两阶段模糊法在海河水系水质评价中的应用

将两阶段模糊法用于海河水系监测断面的水质评价.与传统模糊聚类法不同,该方法首先将国家地表水指标转化为等级打分,再构造模糊隶属函数,确定评价对象对标准水质水平的隶属度;之后运用改进的模糊c-均值法(FCM法)选取适当模糊指标和先验类中心,对评价对象进行聚类,得到替代原观测值的一个全局水质指标,数值在0～100之间,以利于直观评价.计算了海河水系2004～2006年4个断面的432个数据,考察了季节波动规律和不同年度变化的情况,并对水质进行了综合评价.两阶段模糊法对水质评价综合指标有较好的分类能力,能够得到十分科学的评价结果,且计算过程相对FCM法简便,可以运用于河流水质综合评价.     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Analysis of Extreme Summer Rainfall Using Climate Teleconnections and Typhoon Characteristics in South Korea1

Abstract: It is now widely acknowledged that climate variability modulates the frequency of extreme hydrological events. Traditional methodologies for hydrologic frequency analysis are not devised to account for variation in the exogenous teleconnections. Flood frequency analysis is further plagued by the assumptions of stationary in the causal structure as well as ergodicity. Here, we propose a dynamical hierarchical Bayesian analysis to account for exogenous forcing that govern the summer season rainfall. The precursors for Korean summer rainfall at different frequencies are identified utilizing wavelet and independent component analyses. The sea surface temperatures, the ensemble of rainfall predictions by General Circulation Model, in addition to the typhoon attributes were found to have direct correlation with extreme rainfall events and were used as inputs to the logistic regression model. The model parameters are estimated using Markov Chain Monte Carlo and the resulting posterior distributions associated with individual inputs are analyzed to advance our understanding of the spatiotemporal impact of the teleconnections. Eight rainfall stations throughout Korea are considered in this analysis. We demonstrate that the probability of occurrence of extreme events could be successfully projected at a 90% rate of correct classification of extreme events.     

复合顺序对三维花状TiO_2-FeOOH复合材料吸附及光催化性能的影响

以TiCl_4为钛源、Fe(NO_3)3·9H_2O为铁源,采用分子自组装法在活性炭表面制备了不同复合顺序的TiO_2-FeOOH复合材料,研究了其对甲基橙的吸附和光催化性能。实验结果表明,TiO_2-FeOOH复合材料由锐钛矿TiO_2和α-针铁矿复合层组成,并呈直径为200~300 nm的三维花状多级结构,这种独特的多级结构有利于实现吸附与光催化的协同作用。UV-Vis吸收光谱显示TiO_2与FeOOH的复合减小了TiO_2的禁带宽度,但TiO_2/FeOOH/AC、TiO_2/FeOOH/AC和TiO_2/AC对质量浓度为10 mg/L的甲基橙溶液处理6 h后,溶液脱色率分别为78.8%,33.9%,57.5%,表明复合顺序对TiO_2-FeOOH复合材料的界面电子传递过程及光催化性能有重大影响。     

基于主成分—聚类分析的贫困县（区）经济发展研究——以陕南地区国家级贫困县（区）为例

采用国内生产总值、人均纯收入、城镇固定资产投资、社会消费品零售总额等11个指标,运用主成分分析法对陕南地区20个国家级贫困县（区）的经济发展水平进行了评价,在此基础上运用聚类分析法将贫困县（区）分成四种类型：第一类是＂第二、第三产业并进型＂的汉滨区,第二类是＂特色农业带动型＂的商州区,第三类是＂优势矿业驱动型＂的洛南县和旬阳县,第四类是＂传统农业主导型＂的其余16个贫困县,针对各类型贫困县（区）提出相应的发展对策。     

2022年青岛市沿海区域臭氧污染特征及传输影响分析

基于2022年1—12月青岛市沿海区域臭氧（O₃）自动监测数据和气象观测资料,对O₃污染变化特征及影响因素进行了分析,结合后向轨迹聚类与潜在源区分析等方法,对O₃外来输送通道及潜在源分布情况进行分析研究。结果表明：青岛市沿海区域O₃污染主要集中在4—10月份,日变化特征呈单峰单谷趋势,峰值出现在15：00—16：00;气象因素中,地面短波辐射对O₃浓度变化的相对贡献最大,偏南风易导致O₃污染;受二氧化氮（NO₂）滴定作用以及海陆风转换影响,沿海区域O₃峰值与谷值均滞后青岛城区1 h左右;O₃生成整体处于VOCs控制区,1-丁烯、正丁烷与异戊烷是O₃污染期间导致O₃浓度上升的关键组分;O₃污染的主要潜在源区为长三角北部和黄海近岸海域,以及山东中南部地区。     

区域生态质量评价指标选择基础框架及其实现

论述了区域生态质量评价的理论基础、基本单元、指标选择原则及方法、基本评价指标及其实现 ,旨在为区域生态质量评价指标框架的建立打下基础     

Evolution of road risk disparities at small-scale level: example of Belgium

PROBLEM: Road accident outcomes are traditionally analyzed at state or road network level due to a lack of aggregated data and suitable analytical methods. The aim of this paper is to demonstrate usefulness of a simple spatiotemporal modeling of road accident outcomes at small-scale geographical level. METHOD: Small-area spatiotemporal Bayesian models commonly used in epidemiological studies reveal the existence of spatial correlation in accident data and provide a mechanism to quantify its effect. The models were run for Belgium data for the period 2000-2005. Two different scale levels and two different exposure variables were considered under Bayesian hierarchical models of annual accident and fatal injury counts. The use of the conditional autoregressive (CAR) formulation of area specific relative risk and trend terms leads to more distinctive patterns of risk and its evolution. The Pearson correlation tests for relative risk rates and temporal trends allows researchers to determine the development of risk disparities in time. RESULTS: Analysis of spatial effects allowed the identification of clusters with similar risk outcomes pointing toward spatial structure in road accident outcomes and their background mechanisms. From the analysis of temporal trends, different developments in road accident and fatality rates in the three federated regions of Belgium came into light. Increasing spatial disparities in terms of fatal injury risk and decreasing spatial disparities in terms of accident risk with time were further identified. IMPACT ON INDUSTRY: The application of a space-time model to accident and fatal injury counts at a small-scale level in Belgium allowed identification of several areas with outstandingly high accident (injury) records. This could allow more efficient redistribution of resources and more efficient road safety management in Belgium.     

基于灰色聚类方法的湖泊营养状态综合评价

将湖泊水体的营养状态看作一个灰色系统,建立用于识别湖泊营养状态属性的灰色聚类综合评价模型,将水质级别作为一个灰类,水质状态作为灰色变量,根据灰色白化权函数聚类方法来确定水体营养状况归类。以太湖为例,基于分布全湖的20个监测点数据,运用灰色聚类法对其进行富营养状态综合评价,结果表明,监测时段太湖大部分水体基本处于中营养水平,局部湖面达到中度富营养状态,客观地反映了太湖湖区水体营养状况。