A view from the countryside

Guest Editorial

A view from the countryside     

A study on the interactions among several plant secondary compounds and aphids

The research, focused on the specific interactions among seven plant secondary compounds and aphids, was carried out and the preliminary results showed: (1) "T-typed tube method" was regarded as the best method compared with others designed to observe the interactions. (2) Cabbage aphid was able to be attracted by laurolene while it was not susceptible to α-pinene, β-pinene and diamylene. (3) Gossypol, a major secondary substance in cotton, was able to be implicated as feeding attractant to cotton aphid. (4) Rutin might be implicated repellent to cotton aphid.     

污染物在地下水毛细饱水带的运移规律浅释

分析了毛细饱水带的水动力学特性，指出在地下水污染研究中，污染物在毛细饱和水带和潜水怪具有相同的水平运动规律，并以实例分析说明该带对污染物运移的重要性。建议在研究和一非饱和条件地下水及污染物运动问题时把毛细饱水带与潜水含水层统一为饱马毛细饱水带顶面作为饱水面。     

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.     

雨天硫酸盐化速率样品扩散分析

选择采样期不同降雨天数的碱片，测定余边的硫酸盐化速率含量，其结果为：采样期全月无雨时，余边上的硫酸盐均未检出；半月以上至全月有雨时检出率为１００％，检出量占样品量的２．７０～１５．９５％。经扩散试验，查清了余边上的硫酸盐主要是有效面积上采集的样品的扩散，并提出了相应的修正方法。     

连续采样与五日法采样效果及费用的对比分析

通过对连续采样与五日法采样五种方法监测结果及所需费用的比较，得出连续采样方法监测结果更具有代表性，合理性，一次性投资大，但运行费用并不高的结论。     

奎屯市区城市废水排污系统网络化的探讨

根据奎屯市区城市废水的排放量大、排污口多、污染物浓度高以及排污下水管道污水处理设施的建成现状等特点，提出了以污水处理厂为治理中心，市区排污管道网络化的改造设想、存在问题及对策。     

Faculty position announcement

Faculty position announcement     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

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.