Investigation of chlorinated phenols sorption mechanisms on different layers of the Danube alluvial sediment

The characteristics of the Danube river alluvial sediment are of great importance in assessing the risk for transport of pollutants to drinking water sources. Characterization of the sediment column layers has shown that the alluvial sediment, sampled near the city of Novi Sad, is a mesoporous sandy material with certain differences in the properties of individual layers. In order to investigate the sorption mechanisms of four chlorinated phenols (CPs) on the alluvial deposit, static sorption experiments were performed at pH 4, 7 and 10. The results of sorption experiments, confirmed by principal components analysis sugest different mechanisms govern the sorption process at different pH conditions. This can be attributed to the molecular characteristics of CPs, geosorbent properties and to variations in the surface charge of the sorbent at different pH conditions.     

水环境监测点位优化数学模型探讨

根据雨山湖的环境特征和水质状况,在加密监测获取更多的信息的基础上应用多元统计定量分析,选用主成份分析统计模式,用编制计算机程序处理数据,获得优化点数和确定点位.经可靠性检验证明优化方案完全能满足环境监测和环境管理的要求.     

区域环境--经济协调发展定量分析方法研究

本文对目前常用的环境与经济协调发展分析方法的优劣和特点进行了具体分析，指出现在常用的进行可持续发展水平定量分析的层次分析法存在明显的缺陷，以及现有的几种评价环境与经济之间协调程度的方法的局限性。提出了较为客观实用的评估方法，即依据系统分析和数理统计的原理利用主成分分析进行环境——经济协调度分析，并对环境——经济协调度定量评估的步骤、方法包括测算方法的选择、数学模型的建构进行深入的探讨和研究，以南阳市为例对该方法进行了验证。     

不同主体功能定位的县域空间功能区划研究

文章通过判别分析法,尝试构建一套针对不同主体功能定位县通用的指标体系,以反映社会经济发展状况、生态环境保护重要性、国土空间开发支撑条件。通过在广西10个不同主体功能定位县的实践应用,将县域空间划分为城镇空间、农业空间和生态空间,重点开发区、农产品主产区、重点生态功能区的城镇空间分别小于30%、20%和10%,农产品主产区的农业空间和重点生态功能区的生态空间均大于50%。研究成果为全省范围推广县域空间功能区划提供了科学依据。     

五大连池药泉湖水质评价

采用主成分分析法在2010年平水期对五大连池药泉湖6个采样点的8个水质指标进行分析,同时研究药泉湖水体污染的空间分布特征。结果表明:高锰酸盐指数是影响药泉湖水质的首要因子,即有机污染对水体污染的贡献最大;瀑布出水口采样点污染最严重,该出水口为药泉湖观光景区入口,因此要密切关注药泉湖人为点源污染,加大景区环境管制力度。     

Use of principal component analysis to profile temporal and spatial variations of chlorinated solvent concentration in groundwater

This work aims at evaluating spatial distribution patterns of concentration variations for chlorinated solvents in groundwater, based on principal component analysis and geographic information system (GIS) tools. The study investigates long-time series of chlorinated solvent concentrations in groundwater measured for 18 contaminated industrial sites. The characterization of contaminant plumes and delineation of pollutant sources are essential for choosing appropriate monitoring and remediation strategies, as contaminated groundwaters are characterized by complex patterns of spatial and temporal concentration variability, with wide unpredictable fluctuations over time. The present work describes the results of a new exploratory statistical method called the Variability Index Method (VIM) applied to environmental data to assess the performance of using concentration variations as molecular tracers to reveal aquifer dynamics, industrial impacts, and point sources for contamination plumes. The application of this method provides a useful assessment of controls over contaminant concentration variations as well as support for remediation techniques.     

Principal component analysis for soil contamination with organochlorine compounds

In order to investigate the distribution pattern of individual organochlorine compounds in soil samples collected from the sites (Canary Island – Spain, China, Germany, India, Romania, Russia, Serbia, Swiss, UK) affected by industrial activities to the more remote areas, principal component analysis was performed on the data taken from literature. Loading plots pointed out the strong correlation among the variables. Score plots revealed similar PCB- and OCP-soil patterns for majority of the investigated sites. Nevertheless, the temporal differences of PCB-soil loads have been identified: the late 1990s and early 2000s concentrations are similar to those of the early 1940s, and they are below the levels existed in 1980. The most pronounced PCB concentrations characterized the soil from 1966. For OCPs the influence of sites location on the detected concentration has been revealed: China and India were characterized by comparably higher loads of DDX (DDT and its metabolites) and of HCH-isomers, respectively.     

基于统计学方法的南方某市管网水水质特征研究

为综合解析南方某市管网末梢水水质的时空变化特征、识别主要响应指标及指标间相关性,以该市近4年供水管网末梢水水质监测数据为研究对象,利用主成分分析法和对应分析法对管网末梢水质进行了特征研究。主成分分析法评价结果表明:该市管网末梢水年度总体水质存在差异,其中以2016年为最佳;夏秋季节更易发生水质异常现象,5~9月应加强管网末梢水水质管控;区域上看已完成深度处理改造的梅林水厂供水覆盖区管网末梢水水质最优,南山水厂供水覆盖区管网末梢水水质最差;水质指标硫酸盐、氯化物、硬度之间,Fe、浊度、总氯之间具有相对其他水质指标更高的相关性。对应分析法结果表明:管网末梢水水质主要响应指标可重点关注Fe、浊度、耗氧量、总氯和硫酸盐;其中Fe、浊度、耗氧量呈正相关性强,上述指标与硫酸盐、总氯呈负相关性。     

快速城市化背景下的思雅河流域水环境分析

通过对思雅河流域连续3年的水环境质量监测,结合遥感影像获得其土地利用类型变化,据此研究城市化对水环境的影响。结果表明:2012—2014年间思雅河流域自然、农业景观面积占比减少,而建设用地面积持续增加;研究期间除TDS年均值略微下降外,其余水质指标的年均值几乎均呈递增趋势,尤以TSS和COD增加最显著;TSS、COD、NH 3-N和TP的相对标准偏差均>0.1,显著高于其余参数,说明其波动程度较大。主成分分析表明水环境变化最主要的影响因子为第一主成分(TSS、COD、NH 3-N和TP)。贵安新区大学城建设活动加重了思雅河流域水体污染趋势。     

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

Introduction: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. Results: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.