基于Matlab的BP神经网络在大气污染物浓度预测中的应用

为了寻求有效控制和改善环境质量的相应措施,选用了英国伦敦Bloomsbury监测站的PM10小时平均浓度监测资料,采用“提前终止法”泛化改进的BP神经网络模型,预测PM1024 h内的小时平均浓度。结果表明：采用BP神经网络法对大气污染物浓度进行预测,预测相对误差在2%-48%之间,且绝大部分在2%-17%之间,预测精度较高,泛化能力较好,为大气污染物浓度预测提供了一种全新的思路和方法。     

多频管中电流法防腐层检测评级参数影响研究

多频管中电流法通过计算管道防腐层绝缘电阻来判断管道外防腐层的状态,近年来得到较为广泛的应用.但对于丘陵山地的埋地输油气气管道,其评价结果与实际状态有一定差距.通过研究发现,评价结果与实际状态不符的主要原因是:管道分布电容、管道分布电感在GDWFF处理软件中对防腐层评价结果影响较大,尤其是管道分布电感是关键参数.为此,修正了多频管中电流法评价软件给出的管道分布电感推荐值,并采用现场开挖的方式进行验证,说明评价结果更符合山地实际.     

重钢LF炉除尘系统工艺分析与改建

论述了关于重庆钢铁股份有限公司炼钢厂LF炉除尘工艺的现状和系统的不足,并且就相关技术问题进行了分析与研讨,提出了相应的处理方案。     

沈阳市环境铅污染对儿童健康的影响

实地调查采样、结合GIS技术进行室内综合分析,系统研究了沈阳市大气、土壤、灰尘中铅污染对儿童健康的影响.结果表明,沈阳市环境中铅暴露普遍,铅污染较严重,铅浓度空间分异大,已形成4个高浓度中心.并且大气铅浓度有明显的日、季和年变化规律沈阳市环境铅污染已经影响了儿童健康,0～10岁儿童(ZPP＞2.3μmol/L)血铅范围是10.98～511.2μg/L,平均值是135.59μg/L,40%的儿童血铅水平超标.儿童血铅与大气铅相关性最大,其次是土壤和灰尘.实行无铅汽油之后,大气铅污染有了很大改善,但是土壤和灰尘中的铅容易以扬尘的方式再次进入大气,存在潜在风险,应引起有关部门足够的重视.     

Ecological issues and risk assessment in China

As a result of economic development and population explosion, global ecological environments have been severely disturbed and markedly changed. An ecological crisis involving desertification, soil erosion, degradation of land quality, loss of biodiversity and global climate change has been brought about all over the world. In order to manage ecosystems efficiently, it is necessary to assess ecological risk at multiple scales. Ecological risk is the probability that a region and/or site will experience defined ecological or environmental problems. In this paper, the ecological risks of soil erosion, desertification, and acid deposition have been assessed on a national scale according to natural and human factors, such as topography, soil, vegetation and climate. This assessment has provided very useful information for ecological environmental management in China.     

地震应急期关键时间阶段划分研究

在研究我国地震应急救援相关法律法规、预案,以及汶川、玉树、盈江、彝良和海地、东日本等国内外地震应急救援案例的基础上,将地震应急期划分成应急启动、紧急救援、过渡性安置等3个阶段,并对各阶段的持续时间、主要工作及其先后次序进行了梳理.在实际抗震救灾工作中,这3个阶段不是互不相干、相互独立的,而是存在一定的时间相关性.这就需要指挥者对不同阶段的重点工作优先考虑,同时兼顾各项相关工作,保证抗震救灾工作的顺利进行.     

冰雪灾害中交通工程的损害类型及原因分析

通过对已建及在建的高速公路在冰灾中的损害调查,详细列出了损害的类型并进行了原因分析。并将此次灾难中暴露出的设计、施工和管理方面的问题进行了分解。     

助剂作用下超声浸取电解锰渣

用8-羟基喹啉、黄原酸钾、十六烷基三甲基溴化铵、磷酸三丁酯、柠檬酸5种物质作浸取助剂,考察了助剂作用下超声辅助浸取法从电解锰渣中提取锰的工艺条件。实验结果表明：用1%（质量分数）柠檬酸作浸取助剂,在固液比（g/mL）为1∶4、酸矿比（mL/g）为0.3∶1、浸取温度为70℃的条件下,超声浸取15min,锰浸出率平均可达57.28%,是加热酸浸法锰浸出率的2.72倍,是无助剂超声辅助浸取法锰浸出率的1.52倍。     

Diethylenetriaminepentaacetic Acid (DPTA)-modified Magnetic Cellulose Nanocrystals can Efficiently Remove Pb(II) from Aqueous Solution

Journal of Polymers and the Environment - Surface modification of cellulose nanocrystals (CNC) is essential for improving their reactivity and adsorption capacity. Oxidation, as a conventional...     

Water-dispersible nano-pollutions reshape microbial metabolism in type-specific manners: A metabolic and bacteriological investigation in Escherichia coli

• Water-dispersible nano-pollutions exhibit type-specific toxic effects on E. coli. • Global metabolite profiling was used to characterize metabolic disruption patterns. • Key dysregulated metabolites responsive to nano-pollution exposures were found. • Amino acid metabolism and purine metabolism are perturbed at nano-pollutions. Incomplete separation and recycling of nanoparticles are causing undesirable nanopollution and thus raising great concerns with regard to nanosafety. Since microorganisms are important regulator of physiological processes in many organisms, the interaction between nanopollution and microbial metabolomics and the resultant impact on the host’s health are important but unclear. To investigate how typical nanopollution perturbs microbial growth and metabolism, Escherichia coli (E. coli) in vitro was treated with six water-dispersible nanomaterials (nanoplastic, nanosilver, nano-TiO₂, nano-ZnO, semiconductor quantum dots (QDs), carbon dots (CDs)) at human-/environment-relevant concentration levels. The nanomaterials exhibited type-specific toxic effects on E. coli growth. Global metabolite profiling was used to characterize metabolic disruption patterns in the model microorganism exposed to different nanopollutants. The percentage of significant metabolites (p<0.05, VIP>1) accounted for 6%–38% of the total 293 identified metabolites in each of the nanomaterial-contaminated bacterial groups. Metabolic results also exhibited significant differences between different nanopollutants and dose levels, revealing type-specific and untypical concentration-dependent metabolic responses. Key metabolites responsive to nanopollution exposures were mainly involved in amino acid and purine metabolisms, where 5, 4, and 7 significant metabolic features were included in arginine and proline metabolism, phenylalanine metabolism, and purine metabolism, respectively. In conclusion, this study horizontally compared and demonstrated how typical nanopollution perturbs microbial growth and metabolomics in a type-specific manner, which broadens our understanding of the ecotoxicity of nanopollutants on microorganisms.