木沥河流域氮素污染及其污染源解析

为甄别粤港澳大湾区木沥河流域氮污染物来源,本研究应用铵盐同位素示踪技术、硝酸盐同位素示踪技术和多元线性混合模型等方法有效识别了该流域氮素来源的变化.结果表明,木沥河流域氮素污染严重,木沥河水体NH~+_4-N和NO~-_3-N浓度显著高于上游两条支流;除此之外,大坑山支流断面和木沥河下游养殖区断面虽无明显人口居住,仍然面临较高的氮素污染风险.铵盐同位素和硝酸盐同位素定性分析结果表明,木沥河流域氮素污染主要来源于土壤、肥料、大气颗粒物和动物及人排泄物;多元线性混合模型计算结果显示,大坑山支流上流处氮素主要来源为大气沉降,其贡献率在80%左右;鸡笼坑支流上游土壤有机氮平均贡献率可达33%,大于大坑山支流(9%)和木沥河(24%);大坑山支流下游、鸡笼坑支流下游和木沥河中上游污水及粪肥对氮素污染贡献率最高可达70%;值得注意的是,养殖区断面远离人口聚集地,各种养殖已被清理,但污水及粪肥的贡献率仍高达56%,远高于木沥河下游的淡水河水闸断面(3%),这可能要归咎于沉积物中残留的禽畜排泄物.本研究定性和定量地分析了木沥河流域氮素来源,为大湾区的水质管理和污染源治理提供了理论依据.     

滇池流域入湖河流溶解性有机质的分布及来源

基于三维荧光光谱和平行因子分析方法,探究了2018年滇池流域主要入湖河流丰水期和枯水期水体溶解性有机质（DOM）组成,并结合主成分分析和多元线性回归分析对河流中DOM各荧光组分的相对贡献进行了定量分析.结果表明：入滇河流水体DOM包含5种荧光组分,分别为类腐殖质荧光组分C1、C3、C5和类蛋白色氨酸荧光组分C2、C4,且5种荧光组分之间具有同源性,污染严重河流水体DOM的荧光强度相对较高;荧光指数、自生源指数、腐殖化指数综合表明,入滇河流水体DOM的内源特性较强,自生源特征明显,生物可利用性较高,腐质化程度低,分子结构不稳定.主成分分析和多元线性回归分析结果显示：枯水期DOM的荧光组分中类腐殖物质的平均贡献率占30.1%,类蛋白色氨酸物质的平均贡献率占69.9%;丰水期DOM组分中类腐殖物质的平均贡献率占54.3%,类蛋白色氨酸物质的平均贡献率占45.7%.     

基于TLSER关系预测POPs在PUF与空气中的分配系数

为开发一种简洁高效预测K_PA值的方法,本文收集了包含多氯联苯（PCBs）、苯类（Benzenes）、多环芳香烃（PAHs）和杀虫剂（Pesticides）等,共95种污染物在聚氨酯泡沫塑料（PUF）与空气中分配系数（K_PA）的实验值,基于理论线性溶解能关系（TLSER）,运用逐步多元线性回归（MLR）构建了预测K_PA值的模型.结果表明,模型的决定系数R_adj²为0.901,外部验证系数Q_ext²为0.748.TLSER模型有较好的拟合度、稳健性和预测能力,可以预测应用域内POPs在PUF和空气之间的分配系数.     

模型预测在土地利用规划中的应用

阐述了4种预测模型的原理，并以新疆喀什市的城市用地预测为例，通过时间序列建立了4种预测模型，对城市用地实际值进行预测与分析，从而得出各种模型的特点。认为只有选择最适合的预测模型，才能为土地利用规划提供科学依据。     

AQUIFER MANAGEMENT UNDER TRANSIENT AND STEADY-STATE CONDITIONS1

ABSTRACT: The equations of transient and steady-state flow in two-dimensional artesian aquifers are approximated using finite differences. The resulting linear difference equations, combined with other linear physical and management constraints and a linear objective function, comprise a linear programming (LP) formulation. Solutions of such LP models are used to determine optimal well distributions and pumping rates to meet given management objectives for a hypothetical transient problem and for a steady-state field problem.     

THE USE OF MULTIPLE REGRESSION MODELS IN PREDICTING SEDIMENT YIELD1

ABSTRACT. Four commonly used models for predicting sediment yield are analyzed and compared using previously published data. Three of these models involve logarithmic transformations. Some of the problems involved in transforming data are discussed in the context of logarithmic transformations. These problems are illustrated using the results of standard regression analyses and economic loss function analyses. For the data analyzed, the linear model is preferable to each of the logarithmic models on the basis of each analysis, and the usual multiple objective nature of the model choice problem is thus modified. The extent to which these results can be generalized is discussed in the context of model choice.     

Fuzzy goal programming for biodiesel production

ABSTRACT

The energy consumed around the world and especially in Morocco is dominated by oil products. The latter; whether in the global or the national context; are mainly due to diesel, whose climate impacts are well established, suggesting the search for a greener alternative. Despite its virtues, biodiesel is struggling to impose itself for purely economic reasons. Thus, in order to offer a sustainable solution, while keeping in mind the unpredictable fluctuations (price, demand …). This work proposes a generalization of fuzzy goal programming into fully fuzzy goal programming and a relative application, where the aim is to have a final product at a competitive price.     

固相萃取—气相色谱法测定水样中痕量正构烷烃的研究

以正构烷烃为目标化合物,研究了影响固相萃取效率的某些因素,采用双柱串联模式评价了固定相的保留效果,探讨了有机改性剂对固相萃取效率的重要影响及作用机理,建立了水样中痕量正构烷烃的分析方法.在实验条件下,对于正构烷烃含量为250(g/I的水样,方法的平均回收率为98.5%,相对标准偏差为9.9%(n=5).     

Characterizing the Spatial Pattern of Soil Carbon and Nitrogen Pools in the Turkey Lakes Watershed: A Comparison of Regression Techniques

There is considerable spatial heterogeneity in organic carbon (C), total nitrogen (N), and potentially mineralizable nitrogen (PMN) pools in the soils of the Turkey Lakes Watershed. We hypothesized that topography regulates the spatial pattern of these pools through a combination of static factors (slope, aspect and elevation), which influence radiation, temperature andmoisture conditions, and dynamic factors (catenary position,profile and planar curvature), which influence the transport ofmaterials downslope. We used multiple linear regression (MLR)and tree regression (TR) models as exploratory techniques todetermine if there was a topographic basis for the spatialpattern of the C, N and PMN pools. The MLR and TR modelspredicted similar integrated totals (i.e., within 5% of eachother) but dissimilar spatial patterns of the pools. For thecombined litter, fibric and hemic layer, the MLR models explaineda significant portion of the variance (R² = 0.38, 0.23 and0.28 for C, N and PMN, respectively), however, the residuals werelarge and biased (the smallest contents were over-predicted andthe largest contents were under-predicted). The TR models (9-branch), in contrast, explained a greater portion of the variance (R² = 0.75, 0.67 and 0.62 for C, N and PMN, respectively) and the residuals were smaller and unbiased. Based on our sampling strategy, the models suggested that static factors were most important in predicting the spatial pattern of the nutrient pools. However, a nested sampling strategy that included scales where both static (among hillslopes) and dynamic (within hillslope) factors result in a systematic variation in soil nutrient pools may have improvedthe predictive ability of the models.     

SPATIAL VARIABILITY IN WATER‐BALANCE MODEL PERFORMANCE IN THE CONTERMINOUS UNITED STATES1

ABSTRACT: A monthly water‐balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil‐moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R‐square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long‐term mean and standard deviation of annual precipitation; temperature and runoff; and low‐flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R‐square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.