Presenter/Author Information

Marina G. Erechtchoukova
Peter A. Khaiter

Keywords

monitoring design, water quality parameter, constrained optimization model

Start Date

1-7-2012 12:00 AM

Description

The issues of possible improvements, increased efficiency and/oroptimization of monitoring systems in general, and monitoring designs, inparticular, attract the attention of researchers for years. Application of formaltechniques for these purposes looks appealing since it may validate suggestedprocedures or justify expenses required for data collection. The paper describes anapproach to the development of sampling programs as a solution of the operationresearch model which minimizes the total number of water samples collected overan investigated period of time under the condition that the uncertainty of estimatesderived from the monitoring data is kept below an acceptable level. Sinceconcentrations of water constituents exhibit different variability, the numbers ofobservations required to achieve the same uncertainty level in their estimates varysignificantly. In order to make a practically meaningful recommendation on thefrequencies of observations, it is necessary to compromise temporal monitoringdesigns for all water quality parameters whose concentrations are derived from thesame grab water sample. Given that concentrations of these parameters areformed under common hydrological and climatic conditions, it is reasonable toassume that series of concentrations are somehow related. It had been shown thatif such dependencies are detected, they can be used to develop temporalmonitoring designs common for water quality parameters determined from thesame water sample and can significantly reduce the total number of observationsrequired for water quality assessment. The proposed approach has been tested onobservation data collected on a section of a small river in a highly urbanized area.The proposed approach may help to develop efficient monitoring designs with thereasonable cost of sampling programs by considering subsets of the water qualityparameters.

Share

COinS
 
Jul 1st, 12:00 AM

Model-Driven Approach to Optimization of Monitoring Designs for Multiple Water Quality Parameters

The issues of possible improvements, increased efficiency and/oroptimization of monitoring systems in general, and monitoring designs, inparticular, attract the attention of researchers for years. Application of formaltechniques for these purposes looks appealing since it may validate suggestedprocedures or justify expenses required for data collection. The paper describes anapproach to the development of sampling programs as a solution of the operationresearch model which minimizes the total number of water samples collected overan investigated period of time under the condition that the uncertainty of estimatesderived from the monitoring data is kept below an acceptable level. Sinceconcentrations of water constituents exhibit different variability, the numbers ofobservations required to achieve the same uncertainty level in their estimates varysignificantly. In order to make a practically meaningful recommendation on thefrequencies of observations, it is necessary to compromise temporal monitoringdesigns for all water quality parameters whose concentrations are derived from thesame grab water sample. Given that concentrations of these parameters areformed under common hydrological and climatic conditions, it is reasonable toassume that series of concentrations are somehow related. It had been shown thatif such dependencies are detected, they can be used to develop temporalmonitoring designs common for water quality parameters determined from thesame water sample and can significantly reduce the total number of observationsrequired for water quality assessment. The proposed approach has been tested onobservation data collected on a section of a small river in a highly urbanized area.The proposed approach may help to develop efficient monitoring designs with thereasonable cost of sampling programs by considering subsets of the water qualityparameters.