芦苇植株对湿地温室气体排放的影响及其日变化特征

利用静态箱-气相色谱法对夏季(7月、8月和9月)长江河口湿地芦苇植被CO_2、CH_4和N_2O的叶面通量、茎秆扩散速率以及沉积物通量的日变化进行研究。结果显示,通过芦苇叶片排放的N_2O与CH_4的量分别为2.99μg/(m~2·h)和15.36μg/(m~2·h),CO_2则呈现白天吸收(-120.86 mg/(m~2·h))、夜间排放(69.39 mg/(m~2·h))的特点。芦苇茎秆N_2O、CH_4和CO_2平均扩散速率分别为1.96μg/h、142.45μg/h和10.69 mg/h,沉积物平均排放通量为N_2O 8.18μg/(m~2·h)、CH_41.58 mg/(m~2·h)、CO_2169.66 mg/(m~2·h)。芦苇茎秆和沉积物界面CH_4和CO_2的排放均呈现出明显的"单峰型"昼夜变化规律,其排放峰值集中在日照及温度最高的9:00至15:00。芦苇植株是影响温室气体排放变化的因素之一。芦苇植株在光合作用下吸收CO_2并促进CH_4的排放,而芦苇发达的根系及茎秆是温室气体排放的主要途径。同时,Pearson相关性分析表明温度对芦苇群落CH_4和NO2的排放影响显著,但与CO_2通量的相关性不明显。土壤氧化还原电位对3种气体的排放均有显著影响。     

水稻土中五氯酚的降解转化动态及其对微生物群落的影响

采用室内培养实验,研究厌氧条件下水稻土中五氯酚(Pentachlorophenol,PCP)的还原转化与微生物群落组成变化。结果表明,室内培养实验条件下,PCP在水稻土样品中降解比较迅速,在反应17 d时,实验添加的PCP能够完全被还原转化。高通量测序结果显示PCP的添加明显改变了水稻土壤的微生物群落结构,其微生物多样性显著下降,在PCP降解完之后逐渐恢复。原始土壤以及添加PCP的土壤样品中其优势微生物主要分布在变形菌门(Proteobacteria)。PCP添加刺激了水稻土中伯克氏菌科(Burkholderiaceae)、丛毛单胞菌科(Comamonadaceae)、地杆菌科(Geobacteraceae)、红环菌科(Rhodocyclaceae)和假单胞菌科(Pseudomonadaceae)等脱氯相关的微生物菌群繁殖,成为PCP降解过程中的优势菌群,有利于PCP还原降解。本研究结果可为水稻土中有机氯农药污染物的微生物降解脱毒提供理论依据。     

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.     

水中Mg~(2+)的测试管快速测定

研制了一种测定水中Mg2 + 的测试管 ,测定范围为 0 .5mg/L～ 2 .0mg/L。该测试管适用于现场应急监测 ,具有快速、简便、抗干扰能力强和价格低廉等特点     

广州市大气细粒子的化学组成与来源

对广州市四个不同功能区(石井、荔湾、天河和海珠)的夏季大气PM2.5进行了为期一个月的监测,并测试分析了其化学组成(有机碳/元素碳、水溶性离子和元素)。结果表明,广州市夏季PM2.5的平均浓度为97.54μg/m3,其化学组分有机物、SO42-和EC对PM2.5质量浓度贡献最大,分别占PM2.5质量浓度的42%~52%、25%~47%和10%~17%。化学质量平衡模型研究表明,机动车排放和煤燃烧是对广州市大气PM2.5影响最大的污染源,其贡献率分别为54%~75%和32%~52%。     

湛江南桥河、北桥河水重金属含量及分布的研究

通过硝酸-过氧化氢微波消解样品,采用ICP-AES法测定了河水中锌、铅、镉、砷、镍、铁、铬、铜等重金属元素的含量,并分析了这些重金属元素在不同河段、不同水期含量的分布情况。测定结果的RSD<9%,加标回收率在86.0%~103.4%之间,检出限在0.832~2.42μg/L之间,该方法快速、稳定、效果较好。     

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.