亚慢性氟暴露致小鼠海马损伤的影响及分子机制研究

为探明亚慢性氟中毒致小鼠海马损伤及分子机制,选用ICR雄性小鼠140只,随机分为对照(C)、低氟(LF)、高氟(HF)、低氟(LF)+L钙离子通道激动剂(FPL)、低氟(LF)+L钙离子通道抑制剂(NIF)、高氟(HF)+激动剂(FPL)、高氟(HF)+抑制剂(NIF) 7组,分别饮用自来水、5、30 mg·L~(-1)氟化钠水溶液90 d,氟染毒第84 d分别腹腔注射生理盐水、FPL64176、Nifedipine7d.同时,采用开场行为及水迷宫检测学习记忆能力;通过HE切片染色观察海马形态结构;根据试剂盒说明检测脑组织谷胱甘肽过氧化物酶(GSH-PX)、总超氧化物歧化酶(SOD)酶活性及丙二醛(MDA)含量;利用RT-PCR和Western Blot法分别检测海马内凋亡相关分子CaMKⅡ和Bax、Bcl-2基因/蛋白表达.结果发现,HF组、LF+FPL组和HF+FPL组小鼠毛发粗糙,暴躁不安.LF、HF组小鼠自发活动、探究行为及空间学习记忆力显著下降(p0.05),海马细胞损伤,SOD、GSH-PX活性极显著下降(p0.01),CaMKⅡ基因/蛋白表达水平显著(p0.05)或极显著(p0.01)上调,Bax基因/蛋白表达水平显著(p0.05)或极显著(p0.01)上调,Bcl-2基因/蛋白表达水平显著(p0.05)或极显著(p0.01)下调,Bax与Bcl-2基因/蛋白表达水平比值显著(p0.05)或极显著(p0.01)上调;LF+FPL、HF+FPL处理加剧了上述基因/蛋白表达水平,LF+NIF、HF+NIF组可逆转上述基因/蛋白表达水平.结果提示,氟中毒致脑损伤与L型钙离子通道及下游相关分子表达异常密切相关,而L钙离子通道抑制剂(Nifedipine)能改善氟暴露造成的海马损伤.     

Seasonal variation and size distribution of biogenic secondary organic aerosols at urban and continental background sites of China

Size-resolved biogenic secondary organic aerosols(BSOA) derived from isoprene and monoterpene photooxidation in Qinghai Lake, Tibetan Plateau(a continental background site) and five cities of China were measured using gas chromatography/mass spectrometry(GC/MS). Concentrations of the determined BSOA are higher in the cities than in the background and are also higher in summer than in winter. Moreover, strong positive correlations(R2= 0.44–0.90) between BSOA and sulfate were found at the six sites,suggesting that anthropogenic pollution(i.e., sulfate) could enhance SOA formation,because sulfate provides a surface favorable for acid-catalyzed formation of BSOA. Size distribution measurements showed that most of the determined SOA tracers are enriched in the fine mode( 3.3 μm) except for cis-pinic and cis-pinonic acids, both presented a comparable mass in the fine and coarse( 3.3 μm) modes, respectively. Mass ratio of oxidation products derived from isoprene to those from monoterpene in the five urban regions during summer are much less than those in Qinghai Lake region. In addition, in the five urban regions relative abundances of monoterpene oxidation products to SOA are much higher than those of isoprene. Such phenomena suggest that BSOA derived from monoterpenes are more abundant than those from isoprene in Chinese urban areas.     

海洋平台阴极保护与监测技术的应用

针对海洋平台全浸区的腐蚀情况,着重介绍了阴极保护及其原理、种类及特点,监测系统的组成及设计,探讨阴极保护及监测系统对海洋平台水下防腐的应用意义。     

碎米荠对硒、镉超富集特性研究

超富集植物在营养元素生物强化及重金属污染植物修复技术领域有重大应用前景。通过野外调查和栽培实验,研究了碎米荠植物对硒镉的吸收富集特征。研究表明:野生碎米荠中Se含量达数百mg/kg,叶片中Se最高含量为1 365 mg/kg。地上部Se的生物富集系数在2.8~43.8的范围内,大部分超过10;生物转移系数在0.46~1.88之间,大部分大于1。地上部Cd含量都超过了100 mg/kg,叶中的Cd含量大部分在400~800 mg/kg之间。富集系数都大于1,且农田及小溪流域植株地上部Cd的生物富集系数高达几十甚至上百,Cd的生物转移系数大部分超过1。露天栽培条件下,碎米荠中的硒镉含量随盆栽土壤中硒镉浓度的上升呈现出先增加后降低的特征,当Se含量为50 mg/kg时植物中的Se含量最高,此时地上部Se含量为995 mg/kg,地下部Se含量1 100 mg/kg。当Cd浓度为80 mg/kg时,植株当中Cd含量最高,地上部和地下部中的Cd含量分别高达550 mg/kg、337 mg/kg。研究认为湖北渔塘坝发育的碎米荠植物是硒镉多元素超富集植物,在硒镉生态环境污染修复方面具有重大应用前景。     

高速铁路选线对生态环境的影响分析研究

高速铁路选线要充分考虑对生态环境的影响,在路基、桥涵、隧道等形式通过环境敏感点时,要从工程技术和生态环境保护的角度出发,避免高速铁路建设和运营对当地的水资源环境、自然保护区造成破坏。根据高速铁路周边的环境信息,在路基填土高度上结合地形、地质和水文自然特征确定合理的填土高度。在高速铁路跨越生态保护区和湖泊等线路中,贯彻生态选线的原则,准确确定桥涵净空,在工程设计、施工上采用系列的生态保护措施。     

绿色建筑的节能环保设计思路分析研究

绿色建筑具有透光好、保温性与通风性好等优点,为了实现节能减排,提高宜居性,进行绿色建筑的节能环保设计,考虑低碳节能效果,在建筑外墙使用抹整体式保温系统,楼顶使用现浇整体式保温系统进行建筑的保温墙设计,考虑建筑的通风性,建筑使用通透性的设计方案,设计空中楼顶花园,提高隔热防晒效果的同时,提高绿化覆盖面积。合理布局楼高和楼间距,提高建筑的采光性,设计景观生物廊道,采用底层架空式结构实现自然通风和采光,实现绿色建筑的节能优化设计。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).