A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

GPU Accelerated Lattice Boltzmann Simulation of a Cyanide-Release Accident in the Danjiangkou Reservoir

A graphics processing unit (GPU) accelerated two-dimensional contamination transport model is developed using the lattice Boltzmann method (LBM). The intrinsic parallel features of LBM make it particularly suitable for implementation on GPUs. After validation with two benchmarks, the model was applied to a cyanide-release accident in the Danjiangkou Reservoir, using a numerical combination of hydrodynamics and solute transport. The results show good agreement with an Environmental Fluid Dynamics Code (EFDC) model. A single GPU workstation speedup reaches 1.58 times that of a central processing unit (CPU) calculation alone. This speedup provides the opportunity for applying the model as a pollution accident information and decision support system for the Middle Route of the South to North Water Diversion Project in China.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

污染土壤中铅的测定方法比对

采用原子吸收光谱法(AAS法)、电感耦合等离子发射光谱法(ICP-OES法)和X-荧光光谱法(XRF法)分别测定污染土壤中的铅,并对检出限、精密度、准确度等指标进行了比对。结果表明,3种方法均能满足质量控制的要求,测定结果之间无显著性差异; 3种方法中ICP-OES法和XRF法可同时测定多元素,但XRF法更快速简便。     

The Impact of Succession Stage on Share and Traits of Clonal Plants in Abandoned <Emphasis Type="Italic">Molinion caeruleae</Emphasis> Meadows

The impact of succession stage on the share of clonal plants in the plant cover, seed rain and seedling pool has been studied in the years 2014 and 2015. The investigations were carried out in abandoned Molinion caeruleae meadows dominated by small-statured species (Patch I), tall growing large-tussock grasses (Patch II) and shrub-willows (Patch III). Although the clonal taxa dominated in the plant cover, seed rain and seedling pool, their share in subsequent study sites was quite different. The spectra of life-history traits relevant to clonality in the plant cover and seed rain were similar and did not vary significantly among subsequent locations, while they mostly differed as observed in the seedling pool. The various spectra observed in seedling pool in subsequent study sites might be due to fact that the seed germination and recruitment of offspring are strongly influenced by habitat conditions which varied remarkably in the successive Patches.     

Periphyton Developed on Artificial Substrates: Effect of Substrate Type and Incubation Depth

The aim of this study was to assess the effect of substrate type and incubation depth on periphyton that had developed on artificial substrates. Uniform rectangular tiles made out of artificial substrates: glass, ceramic, willow tree and yew tree, were fixed on a floating buoy and deployed at three different depths in a photic zone of the Sava Lake (Belgrade, Serbia). Non-taxonomic attributes in the developed biofilm were estimated week-by-week from the start of the experiment in July, until its end in September 2014. Through assessment of substrate type and depth of incubation effect we concluded that these parameters for the fact influence periphyton development and composition. Glass was preferred by autotrophic component over ceramic and wooden substrates. In general, substrate type effect was diminished by increasing incubation depth. When non-taxonomic parameters are to be used in biomonitoring studies, our results suggest that glass substrate and shallow layer of water column (up to 50 cm) for incubation should be preferred.     

基于DMSP-OLS夜间灯光数据的长江经济带城镇体系空间格局演变（1992～2013）

研究长江经济带城镇体系空间格局演变,对于透视长江经济带城镇体系空间格局演变特征和规律以及促进长江经济带城镇体系空间格局优化发展有着重要意义。利用多时相同期Landsat卫星数据以及运用聚类分析方法对长江经济带夜间灯光数据进行校正,并采用其他辅助数据法对比分析获取城镇建成区面积的阈值,在此阈值基础之上选取景观总面积、总斑块数量和聚集度指数等总共8个典型景观格局指标, 继而使用FRAG-STATS 4.2 软件对1992~2013年间长江经济带城镇发展的空间格局变化进行计算分析。研究结果表明：(1）1992~2013年间,长江经济带城镇化快速扩展,城镇建成区面积增加了9.68倍,斑块数量增加了2.39倍,斑块密度也得到较大的增加,反映出长江经济带城镇数量不断增加和城镇建成区面积不断扩大;（2）景观斑块形状复杂度逐渐上升,中小城市不断壮大,同时出现了较多的新兴小城镇,总边界长度和平均边界密度的年均增长速度分别为21.56%和21.58%,体现了长江经济带城镇间聚集度越来越高,相互影响也越来越大;(3）最大斑块指数和聚集度指数两个指标在整体上呈现出逐年下降的态势,但是景观总面积、总斑块数量和斑块密度等8个指标在演变过程中均存在一定的波动和无序化,反映出长江经济带在城镇化过程中呈现出不规则和无序化的特征。     

Investigating the oil curse in OECD and Non-OECD oil-exporting economies using green measures of income

Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations may not portray the true incomes of resource-intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without adjusting for the value of depleted natural resource stock. This treatment often leads to a positive bias in the national income computations of resource-rich economies. Unlike previous studies, we test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. Also, the paper deviates from most empirical studies in the literature by using the Arellano–Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil-exporting economies. Additionally, we employ two alternative measures of resource intensity in our explorations; the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income.     

Sustainability indicators in the swine industry of the Brazilian State of Santa Catarina

In recent decades, there has been a growing concern about measuring and evaluating the results of intensive production practices, such as the mode of integration practiced on a large scale by agro-industries. The present study discusses the application and the results of a Sustainability Indicators System in the context of the swine industry in the Brazilian State of Santa Catarina. These indicators, focused on the level of sustainability maturity, can be used as a way to diagnose and compare the efficiency of the industry from a broader perspective. The research findings indicate that most of the pig farms that have been studied can be classified as “in search of sustainability or sustainable.” Nevertheless, there are differences among the dimensions and between the most ranked farms and the others which must be considered for a better design and application of public policies and company strategies toward an effective and balanced social, economic and environmental sustainability.