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Efficient sequential sampling strategies for environmental monitoring
Author(s) -
Mukhopadhyay Nitis,
Bendel Robert B.,
Nikolaidis Nikolaos P.,
Chattopadhyay Saibal
Publication year - 1992
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/92wr00916
Subject(s) - sampling (signal processing) , population , environmental monitoring , sampling design , sample (material) , environmental science , computer science , sample size determination , resource (disambiguation) , percentile , environmental pollution , cumulative distribution function , statistics , environmental engineering , mathematics , probability density function , environmental protection , computer network , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision
Assessments of resources at risk to anthropogenic pollution require extensive environmental monitoring. In addition, such assessments are required to have a long‐term monitoring component in order to evaluate not only the status but also the trend of the resources at risk to ecological stresses. There is a need to identify statistical methodologies that would provide effective and cost‐saving environmental monitoring designs, since such monitoring surveys are very expensive. In this paper the purely sequential, accelerated sequential, and three‐stage procedures are evaluated as effective fixed‐precision sampling procedures for environmental monitoring. Current monitoring designs utilize a sampling methodology where each resource is assigned a population inclusion probability, with the intent of describing the distribution of the whole population of resources at risk to anthropogenic environmental stresses. This study assumes that existing designs accurately describe the population distribution. A simultaneous fixed‐precision estimation procedure is developed as an efficient method of estimating practically relevant percentiles of the cumulative distribution function, using water quality data from the Eastern Lake Survey as a lake population distribution. Accelerated sequential and three‐stage procedures are shown to be better alternatives to the purely sequential procedure, requiring fewer sampling operations without any substantial loss of efficiency. Depending upon the precision required, all procedures showed potential reductions in sample size by as much as 60%. These types of designs for environmental monitoring are expected to be advantageous in national monitoring efforts directed toward the assessment of the status and trends of various ecological indicators.