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。     

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。     

Methane emissions from natural wetlands

Methane is considered one of the most important greenhouse gases in the atmosphere. Because of the strict anaerobic conditions required by CH₄-generating microorganisms, natural wetland ecosystems are one of the main sources of biogenic CH₄. The total natural wetland area is estimated to be 5.3 to 5.7 × 10¹² m², making up less than 5% of the Earth's land surface. However, natural wetland plays a disproportionately large role in CH₄ emissions. Wetlands are likely the largest natural sources of CH₄ to the atmosphere, accounting for about 20% of the current global annual emission. Out of the total amount of CH₄ emitted, northern wetlands contribute 34%, temperate wetlands 5%, and tropical systems about 60%.Because of the unique characteristics and high productivity, wetland ecosystems are important in the global carbon cycle. Natural wetlands are permanently or temporarily saturated. Strict anaerobic conditions consequently develop, which allows methanogenesis to occur. But the thin oxic layer and the oxic plant rhizophere promote activity of CH₄-oxidizing bacteria or methanotrophs. Thus, both CH₄ formation and consumption in wetland systems are microbiological processes and are controlled by many factors. Eight of the controlling factors, including carbon supply, soil oxidation-reduction status, pH, temperature, vegetation, salinity and sulfate content, soil hydrological conditions and CH₄ oxidation are discussed in this paper.     

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.     

土地沙漠化监测指标体系的探讨

评述国内外的沙漠化监测指标体系,根据我国实际情况分析沙漠化监测的主要内容,提出建立沙漠化监测指标体系的原则并设置了监测指标体系.     

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。     

污染土壤的植物修复技术进展

植物修复技术是通过使用绿色植物修复污染土壤。它主要源于一系列农艺学，生物学和工程学技术，利用影响植物的生物、微生物及植物的化学和物理作用复污染场地，标签昨技术在经济，美学和技术方面比传统的工程修复技术有更大的优点，概述了植物修复领域的研究动向，以便为修复工作者提供背影资料和研究方法。