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.     

高效液相色谱测定花粉素中水溶性维生素

本文介绍一种灵敏而准确的高效液相色谱测定花粉素中五种水溶性维生素(即B_1、B_2、B_5、B_6和B_(12))方法。研究了移动相醋酸盐缓冲溶液的浓度和PH对色谱分离效果的影响,水溶性维生素的定量分析是用日立3011-N的填充柱(其内径为4毫米,长250毫米),以含有2.5％乙醇的醋酸钠-醋酸组成的缓冲溶液(PH6)作为移动相,在波长254纳米处进行紫外检测,变异系数和回收率分别为2-5％和91.0-101.5％。     

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.     

城市大气中颗粒物的研究现状及健康效应

综述了大气尘(TSP、PM10、PM2.5)的研究现状,重点围绕TSP、PM10、PM2.5的物理特征、化学组成(有机组成、重金属元素的总含量及其各化学形态分布)及其源解析进行描述,也提及了大气颗粒物与健康的关系.     

Monitoring of Butter and Ghee (Clarified Butter Fat) for Pesticidal Contamination from Cotton Belt of Haryana, India

Butter (45) and ghee (55) samples were collected from rural and urban areas of cotton growing belt of Haryana and analysed for detecting the residues of organochlorine, synthetic pyrethroid and organophosphate insecticides. The estimation was carried out by using multi residue analytical technique employing GC-ECD and GC-NPD systems equipped with capillary columns. Butter samples were comparatively more contaminated (97%) than ghee (94%), showing more contamination with organochlorine insecticides from urban samples. About 11% samples of butter showed endosulfan residues above MRL value and 2% samples had residues of synthetic pyrethroids and organophosphates each above their respective MRL values. In ghee, residues of HCH & DDT both and of endosulfan exceeded the MRL values in 5 and 20% samples, respectively. Among organophosphates, only chlorpyriphos was detected with 9% samples showing its residue above MRL value. Irrespective of contamination levels, residues above the MRL values were more in ghee. More extensive study covering other agricultural regions/zones of Haryana has been suggested to know the overall scenario of contamination of milk products.     

Evaluation of Water Quality in the Chillán River (Central Chile) Using Physicochemical Parameters and a Modified Water Quality Index

The Chillán River in Central Chile plays a fundamental role in local society, as a source of irrigation and drinking water, and as a sink for urban wastewater. In order to characterize the spatial and temporal variability of surface water quality in the watershed, a Water Quality Index (WQI) was calculated from nine physicochemical parameters, periodically measured at 18 sampling sites (January–November 2000). The results indicated a good water quality in the upper and middle parts of the watershed. Downstream of the City of Chillán, water quality conditions were critical during the dry season, mainly due to the effects of the urban wastewater discharge. On the basis of the results from a Principal Component Analysis (PCA), modifications were introduced into the original WQI to reduce the costs associated with its implementation. WQI_DIR2 and WQI_DIR, which are both based on a laboratory analysis (Chemical Oxygen Demand) and three (pH, temperature and conductivity), respectively, four field measurements (pH, temperature, conductivity and Dissolved Oxygen), adequately reproduce the most important spatial and temporal variations observed with the original index. They are proposed as useful tools for monitoring global water quality trends in this and other, similar agricultural watersheds in the Chilean Central Valley. Possibilities and limitations for the application of the used methodology to watersheds in other parts of the world are discussed.     

固相萃取富集高效液相色谱法测定环境水样中的重金属元素

研究了用固相萃取富集高效液相色谱法测定环境水样中痕量重金属镍、铜、银、铅、镉和汞的方法。环境水样中的镍、铜、银、铅、镉和汞用四 -(对二甲氨基苯基 ) -卟啉 (T4 -DMAPP)柱前衍生 ,用C18固相萃取小柱萃取富集镍、铜、银、铅、镉和汞的T4 -DMAPP络合物 ,富集倍数为 1 0 0倍 ,然后用甲醇和四氢呋喃梯度洗脱为流动相 ,WatersXter raTMRP18( 3 9mm× 1 5 0mm)色谱柱为固定相分离 ,用二极管矩阵检测器检测。镍、铜、银、铅、镉和汞的检测限分别为3、2、4、3、1 5ng/L和 3ng/L。方法相对标准偏差为 1 8%— 3 2 % ,标准回收率为 92 %— 1 0 7%。该方法用于测定环境水样中的痕量重金属 ,结果令人满意     

新疆物理监测能力现状及建议

针对新疆物理监测工作(包括噪声监测、振动监测、辐射监测等)现状及环保监测系统物理监测的人员、仪器、能力等情况，分析了物理监测工作存在的不足，并对今后更好的开展物理监测工作提出建设性意见。     

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。     

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.