Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

River reaches are frequently classified with respect to various mode of water utilization depending on the quantity and quality of water resources available at different location. Monitoring of water quality in a river system must collect both temporal and spatial information for comparison with respect to the preferred situation of a water body based on different scenarios. Designing a technically sound monitoring network, however, needs to identify a suite of significant planning objectives and consider a series of inherent limitations simultaneously. It would rely on applying an advanced systems analysis technique via an integrated simulation-optimization approach to meet the ultimate goal. This article presents an optimal expansion strategy of water quality monitoring stations for fulfilling a long-term monitoring mission under an uncertain environment. The planning objectives considered in this analysis are to increase the protection degree in the proximity of the river system with higher population density, to enhance the detection capability for lower compliance areas, to promote the detection sensitivity by better deployment and installation of monitoring stations, to reflect the levels of utilization potential of water body at different locations, and to monitor the essential water quality in the upper stream areas of all water intakes. The constraint set contains the limitations of budget, equity implication, and the detection sensitivity in the water environment. A fuzzy multi-objective evaluation framework that reflects the uncertainty embedded in decision making is designed for postulating and analyzing the underlying principles of optimal expansion strategy of monitoring network. The case study being organized in South Taiwan demonstrates a set of more robust and flexible expansion alternatives in terms of spatial priority. Such an approach uniquely indicates the preference order of each candidate site to be expanded step-wise whenever the budget limitation is sensitive in the government agencies.     

Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP)

River water quality sampling frequency is an important aspect of the river water quality monitoring network. A suitable sampling frequency for each station as well as for the whole network will provide a measure of the real water quality status for the water quality managers as well as the decision makers. The analytic hierarchy process (AHP) is an effective method for decision analysis and calculation of weighting factors based on multiple criteria to solve complicated problems. This study introduces a new procedure to design river water quality sampling frequency by applying the AHP. We introduce and combine weighting factors of variables with the relative weights of stations to select the sampling frequency for each station, monthly and yearly. The new procedure was applied for Jingmei and Xindian rivers, Taipei, Taiwan. The results showed that sampling frequency should be increased at high weighted stations while decreased at low weighted stations. In addition, a detailed monitoring plan for each station and each month could be scheduled from the output results. Finally, the study showed that the AHP is a suitable method to design a system for sampling frequency as it could combine multiple weights and multiple levels for stations and variables to calculate a final weight for stations, variables, and months.     

Air quality monitoring for multiple pollutants: Optimization of a network around a hypothetical potash plant and two thermal power stations in open countryside

A method is presented for the design of multi-pollutant air quality monitoring networks (AQMN). This technique leads to an optimal network, i.e. a network providing a maximum of information with a minimum of measurement devices. The spatial correlation analysis technique is used to compare the information given by the potential sites that may form the network. The concept of potential of violation is defined to take into account the number of times that the maximum emission values tolerated by law are exceeded. Both objectives are weighted automatically through an adjustable parameter, b, for which an estimation procedure has been developed in this study, depending on the purpose of the network. Several methods are described, allowing simultaneous consideration of different pollutants. As an illustration of these methods, a number of air quality monitoring networks is designed to perform an analysis of the environmental impact due to a hypothetical potash processing plant and two thermal power stations.     

Optimal Design of a Multi-Pollutant Air Quality Monitoring Network in a Metropolitan Region Using Kaohsiung, Taiwan as an Example

An air quality monitoring network (AQMN) usually performs the basic function of assessment of regional air quality and demonstration of compliance with ambient air quality standards in an urban area. Different pollutants, however, may present different characteristic variabilities due to their specific emission patterns, rates of diffusion, and transport and transformation behaviors. But the costs of siting in a pollutant-specific monitoring network would be higher than that for a common network with respect to several pollutants monitored simultaneously. This paper presents a survey of multi-pollutant design principles and optimal searches for siting patterns of an AQMN using both simulation and optimization models as a combined tool. While conservative, quasi-stable, and reactive pollutants are considered in the design principles, cost, coverage effectiveness, and spatial correlation characteristics are included in the multi-criteria decision making process. For illustrative purpose, a series of technical settings and two types of objectives were examined in the case study for the city of Kaohsiung in Taiwan.     

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.     

Optimal Space-time Coverage and Exploration Costs in Groundwater Monitoring Networks

A method to determine the optimal subset of stations from a reference level groundwater monitoring network is proposed. The method considers the redundancy of data from historical time series, the times associated with the total distance required to run through the entire monitoring network, and the sum of the times for each monitoring station. The method was applied to a hypothetical case-study consisting of a monitoring network with 32 stations. Cost-benefit analysis was performed to determine the number of stations to include in the new design versus loss of information. This optimisation problem was solved with simulated annealing. Results showed that the relative reduction in exploration costs more than compensates for the relative loss in data representativeness.     

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.     

美国空气质量监测的经验与启示

2013年9月10日，国务院发布《大气污染防治行动计划》，为当前和今后一个时期全国大气污染防治工作提供行动指南。近年来，中国空气质量监测工作发展迅猛，但新的空气质量管理目标对空气质量监测水平提出了更高的要求。为了加强中国空气质量监测能力，文章总结了美国空气质量监测四十多年的发展历程，介绍了美国系统谋划监测战略、合理布设监测网络、严格控制数据质量、开展污染组分分析、科学应用监测结果、积极推动公众参与等方面的经验，为中国发展和完善空气质量监测体系提供参考，为切实改善空气质量提供技术支持。     

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 River Water Quality Monitoring Networks: A Case Study

Karoon River, from Gotvand Dam to Persian Gulf with more than 450 km in length and an annual discharge of 11,891 million cubic meters, is the largest river in Iran. Increasing water withdrawal from and wastewater discharge to the river has endangered the aquatic life of this important ecosystem. Furthermore, the drinking and in-stream water quality standards have been violated in many instances. In this paper, a river water quality monitoring network is designed, including determination of sampling frequencies as well as location of water quality monitoring stations. In this regard, two models are developed. The first model is a Genetic Algorithm-based optimization model and the second one is a combination of Kriging method and Analytical Hierarchy Process. The temporal variation of the concentration of water quality variables along Karoon and Dez Rivers are evaluated and the main water quality indicators are selected. Then, thirty five stations are selected and the application of Entropy Theory in calculating the sampling frequency is demonstrated. The results show the significant value of the proposed methodology in the design of monitoring network.     

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

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

A Water Quality Monitoring Network Design Methodology for the Selection of Critical Sampling Points: Part I

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.     

Optimization models for siting water quality monitoring stations in a catchment

A water quality monitoring network (WQMN) must be designed so as to adequately protect the water quality in a catchment. Although a simulated annealing (SA) method was previously applied to design a WQMN, the SA method cannot ensure the solution it obtained is the global optimum. Therefore, two new linear optimization models are proposed in this study to minimize the deviation of the cost values expected to identify the possible pollution sources based on uniform cost (UC) and coverage elimination uniform cost (CEUC) schemes. The UC model determines the expected cost values by considering each sub-catchment being covered by which station, while the CEUC model determines the coverage of each station by eliminating the area covered by any upstream station. The proposed models are applied to the Derchi reservoir catchment in Taiwan. Results show that the global optimal WQMN can be effectively determined by using the UC or CEUC model, for which both results are better than those from the SA method, especially when the number of stations becomes large.     

Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations

One of the difficulties in accurate characterization of unknown groundwater pollution sources is the uncertainty regarding the number and the location of such sources. Only when the number of source locations is estimated with some degree of certainty that the characterization of the sources in terms of location, magnitude, and activity duration can be meaningful. A fairly good knowledge of source locations can substantially decrease the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. A methodology is developed to use a sequence of dedicated monitoring network design and implementation and to screen and identify the possible source locations. The proposed methodology utilizes a combination of spatial interpolation of concentration measurements and simulated annealing as optimization algorithm for optimal design of the monitoring network. These monitoring networks are to be designed and implemented sequentially. The sequential design is based on iterative pollutant concentration measurement information from the sequentially designed monitoring networks. The optimal monitoring network design utilizes concentration gradient information from the monitoring network at previous iteration to define the objective function. The capability of the feedback information based iterative methodology is shown to be effective in estimating the source locations when no such information is initially available. This unknown pollution source locations identification methodology should be very useful as a screening model for subsequent accurate estimation of the unknown pollution sources in terms of location, magnitude, and activity duration.     

Genetic Algorithm Usage in Water Quality Monitoring Networks Optimization in Gediz (Turkey) River Basin

Selection procedure of the optimum station combination for decreasing the station number from 33 to 14 in water quality monitoring network of Gediz river basin was applied using an optimization method. Gediz basin is one of the important basins and it covers 2.3% of the total surface area of Turkey. The technique includes two stages as the data preparation and the optimization. In the data preparation stage, firstly, alternative station combinations decreased and then station combination scores obtained from assigned selection criteria for point and nonpoint pollution management objectives. Finally, genetic algorithm applied to select the best combination. The results were compared with a prior solution that used dynamic programming as the optimization technique.     

Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.     

The need for long-term stream monitoring programs in forest ecosystems of the Pacific Northwest

Concepts, planning and design procedures are examined that are needed in the development of long-term stream monitoring programs in forested regions. A long-term stream monitoring program is viewed as the key component for bringing together management organizations, researchers and decision-makers to improve the management of natural resources. The keystones of such ecosystem monitoring are long-term data records that provide the basis for analysis of environmental assessment objectives, predictions and analysis of outcomes which in-turn can be used to modify and improve future projects. Management organizations that initiate long-term monitoring programs are urged to use monitoring actions and information to facilitate decision-making processes that pertain to conserving and allocating resources for future beneficial uses. Recommendations are provided for careful planning and definition of interactive activities of monitoring programs and that should provide information feedbacks that can be used to evaluate issues pertaining to beneficial uses of resources. Procedural requirements and literature sources are suggested for developing long-term stream monitoring programs. They include reviews of background and historical information to provide precise definitions of long-term objectives, planning considerations and monitoring methods. Examples are given of specific procedures that need to be identified during the planning process. They include the application of management standards to variable conditions encountered within natural ecosystems and the detection of the timing of recovery phases of stream ecosystem development following a disturbance. These procedures are viewed as being essential for improving applications of management standards and perceived thresholds to stream and watershed ecosystems monitoring programs.     

数据融合技术在环境监测网络中的应用与思考

按照环境监测网络的信息处理结构,将网络划分为采集层、网络层和应用层,归纳了数据级融合、特征级融合、决策级融合3层融合级别,介绍了加权平均、卡尔曼滤波、人工神经网络、支持向量机、遗传算法、贝叶斯系统等在环境监测数据分析中的应用,指出当前面临的数据处理技术不成熟、评价体系不完善和数据质量监管能力不足等问题,提出优化网络结构、数据多元应用和强化决策支持等研究建议。     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

Wmo's activities on background atmospheric pollution and integrated monitoring and research

As early as 1968, WMO decided to start a programme on atmospheric pollution. Consequently, a Panel of Experts on Meteorological Aspects of Atmospheric Pollution was established. It was also decided to operate a network of background air pollution monitoring stations. With increasing public concern on environmental pollution impacts, a growing number of WMO Members joined the programme. The Environmental Pollution Monitoring and Research Programme, as well as the World Climate Programme launched in the late seventies, will provide information on a possible influence of pollution on climate.When the network of background ait pollution monitoring started, some Members had already proposed to carry out multimedia monitoring at suitable stations. Later on, it became obvious that more information is required on levels and trends of pollutants in media interacting with the atmosphere and a project on integrated monitoring was established, the purpose of which is to define the objectives and uses of integrated monitoring and to establish procedures for routine standardized integrated monitoring of the of the environment.Pilot projects presently being carried out in a few Member countries are meant to provide most of the information required for the implementation of global background integrated environmental monitorting.