基于主成分分析-多元线性回归的松花江水体中多环芳烃源解析

对松花江全流域14个监测断面的16种美国环保局优先控制的多环芳烃(PAHs)的主要来源及其贡献率应用主成分因子分析-多元线性回归模型(PCA-MLR)进行了来源解析。结果表明:松花江全流域为化石和石油燃料的复合PAHs污染,水体环境中PAHs首要污染源为化石燃料燃烧和交通污染,合计贡献率为63.1%,第二大污染源为工业和民用燃煤污染,合计贡献率为36.9%,沿江的石化、石油基地、大型焦化厂、电厂都是PAHs的主要来源。     

城市小流域面源污染输出特征及污染负荷分类核算研究

以南京市上秦淮片区二横沟小流域为例,在污染源调查的基础上,采用分类法,利用改进后的污染输出系数模型进行流域污染负荷核算,得到外源污染物入河负荷量及各污染源的贡献率。研究结果表明,上秦淮片区二横沟流域内污染物排放负荷总量为COD 229.9 t/a、TN 25.8 t/a、NH3-N 22.4 t/a、TP 1.7 t/a,入河负荷量为COD 47.1 t/a、TN 5.5 t/a、NH3-N 4.6 t/a、TP 0.36 t/a,主要来自面源污染,点源污染较小。其中,生活面源污染年入河贡献率最高,为44.5%,林地面源污染贡献率最低,为0.1%,水产面源、道路面源、水田面源和旱地面源贡献率分别为31.6%、16.2%、6.2%和1.4%。     

太浦河界标断面水质达标综合治理对策

简述了太浦河界标断面2020年水质考核目标以及区域水污染现状。指出,界标断面水质主要超标因子为DO和TP,农业面源污染、工业污染、生活污水处理相对滞后、内源污染、部分黑臭河道河段清淤不彻底是导致区域水污染的主要因素。提出,要推进农业面源污染治理,强化工业污染源治理,加强区域性污染物控制以及生活污染源整治,促进河湖生态系统恢复,加强自动监测站管理工作。     

京杭大运河王江泾断面水质达标综合治理对策

简述了京杭大运河王江泾断面水质及区域水污染现状,指出工业污染、农业面源污染、污水集中处理设施滞后、内源污染是区域水污染的主要因素。提出,对区域水环境综合整治,实现京杭大运河王江泾断面水质稳定达标,必须推进产业结构调整与空间布局优化,推进点源污染整治,推进农业面源污染治理,推进生活污染源整治,推进河道整治,提高环境监管及应急水平。     

北京市妫水河水质现状评价

2017年分3个时期(丰水期、平水期、枯水期)采集妫水河及其支流12个断面表层水样,分析了COD、氨氮、TP、DO、pH值等水质指标,采用单因子评价法、水污染指数法、主成分分析法和模糊综合评价法对水质进行评价,根据4种水质评价方法的评价结果,分析4种方法在小流域河流评价中的适用性。结果表明:目前妫水河12个断面中S2、S10和Z2 3个断面水质较差,基本属于地表Ⅴ类水,污染程度为重度污染;其他9个断面水质等级为地表Ⅳ类水,污染程度为中度污染;中段世园会段水质较好,优于上段和下段,主要污染指标为COD;妫水河主要污染源于农业和生活,枯水期水质优于平水期和丰水期。水污染指数法更适合小流域水体水质的定性定量评价,且计算方便简单。     

饮水污染事故污染源的确认

报道了造成南京某水厂饮水污染事故污染源的寻找和认定过程中应用的现场和实验室分析合的调查方法。结果表明，这次污染事故是某化工厂的环己胺生产过程中产生的残液，污染了排涝沟水，当向长江排出被化工废水污染的降雨积水时，造成了该水厂取水口的源水污染，引发饮水污染事故。饮水中污染物为二环己胺。     

2009—2017年南充市城区酸雨特征分析

为了更为有效地治理酸雨污染,根据南充市环境监测中心站提供的2009—2017年降水监测数据,对南充市城区酸雨污染情况进行了分析及源解析。结果表明:2009—2017年,南充市降水pH从4.60波动上升至5.6以上,酸雨频率波动下降,酸雨污染情况有所改善;[SO4^2-]/[NO3^-]从4.92下降至0.86,酸雨污染类型从硫酸型转变为硝酸-硫酸型。同时,对比分析2014—2016年大气污染物排放源可知,NOx排放源中,工业污染源占比由14%降至11%,生活污染源由2%上升至5%;SO2排放源中,工业污染源占比由62%降至43%,生活污染源由38%上升至57%,表明南充市SO2污染已从以工业污染源为主转变为工业污染源与生活污染源并重。     

台州市区臭氧污染特征及来源解析

利用台州市区2013—2017年O_3监测数据分析其污染特征,并采用CMAQ模型研究各类污染源对O_3的贡献率。结果表明:台州市区O_3年均浓度稳定,月均浓度4—10月较高,日小时浓度呈单峰型,峰值出现在13:00左右;在温度较高、相对湿度50%～80%、风速1.0 m/s～3.0 m/s、风向为偏东时O_3浓度相对较高,易出现超标现象;本地排放源是O_3形成的主要来源,各季节贡献率略有差异,分别为春季(72.28%)、夏季(69.95%)、秋季(69.24%)、冬季(66.28%);工艺过程源、道路移动源和居民生活源是O_(3 )形成的3大来源,贡献率分别为26.32%、12.89%和9.91%。     

湖北典型矿冶城市土壤重金属污染评价及源解析

通过对湖北省典型矿冶城市表层土壤采样,测定其中Cd、Co、Cr、Cu、Mn、Ni、Pb和Zn等8种重金属的质量比,探究土壤重金属空间分布与污染程度,并定量分析各污染源的贡献率。结果表明:Cd、Cu、Pb和Zn污染严重且具有类似的污染空间分布,均在工矿活动区域富集,Co和Cu高值区均出现在铜矿附近。Cd、Cr和Cu污染程度较高,对土壤生态环境有较大的不利影响。研究区内主要受综合采矿源、铜矿采矿-冶炼源影响,对土壤重金属的贡献率分别为41%和31%。     

四川邛海滨湖公路路面径流污染及其生态风险研究

通过在雨季和旱季各2次典型降雨期间对邛海滨湖公路路面径流连续采样监测,结果表明,该路面径流的主要污染因子为SS、COD、TP、TN和Pb,旱季的径流污染程度较雨季重,主干路的路面径流污染最严重。因子分析表明,路面径流的主要污染因子为重金属和营养盐,其方差贡献率分别为64. 546%和13. 596%。潜在生态风险评估表明,路面径流中重金属平均潜在生态风险处于轻微级别,6种重金属中除Pb在主干路达到中等风险外,其余均为轻微风险。     

不同水体中环境类雌激素污染状况调查与分析

对80个不同水体样品进行了环境雌激素检测,共有42个样品为阳性(阳性率52.5%),阳性样品主要来自于医疗废水、市政排污口、污水处理厂及与工业污染源废水,水源水中未检出。42个阳性样品检测值均超过EPA标准规定的壬基酚4 d平均浓度限值(6.6μg/L),其中12个样品检测值超过了该标准中规定的壬基酚小时平均浓度限值(28μg/L)。     

Assessment of elemental distribution and trace element contamination in surficial wetland sediments, Southern Tibetan Plateau

In order to analyze and evaluate different trace metals on surface water of the Changjiang River, concentrations of dissolved trace metals (Cu, Ni, Fe, Co, Sc, Al, Zn, Pb, Cd, Se, As, Cr, and Hg), major elements(Ca and Mg), and nutrient(NO $_{3}^{-})$ were measured. Samples were taken at 76 positions along Changjiang River in flood and dry seasons during 2007?C2008. Spatial distributions identified two main large zones mainly influenced by mineral erosion (sites 1?C22) and anthropogenic action (sites 23?C76), respectively. Principal component analysis (PCA) and hierarchical cluster analysis were used to identify the variance distinguishing the origin of water. Four significant components were extracted by PCA, explaining 74.91% of total variable. Cu, Ni, Fe, Co, Sc, Al, Ca, and Mg were mainly associated with the weathering and erosion of various rocks and minerals, while an anthropogenic source was identified for Cd and As. Although erosion was one source of Pb and Zn, they were also input by atmospheric deposition and industrial pollutions. NO $_{3}^{-}$ and Se were mainly associated with agriculture activities. However, Hg and Cr showed different sources. CA confirmed and completed the results obtained by PCA, classifying the data into two large groups representing different areas. Group 1 referred to the upper reaches which represented samples mainly corresponding to natural background areas. Group 2 referred to the middle and lower reaches including samples under anthropogenic influence. Meanwhile, group 2 was subdivided into three new groups, representing agricultural, industrial, and various artificial pollution sources, respectively.     

城市生活污染对浏阳河长沙城区段水质的影响

对浏阳河长沙城区段的水质现状和污染来源进行了调查。结果表明,浏阳河河水流经长沙城区后,氨氮和总磷浓度分别升高了9.49倍和7.75倍,但铁、锰和高锰酸盐指数等其他监测指标无显著变化。浏阳河长沙城区段的污染主要是由于区域内生活污水直接排入和污水处理厂尾水排入导致清污比例严重失调引起的,城市生活污染对浏阳河长沙城区段水质下降的贡献最大。此外,湘江长沙综合枢纽工程库区回水的顶托作用对浏阳河长沙城区段水质也有一定影响。     

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

流域重点工业污染源识别特征成分谱建立技术

研究了基于生产排放全过程、多相、多类污染物并举的污染源识别特征成分谱建立技术。首先建立污染源排放完整图谱,包括原辅料、中间物质、产品、各工艺废水污染物、水处理设施进口和出口污染物等。然后从排放完整图谱中解析出特征污染物。对于废水中的常规污染物和金属污染物,采用与受纳水体浓度相比较的方式得到污染源识别特征污染物,建议将浓度超过受纳水体1倍的污染物定为特征污染物。对于有机污染物,将质量分数之和大于90%的污染物集定为特征有机污染物,并按照有机物类别进行分类。最后开发建立动态的水污染源排放数据库。应用该技术建立了石化行业典型企业的排放特征成分谱,发现这些特征组分具有很好的代表性,为水污染源的识别提供了基础数据。     

Distribution and Statistical Analysis of Leachable and Total Heavy Metals in the Sediments of the Suez Gulf

The concentrations of nine heavy metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pd and Zn) in the labile and total fractions of muddy and sandy sediment samples collected from twelve sites in Suez Gulf during April 1999 were studied to evaluate the pollution status of the Suez Gulf. The enrichment factors (EF) for each element were calculated. There are extremely high concentrations of Cd, Ni, Pb and slightly concentration of Cr and Cu in both muddy and sandy sediments. The concentration of Zn was moderately high and can be considered as seriously contaminate Metal pollution index (MPI) shows high values ranged between 46 to 156 and 40 to 232 for both sandy and muddy sediments, respectively. Concentrations of heavy metals were normalized against iron for total fraction in both of sandy and muddy sediments. Principal component analysis (PCA) was studied on the data matrix obtained and represented three-factor model explaining 92.22% for labile and 88.82% for total fractions of muddy sediment. The main source of contamination is the offshore oil fields and industrial wastes. This is largely a result of ineffective and inefficient operation equipment, illegal discharge of dirty ballast water from tankers and lack of supervision and prosecution of offenders.     

Analysis of heavy metal concentrations in sediments of selected estuaries of Malaysia—a statistical assessment

Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.     

Spatial and seasonal characteristics of river water chemistry in the Taizi River in Northeast China

Anthropogenic activities have led to water quality deterioration in many parts of the world, especially in Northeast China. The current work investigated the spatiotemporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons. One-way analysis of variance indicated that the 20 measured variables (except pH, 5-day biological oxygen demand, permanganate index, and chloride, orthophosphate, and total phosphorus concentrations) showed significant seasonal (p?≤?0.05) and spatial (p?<?0.05) variations among the mainstream and major tributaries of the river. Hierarchical cluster analysis of data from the different seasons classified the mainstream and tributaries of the river into three clusters, namely, less, moderately, and highly polluted clusters. Factor analysis extracted five factors from data in the different seasons, which accounted for the high percentage of the total variance and reflected the integrated characteristics of water chemistry, organic pollution, phosphorous pollution, denitrification effect, and nitrogen pollution. The results indicate that river pollution in Northeast China was mainly from natural and/or anthropogenic sources, e.g., rainfall, domestic wastewater, agricultural runoff, and industrial discharge.     

连云港地区非点源污染的调查分析

随着工业点源废水治理达标排放的实现,非点源废水已成为连云港市地表水环境污染的主要因素。通过现场调查、理论计算,结合连云港市第一次全国污染源普查结果和相关统计资料,核算出连云港地区非点源废水污染物的排放量。     

Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey

The application of different multivariate statistical approaches for the interpretation of a complex data matrix obtained during the period 2004-2005 from Uluabat Lake surface water is presented in this study. The dataset consists of the analytical results of a 1 year-survey conducted in 12 sampling stations in the Lake. Twelve parameters (T, pH, DO, [Formula: see text], NH(4)-N, NO(2)-N, NO(3)-N, [Formula: see text], BOD, COD, TC, FC) were monitored in the sampling sites on a monthly basis (except December 2004, January and February 2005, a total of 1,296 observations). The dataset was treated using cluster analysis, principle component analysis and factor analysis on principle components. Cluster analysis revealed two different groups of similarities between the sampling sites, reflecting different physicochemical properties and pollution levels in the studied water system. Three latent factors were identified as responsible for the data structure, explaining 77.35% of total variance in the dataset. The first factor called the microbiological factor explained 32.34% of the total variance. The second factor named the organic-nutrient factors explained 25.46% and the third factor called physicochemical factors explained 19.54% of the variances, respectively.