Development and field validation of a predictive copper toxicity model for the green alga Pseudokirchneriella subcapitata.

Abstract

In this study, the combined effects of pH, water hardness, and dissolved organic carbon (DOC) concentration and type on the chronic (72-h) effect of copper on growth inhibition of the green alga Pseudokirchneriella subcapitata were investigated. Natural dissolved organic matter (DOM) was collected at three sites in Belgium and The Netherlands using reverse osmosis. A full central composite test design was used for one DOM and a subset of the full design for the two other DOMs. For a total number of 35 toxicity tests performed, 72-h effect concentration resulting in 10% growth inhibition (EbC10s) ranged from 14.2 to 175.9 micrograms Cu/L (factor 12) and 72-h EbC50s from 26.9 to 506.8 micrograms Cu/L (factor 20). Statistical analysis demonstrated that DOC concentration, DOM type, and pH had a significant effect on copper toxicity; hardness did not affect toxicity at the levels tested. In general, an increase in pH resulted in increased toxicity, whereas an increase of the DOC concentration resulted in decreased copper toxicity. When expressed as dissolved copper, significant differences of toxicity reduction capacity were noted across the three DOM types tested (up to factor 2.5). When expressed as Cu2+ activity, effect levels were only significantly affected by pH; linear relationships were observed between pH and the logarithm of the effect concentrations expressed as free copper ion activity, that is, log(EbC50Cu2+) and log(EbC10Cu2+): (1) log(EbC50Cu2+)= - 1.431 pH + 2.050 (r2 = 0.95), and (2) log(EbC10cu2+) = -1.140 pH -0.812 (r2 = 0.91). A copper toxicity model was developed by linking these equations to the WHAM V geochemical speciation model. This model predicted 97% of the EbC50dissolved and EbC10dissolved values within a factor of two of the observed values. Further validation using toxicity test results that were obtained previously with copper-spiked European surface waters demonstrated that for 81% of tested waters, effect concentrations were predicted within a factor of two of the observed. The developed model is considered to be an important step forward in accounting for copper bioavailability in natural systems.