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Framework for derivation of water quality criteria using the biotic ligand model: Copper as a case study
Author(s) -
Gondek John C,
Gensemer Robert W,
Claytor Carrie A,
Canton Steven P,
Gorsuch Joseph W
Publication year - 2018
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.4062
Subject(s) - representativeness heuristic , consistency (knowledge bases) , sampling (signal processing) , set (abstract data type) , computer science , data mining , quality (philosophy) , process (computing) , water quality , data quality , risk analysis (engineering) , engineering , mathematics , business , ecology , statistics , operations management , metric (unit) , philosophy , filter (signal processing) , epistemology , artificial intelligence , computer vision , biology , programming language , operating system
Acceptance of the biotic ligand model (BLM) to derive aquatic life criteria, for metals in general and copper (Cu) in particular, is growing among regulatory agencies worldwide. Thus, it is important to ensure that water quality data are used appropriately and consistently in deriving such criteria. Here we present a suggested BLM implementation framework (hereafter referred to as “the Framework”) to help guide the decision‐making process when designing sampling and analysis programs for use of the BLM to derive water quality criteria applied on a site‐specific basis. Such a framework will help inform stakeholders on the requirements needed to derive BLM‐based criteria, and thus ensure that the appropriate types and amount of data are being collected and interpreted. The Framework was developed for calculating BLM‐based criteria when data are available from multiple sampling locations on a stream. The Framework aspires to promote consistency when applying the BLM across data sets of disparate water quality, data quantity, and spatial and temporal representativeness and is meant to be flexible to maximize applicability over a wide range of scenarios. Therefore, the Framework allows for a certain level of interpretation and adjustment to address the issues unique to each data set. Integr Environ Assess Manag 2018;14:736–749. © 2018 SETAC