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.     

River water quality and pollution sources in the Pearl River Delta, China

Some physicochemical parameters were determined for thirty field water samples collected from different water channels in the Pearl River Delta Economic Zone river system. The analytical results were compared with the environmental quality standards for surface water. Using the SPSS software, statistical analyses were performed to determine the main pollutants of the river water. The main purpose of the present research is to investigate the river water quality and to determine the main pollutants and pollution sources. Furthermore, the research provides some approaches for protecting and improving river water quality. The results indicate that the predominant pollutants are ammonium, phosphorus, and organic compounds. The wastewater discharged from households in urban and rural areas, industrial facilities, and non-point sources from agricultural areas are the main sources of pollution in river water in the Pearl River Delta Economic Zone.     

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).     

梅梁湖的富营养化状况及治理对策讨论

监测结果表明，梅梁湖水体ＴＰ，ＴＮ含量分别为０．１ｍｇ／Ｌ和３．４ｍｇ／Ｌ藻类生物量达９－１３亿个细胞／Ｌ，富营养化相当严重，污染的主要原因，工农业，渔业，畜牧业和生活污水的直接排放，使水全营养盐含量较高，藻类大量繁殖，可采用“控源截汛”，“清除淤泥”，“调水搞活”及生物治理等综合性措施，对梅梁湖的富营养化进行了治理。     

对修改《征收排污费暂行办法》的一些建议

分析了《征收排污费暂行办法》在新形势下存在的问题，并从排污费的征收，管理和使用等方面提出了修改建议。     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。