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1.
Integr Environ Assess Manag ; 13(4): 746-753, 2017 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-27743466

RESUMEN

Pollutant loads are a means for assessing regulatory compliance and setting targets to reduce pollution entering receiving waterbodies. However, a pollutant load is often comprised of multiple chemicals, which may exert joint toxicity on biota. When the ultimate goal for assessing pollutant loads is to protect ecosystems from adverse effects of toxicants, then the total pollutant load needs to be calculated based on the principles of mixture toxicology. In this article, an improved method is proposed to convert a pollutant load to a toxicity-based load (toxic load) using a modified toxic equivalency factor (TEF) derivation method. The method uses the relative potencies (RePs) of multiple species to represent the response of the ecological community. The TEF is calculated from a percentile of a cumulative distribution function (CDF) fitted to the RePs. The improvements permit the determination of which percentile of the CDF generates the most environmentally relevant and robust toxic loads. That is, environmental relevance ensures that a reduction in the toxic load is likely to result in a corresponding improvement in ecosystem health and robustness ensures that the calculation of the toxic loads is not biased by the reference chemical used. The improved methodology will therefore ensure that correct management decisions will be made and ultimately, a reduction in the toxic load will lead to a commensurate improvement in water quality. Integr Environ Assess Manag 2017;13:746-753. © 2016 SETAC.


Asunto(s)
Sustancias Peligrosas/toxicidad , Pruebas de Toxicidad/métodos , Carcinógenos/toxicidad , Pruebas de Toxicidad/normas
2.
Integr Environ Assess Manag ; 13(4): 754-764, 2017 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-27775206

RESUMEN

Pollutant loads are widely used to set pollution reduction targets and assess regulatory compliance for the protection of receiving waterbodies. However, when a pollutant load consists of a mixture of chemicals, reducing the overall load (mass) will not necessarily reduce the toxicity by a similar amount. This can be overcome by setting targets based on toxicity-based loads (toxic loads, TLs), where the load is modified according to the relative toxicity (expressed as toxic equivalency factors [TEFs]) of each toxicant. Here, we present the second article of a 2-part series in which a case study is used to demonstrate the application of the toxic load method proposed in Part 1. The toxic load method converts a pollutant load, comprised of multiple chemicals, to a toxic load, using a modified TEF approach. The modified approach uses a cumulative distribution of relative potency (ReP) estimates of multiple species to determine a TEF. It further improves upon previously published methods by including two tests to select the optimal percentile of the ReP distribution to determine the TEF. The first test is a test for environmental relevance that compares results against an independent mixture method, identifying the percentile that produces the most environmentally relevant TEFs and TLs. The second is a test for robustness which ensures the results are independent of the ReP of the selected reference chemical. Here, the TL method is applied to mixtures of pesticides that are discharged from agricultural land to the Great Barrier Reef (GBR) to test its utility. In this case study, the most environmentally relevant and robust TLs were generated using the 75th percentile of the ReP cumulative distribution. The results demonstrate that it is essential to develop pollution reduction targets based on toxic loads and making progress to meeting them will lead to a commensurate reduction in toxic effects caused by toxicants in waters of the GBR. Integr Environ Assess Manag 2017;13:754-764. © 2016 SETAC.


Asunto(s)
Arrecifes de Coral , Monitoreo del Ambiente/métodos , Pruebas de Toxicidad/métodos , Contaminantes Químicos del Agua/toxicidad , Queensland , Contaminantes Químicos del Agua/análisis
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