乌鲁木齐市餐饮业油烟污染的调查及防治初探

通过对乌鲁木齐市20家餐饮业油烟污染的调查分析，结果表明，乌鲁木齐市餐饮业油烟污染严重，并提出了防治措施。     

Water Quality Evaluation and Trend Analysis in Selected Watersheds of the Atlantic Region of Canada

Water quality indices (WQIs) have been developed to assess the suitability of water for a variety of uses. These indices reflect the status of water quality in lakes, streams, rivers, and reservoirs. The concept of WQIs is based on a comparison of the concentration of contaminants with the respective environmental standards. The number, frequency, and magnitude by which the environmental standards for specific variables are not met in a given time period are reflected in WQIs. Further, the water quality trend analysis predicts the behavior of the water quality parameters and overall water quality in the time domain. In this paper, the concept of WQI was applied to three selected watersheds of Atlantic region: the Mersey River, the Point Wolfe River, and the Dunk River sites. To have robust study, two different water quality indices are used: Canadian Water Quality Index (CWQI), and British Columbia Water Quality Index (BWQI). The complete study was conducted in two steps. The first step was to organize and process the data into a format compatible with WQI analysis. After processing the input data, the WQI was calculated. The second step outlined in the paper discusses detailed trend analysis using linear and quadratic models for all the three sites. As per the 25 years trend analysis, overall water quality for agriculture use observed an improving trend at all the three sites studied. Water quality for raw water used for drinking (prior to treatment) and aquatic uses has shown improving trend at Point Wolfe River. It is further observed that pH, SO₄, and NO₃ concentrations are improving at Dunk River, Mersey River, and Point Wolfe River sites. To ascertain the reliability and significance of the trend analysis, a detailed error analysis and parametric significance tests were also conducted It was observed that for most of the sites and water uses quadratic trend models were a better fit than the linear models.     

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.     

The Chemistry of Soils, Rocks and Plant Bioindicators in Three Ecosystems of the Holy Cross Mountains, Poland

In June of 2000, biogeochemical study was carried out in three ecosystems of the Holy Cross Mountains (south-central Poland). This paper presents element concentrations and stable sulfur (and in one site lead) isotope ratios in rocks, detailed soil profiles, and plant bioindicators including epiphytic lichen Hypogymnia physodes (L.) Nyl., mosses (Pleurozium schreberi (Brid.) Mitt., Hypnum cupressiforme Hedw. S. Str., Hylocomium splendens (Hedw.) B.S.G.), Scots pine (Pinus sylvestris L.), common birch (Betula pendula Roth.), aspen (Populus tremula L.) and English oak (Quercus robur L.). Chemical analyses were performed with ICP-AES and AAS methods in the accredited laboratory (Central Chemical Laboratory of the Polish Geological Institute in Warsaw). The principal objective of this study was to compare the chemical composition of rocks, soils and selected plant bioindicators between investigation sites using the same methods of sample collection, preparation and analyses. The results of this study have shown that there is a high variability in concentrations of elements in plant bioindicators from the same sites that can not be explained only by soil properties or anthropogenic influence. This conclusion indicates that for biomonitoring purposes (especially with vascular plants) we cannot neglect individual features of the species examined.     

Study on the effect of porous fence on air quality and traffic noise level around a double-decked road structure

Fence for traffic noise control sometimes causes adverse effect on air pollution. Thus in this study, performance of porous fence as a tool for control of both air pollution and noise pollution was evaluated. A two-dimensional numerical model for flow and pollutant concentration and an analytical model for traffic noise were utilized in the analysis of a double-decked road structure with fences only at ground (Case 1) and at both ground and upper deck (Case 2). Porous fences were assumed only at the ground level since the solid fences at the upper deck usually leads to desirable result on air pollution. Effects of the variable porosity on air quality and noise level near road were evaluated. Obtained results showed: (1) flow pattern in leeward of fence was drastically changed at 40–50% porosity in Case 1 and 50% in Case 2. The porosity larger than 40% excluded presence of a circulation behind the fence. (2) Effect of porous fence on air pollution was different in Cases 1 and 2. In Case 1, the porous fence generally resulted in the reduction of air pollution at the ground level; on the other hand, in Case 2, it rather led to increase of the concentration. (3) Traffic noise level was also largely changed by the porosity of the fence; an example of simultaneous evaluation of the effects of porous fence on both air and noise pollution in Case 1 showed that the fence of 60% porosity leads to reduction of air pollution by 20% compared with solid fence case, and reduction of noise pollution by 4–6% in dB compared with no fence case, at l m high and 10 m from the road.     

Sulphate, Nitrogen and Base Cation Budgets at 21 Forested Catchments in Canada, the United States and Europe

To assess the concern over declining base cation levels in forest soils caused by acid deposition, input-output budgets (1990s average) for sulphate (SO₄), inorganic nitrogen (NO₃-N; NH₄-N), calcium (Ca), magnesium (Mg) and potassium (K) were synthesised for 21 forested catchments from 17 regions in Canada, the United States and Europe. Trend analysis was conducted on monthly ion concentrations in deposition and runoff when more than 9 years of data were available (14 regions, 17 sites). Annual average SO₄ deposition during the 1990s ranged between 7.3 and 28.4 kg ha⁻¹ per year, and inorganic nitrogen (N) deposition was between 2.8 and 13.8 kg ha⁻¹ per year, of which 41–67% was nitrate (NO₃-N). Over the period of record, SO₄ concentration in deposition decreased in 13/14 (13 out of 14 total) regions and SO₄ in runoff decreased at 14/17 catchments. In contrast, NO₃-N concentrations in deposition decreased in only 1/14 regions, while NH₄-N concentration patterns varied; increasing at 3/14 regions and decreasing at 2/14 regions. Nitrate concentrations in runoff decreased at 4/17 catchments and increased at only 1 site, whereas runoff levels of NH₄-N increased at 5/17 catchments. Decreasing trends in deposition were also recorded for Ca, Mg, and K at many of the catchments and on an equivalent basis, accounted for up to 131% (median 22%) of the decrease in acid anion deposition. Base cation concentrations in streams generally declined over time, with significant decreases in Ca, Mg and K occurring at 8, 9 and 7 of 17 sites respectively, which accounted for up to 133% (median 48%) of the decrease in acid anion concentration. Sulphate export exceeded input at 18/21 catchments, likely due to dry deposition and/or internal sources. The majority of N in deposition (31–100%; median 94%) was retained in the catchments, although there was a tendency for greater NO₃-N leaching at sites receiving higher (<7 kg ha^-1 per year) bulk inorganic N deposition. Mass balance calculations show that export of Ca and Mg in runoff exceeds input at all 21 catchments, but K export only exceeds input at 16/21 sites. Estimates of base cation weathering were available for 18 sites. When included in the mass balance calculation, Ca, Mg and K exports exceeded inputs at 14, 10 and 2 sites respectively. Annual Ca and Mg losses represent appreciable proportions of the current exchangeable soil Ca and Mg pools, although losses at some of the sites likely occur from weathering reactions beneath the rooting zone and there is considerable uncertainty associated with mineral weathering estimates. Critical loads for sulphur (S) and N, using a critical base cation to aluminium ratio of 10 in soil solution, are currently exceeded at 7 of the 18 sites with base cation weathering estimates. Despite reductions in SO₄ and H⁺ deposition, mass balance estimates indicate that acid deposition continues to acidify soils in many regions with losses of Ca and Mg of primary concern. The U.S. Government's right to retain a non-exclusive, royalty free licence in and to any copyright is acknowledged. The Canadian Crown reserves the right to retain a non-exclusive, royalty free licence in and to any copyright.     

An integrated assessment model for cross-country pipelines

The cross-country petroleum pipelines are environmentally sensitive because they traverse through varied terrain covering crop fields, forests, rivers, populated areas, desert, hills and offshore. Any malfunction of these pipelines may cause devastating effect on the environment. Hence, the pipeline operators plan and design pipelines projects with sufficient consideration of environment and social aspects along with the technological alternatives. Traditionally, in project appraisal, optimum technical alternative is selected using financial analysis. Impact assessments (IA) are then carried out to justify the selection and subsequent statutory approval. However, the IAs often suggest alternative sites and/or alternate technology and implementation methodology, resulting in revision of entire technical and financial analysis. This study addresses the above issues by developing an integrated framework for project feasibility analysis with the application of analytic hierarchy process (AHP), a multiple attribute decision-making technique. The model considers technical analysis (TA), socioeconomic IA (SEIA) and environmental IA (EIA) in an integrated framework to select the best project from a few alternative feasible projects. Subsequent financial analysis then justifies the selection. The entire methodology has been explained here through a case application on cross-country petroleum pipeline project in India.     

Distribution of Chlorinated Organic Pollutants in Harbor Sediments of Livorno (Italy): A Multivariate Approach to Evaluate Dredging Sediments

Dredging is a very important procedure for harbor management. InItaly the guidelines for the offshore dumping of dredged materials are issued by the Ministry of Environment. Theydescribed a few steps of dredging activities, such as thesampling strategy, but do not deal with limits or guide-valuesfor the chemical, physical and biological composition of theresulting sediments. The quality of dredged materials is mainlydependent on the presence of inorganic and organic pollutants.In particular, polychlorinated biphenyls (PCBs) and organo-chlorinated pesticides are seen as a high priority inmarine environment by international organizations because oftheir persistence, toxicity and bioaccumulation capacity.In this article the presence of some PCBs and organo-chlorinatedpesticides in sediment samples collected from the harbor ofLivorno (Northern Tyrrhenian Sea) was investigated. Theconcentration of HCHs, Aldrin, Chlordanes, DDEs, DDTs, and PCBsin 12 representative sites ranged between <1 g kg^–1and 95, 19, 32, 35, 107, and 111 g kg^–1, respectively.The application of univariate and multivariate statisticaltechniques, such as linear regression analysis and principalcomponent analysis, to the experimental data showed a differentdistribution of PCBs in the two sediment layers. On thecontrary, the vertical distribution of the other investigatedpollutants was more homogeneous and affected by randomvariability. The multivariate approach was an important tool to establish more rational criteria for the management of dredged materials.     

Climate impact on social systems: the risk assessment approach

A novel approach to the problem of estimating climate impact on social systems is suggested. This approach is based on a risk concept, where the notion of critical events is introduced and the probability of such events is estimated. The estimation considers both the inherent stochasticity of climatic processes and the artificial stochasticity of climate predictions due to scientific uncertainties. The method is worked out in some detail for the regional problem of crop production and the risks associated with global climate change, and illustrated by a case study (Kursk region of the FSU). In order to get local climatic characteristics (weather), a so-called statistical weather generator is used. One interesting finding is that the 3% risk level remains constant up to 1.0–1.1°C rise of mean seasonal temperature, if the variance does not change. On the other hand, the risk grows rapidly with increasing variance (even if the mean temperature rises very slowly). The risk approach is able to separate two problems: (i) assessment of global change impact, and (ii) decision making. The main task for the scientific community is to provide the politicians with different options; the choice of admissible (from the social point of view) critical events and the corresponding risk levels is the business of decision makers.