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。     

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.     

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。     

上海地区二氧化硫浓度和风速的统计关系

应用上海市1986年6月至1987年5月间5个自动监测站的全年每日逐时SO_2浓度平均值和同步的风速平均值资料,按季进行比较、归纳,得到风速—SO_2浓度的诊断判据,并利用1984年全年的相应资料进行验证,准确率达85％;得到了各季浓度—风速的回归方程,和浓度—风速、温差的二元回归方程、复相关系数。     

NBIAS中污泥龄对脱氮除磷效果的影响

探讨无回流间歇曝气系统 (简称 NBIAS) ,处理城市污水的污泥龄 ( SRT)对脱氮除磷及有机物去除效果的影响。通过原污水流程试验和人工合成污水静态模拟实验 ,试验结果表明 :在水温为 2 0℃～ 30℃ ,污泥负荷为 0 .2 5～0 .45kg CODcr/ kg MLSS· d,SRT为 1 8～ 2 2 d,HRT为 8～ 1 0 h时 ,利用原污水作为碳源 ,NBIAS在保持较高的 CODcr去除率的同时 ,总氮和总磷的去除率均在 80 %以上。