Water Quality Monitoring Network Research Articles

Impact of sensor measurement error on sensor positioning in water quality monitoring networks

This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network.

Estimating sampling and analysis uncertainties to assess the fitness for purpose of a water quality monitoring network

In order to verify that the protocols used for water quality monitoring of surface waters within the Long-term Environmental Research Monitoring and Testing System (OPE), located in the north-eastern part of France in relation with a geological disposal for radioactive waste project, are fit for purpose, a validation study was conducted following the methodology described in the Eurachem/citac and Nordtest guidance documents on uncertainty arising from sampling. As one of the objectives of the OPE water monitoring programme was to investigate the spatial and temporal variability of water quality, quality requirements were set to having a measurement variance, including sampling and analytical contributions, less than 20 % of the total variance to minimise the impact of measurement over the observed environmental variability. The replicate method was then selected in order to estimate the measurement uncertainty, including the sampling contribution, as well as the spatial and temporal variability of water quality of surface waters. To minimise costs, a single-split level was selected. Analytical uncertainties were assessed from inter-laboratory data and/or internal quality control data from the last 2–5 years. Finally, ANOVA was applied to the data sets after elimination of outliers. Results showed that for pH, electrical conductivity, turbidity and nitrate, the sampling uncertainty was negligible, whereas for other parameters such as dissolved oxygen, total suspend solids, total organic carbon, nitrite and phosphate, the sampling contribution to the measurement uncertainty was largely significant. For all parameters except calcium, the sampling and analytical protocols were considered fit for purpose.

Chesapeake Bay recovery and factors affecting trends: Long-term monitoring, indicators, and insights

Monitoring the outcome of restoration efforts is the only way to identify the status of a recovery and the most effective management strategies. In this paper, we discuss Chesapeake Bay and watershed recovery and factors influencing water quality trends. For over 30 years, the Chesapeake Bay Program Partnership’s long-term tidal and watershed water quality monitoring networks have measured physical, chemical and biological parameters throughout the bay and its surrounding watershed underpinning an adaptive management process to drive ecosystem recovery. There are many natural and anthropogenic factors operating and interacting to affect the watershed and bay water quality recovery responses to management actions. Across habitats and indicators, the bay and its watershed continue to express a diverse spatial and temporal fabric of multiscale conditions, stressors and trends that show a range of health conditions and impairments, as well as evidence of progress and degradation. Recurrent independent reviews of the monitoring program have driven a culture of continued adaptation of the monitoring networks to reflect ever evolving management information needs. The adherence to bay and watershed-wide consistent monitoring protocols provides monitoring data supporting analyses and development of scientific syntheses that underpin indicator and model development, regulatory assessments, targeting of management actions, evaluation of management effectiveness, and directing of priorities and policies.

Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin

ABSTRACTWater monitoring networks are generally classified into surface water, precipitation, groundwater or water quality monitoring networks. The design of these networks typically occurs in isolation from each other. We present a regionalization approach to identify homogeneous subregions of large basins that are suitable as areas for the optimization of an integrated water monitoring network. The study area, which comprises a portion of the St. Lawrence Basin, was spatially divided using ecological units. For each ecological unit, 21 attributes were derived including both environmental and hydrological indicators. A spatially constrained regionalization technique was applied to define the final regions. A scree plot was used to determine the number of regions. The sensitivity of the technique to the correlation in the attribute data was removed by utilizing principal component analysis to reduce correlation between attribute data. During regionalization, the component values were weighted by their proportion of the total variance explained. The four regions in the final configuration had areas from 19% to 31% of the total area, 63,597 km2. For the St. Lawrence Basin, this approach is effective for defining homogeneous regions that can be used in further research on the optimization of integrated water monitoring networks. The approach is portable to other regions and can incorporate any set of attribute data that is valuable to the regionalization objective.

Optimization of the monitoring network on the River Tisza (Central Europe, Hungary) using combined cluster and discriminant analysis, taking seasonality into account.

The most essential requirement for water management is efficient and informative monitoring. Operating water quality monitoring networks is a challenge from both the scientific and economic points of view, especially in the case of river sections ranging over hundreds of kilometers. Therefore, spatio-temporal optimization is vital. In the present study, the optimization of the monitoring system of the River Tisza, the second largest river in Central Europe, is presented using a generally applicable and novel method, combined cluster and discriminant analysis (CCDA). This area for the study was chosen because, spatial inhomogeneity of a river's monitoring network can more easily be studied in a mostly natural watershed - as in the case of the River Tisza - since the effects of man-made obstacles: e.g water barrage systems, hydroelectric power plants, artificial lakes, etc. are more pronounced. Furthermore, since the temporal sampling frequency was bi-weekly, the opportunity of optimizing the monitoring system on a temporal (monthly) scale arose. In the research, 15 water quality parameters measured at 14 sampling sites in the Hungarian section of the River Tisza were assessed for the time period 1975-2005. First, four within-year sections ("hydrochemical seasons") were determined, characterized with unequal lengths, namely 2, 4, 2, and 4 months long starting with spring. Homogeneous groups of sampling sites were determined in space for every season, with the main separating factors being the tributaries and man-made obstacles. Similarly, an overall pattern of homogeneity was determined. As an overall result, the 14 sampling sites could be grouped into 11 homogeneous groups leading to the possibility of reducing the number of sampling locations and thus making the monitoring system more cost-efficient.

Development of a decision-making methodology to design a water quality monitoring network

The number of water quality monitoring stations in the USA has decreased over the past few decades. Scarcity of observations can easily produce prediction uncertainty due to unreliable model calibration. An effective water quality monitoring network is important not only for model calibration and water quality prediction but also for resources management. Redundant or improperly located monitoring stations may cause increased monitoring costs without improvement to the understanding of water quality in watersheds. In this work, a decision-making methodology is proposed to design a water quality monitoring network by providing an adequate number of monitoring stations and their approximate locations at the eight-digit hydrologic unit codes (HUC8) scale. The proposed methodology is demonstrated for an example at the Upper Colorado River Basin (UCRB), where salinity is a serious concern. The level of monitoring redundancy or scarcity is defined by an index, station ratio (SR), which represents a monitoring density based on water quality load originated within a subbasin. By comparing the number of stations from a selected target SR with the available number of stations including the actual and the potential stations, the suggested number of stations in each subbasin was decided. If monitoring stations are primarily located in the low salinity loading subbasins, the average actual SR tends to increase, and vice versa. Results indicate that the spatial distribution of monitoring locations in 2011 is concentrated on low salinity loading subbasins, and therefore, additional monitoring is required for the high salinity loading subbasins. The proposed methodology shows that the SR is a simple and a practical indicator for monitoring density.

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.

Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques

The objective of this research is to develop a tool for planning and managing the water quality of River Godavari. This is achieved by classifying the pollution levels of Godavari River into several categories using water quality index and a clustering approach that ensure simple but accurate information about the pollution levels and water characteristics at any point in Godavari River in Maharashtra. The derived water quality indices and clusters were then visualized by using a Geographical Information System to draw thematic maps of Godavari River, thus making GIS as a decision support system. The obtained maps may assist the decision makers in managing and controlling pollution in the Godavari River. This also provides an effective overview of those spots in the Godavari River where intensified monitoring activities are required. Consequently, the obtained results make a major contribution to the assessment of the State’s water quality monitoring network. Three significant groups (less polluted, moderately and highly polluted sites) were detected by Cluster Analysis method. The results of Discriminant Analysis revealed that five parameters i.e. pH, Dissolved Oxygen (DO), Faecal Coliform (FC), Total Coliform (TC) and Ammonical Nitrogen (NH3-N) were necessary for analysis in spatial variation. Using discriminant function developed in the analysis, 100% of the original sites were correctly classified.

Analysis on Distribution Trends of Affecting Red Tide Factors in the Beibu Gulf Inshore

Analysis on distribution trends of affecting red tide factors in the Beibu Gulf inshore based on data were monitored by automatic water quality monitoring network in 2011 and 2013, providing the scientific basis for preventing disasters and reducing damages of the red tide. Results showed that, average water temperature of Beibu Gulf are maintained in 21.5 °C ~27 °C in two years, the same station maximum temperature difference is 2.72 °C, and is gradually increased since West to East and rose yearly; pH are maintained in 7.8~8.3, are partial alkaline and relative compared stable, then pH of enclosed sea are below offshore waters, which in pH interval of red tide easy outbreak, existing high risk. DO concentration are between 6.3~7.7mg/L, does not have the foundation of extensive outbreak red tides. Lower percentage of DO in enclosed sea and with less material flux in offshore waters, so enclosed sea are high-incidence areas. Chlorophyll concentration in 2013 are almost lower than 2011, and smaller fluctuations. The environmental capacity of the waters in the Beibu Gulf is getting saturated; therefore, it should closely monitor water temperature, pH, and DO and chlorophyll concentration for predicting red tide.

Statistical Assessment of the Surface Water Quality Monitoring Network in Saskatchewan

Surface water quality monitoring is one of the responsibilities of a number of provincial and federal environmental departments in Canada. In Saskatchewan, the Ministry of Environment is responsible for the province water quality monitoring network. The sampling effort was initiated 40 years ago and has been ongoing since, with varying degrees of spatial and temporal coverage. The main objective of the Saskatchewan monitoring network is the assessment of ambient water quality status. In addition, one of the main uses of the generated water quality data is the calculation of a Water Quality Index. The adequacy of the monitoring network to perform these tasks needs to be validated. The objective of this study is to provide a statistical assessment of two of the monitoring network main aspects, the water quality variables and their sampling frequency. A new rationalization approach is applied for the assessment and reselection of water quality variables. The proposed approach provides, in a systematic way, the optimal combinations of variables to continue measuring, variables that may be redundant and could be considered for discontinuance, and variables that may need to be added to the list of variables being measured. The confidence interval around the mean is used as the main criterion for the sampling frequency assessment. A design chart is provided for the sampling frequency assessment, which is easy to use, and provides an initial assessment of the number of samples required to provide a mean value with a predefined error percentage.

Estimating lake carbon fractions from remote sensing data

Issues like monitoring lake water quality, studying the role of lakes in the global carbon cycle or the response of lakes to global change require data more frequently and/or over much larger areas than the in situ water quality monitoring networks can provide. The aim of our study was to investigate whether it is feasible to estimate different lake carbon fractions (CDOM, DOC, TOC, DIC, TIC and pCO2) from space using sensors like OLCI on future Sentinel 3. In situ measurements were carried out in eight measuring stations in two Swedish lakes within 2days of MERIS overpass. The results suggest that the MERIS CDOM product was not suitable for estimating CDOM in lakes Mälaren and Tämnaren and was not a good proxy for mapping lake DOC and TOC from space. However, a simple green to red band ratio and some other MERIS products like the total absorption coefficient, turbidity index, suspended matter and chlorophyll-a were correlated with different carbon fractions and could potentially be used as proxies to map these lake carbon fractions (CDOM, DOC, TOC, DIC, TIC and pCO2) from space.

A water quality monitoring network design using fuzzy theory and multiple criteria analysis.

A proper water quality monitoring design is required in a watershed, particularly in a water resource protected area. As numerous factors can influence the water quality monitoring design, this study applies multiple criteria analysis to evaluate the suitability of the water quality monitoring design in the Taipei Water Resource Domain (TWRD) in northern Taiwan. Seven criteria, which comprise percentage of farmland area, percentage of built-up area, amount of non-point source pollution, green cover ratio, landslide area ratio, ratio of over-utilization on hillsides, and density of water quality monitoring stations, are selected in the multiple criteria analysis. The criteria are normalized and weighted. The weighted method is applied to score the subbasins. The density of water quality stations needs to be increased in priority in the subbasins with a higher score. The fuzzy theory is utilized to prioritize the need for a higher density of water quality monitoring stations. The results show that the need for more water quality stations in subbasin 2 in the Bei-Shih Creek Basin is much higher than those in the other subbasins. Furthermore, the existing water quality station in subbasin 2 requires maintenance. It is recommended that new water quality stations be built in subbasin 2.

Challenges and Solutions of Urban Hydrology in Beijing

Since the reform and opening-up in 1978, the city of Beijing as the capital of China has been going through a rapid urbanization. Water as the public, fundamental and strategic resources for the social and economic development as well as the survival basis of human and surroundings, is constrained to the urbanization of Beijing. In order to solve the hydrological challenges by urbanization, as the municipal water information service, Beijing Hydrological Center (BHC) is currently focusing on the safe water supply based on the mega-city hydrological development concept and making efforts to deal with new hydrological problems caused by the rapid urbanization. After years of study on theories and practices, the solutions to intense water scarcity, such as new well fields development, South-North water diversion, water resource and quality monitoring networks improvement, reclaimed water usage and so on, have provided strong support for Beijing turning into an international city. In this paper, the current water problems of Beijing are described in details on water shortage, water quality deterioration, drinking water safety and problems concerning water drainage system and flood control. The experiences of hydrometric, water quality and groundwater monitoring networks are provided as measures for water resources management. Additionally, the previous and current research work which provides technical and scientific support for decision-making of water resources management in Beijing is discussed in this paper.

Dynamic programming approach (DPA) for assessment of water quality network: a case study of the Sefīd-Rūd River

Due to financial constraints, water quality monitoring network design is an efficient way to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. Considering the objectives of water usage, we evaluate existing water quality sampling stations of the Sefīd-Rūd River, located in the north of Iran, which has been used for irrigation and drinking water. There are several methods for assessment of existing monitoring stations such as Sanders method, multiple criteria decision making and dynamic programming approach (DPA) which DPA was opted in this study. The results showed that out of 21 existing monitoring stations, eight and seven stations should be retained on the upstream and downstream of the basins, respectively. Although the area of the downstream basin is larger than the upstream basin, the number of stations which was retained was less than upstream. This may mean that water quality of the downstream is more favorable than the upstream. Meanwhile the technique may be also suitable for optimization of the water quality network.

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India).

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. The effectiveness of existing river water quality monitoring network is assessed. Significance of seasonal redesign of the monitoring network is demonstrated. Rationalization of water quality parameters is performed in a statistical framework.

Achnanthidium barbei sp. nov. and Achnanthidium costei sp. nov., two new diatom species from French rivers

Two new benthic freshwater species belonging to the genus Achnanthidium Kützing were found in French rivers located in the Languedoc-Roussillon region. Achnanthidium barbei and A. costei are described as new species based on light and scanning electron microscopy observations. These species belong to the ‘A. minutissimum complex’ based on the short, straight distal raphe fissures. Achnanthidium barbei and A. costei were found in rivers characterized by an alkaline pH and high phosphorus. Achnanthidium costei occurs in waters with very high conductivity and sulfate. The most characteristic morphological features of A. barbei are the bent frustule with recurved apices in girdle view, clearly lanceolate valves with convex margins and distinctly protracted apices, and the presence of up to six areolae per stria. Achnanthidium costei is characterized by a linear raphe valve outline and a wide axial area on the rapheless valve. Both species are compared with the most similar species: A. caravelense Novais & Ector, A. ennediense (Compère) Compère & Van de Vijver, A. eutrophilum (Lange-Bertalot) Lange-Bertalot, A. lineare W. Smith, A. minutissimun (Kützing) Czarnecki., A. pseudolineare Van de Vijver, Novais & Ector, A. sublineare Van de Vijver, Novais & Ector and A. tepidaricola Van de Vijver & M. de Haan. The ecological preferences of both species are assessed based on physicochemical analyses carried out on sites that are part of water quality monitoring networks.

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leca River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leca River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Optimal sampling network for monitoring the representative water quality of an entire reservoir on the basis of information theory

This study presents a method for optimal selection of sampling stations for representative and effective water quality monitoring of a reservoir on the basis of information theory. We adopt an objective function used for the design of other hydrological monitoring networks such as rain gauge networks. Unlike rain gauges, in which only a single variable of rainfall is of interest, water quality monitoring stations measure multiple water quality variables. To consider this nature, a new concept of multi-variate weighted total information is proposed. This allows us to consider the relative importance between multiple water quality variables in the design of water quality monitoring networks. The proposed methodology is applied to Lake Yongdam, South Korea, where water quality has been extensively observed at several points. The optimal combination of sampling sites selected with the proposed method is found to show little redundancy in comparison with a previous independent study that presents statistical analysis of the same dataset. Further, the water quality data averaged over all stations are very close to those averaged over the selected sites only, implying that the optimal combination of sampling sites is representative of all sites.

Single Criterion and Multiple Criteria Analysis: A Comparison of Water Quality Monitoring Designs for a River System

The design of a water quality monitoring network is of critical importance to watershed management. By employing both a single criterion analysis and a multiple criteria analysis, this study assessed and compared water quality monitoring designs for a river system in the Taipei Water Resource Domain (TWRD), a major water resource protected area in northern Taiwan. The case area for this work was divided into 22 sub-basins; and each sub-basin was generally based upon a river network. With a single criterion analysis, inputting different individual variables produced significantly different results. Thus, the sub-basins seen to have a greater need for a water quality monitoring system varied greatly. This study, further, observed that a multiple criteria analysis is more objective than a single criterion analysis. When considering multiple factors, this report utilized seven criteria, the top five sub-basins with a greater necessity for improved water quality monitoring records are, in order, Sub-basins 2, 5, 21, 20, and 7. This indicates that the need for a water quality monitoring system in the Sin-Dian Creek Basin and in the Bei-Shih Creek Basin is greater than that in the Nan-Shih Creek Basin. This result is a useful reference for the creation of an improved water quality management strategy in the TWRD.

水质监测系统整体架构及网络设计

随着社会的发展，人们对环境保护的重视，如何对环境进行智能监测控制具有重大的研究意义。水质监测系统架构及网络设计，先设计了以声波实现通信和组网水下数据采集节点，再在每个区域(水面)加入数据汇集节点，数据汇集节点采用ZigBee系统进行自动组网和数据汇集，由GPRS传递给服务器。 With the development of society, people pay more attention to environmental protection. How to intelligently monitor and control the environment has important research significance. Water quality monitoring system architecture and network design, first designed underwater acoustic communication and network data acquisition node, then added data acquisition nodes in each region (water), which used ZigBee system for automatic networking and data collection, by GPRS are passed to the server.