Efficiency of sediment quality guidelines to predict toxicity: the case of the St. Lawrence River | Efficience de seuils de qualité des sédiments vis à vis de la toxicité : le cas du fleuve Saint Laurent
2010
Desrosiers, Mélanie | Babut, Marc | Pelletier, M. | Bélanger, C. | Thibodeau, S. | Martel, L. | Milieux aquatiques, écologie et pollutions (UR MALY) ; Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF) | MINISTERE DU DEVELOPPMENT DURABLE DE L'ENVIRONNEMENT ET DES PARCS DU QUEBEC CAN ; Partenaires IRSTEA ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) | ENVIRONNEMENT CANADA MONTREAL CAN ; Partenaires IRSTEA ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
[Departement_IRSTEA]Eaux [TR1_IRSTEA]BELCA
显示更多 [+] 显示较少 [-]International audience
显示更多 [+] 显示较少 [-]英语. Multitiered frameworks that are designed for risk assessment of contaminated sediment rely on sediment quality guidelines (SQGs) at the first tier or screening level. In the case of contamination by multiple pollutants, results can be aggregated under indices such as the mean quotient. A decision is then reached (e.g., to dispose of dredged materials in open water) without further investigation, provided that the SQGs or the specific values of indices or quotients derived from the SQGs are not exceeded. In this way, SQGs and quotients play a critical role in environmental protection. As part of the development of a tiered framework to assess the environmental risk of materials dredged from the St. Lawrence River, we evaluated various quotients based on SQGs available for this river with a data set that matches chemistry and toxicity test endpoints. The overall efficiency of all tested quotients was rather low, and we then examined factors such as sediment grain size, nutrients, metal-binding phases (e.g., Al, Fe), and dissolved organic carbon to explain misclassified samples. This examination led to the design of a modified tier 1 framework in which SQGs are used in combination with decision rules based on certain explanatory factors.
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