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。     

塔里木油田环境空气质量状况及评价

根据塔里木油田１９９３－１９９６年环境空气监测数据，对其环境空气质量状况进行了分析和评价。     

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.     

灰色系统模型GM（1,1）在大气环境浓度预测中的应用

运用灰色系统理论，结合实例详细介绍了ＧＭ（１，１）模型在大气环境浓度预测中的应用。     

灰色系统模型在总悬浮物预测中的应用

以１９８６－１９９４年东北某城市总悬浮物统计资料为依据，应用灰色系统理论ＧＭ（１，１）模型对总量浮物数值进行预测分析。     

选择双波长分光光度法测定水中余氯

余氯在pH6.5条件下与PADA发生显色反应,本文根据稳定悬浮颗粒对光的吸收行为研究选择波长测定水体中余氯,实验证明在450～515nm波长范围内任意选择两波长比色按照推导公式和模型计算余氯浓度,测定方便结果准确,且计算模型稳定,几乎不受操作环境影响。适合于废水和天然水的监测。