碳纳米管径向尺寸对碳纳米管自支撑膜孔隙结构及通量的影响

为了探索碳纳米材料膜在水处理中应用的可能性,本实验选取4种径向尺寸的碳纳米管(Carbon Nanotubes, CNTs),采用压力辅助过滤技术制备CNTs自支撑膜.通过对CNTs自支撑膜的平均孔径、孔径分布及截留分子量(Molecular weight Cut-Off,MWCO)等进行分析,研究了CNTs径向尺寸变化对CNTs自支撑膜孔隙结构的影响.结果表明,CNTs的径向尺寸越小,制备得到的CNTs自支撑膜越趋向于形成小孔径的膜孔,且孔径分布更加均匀,孔隙面积比越高,膜的MWCO越小.单壁碳纳米管(Single-walled carbon nanotube, SWNT)与多壁碳纳米管(Multi-walled carbon nanotube, MWNT)相比,前者更容易形成膜平均孔径更小且孔径分布更均匀的CNTs自支撑膜.膜通量测定结果表明,研究制备的CNTs自支撑膜的通量属于低压膜范围,通量在230～347 L·m~(-2)·h~(-1)·bar~(-1)之间.其中,虽然由径向尺寸较小的CNTs所制备的CNTs自支撑膜的平均孔径更小,但由于其具有更高的孔隙面积比和更小的膜厚度,其纯水通量相对高于由大径向尺寸CNTs制备的CNTs自支撑膜.     

北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)时空统计分析的主要研究内容.然而,现有的方法直接从监测站点的角度对时空分布特征进行分析,难以有效地揭示PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)与其它污染物间关系进行建模的过程中,缺乏同时考虑时间异质性与空间异质性,从而不能准确地描述依赖关系的时空变异特征.为此,首先借助于空间聚类分析技术,对北京市2014年PM_(2.5)浓度的聚集结构进行探测,在此基础上,通过聚集结构来分析PM_(2.5)季节性时空分布特征.然后,利用地理时空加权回归对北京市PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)浓度均表现为北部浓度低、南部浓度高的空间分布特征;地理时空加权回归具有更好的拟合效果,由回归系数进一步可发现,春夏季PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.     

京津冀地区高分辨率PM2.5浓度时空变化模拟与分析

为了全覆盖、高分辨率和高精度识别京津冀地区大气PM_2.5质量浓度时空变化,选取多角度大气校正算法遥感反演的1km AOD为主要预测因子,多种气象要素和土地利用要素为辅助预测因子,构建了混合效应模型+地理加权回归模型的两阶段统计模型,并针对京津冀地区PM_2.5污染较严重的特点,模型中引入了AOD²等独特预测因子.通过上述两阶段模型定量预测了研究区2017年1 km²空间分辨率的每日PM_2.5质量浓度.结果表明,模型交叉验证的决定系数R²为0.94,斜率为0.95,均方根预测误差为13.14 μg·m^-3,在前人基础上预测精度进一步提升,可用于PM_2.5浓度时空变化预测与分析.2017年,京津冀地区PM_2.5浓度年均值为44.96 μg·m^-3,年均值范围在0~89.89 μg·m^-3之间.PM_2.5浓度时空变化差异性明显,整体上呈现"平原西南部浓度高、平原东北部浓度中等和山区高原浓度低"的空间分布格局以及"冬季浓度高、夏季浓度低和春秋过渡"的季节变化特点.模型预测结果的高时空分辨率可以支持流行病学研究在较小区域的暴露评估和识别小尺度污染源的时空变化,分析发现在大气污染防治行动计划实施以来,污染较严重的冀中南山麓平原区可能出现了重要污染源的空间变化.模型预测与分析结果可以为京津冀大气污染防治提供科学支撑.     

Normalized Brain Weight as a Characteristic of the Relationship between the Body and Brain Weights in Birds

The growth of many internal organs of vertebrates is isometrically related to the growth of the body. Therefore, calculation of the index of an organ makes it possible to eliminate the basic dependence of organ weight on body weight and to reveal the effects of second-order factors. The relationship between the brain and body weights is allometric in all vertebrates, from fishes to birds and mammals, including man. To make this relationship isometric, brain weight should be divided by the square root of body weight, rather than body weight proper. This parameter was previously named normalized brain weight (NBW). The advantage of using NBW instead of cerebral index has been demonstrated for 88 subarctic populations of different bird species.     

大气臭氧浓度升高对丛枝菌根(AM)及其功能的影响

通过模拟大气O₃浓度(0.02μL·L^-1、0.1μL·L^-1和0.2μL·L^-1)升高,观察其对丛枝菌根(AM)及功能的影响.结果表明,大气O₃浓度升高对菌根侵染率影响较小,但对孢子和外生菌丝影响显著,高浓度O₃时的孢子数比自然O₃浓度时增加1倍,低、高浓度O₃分别使AM外生菌丝量比自然浓度时下降48.7%和85.6%.随着O相似文献   

臭氧活性炭工艺中卤乙酸生成潜能与相对分子质量分布关系的研究

通过臭氧生物活性炭和微曝气生物活性炭(O₃/BAC和micro-aeration/BAC)2套工艺研究其对不同相对分子质量有机物去除特点和不同相对分子量有机物生成的卤乙酸及其去除特性.结果表明,O₃/BAC工艺对相对分子质量区间>30×10³的有机物去除率超过90%.O₃/BAC与micro-aeration/BAC出水中,UV₂₅₄值表示相对分子质量<103的有机物超过50%,相对分子量区间在10×10³～30×10³的有机物占20%～30%;在O₃/BAC和微曝气/BAC工艺出水中,以相对分子质量<10³的有机物生成的卤乙酸最多,生成DCAA、TCAA、DBAA分别为97.00、38.55、2.10μg/L和104.00、42.75和2.92μg/L;采用各处理单元不同分子量有机物与氯反应生成的DCAA、TCAA、DBAA和THAAs与相对应的UV₂₅₄值进行线性拟合,相关系数分别为0.827、0.851 3、0.815 7和0.878.UV₂₅₄与臭氧生物活性炭处理工艺出水中的卤乙酸生成潜能具有较好的线性关系.     

层次分析法应用于黑龙江省生态环境质量评价研究

将层次分析法应用于生态省建设指标体系的研究,构建了黑龙江省生态省建设指标体系.采用专家调查问卷的方式构造判断矩阵,应用MATLAB6.5计算软件编程,确定了各指标的权重.运用环境质量综合指数模型,对黑龙江省的生态环境质量进行分级,评价黑龙江省生态省建设所取得的进展.结果表明,应用层次分析法定量地评价省域范围生态环境质量是切实可行的,黑龙江省自开展生态省建设以来,生态环境得到了明显改善.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.