重点源大气砷铅污染排放模型及特征

在我国的非常规性污染物质中,大气重金属砷、铅已越来越多地被关注和重视。文章综述了国内外人为源对大气中重金属排放的贡献,结果显示中国是全球人为活动向大气排放重金属最多的国家之一,燃煤和有色金属冶炼行业在相当长的时间内都将是最主要且最为重要的人为排放源。通过系统调研燃煤及有色金属冶炼业资源及产业布局状况,构建目前我国大气重金属相关清单模型,进行了我国重点源大气砷、铅排放清单分析,结果表明：（1）2000-2008年我国燃煤大气砷、铅排放量共为93733t,年均增长率为7．93％,2004-2008年有色金属冶炼业大气砷、铅排放量共为18836t,年均增长率为15．2％;（2）2000-2008年各经济部门中电力部门燃煤大气砷、铅放量始终最高,占燃煤大气排放总量的44．6％-57．1％,且呈逐年升高的趋势;（3）2000-2008年各省区中山西、河北、河南和湖南省是大气砷、铅的排放大户。其中,燃煤大气砷、铅排放量主要集中在人口密集、工业集中、经济发展速度较快的北部和中东部省区,包括山西、山东、河北、河南和江苏五省,占全国燃煤排放总量的39．1％,有色金属冶炼大气砷、铅排放量主要集中在我国有色金属工业较为发达的河南及湖南省,占全国有色金属冶炼业排放总量的47．3％。可以看出,我国需要高度重视大气重金属砷、铅的污染防治,加强排放控制基础能力建设,加快建立适合中国的大气砷、铅污染防治技术政策体系。     

Modelling the fate of marine turtle hatchlings

The SLIM oceanographic model was used to examine the fate of hatchling flatback turtles (Natator depressus) in the first two weeks of their dispersal starting at Wild Duck Island, a major turtle-nesting site in the central Great Barrier Reef region of Australia. We ran simulations to investigate the effects of spring versus neap tides, hatchling's swimming behaviour during their first three days at sea, and the location of nesting beaches. The model predicted that up to 50% of the turtle hatchlings entering the sea from the windward, southern beach remained after 14 days in shallow, nearshore waters, irrespective of tides and swimming. These waters are turbid and may be inhospitable to hatchlings. In contrast >80% of hatchlings dispersing from the leeward, northern beach were dispersed to deeper water (10-40 m) in a quasi-stationary dispersion core centred around 10 km north of the nesting beach after 14 days and the offshore spread of the turtle plume was enhanced by the hatchling's seaward swimming during the first three days. This was due to the presence of a coastal boundary layer and a stagnation zone around the northern side of island, but not the southern side. The model confirmed that dispersal from eastern Queensland flatback turtle rookeries is restricted to the lagoons and coastal waters, and that water circulation and hatchling's swimming control dispersion. The model explains why more turtles nest on the northern than the southern side. This study highlights the usefulness of oceanographic models to increase knowledge about a cryptic life stage of marine turtles.     

Regression models for octanol‐water partition coefficients,and for bioconcentration in fish

Octanol‐water partition coefficients (P) of a number of organochlorine insecticides (OCs) are presented. The merits of log‐log regressions between experimental ? values and calculated estimates of P, solute activity coefficients in water, solute molecular surface area data, and reversed‐phase liquid‐chromatographic net retention data, are critically evaluated for several classes of pollutants: polycyclic aromatic hydrocarbons (PAHs), chlorinated benzenes, chlorinated biphenyls and OCs. Special attention is paid to the predictive accuracy of such semi‐empirical regressions in connection with possible effects of solute molecular shape and polarity.

Finally, bioconcentration and ‐accumulation of hydrophobic pollutants in fish are briefly discussed.     

Aphid population response to agricultural landscape change: A spatially explicit,individual-based model

A spatially explicit individual-based simulation model has been developed to represent aphid population dynamics in agricultural landscapes. The application of the model to Rhopalosiphum padi (L.) population dynamics is detailed, including an outline of the construction of the model, its parameterisation and validation. Over time, the aphids interact with the landscape and with one another. The landscape is modified by varying a simple pesticide regime, and the multi-scale spatial and temporal implications for a population of aphids is analysed. The results show that a spatial modelling approach that considers the effects on the individual of landscape properties and factors such as wind speed and wind direction provides novel insight into aphid population dynamics both spatially and temporally. This forms the basis for the development of further simulation models that can be used to analyse how changes in landscape structure impact upon important species distributions and population dynamics.     

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.     

Calculating losses of crops in Denmark caused by high ozone levels

In order to help guide air pollution legislation at the European level, harmful air pollution effects on agriculture crops and the consequent economic implications for policy have been studied for more than a decade. Ozone has been labeled as the most serious of the damaging air pollutants to agriculture, where growth rates and consequently yields are dramatically reduced. Quantifying the effects has formed a key factor in policymaking. Based on the widely held view that AOT40 (Accumulated exposure Over Threshold of 40 ppb) is a good indicator of ozone-induced damage, the Danish Eulerian Model (DEM) was used to compute reduced agriculture yields on a 50 km×50 km grid over Europe. In one set of scenarios, a ten year meteorological time series was combined with realistic emission inventories. In another, various idealized emission reduction scenarios are applied to the same meteorological time series. The results show substantial inter-annual variability in economic losses, due in most part to meteorological conditions which varied much more substantially than the emissions during the same period. It is further shown that, taking all uncertainties into account, estimates of ozone-induced economic losses require that a long meteorological record is included in the analysis, for statistical significance to be improved to acceptable levels for use in policy analysis. In this study, calculations were made for Europe as a whole, though this paper presents results relevant for Denmark.     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

Developing an empirical model of phytoplankton primary production: a neural network case study

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.     

‘A powerful alternative to Williams’ test with application to toxicological dose-response relationships of normally distributed data

The comparison of increasing doses of a treatment to a negative control is frequently part of toxicological studies. For normally distributed data Williams (1971, 1972) introduced a maximum likelihood test under total order restriction. But until now there seems to have been no solution for the arbitrary unbalanced case. According to the idea proposed by Robertson et al. (1988) we will apply in this article the basic concept of Williams on the class of multiple contrast tests for the general unbalanced parametric set-up. Simulation results for size and power and two examples for estimating the minimal toxic dose (MTD) are given.     

The representation of cyanobacteria life cycle processes in aquatic ecosystem models

Two simplified versions of a numerical life cycle model for diazotrophic cyanobacteria (of the order Nostocales) are developed and evaluated. Both consider two-life cycle stages, one growing, nitrogen-fixing stage and one stage that combines the resting, germinating and vegetative stages. The versions differ in the vertical resolution of the non-diazotrophic stage: version 1 collects the biomass in one layer at the bottom, version 2 considers sinking and rising of biomass explicitly. The results of the two versions are compared with a complex cyanobacteria life cycle model which describes four different life cycle stages each with two internal quotas for energy and nitrogen. The two simplified approaches show a good agreement with respect to the main characteristics of cyanobacteria dynamics (timing and duration of blooms, magnitude of nitrogen fixation, interannual variability). Our model study shows that both simplified approaches are suitable to be implemented into three-dimensional coastal or lake models.