Distribution of Zooplankton along the Longitudinal Profile of Two Disturbed Small Rivers of the Upper Volga Basin

The abundance and trophic structure of zooplankton along the longitudinal profile of two typical rivers in the Yaroslavl sector of the Volga region are determined by anthropogenic and zoogenic factors. The distribution of zooplankton under the influence of these factors is described by the concept of patch dynamics. The abundance of zooplankton reaches the highest values in the ameliorated upper reaches of rivers and in beaver ponds.     

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

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.     

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.     

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.     

Estimating Trophic State Proportions of a Regional Lake Population: Are Larger Samples Always Better?

During the summers of 1991–1994, the Environmental Monitoringand Assessment Program (EMAP) sampled 344 lakes throughout thenortheastern United States using a proportional stratified sampling design based on lake size. Approximately one-quarter ofthe 344 lakes were sampled each year (4 years) for totalphosphorus to determine the proportion (and associated95% confidence intervals) of the northeast lake population 1ha (11,076 ± 1,699 lakes) that was in oligotrophic,mesotrophic, eutrophic, or heupereutropic (4 classes) conditionaccording to the total phosphorus criteria of the North AmericaLake Manegement Society. Estimates for the second, third, andfourth yr were developed as cumulative of the previous yrsamples and the current yr samples for the northeast as a wholeand for each of its three ecoregions (4 regions). New confidence intervals were computed for each cumulative yrcondition estimate. This produced a total (4 years × 4classes × 4 regions) of 64 cumulative yr tropic conditionestimates. Confidence intervals for 21% of these estimates didnot shorten with increased sample size. This phenomena raisedquestions about the accuracy of estimates based on cumulativesampling procedures. We explain why and how the phenomenon comesabout with both straight random and proportional randomsampling. Further, we present an example of the effects thisphenomenon has on lake tropic state condition estimates in thenortheastern United States.     

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.     

Effect of free-air CO2 enrichment on nematode communities in a Chinese farmland ecosystem

At a rice-wheat rotational free-air CO2 enrichment(FACE) platform, the effect of elevated atmospheric CO2 on soil nematode communities in a farmland ecosystem was studied. Wheat plots were exposed to elevated atmospheric CO2 (ambient 370 μl/L 200 μl/L).32 families and 40 genera of nematode were observed in soil suspensions during the study period. Under FACE treatment, the numbers of total nematodes, bacterivores and fungivores exhibited an increasing trend. Because of the seasonal variation of soil temperature and moisture, the effect of elevated atmospheric CO2 on soil nematodes was only observed under favorable conditions. The response of nematode communities to elevated atmospheric CO2 may indicate the change of soil food web.     

Rural landscape planning case study of Nanhua State Farm

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Ecological networks and greenways in Europe: reasoning and concepts

The paper gives an overview of approaches towards ecological networks throughout Europe.It does not intend to present a complete picture,but to highlight comon developments within countries and regions and show common principles and differences between countries and regions that have to be taken into account when developing a joint European intiative,Countries or regions that have not been included can be active in the same way ,but information was not accessible to be authors for different reasons.This overview shows the comparable trends in decline of landscapes and the diversity in approaches to biodiversity conservation and nature conservation planning.Understanding the differences and common issues are of utmost imprtance to generalise common principles and to understand the way neighbours and other European partners approach problems.