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.     

The efficacy of protoporphyrin as a predictive biomarker for lead exposure in canvasback ducks: Effect of sample storage time

We used 363 blood samples collected from wild canvasback dueks (Aythya valisineria) at Catahoula Lake, Louisiana, U.S.A. to evaluate the effect of sample storage time on the efficacy of erythrocytic protoporphyrin as an indicator of lead exposure. The protoporphyrin concentration of each sample was determined by hematofluorometry within 5 min of blood collection and after refrigeration at 4 °C for 24 and 48 h. All samples were analyzed for lead by atomic absorption spectrophotometry. Based on a blood lead concentration of 0.2 ppm wet weight as positive evidence for lead exposure, the protoporphyrin technique resulted in overall error rates of 29%, 20%, and 19% and false negative error rates of 47%, 29% and 25% when hematofluorometric determinations were made on blood at 5 min, 24 h, and 48 h, respectively. False positive error rates were less than 10% for all three measurement times. The accuracy of the 24-h erythrocytic protoporphyrin classification of blood samples as positive or negative for lead exposure was significantly greater than the 5-min classification, but no improvement in accuracy was gained when samples were tested at 48 h. The false negative errors were probably due, at least in part, to the lag time between lead exposure and the increase of blood protoporphyrin concentrations. False negatives resulted in an underestimation of the true number of canvasbacks exposed to lead, indicating that hematofluorometry provides a conservative estimate of lead exposure.The U.S. Government's right to retain a non-exclusive, royalty-free licence in and to any copyright is acknowledgedDeceased     

Scenario Analysis for the San Pedro River, Analyzing Hydrological Consequences of a Future Environment

Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits and consequences. The San Pedro River in Arizona and Sonora, Mexico is an area that has undergone rapid changes in land use and cover, and subsequently is facing keen environmental crises related to water resources. It is the location of a number of studies that have dealt with change analysis, watershed condition, and most recently, alternative futures analysis. The previous work has dealt primarily with resources of habitat, visual quality, and groundwater related to urban development patterns and preferences. In the present study, previously defined future scenarios, in the form of land-use/land-cover grids, were examined relative to their impact on surface-water conditions (e.g., surface runoff and sediment yield). These hydrological outputs were estimated for the baseline year of 2000 and predicted twenty years in the future as a demonstration of how new geographic information system-based hydrologic modeling tools can be used to evaluate the spatial impacts of urban growth patterns on surface-water hydrology.     

Mercury and trace elements in cloud water and precipitation collected on Mt. Mansfield,Vermont

The lack of high quality measurements of Hg and trace elements in cloud and fog water led to the design of a new collector for clean sequential sampling of cloud and fog water. Cloud water was collected during nine non-precipitating cloud events on Mt. Mansfield, VT in the northeastern USA between August 1 and October 31, 1998. Sequential samples were collected during six of these events. Mercury cloud water concentrations ranged from 7.5 to 71.8 ng l(-1), with a mean of 24.8 ng l(-1). Liquid water content explained about 60% of the variability in Hg cloud concentrations. Highest Hg cloud water concentrations were found to be associated with transport from the Mid-Atlantic and Ohio River Valley, and lowest concentrations with transport from the north of Mt. Mansfield out of Canada. Twenty-nine event precipitation samples were collected during the ten-week cloud sampling period near the base of Mt. Mansfield as part of a long-term deposition study. The Hg concentrations of cloud water were similar to, but higher on average (median of 12.5 ng l(-1)) than Hg precipitation concentrations (median of 10.5 ng l(-1)). Cloud and precipitation samples were analyzed for fifteen trace elements including Mg, Cu, Zn, As, Cd and Pb by ICP-MS. Mean concentrations were higher in cloud water than precipitation for elements with predominately anthropogenic, but not crustal origin in samples from the same source region. One possible explanation is greater in-cloud scavenging of crustal elements in precipitating than non-precipitating clouds, and greater below-cloud scavenging of crustal than anthropogenic aerosols.     

Dissolved and Total Copper in a Coal Ash Effluent and Receiving Stream: Assessment of In Situ Biological Effects

An in situ chemical and biological study was conducted in the lower Muskingum River (southeast Ohio, U.S.A.) to evaluate potential effects of copper (Cu) discharged from a coal ash effluent. Effluent total Cu, dissolved Cu, TSS and pH measurements were performed monthly during January-December 1995. Benthic macroinvertebrates were sampled at five river locations using artificial substrate samplers, and in situ Cu analyses were conducted. Effluent Cu (total) ranged from 8 to 142 microg L(-1) (mean = 58 microg L(-1)), but dissolved Cu never exceeded 78 microg L(-1) (mean = 20 microg L(-1)). The mean ratio of dissolved Cu to total Cu in these samples was 32%. Total Cu concentrations at the biological sampling sites adjacent to the effluent discharge were higher than levels at ambient sites, but dissolved Cu levels were similar among all sites. The macroinvertebrate community proximal to the coal ash effluent had the highest number of taxa and total number of individuals; a high number of mayfly and caddis fly taxa; and the highest Invertebrate Community Index score. The high water velocity of the discharge (which likely contained particulate organic matter) apparently created a favorable microhabitat that, combined with Cu-complexing constituents in the discharge, superceded potential adverse effects of high Cu levels. This study emphasizes the importance of instream biological data when obtained in conjunction with chemical analyses.     

Integrated assessment of potential health impact of global environmental change: Prospects and limitations

Estimating the future health impact of global environmental change requires scientific methods that extend beyond conventional health risk assessment in relation to existing exposures. The dynamic and non‐linear nature of these changes in large complex biophysical systems, the interactions between them, and the reference to future scenarios all contribute uncertainty. Potential health impacts can be estimated from historical analogues, by mathematical modelling, or by reasonable foresight (especially in relation to social and economic disruptions). Integrated assessment methods draw upon all these techniques. In particular, integrated mathematical modelling techniques are evolving, as scientists (and policy‐makers) come to terms with this complex scenario‐based impact assessment task.     

Control of rumen methanogenesis

During the last decades, considerable research on methane production in the rumen and its inhibition has been carried out. Initially, as methane production represents a significant loss of gross energy in the feed (2–15%), the ultimate goal of such intervention in rumen fermentation was an increase in feed efficiency. A second reason favouring research on methane inhibition is its role in the global warming phenomenon and in the destruction of the ozone layer. In this review, the authors describe briefly several interventions for reducing methane emission by ruminants. The objective can be reached by intervention at the dietary level by ration manipulation (composition, feeding level) or by the use of additives or supplements. Examples of additives are polyhalogenated compounds, ionophores and other antibiotics. Supplementation of the ration with lipids also lowered methanogenesis. More biotechnological interventions, e.g., defaunation, probiotics and introduction of reductive acetogenesis in the rumen, are also mentioned. It can be concluded that drastic inhibition of methane production is not unequivocally successful as a result of several factors, such as: instantaneous inhibition often followed by restoration of methanogenesis due to adaptation of the microbes or degradation of the additive, toxicity for the host animal, negative effects on overall digestion and productive performance. Therefore, methanogenesis and its inhibition cannot be considered as a separate part of rumen fermentation and its consequences on the animal should be taken into account.     

Classification of local- and landscape-scale ecological types in the southern Appalachian Mountains

Five local ecological types based on vegetative communities and two landscape types based on groups of communities, were identified by integrating landform, soil, and vegetation components using multivariate techniques. Elevation and several topographic and soil variables were highly correlated with types of both scales. Landscape ecological types based only on landform and soil variables without vegetation did not correspond with types developed using vegetation. Models developed from these relationships could allow classification and mapping of extensive areas using geographic information systems.     

A national framework for monitoring and reporting on environmental sustainability in Canada

In 1991, a collaborative project to revise the terrestrial component of a national ecological framework was undertaken with a wide range of stakeholders. This spatial framework consists of multiple, nested levels of ecological generalization with linkages to existing federal and provincial scientific databases. The broadest level of generalization is the ecozone. Macroclimate, major vegetation types and subcontinental scale physiographic formations constitute the definitive components of these major ecosystems. Ecozones are subdivided into approximately 200 ecoregions which are based on properties like regional physiography, surficial geology, climate, vegetation, soil, water and fauna. The ecozone and ecoregion levels of the framework have been depicted on a national map coverage at 1:7 500 000 scale. Ecoregions have been subdivided into ecodistricts based primarily on landform, parent material, topography, soils, waterbodies and vegetation at a scale (1:2 000 000) useful for environmental resource management, monitoring and modelling activities. Nested within the ecodistricts are the polygons that make up the Soil Landscapes of Canada series of 1:1 000 000 scale soil maps. The framework is supported by an ARC-INFO GIS at Agriculture Canada. The data model allows linkage to associated databases on climate, land use and socio-economic attributes.     

Development of Ecological Land Classification and mapping in support of forest management in northern Newfoundland,Canada

For the sustainable development of forest land, as recently prescribed by the Canadian Forest Strategy, a land classification project in northern Newfoundland was initiated to support the local forest management activities. The method adopted here is a modification of the Canadian Committee for Ecological Land Classification's (CCELC) system, and it applies various levels of mapping to uniform areas based on geomorphology, soils, vegetation, climate, water, and fauna.In this study, all CCELC levels were mapped; resulting maps were digitized and imported into a Geographic Informations System (GIS). The GIS data base contained the following maps: 1) digital terrain model, 2) bedrock geology, 3) surficial geology, 4) forest inventory, and 5) various levels of the ecological land classification, including Vegetation Types at the lowest level. In addition to the mapping, mensurational data were analyzed to provide stand and stock tables for each of the forest types, including growth curves that could be entered into specific forest growth modelling systems to predict wood supply scenarios based upon different management interventions.