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.     

Determination of organophosphorus pesticides and related compounds in water samples by membrane extraction and gas chromatography

A membrane extraction-gas chromatography method was developed fordetermination of organophosphorus pesticides and related compounds including methamidophos, DDVP, dimethoate, methyl parathion, parathion, thiophosphoric acid trimethyl ester, and thiophosphoramidic acid dimethyl ester in water samples. In thismethod, surface-modified acetic cellulose membranes were used to extract the target analytes in water samples, the extracted analytes were back-extracted into a small amount of methanol, andgas chromatography with pulsed flame photometric detector (GC-PFPD) was used to determine the concentrations of targetanalytes in the extracts. The recoveries obtained for thetarget analytes spiked into the water samples ranged from 66to 94%. The method detection limit for each target analyte was 0.05 g L^-1. The method developed in this study had shown the advantages of being cheap, simple, fast, and reliable. It had been used successfully to determine the concentrations of target analytes in river water samples.     

中国水环境的NPSP问题及对策

介绍了我国水环境中的非点源污染（ＮｏｎｐｏｉｎｔＳｏｕｒｃｅｐｏｌｌｕｔｉｏｎ，缩写为ＮＰＳＰ）的情况，强调农业生产是最主要的非点源污染。同时，针对我国水污染防治的政策目标，提出一些建议。     

博斯腾湖水质矿化度模型及预测研究

根据博斯腾湖１９８５～１９９５年水质监测数据和出入湖水量等水文数据观测值，采用水质扩散模型和盐量平衡关系推导出博斯腾湖大湖区（简称博湖）水质矿化度模型，并预测了几种情况下博湖水质矿化度及近几年变化趋势，分析了影响博湖水质矿化度的主要因素，为博湖的近期和远期环境保护规划、环境管理等提供科学依据．     

建立锡山市跨行政区水域边界监控点的必要性和可行性

分析了建立锡山市跨行政区水域边界监控点的必要和可行性，并阐述了建立锡山市排污总量控制体系的重要性，以便提高整个区域的水环境质量。     

某铀矿工人头发中总α,总β,铀,钍放射性水平的调查

本文对某铀矿职业工人和非职业工人头发中的总β、总α放射性水平和铀、钍含量进行了分析测定.结果表明,该矿工人体内铀和钍的蓄积量均在正常放射性本底水平范围内,从头发中检验未观察到工人体内铀、钍放射性水平有明显的升高.     

我国海洋环境污染监测质量保证的回顾与展望

我国海洋环境污染监测质量保证的发展历史可大致分为三个阶段：孕育阶段（１９７２～１９７７年）起步阶段（１９７８～１９８３年）和发展阶段（１９８４～）。各个阶段有不同的特色和侧重点，１９７８年前主要探索与污染物质的分析方法，１９８４年后是监测质量保证工作进入管理时期。     

A study of the elemental leachability and retention capability of compost

In this work a comparison is made between the different approaches that can be taken to evaluate the mobility of elements in compost. The practical consequences of the results obtained are also discussed in terms of methods for cleaning up compost and using compost in environmental remediation. The mobility of potentially toxic elements in compost is evaluated by leaching with four selected eluents, i.e. diluted sulfuric acid, oxalate, citrate and EDTA. In contrast to the chelating agents, diluted sulfuric acid was found to generally have a low leaching capability for removal of heavy metals from compost. This implies that the risk of heavy metal leaching caused by natural rainfall is likely to be low. The results obtained in the leaching experiment were compared with previous results obtained from sequential fractionation. This comparison confirmed that both methods gave similar results for predicting the lability of elements in compost. A non-linear regression analysis of the leaching curves was also conducted. The leaching curves for elements with high lability could be fitted with a two components model. The labile components identified by the kinetic model are approximately in accordance with the fractions obtained from the first step of the sequential extraction method. The kinetic speciation method is shown to be a relatively rapid and simple procedure for compost which gives more information about element lability than simple leaching experiments. The leaching reagents used in this work were not effective enough to be used for cleaning up compost with a high metal content. Compost was however shown to have a high affinity for heavy metals, with the order of affinity of metal for the compost being very similar to that seen for humic acid. Compost may therefore prove to be a good remediation material for metal contaminated waste.     

苏州河干流水质模型的开发研究

对《苏州河水质模型的开发》课题完成的大部分内容进行了介绍，包括水质模型的选择及其原理、模型研究的内容与开发方法、干流水质模型的率定与验证，并据此给出了关于苏州河水环境特性的一些参数值，计算的基准时间为1999年夏季苏州河第3次调水试验时期。需要指出的是，苏州河正处于动态的综合整治过程中，模型中有关参数也会随其相应改变。