm-2)下BDD电极对吲哚的去除率与矿化率,结合降解过程中碳和氮形态的变化与守恒情况,分析吲哚的降解机制.结果表明,BDD电极对吲哚有良好的去除效果,电流密度为10、20、30 m A·cm-2时,吲哚达到100%去除的时间分别为8、5和4 h;TOC去除率、CO_2产生量均随电流密度的增加而增大,证明矿化率与电流密度成正相关;电解产生的CO_2气体与TOC、无机碳(TIC)构成了碳守恒体系.4~5 h时,体系TOC、TON和CO_2产生量均没有变化,表明电解产生的靛红具有较高的稳定性,此时为中间产物积累阶段;XPS表征进一步证实了中间产物靛红、苯醌等在电极表面的吸附,随着电解时间的延长,这些吸附的中间产物可进一步被降解.本研究从气态产物检测及碳氮形态分析与守恒的角度阐释吲哚矿化过程,对于辅助揭示有机物的电解过程有重要意义.     

高级氧化联合生物活性炭工艺深度净化污水中PPCPs研究进展

污水处理厂传统的二级生化处理对药品及个人护理用品(Pharmaceutical and Personal Care Products, PPCPs)等新兴污染物的去除率较低, PPCPs随出水排放会给生态环境带来较大的风险,并会经过生物富集最终危害到人体健康,因此需要通过进一步的深度净化以削减其风险.本文综述了常见PPCPs在污水处理系统的分布特征及生态风险,阐述了高级氧化工艺(Advanced oxidation processes, AOPs)、生物活性炭工艺(Biological activated carbon, BAC)以及高级氧化联合生物活性炭工艺(AOPs-BAC)深度净化PPCPs的净化原理,在此基础上,综述了AOPs-BAC工艺对污水中PPCPs的深度净化效果及主要影响因素,并探讨了AOPs-BAC工艺对污水中PPCPs的风险削减能力,为污水中PPCPs深度净化工艺的研发及PPCPs的生态风险控制提供了参考.