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.     

地表水样品自然沉降时间对总磷测定结果的影响分析

地表水中总磷含量高低是评定水质优劣的一项重要指标,分析数据表明丰水期中总磷主要来源于泥沙中吸附的有机磷和无机磷,而溶解态磷含量较少;水样中总磷含量随着自然沉降时间的延长而逐渐降低.因此样品采集回来后样品的自然沉降时间是影响地表水中总磷测定结果的一个主要原因之一.     

“入世”对我国环境监测工作的影响及对策初探

加入 WTO后 ,我国环境监测工作将面临重大的挑战和机遇 ,文章初步分析了入世对我国环境监测工作的正负面影响以及现存的若干问题 ,积极探索了环境监测工作的新战略和新思路 ,提出了创建新体制和适应新形势的对策。     

采样系统中金属氧化物对SO2测定值影响分析

通过实验与实际工程相结合，定量描述了大气采样系统中金属氧化物的存在对ＳＯ２测定值的影响，并阐述了其反应机理，提出了相应措施。     

工作场所空气中硫酸雾（SO3）的分析

采用碘量法测定工作场所空气中的三氧化硫及硫酸雾，三氧化硫及硫酸雾经中性水吸收后与碘化钾和碘酸钾反应，析出的碘与淀粉指示剂作用产生蓝色，进行比色测定。     

地面水环境质量评价的有时序多目标决策方法

将有时序多目标决策方法应用于地面水环境质量优势的比较，介绍了决策方法的原理和评价步骤。实例分析表明该方法计算过程简便，分辨率高，排序结果与实际环境质量状况相符合。     

发光细菌法评价工业废水的毒性

用发光细菌测定法估算了两城市污水对河道水体污染的分担率，评价了工厂排放废水的毒性，并据此确定了污染源监督管理中应优先治理的工厂（车间）废水。     

地下水环境质量变化动态研究

以邢台市环境保护监测站１０年来的监测资料为依据，运用单因子比值法，环比发展速率外推法和直线回归法，对地下水环境质量现状及１０年的动态变化进行了分析，研究和评述，并对发展趋势做了预测性评价。     

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.     

用密切值法评价沙湾荒漠区生态环境质量

利用多目标决策中的密切值法进行沙湾荒漠区生态环境综合评价。该方法简便、适用、且分辨率较高，对各监测点群的定量描述符合实际状况。