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.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).     

A modular approach to Integrated Assessment modeling

In this paper, we present a new approach to model coupling that probably forms the methodological basis of a new generation of Integrated Assessment models. This approach respects the knowledge and expertise that is embodied in existing models and encourages their gradual evolution. Modularity is the guiding principle. Our approach is distinguished by the way modules are coupled which is based on an interplay of a job control module, a numerical coupling module, and a couple of stand-alone functional modules. The numerical coupling module - the core component - serves to treat the feedbacks between the functional modules. A first implemented example that couples an economic and a climate module by means of a two-phase meta-optimization is presented here. The algorithm and mathematical structure behind are discussed as well as important features such as convergence behavior and reliability.     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。     

环境样品中痕量锰的催化动力学测定法

对环境样品中痕量锰的催化动力学测定方法进行简要评述，引述文献１６篇。     

Optimizing the removal of small fish passage barriers

Removing small artificial barriers that hinder upstream migrations of fish is a major problem in riparian habitat restoration. Because of budgetary limitations, it is necessary to prioritize barrier removal and repair decisions. These have usually been based on scoring and ranking procedures, which, although simple to use, can be very inefficient in terms of increasing the amount of accessible instream habitat. We develop a novel decision-making approach, based on integer programming techniques, which optimizes repair and removal decisions. Results show based on real datasets of barrier culverts located in Washington State that scoring and ranking is over 25% below the optimum on average and a full 100% below in the worst case, producing no net habitat gain whatsoever. This is compared to a dynamic programming method that was able to find optimal solutions in less than a second, even for problems with up to several hundred variables, and a heuristic method, which found solutions with less than a 1% average optimality gap in even less time.     

大气微生物与气象条件及大气污染物的灰色关联分析

根据大气微生物浓度、气象因子和大气污染理化因子的监测结果，应用灰色关联分析原理，分析研究大气微生物浓度与气象条件和大气污染物的关联程度，说明气象因子对各类微生物浓度的影响大于大气污染因子，特别是对耐高渗透压霉菌。     

用好防灾科技学院教育平台创建和弘扬防震减灾文化-恭贺母校升本暨校庆30周年

本文对防震减灾文化的组成、创建动力及其方法进行了探讨.初次提出了防震减灾文化的概念,并对创建防震减灾文化的目的和如何创建、弘扬防震减灾文化提出了看法和观点.     

用好防灾科技学院教育平台 创建和弘扬防震减灾文化——恭贺母校升本暨校庆30周年

本文对防震减灾文化的组成、创建动力及其方法进行了探讨。初次提出了防震减灾文化的概念，并对创建防震减灾文化的目的和如何创建、弘扬防震减灾文化提出了看法和观点。     

区域环评中废水排放量的预测方法研究

阐述了区域环评污染物排放源强的预测原则,对区域开发活动中废水量和COD排放量的预测方法作了详细论述,并以温州滨海新区环评为例作了实证研究.