广东省生活垃圾焚烧设施环境与健康风险评估构架研究

生活垃圾焚烧设施的建立是解决城市生活垃圾逐年增多的有效方式之一,但其所引发的环境与健康问题成为公众关注的热点。基于广东省对生活垃圾焚烧设施的研究,以环境管理为目标,结合国内外在该领域的研究现状,提出广东省生活垃圾焚烧设施环境与健康风险评估构架。通过该构架的多年实践,总结了广东省生活垃圾焚烧设施环境与健康风险评估研究所取得的成效,有望将该成果推而广之,从而使该行业实现绿色生态的蓬勃发展。     

呼兰河哈尔滨段水环境质量现状及污染源解析

为了解呼兰河哈尔滨段水环境质量现状及污染物来源,在平水期和枯水期,对呼兰河哈尔滨段沿线进行现场勘查及断面水质监测。结果表明,呼兰河哈尔滨段沿岸集中式排污点源、工业点源、农村生活污染、畜禽粪便、农田径流等各类污染源化学需氧量总入河量为7 671.02吨/年,氨氮总入河量为1 195.16吨/年。松北区的主要污染贡献源为肇兰新河支流污染;呼兰区的主要污染贡献源为集中式排污点源和农业面源污染。     

天津市滨海新区PM_(2.5)污染时空分布及影响因素研究

为了降低天津市滨海新区中PM_(2.5)的污染,需要对天津市滨海新区PM_(2.5)污染的时空分布和影响因素进行研究。研究天津市滨海新区近年来PM_(2.5)的时空分布特征,并选取PM_(2.5)的相关指标,对天津市滨海新区PM_(2.5)污染影响因素进行分析。结果表明,在天津市滨海新区的冬季时,PM_(2.5)的质量浓度值最高,在滨海新区的夏季时,PM_(2.5)的质量浓度值最低。PM_(2.5)在天津市滨海新区昼间大气中的质量浓度低于夜间大气中的质量浓度。     

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。     

考虑环境污染物减排的石油储运策略研究

随着经济的快速发展,科技水平不断地提高,中国石油行业发展迅速,石油储运的技术也在不断提高。对考虑环境污染物减排的石油储运策略进行研究,对石油储运的过程中出现污染源以及产生的危害进行论述,在此基础上对石油储运污染物减排存在的障碍性因素进行说明,最后针对上述问题,提出石油储运策略对污染物减排相应的解决措施,由变频调速污染物的减排技术、可燃气体的排放与回收技术等措施对石油储运策略的污染物减排进行优化,减少污染物的排放,对生态环境进行保护。     

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.     

连续采样与五日法采样效果及费用的对比分析

通过对连续采样与五日法采样五种方法监测结果及所需费用的比较，得出连续采样方法监测结果更具有代表性，合理性，一次性投资大，但运行费用并不高的结论。     

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.