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.     

Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index

Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index’s usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.     

Development of groundwater quality index

Assessing the water quality status for special use is the main objective of any water quality monitoring studies. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number which represents the water quality level. In fact, developing WQI in an area is a fundamental process in the planning of land use and water resources management. In this study, a simple methodology based on multivariate analysis is developed to create a groundwater quality index (GWQI), with the aim of identifying places with best quality for drinking within the Qazvin province, west central of Iran. The methodology is based on the definition of GWQI using average value of eight cation and anion parameters for 163 wells during a 3-year period. The proportion of observed concentrations to the maximum allowable concentration is calculated as normalized value of each parameter in observing wells. Final indices for each well are calculated considering weight of each parameter. In order to assess the groundwater quality of study area, the derived indices are compared with those of well-known mineral waters. Using developed indices, groundwater iso-index map for study area and the map of areas of which the indices are near to mineral waters was drawn. In the case study, the GWQI map reveals that groundwater quality in two areas is extremely near to mineral water quality. Created index map provides a comprehensive picture of easily interpretable for regional decision makers for better planning and management.     

Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking framework

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework (R _WQI framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R _WQI framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R _WQI framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events.     

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.     

Statistical needs in national water quality monitoring programs

The concept that a few well chosen, strategically placed, water quality stations can provide valuable scientific information to water managers is common to many countries. Historically within Canada, water quality regional networks (Great Lakes network, Prairie Provinces Water Board network, Long Range Transport of Airborne Pollutants network, etc.) have been successfully operating for many years. This paper will describe the difficulties associated with developing a national water quality network for a country the size of Canada. In particular, it will describe some of the statistical tools presently being used in regional networks which are suitable for a national network, and discuss the need to develop new statistical tools for environmental monitoring in the 1990's.     

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.     

Assessing the water quality index of water treatment plant and bore wells, in Delhi, India

Water quality monitoring exercise was carried out with water quality index (WQI) method by using water characteristics data for bore wells and a water treatment plant in Delhi city from December 2006 to August 2007. The water treatment plant received surface water as raw water, and product water is supplied after treatment. The WQI is used to classify water quality as excellent, good, medium, bad, and very bad. The National Sanitation Foundation WQI procedure was used to calculate the WQI. The index ranges from 0 to 100, where 100 represents an excellent water quality condition. Water samples were collected monthly from a bore well in Nehru Camp (site 1), a bore well in Sanjay Gandhi pumping station (site 2), and water treatment plant in Haiderpur (site 3). Five parameters were analyzed, namely, nitrate, pH, total dissolved solids, turbidity, and temperature. We found that the WQI was around 73–80 in site 3, which corresponds to “good,” and it decreased to 54.32–60.19 and 59.93–70.63 in site 1 and site 2, respectively, indicating that these bore wells were classified as “medium” quality.     

Assessment of irrigation water quality. A proposal of a quality profile

Water quality indices provide a simple and understandable tool for managers on the quality and possible uses for irrigation water, however an individual quality factor alone is not enough to evaluate the irrigation water quality because it could be restrictive and sometime it could give an unfavorable qualification. The aim of this paper was propose a quality profile of irrigation water using the preexisting water quality indices to be applied to arid and semi-arid regions. As a case studied, the water of the Del Molle River (Nogolí, San Luis, Argentina) was researched. Samples were collected during the period October 2005-May 2006. Conductivity, pH, total hardness, sulphate, nitrate, nitrite, alkalinity, chloride, sodium, potassium, TDS, DO and phosphate were analyzed. The irrigation water quality, according to Riverside Norm, belongs to C(2)-S(1) class, according to Wilcox Norm as excellent to good, according to Scott quality factor it is good and according to SAR < 10 and according to RCS it is recommendable. From the obtained data, it can be concluded that the water quality profile was good, so it is useful for normal irrigation agriculture.     

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.     

Spatiotemporal trend analysis of recent river water quality conditions in Japan

In order to promote pollutant monitoring and preservation of water resources, we evaluate the spatiotemporal trends in recent water quality conditions in Japanese rivers. Trend analysis is conducted on the 92 major rivers in Japan using the available water quality data recorded from 1992 to 2005 and the characteristics of major pollutants in these rivers are analyzed. Spatial and temporal analysis of trends for six water quality indicators is conducted using the Mann Kendall test, a non-parametric statistical method. The indicators analyzed are biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP) and pH. The majority of sampling locations monitoring BOD, COD, TN and TP show trends toward decreasing concentrations over time. Many sampling locations show increasing DO concentrations. Our results show that water quality in Japanese rivers has improved dramatically over the past decade, although there are still problems in some places, most notably in the Hokkaido, Kanto, Kinki and Kyushu regions. The improvements seen in water quality appear to be the result of improved wastewater treatment and other water quality improvement efforts achieved through government initiative.     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

A comparative chemometric study for water quality expertise of the Athenian water reservoirs

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.     

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.     

Multi-objective, decision-based assessment of a water quality monitoring network in a river system

Water quality monitoring network design has historically tended to use experience, intuition and subjective judgement in locating monitoring stations. Better design procedures to optimize monitoring systems need to simultaneously identify significant planning objectives and consider a number of social, economic and environmental constraints. The consideration of multiple objectives may require further decision analysis to determine the preference weights associated with the objectives to aid in the decision-making process. This may require the application of an optimization study to extract such information from decision makers or experts and to evaluate the overall effectiveness of locating strategies. This paper assesses the optimal expansion and relocation strategies of a water quality monitoring network using a two-stage analysis. The first stage focuses on the information retrieval of preference weights with respect to the designated planning objectives. With the aid of a pre-emptive goal programming model, data analysis is applied to obtain the essential information from the questionnaire outputs. The second stage then utilizes a weighted multi-objective optimization approach to search for the optimal locating strategies of the monitoring stations in the river basin. Practical implementation is illustrated by a case study in the Kao-Ping River Basin, south Taiwan.     

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.     

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO₂ pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO₂ annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NO_x emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO₂ concentration is above the annual limit value.     

Optimizing water quality monitoring networks using continuous longitudinal monitoring data: a case study of Wen-Rui Tang River, Wenzhou, China

Identification of representative sampling sites is a critical issue in establishing an effective water quality monitoring program. This is especially important at the urban-agriculture interface where water quality conditions can change rapidly over short distances. The objective of this research was to optimize the spatial allocation of discrete monitoring sites for synoptic water quality monitoring through analysis of continuous longitudinal monitoring data collected by attaching a water quality sonde and GPS to a boat. Sampling was conducted six times from March to October 2009 along a 6.5 km segment of the Wen-Rui Tang River in eastern China that represented an urban-agricultural interface. When travelling at a velocity of ～2.4 km h(-1), this resulted in water quality measurements at ～20 m interval. Ammonia nitrogen (NH(4)(+)-N), electrical conductivity (EC), dissolved oxygen (DO), and turbidity data were collected and analyzed using Cluster Analysis (CA) to identify optimal locations for establishment of long-term monitoring sites. The analysis identified two distinct water quality segments for NH(4)(+)-N and EC and three distinct segments for DO and turbidity. According to our research results, the current fixed-location sampling sites should be adjusted to more effectively capture the distinct differences in the spatial distribution of water quality conditions. In addition, this methodology identified river reaches that require more comprehensive study of the factors leading to the changes in water quality within the identified river segment. The study demonstrates that continuous longitudinal monitoring can be a highly effective method for optimizing monitoring site locations for water quality studies.     

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.