Concentrations of Heavy Metals in Sardinella gibbosa (Bleeker, 1849) from the Coast of Balochistan, Pakistan

Critical knowledge gaps and relevant variables requiring consideration when performing aquatic ecotoxicity assays

The increasing diversity and complexity of contaminants released in the environment continuously lead to new challenges when applying ecotoxicity assays. This paper comprises a review concerning exposure assessment and highlights important variables that should be taken into account when investigating aquatic media toxicity under both laboratory or field conditions. Thus, to reflect as much as possible what occurs in nature, ecotoxicity assays must carefully consider these variables in their experimental design. This includes contaminant properties, the selected bioindicators and biomarkers, the dose mode/regime, concentration vs. load, exposure to single vs. multiple contaminants and exposure of single vs. multiple species. Many of these, however, are not usually taken into account, leading to critical knowledge gaps in this area, discussed in detail herein.

Heavy metal monitoring, analysis and prediction in lakes and rivers: state of the art

Several research efforts have been conducted to monitor and analyze the impact of environmental factors on the heavy metal concentrations and physicochemical properties of water bodies (lakes and rivers) in different countries worldwide. This article provides a general overview of the previous works that have been completed in monitoring and analyzing heavy metals. The intention of this review is to introduce the historical studies to distinguish and understand the previous challenges faced by researchers in analyzing heavy metal accumulation. In addition, this review introduces a survey on the importance of time increment sampling (monthly and/or seasonally) to comprehend and determine the rate of change of different parameters on a monthly and seasonal basis. Furthermore, suggestions are made for future research to achieve more understandable figures on heavy metal accumulation by considering climate conditions. Thus, the intent of the current study is the provision of reliable models for predicting future heavy metal accumulation in water bodies in different climates and pollution conditions so that water management can be achieved using intelligent proactive strategies and artificial neural network (ANN) techniques.