模糊物元识别模型在巢湖水体富营养化评价中的应用研究

选取TN、TP、Chl-a、COD、BOD5、DO和SD等7项指标,应用模糊物元识别模型对2000~2007年巢湖的12个水质监测点的富营养化等级进行了模糊识别。通过与巢湖同期水质浓度变化及其分布区域进行对比分析,验证了模糊物元识别模型在巢湖水体富营养化评价中的适用性。巢湖富营养化评价结果表明,2000~2007年富营养化呈逐年加重的趋势,据采样点模糊物元计算结果得知西巢湖(1#~6#)的富营养化状况比东巢湖(7#~12#)更严重,西巢湖贴近度最高值为0.9974,接近极富营养状态,而东巢湖的最高值仅为0.5866。从年际变化上看,2000~2007年东巢湖富营养化状况变化不大,营养状态级处在较低的水平,而西巢湖的富营养状况变化则较大,营养状态级则处在较高水平。     

太湖西岸水质变化趋势及主要驱动因子

基于2009—2015年太湖西岸10个监测断面的8个水质指标数据,采用季节性Kendall检验法对其指标的浓度变化趋势进行分析,并用主成分分析法结合相关分析评价历年水质状况,分析影响太湖西岸水质的主要驱动因子。结果表明:(1)2009—2015年太湖西岸COD下降趋势显著,DO非显著上升,电导率、高锰酸盐指数、BOD5、氨氮、TP和TN均表现为高度显著下降趋势。(2)主成分分析从原始信息中提取出两个主成分,共解释了73.645%的结果,分别代表水质氮磷营养盐和有机污染。综合得分表明,2009—2015年太湖西岸水质呈逐年改善趋势。水质污染在空间上表现为北部向南部递减的趋势。(3)相关分析表明,氨氮和COD是影响该区域水质的主要驱动因子。     

西安某人工湖水质时空分布特征及其荧光特性

2013年10月到2014年9月,通过对西安某人工湖水温、DO、SD、pH、TN、TP、COD、叶绿素a等10项水质指标进行监测,评价了水体的营养状态,分析了水质的时空分布特征及其可能污染来源,并对其进行三维荧光光谱分析。结果表明,实验期间,该人工湖在5—7月处于中度富营养化状态,其他月份均处于轻度富营养化状态。在时间上,NO-3-N、NH+4-N和TN在14年2月到5月明显高于其他月份,其中NO-3-N是构成TN的主要存在形态,TP含量变化无明显规律,COD在14年3月到5月含量较高,5—7月是人工湖的藻类暴发期,叶绿素a较其他月份高,在空间上,NO-3-N、NH+4-N和TN各采样点间没有显著差异,TP、COD和叶绿素a波动较大,这可能与采样点附近的局部污染有关。9月份湖内的溶解性有机物主要是腐殖酸类,主要来源是微生物的生命活动和死亡分解,含量沿湖体采样点下降。     

基于玄武岩纤维载体的生物膜法净化污染河道水体

为考察不同水体环境状况下,所构建的基于玄武岩纤维的污染河道水体净化技术的净化效能,通过模拟不同p H、温度及DO的污染河道水体环境,研究了水体中的COD、氨氮及TP指标变化规律,得到所构建的净化技术对COD、氨氮及TP的削减速率。研究结果表明,不同温度环境下COD、氨氮及TP的净化效率会随着时间增加而增强;不同DO环境下COD、氨氮及TP的净化效率随时间增加而增强;不同p H条件下COD净化效率随着时间增加而持续增强,氨氮净化效率随时间增加至某一极值后趋于平稳,而TP的净化效率会随着时间增加表现先升高后降低的趋势。此外,实验期间不同水体环境下COD、氨氮与TP皆在温度20~25℃,p H为7,DO为2~4 mg/L时分别获得93%、90%和36%的最大净化效率;根据实验结果得出的最适宜水体环境为温度20~25℃,p H为7,DO为2~4 mg/L;COD、氨氮及TP削减速率分别为0.63~0.84、3.6×10-3~4.0×10-2和7.1×10-4~2.9×10-3kg/(m3·d)。     

洪湖水质时空特征及污染驱动力分析

运用主成分分析法和聚类分析法,对2016年洪湖8个常规监测点的8项水质指标进行数据分析,识别洪湖主要污染因子,判别洪湖水质的时空差异性,揭示洪湖水质与污染源的内在联系,分析污染成因。结果表明,2016年洪湖8个监测点均出现超标现象,且入湖断面水质浓度大于出湖断面水质浓度,超标因子主要为COD、TP、TN、氨氮,超标最严重的监测点位为蓝田。丰水期有机污染严重,污染源主要为洪湖内水产围网养殖;枯水期氮磷超标严重,污染主要来源于洪湖流域农业种植及畜禽养殖等;平水期湖体内外源污染都占较大比重。因此,为提高洪湖水质保护水生态环境,应拆除洪湖内围网,并加强对外源污染的控制。     

沉水植被构建对上海辰山植物园景观湖水质的影响

以辰山植物园景观湖为研究对象,研究了景观湖建成初期沉水植物群落构建对水体水质因子的影响;并应用相关性分析(correlation analysis)和主成分分析(principal components analysis)对各水质和生态因子之间的相互关系进行了研究。结果表明,景观湖沉水植被建成1年后,水体水质处于Ⅲ类,其中叶绿素a含量5.48±1.930μg/L、总氮1.40±0.136mg/L、总磷0.080±0.015 mg/L、透明度108±20 cm、高锰酸盐指数5.50±1.26 mg/L;除高锰酸盐指数(CODMn)外,其他水质指标均较构建前有显著改善;并且景观湖水质显著优于外河道补充水源水质。相关性分析表明,景观湖水体叶绿素a含量与N、P营养盐呈显著正相关,但却与水温呈极显著负相关,这显示了草型景观湖水质的显著特征;主成分分析显示,第一主成分包含硝态氮、亚硝态氮、总磷、活性磷、叶绿素、水温、透明度和pH等变量,第二主成分包括高锰酸盐指数和氨氮等变量;第一主成分中的水质参数可作为辰山植物园景观湖后续水生态管理中的主要监测对象;此外,主成分分析还显示TP对水体叶绿素的增加具有很高正权重。     

冬季西安某人工湖污染物分布特征及其相关性分析

以西安某人工湖为研究对象,在湖体中布设9个采样点,并且在2013.12—2014.1期间采样3次,对其中TN、TP、COD、NH+4-N和NO-3-N等污染物分布及其与叶绿素a浓度的相关性进行了相应的研究。结果表明,湖体中NH+4-N、NO-3-N、TN、TP、COD和叶绿素a的浓度分别为:0.8~1.7 mg/L、1.1~2.9 mg/L、2.5~4.8 mg/L、0.04~0.14 mg/L、20~24.8 mg/L和0.82~2.14μg/L。人工湖进、出水口污染物浓度整体较低,在靠近车流量较大的3座交通桥的采样点4、5和8采样后所测各项污染指标均较其他点高,由此说明在研究期间交通桥可能成为此人工湖的点污染源。污染物相关性分析结果显示,在冬季低温条件下(4~6℃)叶绿素a与TN、TP、NH+4-N、NO-3-N的相关系数分别为:R2=0.55~0.75,P0.05;R2=0.4~0.65,P0.05;R2=0.48~0.62,P0.05;R2=0.15~0.44,P0.05;COD和浊度呈极显著相关:R2=0.67~0.85,P0.01。     

基于MIKE21研究生态补水对巢湖水质时空分布的影响

选取引江济淮工程中引江济巢段的2条生态补水路线,设计3种生态补水方案：方案一为西兆线、菜巢线双线引入巢湖,经裕溪河巢湖闸流出;方案二为兆河单线引入巢湖,由白石天河的白山节制枢纽流出;方案三为生态补水经兆河单线进入巢湖,经裕溪河巢湖闸流出。利用MIKE21软件建立巢湖水动力水质耦合模型,模拟巢湖水动力和水质时空变化过程及4个敏感区域(南淝河入湖口、峔山岛、忠庙和饮用水水源地)的水质改善效果。总体看来,方案二对南淝河入湖口、峔山岛、忠庙水质改善效果最优,而方案三使饮用水水源地总磷(TP)浓度降幅最大。方案二降低了峔山岛、忠庙区域蓝藻暴发的潜在风险,使总氮(TN)/TP分别从14.7、13.4降至7.4、4.0;方案三使饮用水水源地的TN/TP从10.0降至4.8,降低了饮用水水源地蓝藻暴发的潜在风险,但3个生态补水方案中南淝河入湖口区域的TN/TP均在15～20,表明蓝藻暴发风险依然较高。引江济巢工程对巢湖水环境改善有积极的效果,可以保证巢湖市饮用水水源地的水质安全,但由于西半湖的水动力环境未得到改善,蓝藻暴发的潜在风险依然较高,因此,要优化生态补水方案,并有效控制入湖河流流域的地表径流污...     

松花江水质的多元统计分析

采用主成分分析法、因子分析法对松花江干流11个断面的水质监测数据按照不同水期进行分析,研究了各水质参数的相关性并对污染来源及不同水期内反映水质的重要参数进行了初步探讨.结果表明,水中可生物降解污染物对水体DO有一定的影响,水体温度是控制耗氧性污染物降解的主要因素.枯水期NH3-N与生活污水排放有重要关系,且在TN中占有较高比例.丰水期NH3-N与挥发酚相关性增加,这可能与汛期内煤化工类企业排放不达标有关；平水期,NH3-N与NO3-N相关性增加,可能平水期的污染来源及负荷与其他水期有差别.通过因子分析发现,在不同水期有不同的水质参数影响水质,枯水期大肠杆菌、TP是反映水质的重要参数；丰水期NH3-N、TP、石油类、pH为影响水质的主要水质参数；平水期DO、TN、电导率是反映松花江水质状况的重要水质参数,应当针对不同水期的污染负荷构成制定相应的污染防治方案.     

景观富营养水体生态修复对浮游植物群落结构的影响

对以再生水为补给水源的北京稻香湖园林水系进行生态修复实验。通过对浮游植物群落和理化指标的分析,探讨浮游植物对生态修复的响应和修复效果。结果显示,生态修复实验实施后,TN、TP、COD和BOD5分别下降62.55%、66.42%、48.53%和40.95%,富营养程度明显降低;浮游植物密度和Chl-a含量分别下降86.1%和77.4%;Shan-non-Weaver物种多样性指数值从生态修复前低于2上升到2.74,且优势种群发生演替。相关分析表明:TP、PO4-P、pH与浮游植物密度呈极显著正相关(P(0.01);TN、NH4-N、NO2-N、COD和DO与浮游植物密度呈显著正相关(P(0.05);Chl-a含量与浮游植物密度呈极显著正相关(P(0.01)。浮游植物群落结构对城市富营养景观水系的生态修复措施表现出明显的响应。     

Application of nanobalance technique and principal component analysis for detection of the soil fumigant Telone residues in the air

Quartz crystal nanobalance (QCN) technique is considered as a powerful mass sensitive sensor for monitoring of materials in the sub-nanogram level. In the current study, a method based on QCN technique developed to determine Telone in air. Various coating materials including methyl phenyl silicon, 75% phenyl (OV25) and molecularly imprinted polymer (MIP) were employed. The frequency shift of OV25-modified quartz crystal was found to be linear against organohalogen compounds [Telone (soil fumigant), Koril (Herbicide), Endosulfan (organochlorine insecticide) and Chloroform (solvent)] concentrations in the range of 2.4 to 48 mg L^?1 for Telone vapor and 4.8–24 mg L^?1 for three other vapors. The correlation coefficients for Telone, Koril, Endosulfan and Chloroform were 0.992, 0.996, 0.989 and 0.991, respectively. The principal component analysis was also utilized to process the frequency response data of the organic vapors. Using principal component analysis, it was found that more than 93.85% of the data variance could still be explained by use of two principal components (PC1 and PC2). Subsequently, the successful discrimination of Telone and other compounds was quite possible through the principal component analysis of the transient responses of the OV25-modified electrode. In the second method, a molecularly imprinted polymer-coated sensor for Telone was developed. Molecularly imprinted polymer coated quartz crystal (MIP-QCN) showed a selective response to Telone and gave a linear relationship between frequency shift and amount of Telone from 1 to 48 mg L^?1. In this investigation, the proficiency of MIP-QCN and OV25-modified QCN sensors were compared.     

Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score—multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.     

Spatial and Seasonal Variations of Disinfection Byproducts (DBPs) in Drinking Water Distribution Systems of Istanbul City,Turkey

This study presents the seasonal and spatial variations of trihalomethanes (THMs) and haloacetic acids (HAAs) in 30 sampling points within three water distribution systems of Istanbul City, Turkey. The effects of surface water quality, seasonal variation, and species differences were examined. The occurrence of chlorinated THMs and HAAs levels was considerably lower in the system in which raw water is subjected to pre-ozonation versus pre-chlorination. Seasonal analysis of the data indicated that the median concentration of four THMs (THM₄) was higher than nine HAAs (HAA₉) concentrations in all three distribution systems sampling points. For all distribution systems monitored, the highest median THM₄ and HAA₉ concentrations were observed in the spring and summer season, while the lowest concentrations of these disinfection byproduct (DBP) compounds were obtained in the fall and winter period. Due to the higher level of bromide in supplying waters of these two systems, moderate levels of brominated DBP species have been observed in the Kagithane and Buyukcekmece distribution systems districts. In fact, Spearman partial correlations (Spearman rank correlation coefficients [rs]) tend to be higher among analogues in terms of number and types of substituent, especially TCAA with TCM (rs 0.91), and DBAA with DBCM (rs 0.90). In contrast, the hydraulic (residence time and flow rate) and chemical mechanisms (hydrolysis, volatilization, and adsorption) affect the fate and transport of DBPs in distribution systems. Seasonal and spatial variations of DBPs presented in this study have important implications on regulatory issues and from an epidemiological point of view.     

Groundwater quality assessment and pollution source apportionment in an intensely exploited region of northern China

Deterioration in groundwater quality has attracted wide social interest in China. In this study, groundwater quality was monitored during December 2014 at 115 sites in the Hutuo River alluvial-pluvial fan region of northern China. Results showed that 21.7% of NO₃ ^? and 51.3% of total hardness samples exceeded grade III of the national quality standards for Chinese groundwater. In addition, results of gray relationship analysis (GRA) show that 64.3, 10.4, 21.7, and 3.6% of samples were within the I, II, IV, and V grades of groundwater in the Hutuo River region, respectively. The poor water quality in the study region is due to intense anthropogenic activities as well as aquifer vulnerability to contamination. Results of principal component analysis (PCA) revealed three major factors: (1) domestic wastewater and agricultural runoff pollution (anthropogenic activities), (2) water-rock interactions (natural processes), and (3) industrial wastewater pollution (anthropogenic activities). Using PCA and absolute principal component scores-multivariate linear regression (APCS-MLR), results show that domestic wastewater and agricultural runoff are the main sources of groundwater pollution in the Hutuo River alluvial-pluvial fan area. Thus, the most appropriate methods to prevent groundwater quality degradation are to improve capacities for wastewater treatment and to optimize fertilization strategies.     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

古运河(镇江段)沉积物酶活性及其与氮磷阴离子的相关性

以古运河镇江段为研究对象,研究了表层沉积物中碱性磷酸酶活性、硝酸还原酶活性和亚硝酸还原酶活性的时空变化以及3种酶活性与河流表层沉积物中氮磷阴离子浓度及上覆水水质的相关性。结果表明,镇江古运河表层沉积物中碱性磷酸酶活性、硝酸还原酶活性和亚硝酸还原酶活性具有显著的时间和区域性差异,枯水期(12月)各酶活性较高,分别介于0.29~0.32 mg/(g·24 h)、0.13~0.19 mg/(g·24 h)、3.05~5.21 mg/(g·24 h);丰水期(5月)各酶活性较低,变化范围分别为0.18~0.29 mg/(g·24 h)、0.08~0.14 mg/(g·24 h)、2.95~3.64 mg/(g·24 h)。冗余分析结果显示,古运河表层沉积物酶活性在枯水期差异较大,丰水期差异较小,各酶活性与其相关离子浓度呈显著正相关(P0.05),硝酸还原酶和亚硝酸还原酶与上覆水的水质呈正相关。3种酶活性可被作为判断古运河镇江段沉积物富营养化负荷的参考性指标,硝酸还原酶和亚硝酸还原酶还可作为评价古运河镇江段水质的参考性指标。     

Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies

In this work the performance and theoretical background behind two of the most commonly used receptor modelling methods in aerosol science, principal components analysis (PCA) and positive matrix factorization (PMF), as well as multivariate curve resolution by alternating least squares (MCR-ALS) and weighted alternating least squares (MCR-WALS), are examined. The performance of the four methods was initially evaluated under standard operational conditions, and modifications regarding data pre-treatment were then included. The methods were applied using raw and scaled data, with and without uncertainty estimations. Strong similarities were found among the sources identified by PMF and MCR-WALS (weighted models), whereas discrepancies were obtained with MCR-ALS (unweighted model). Weighting of input data by means of uncertainty estimates was found to be essential to obtain robust and accurate factor identification. The use of scaled (as opposed to raw) data highlighted the contribution of trace elements to the compositional profiles, which was key to the correct interpretation of the nature of the sources. Our results validate the performance of MCR-WALS for aerosol pollution studies.     

Methyl Tertiary Butyl Ether (MTBE) and Other Volatile Organic Compounds (VOCs) in Public Water Systems,Private Wells,and Ambient Groundwater Wells in New Jersey Compared to Regulatory and Human-Health Benchmarks

Potential threats to drinking water and water quality continue to be a major concern in many regions of the United States. New Jersey, in particular, has been at the forefront of assessing and managing potential contamination of its drinking water supplies from hazardous substances. The purpose of the current analysis is to provide an up-to-date evaluation of the occurrence and detected concentrations of methyl tertiary butyl ether (MTBE) and several other volatile organic compounds (VOCs) in public water systems, private wells, and ambient groundwater wells in New Jersey based on the best available data, and to put these results into context with federal and state regulatory and human-health benchmarks. Analyses are based on the following three databases that contain water quality monitoring data for New Jersey: Safe Drinking Water Information System (SDWIS), Private Well Testing Act (PWTA), and National Water Information System (NWIS). For public water systems served by groundwater in New Jersey, MTBE was detected at a concentration ≥10 μg/L, ≥20 μg/L, and ≥70 μg/L at least once in 30 (2%), 21 (1.4%), and five (0.3%) of sampled systems from 1997 to 2011, respectively. For private wells in New Jersey, MTBE was detected at a concentration ≥10 μg/L, ≥20 μg/L, and ≥70 μg/L at least once in 385 (0.5%), 183 (0.2%), and 46 (0.05%) of sampled wells from 2001 to 2011, respectively. For ambient groundwater wells in New Jersey, MTBE was detected at a concentration ≥10 μg/L, ≥20 μg/L, and ≥70 μg/L at least once in 14 (2.1%), 9 (1.3%), and 4 (0.6%) of sampled wells from 1993 to 2012, respectively. Average detected concentrations of MTBE, as well as detected concentrations at upper-end percentiles, were less than corresponding benchmarks for all three datasets. The available data show that MTBE is rarely detected in various source waters in New Jersey at a concentration that exceeds the State's health-based drinking water standard or other published benchmarks, and there is no evidence of an increasing trend in the detection frequency of MTBE. Other VOCs, such as tetrachloroethylene (PCE), trichloroethylene (TCE), and benzene, are detected more often above corresponding regulatory or human-health benchmarks due to their higher detected concentrations in water and/or greater toxicity values. The current analysis provides useful data for evaluating the nature and extent of historical and current contamination of water supplies in New Jersey and potential opportunities for public exposures and health risks due to MTBE and other VOCs on a statewide basis. Additional forensic or forecasting analyses are required to identify the sources or timing of releases of individual contaminants at specific locations or to predict potential future water contamination in New Jersey.