Status and habitat preferences for endemic inhabitants of fiddler crab Uca formosensis in Hsiang-Shan wetland, Taiwan

This article reports on soil samples collected from Hsiang-Shan wetland, Taiwan. Canonical discriminant analysis (CDA) was applied to identify an existing habitat type's scheme by identifying the physico-chemical properties of sediment in Hsiang-Shan wetland. The three constructed discriminant functions (CDFs) showed a marked contribution by most of the discriminant variables, and the recognition capacities in these three CDFs were 49.5, 32.8 and 17.7%. Our study revealed that the most important latent factors in Hsiang-Shan wetland are soil texture-caused factor, ocean current-caused factor, nutrient-caused factor, and the redox reaction-caused factor. And the most sensitivity parameters in this habitat followed the descending order: OBD, EC, Eh, sand, TN, porosity, STP, silt, VCP and pH. And the inhabited sediment properties for U. formosensis in terms of soil texture are sand, silt, and clay (34.05, 29.72, and 32.35%, respectively): that is clay loam soil. We also found that U. formosensis preferred to inhabit the upper intertidal zone, spending 8.41% of the time submerged. Vegetation coverage on the ground was less than 2.20%, showing that it preferred to live in a bare intertidal habitat. Concerning nest choosing, excavating burrows is more difficult when a high soil penetration force is required, and in this study the soil penetration force for 20 cm was found to be is 45.98 N/cm(2). The results will be helpful in developing a methodology for use by the government in refining its management programs.     

Assessment of water quality using multivariate statistical techniques in the coastal region of Visakhapatnam,India

The present study was intended to develop a Water Quality Index (WQI) for the coastal water of Visakhapatnam, India from multiple measured water quality parameters using different multivariate statistical techniques. Cluster analysis was used to classify the data set into three major groups based on similar water quality characteristics. Discriminant analysis was used to generate a discriminant function for developing a WQI. Discriminant analysis gave the best result for analyzing the seasonal variation of water quality. It helped in data reduction and found the most discriminant parameters responsible for seasonal variation of water quality. Coastal water was classified into good, average, and poor quality considering WQI and the nutrient load. The predictive capacity of WQI was proved with random samples taken from coastal areas. High concentration of ammonia in surface water during winter was attributed to nitrogen fixation by the phytoplankton bloom which resulted due to East India Coastal Current. This study brings out the fact that water quality in the coastal region not only depends on the discharge from different pollution sources but also on the presence of different current patterns. It also illustrates the usefulness of WQI for analyzing the complex nutrient data for assessing the coastal water and identifying different pollution sources, considering reasons for seasonal variation of water quality.     

Modeling the effect of land use/land cover on nitrogen,phosphorous and dissolved oxygen loads in the Velhas River using the concept of exclusive contribution area

Non-point source water pollution is a major problem in most parts of the world, but is also very difficult to quantify and control since it is not easily separated from point sources and can theoretically originate from the whole watershed. In this article, we evaluate the relationship between land use and land cover and four water pollution parameters in a watershed in Southeast Brazil. The four parameters are nitrate, total ammonia nitrogen, total phosphorous, and dissolved oxygen. To help concentrate on non-point source pollution, only data from the wet seasons of the time period (2001–2013) were analysed, based on the fact that precipitation causes runoff which is the main cause of diffuse pollution. The parameters measured were transformed into loads, which were in turn associated with an exclusive contribution area, so that every measuring station could be considered independent. Analyses were also performed on riparian zones of different widths to verify if the effect of the land cover on the water quality of the stream decreases with the increased distance. Pearson correlation coefficients indicate that urban areas and agriculture/pasture tend to worsen water quality (source). Conversely, forest and riparian areas have a reducing effect on pollution (sink). The best results were obtained for total ammonia nitrogen and dissolved oxygen using the whole exclusive contribution areas with determination coefficients better than R²≈0.8. Nitrate and total phosphorous did not produce valid models. We suspect that the transformation delay from total ammonia nitrogen to nitrate might be an important factor for the poor result for this parameter. For phosphorous, we think that the phosphorous sink in the bottom sediment might be the most limiting factor explaining the failure of our models.     

采煤塌陷区水污染源解析研究

应用SPSS17.0中因子分析模型对淮南市潘一矿采煤塌陷水体常规水质指标进行污染源解析,结果得出4种主要的污染源,F1主要代表了氮磷肥引起的农业面源污染、矿井水和浅层地下水的混合污染,方差贡献率为42.991%,F2代表了矿井水污染,方差贡献率为20.180%,F3主要代表了农村污水和矿业废水混合污染,方差贡献率为13.299%,F4主要代表了煤矸石淋溶水污染,方差贡献率为11.546%。     

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.     

基于多元统计分析的石头口门水库汇水流域水质综合评价

根据石头口门水库汇水流域的4个监测断面2001～2007年的水质监测数据,应用多元统计分析方法(聚类分析与因子分析)确定主要污染因子并计算权重,从而对流域的水质进行综合评价。结果表明,通过因子分析,提取了3个公因子,第一主因子主要包括溶解氧、氨氮、总氮、高锰酸盐指数、化学需氧量、生化需氧量;第二主因子的主要代表指标是总磷;氟化物、总大肠菌群数对第三主因子贡献明显。由综合评价结果得出,石头口门水库总体属Ⅲ类水质,主要污染因子为总磷;饮马河(烟筒山断面)和岔路河(星星哨水库断面)水质属Ⅲ类,主要受第一主因子影响;双阳河(新安断面)水质属Ⅴ类。流域水质主要受到了农业非点源污染和生活污染的影响。     

基于多元统计方法的某地浅层地下水污染来源分析

为查明某地农田灌溉水井水质污染致使作物生长受损事件的污染来源,对研究区10眼水井进行了水质检测分析,并采用多元统计方法判断污染来源。结果表明:研究区水样中全盐量普遍超过农田灌溉水质标准,总硬度、硫酸盐、氨氮和氰化物等也存在不同程度的超标;水样中全盐量、氨氮与氰化物的含量之间存在显著正相关,具有共同的来源,且与河流A补给关系密切;地下水中盐分过高是造成作物受损的主要原因;地下水中全盐量、氨氮及氰化物等主要污染物来源于上游的焦化企业。基于多元统计方法的地下水污染来源分析结果可为当地地下水污染防治及管控提供环境管理依据。     

Assessment of temporal variation in water quality of some important rivers in middle Gangetic plains, India

The study explains water quality of three important tributaries of the Ganga River in the middle Gangetic plains in India. Seasonal changes in the water quality of the studied rivers: Gandak, Ghaghra, and Sone were observed. During monsoon, several water quality parameters show considerable changes due to increased runoff from the catchments and other seasonal factors. Multivariate discriminant analysis delineated a few parameters responsible for temporal variation in water quality. Seasonal variation in water quality of the Gandak River was rendered by seven parameters??turbidity, sulfate, pH, phosphate, water temperature, total alkalinity, and sodium, while total alkalinity and water temperature were responsible for seasonal discrimination in water quality of Ghaghra River. Water temperature, turbidity, total dissolved solids, total suspended solids, calcium, and phosphate were important for seasonal discrimination in water quality of Sone River. The seasonal changes in water quality of the rivers were due to seasonal effects and catchment characteristics. The discriminant functions classified most of the cases correctly.     

Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health

Three representative areas (lowland, semi-mountainous, and coastal) have been selected for the collection of drinking water samples, and a total number of 28 physical, chemical, and biological parameters per water sample have been determined and analyzed. The mean values of the physical and chemical parameters were found to be within the limits mentioned in the 98/83/EEC directive. The analysis of biological parameters shows that many of the water samples are inadequate for human consumption because of the presence of bacteria. Cluster analysis (CA) first was used to classify sample sites with similar properties and results in three groups of sites; discriminant analysis (DA) was used to construct the best discriminant functions to confirm the clusters determined by CA and evaluate the spatial variations in water quality. The standard mode discriminant functions, using 17 parameters, yielded classification matrix correctly assigning 96.97% of the cases. In the stepwise mode, the DA produced a classification matrix with 96.36% correct assignments using only ten parameters (EC, Cl^???, NO₃ ^???, HCO₃ ^???, CO₃ ^???2, Ca^?+?2, Na^?+?, Zn, Mn, and Pb). CA and factor analysis (FA) are used to characterize water quality and assist in water quality monitoring planning. CA proved that two major groups of similarity (six subclusters) between 17 physicochemical parameters are formed, and FA extracts six factors that account for 66.478% of the total water quality variation, when all samples’ physicochemical data set is considered. It is noteworthy that the classification scheme obtained by CA is completely confirmed by principal component analysis.     

不同水质评价方法在丹江口流域水质评价中应用比较

采用合理的水质评价方法,准确地描述河流水质状况,才能为水质管理提供治理方案。以丹江口水源地为例,选用单因子指数评价方法、综合污染指数评价方法、模糊综合评判法、主成分分析法、水污染指数法对丹江口水源地7个河流断面进行水质评价并对比评价效果。结果表明,水污染指数法操作简便、评价结果直观明了,具有广泛的应用范围。     

长春南湖底泥疏浚前后水因子分析及动态变化

监测了长春南湖底泥疏浚后的DO、NH4＿N、NO2＿N、NO3＿N、BOD5、CODCr、TN、TP、SS、pH、SD和水温等12项水化学指标。用因子分析方法找出了底泥疏浚前后影响南湖水质的主要因子,分析了底泥疏浚前后南湖水质变化的特征和底泥疏浚对南湖水质的影响,分析结果表明,南湖疏浚前主要污染物是总磷,疏浚后总磷对水质的影响降低,悬浮物作用增大。     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

官厅水库入库断面水质多指标评价与演变特征分析

水体质量状况及其污染特征是流域水污染防治规划和治理措施制定的前提和基础。采用主成分分析和层次聚类分析等多元统计分析方法,选取溶解氧、高锰酸盐指数、化学需氧量等10个不同类型的监测指标,综合评价了官厅水库入库断面八号桥2006—2017年丰水期和枯水期水质年际变化特征,识别了不同阶段关键污染指标。结果表明:各年份丰水期(9月)大多数水质指标均优于枯水期(5月),特别是粪大肠菌群和氨氮,但总磷和高锰酸盐指数的差异较小。根据水质指标年际变化情况,可将研究期分为污染严重阶段(2006—2007年)、污染改善阶段(2008—2015年)和污染全面好转阶段(2016—2017年)。大部分水质指标呈现逐年好转的趋势,特别是粪大肠菌群、氨氮和五日生化需氧量,但总磷仍是官厅水库入库河流的重要污染指标。     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Application of Composite Water Quality Identification Index on the water quality evaluation in spatial and temporal variations: a case study in Honghu Lake,China

Composite Water Quality Identification Index (CWQII) and multivariate statistical techniques were used to investigate the temporal and spatial variations of water quality in Honghu Lake. The aims are to explore the characteristics of water quality trends in annual, monthly, and site spatial distribution and to identify the main pollution factors. The results showed that the values of CWQII increased from 2.0 to 4.0 from the years 2001 to 2005, then decreased from 2006 and kept a balance between 2.0 and 3.0 from 2006 to 2011, indicating that the water quality of Honghu Lake deteriorated from 2001 to 2005 and has gradually improved since 2006, which were likely achieved after water protection measurements taken since 2004. The monthly change rules of water quality were influenced by a superposition of natural processes and human activities. In samples numbered 1–9 from upstream to downstream, the maximum values of CWQII often occurred in sample site 9 while the minimum ones often occurred in sample site 2, indicating that the water quality near the upstream tributary was the poorest and that in the core zone was the best. Incoming water from the trunk canal of the Sihu area upstream was the largest pollution source. The sensitive pollution nutrients were mainly caused by the total nitrogen, followed by the total phosphorus.     

汉江水质评价的化学计量学研究

运用因子分析法对汉江各主要水质断面进行水质因子分析及综合评价,通过各主因子的方差贡献及因子得分得出各水质因子的赋权值,从而对所取断面进行水质污染程度的综合评价、分析与排序。同时运用聚类分析法对汉江17个断面的水质污染相似性进行分析,给出分类处理结果。     

丹江口库区及其上游流域水质污染特征及评价

根据丹江口库区及其上游河流5个区域42个断面2012-2013年的水质监测数据,采用主成分分析法确定主要污染因子及权重,对不同流域的水质进行综合评价。第一主成分包括总氮、溶解氧、五日生化需氧量、总磷、氨氮、高锰酸盐指数,第二主成分为氟化物、粪大肠菌群,第三主成分为化学需氧量;其权重分别为5．022,2．256,1．508。评价结果表明,湖北十堰市和丹江口市流域水环境污染相对较重,其次为河南南阳市、陕西商洛市、陕西安康市以及陕西汉中市流域。     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

杭州市钱塘江干支流水质多元统计分析

运用多元统计方法分析了杭州市钱塘江干支流上26个断面的水质监测指标。利用系统聚类分析方法将断面所在河流分为3组,与钱塘江流域污染空间分布现状基本一致。对各组水质的主成分分析表明,第1组河流水质以有机污染为主,水体中氮、磷营养盐浓度较高,水体污染程度较轻,污染来源相对单一;第2组河流水体受有机物、重金属、石油类等多个污染指标的影响,水体水质较第1组差,污染来源相对复杂;第3组河流水体既有一般有机污染,也有重金属、有毒有害物质的污染,水体水质污染严重。     

基于环境库兹涅茨曲线的中国城市环境空气质量主要影响因素

当前,中国城市环境空气污染形势十分严峻,空气质量呈现出典型的区域性特征。研究对2006—2012年各地区环境空气质量数据和经济社会发展指标统计资料面板数据进行分析,结果表明:研究选取时段内多数空气质量指标与人均国内生产总值之间的关系并不符合典型的环境库兹涅茨曲线(倒U型曲线),无显著相关性,但NO2质量浓度与人均国内生产总值之间呈现出倒N型曲线,空气质量综合指数与人口密度之间也呈现出倒N型曲线。空气质量综合指数与国民经济中第二产业占比和第三产业占比之间没有显著的相关关系,但与第一产业占比呈显著的负相关关系。空气质量综合指数与主要污染物单位面积排放量呈显著的正相关关系,与单位面积能源消费总量、单位面积煤炭消费量均呈显著的正相关关系,表明以煤炭为主要能源类型的能源消费带来的污染物排放是影响空气质量的主要因素。空气质量综合指数与降水量呈显著的负相关关系,降水量等气象条件对空气质量有一定影响,在开展大气污染防治时,应综合考虑各地的自然因素特征,合理确定工作目标和防治对策。