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.     

新疆生态功能区划初探

“新疆生态功能区划”参考了“中国综合生态环境区划方案”，以《生态功能区划暂行规程》为依据，充分考虑新疆独特的地理分布格局，将新疆分为5个生态区，18个生态亚区，79个生态功能区。“新疆生态功能区划”对科学有效地管理新疆生态环境，因地制宜地实施保护和治理策略，保证社会经济可持续发展具有重要意义。     

兰州市功能区大气细菌污染和城市绿地系统杀菌效应分析

在测定兰州市城市不同功能区和不同类型绿地系统空气细菌数量的基础上,分析了各功能区空气细菌污染状况,以及人流量和车流量与空气细菌数量之间的关系;探讨了不同绿地系统的杀菌效应,指出各绿地系统杀菌能力:乔、灌、草>乔、灌>草、灌>草坪。     

以江苏省河流湖泊为例探讨基于物种与面积关系确定大型底栖动物最适及高效采样量

以江苏省河流湖泊为研究对象,选取不同水体代表性点位,运用多生境采样法和形态学鉴定获取淡水大型底栖无脊椎动物的物种数及对应的采样面积,拟合实际结果的物种数与面积关系对数函数曲线。所研究采样量包括采样面积值、样方及样带数。研究发现,不同水体点位最适采样量面积为1.45~12.75 m²,样方及样带总数为6~16个,其要求十分耗时耗力。为解决这一问题,以物种数与面积关系对数函数的一阶导数曲线y≈1值为临界点,提出"高效采样量"概念,并提出3条采样原则供讨论。同时得到江苏地区高效采样量:涉水可过河流采样面积为1.36 m²,涉水不可过河流采样面积为1.5 m²,不可涉水河流和浅水湖泊采样面积为1.75 m²,均需采集4个样方,1个样带。     

煤工尘肺患者生存质量与家庭功能和社会支持的关系研究

为了解煤工尘肺患者的生存质量的现状及其与家庭功能和社会支持之间的关系,采用SF-36量表、家庭功能评定量表和社会支持评定量表对212名煤工尘肺患者进行问卷调查。单因素分析显示,煤工尘肺患者在SF-36量表的躯体疼痛、总体健康、活力、情感职能、精神健康5个维度的得分与对照组的差异有统计学意义（p〈0.05）。生存质量得分与家庭功能得分呈负相关,生存质量得分与社会支持得分呈正相关。以生存质量得分为因变量,以家庭功能和社会支持的分量表得分为自变量进行多元逐步回归分析显示,生存质量的影响因素为社会支持利用度、家庭功能的总的功能、情感介入、社会支持的客观支持和问题解决。研究结果表明,煤工尘肺患者生存质量较低,与其家庭功能和社会支持有密切关系,需要从家庭功能和社会支持采取综合措施,提高煤工尘肺患者的生存质量。     

基于Web的安全评价辅助系统的研究

随着我国经济的迅速发展,安全生产的问题在国民经济中的地位越来越重要,安全评价工作正在各个行业开展。目前,对系统安全状况的评价主要通过人工方式,其主要缺点有评价数据精度不高、评价速度慢、评价操作复杂等。本文设计了一个适合政府部门和企业的安全评价辅助系统。将计算机技术应用于安全评价过程中,较好的克服了人工方式的缺点,并能将评价信息及时完整的反馈给安全工作者。该系统运用MicrosoftSQLServer2000和．NET技术,构建了基于B／S／D／C结构的安全评价辅助系统平台,实现了安全评价报告的撰写等功能。其具有一定的实用性,能满足一般用户对安全评价报告的撰写、快速浏览添加以及统计管理等功能。     

基于模糊均生函数和最优子集回归的飞行事故率预测

为有效预防飞行事故的发生,针对飞行事故率具有随机波动性和趋势性的特点,采用模糊均生函数(FMGF)和最优子集回归(OSR)建立飞行事故率预测模型。该方法把FMGF延拓序列作为预测因子加入OSR方程,将FMGF分析和因子筛选相结合作OSR,进而对飞行事故率进行预测。通过对美国空军1988—2004年的飞行事故率进行拟合预测,结果表明:将FMGF模型和OSR模型有机结合,能够有效刻画飞行事故率的随机波动特性,并且其预测结果的相对误差也较小。     

堆积体边坡渐进性破坏的随机模型

为精确模拟堆积体边坡变形破坏过程,根据堆积体所固有的非连续性、非均质性、各向异性等特点,以及边坡变形破坏的渐进性特征,采用改进的有限单元法(FEM)对堆积体边坡进行模拟。考虑堆积体边坡从连续位移函数到不连续位移函数的突变。破裂发生前,用连续位移函数进行计算;破裂发生后,引入不连续位移函数。研究结果表明,破裂面上不同点的安全系数(FOS)是不同的,但都随着破坏过程而降低。同时,可以得出不同非均匀程度下安全系数的概率分布。     

生态系统服务的核算方法

生态系统服务是指对人类生存及生活质量有贡献的生态系统产品和生态系统功能 ,包括由自然生态过程产生并维持的环境和资源条件。目前已有的生态系统服务核算方法可以分为市场价值法、替代市场法和假想市场法。     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.