催化湿式氧化处理H-酸溶液的反应动力学

两阶段一级反应动力学模型和广义动力学模型被用来描述湿式氧化 ( WAO)及催化湿式氧化 ( CWAO)反应过程 ,并确定了动力学参数 .2个模型的计算值均与实验值相符 ,而广义动力学模型相对更准确些 .2个模型均表明反应分 2个步骤 :首先是H-酸被迅速氧化成小分子有机酸 ,后者再缓慢氧化 .这 2个步骤由模型参数加以表征 ,因而模型可被用来对 CWAO催化剂进行评价 .     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

A thermodynamic model of freshwater Antarctic lake ice

Antarctic lakes with simple plankton ecosystems are believed to be sensitive biological indicators of climate change. Models of the physical environment, in particular the ice layer, support understanding of how the ecosystems respond to meteorological variables. This paper describes how data from a previously reported automatic measuring probe and meteorological data from Davis station were used to develop a detailed thermodynamic model of the ice layer on Crooked Lake, one of the largest and deepest freshwater lakes in Antarctica. The general model structure is similar to a previously reported model of sea ice but with modifications specific to the Antarctic freshwater lake case informed by the data. The model inputs are atmospheric variables as well as water temperature, ice albedo and the radiation extinction coefficient for the ice. Heat and radiation fluxes at the ice–air and ice–water boundaries are calculated using equations chosen for their suitability for the Antarctic. In the case of shortwave radiation, equations were fitted to data from the automatic probe. Using the heat fluxes to establish boundary conditions, and incorporating the known thermodynamic properties of ice, the temperature profile within the ice and the resulting growth and melt of the ice can be calculated. The model uses a largely mechanistic approach, with most equations taken from established thermodynamic theories or empirical studies and only one adjustable parameter related to the sensible heat flux from the water, which is not easily calculated from the available data. It was found to accurately reproduce ice temperature and ice thickness data for the year 2003, with r² = 0.89, n = 2005. Finally, the model was simplified to run with air temperature as the only input variable and was shown to perform well—this suggests that freshwater lake ice is affected more by air temperature than any other variable, and is therefore a useful indicator of climate change in its own right.     

Calibration and uncertainty analysis of a simple model of silica-limited diatom growth in the Elbe River

A simple Lagrangian water quality model was designed to investigate the hypothesis of sporadic silica limitations of diatom growth in the lower Elbe River in Germany. For each fluid parcel a limited reservoir of silica was specified to be consumed by diatoms. The model's simplicity notwithstanding, a set of six selected model parameters could not be fully identified from existing observations at one station. After the introduction of prior knowledge of the ranges of meaningful parameter values, calibration of the over-parameterised model manifested itself primarily in the generation of posterior parameter covariances. Estimations of the covariance matrix based on (a) second order partial derivatives of a quadratic cost function at its optimum and (b) Monte Carlo simulations exploring the whole space of parameter values gave consistent results. Diagonalisation of the covariance matrix yielded two linear parameter combinations that were most effectively controlled by data from periods with and without lack of silica, respectively. The two parameter combinations were identified as the essential inputs that govern the successful simulation of intermittently decreasing chlorophyll a concentrations in summer. A satisfactory simulation of the pronounced chlorophyll a minimum in spring, by contrast, was found to be beyond the means of the simple model.     

桑叶表面氟化物吸附积累规律的统计研究

以各气象因素和大气氟化物浓度为生态因子,对大田桑园中各叶位桑叶的氟化物的吸附积累规律,进行了统计分析和研究,建立了各叶位桑叶的氟化物的吸附积累模型。     

我国现行大气环境影响评价预测模型的若干问题及其改进

我国现行大气环境影响预测评价模型存在以下几个问题:大气混合层高度求算偏低;稳定条件下大气扩散计算结果不合理;模型未考虑非扩散因素影响(干湿沉积、化学转化);非正常排放模型在小风、静风条件下计算结果偏大;熏烟模型计算的不可操作性等。本文针对这些问题提出相应的改进方法,并进行了实例计算。     

Dispersion modeling of LNG-Vapor on land – A CFD-Model evaluation study

Based on methane from renewable resources, LNG is an alternative fuel for heavy and long-distance traffic in land transport. Contrary to its positive properties, the fuel contains risks from an explosion and extremely low temperatures for personal and infrastructure safety. CFD-models are suitable for doing risk analyses for arbitrary scenarios. For examining how to model for risk research the dispersion of LNG-vapor, this paper contains a model variant study, with an evaluation by experiments.This paper describes the use of the CFD-code ANSYS Fluent for simulating experiments of the ‘LNG Safety Program Phase II‘. The content of the well-documented experiments was the research of the vaporization rate of LNG on land and the dispersion of LNG-vapor in the air. Based on the comparison to two experiments, overall 12 CFD-model variants with varying thermal and turbulence parameters were examined how they affect the transient LNG-vapor dispersion in air.The definition of turbulence-boundary-condition at the domain borders had the biggest impact on modeling, followed by the turbulence model. The most accurate model variant had been applied for observing the spreading behavior of LNG-vapor in the air after evaporation on land and analyzing the influence of the LNG-composition to the dispersion. The results show that the mixture of LNG-vapor and the air in the free field is cooler than the ambient air and spreads like a heavy gas on the ground.     

室内准稳态压力火灾烟气沉降模型的改进和实验验证

对室内火灾及烟气的研究给消防工程领域带来了诸多益处,如疏散时间预测、基于性能化的设计分析等。Zukoski (Fireand Materials,1978,2(2): 54-62)在室内火灾烟气运动的开创性研究给出了基于质量和能量平衡的理论方法,为后续研究奠定了基础。然而实验发现,由于忽略热损失偏差,模型对近壁面火源的火灾烟气沉降时间的预测缺乏准确度。本文通过空间重构的方法消除房间长宽比因素造成的热损失偏差,引入两个参数进行表征,以完善该模型。完善后的模型预测烟气沉降时间与实验吻合度非常好,验证了该方法的有效性。在计算分析基础上,将两个参数对模型的影响作了评估,发现其一对模型预测结果的影响远大于另一参数。火源大小确定后,烟气充满房间的时间范围可确定,火源功率越大,时间范围越小。     

Testing of the Storm Water Management Model Low Impact Development Modules

Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency’s Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM’s LID modules. The calibrated LID modules’ Nash–Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) ?9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.     

Performance of the National Water Model in Iowa Using Independent Observations

This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (r_s), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km²) basins (r_s = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km²) basins (r_s = 0.6, E = ?0.25, KGE = ?0.2). Analysis with independent IFCBS observations was used to report performance at large basins (r_s = 0.6, KGE = 0.1) and small basins (r_s = 0.2, KGE = ?0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak.