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.     

Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000?C2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling sites. Hierarchical cluster analysis grouped the 12 months into two periods and the 16 sampling sites into three groups based on similarity in water quality characteristics. Discriminant analysis (DA) provided better results both temporally and spatially. DA also offered an important data reduction as it only used four parameters for temporal analysis, affording 84.2% correct assignations, and eight parameters for spatial analysis, affording 96.1% correct assignations. Principal component analysis/factor analysis identified four latent factors standing for organic pollution, industrial pollution, nonpoint pollution, and fecal pollution, respectively. KN1, KN4, KN5, and KN7 were greatly affected by organic pollution, industrial pollution, and nonpoint pollution. The main pollution sources of TN1 and TN2 were organic pollution and nonpoint pollution, respectively. Industrial pollution had high effect on TN3, TN4, TN5, and TN6.     

Spatial distribution and source identification of persistent pollutants in marine sediments of Hong Kong

A data matrix, obtained during a 3-year monitoring period (2007–2009) from 45 sampling sites in Hong Kong marine, was subjected to determine the spatial characterization and identify the sources of main pollutants. Indicator analyses indicated that polycyclic aromatic hydrocarbons (PAHs), nickel, manganese, and arsenic (As) were at safe levels. Five heavy metals (zinc, lead, cupper, cadmium, chromium (Cr)) were moderate to severe enrichment at some sites. Inner Deep Bay and Victoria Harbor were considered as hot spots for PAHs and the heavy metals, while Tolo Harbor was highly polluted by the heavy metals. Cluster analysis classified the 45 sampling sites into three groups, representing different pollution levels. Principal component analysis/factor analysis identified four principal components (PCs) and explained 84.9 % of the total variances, standing for persistent pollution, N factor, P and Cr factor, and As factor, respectively. Group A was highly polluted by persistent pollution, group B was the less polluted group, and subgroup B1 was less affected by PC3 and PC4 than subgroup B2. Group C, considered as the moderately polluted group, was greatly affected by N factor or persistent pollution, while subgroup C2 received more N pollution than subgroup C1.     

釜溪河自贡城区段季节性低氧成因研究

釜溪河为沱江一级支流,在自贡城区段设有国考碳研所断面。收集碳研所断面近10年来水质自动站数据,分析溶解氧(DO)变化特征,采样调查釜溪河自贡城区段水质及河道底泥污染状况,采用相关性分析、数值模拟等,研究分析釜溪河自贡城区段溶解氧分布特征及碳研所断面季节性低氧成因。研究结果表明,碳研所断面的溶解氧质量浓度变化特征呈现春末夏初最低,白天高晚上低的特征。釜溪河碳研所断面河水耗氧类污染物质量浓度较沱江流域内其他断面高,耗氧强度较大,溶解氧质量浓度较沱江流域其他断面偏低;其次,研究河段中釜溪河污水厂以下河段受污水厂低氧水排入和金子凼堰底层低氧水下泄影响,其溶解氧水平整体较污水厂以上河段低;最后,河段底泥有机质含量较高,春夏季气温升高将导致微生物分解活性增强大量消耗溶解氧,同时,闸坝和外来水体排入的水文扰动造成污水厂以下河段水温梯度弱,表层溶解氧易受底层低氧水影响,促使断面形成季节性低氧现象。溶解氧预测模型结果也进一步证实了温度变化和垂向温度梯度弱是碳研所断面溶解氧质量浓度季节性偏低的主要因素。     

Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region,Tunisia

In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group “A,” water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group “B,” the water level contains much less high-frequency variation. The wells of group “C” represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.     

南京市地表水溶解氧分布特征及低氧成因初探——以外秦淮河七桥瓮为例

于2018—2021年对南京市及国考断面七桥瓮进行水质调查,分析其溶解氧变化特征,采用水质水量联合评价及皮尔逊相关分析法,并结合水文气象等相关信息,对南京市地表水溶解氧分布特征及国考七桥瓮断面低氧成因进行研究分析。结果表明,南京市地表水溶解氧浓度夏季最低,中心主城区及附近区域溶解氧浓度均相对较低。七桥瓮断面溶解氧浓度在2.25~11.07 mg/L,其中5—9月溶解氧易出现超标波动。溶解氧浓度昼间高于夜间,与pH值呈正相关关系,与水温、高锰酸盐指数、氨氮、总磷均呈负相关关系。水温和上游来水带入的耗氧污染物是七桥瓮断面溶解氧偏低的主要成因,其中,溶解氧浓度与水温相关性最为显著。研究结论可为七桥瓮断面稳定达标提供基础支撑,为秦淮河流域精准治污提供技术依据,为南京市水环境多源同治提供治理思路。     

The impact of traditional coffee processing on river water quality in Ethiopia and the urgency of adopting sound environmental practices

Although waste from coffee processing is a valuable resource to make biogas, compost, and nutrient-rich animal food, it is usually dumped into nearby water courses. We carried out water quality assessment at 44 sampling sites along 18 rivers that receive untreated waste from 23 coffee pulping and processing plants in Jimma Zone, Ethiopia. Twenty upstream sampling sites free from coffee waste impact served as control, and 24 downstream sampling sites affected by coffee waste were selected for comparison. Physicochemical and biological results revealed a significant river water quality deterioration as a result of disposing untreated coffee waste into running water courses. During coffee-processing (wet) season, the highest organic load (1,900?mg/l), measured as biochemical oxygen demand, depleted dissolved oxygen (DO) to a level less than 0.01?mg/l, and thus curtailed nitrification. During off season, oxygen started to recuperate and augmented nitrification. The shift from significantly elevated organic load and reduced DO in the wet season to increased nitrate in the off season was found to be the determining factor for the difference in macroinvertebrate community structure as verified by ordination analysis. Macroinvertebrate diversity was significantly reduced in impacted sites during the wet season contrary to the off season. However, there was a significant difference in the ratio of sensitive to pollution-tolerant taxa in the off season, which remained depreciated in the longer term. This study highlights the urgency of research exploring on the feasibility of adopting appropriate pollution abatement technologies to implement ecologically sound coffee-processing systems in coffee-growing regions of Ethiopia.     

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。     

Near-field loading dynamics of total phosphorus and short-term water quality variations at a rainbow trout cage farm in Lake Huron

Aquatic total phosphorus (Tot-P) is measured at fish-cages in Lake Huron for environmental regulatory compliance. An improved understanding of how Tot-P is manifested in the near-field (相似文献   

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.     

Analysis of hypoxia in the western interior parts of the Ariake Sea, Japan, using a box model

To clarify the mechanism of hypoxia in the western interior parts of the Ariake Sea (WIAS), field observation data collected in the period of 1972-2004 were analyzed using a two-layer box model. Monthly averages of advection velocity, vertical diffusion coefficient (K(z)), and biochemical oxygen consumption rate (R) in WIAS were evaluated quantitatively during the above period. The estimated advection velocity comparatively corresponded to the observed residual flow pattern of bay head in summer and winter. The estimated K(z) was relatively high (0.6-5.3 cm(2) s(?-1)) from September to March but lower (0.2-0.4 cm(2) s(?-1)) from April to August. The estimated R ranged from 0.30 to 0.46 mg L(?-1) day(?-1) during May to August. In summer, the temporal variation of dissolved oxygen (DO) concentration in the lower layer was controlled largely by K ( z ) and R. Monthly variations of K(z), R, and degree of density stratification (P) in the 1970s, the 1980s, and the 1990s-early 2000s were analyzed. P, K ( z ), and R were not significantly different among the calculated periods (p = 0.93, 0.23, and 0.49). However, the variations of R in summer between the 1970s and the other calculated periods changed. DO consumption period was longer in the 1980s and the 1990s-early 2000s than in the 1970s. R in the 1980s was highest among the calculated periods. The increase in R in the 1980s was caused by the increase in organic matter load originating from red tide phytoplankton due to a decrease in the suspension feeders.     

Effects of land use types on surface water quality across an anthropogenic disturbance gradient in the upper reach of the Hun River, Northeast China

Surface water quality is vulnerable to pollution due to human activities. The upper reach of the Hun River is an important water source that supplies 52 % of the storage capacity of the Dahuofang Reservoir, the largest reservoir for drinking water in Northeast China, which is suffering from various human-induced changes in land use, including deforestation, reclamation/farming, urbanization and mine exploitation. To investigate the impacts of land use types on surface water quality across an anthropogenic disturbance gradient at a local scale, 11 physicochemical parameters (pH, dissolved oxygen [DO], turbidity, oxygen redox potential, conductivity, biochemical oxygen demand [BOD₅], chemical oxygen demand [COD], total nitrogen [TN], total phosphorus [TP], NO ₃ ^? -N, and NH ₄ ⁺ -N) of water from 12 sampling sites along the upper reach of the Hun River were monitored monthly during 2009–2010. The sampling sites were classified into four groups (natural, near-natural, more disturbed, and seriously disturbed). The water quality exhibited distinct spatial and temporal characteristics; conductivity, TN, and NO ₃ ^? -N were identified as key parameters indicating the water quality variance. The forest and farmland cover types played significant roles in determining the surface water quality during the low-flow, high-flow, and mean-flow periods based on the results of a stepwise linear regression. These results may provide incentive for the local government to consider sustainable land use practices for water conservation.     

1992—2012年浙江省酸雨变化特征及成因分析

为了解20世纪90年代以来,浙江地区酸雨污染的演变特征及成因,以杭州地区为代表,结合全省酸雨监测数据,揭示1992—2012年浙江省酸雨变化特征及成因。结果表明：1992—2012年,浙江地区酸雨的污染大致经历了3个阶段：1992—1999年为酸雨改善期;2000—2004年酸雨污染再次出现了恶化;2005—2012年为酸雨再次改善期。SO₂排放减少是近年来浙江地区酸雨污染出现好转的重要原因,但NO_x对酸雨的贡献有增加的趋势,酸雨污染类型逐渐由"硫酸型"为主转变为"硫酸硝酸混合型",机动车排放对浙江地区酸雨污染的贡献已不容忽视。全省强酸雨区主要分布在浙北、浙中和东部沿海等经济发达地区,经济相对落后的浙西南地区酸雨污染较轻,酸雨污染与地区经济发展密切相关,控制本地污染排放对于防治和减轻当地酸雨污染具有重要意义。     

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.     

Application of Water Quality Indices and Dissolved Oxygen as Indicators for River Water Classification and Urban Impact Assessment

The usefulness of water quality indices, as the indicators of water pollution, for assessment of spatial-temporal changes and classification of river water qualities was verified. Four water quality indices were investigated: WQI (considering 18 water quality parameters), WQI(min) and WQI(m) (considering five water quality parameters: temperature, pH, DO, EC and TSS) and WQI(DO) (considering a single parameter, DO). The water quality indices WQI(min), WQI(m) and WQI(DO) could be of particular interest for the developing countries because of the minimum analytical cost involved. As a case study, water quality indices were used to evaluate spatial and temporal changes of the water quality in the Bagmati river basin (Nepal) for the study period 1999-2003. The results allowed us to determine the serious negative effects of the city urban activity on the river water quality. In the studied section of the river, the water quality index (WQI) was 71 units (classified as good) at the entry station and 47.6 units (classified as bad) at the outlet station. For the studied period, a significant decrease in water quality (mean WQI decrease = 11.6%, p = 0.042) was observed in the rural areas. A comparative analysis revealed that the urban water quality was significantly bad as compared with rural. The analysis enabled to classify the water quality stations into three groups: good water quality, medium water quality and bad water quality. WQI(min) resulted in overestimation of the water quality but with similar trend as with WQI and is useful for the periodic routine monitoring program. The correlation of WQI with WQI(min) and DO resulted two new indices WQI(m) and WQI(DO), respectively. The classification of waters based on WQI(m) and WQI(DO) coincided in 90 and 93% of the samples, respectively.     

Chemical compositions and sources of atmospheric PM10 in heating, non-heating and sand periods at a coal-based city in northeastern China

Mass concentrations and chemical components (18 elements, 9 ions, organic carbon [OC] and elemental carbon [EC]) in atmospheric PM(10) were measured at five sites in Fushun during heating, non-heating and sand periods in 2006-2007. PM(10) mass concentrations varied from 62.0 to 226.3 μg m(-3), with 21% of the total samples' mass concentrations exceeding the Chinese national secondary standard value of 150 μg m(-3), mainly concentrated in heating and sand periods. Crustal elements, trace elements, water-soluble ions, OC and EC represented 20-47%, 2-9%, 13-34%, 15-34% and 13-25% of the particulate matter mass concentrations, respectively. OC and crustal elements exhibited the highest mass percentages, at 27-34% and 30-47% during heating and sand period. Local agricultural residuals burning may contribute to EC and ion concentrations, as shown by ion temporal variation and OC and EC correlation analysis. Heavy metals (Cr, Ni, Zn, Cu and Mn) from coal combustion and industrial processes should be paid attention to in heating and sand periods. The anion/cation ratios exhibited their highest values for the background site with the influence of stationary sources on its upper wind direction during the sand period. Secondary organic carbon were 1.6-21.7, 1.5-23.0, 0.4-17.0, 0.2-33.0 and 0.2-21.1 μg m(-3), accounting for 20-77%, 44-88%, 4-77%, 8-69% and 4-73% of OC for the five sampling sites ZQ, DZ, XH, WH and SK, respectively. From the temporal and spatial variation analysis of major species, coal combustion, agricultural residual burning and industrial emission including dust re-suspended from raw material storage piles were important sources for atmospheric PM(10) in Fushun at heating, non-heating and sand periods, respectively. It was confirmed by principal component analysis that coal combustion, vehicle emission, industrial activities, soil dust, cement and construction dust and biomass burning were the main sources for PM(10) in this coal-based city.     

Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan

This study reports the spatio-temporal changes in water quality of Nullah Aik, tributary of the Chenab River, Pakistan. Stream water samples were collected at seven sampling sites on seasonal basis from September 2004 to April 2006 and were analyzed for 24 water quality parameters. Most significant parameters which contributed in spatio-temporal variations were assessed by statistical techniques such as Hierarchical Agglomerative Cluster Analysis (HACA), Factor Analysis/Principal Components Analysis (FA/PCA), and Discriminant Function Analysis (DFA). HACA identified three different classes of sites: Relatively Unimpaired, Impaired and Less Impaired Regions on the basis of similarity among different physicochemical characteristics and pollutant level between the sampling sites. DFA produced the best results for identification of main variables for temporal and spatial analysis and separated eight parameters (DO, hardness, sulphides, K, Fe, Pb, Cr and Zn) that accounted 89.7% of total variations of spatial analysis. Temporal analysis using DFA separated six parameters (E.C., TDS, salinity, hardness, chlorides and Pb) that showed more than 84.6% of total temporal variation. FA/PCA identified six significant factors (sources) which were responsible for major variations in water quality dataset of Nullah Aik. The results signify that parameters identified by statistical analyses were responsible for water quality change and suggest the possibility of industrial, municipal and agricultural runoff, parent rock material contamination. The results suggest dire need for proper management measures to restore the water quality of this tributary for a healthy and promising aquatic ecosystem and also highlights its importance for objective ecological policy and decision making process.     

Seasonal Variation of Toxic Benzene Emissions in Petroleum Refinery

Petroleum refineries are largest chemical industries that are responsible for the emission of several pollutants into the atmosphere. Benzene is among the most important air pollutants that are emitted by petroleum refineries, since they are involved in almost every refinery process. Volatile organic compounds (VOCs) are a major group of air pollutants, which play a critical role in atmospheric chemistry. These contribute to toxic oxidants, which are harmful to ecosystem, human health and atmosphere. The variability of pollutants is an important factor in determining human exposure to these chemicals. The ambient air concentrations of benzene were measured in several sites around the Digboi petroleum refinery, near the city of Gowahati in northeast India, during winter and summer 2004. The seasonal and spatial variations of the ambient air concentrations of this benzene were investigated and analyzed. An estimation of the contribution of the refinery to the measured atmospheric levels of benzene was also performed. The ambient air mixing ratios of benzene in a large area outside the refinery was generally low, in ppbv range, much lower than the ambient air quality standards. This article presents the temporal and spatial variation of air pollution in and around petroleum refinery and showed that no health risk due to benzene is present in the areas adjacent to the refinery.     

Water Quality Assessment of an Untreated Effluent Impacted Urban Stream: The Bharalu Tributary of the Brahmaputra River, India

Guwahati, the lone city on the bank of the entire midstream of the Brahmaputra River, is facing acute civic problem due to severe depletion of water quality of its natural water bodies. This work is an attempt towards water quality assessment of a relatively small tributary of the Brahmaputra called the Bharalu River flowing through the city that has been transformed today into a city drainage channel. By analyzing the key physical, chemical and biological parameters for samples drawn from different locations, an assessment of the dissolved load and pollution levels at different segments in the river was made. Locations where the contaminants exceeded the permissible limits during different seasons were identified by examining spatial and temporal variations. A GIS developed for the watershed with four layers of data was used for evaluating the influence of catchment land use characteristics. BOD, DO and total phosphorus were found to be the sensitive parameters that adversely affected the water quality of Bharalu. Relationship among different parameters revealed that the causes and sources of water quality degradation in the study area were due to catchments input, anthropogenic activities and poor waste management. Elevated levels of total phosphorus, BOD and depleted DO level in the downstream were used to develop an ANN model by taking total phosphorus and BOD as inputs and dissolved oxygen as output, which indicated that an ANN based predictive tool can be utilized for monitoring water quality in the future.     

春节期间烟花爆竹燃放对哈尔滨市区空气质量的影响

为研究烟花爆竹燃放对哈尔滨市区空气质量的影响,及时捕捉污染过程的变化趋势并分析污染的成因,2016年2月7—9日对哈尔滨市区内4个监测点位(省监测站、松北商大、动力和平路和省农科院)颗粒物浓度变化趋势进行解析,同时结合单颗粒气溶胶飞行质谱仪、3D可视激光雷达等相关仪器分析结果进一步确定污染成因。结果表明,连续监测期间4个监测点位均出现重度污染时段,且与除夕夜烟花爆竹集中燃放时间高度吻合,说明烟花爆竹的集中燃放会在短时间内造成严重的大气污染;同时烟花爆竹的燃放会对颗粒物化学组分造成明显的影响,受影响最大的是Mg~(2+)和Cl~-,且在燃放期有机碳、元素碳、混合碳占比升高。