Water Quality Modeling of Municipal Discharges from Sea Outfalls,Mumbai

The island city of Mumbai with a population of about 10 million generates about 2000 million liters per day (mld) of sewage from the seven service areas of the city sewerage network and discharges it into the adjoining west coast and the two creeks in the Arabian Sea. This has resulted in degradation of coastal water quality, contamination of the adjoining beaches and seafronts. The Municipal Corporation of Greater Mumbai has therefore, undertaken the task of delineating appropriate sewage disposal system to achieve cleaner marine ecosystem through marine outfalls at specific locations. This paper presents the results of the mathematical simulations on the impacts of discharge is-a-vis the length of the outfall and level of land treatment apriori. The results of the simulation indicate the level of bacterial pollution to be higher near the diffuser locations as compared to nearshore regions. 48 hsimulation result analysis shows that FC counts nearthe diffuser location will be in the range of 2000–8000 counts per 100 ml.     

Water quality assessment, by statistical analysis, on rural and urban areas of Chocancharava River (Río Cuarto), Córdoba, Argentina

Water quality has degraded dramatically in the Chocancharava River (Río Cuarto, Córdoba, Argentina) due to point and non-point sources. This paper aims to assess spatial and temporal variations of physical and chemical parameters of the river. Six sampling sites and six sampling campaigns were developed. During the period 2007–2008, wet and dry seasons were included. A statistical analysis was carried out with 23 physical and chemical variables. Then, a new statistical analysis was carried out including the Riparian Corridors Quality Index and the physical and chemical variables (24 variables). Considering a multivariate system, analysis of variance, principal component analysis and cluster analysis were used. From the statistical analysis, the river was divided into two zones with different degrees of contamination. The most polluted zone is due to pollution inputs of urban, industrial and agricultural sources. This area showed a remarkable deterioration in water quality, mainly due to wastewater discharges. According to Riparian Quality, better results were found in sections of poor water quality, due to the fact that the river bank forest was less degraded downstream of the sewage discharge.     

Hydrodynamic assessment of sewage impact on water quality of Malad Creek, Mumbai, India

The rapid population growth and uncontrolled development in the coastal zone have led to major pollution impacts on creeks, estuarine, and coastal environment. Water quality models are valuable tools to understand the environmental processes for prediction of pollution impacts and evaluate future trends for management. Presently, the Malad creek in west coast of Mumbai receives wastewater and sewage from open drains and partially treated sewage from Malad and Versova treatment plants. The objective of the paper is to assess the environmental quality and estimate the extent of improvement in different parts of the creek by enhancing the collection efficiency and adequate treatment of sewage as well as disposal through ocean outfall. A hydrodynamic and water quality simulation has been carried out for the present condition in the creek and calibrated and validated with two different season data for better representation of the system. Calibrated model has been used to generate future scenarios based on various options. Among scenarios, option of treated effluent diverted to propose outfall and improvement in collection of unorganized flow through sewerage up to 40% and 60% are found most significant for biochemical oxygen demand reduction and increase in dissolved oxygen. Fecal coliform reduction is also found drastically but still very high against standard. To improve the environmental quality of the creek, still upper stretch requires more dilution and flushing due to narrow width and contribution of heavy pollution from open drains.     

Assessment of groundwater quality in Puri City, India: an impact of anthropogenic activities

Puri City is situated on the east coast of India and receives water supply only from the groundwater sources demarcated as water fields. The objective of this paper is to assess and evaluate the groundwater quality due to impact of anthropogenic activities in the city. Groundwater samples were collected from the water fields, hand pumps, open wells, and open water bodies during post-monsoon 2006 and summer 2007. Groundwater quality was evaluated with drinking water standards as prescribed by Bureau of Indian Standards and Environmental Protection Agency to assess the suitability. The study indicated seasonal variation of water-quality parameters within the water fields and city area. Groundwater in the water fields was found to be suitable for drinking after disinfection. While in city area, groundwater quality was impacted by onsite sanitary conditions. The study revealed that groundwater quality was deteriorated due to the discharge of effluent from septic tanks, soak pits, pit latrines, discharges of domestic wastewater in leaky drains, and leachate from solid waste dumpsite. Based on observed groundwater quality, various mitigation measures were suggested to protect the water fields and further groundwater contamination in the city.     

Domestic Water Contamination in Rapidly Growing Megacities of Asia: Case of Karachi, Pakistan

The development of essential services including water and sanitation in many megacities of the economically developing countries of Asia cannot keep pace with their rapidly growing population and accompanying urban and industrial development. The inadequate water supply and poor sanitation services lead to contamination of their water supply. It also leads to the input of sewage water into the groundwater. The problem is seriously acute in Karachi, the largest city in Pakistan with a population of over 12 million and growing at 6 percent. This paper examines the problem of water contamination in Karachi. The paper presents the data on water quality from various sources, mainly municipal water supply, vendors and well water; the three major sources of water for domestic use in Karachi. Except municipal water from some areas and during certain periods, water from most other sources contain coliform bacteria, and in many cases faecal coliform, in amounts several magnitudes higher than any standards permit. Many samples have also been found to contain heavy metals including Chromium, Lead, Nickel and Arsenic in amounts excessive of permitted standards. The probable sources of contaminants for the various types of water (piped, vendors, wells) indicate that groundwater may be the main contributor. The very source of this groundwater is predominantly from sewage. The health hazards from consuming such contaminated water are obvious. The paper also evaluates the solutions that are being practiced, proposed or may be feasible, as well as those that are evolving.     

Evaluation of a fast wastewater odour characterisation procedure using a chemical sensor array

Sewage treatment works are one of the major sources that cause atmospheric odour pollution. The increase in the number of complaints about odour nuisance is due to the increase in environmental concerns. Unfortunately, the legislation on odour nuisance from sewage treatment works is very limited. In order to determine suitable thresholds on which to base legal standards, reliable and efficient odour measurement methods need to be defined. A chemical sensor array was developed for the purpose of measuring wastewater odour. This paper describes the development of the chemical sensor system which is specifically tuned to odours of wastewater origin and which can give an electronic measure of the wastewater odours. Odour emissions from a wastewater treatment facility were detected by using a quartz crystal microbalance (QCM) sensor array. The array consists of nine sensor elements, which were coated with different materials. In this paper, the usage of these novel instruments in the water industry was shown.     

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.     

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.     

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.     

Stable isotopes and C/N ratios in marine sediments as a tool for discriminating anthropogenic impact

Sources of anthropogenic pollution were categorised by combining elemental and isotopic techniques (C/N ratios and δ(13)C, δ(15)N) on samples of surficial sediments in the harbour of Naples (Southern Tyrrhenian Sea, Italy). The study area, due to the intense and diversified industrial and commercial activities, is an appropriate natural laboratory to verify reliability and relevance of geochemical methodologies applied to exploration of sources, pathways and fate of contaminants in highly polluted coastal marine systems. Application of cluster analysis to the whole dataset and resolution of a δ(13)C and δ(15)N Moore-Penrose system equation provided information to reliably discriminate and identify anthropogenic point sources in the studied sediments. In particular, effects of oil spilling and wastewater discharge within the area of the harbour of Naples were clearly discriminated thus suggesting high-potential of the two numerical techniques, applied to the C and N elemental and isotopic values, to explore effects of anthropogenic impact in marginal and confined coastal basins. The δ(15)N values showed high sensitivity to discriminate sewage discharges (treated or untreated organic matter), clearly indicating the point of emissions. The δ(13)C shows indirect capability of discriminating crude oil and petroleum products.     

洋河下游农灌区农作物受灾与水质污染的关系

根据情况调查设计严密而合乎逻辑的监测方案：在农灌水质标准缺乏的情况下，根据监测数据和有关资料找出致害污染物；对本次监测数据和历年常规监测数据进行分析，确定污染源；最后提出防治污染的对策。结论是致死污染物为阿特拉津和乙草胺．而高锰酸盐指数、ｐＨ和悬浮物的超标是致害的重要有关因素；致害污染物主要来源了宣化区废水；严格监控农药厂废水、兴建污水处理厂和改善农灌措施是防治污染的途径。后来的事实完全证实了本结论和对策的正确性。     

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%。     

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.     

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。     

Impact of sewage contaminated water on soil, vegetables, and underground water of peri-urban Peshawar, Pakistan

The use of sewage-contaminated municipal water for irrigation of crops is an old practice in many big cities of Pakistan. Since the wastewater is rich in nutrients, it increases crops yield substantially but at the cost of food quality. The objective of this study was to investigate sewage water irrigation as a source of accumulation of heavy metals in soil and its subsequent transfer to crops and underground water. Sewage water, soil, groundwater, and crop samples were collected from selected areas around Peshawar city and analyzed for heavy metals concentration by atomic absorption spectroscopic method. Analysis of data revealed a considerable impact of the irrigation practices in the peri-urban Peshawar. Statistical analysis of the data showed a positive correlation between heavy metals concentration and soil carbon contents on the one hand and cation exchange capacity on the other. A strongly negative correlation was observed between metal contents and soil pH. The vertical movement of heavy metals from contaminated soil has polluted crops and underground water. The results indicated higher concentration of toxic metals in soil accumulated due to long-term sewage-contaminated water irrigation and their subsequent transfer to our food chain. The practice, if continued un-noticed may pose a threat of phytotoxicity to the local population.     

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.     

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。     

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.     

MONITORING OF SPATIAL AND TEMPORAL HYDROCHEMICAL CHANGES IN GROUNDWATER UNDER THE CONTAMINATING EFFECTS OF ANTHROPOGENIC ACTIVITIES IN MERSIN REGION, TURKEY

The development of groundwater resources for water supply is a favored way in Turkey. The Berdan alluvial aquifer in Mersin is particularly productive, but little is known about the natural phenomena that govern the groundwater quality and the contamination sources in this region. During 2001 and 2002, water samples for chemical analysis were obtained from 27 wells and from two points of Berdan River and analyzed by ICP. Main chemical characteristics of sampled groundwater define two aquifers, which were also determined by hydrogeological investigations. The groundwater produced from some of the wells was affected by anthropogenic activities temporally and spatially by seawater intrusion. Berdan River is polluted with the wastewater discharges and river water also influences the groundwater quality.     

Assessment of water pollution in the Antuã River basin (Northwestern Portugal)

The Antuã River, located in northwestern Portugal, drains a region with a high population density and a strong economic dynamism. These factors, together with a lack of facilities for appropriate treatment of domestic and industrial sewage, are putting increasing pressure on water resources. In this context, the aim of the present study was to identify point sources of pollution and to assess the surface water quality in the Antuã basin by monitoring physicochemical variables. A total of 40 point sources of wastewater, including some with a high pollution load, were detected in the most populated and industrialized areas of the São João da Madeira and Oliveira de Azeméis municipalities. These sources explained the strong degradation of water quality observed in the upper and medium Antuã River and in one of its tributaries, where maxima of 49 mg l^?1 for biochemical oxygen demand, 29 mg l^?1 for Kjeldahl nitrogen and 3.7 mg l^?1 for total phosphorus, were found after five surface water monitoring campaigns. Despite the relevance of pollution problems, a considerable water quality improvement, promoted by favourable reaeration conditions, was observed in the final stretch of the river, giving evidence of a great self-depuration capacity. However, the Antuã is a significant contributor of nutrients to the Ria de Aveiro, the coastal lagoon where the river meets the Atlantic Ocean.