植物体内水杨酸分析方法的探讨及其应用(Investigation and Application of Analysis Methods of Salicylic Acid in Plant Tissue)

准确测定植物中水杨酸的含量是深入研究其重要作用和抗病机理的必要前提.采用高效液相色谱(HPLC)对植物中内源水杨酸进行了分析,比较了紫外检测器和荧光检测器不同的检测结果,发现荧光监测器灵敏度更高,是紫外检测器的15倍.论文同时比较了使水杨酸与其他组分很好分离的液相色谱条件,结果表明,pH5.5的乙酸钠:甲醇(9:1,V/V)的流动相效果最好.用该方法测得的精密度的相对标准偏差为1.73%,重现性和稳定性均较好(稳定性的相对标准偏差为4.6%).论文以模式植物拟南芥(Arabidopsis thaliana)的野生型和水杨酸突变体两种类型为供试材料,对其体内水杨酸进行了提取测定,检测结果发现野生型体内的水杨酸的含量大约是水杨酸突变体的9倍,与文献报道吻合.     

融合视频监控系统设计

有线视频监控业务已经得到了广泛的应用,移动视频监控也具有突出的优势,3G出现以后,固定移动网络融合的优势带来了全新的监控业务模式.本文仅简单介绍融合监控系统的设计,以及其应用场景.     

基于GASA-BP神经网络的煤层瓦斯含量预测方法研究

为提高煤层瓦斯含量预测的精准度和效率,提出1种利用遗传算法(GA)和模拟退火算法(SA)混合初始化BP神经网络(BPNN)的瓦斯含量预测新模型(GASA-BPNN模型)。利用灰色关联分析法(GRA)筛选瓦斯含量主控因素并作为GASA-BPNN预测模型的输入。为解决BPNN收敛速度慢和易陷入局部极小陷阱的问题,将GA和具有时变概率突跳性的SA整合为GASA算法协同初始化BPNN的权值和阈值,有效地提高BPNN的参数学习能力。将该模型应用于煤炭生产现场,结果表明：BPNN模型、GA-BPNN模型和GASA-BPNN模型瓦斯含量预测总平均相对误差分别为15.79%,9.03%,5.56%。相比BPNN模型和GA-BPNN模型,GASA-BPNN模型对样本的泛化能力更强,参数训练速度最快并且预测精准度最高。     

Measurement of Particulate Bound Mercury,Gaseous Elemental Mercury,and Reactive Gaseous Mercury Concentrations by Using an Ambient Air Mercury Collection System

In this investigation, the concentrations of gaseous elemental mercury (GEM), reactive gaseous mercury (RGM) and particulate bound mercury (PBM) in ambient air were measured at the Hung Kuang (traffic) sampling site during September 27 to October 6, 2014. An ambient air mercury collection system (AAMCS) was utilized to measure simultaneously PBM, GEM, and RGM concentrations in ambient air. The results thus obtained demonstrate that the mean concentrations of PBM, GEM, and RGM were 38.57 ± 11.4 (pg/m³), 17.67 ± 5.56 (ng/m³) and 10.78 ± 2.8 (pg/m³), respectively, at this traffic-sampling site. The mean GEM/PBM and GEM/RGM concentration ratios were 458 and 1639, respectively. The results obtained herein demonstrate that AAMCS can be utilized to collect three phases of mercury simultaneously. The mean PBM, GEM, and RGM concentrations herein were compared with others found in Asia, America, Europe and Antarctica. The mean PBM, GEM, and RGM concentrations were found to be lowest in Asia and Antarctica. The mean PBM concentration in Europe was approximately eight times that in this investigation. The mean GEM and RGM concentrations in this study were 1.21 and 170 times those found in the United States.     

华北平原耕作土壤特性对基因工程菌迁移的影响

基因工程菌在土壤中的迁移是影响污染土壤生物强化修复的重要因素.在华北平原饱和耕作土壤中,考察了1株阿特拉津降解基因工程菌迁移留存及其影响因素.结果表明,在饱和耕作土壤中,平流渗透是基因工程菌迁移的主要机制,其过程可用过滤模型拟合.土壤性质对孔隙水流和基因工程菌迁移具有显著影响.随着土壤粒径、孔隙率和土壤砂粒组分增加,土壤水力传导率常数增大,基因工程菌过滤系数减小,土壤对基因工程菌过滤留存作用降低.土壤条件不变时,增加入渗流量也会增大土壤水力传导率常数,减小基因工程菌过滤系数.饱和土壤中,水力传导率常数为5.02~6.70 m·d-1时,基因工程菌在土壤中的过滤系数为0.105~0.274,二者存在显著负相关关系.     

水稻甲基汞富集对大气气态单质汞浓度升高的响应研究

水稻具有很强的甲基汞(MeHg)富集能力,从而增加了汞污染地区居民甲基汞暴露的风险.本研究利用开顶气室熏蒸实验和土壤加汞培育实验探究水稻各组织中MeHg富集对大气中的气态单质汞(GEM)浓度升高的响应.结果表明:水稻根中甲基汞含量与土壤中甲基汞含量显著正相关(r=0.9462~0.9870,p0.05),与大气汞含量无线性关系(p0.05),表明水稻根中的MeHg主要来自土壤.水稻茎中甲基汞含量随土壤甲基汞含量的增加而线性增加(上部茎:r=0.8560,p0.05;下部茎:r=0.9178~0.9484,p0.05),与大气汞含量没有相关性(p0.05),但在气室熏蒸实验中上部茎中的甲基汞含量高于下部茎;而在土壤加汞培育实验中下部茎中的甲基汞含量高于上部茎,表明水稻茎主要受土壤汞的影响,且大气汞可能对水稻上部茎甲基汞的富集有一定的影响.水稻叶中甲基汞含量与土壤甲基汞含量显著正相关(r=0.9708,p0.01),与大气汞含量无线性关系,但在气室熏蒸实验中实验组叶中甲基汞的含量高于对照组,表明水稻叶中的甲基汞可能受土壤甲基汞和大气汞的共同影响.水稻籽粒中甲基汞的含量与土壤甲基汞含量呈显著正相关(r=0.9046~0.9865,p0.05),与大气汞含量无明显的线性关系(p0.05),表明水稻籽粒中的甲基汞主要来自土壤,但在水稻上部茎和叶中甲基汞含量受一定程度大气汞含量影响的情况下势必会对水稻籽粒甲基汞的富集产生影响,但究竟有多大程度影响或量化其贡献还需进一步实验研究.     

100 years of high GEM concentration in the Central Italian Herbarium and Tropical Herbarium Studies Centre (Florence, Italy)

Up to 1980s, the most used preservative for herbaria specimens was HgCl₂, sublimating at ambient air conditions; ionic Hg then reduces to Hg⁰ (gaseous elemental mercury, GEM) and diffuses throughout poor ventilated environments. High GEM levels may indeed persist for decades, representing a health hazard. In this study, we present new GEM data from the Central Italian Herbarium and Tropical Herbarium Studies Centre of the University of Florence (Italy). These herbaria host one of the largest collection of plants in the world. Here, HgCl₂ was documented as plant preservative up to the 1920s. GEM surveys were conducted in July 2013 and July and December 2017, to account for temporal and seasonal variations.Herbaria show GEM concentrations well above those of external locations, with peak levels within specimen storage cabinets, exceeding 50,000?ng/m³. GEM concentrations up to ~ 7800?ng/m³ were observed where the most ancient collections are stored and no ventilation systems were active. On the contrary, lower GEM concentrations were observed at the first floor. Here, lower and more homogeneously distributed GEM concentrations were measured in 2017 than in 2013 since the air-conditioning system was updated in early 2017.GEM concentrations were similar to other herbaria worldwide and lower than Italian permissible exposure limit of 20,000?ng/m³ (8-hr working day). Our results indicate that after a century from the latest HgCl₂ treatment GEM concentrations are still high, i.e., the treatment itself is almost irreversible. Air conditioning and renewing is probably the less expensive and more effective method for GEM lowering.     

固定化GEM/CAS串联工艺强化处理阿特拉津废水

考察了固定化基因工程菌强化处理(GEM)/传统活性污泥处理(CAS)串联工艺对阿特拉津废水的处理效果,水力停留时间(HRT)对处理效果的影响,基因工程菌的生长和流失情况.结果表明,当HRT为4～24h,阿特拉津初始浓度为20mg/L,以实际生活污水为碳源时,串联工艺均可以实现对高浓度高负荷的阿特拉津生物强化处理.水力停留时间为24h时,固定化细胞反应器(串联工艺A段)的处理效果最好,阿特拉津平均去除率为96.64%,出水浓度为0.56ms/L.水力停留时间为12、8和4h时,平均去除率分别为88.59%、89.79%、88.61%.反应器在以上4个HRT时, COD平均去除率分别为72.76%、64.59%、66.16%和65.84%. 在整个反应过程中,没有出现大量工程菌流失的现象,同时在固定化颗粒的表面以及浅层均观察到了大量工程菌菌体,固定化颗粒的表面还出现了生物膜和菌胶团,反应结束时,颗粒形态完好,强度满足本工艺条件下长期使用的需求.     

A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation

Sensitivity analysis consists of an integral and important validatory check of a computer simulation model before the code is used in performing any kind of analysis operation. The present paper demonstrates the use of a relatively new method and tool for conducting global sensitivity analysis (GSA) for environmental models, providing simultaneously the first GSA study of the widely used 1d soil–vegetation–atmospheric transfer (SVAT) model named SimSphere. A software platform called the Gaussian emulation machine for sensitivity analysis (GEM SA), which has been developed for performing a GSA via Bayesian theory, is applied to SimSphere model in order to identify the most responsive model inputs to the simulation of key model outputs, detect their interactions and derive absolute sensitivity measures concerning the model structure. This study is also very timely in that, use of this particular SVAT model is currently being considered to be used in a scheme being developed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2016.The employed GSA method was found capable of identifying the most responsive SimSphere inputs and also of capturing their key interactions for each of the simulated target quantities on which the GSA was conducted. The most sensitive model inputs were the topography parameters (slope, aspect) as well as the fractional vegetation cover and soil surface moisture availability. The implications of these findings for the future use of SimSphere are discussed.     

大气汞浓度升高对水稻叶片生理效应的影响研究

采用大田开顶式气室熏气实验,研究大气汞浓度升高对水稻叶片气体交换参数、脯氨酸、丙二醛的积累以及超氧化物歧化酶活性的影响。实验结果显示,水稻叶片净光合速率(Pn)和气孔导度(Gs)随大气汞浓度的升高均较对照略微下降,表明大气汞浓度的升高对水稻光合作用和气孔开放程度有一定影响;扬花期水稻胞间CO2浓度(Ci)随大气汞浓度的升高明显降低(P0.05)表明Pn的略微下降属于气孔限制,同时蒸腾速率(Tr)显著增加(P0.01)表明大气汞对水稻的蒸腾生理功能有一定的影响。乳熟期水稻叶片气体交换参数与大气汞浓度无显著差异(P0.05),且各指标均低于扬花期。水稻叶片脯氨酸(Pro)含量在拔节期随大气汞浓度的升高而显著增加(P0.05),扬花期先升高后下降,在45 ng·m-3时达到最大,成熟期无显著差异(P0.05);水稻叶片丙二醛(MDA)含量在拔节期先升高后下降,45 ng·m-3时达到最大,扬花期和成熟期均无显著差异(P0.05);水稻叶片超氧化物歧化酶(SOD)活性在拔节期先升高后下降,15 ng·m-3时达到最大,扬花期无显著差异(P0.05)。以上结果表明大气汞浓度的升高可以引起水稻叶片膜脂过氧化以及脯氨酸和丙二醛含量的积累,且随着体内Pro、MDA和SOD对大气汞胁迫的协同反应,水稻对逆境的适应能力增强,对汞胁迫产生了耐受性。