Spatial distribution and temporal variation of chemical properties of drainage watercourses in rural and peri-urban areas of Novi Sad (Serbia)—a case study

Waters are among to the most vulnerable environmental resources exposed to the impact of various point and non-point pollutants from rural/urban activities. Systematic and long-term monitoring of hydro-resources is therefore of crucial importance for sustainable water management, although such practice is lacking across many (agro-)hydro-ecosystems. In the presented study, for the first time, the spatial distribution (covering almost 9000 ha) and temporal variation (2006–2013) in certain quality parameters was characterized in drainage watercourses Tatarnica and Subic, whose catchment is rural and suburban areas close to the city of Novi Sad, Republic of Serbia. Based on majority of observed parameters, both watercourses belonged to I and II water quality classes, with occasional presence of certain parameters (e.g., suspended solids, total phosphorus; ammonium) at extreme values exacerbating both watercourses to classes IV and V. The value of the synthetic pollution index (i.e., a combined effect of all considered parameters) showed a higher degree of water pollution in watercourse Subic (on average 2.00) than Tatarnica (on average 0.72). Also, cluster analysis for watercourse Tatarnica detected two groups of parameters (mostly related to nutrients and organic matter), indicating more complex impacts on water quality during the observed period, in which elucidation thus established water quality monitoring program would be of great importance.     

农业面源污染对我国农村地表水的影响与对策研究

基于2015—2021年我国农村地表水环境质量监测数据,分析了农村地表水环境质量状况特征;选取农业农村社会经济活动相关参数,与农村地表水中主要超标因子的超标比例进行了相关性分析;以2020年为基准年,对全国31个行政区,涵盖农村地表水水质状况、农业农村活动水平和污染压力、环境容量3个方面的9个指标进行了聚类分析。结果表明,我国农村地表水的变化趋势、季节特点和主要超标因子等表现出明显的农业面源污染特征;乡村人口、农业投入品使用量和经济作物种植比例等参数与主要超标指标具有较强的相关性（R＞0.9）;聚类分析将全国31个行政区划分为7种不同的农业面源污染类型。提出,应根据不同地区农业面源污染特点,因地制宜地推进标准化规模养殖、畜禽粪污资源化利用、化肥减量行动、高效低风险农药推广等农业面源污染治理措施,进一步加大农村生活污水处理设施建设,同时,完善农村环境质量监测网络,加强农业面源污染监测和评估。     

Evaluating climatic and non-climatic stresses for declining surface water quality in Bagmati River of Nepal

Both climatic and non-climatic factors affect surface water quality. Similar to its effect across various sectors and areas, climate change has potential to affect surface water quality directly and indirectly. On the one hand, the rise in temperature enhances the microbial activity and decomposition of organic matter in the river system and changes in rainfall alter discharge and water flow in the river ultimately affecting pollution dilution level. On the other hand, the disposal of organic waste and channelizing municipal sewage into the rivers seriously worsen water quality. This study attempts to relate hydro-climatology, water quality, and impact of climatic and non-climatic stresses in affecting river water quality in the upper Bagmati basin in Central Nepal. The results showed that the key water quality indicators such as dissolved oxygen and chemical oxygen demand are getting worse in recent years. No significant relationships were found between the key water quality indicators and changes in key climatic variables. However, the water quality indicators correlated with the increase in urban population and per capita waste production in the city. The findings of this study indicate that dealing with non-climatic stressors such as reducing direct disposal of sewerage and other wastes in the river rather than emphasizing on working with the effects from climate change would largely help to improve water quality in the river flowing from highly populated urban areas.     

庞庄煤矿煤矸石淋溶液对周边水体影响分析

为研究庞庄煤矿张小楼煤矸石堆场淋溶液对其场地和周边地表水、地下水水质影响情况,围绕煤矸石堆场及周边采集13组水样做分析比对。结果表明,煤矸石堆场淋溶液对周边地表水体形成普遍污染,且污染物浓度随着污染距离增加逐步降低,一般化学污染指标主要包括溶解性总固体、硫酸盐、铁、锰、氨氮等,毒理学污染指标包括亚硝酸盐、氟化物、砷,超标倍率约为0.02~5.36。受距离及成井深度等因素影响,地下水体未遭受淋溶液明显的污染和影响。     

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。     

Assessment of seasonal variations of surface water quality characteristics for Porsuk Stream

In this study, the factor analysis technique is applied to surface water quality data sets obtained from Porsuk stream in Turkey, generated during 10 years (1995-2005) monitoring of 29 parameters at one site (Esenkara) for all four seasons. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to mineral and inorganic nutrients, organic pollution, microbiological pollution in winter and spring; mineral and nutrients in summer; microbiological and nutrient pollution in fall. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water.     

Application of physicochemical data for water-quality assessment of watercourses in the Gdansk Municipality (South Baltic coast)

The paper presents water-quality evaluation based on an 8-year monitoring programme in the Gdansk Municipality region, on the Southern coast of the Baltic Sea. The studies were carried out from 2000 to 2007 by surface water analysis at 15 various sites within eight watercourses. Sampling sites included rather urbanized or developed lands, farming fields and non-polluted city recreational areas such as parks and forests. Most of the watercourses were sampled monthly at two locations, one within the upper course of the watercourse and the other near its mouth. In all samples, eight parameters of water quality were determined: total suspended solids, dissolved oxygen, water temperature, oxygen saturation, 5-day biochemical oxygen demand, chemical oxygen demand, total phosphorus and total nitrogen concentration. Interpretation of the obtained results revealed that examination of those basic physicochemical parameters permits to discriminate initially watercourses with respect to level of water contamination. During the research, a large dataset was obtained and it was described by both basic statistical parameters and chemometric method of cluster analysis. The paper presents relations between analysed parameters and influence of land exploitation mode on water quality and describes variation of the results both in space and time.     

上海市浅层地下水在线监测技术要点

地下水在线监测技术可以实现地下水水质的高频监测,是未来发展的重要趋势。梳理国内外地下水在线监测技术研究进展,以上海市典型水文地质特征与环境质量状况为例,探讨地下水在线监测点位布设、指标筛选、监测方式及监测井设计等技术要点。首先,优化监测点位布设,对需要开展高频监测的区域或重点风险源开展在线监测,以代表性点位反映总体地下水环境质量状况。其次,综合筛选监测指标,除常规参数外,优先选取水体中的氨氮、高锰酸盐指数等作为在线监测指标,在具有潜在有机污染的区域,选取水中有机物、水中油等作为有机污染指示性指标。应进一步加强指标之间的相关性分析,为指示性指标的确立提供依据。再次,合理确定监测方式,根据取样方式以及污染源风险等级,设置相应的微型站和小型站。最后,优化监测井设计技术方案,进一步研究不同井管材质对地下水中无机或有机污染物的长期吸附(解吸)作用。     

Use of PFU protozoan community structural and functional characteristics in assessment of water quality in a large, highly polluted freshwater lake in China

Structural and functional parameters of protozoan communities colonizing on PFU (polyurethane foam unit) artificial substrate were assessed as indicators of water quality in the Chaohu Lake, a large, shallow and highly polluted freshwater lake in China. Protozoan communities were sampled 1, 3, 6, 9 and 14 days after exposure of PFU artificial substrate in the lake during October 2003. Four study stations with the different water quality gradient changes along the lake were distinguishable in terms of differences in the community's structural (species richness, individual abundance, etc.) and functional parameters (protozoan colonization rates on PFU). The concentrations of TP, TN, COD and BOD as the main chemical indicators of pollution at the four sampling sites were also obtained each year during 2002-2003 for comparison with biological parameters. The results showed that the species richness and PFU colonization rate decreased as pollution intensity increased and that the Margalef diversity index values calculated at four sampling sites also related to water quality. The three functional parameters based on the PFU colonization process, that is, S(eq), G and T90%, were strongly related to the pollution status of the water. The number of protozoan species colonizing on PFU after exposure of 1 to 3 days was found to give a clear comparative indication of the water quality at the four sampling stations. The research provides further evidence that the protozoan community may be utilized effectively in the assessment of water quality and that the PFU method furnishes rapid, cost-effective and reliable information that may be useful for measuring responses to pollution stress in aquatic ecosystems.     

滨海新区地表水水质评价与污染源分析

滨海新区在大力发展工业的同时,面临水资源紧缺与水环境恶化等问题,基于2020年至2021年滨海新区内15个监测站位的丰水期、枯水期实测数据,通过改进型加拿大水质指数模型对滨海新区地表水进行水质评价,在水质评价的基础上,利用相关性分析与绝对主成分-多元线性回归模型分析影响地表水水质状况的污染源。15个水质监测站位水质评价结果表明：丰水期水质指数为32.27～82.80,良好水质站位1个,中等水质站位6个,较差水质站位7个,差等水质站位1个;枯水期水质指数为47.28～81.36,良好水质站位1个,中等水质站位12个,较差水质站位2个。污染源分析结果表明：丰水期中,污染源为生活污水、农业面源污染、养殖尾水点源污染;枯水期中,主要污染源为工业废水、生活污水、养殖尾水面源污染。     

The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin,Brazil

This study sought to evaluate and propose adjustments to the water quality monitoring network of surface freshwaters in the Paraopeba river basin (Minas Gerais, Brazil), using multivariate statistical methods. A total of 13,560 valid data were analyzed for 19 water quality parameters at 30 monitoring sites, over a period of 5 years (2008–2013). The cluster analysis grouped the monitoring sites in eight groups based on similarities of water quality characteristics. This analysis made it possible to detect the most relevant monitoring stations in the river basin. The principal components analysis associated with non-parametric tests and the analysis of violation of the standards prescribed by law, allowed for identifying the most relevant parameters which must be maintained in the network (thermotolerant coliforms, total manganese, and total phosphorus). The discharge of domestic sewage and industrial wastewater, that from mining activities and diffuse pollution from agriculture and pasture areas are the main sources of pollution responsible for the surface water quality deterioration in this basin. The BP073 monitoring site presents the most degraded water quality in the Paropeba river basin. The monitoring sites BP094 and BP092 are located geographically close and they measure similar water quality, so a possible assessment of the need to maintain only one of the two in the monitoring network is suggested. Therefore, multivariate analyses were efficient to assess the adequacy of the water quality monitoring network of the Paraopeba river basin, and it can be used in other watersheds.     

Development of an Overall Index of Pollution for Surface Water Based on a General Classification Scheme in Indian Context

Various National and International Agencies involved in water quality assessment and pollution control have defined water quality criteria for different uses of water considering different indicator parameters. Classification schemes for water quality criteria/standards developed by these agencies differ inaddition to terminologies used such as Action level, Guide level etc. in defining the concentration values in these classes. In the present article a general classification scheme viz. Excellent,Acceptable, Slightly Polluted, Polluted and Heavily Polluted water is proposed for surface water quality assessment. The concentration ranges in these classes are defined in Indianscenario considering Indian Standards and CPCB criteria. Standardsby the European Community (EC), WHO etc. and the reported factsabout the pollution effects of important water quality indicatorparameters on the surrounding were also taken into account. Themathematical equations to transform the actual concentrationvalues into pollution indices are formulated and correspondingvalue function curves are plotted. Based on the individual indexvalues, an `Overall Index of Pollution' (OIP) is estimated. Theapplication of OIP is demonstrated at a few sampling stations onriver Yamuna based on observed water quality data. The general classification scheme along with concentrationranges defined in these classes will be of immense use fordetermining the surface water quality status with reference tospecific individual parameter, and the OIP for assessing theoverall water quality status in Indian context.     

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.     

Water quality of the Ribeirão Preto Stream, a watercourse under anthropogenic influence in the southeast of Brazil

It is known that Brazil still has a privileged position of water quantity and quality, but water use has not proceeded in a responsible manner and often results in impairment of quality. This study aims to evaluate limnological parameters, parasites and bacteria, and concentrations of heavy metals (Cd, Pb, Cu, Cr, Mn, Hg, and Zn) in surface water of Ribeirão Preto Stream. The Ribeirão Preto Stream is located in urban areas under anthropogenic influence. The results showed that the levels of dissolved oxygen values were lower than those established by the National Environmental Council (CONAMA Resolution No 357/2005). The reading of electrical conductivity showed values typical of impacted environments. The parasitological analysis revealed the presence of nematode larvae. The bacteriological analysis showed higher values for total coliform and Escherichia coli than those set by the Brazilian National Environment Council (CONAMA). The heavy metals Cd, Pb, Cu, Cr, Mn, Hg, and Zn showed concentrations in accordance with the guidelines established by CONAMA. The results provide data on the quality of these waters and showed the necessity to protect the watercourse from point sources of contamination, recommending their continued monitoring.     

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.     

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.     

基于灰色关联和系统聚类的大连市环境质量影响因素分析

采用灰色关联度和系统聚类分析方法,定量分析大连市社会、经济、自然、人口、能源资源和污染排放等有代表性的指标与生态环境质量的关联程度。结果表明,大连市社会经济发展与环境质量关联度为中等,经济指标与环境质量指标关联度较强,社会、人口、污染排放指标关联度中等,能源和自然指标关联度较弱。金普新区、甘井子区和长兴岛是潜在环境风险区域,需要重点关注环境质量变化趋势。普兰店区、瓦房店市和庄河市虽然属于低排放区域,但环境质量风险相对较大,应进一步加强环境风险管控。     

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.     

The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses

This study investigates six water quality monitoring stations in the watershed of the Feitsui Reservoir. It uses nine parameters of water quality collected in an interval of two and half years for factor analyses, which was first conducted to determine four types of factors, respectively, those for organic pollution, eutrophication, seasonal influence, and sediment pollution. The analysis results effectively help to determine water quality in the watershed of the reservoir. The authors reutilize analysis of moment structures (AMOS) to acquire further results in order to confirm the goodness of fit of the previous factor analysis model. During the confirmation, we examine the hypothesized orthogonal results as well as utilize oblique rotation to explore the goodness of fit of the reflective indicators of the orthogonal rotation. As shown in the algorithm results, as long as the covariance curve is included in the four factors, no related issues are detected in the goodness of fit of reflective indicators and interior and external quality is reported with excellence. The orthogonal model, thus, stands. Additionally, when the analysis of structural equation modeling (SEM) is conducted, sample data mismatches the hypotheses of multivariate normality. Therefore, this study adopts the generalized least square (GLS) for an algorithm. Research results of this study have been submitted to the reservoir management authorities in Taiwan for the improvement of statistical application and strategic evaluation of water quality monitoring data in order to strengthen the managerial effectiveness of water quality in watersheds.     

Application of multivariate statistical techniques to evaluation of water quality in the Mała Wełna River (Western Poland)

The paper presents the results of determinations of physico-chemical parameters of the Ma?a We?na waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.