Elzwayie Adnan, Afan Haitham Abdulmohsin, Allawi Mohammed Falah, El-Shafie Ahmed
Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Environ Sci Pollut Res Int. 2017 May;24(13):12104-12117. doi: 10.1007/s11356-017-8715-0. Epub 2017 Mar 29.
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.
已经开展了多项研究工作,以监测和分析环境因素对全球不同国家水体(湖泊和河流)中重金属浓度和理化性质的影响。本文对之前在监测和分析重金属方面已完成的工作进行了总体概述。本综述的目的是介绍历史研究,以区分和理解研究人员在分析重金属积累时所面临的先前挑战。此外,本综述介绍了一项关于时间增量采样(每月和/或季节性)重要性的调查,以理解和确定不同参数在月度和季节基础上的变化率。此外,还针对未来研究提出了建议,即通过考虑气候条件来获得关于重金属积累的更易懂的数据。因此,本研究的目的是提供可靠的模型,用于预测不同气候和污染条件下水体中未来的重金属积累情况,以便能够使用智能主动策略和人工神经网络(ANN)技术实现水资源管理。