Abstract
ABSTRACT: Recent developments in water quality monitoring have generated interest in combining non‐probability and probability data to improve water quality assessment. The Interagency Task Force on Water Quality Monitoring has taken the lead in exploring data combination possibilities. In this paper we take a developed statistical algorithm for combining the two data types and present an efficient process for implementing the desired data augmentation. In a case study simulated Environmental Protection Agency (EPA) Environmental Monitoring and Assessment Program (EMAP) probability data are combined with auxiliary monitoring station data. Auxiliary stations were identified on the STORET water quality database. The sampling frame is constructed using ARC/INFO and EPA's Reach File‐3 (RF3) hydrography data. The procedures for locating auxiliary stations, constructing an EMAP‐SWS sampling frame, simulating pollutant exposure, and combining EMAP and auxiliary stations were developed as a decision support system (DSS). In the case study with EMAP, the DSS was used to quantify the expected increases in estimate precision. The benefit of using auxiliary stations in EMAP estimates was measured as the decrease in standard error of the estimate.
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