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.     

监测分析中结果的表达─兼谈环境监测标准化

对环境监测中的数据、统计、有效数字、修约，以及计量单位、结果表达、原始数据的记录等既平常又十分重要易被忽略的重要问题作了概述，这些问题的解决才能保证监测结果的准确性和可靠性．     

Tree species in relation to soil moisture regime in northwestern Ontario,Canada

Selected data on site, vegetation cover, and soil, including soil moisture regime (SMR), were collected from 2 167 field plots in northwestern Ontario, Canada. SMR provides an estimate of an averaged, annualized soil moisture supply throughout a complete vegetation cycle. SMR is based upon a relative scale that subjectively ranks sites from , 0, and 1 through 9 along a soil moisture continuum which relates to a dry to wet gradient. SMR may be generally correlated to tree growth, stand composition, degree of competition, nutrient availability and overall site quality.This paper reports on relationships between SMR and major tree species. Results highlight relationships between SMR class and the broad ecological ranges of several tree species. In northwestern Ontario, the determination of SMR can help resource managers to better understand the ecology of boreal sites.     

Comparative rice seed toxicity tests using filter paper,growth pouch-tm,and seed tray methods

Paper substrate, especially circular filter paper placed inside a Petri dish, has long been used for the plant seed toxicity test (PSTT). Although this method is simple and inexpensive, recent evidence indicates that it gives results that are significantly different from those obtained using a method that does not involve paper, especially when testing metal cations. The study compared PSTT using three methods: filter paper, Growth Pouch-TM, and seed tray. The Growth Pouch-TM is a commercially available device. The seed tray is a newly designed plastic receptacle placed inside a Petri dish. The results of the Growth Pouch-TM method showed no toxic effects on rice for Ag up to 40 mg L^–1 and Cd up to 20 mg L^–1. Using the seed tray method, IC₅₀ (50% inhibitory effect concentration) values were 0.55 and 1.4 mg L^–1 for Ag and Cd, respectively. Although results of filter paper and seed tray methods were nearly identical for NaF, Cr(VI), and phenol, the toxicities of cations Ag and Cd were reduced by using the filter paper method; IC₅₀ values were 22 and 18 mg L^–1, respectively. The results clearly indicate that paper substrate is not advisable for PSTT.     

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.     

工作场所空气中硫酸雾（SO3）的分析

采用碘量法测定工作场所空气中的三氧化硫及硫酸雾，三氧化硫及硫酸雾经中性水吸收后与碘化钾和碘酸钾反应，析出的碘与淀粉指示剂作用产生蓝色，进行比色测定。     

地面水环境质量评价的有时序多目标决策方法

将有时序多目标决策方法应用于地面水环境质量优势的比较，介绍了决策方法的原理和评价步骤。实例分析表明该方法计算过程简便，分辨率高，排序结果与实际环境质量状况相符合。     

中国海洋大气监测方案的设想

概述了国内外海洋大气环境监测的基本情况，提出了我国开展海洋大气环境监测的必要性，在我国开展海洋大气环境监测必须解决的几个新课题，并根据国家海洋局《海洋大气监测方案》（讨论稿）提出了我国开展海洋大气监测的设想。     

发光细菌法评价工业废水的毒性

用发光细菌测定法估算了两城市污水对河道水体污染的分担率，评价了工厂排放废水的毒性，并据此确定了污染源监督管理中应优先治理的工厂（车间）废水。     

地下水环境质量变化动态研究

以邢台市环境保护监测站１０年来的监测资料为依据，运用单因子比值法，环比发展速率外推法和直线回归法，对地下水环境质量现状及１０年的动态变化进行了分析，研究和评述，并对发展趋势做了预测性评价。