title: Critical load analysis in hazard assessment of metals using a Unit World Model.
abstract: A Unit World approach has been used extensively to rank chemicals for their hazards and to understand differences in chemical behavior. Whereas the fate and effects of an organic chemical in a Unit World Model (UWM) analysis vary systematically according to one variable (fraction of organic carbon), and the chemicals have a singular ranking regardless of environmental characteristics, metals can change their hazard ranking according to freshwater chemistry, notably pH and dissolved organic carbon (DOC). Consequently, developing a UWM approach for metals requires selecting a series of representative freshwater chemistries, based on an understanding of the sensitivity of model results to this chemistry. Here we analyze results from a UWM for metals with the goal of informing the selection of appropriate freshwater chemistries for a UWM. The UWM loosely couples the biotic ligand model (BLM) to a geochemical speciation model (Windermere Humic Adsorption Model [WHAM]) and then to the multi-species fate transport-speciation (Transpec) model. The UWM is applied to estimate the critical load (CL) of cationic metals Cd, Cu, Ni, Pb, and Zn, using three lake chemistries that vary in trophic status, pH, and other parameters. The model results indicated a difference of four orders of magnitude in particle-to-total dissolved partitioning (K(d)) that translated into minimal differences in fate because of the short water residence time used. However, a maximum 300-fold difference was calculated in Cu toxicity among the three chemistries and three aquatic organisms. Critical loads were lowest (greatest hazard) in the oligotrophic water chemistry and highest (least hazard) in the eutrophic water chemistry, despite the highest fraction of free metal ion as a function of total metal occurring in the mesotrophic system, where toxicity was ameliorated by competing cations. Water hardness, DOC, and pH had the greatest influence on CL, because of the influence of these factors on aquatic toxicity.
authors.affiliation: Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Nilima
authors.initials: N
authors.lastname: Gandhi
authors.forename: Satyendra P
authors.initials: SP
authors.lastname: Bhavsar
authors.forename: Miriam L
authors.initials: ML
authors.lastname: Diamond
mesh_terms: D000327:Adsorption; D000818:Animals; D059001:Aquatic Organisms / Q000187:drug effects / Q000378:metabolism; D003621:Daphnia / Q000187:drug effects / Q000378:metabolism; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000737:chemistry; D008670:Metals / Q000032:analysis* / Q000378:metabolism / Q000633:toxicity; D008956:Models, Chemical; D009930:Organic Chemicals / Q000032:analysis; D018570:Risk Assessment; D014867:Water / Q000032:analysis; D014872:Water Movements; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000378:metabolism / Q000633:toxicity; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data; D014881:Water Supply / Q000592:standards / Q000706:statistics & numerical data
chemical_list: D008670:Metals; D009930:Organic Chemicals; D014874:Water Pollutants, Chemical; D014867:Water