叶片微观结构变化对其颗粒物滞纳能力的影响

叶片作为植物滞留大气颗粒物最主要的载体,其表面微观结构特征和粗糙度的差异是颗粒物滞纳能力的重要决定因素.叶片微观结构会随着生长(内部因素)以及环境污染强度(外部因素)发生变化,然而现有的粉尘喷洒模拟实验,一般持续时间较短,而微观结构变化响应具有明显的滞后性,其结果无法客观反映由内外因素作用引起的微观结构变化对颗粒物滞纳能力的影响.本研究利用新叶和老叶研究叶片生长,并选择自然状态下不同污染源条件研究污染强度,分析叶片表面微观结构的变化及其对颗粒物滞纳能力的影响.研究得到3种常绿树种(矮紫杉Taxus cuspidata var.、侧柏Platycladus orientalis和油松Pinus tabuliformis)的老叶滞纳TSP、 PM_(10)、 PM_(2.5)和PM_1量均高于新叶,随着叶片的生长其颗粒物滞纳量在增大,且新叶与老叶对不同粒径颗粒物的滞纳量间均存在极显著的差异.生长中叶片粗糙度Rq值的增大是老叶颗粒物滞纳能力增大的主要原因. 5个树种(侧柏、油松、国槐Sophora japonica、毛白杨Populus tomentosa和银杏Ginkgo biloba)TSP和PM_(10)滞纳量为重度污染区高于相对清洁区.而PM_(2.5)和PM_1滞纳量则是油松、银杏和侧柏为重度污染区高于相对清洁区,国槐和毛白杨为相对清洁区高于重度污染区.不同污染强度区域间叶片TSP、PM_(10)和PM_(2.5)滞纳量存在着极显著的差异,PM_1滞纳量也存在着差异.主要归因于与相对清洁区相比,重度污染区叶片的气孔指数降低,蜡质层退化,表面纹理和细胞边界更加不规则,绒毛变长,变硬,叶片微观结构的这些变化使得重度污染区叶片粗糙度Rq值高于相对清洁区,且叶片背面的增加较正面更明显.研究结果将为深入揭示叶片颗粒物滞纳能力的驱动因素,以及提出更科学地提升净化颗粒物功能的城市森林管理措施提供数据支持.     

高炉炉顶煤气中HCl气体脱除的试验研究

高炉炉顶煤气中HCl气体会加速TRT叶片和煤气管道腐蚀。脱除高炉炉顶煤气中HCl气体可以采用石灰石、轻烧石灰石、Na_2CO_3和KOH作为脱氯剂的活性组分,脱氯剂粒度控制为3~6 mm、脱氯剂料层厚度控制为200 mm以上并适当提高煤气温度来改善高炉煤气中HCl气体的脱除过程。     

型煤燃烧固硫中的温度效应

以氧化钙作为固硫剂的型煤中适当加入Ｆｅ,Ｓｉ添加剂组分,燃烧生成新的固硫体系后进行了２种不同温度条件下的进样实验。１．型煤样品分成６份,进行逐渐升温实验,分别加热到１１７０℃－１２２０℃,温度间隔１０℃,恒温１／４ｈ,２．型煤样品分成７份,进行先升温后进样实验,将加热炉分别升温至要求温度１０００℃－１２２０℃＋后进样,温度间隔５０℃,恒温１／２ｈ,实验表明：在Ｆｅ,Ｓｉ体系的型煤燃烧过程中,当进样     

The relevance of James Lovelock’s research and philosophy to environmental science and academia

James E. Lovelock, famed for his Gaia hypothesis, which views the Earth as a living integrated and interconnected self-regulating system whose equilibrium comes about from complex energy-based interactions and feedback loops, ultimately sustaining life, passed away at the end of July, 2022 at the age of 103. Not only are the adaptive mechanisms of Gaia central to the conversation of environmental homeostasis, they lie at the heart of climate change and global warming. Lovelock is also remembered as the co-inventor of the electron capture detector that eventually allowed for the sensitive detection of chlorofluorocarbons and pesticides. Finally, Lovelock’s free-spirited nature and research independence allow academia to rethink current research’s modus operandi.     

驾驶员注意涣散检测技术研究

通过对注意涣散时驾驶员头部运动及面部表情变化特征的分析,系统实时监测驾驶员眼睛、嘴巴位置和运动状态信息,构建驾驶员注意涣散特征表征参量,实现对驾驶员注意涣散状态信息的检测与提取。驾驶员注意涣散表征量具有复杂的非线性特征,利用BP神经网络非线性识别的优势对驾驶员注意特征进行模式分类,实现驾驶员不同注意涣散状态下的特征捕捉。同时采用Dempster-Shafer证据推理技术,对驾驶注意涣散多源表征信息进行决策融合,实现对驾驶员注意涣散状态的判断。结果表明,BP神经网络与D-S规则多源信息决策融合技术的运用提高了驾驶员注意涣散特征检测的准确性和可靠性。     

数字化控制系统信息显示特征对操纵员信息 捕获绩效的影响及优化研究

随着计算机技术和仪控技术的发展,数字化仪控系统逐步在大型控制室得到应用,主控室信息界面随之也出现了 “巨量信息与有限显示”的矛盾。通过分析数字化主控室信息显示的特点,基于认知心理学的相关理论,通过实验研究 信息显示率、信息提供率、数据更新率对操纵员信息捕获绩效的影响。实验结果表明:对操纵员信息捕获绩效影响最主 要的因素是信息显示率,其次是信息提供率,最后是数据更新率;信息显示率与信息提供率对被试信息捕获绩效的影响 具有较高的灵敏度,而数据更新率的变化对被试信息捕获绩效的影响并不显著;信息显示的最佳匹配组合是信息显示率 为56.4 bit,信息提供率为9.4 bit,数据更新率为3 s。研究成果可为数字化主控室信息显示的设计提供理论基础。     

Optimal timing of carbon capture policies under learning-by-doing

Using a standard Hotelling model of resource exploitation, we determine the optimal energy consumption paths from three options: dirty coal, which is non-renewable and carbon-emitting; clean coal, which is also non-renewable but carbon-free thanks to carbon capture and storage (CCS); and solar energy, which is renewable and carbon-free. We assume that the atmospheric carbon stock cannot exceed an exogenously given ceiling. Taking into account learning-by-doing in CCS technology, we show the following results: (i) clean coal exploitation cannot begin before the outset of the carbon constrained phase and must stop strictly before the end of this phase; (ii) the energy price path can evolve non-monotonically over time; and (iii) when the solar cost is low enough, an unusual energy consumption sequence along with solar energy is interrupted for some time and replacement by clean coal may exist.     

Targeting for optimal grid-wide deployment of carbon capture and storage (CCS) technology

Carbon capture and storage (CCS) techniques are considered as one of the promising approaches to reduce carbon dioxide (CO₂) emissions from fossil fuel based power generation, which still accounts for a significant portion of greenhouse gas emissions in the world. CCS technology can be used to mitigate greenhouse gas emissions, with the additional advantage that it allows continuing use reliable and inexpensive fossil fuels. However, CCS retrofit entails major capital costs as well as a reduction of overall thermal efficiency and power output. Thus, it is essential for planning purposes to implement the minimal extent of CCS retrofit while meeting the specified carbon emission limits for the power sector. At the same time, it is necessary to plan for compensatory power generation capacity to offset energy losses resulting from CCS retrofit. In this paper, an algebraic targeting technique is presented for planning of grid-wide CCS retrofits in the power generation sector with compensatory power. The targeting technique is developed based on pinch analysis. In addition, the proposed methodologies are illustrated through case studies based on grid data in India and the Philippines. Sensitivity analysis is carried out to determine the suitable CCS technology and compensatory power source which satisfy emission limits.     

Fuzzy optimization of multi-period carbon capture and storage systems with parametric uncertainties

Carbon capture and storage (CCS) is an important technology option for reducing industrial greenhouse gas emissions. In practice, CO₂ sources are easy to characterize, while the estimation of relevant properties of storage sites, such as capacity and injection rate limit (i.e., injectivity), is subject to considerable uncertainty. Such uncertainties need to be accounted for in planning CCS deployment on a large scale for effective use of available storage sites. In particular, the uncertainty introduces technical risks that may result from overestimating the limits of given storage sites. In this work, a fuzzy mixed integer linear program (FMILP) is developed for multi-period CCS systems, accounting for the technical risk arising from uncertainties in estimates of sink parameters, while still attaining satisfactory CO₂ emissions reduction. In the model, sources are assumed to have precisely known CO₂ flow rates and operating lives, while geological sinks are characterized with imprecise fuzzy capacity and injectivity data. Three case studies are then presented to illustrate the model. Results of these examples illustrate the tradeoff inherent in planning CCS systems under parametric uncertainty.     

Control of chlorinated aromatic compounds from a municipal solid waste incinerator using limestone addition

In this study, limestone powder was directly added to synthetic MSW, which was fed into a small‐scale fluidized bed incinerator. The concentration of CBzs and CPs in the flue gas were measured before and after a secondary combustion air injection. Finally, the PCDDs and PCDFs concentrations were also measured in the flue gas after the secondary combustion zone. The CaCO₃ added to synthetic MSW not only controls HCl in the flue gas, but suppresses the formation of chlorinated aromatic compounds like CBzs, CPs, PCDDs, and PCDFs. The main mechanisms to control the formation of chlorinated aromatic compounds was more likely related to the suppression of catalytic capability of flyash than the HCl reduction in the flue gas. However, the NO concentration was increased by catalytic reaction of limestone in the fluidized bed.