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。     

大豆过敏原P34蛋白基因检测技术与环境污染的关系研究

环境污染易引起大豆种植区域出现过敏原P34蛋白基因变异情况,当前蛋白基因检测方法不能满足检测需求,提出基于探针引物的大豆过敏原P34蛋白基因荧光PCR定量检测技术,针对大豆过敏原特异性基因P34蛋白基因序列进行设计;通过DNA提取、浓度测定,荧光定量PCR检测,实现大豆过敏原P34蛋白基因检测。实验结果表明,所提方法能够准确检测出包括环境污染区域在内的大豆食品过敏原P34蛋白基因成分,具有很好的应用前景,满足市场监督检测的需要。     

绿色环境保护节能建筑的设计与实现研究

为了实现建筑行业的可持续发展,设计并实现绿色环保节能建筑,将节能环保理念渗透在建筑设计的每个细节中。分析融入节能环保理念的建筑设计过程,对其中的架构环保节能设计、节能环保材料的选用以及节能环保技术发展进行研究,并重点分析了资源可持续利用、太阳能利用以及电气节能技术在绿色环保节能建筑设计中的应用过程。通过绿色建筑节能环保总设计,绿色节能环保建筑构造的优化设计以及绿色建筑的暖通空调设计,实现绿色环保节能建筑的高质量设计。     

污染物在地下水毛细饱水带的运移规律浅释

分析了毛细饱水带的水动力学特性，指出在地下水污染研究中，污染物在毛细饱和水带和潜水怪具有相同的水平运动规律，并以实例分析说明该带对污染物运移的重要性。建议在研究和一非饱和条件地下水及污染物运动问题时把毛细饱水带与潜水含水层统一为饱马毛细饱水带顶面作为饱水面。     

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).     

Groundwater quality under the influence of spent mushroom substrate weathering

Nitrate and other solutes resulting from field-weathering of spent mushroom substrate (SMS) percolate into underlying soils and may migrate to groundwater. A field trial was conducted to investigate the potential influences of SMS weathering on groundwater quality. Spent mushroom substrate was deposited at 90 and 150 cm pile depths over a Typic Hapludult and weathered for 2 years. Eight casing wells were installed around the SMS piles to monitor the quality changes of groundwater with a high seasonal water table of 760 cm below the surface. Although leachate solutes had moved more than 200 cm deep in soil from the surface, no significant changes of groundwater quality caused by SMS weathering were observed even one year after removal of the SMS piles (3 years total). The groundwater had pH, electrical conductivity (EC) and dissolved organic carbon (DOC) of 4.3-5.7, 0.2-0.3 dS m(-1) and 0.7-2.2 mg L(-1), respectively. The major inorganic ions were Mg(2+), Ca(2+), Na(+), Cl(-), SO(4)(2-) and NO(3)(-), with a concentration range of 2.5-68.3 mg L(-1). The results suggest that SMS leachate solutes migrated fairly slow in deep subsurface soils of the experimental field. Considering that leachate solutes may move several meters in soil through preferential flow channels, weathering of SMS in fields with a high seasonal groundwater table >or=5 m below the ground is recommended. Conservatively, SMS weathering should be conducted on compact surfaces and leachate be collected and reused as liquid fertilizers.     

Measurements of fractionated gaseous mercury concentrations over northwestern and central Europe, 1995-99

Although it makes up only a few per cent. of total gaseous mercury (TGM) in the atmosphere, the fraction of oxidised (divalent) mercury plays a major role in the biogeochemical cycle of mercury due to its high affinity for water and surfaces. Quantitative knowledge of this fraction present in mixing ratios in the parts-per-10(15) (ppq) range is currently very scarce. This work is based on approximately 220 data for divalent gaseous mercury (DGM) collected during 1995-99 in ambient air. Over the course of the measurements, the sampling and analytical methods were modified and improved. This is described here in detail and includes transition from wet leaching and reduction procedures to thermo-reductive desorption, the use of annular as well as tubular denuders and adoption of an automated sampling system. The concentration of DGM exhibited a strong seasonal behaviour in contrast to atomic gaseous mercury, with low values in winter and maximum values in summer. The DGM/TGM ratios were frequently found to be below the detection limit (< or = 1%) and in the range 1-5%. A trend of diurnal DGM patterns was observed and implies photolytically induced sources. Scavenging of DGM during rain events was also noticed.