东亚大气可吸入颗粒物时空分布的数值模拟研究

利用嵌套网格空气质量模式(Nested Air Quality Prediction Model System,NAQPMS)模拟研究了2010年东亚地区可吸入颗粒物(PM10)的时空演变,并初步评估了其对人群健康的危险度.结果表明,NAQPMS模式能够合理地反映东亚地区PM10的时空分布,不同季节观测值和模拟值的总体相关系数达到0.65~0.85.2010年东亚PM10的地面浓度高值区(100μg·m-3)出现在我国华北、华中和内蒙古中西部等地区.其中,无机盐(硫酸盐、硝酸盐和铵盐)对我国东部PM10的贡献最大(10~70μg·m-3,20%~50%);一次PM10次之(5~50μg·m-3,10%~30%),有机物(5~30μg·m-3,10%~20%)和黑炭(3~20μg·m-3,3%~5%)紧随其后.PM10可以引起我国东部人群急性总死亡率增加2%~5%,对我国居民的健康水平构成了一定威胁.     

体育馆出口疏散能力的实验研究

为研究出口单位流量与人流时间之间的关系,以某体育馆为例,进行对某一出口常态下的人流观测实验.通过理论计算和建立Cubic回归模型对采集数据进行了统计分析.发现:单位出口流量最大值为2人/(m·s),稳定值为0.8-1.2人/(m·s),最小值为0.2人/(m·s).出口单位流量是人流时间的三次函数.研究结果表明:出口单位流量是随着时间的变化而变化,不是一个定值,而且单位出口流量稳定值分布在初期.本文对性能化防火设计有一定的参考价值.     

Vertical migration of radionuclides in undisturbed grassland soils

Literature data on numerical values obtained for the parameters of the two most popular models for simulating the migration of radionuclides in undisturbed soils have been compiled and evaluated statistically. Due to restrictions on the applicability of compartmental models, the convection–dispersion equation and its parameter values should be preferred. For radiocaesium, recommended values are derived for its effective convection velocity and dispersion coefficient. Data deficiencies still exist for radionuclides other than caesium and for soils of non-temperate environments.     

Evaluating and reducing a model of radiocaesium soil-plant uptake

An existing model of radiocaesium transfer to grasses was extended to include wheat and barley and parameterised using data from a wide range of soils and contact times. The model structure was revised and evaluated using a subset of the available data which was not used for model parameterisation. The resulting model was then used as a basis for systematic model reduction to test the utility of the model components. This analysis suggested that the use of 4 model variables (relating to radiocaesium adsorption on organic matter and the pH sensitivity of soil solution potassium concentration) and 1 model input (pH) are not required. The results of this analysis were used to develop a reduced model which was further evaluated in terms of comparisons to observations. The reduced model had an improved empirical performance and fewer adjustable parameters and soil characteristic inputs.     

A review and test of predictive models for the bioaccumulation of radiostrontium in fish

Empirical relations between the ⁹⁰Sr concentration factor (CF) and the calcium concentration in freshwater aquatic systems have previously been determined in studies based on data obtained prior to the Chernobyl accident. The purpose of the present research is to review and compare these models, and to test them against a database of post-Chernobyl measurements from rivers and lakes in Ukraine, Russia, Belarus and Finland. It was found that two independently developed models, based on pre-Chernobyl empirical data, are in close agreement with each other, and with empirical data. Testing of both models against new data obtained after the Chernobyl accident confirms the models’ predictive ability. An investigation of the influence of fish size on ⁹⁰Sr accumulation showed no significant relationship, though the data set was somewhat limited.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.     

Modelling the fate of hydrophobic organic contaminants in a boreal forest catchment: A cross disciplinary approach to assessing diffuse pollution to surface waters

The fate of hydrophobic organic compounds (HOCs) in soils and waters in a northern boreal catchment was explored through the development of a chemical fate model in a well-characterised catchment system dominated by two land types: forest and mire. Input was based solely on atmospheric deposition, dominated by accumulation in the winter snowpack. Release from soils was governed by the HOC concentration in soil, the soil organic carbon fraction and soil-water DOC content. The modelled export of selected HOCs in surface waters ranged between 11 and 250 ng day⁻¹ during the snow covered period, compared to 200 and 9600 ng/d during snow-melt; highlighting the importance of the snow pack as a source of these chemicals. The predicted levels of HOCs in surface water were in reasonable agreement to a limited set of measured values, although the model tended to over predict concentrations of HOCs for the forested sub-catchment, by over an order of magnitude in the case of hexachlorobenzene and PCB 180. This possibly reflects both the heterogeneity of the forest soils and the complicated and changing hydrology experienced between the different seasons.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

A method of approach to landscape stability. Part 1: Fundamentals and methodology

A four-stage method of providing conditions for improving the stability of a landscape sector is presented. In the first stage, structure and function of the landscape system is examined, predominantly based on the results of monitoring. In the second stage, a method is suggested for applying monitoring data to a dynamic structure with complex functions of the territory under examination. In the third stage, the territory is optimized as to the function of particular components within it. The optimization consists in controlling the dynamics of the flows of material, energy, and population within the sector. In the fourth stage, the holistic function of the landscape strip should be monitored with respect to representative key factors. The entire concept is based on assuming the existence of destabilizing processes leading to ecocritical situations and determining mitigating factors using heuristic methods of optimization.     

THE REGULATED RIPARIAN VERSION OF THE ASCE MODEL WATER CODE: THE THIRD WAY TO ALLOCATE WATER1

ABSTRACT: Lawyers, engineers, and hydrologists are accustomed to thinking of water law as falling into one of two incompatible models: riparian rights (under which water is allocated by courts according to the relative reasonableness of the competing uses) and appropriative rights (under which water is allocated according to the temporal priority of the competing uses, largely by the action of the water users themselves but perfected by the issuance of an administrative permit). Usually unnoticed is the existence of a third approach, which I have dubbed “regulated riparianism.” Under regulated riparianism, water is allocated by water permits issued after an administrative determination of the reasonableness of the proposed use before the use is commenced. This system, now in place in about half of the states east of Kansas City (plus Hawaii), thus is fundamentally different from either the traditional ripanan rights that it replaces or the appropriative rights found in western states.