Spatial and temporal variations of nitrogen pollution in Wen-Rui Tang River watershed, Zhejiang, China

Water quality has degraded dramatically in Wen-Rui Tang River watershed, Zhejiang, China, especially due to rapid economic development since 1995. This paper aims to assess spatial and temporal variations of the main pollutants (NH??-N, TN, BOD(5), COD(Mn), DO) of water quality in Wen-Rui Tang River watershed, using the geographic information system, cluster analysis (CA) and principal component analysis (PCA). Results showed that concentrations of BOD(5), COD(Mn), NH??-N, and TN were significantly higher in tertiary rivers than in primary and secondary rivers. From April 2006 to March 2007, the concentrations of NH? ?-N (2.25-57.9 mg/L) and TN (3.78-70.4 mg/L) in all samples exceeded Type V national water quality standards (≥2 mg/L), while 5.3% of all COD(Mn) (1.83-27.5 mg/L) and 33.6% of all BOD(5) (0.34-50.4 mg/L) samples exceeded Type V national water quality standards (COD(Mn)?≥ 15 mg/L, BOD(5)?≥ 10 mg/L). Monthly changes of pollutant concentrations did not show a clear pattern, but correlation analysis indicated that NH??-N and TN in tertiary rivers had a significant negative correlation with 5-day cumulative rainfall and monthly rainfall, while there were no significant correlations in primary and secondary rivers. The results of CA and spatial analysis showed that the northern part of Wen-Rui Tang River watershed was the most seriously polluted. This region is characterized by the high population density and industrial and commercial activities. The PCA and spatial analysis indicated that the degraded water quality is caused by anthropogenic activities and poor wastewater management.     

Optimal redesign of groundwater quality monitoring networks: a case study

Assessment and redesign of water quality monitoring networks is an important task in water quality management. This paper presents a new methodology for optimal redesign of groundwater quality monitoring networks. The measure of transinformation in discrete entropy theory and the transinformation–distance (T–D) curves are used to quantify the efficiency of sampling locations and sampling frequencies in a monitoring network. The existing uncertainties in the T–D curves are taken in to account using the fuzzy set theory. The C-means clustering method is also used to classify the study area to some homogenous zones. The fuzzy T–D curve of the zones is then used in a multi-objective hybrid genetic algorithm-based optimization model. The proposed methodology is utilized for optimal redesign of monitoring network of the Tehran aquifer in the Tehran metropolitan area, Iran.     

Revising river water quality monitoring networks using discrete entropy theory: the Jajrood River experience

This paper aims at evaluating and revising the spatial and temporal sampling frequencies of the water quality monitoring system of the Jajrood River in the Northern part of Tehran, Iran. This important river system supplies 23% of domestic water demand of the Tehran metropolitan area with population of more than 10 million people. In the proposed methodology, by developing a model for calculating a discrete version of pair-wise spatial information transfer indices (SITIs) for each pair of potential monitoring stations, the pair-wise SITI matrices for all water quality variables are formed. Also, using a similar model, the discrete temporal information transfer indices (TITIs) using the data of the existing monitoring stations are calculated. Then, the curves of the pair-wise SITI versus distance between monitoring stations and TITI versus time lags for all water quality variables are derived. Then, using a group pair-wise comparison matrix, the relative weights of the water quality variables are calculated. In this paper, a micro-genetic-algorithm-based optimization model with the objective of minimizing a weighted average spatial and temporal ITI is developed and for a pre-defined total number of stations, the best combination of monitoring stations is selected. The results show that the existing monitoring system of the Jajrood River should be partially strengthened and in some cases the sampling frequencies should be increased. Based on the results, the proposed approach can be used as an effective tool for evaluating, revising, or redesigning the existing river water quality monitoring systems.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

Optimizing booster chlorination in water distribution networks: a water quality index approach

The optimization of chlorine dosage and the number of booster locations is an important aspect of water quality management in distribution networks. Booster chlorination helps to maintain uniformity and adequacy of free residual chlorine concentration, essential for safeguarding against microbiological contamination. Higher chlorine dosages increase free residual chlorine concentration but generate harmful by-products, in addition to taste and odor complaints. It is possible to address these microbial, chemical, and aesthetic water quality issues through free residual chlorine concentration. Estimating a water quality index (WQI) based on regulatory chlorine thresholds for microbial, chemical, and aesthetics criteria can help engineers make intelligent decisions. An innovative scheme for maintaining adequate residual chlorine with optimal chlorine dosages and numbers of booster locations was established based on a proposed WQI. The City of Kelowna (BC, Canada) water distribution network served to demonstrate the application of the proposed scheme. Temporal free residual chlorine concentration predicted with EPANET software was used to estimate the WQI, later coupled with an optimization scheme. Preliminary temporal and spatial analyses identified critical zones (relatively poor water quality) in the distribution network. The model may also prove useful for small or rural communities where free residual chlorine is considered as the only water quality criterion.     

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin,northeastern China,from 2011 to 2015

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD₅) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.     

Continuous high-frequency monitoring of estuarine water quality as a decision support tool: a Dublin Port case study

High-frequency, continuous monitoring using in situ sensors offers a comprehensive and improved insight into the temporal and spatial variability of any water body. In this paper, we describe a 7-month exploratory monitoring programme in Dublin Port, demonstrating the value of high-frequency data in enhancing knowledge of processes, informing discrete sampling, and ultimately increasing the efficiency of port and environmental management. Kruskal–Wallis and Mann–Whitney tests were used to show that shipping operating in Dublin Port has a small–medium effect on turbidity readings collected by in situ sensors. Turbidity events are largely related to vessel activity in Dublin Port, caused by re-suspension of sediments by vessel propulsion systems. The magnitudes of such events are strongly related to water level and tidal state at vessel arrival times. Crucially, measurements of Escherichia coli and enterococci contamination from discrete samples taken at key periods related to detected turbidity events were up to nine times higher after vessel arrival than prior to disturbance. Daily in situ turbidity patterns revealed time-dependent water quality “hot spots” during a 24-h period. We demonstrate conclusively that if representative environmental assessment of water quality is to be performed at such sites, sampling times, informed by continous monitoring data, should take into account these daily variations. This work outlines the potential of sensor technologies and continuous monitoring, to act as a decision support tool in both environmental and port management.     

Monitoring, assessment and modelling using water quality data in the Saale River Basin, Germany

The European Water Framework Directive (WFD) is the overall driver for this environmental study and currently requires the identification of patterns and sources of pollution (monitoring) to support objective ecological sound decision making and specific measures to enhance river water quality (modelling). The purpose of this paper is to demonstrate in a case study the interrelationship between (1) hydrologic and water quality monitoring data for river basin characterization and (2) modelling applications to assess resources management alternatives. The study deals with monitoring assessment and modelling of river water quality data of the main stem Saale River and its principal tributaries. For a period of 6 years the data, which was sampled by Environmental Agencies of the German states of Thuringia, Saxony and Saxony-Anhalt, was investigated to assess sources and indicators of pollution. In addition to the assessment a modelling exercise of the routing of different pollutants was carried out in the lower part of the test basin. The modelling is a tool to facilitate the evaluation of alternative measures to reduce contaminant loadings and improve ecological status of a water body as required by WFD. The transport of suspended solids, salts and heavy metals was modelled along a selected Saale reach under strong anthropogenic influence in terms of contaminants and river morphology between the city of Halle and the confluence with the Elbe River. The simulations were carried out with the model WASP5 which is a dynamic flood-routing and water quality model package developed by the US Environmental Protection Agency.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.     

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.     

Assessment of domestic water quality: case study, Beirut, Lebanon

In urban cities, the environmental services are the responsibility of the public sector, where piped water supply is the norm for urban household. Likewise, in Beirut City (capital of Lebanon) official water authorities are the main supplier of domestic water through a network of piping system that leaks in many areas. Beirut City and its suburbs are overpopulated since it is the residence of 1/3 of the Lebanese citizens. Thus, Beirut suffers deficiency in meeting its water demand. Water rationing, as a remedial action, is firmly established since four decades by the Lebanese Water Authorities. Consumers resorted then to private wells to supplement their domestic water needs. Consequently, household water quality is influenced by external factors relating to well water characteristics and internal factors depending on the types of the pipes of the distribution network and cross connections to sewer pipes. These factors could result in chemical and microbial contamination of drinking water. The objective of this study is to investigate domestic water quality variation in Beirut City emerging form the aforementioned factors. The presented work encircles a typical case study of Beirut City (Ras Beirut). Results showed deterioration pattern in domestic water quality. The predicted metal species and scales within the water pipes of distribution network depended on water pH, hardness, sulfate, chloride, and iron. The corrosion of iron pipes mainly depended on Mg hardness.     

Estimation of chlorophyll-a concentration using field spectral data: a case study in inland Case-II waters, North China

In the remote sensing of chlorophyll-a (Chla) in inland Case-II waters, the assumption that the optical parameter of Chla specific absorption coefficient a*ph remains constant usually restrains application of many models. In this paper, we presented a newly developed model [Rrs(-1)(lambda1) - Rrs(-1)(lambda2)] x Rrs(lambda3) x a*ph(-1)(lambda1) which was improved on a previous three-band model to isolate interferences from a*ph. In terms of the importance of water optical properties in the model development, spectral and absorption characteristics were analyzed for Shitoukoumen Reservoir and Songhua Lake in Northeast China, as typical examples of inland Case-II waters. Both waters showed overwhelming absorption sum of tripton and chromophoric dissolved organic matter (CDOM) owing to their relatively low Chla contents (1.53 to 19.35 microgl(-1)). According to the optical characteristics of waters studied, optimal positions for lambda (1), lambda (2) and lambda (3) were spectrally tuned to be at 664, 684 and 705 nm, respectively. The model allowed accurate Chla estimation with a determination coefficient (R (2)) close to 0.98 and a root mean square error (RMSE) of 0.87 microgl(-1). Comparison of different models further showed the stability of the improved model, implying its potential use in water color remote sensing. Although the findings underline the rationale behind the improved model, an extensive database containing data in different water conditions and water types is required to generalize its application.     

Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China

This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS Data) to monitor water quality parameters including chlorophyll-a, secchi disk depth, total phosphorus and total nitrogen at Chaohu Lake. In this paper, multivariate regression analysis, Back Propagation neural networks (BPs), Radial Basis Function neural networks (RBFs) and Genetic Algorithms-Back Propagation (GA-BP) were applied to investigate the relationships between water quality parameters and the MODIS bands combinations. The study results indicated that a simple, efficient and acceptable model could be established through multivariate regression analysis, but the model precision was relatively low. In comparison, BPs, RBFs and GA-BP were significantly advantageous in terms of sufficient utilization of spectra information and model reliance. The relative errors of BPs, RBFs and GA-BP were below 35%. Based on method comparison, it can be concluded that GA-BP is more suitable for simulation and prediction of water quality parameters by applying genetic algorithm to optimize the weight value of BP network. This study demonstrates that MODIS data can be applied for monitoring some of the water quality parameters of large inland lakes.     

Real-time monitoring of water quality using fish and crayfish as bio-indicators: a review

Water quality monitoring using fish and crayfish as bio-indicators requires an understanding of the state of pollution of waters, choice of bio-indicators, physiological and behavioral endpoints of fish and crayfish, and principles of the methodology and their potential applications. Here, we discuss telemetry, acoustic monitoring, vision-based monitoring, measures of ventilatory activity, electrocardiography, and fiber-optic plethysmography. Assessment of water quality must be based, not only on physicochemical characteristics of the current environment as determined by chemical analyses, but also on observations of the physiology and behavior of its inhabitants. Real-time biomonitoring is suggested as the most reliable method, since it incorporates living organisms into the system to serve as biosensors. The potential application of the methods discussed includes use at water treatment plants and water supply stations for prevention of hazardous toxicological events, and, for aquaculture, in ponds, lakes, and aquariums for monitoring growth, population size, and behavior traits.     

Site location optimization of regional air quality monitoring network in China: methodology and case study

Regional air quality monitoring networks (RAQMN) are urgently needed in China due to increasing regional air pollution in city clusters, arising from rapid economic development in recent decades. This paper proposes a methodological framework for site location optimization in designing a RAQMN adapting to air quality management practice in China. The framework utilizes synthetic assessment concentrations developed from simulated data from a regional air quality model in order to simplify the optimal process and to reduce costs. On the basis of analyzing various constraints such as cost and budget, terrain conditions, administrative district, population density and spatial coverage, the framework takes the maximum approximate degree as an optimization objective to achieve site location optimization of a RAQMN. An expert judgment approach was incorporated into the framework to help adjust initial optimization results in order to make the network more practical and representative. A case study was used to demonstrate the application of the framework, indicating that it is feasible to conduct site optimization for a RAQMN design in China. The effects of different combinations of primary and secondary pollutants on site location optimization were investigated. It is suggested that the network design considering both primary and secondary pollutants could better represent regional pollution characteristics and more extensively reflect temporal and spatial variations of regional air quality. The work shown in this study can be used as a reference to guide site location optimization of a RAQMN design in China or other regions of the world.     

Assessment of water quality of some swimming pools: a case study in Alexandria, Egypt

In spite of the importance and popularity of swimming pools in summer, they have been identified as posing some public health risks to users due to either chemical or microbiological contamination. This study was carried out aiming at assessing the quality of water for some Alexandria's swimming pools in order to determine its compliance with the Egyptian standards no. 418/1995. Five swimming pools were selected randomly from different districts. Physical and chemical parameters, as well as biological examination of a total of 30 samples, were carried out using standard analytical methods. Water samples were collected from the studied swimming pools monthly over 6?months and pool water monitoring was carried out during afternoon of the weekends when the pools were most heavily used. The results indicated overall poor compliance with the standards. Compliance of the pool water to the microbial parameters, residual chlorine, pH, and turbidity were 56.7% (17 samples), 20% (6 samples), 46.7% (14 samples), and 46.7% (14 samples), respectively. Statistical analysis showed significant association between water contamination with microbial indicators and physical–chemical aspects such as residual chlorine, temperature, turbidity, and load of swimmers. Furthermore, Cryptosporidium oocysts and Giardia lamblia cysts has been found in 10% of samples. It was concluded that there is a need to improve disinfection and cleaning procedures, with consideration given to safety, and size of the pool in relation to bathing load. There is also a need to monitor swimming pool water quality continuously, and to increase bather hygienic practices and awareness of the risks as well as training of governmental inspectors.     

Use of neural networks for monitoring surface water quality changes in a neotropical urban stream

This paper reports the using of neural networks for water quality analysis in a tropical urban stream before (2002) and after sewerage building and the completion of point-source control-based sanitation program (2003). Mathematical modeling divided water quality data in two categories: (a) input of some in situ water quality variables (temperature, pH, O2 concentration, O2 saturation and electrical conductivity) and (b) water chemical composition (N-NO2(-); N-NO3(-); N-NH4(+) Total-N; P-PO4(3-); K+; Ca2+; Mg+2; Cu2+; Zn2+ and Fe+3) as the output from tested models. Stream water data come from fortnightly sampling in five points along the Ipanema stream (Southeast Brazil, Minas Gerais state) plus two points downstream and upstream Ipanema discharge into Doce River. Once the best models are consistent with variables behavior we suggest that neural networking shows potential as a methodology to enhance guidelines for urban streams restoration, conservation and management.     

The SSC cycle: a PDCA approach to address site-specific characteristics in a continuous shallow water quality monitoring project

In any water quality-monitoring project there are several critical success factors that must be adequately addressed in order to ensure the implementation and realization of the monitoring objectives. Site selection is one of these critical success factors. The monitoring sites must be selected to comply with the monitoring and data quality objectives. In the real world, ideal monitoring setting conditions are difficult to achieve, and compromises must be made in order to locate the monitoring stations that best represent the environment to be monitored. Site-specific characteristics are all the environmental, logistical and management factors particular to the monitoring site, that could influence the fulfilment of the monitoring and data quality objectives. Therefore, during the site selection process, it is essential to properly consider and evaluate these site-specific characteristics. The SSC cycle was developed with this goal in mind, to assist the monitoring team to systematically address site-specific characteristics. The cycle is a methodology to organize the site-specific characteristics in different categories, and to ensure a comprehensive overview of these characteristics throughout the project life cycle.