Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

This study aims at the classification and water quality assessment of Harike wetland (Ramsar site) in India using satellite images from the Indian Remote Sensing satellite, Resourcesat (IRS P6). The Harike wetland is a converging zone of two rivers, Beas and Sutlej. The satellite images of IRS Linear Imaging Self Scanner (LISS) IV multispectral sensor with three bands (green, red, and near infrared (NIR)) and a spatial resolution of 5.8 m were classified using supervised image classification techniques. Field points for image classification and water sampling were recorded using a Garmin eTrex Global Positioning System. The water quality parameters assessed were dissolved oxygen, conductivity, pH, turbidity, total and suspended solids (SS), chemical oxygen demand, and Secchi disk transparency (SDT). Correlations were established between turbidity and SS, SS and SDT, and total solids and turbidity. Using reflectance values from the green, red, and NIR bands, we then plotted the water quality parameters with the mean digital number values from the satellite imagery. The NIR band correlated significantly with the water quality parameters, whereas, using SDT values, it was observed that the green and the red reflectance bands were able to distinguish the waters from the two rivers, which have different water qualities.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

Assessment of water quality of Rawal Lake by long-time monitoring

Water quality of rivers, natural lakes, and reservoirs in developing countries is being degraded because of the contaminated inflows. There is a serious need for appropriate water quality monitoring for future planning and management of clean water resources. Quality of water in Rawal Lake Pakistan has been investigated in this paper. Flows from the upstream of Rawal Lake and its surrounding villages are highly polluted. Lake water quality parameters like pH, turbidity, alkalinity, calcium, nitrite, sulfate, biological oxygen dissolved, dissolved oxygen, chloride, total dissolved solids (TDS), and coliforms were investigated. Samples of water from different locations of Korang River were collected and tested. Most of the data was collected by field sampling and field visits. However, long-term information was taken from different departments. Statistical parameters (standard deviation, maximum, minimum, mean, mode, kurtosis, skew, and Euclidean distance) of variables were determined. A distinct parameter based on the difference of the maximum value the variable and maximum allowable value of that variable defined by World Health Organization was used for analysis. Grouping and clustering of elements was made on the basis of this parameter. Trend of increasing or decreasing of values of variables over a long time was also taken into account for grouping the variables. It was concluded that the concentration of seven contaminants was higher as compared to the permissible limits under environmental standards. These variables need immediate attention. The environmentally bad conditions of Rawal Lake can only be rectified by appropriate lake environmental supervision, watershed management, and implementation of environmental legislation.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

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.     

Evaluating the effectiveness of routine water quality monitoring in Miyun reservoir based on geostatistical analysis

This study used geographic information system techniques and geostatistics methods to evaluate the effectiveness of routine water quality monitoring in the western segment of the Miyun reservoir in Beijing. Methodologies as well as the sampling design are evaluated. The single-layer evaluation and three integrated evaluation methods including principal component analysis (PCA), ordinary kriging (OK)_Mean, and Mean_Layers were used to validate the effectiveness of evaluation methods, and the effectiveness of each sampling design was validated by comparing their errors. Results indicated that, while a single-layer evaluation only shows the trophic state of water at a specific level, an integrated evaluation synthetically analyzes and evaluates the trophic state of the entire water body. Furthermore, results of the integrated analysis show that a PCA method is more accurate and can represent the trophic state of the entire water body. The OK_Mean and Mean_Layers methods are only able to represent the mean level for trophic state of the entire water body but cannot reflect local trophic state and distribution details. Although methods used in the routine monitoring of Miyun reservoir have some similarities to the OK_Mean and Mean_Layers methods, their range of errors and uncertainty are greater because of a lack of detailed spatial continuous information. The analysis on the number of sampling points shows that, within a certain range of error, minor changes of sampling points will have no obvious impact on the monitoring results. For the routine monitoring of western Miyun reservoir, using only three to five sampling points for monitoring is inadequate. According to our analysis, it is more appropriate to use at least ten sampling points for monitoring these areas.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

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.     

Seasonal monitoring of heavy metals and physicochemical characteristics in a lentic ecosystem of subtropical industrial region, India

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of lentic ecosystem (lakes and reservoirs) pollution due to industrial effluent discharge. In this study, nine metals and 15 physicochemical parameters, collected from four sampling sites in a tropical lake receiving the discharge from thermal power plant, coal mine, and chloralkali industry, during the years from 2004 to 2005, were analyzed. For greater efficacy in monitoring of heavy metals, particle-induced X-ray emission has been used during present investigation. Different statistical techniques like analysis of variance, Pearson correlation, principal component analysis, and factor analysis were employed to evaluate the seasonal correlations of physicochemical parameters. Most of the metals and physicochemical parameters monitored in the present study exhibited high spatial and temporal variability. Pertaining to metal pollution, the most polluted site was Belwadah, i.e., waters and sediments had the highest concentration of all the relevant metals. The reference site was characterized by the presence of low concentrations of metals in waters and in sediments. Based on the high metal concentration recorded in lake ambient, drinking, bathing, and irrigation water should not be used by the local people at the effluent discharge points.     

Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of Kahramanmaraş, Turkey

This study assessed land cover (LC) changes in Kahramanmara? (K.Mara?) and its environs by using multitemporal Landsat and ASTER imagery, respectively belong to 1989, 2000 and 2004. A priori defined nine land cover classes in the classification scheme were urban and built-up, forest, sparsely vegetated areas, grassland, vegetated stream beds, unvegetated stream beds, bare areas, crop fields, and water bodies. Individual classifications were employed using the combination of both unsupervised and supervised classification methods. Iterative Self Organizing Data Analysis (ISODATA) was used to reduce spectral variation in the scenes arising from complex pattern of crop fields. Maximum Likelihood classifier was used in the LC classification of the individual images. Image pairs of consecutive dates were compared by overlaying the thematic LC maps and cross-tabulating the LC statistics. Urbanization and expansion of agriculture were the major reasons for the dramatic LC conversions. The amount of conversion from crop fields to water occurred as large as 927.67 ha, accounting for 73% of the total land-to-water conversion. Conversions to agriculture have mainly been occurred from grasslands and sparsely vegetated areas as large as 1,314.95 and 1,325.84 ha, respectively. Urban coverage doubled in this period as a result of 1,443.45 ha of increase. Urban area increased in the second period from 2,920 to 3,526 ha. Conversions to agriculture occurred at high amounts. A total of 1,075.79 ha area changed from sparsely vegetated areas to crop fields. A landscape-level environmental monitoring scheme based on satellite remote sensing was proposed for effective environmental resource management.     

A multi-variate methodology for analyzing pre-existing lake water quality data

Environmental agencies are given the task of monitoring water quality in rivers, lakes, and other bodies of water, for the purpose of comparing the results with regulatory standards. Monitoring follows requirements set by regulations, and data are collected in a systematic way for the intended purpose. Monitoring enables agencies to determine whether water bodies are polluted. Much effort is spent per monitoring event, resulting in hundreds of data points typically used solely for comparison with regulatory standards and then stored for little further use. This paper devises a data analysis methodology that can make use of the pre-existing datasets to extract more useful information on water quality trends, without new sample collection and analysis. In this paper, measured lake water quality data are subjected to statistical analyses including Principal Component Analysis (PCA) to deduce changes in water quality spatially and temporally over several years. It was found that the lake as a whole changed temporally by season, rather than spatially. Storm events caused the greatest shifts in water quality, though the shifts were fairly consistent across sampling stations. This methodology can be applied to similar datasets, especially with the recent emphasis by the U.S. EPA on protection of lakes as water sources. Water quality managers using these techniques may be able to lower their monitoring costs by eliminating redundant water quality parameters found in this analysis.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Quantifying the connections—linkages between land-use and water in the Kathmandu Valley,Nepal

Land development without thoughtful water supply planning can lead to unsustainability. In practice, management of our lands and waters is often unintegrated. We present new land-use, ecological stream health, water quality, and streamflow data from nine perennial watersheds in the Kathmandu Valley, Nepal, in the 2016 monsoon (i.e., August and September) and 2017 pre-monsoon (i.e., April and May) periods. Our goal was to improve understanding of the longitudinal linkages between land-use and water. At a total of 38 locations, the Rapid Stream Assessment (RSA) protocol was used to characterize stream ecology, basic water quality parameters were collected with a handheld WTW multi-parameter meter, and stream flow was measured with a SonTek FlowTracker Acoustic Doppler Velocimeter. A pixel-based supervised classification method was used to create a 30-m gridded land use coverage from a Landsat 8 image scene captured in the fall of 2015. Our results indicated that land-use had a statistically significant impact on water quality, with built land-uses (high and low) having the greatest influence. Upstream locations of six of the nine watersheds investigated had near natural status (i.e., river quality class (RQC) 1) and water could be used for all purposes (after standard treatments as required). However, downstream RSA measurements for all nine watersheds had RQC 5 (i.e., most highly impaired). Generally, water quality deteriorated from monsoon 2016 to pre-monsoon 2017. Our findings reinforce the importance of integrated land and water management and highlight the urgency of addressing waste management issues in the Kathmandu Valley.     

Using Satellite and Other Data to Estimate the Annual Water Demand of an Irrigation District

Locating and forecasting water needs can assist the location of water in dry regions, and improve the management of reservoirs and the canal network. Satellite, ground data, and agrometeorological data were combined to forecast the volume of irrigation water needed during 1993 and 1994 in an irrigation district of 327 km2 located in the Ebro basin, Spain. The main crops were rice, alfalfa plus forage, winter cereals (barley and wheat), sunflower and maize. Their extent was estimated every year by frame area sampling and a regression estimator with satellite data. Initial crop area statistics were obtained by expansion of the sample areas to the entire study area and then a regression estimator with the multitemporal supervised classification of two Landsat-5 TM images was applied. This procedure improved the precision of the estimates by expansion. Net water requiremets (m3 ha-1) of the above mentioned crops were computed from reference evapotranspiration estimates, crop coefficients and effective precipitation. These computations were performed for an average year, i.e. by using long-term averaged meteorological data. Crop hectarage and net crop water requirements were multiplied to obtain, for the entire study area, the volume (hm3 106 m3) of the net crop water requirements. After subtraction of water taken directly from the rivers and non-productive sunflower, the irrigation water volumes were estimated. The comparison of these forecasts with the volumes of water invoiced by the Ebro Basin Water Authority confirmed the feasibility of forecasting the volume of water applied to an individual irrigation district. This is an objective and practical method for estimating the irrigation water volume applied in an irrigated area.     

Monitoring Studies of Augusta Bay Marine Waters

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a tunction of bme or as spatial function.In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of particular pollution indices. With the increase in the number of sampling points, the amount of data increases and examining the results and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Principal Component Analysis (PCA).The use of the PCA in this work has been applied to the analysis of twenty three different sampling points in three seasonal sampling cruises in the same year. This led to recognition of the influence and the localisation of wastewaters in the Augusta bay after measuring the water pollution parameters.The PCA made evident the difference between some sampling sites whose data were initially thought to be similar where the presence of hot industrial water discharge or urban wastewater determines the permanent water quality.Furthermore, it has allowed a choice of more significant parameters for monitoring programs and more representative sampling site locations.     

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.     

Cloud water and throughfall deposition of mercury and trace elements in a high elevation spruce-fir forest at Mt. Mansfield,Vermont

As part of the Lake Champlain Basin watershed study of mercury (Hg) and pollutant deposition, cloud water and cloud throughfall collections were conducted at the south summit (1204 m) of Mt. Mansfield, Vermont between August 1 and October 31, 1998, for multi-element chemical analysis. A passive Teflon string collector was deployed during non-precipitating events to sample cloud/fog water at timberline, while three sets of paired funnels collected cloud throughfall under the red spruce-balsam fir canopy. Samples were analyzed for concentrations of Hg, major ions, and 10 trace elements. Ultra-clean sampling and analysis techniques were utilized throughout the study. Six events were sampled for cloud water alone and four events were sampled for both cloud water and cloud throughfall. Cloud throughfall chemistry showed substantial modification from incident cloud water. Much higher concentrations of Hg (2.3 x), base cations (Ca2+, K-, Mg2+; 3-18 x) and certain trace elements (Ni, Cu, Mn, Rb, Sr; 2-34 x) were observed in throughfall than in cloud water. These results confirm that cloud water can leach a wide variety of elements from tree foliage and wash off dry deposited elements. Cloud water deposited an average of 0.42 +/- 0.12 mm of water per hour. Estimated cloud water deposition of Hg was 7.4 microg m(-2) for the period August 1-October 31, approximately twice that deposited by rain during this period at a nearby low elevation Hg monitoring site. Our results indicate that cloud water and Hg deposition at Mt. Mansfield are likely to have considerable ecological effects.     

Monitoring water quality in reservoirs for human supply through multi-biomarker evaluation in tropical fish

Paraíba do Sul River is located at a very densely inhabited region of Brazil crossing the three most industrialized states of the country (S?o Paulo, Minas Gerais and Rio de Janeiro states). As a result, industrial and farming residues as well as urban sewage are frequently disposed without appropriate treatment. The current study aimed at investigating the water quality in three reservoirs along the Paraíba do Sul River (Ilha dos Pombos, Santa Cecília and Santa Branca), through physiological, morphological, biochemical, and genetic biomarkers. The bioindicator chosen was the catfish Pimelodus maculatus, sampled during the dry (June 2008) and rainy (February 2009) seasons. Also, some water physicochemical parameters were analyzed from the sampling sites, but displayed no alterations according to the Brazilian Agency for Water Quality Legislation. Branchial carbonic anhydrase activity was inhibited in the dry season, while renal carbonic anhydrase activity was inhibited in the rainy season in the Santa Branca reservoir, indicating disturbance of osmoregulatory and acid-base regulation processes. Histopathological alterations were observed in the gills (neoplasic and tissue hyperplasia processes) and liver (necrosis), indicating serious damage to the functional integrity of these organs. A high incidence of melanomacrophage centers was observed in the liver, suggesting an intense immune response in all reservoirs. Acetylcholinesterase and catalase activity showed also differences corroborating some morphological results. Likewise, the induction of the micronucleus and DNA damage indicate genotoxicity, but mainly in the Santa Branca reservoir. Thus, the health status of P. maculatus warrants caution in the use of the water from the 3 reservoirs for direct human consumption, particularly after the accidental spill of endosulfan in November 2008, three months before the rainy season sampling.     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Environmental classification addresses issues involving the representation and analysis of continuous and variable ecological data. This study creates a methodology to define topo-climatic landscapes (TCL) in the north-west of Catalonia, which is situated in the north-east of the Iberian Peninsula. TCL provide data regarding the ecological behavior of a landscape in terms of its topography, physiognomy, and climate, which are the main drivers of an ecosystem. The variables selected are derived from a variety of different sources, such as remote sensing and climatic atlases. The methodology employed combines unsupervised iterative cluster classification with supervised fuzzy classification. Twenty eight TCL, which can be differentiated in terms of their vegetation physiognomy and vegetation altitudinal range type, were selected for the study area. Furthermore, a hierarchy among the TCL is established which permits the merging of clusters and allows for changes in thematic resolution. By using the topo-climatic landscape map, managers can identify patches with similar environmental conditions and at the same time assess the uncertainty involved in classification.