The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores

Fungal spores are an important component of bioaerosol and also considered to act as indicator of the level of atmospheric bio-pollution. Therefore, better understanding of these phenomena demands a detailed survey of airborne particles.The objective of this study was to examine the dependence of two the most important allergenic taxa of airborne fungi - Alternaria and Cladosporium - on meteorological parameters and air pollutant concentrations during three consecutive years (2006-2008). This study is also an attempt to create artificial neural network (ANN) forecasting models useful in the prediction of aeroallergen abundance.There were statistically significant relationships between spore concentration and environmental parameters as well as pollutants, confirmed by the Spearman’s correlation rank analysis and high performance of the ANN models obtained. The concentrations of Cladosporium and Alternaria spores can be predicted with quite good accuracy from meteorological conditions and air pollution recorded three days earlier.     

Ambient Air Quality Monitoring Network Design for Assessing Human Health Impacts from Exposures to Airborne Contaminants

Existing methods of establishing ambientair quality monitoring networks typically evaluateonly parameters related to ambient concentrations ofthe contaminant(s) of interest such as emissionsource characteristics, atmospheric transport anddispersion, secondary reactions, depositioncharacteristics, and local topography. However,adverse health risks from exposures to airbornecontaminants are a function of the contaminant andthe anatomic and physiologic characteristics of theexposed population. Thus, ambient air qualitymonitoring networks designed for the protection ofpublic health or for epidemiological studiesevaluating adverse health impacts from exposures toambient air contaminants should account for bothcontaminant characteristics and human healthparameters. A methodology has been establishedwhich optimizes ambient air quality monitoringnetworks for assessments of adverse human healthimpacts from exposures to airborne contaminants byincorporating human health risk assessmenttechniques. The use of risk assessment techniquesas the basis for designing ambient air qualitymonitoring networks will help to target limitedfinancial and human resources to evaluate humanhealth risks from exposures to airbornecontaminants.     

人工神经网络和专家系统在污水生物处理系统中的应用

对近年来国内外污水生物处理系统中人工神经网络和专家系统的应用进行了简要的回顾。分析了废水生物处理工艺难于控制的原因及人工神经网络和专家系统的结构和特点。结果表明．国外智能控制发展迅速,并且应用领域遍及污水生物处理的各个方面,国内尚处于起步阶段。简要探讨了废水生物处理智能控制今后应深入研究的问题及方向。     

Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK

Monitoring data from the UK Automatic Urban and Rural Network are used to investigate the relationships between ambient levels of ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂) as a function of NO_x, for levels ranging from those typical of UK rural sites to those observed at polluted urban kerbside sites. Particular emphasis is placed on establishing how the level of ‘oxidant’, OX (taken to be the sum of O₃ and NO₂) varies with the level of NO_x, and therefore to gain some insight into the atmospheric sources of OX, particularly at polluted urban locations. The analyses indicate that the level of OX at a given location is made up of NO_x-independent and NO_x-dependent contributions. The former is effectively a regional contribution which equates to the regional background O₃ level, whereas the latter is effectively a local contribution which correlates with the level of primary pollution. The local oxidant source has probable contributions from (i) direct NO₂ emissions, (ii) the thermal reaction of NO with O₂ at high NO_x, and (iii) common-source emission of species which promote NO to NO₂ conversion. The final category may include nitrous acid (HONO), which appears to be emitted directly in vehicle exhaust, and is potentially photolysed to generate HO_x radicals on a short timescale throughout the year at southern UK latitudes. The analyses also show that the local oxidant source has significant site-to-site variations, and possible reasons for these variations are discussed. Relationships between OX and NO_x, based on annual mean data, and fitted functions describing the relative contributions to OX made by NO₂ and O₃, are used to define expressions which describe the likely variation of annual mean NO₂ as a function of NO_x at 14 urban and suburban sites, and which can take account of possible changes in the regional background of O₃.     

Determining Ecoregions for Environmental and GMO Monitoring Networks

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.     

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.     

Spatial distribution of main forest soil groups in Croatia as a function of basic pedogenetic factors

The model of spatial distribution of main forest soil groups in Croatia was developed as a function of basic pedogenetic factors: lithological substratum, macroclimate and relief. Used data about soil group, lithological substratum, terrain slope and aspect were collected on 1881 soil profiles. Macroclimatic data were estimated for each soil profile by spatial interpolation between meteorological stations. Feedforward neural networks were used as modelling tool. The final model has total classification correctness of 63.5% for training data set and 62.3% for independent test data set. The best result (86.4%) was achieved for fluvisols which are strongly spatially correlated with alluvial sediment in a flood plains. The worst result was achieved for luvisol (14.2%) which mainly comprised very old soils, probably developed under pedogenetic factors different from actual. The model was applied on entire Croatian territory aiming at construction of potential spatial distribution of main forest soils (without human impact), which was compared by the potential spatial distribution of major forest types modelled independently.     

Social network theory in the behavioural sciences: potential applications

Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.     

崇明岛公路两侧蔬菜地土壤和蔬菜重金属污染研究

为了研究崇明岛公路两侧土壤和蔬菜重金属污染状况,采集陈海、北沿公路两侧蔬菜地土壤、蔬菜和路面灰尘样品,测定重金属Pb、Cd、Cu、Zn含量.结果表明,以上海市土壤环境背景值上限值为评价标准,所有土壤样品Pb、Cd、Cu和Zn的超标率分别为10.1%、25.4%、6.5%和8.7%;以HJ 332-2006 食用农产品产地环境质量评价标准中蔬菜地土壤环境质量评价指标限值(pH值6.5～7.5)为标准,Cd超标率在21.0%,仅有不到3%样品Pb含量超标,而没有样品Cu、Zn含量超标.以国家食品卫生标准中规定的重金属限量为标准,路侧蔬菜样品Pb含量超标率为43.2%,Cd为18.6%,Zn和Cu则小于5%.由此可见,崇明岛主要公路两侧土壤污染以Cd为主,而蔬菜污染则以Pb为主.在长期运营的前提下,低交通量公路两侧50 m范围内耕作层土壤和两侧作物会发生一定程度的重金属污染,路面灰尘是路侧土壤和蔬菜的潜在污染源.