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.     

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.     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

Aerosol Scavenging: Model Application and Sensitivity Analysis in the Indian Context

Sulfate aerosols have been found to bethe major contributors to precipitation acidity. Thus,in view of the long-term ecological repercussions theyhave on aquatic ecosystems and their acidity-potential,the present analysis focuses on a case study applicationof the layer-averaged aerosol-scavenging model (Okita et al., 1996) for predicting values of the wet scavengingcoefficient and sulfate concentrations in precipitationsamples on the basis of the information available forsome selected Indian cities. Through sensitivityanalysis (Pandey et al., 1997) the scavengingcoefficient has been found to be very strongly dependenton precipitation intensity. Comparison of modelpredictions has been done with the measured values forDelhi, Mumbai, Calcutta and Chennai in India.     

基于B-P神经网络的环境质量评价方法

提出可将环境质量评价的无论是定量指标还是定性参数转化成"二进制"的"1"或"0",进而将这种二进制数引入B-P网络.通过实例探讨,这种新的B-P网络既适用于定量指标的水质参数又适用于定性指标的水质参数.     

济南市环境空气中多环芳烃的来源识别和解析

根据环境空气污染源标识物的确定和多环芳烃降解行为,利用CMB受体模型进行拟合计算,确定多环芳烃污染源贡献率,并在比较多环芳烃实测值和CMB受体模型计算值的基础上,得出可吸入颗粒物中多环芳烃源解析结果,确定机动车污染源是济南市可吸入颗粒物中多环芳烃的主要贡献源。     

ADMS模型解析城区总悬浮颗粒物来源

以污染源排放数据为基础,应用ADMS-城市扩散模型模拟分析了鞍山市尘各污染源对空气环境质量的贡献。结果表明,来自污染源的浓度贡献值占总量的52%;二次尘及外来尘浓度贡献占48%;矿山开采二次尘的浓度贡献占16%;鞍钢炼铁厂的浓度贡献占19.6%;鞍钢化工总厂浓度贡献占5.6%;供暖锅炉浓度贡献占9.3%。低、中、高架源浓度贡献百分比分别为68.1%、29.0%、2.9%。低架源吨排放量浓度贡献为高架源的2.4倍。     

Prediction of Leachate Level in Kimpo Metropolitan Landfill Site by Total Water Balance

Kimpo metropolitan landfill has received various kinds of wastessince January 1992. The leachate level was measured to be 10.3 m in May 1995 and the level increased to 12.2 m in August 1996. Therefore, to prove the reason for the increasing leachate level, we calibrated hydraulic conductivity of each waste andintermediate layer using the HELP (Hydrologic Evaluation ofLandfill Performance) model. The leachate generation data measured from February 1993 to October 1995 was used in the model calibration. As a result of a model calibration, we obtained anaverage infiltration ratio and used this in analysis of the total water balance to predict elevation of leachate level. Main causes of the elevation of the leachate level were the high water content of the waste and the degradation of the leachate-drainage system caused by the subsidence of a naturalbarrier layer.     

Modeling of non-point source pollution in a Mediterranean drainage basin

SWAT ver. 2000 was used to predict hydrographs, and sediment, nitrate and total phosphorus loadings from a 1349 km² mountainous/agricultural watershed in Northern Greece. The model was calibrated and verified using continuous meteorological data from eight stations within the drainage area, and runoff, sediment and nutrient concentrations measured at nine stations located within the main tributaries of the watershed, for the time period from May 1st, 1998 to January 31st, 2000. Model validation methodology and resulting input parameters appropriate for Mediterranean drainage basins are presented. Predicted by the model hydrographs, sedimentographs and pollutographs are plotted against observed values and show good agreement. Model performance is evaluated using the root mean square error computation and scattergrams of predicted versus observed data. The validated model is also used to test the effectiveness of three alternative cropping scenarios in reducing nutrient loadings from the agricultural part of the watershed. The study showed that this model, if properly validated, can be used effectively in testing management scenarios in Mediterranean drainage basins.     

A study on the orbit of air temperature movement

In this paper, we describe an air temperature movement by mapping its time series to the polar coordinates: the value of the time series is plotted on the radial coordinate and the time on the angular coordinate. In this way, both the yearly and daily air temperature movements from five different locations of China are used to demonstrate that, in an intuitionistic view, air temperature movement is neither random nor chaotic, but has its own orbit. We then propose an elliptic orbit model for the air temperature movement. Our proposed model provides quite good results when it is applied to the evaluation of yearly air temperature movements in eight locations in various parts of the world, and the evaluation of daily air temperature movements in five locations in Hunan Province of China on December 17, 2005. Results show that our approach provides a concise and helpful model for air temperature analysis.