Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping River Basin, Taiwan

Ning, Shu‐Kuang; Chang, Ni‐Bin; Liu, Yang; Chen, Ho‐Wen; Hsu, Hsiu‐Yun

doi:10.1006/jema.2000.0397

Cited by 69 publications

(56 citation statements)

References 16 publications

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“…In this study, QUAL2E has to be calibrated based on one set of observed data collected in the dry season and subsequently validated with an another set of observed data obtained in the wet season. The research finding shows a good match between computed and measured values, which may guarantee the accuracy in the prediction of half-life distance in the optimization analysis (Ning et al, 2001).…”

Section: Calibration and Verificationmentioning

confidence: 52%

“…Detail principles and procedures are explained as follows: 2.1. STAGE I: WATER QUALITY SIMULATION MODEL Simulation analysis is deemed as an integral part of a complete monitoring program design (Ning et al, 2001). The simulation outputs that characterized the internal features of assimilative capacity in a river system will then provide clues of site potential and will largely dictate the later site-selection process in the optimization framework.…”

Section: Analytical Methodologymentioning

confidence: 99%

“…To gain a deeper understanding of the water quality condition in the river reaches, a large-scale sampling campaign was carried out during dry and wet seasons respectively during the time periods of August 1998 and February 1999 (Ning et al, 2001). Each of the two sampling programs consists of a 96 hr survey.…”

Section: Water Quality Surveymentioning

confidence: 99%

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Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

Ning¹,

Chang²

2004

Environ Monit Assess

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Abstract. 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.Keywords: environmental systems analysis, fuzzy multi-objective programming, monitoring network, river basin planning, water quality management NotationThe following symbols are used in this article: SHU-KUANG NING AND NI-BIN CHANGE ij = the distance between the j th monitoring station located at the ith tributary and the nearest potable water intake in the upper stream area (km); L c = allowable overlapped half-life distance in this systems analysis (km); L i = the lower bound of tolerance interval in the ith fuzzy membership function; L ij k = the distance required for a decay of half concentration of the kth pollutant where the j th monitoring station in the ith tributary is located (km); M = the upper bound of the number of monitoring stations (no unit); p = the number of tributary in Kao-Ping River basin; P ij = the population covered within the 10 km in radius where the j th monitoring station in the ith tributary is located (capita); q i = the total number of candidate station in the ith ...

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Section: Calibration and Verificationmentioning

confidence: 52%

Section: Analytical Methodologymentioning

confidence: 99%

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Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

Ning¹,

Chang²

2004

Environ Monit Assess

Self Cite

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“…la luminosité) et par le calcul du taux de photosynthèse et de respiration. Il a été utilisé entre autres afin de simuler la variabilité de l'oxygène dissous en fonction de la demande biochimique en oxygène et de la photosynthèse (New Jersey, VAN ORDEN et UCHRIN, 1993); d'établir les facteurs majeurs influençant la qualité de l'eau pour le développement des objectifs environnementaux de rejets (CHAUDHURY et al, 1998); et d'évaluer de nouvelles stratégies de gestion pour un fleuve à Taiwan (NING et al, 2001). Le modèle QUASAR est aussi un modèle de qualité de l'eau pouvant être d'application générale et permettant de modéliser diverses variables de qualité de l'eau le long d'une rivière, dont la concentration en oxygène dissous (WHITEHEAD et al, 1997).…”

Section: Modèles Générauxunclassified

Dynamique et modélisation de l’oxygène dissous en rivière

Villeneuve¹,

Légaré²,

Painchaud

et al. 2007

rseau

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La concentration en oxygène dissous en milieu fluvial varie selon un cycle diurne (24 h) qu’il est essentiel de considérer dans l’évaluation de l’état d’oxygénation d’un cours d’eau. En principe, seules des mesures en continu recueillies au cours de cycles de 24 h permettent d’évaluer correctement l’état d’oxygénation d’une rivière, ce que, en pratique, les contraintes logistiques et budgétaires ne permettent pas de réaliser. Le présent article vise à faire la synthèse des connaissances sur les facteurs de contrôle et la modélisation des variations diurnes de la concentration en oxygène dissous en rivière. Parmi les facteurs biologiques et physico-chimiques, les activités autotrophe et hétérotrophe sont les facteurs dominants responsables des variations diurnes de l’oxygène dissous. Certains modèles de qualité de l’eau permettent de modéliser la teneur en oxygène et, dans certains cas, la variation diurne. Toutefois, ces modèles sont souvent complexes, d’utilisation ardue et impliquent la mesure directe sur des cycles de 24 h des variables qui régissent la concentration en oxygène dans le milieu. Une démarche est proposée pour l’élaboration d’un modèle et a été appliquée dans une étude de cas réalisée dans la rivière Saint-Charles (Québec, Canada). Un modèle simple (une fonction sinusoïdale) dont les paramètres ont été corrélés à la température et à la concentration moyenne en nitrate a permis de générer des valeurs simulées d’oxygène dissous très proches des valeurs observées in situ. Un modèle alternatif utilisant des valeurs ponctuelles de température et de concentration de nitrate a donné des résultats équivalents. L’approche proposée constitue donc une alternative simple et pratique à la mesure en continu de l’oxygène et permet une évaluation plus réaliste de l’état réel d’oxygénation d’une rivière que la prise de mesures ponctuelles.In rivers, dissolved oxygen concentrations typically show diel variations with maximum values during daytime and minimum values at night. The diel cycle must be taken into account when assessing the state of oxygenation of a watercourse. However, in water quality monitoring programs, dissolved oxygen concentrations are usually obtained from single measurements taken during daytime. The resulting data do not represent the real overall oxygen levels of a watercourse and thus can lead to erroneous conclusions regarding the oxygen status of a river. Continuous data collected over 24‑hour cycles are required for an accurate oxygen status assessment, but in practice, logistic and budget constraints do not allow such samplings. Modelling can be a convenient alternative to direct measurements. However, the water quality models that take into account the diel cycle of oxygen are generally complex to run. The purpose of this study was to review the information relating to the dynamics and the modelling of the diel variations in dissolved oxygen in rivers and to apply a simple model in a case study involving dissolved oxygen, nutrients, temperature and chlorophyll a data collected over 24‑...

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“…They used QUAL2E, a steady state one-dimensional model that applies a fi nite difference scheme for solving the governing equations on pollutants' transport in rivers. Subsequently, simulation of BOD 5 , dissolved oxygen (DO) and total phosphorous (TP) was carried out by Ning et al (2001).…”

Section: Introductionmentioning

confidence: 99%

A Software Sensor for in-Situ Monitoring of the 5-Day Biochemical Oxygen Demand

Kasem

ALabdeh

Noori

et al. 2017

MGPB

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Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD 5 ), the present study developed two software sensors based on artifi cial intelligence techniques. It is aimed to estimate this indicator instantaneously. For this purpose, feed-forward and radial basis function neural networks (FFANN and RBFANN, respectively) were used. FFANN and RBFANN were employed to estimate the maximum values of BOD 5 (BOD 5(max) ) as a function of average, maximum and minimum dissolved oxygen in the Sefi drood River. Also, Levenberg-Marquardt (LM), resilient backpropagation, and scaled conjugate gradient algorithms were used to optimize the FFANN parameters. The results demonstrated that the performance of the LM algorithm in tuning the FFANN was better than the others, in the verifi cation step. Furthermore, the performance of each model was evaluated according to the mean square error, correlation coeffi cient and developed discrepancy ratio. The results showed that the performance of both FFANN and RBFANN models for the prediction of the BOD 5(max) were approximately the same.

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Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping River Basin, Taiwan

Cited by 69 publications

References 16 publications

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

Dynamique et modélisation de l’oxygène dissous en rivière

A Software Sensor for in-Situ Monitoring of the 5-Day Biochemical Oxygen Demand

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