Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

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.     

大气颗粒物中元素碳的直接测定

对原来用元素分析仪测定大气颗粒物样品中有机碳、元素碳的方法[1 ] 进行改进 ,将差减法间接测定元素碳改为一步直接测定元素碳。有机碳、元素碳的测量标准偏差的平均值分别为 0 35 %、0 34% ,提高了元素碳的测量精度 ,同时避免了误差传递 ,解决了差减法测定元素碳时出现负值的情况。     

The collection of PM10 for toxicological investigation: comparisons between different collecting devices

A positive correlation has been established between increased levels of airborne particulate pollution and adverse health effects, the toxicological mechanisms of which are poorly understood. For toxicologists to unambiguously determine thesemechanisms, truly representative samples of ambient PM₁₀ are required. This presents problems, as PM₁₀ collecting equipment commonly employed, such as the Tapered Element Oscillating Microbalance (TEOM®), heat the inflow toexclude moisture or use fibrous filters, resulting in a PM₁₀sample that may have undergone significant chemical change on thefilter surface or is contaminated by filter fibres. Other systems(i.e. Negretti and Partisol) can successfully collect PM₁₀ without chemical alteration or filter contamination. Comparativecollections from Port Talbot, S. Wales suggest that TEOMs and Negretti/Partisol systems collect different PM₁₀'s; the principle difference arising from the TEOM's heating chamber, which precipitates water-soluble ions and volatilises some organic components. This results in both the mass and compositionof the PM₁₀'s being altered. Particle size distributionsfor Negretti and Partisol collections highlighted differences mainly attributed to different flow rates. The results of thiswork demonstrate that simple correlations between PM₁₀ massand adverse health effects are problematic. Furthermore, elucidation of the complex fractionation and chemical changes indifferent collectors is necessary.     

沿江施工产生悬浮物对环境影响分析

通过对太仓港围滩吹填工程施工过程泥浆水的采样分析，研究涉及工程施工过程泥浆扩散和演变规律；进而评价吹填工程施工引起悬浮物的增加对水质、水生生物和鱼类的影响。     

Heavy metals in water bodies purified by suspended substrate of rivers

ＩｎｔｒｏｄｕｃｔｉｏｎＨｅａｖｙｍｅｔａｌｓｉｎｗａｔｅｒｂｏｄｉｅｓａｒｅｓｉｇｎｉｆｉｃａｎｔｌｙｄｉｆｆｅｒｅｎｔｆｒｏｍｏｒｇａｎｉｃｐｏｌｌｕｔａｎｔｓｔｈａｔｃａｎｂｅｍｉｃｒｏｂｉｏｌｏｇｉｃａｌｌｙｄｅｇｒａｄｅｄｏｒｄｅｃｏｍｐｏｓｅｄ .Ｇｅｎｅｒａｌｌｙ ,ｔｈｅｙａｒｅｐｕｒｉｆｉｅｄｔｈｒｏｕｇｈｔｈｅｉｒａｄｓｏｒｐｔｉｏｎｏｎｔｏｓｕｓｐｅｎｄｅｄｓｕｂｓｔｒａｔｅｓｉｎｒｉｖｅｒｓ.Ａｄｓｏｒｐｔｉｏｎｏｆｈｅａｖｙｍｅｔａｌｓｏｎｒｉｖｅｒｓｕｓｐｅｎｄｅｄｓｕｂ…     

太原市工业区大气中TSP浓度及元素成分分析

采用校正空白称量法对太原市工业区大气中ＴＳＰ浓度进行了测定分析，对该区大气ＴＳＰ浓度随时间变化的规律，超标情况及污染特征进行了研究，同时测定了ＴＳＰ中Ｃｒ、Ｐｂ等１２种元素的含量。     

含湿Ca(OH)2颗粒脱硫反应特性的模型研究

在半干法脱硫装置中脱反应主要表现为湿Ｃａ（ＯＨ）２颗粒与ＳＯ２的反应，在液相反应溶解和传质理论基础上，通过对含湿Ｃａ（ＯＨ）２颗粒内反应机理的研究分析提出了含湿Ｃａ（ＯＨ）２颗粒相比发生明显变化。     

有机气溶胶的来源与形成研究现状

介绍了有机气溶胶来源与形成的研究现状,从有机气溶胶的化学组成特征、一次有机气溶胶的来源和二次有机气溶胶的形成机制论述其研究进展.一次有机气溶胶主要来源于烹调油烟、机动车排放、生物质燃烧、工业或民用燃油锅炉释放出的有机物,还有道路扬尘、沥青、刹车尘、轮胎屑、室外香烟烟雾、以及高等植物蜡、细菌活动和草本植物等.大气中的半挥发性有机物可通过物理和化学吸附形成二次有机气溶胶,一些挥发性有机物可通过气相化学反应转化为低挥发性的物质并形成二次有机气溶胶,其主要前体物是芳香族化合物,如苯、甲苯、二甲苯,以及烯烃、烷烃、环烷烃、萜烯和生物排放的非饱和氧化物.