Application of artificial neural network model for the identification the effect of municipal waste compost and biochar on phytoremediation of contaminated soils
作者: Reza RoohiMohammad JafariEsfandiar JahantabMaryam Saffari AmanMehdi MoameriSalman Zare
作者单位: 1Department of Mechanical Engineering, Fasa University, Fasa, Iran
2Department of Reclamation of Arid and Mountainous Regions, Natural Resources Faculty, University of Tehran, Karaj, Iran
3Department of Range and Watershed Management, Faculty of Agriculture, Fasa University, Fasa, Iran
4Faculty of Range and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Iran
5Department of Range and Watershed Management, Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran
刊名: Journal of Geochemical Exploration, 2020, Vol.208
来源数据库: Elsevier Journal
DOI: 10.1016/j.gexplo.2019.106399
关键词: Artificial neural networkBromus tomentellusBiocharMunicipal waste compostPhytoremediation
英文摘要: Abstract(#br)This research was carried out to assessing the potential of Bromus tomentellus for phytoremediation with biochar and municipal waste compost amendments to improving the clean-up efficiency of soils contaminated with chromium (Cr) and zinc (Zn). Soil amendment was added to contaminated soil in three levels (%0: Control; without organic fertilizer, biochar and compost 1%, biochar and compost 2%). It also determines the applicability of artificial neural network (ANN) in the modeling of the extraction process. The physiochemical properties of the contaminated soil, including pH, Electrical Conductivity (ECe), Cation Exchange Capacity (CEC) and Sodium Adsorption Ratio (SAR) were determined. After validation of the applied artificial neural network, the effect of municipal waste...
全文获取路径: Elsevier  (合作)
影响因子:1.952 (2012)

  • compost 堆肥
  • waste 岩屑
  • network 网络
  • neural 神经系统的
  • identification 辨认
  • artificial 人为的
  • municipal 城市
  • contaminated 被污染的
  • effect 效应
  • Application 应用