Metal accumulation in two contiguous eutrophic peri-urban lakes,Chivero and Manyame,Zimbabwe

Concentrations of aluminium, cadmium, chromium, cobalt, copper, iron, lead, nickel and zinc were determined in surface water, benthic sediments, and the gills, liver and stomach muscle tissues of Oreochromis niloticus and Clarias gariepinus in peri-urban lakes Chivero and Manyame, Zimbabwe. Five sites were sampled in each lake once per month in November 2015, February, May, August and November 2016. Pollution load index detected no metal contamination, whereas the geo-accumulation index reflected heavy to extreme sediment pollution, with Fe, Cd, Zn, Cr, Ni and Cu present in both lakes. Significant spatial temporal variations were detected for Al, Cr, Cu and Pb across sites within and between the two lakes. High Fe, Al and Cr concentrations in water and sediments in lakes Chivero and Manyame derive from geogenic background sources in addition to anthropogenic loads and intensity. Elevated concentrations of Al, Pb, Cu, Cd, Fe and Zn detected in gills, liver and stomach tissue of catfish corroborate concentrations in water and sediments, and pose the highest ecological and health risk for hydrobionts in lakes Chivero and Manyame. Contiguity of peri-urban lakes exposes them to similar threats, necessitating creative water management strategies, which ensure ecological continuity.     

Inter- and Intra-Species Extrapolation in Risk Assessment of Different Classes of Pesticides

Risk assessors routinely use the reference dose (RfD) approach for non-cancer risk assessment. In this approach, No-Observed-Adverse-Effect-Level (NOAEL) is divided by the product of uncertainty factors (UFs) and, occasionally, an additional modifying factor (MF), each usually employed by default as factors of 10. In the present investigation, kinetic and dynamic data have been used in order to reduce uncertainties when establishing exposure guidelines for examples of chemicals representing four classes of pesticides (warfarin, lindane, carbaryl and parathion). An intensive search of databases was conducted for these pesticides, and toxicokinetic and toxicodynamic parameters in inter- and intra-species were evaluated. The kinetic and dynamic subfactors were less than the proposed values of Renwick and the International Programme on Chemical Safety (IPCS). The composite factors for all the examined pesticides were less than 100. The present study indicated that in setting exposure levels it is important to incorporate kinetic and dynamic data, as they become available, rather than rely on default uncertainty factors, which are imprecise in many cases.     

Case Studies of Categorical Data-Derived Adjustment Factors

Investigations were performed on representative compounds from five different therapeutic classes to evaluate the use of categorical data-derived adjustment factors to account for interindividual variability. The five classes included antidepressants, angiotensin converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDS), cholesterol lowering agents, and antibiotics. Each of the case studies summarized the mode of action of the class responsible for both the therapeutic and adverse effects and the key pharmacodynamic (PD) and pharmacokinetic (PK) parameters that determine the likelihood of these responses for individual compounds in the class. For each class, an attempt was made to identify the key factors that determine interindividual variability and whether there was a common basis to establish a categorical default adjustment factor that could be applied across the class (or at least across specific subclasses within the class). Linking the PK and PD parameters to the critical endpoint used to establish a safe level of exposure was an important underlying theme throughout the investigations. Despite the wealth of PK and PD information in the published literature on the surrogate compounds representing these classes, it was difficult to derive a categorical adjustment factor that could be applied broadly within each class. The amount of information available may have hindered rather than helped the evaluations. Derivation of categorical defaults for different classes of “common” chemicals may be more straightforward if sufficient data are available. In a few cases (e.g., tricyclic antibiotics, ACE inhibitors and selected antiinflammatory agents) categorical defaults could be proposed, although it is unclear whether the reduction in uncertainty resulting from their application would be offset by the additional uncertainties that may have resulted from their application. Residual uncertainties may remain depending on the level of confidence in the underlying assumptions used to support the categorical defaults. Regardless of the conclusions on the utility of categorical defaults, these investigations provided further support for the use of data-derived adjustment factors on a compound-specific basis.