Yousefi Kebria D, Ghavami M, Javadi S, Goharimanesh M
Department of Civil and Environmental Engineering, Babol Noshirvani University of Technology, P.O. BOX 484, Babol, Mazandaran, Iran.
Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY, USA.
Environ Monit Assess. 2017 Dec 16;190(1):26. doi: 10.1007/s10661-017-6374-8.
In the contemporary world, urbanization and progressive industrial activities increase the rate of waste material generated in many developed countries. Municipal solid waste landfills (MSWs) are designed to dispose the waste from urban areas. However, discharged landfill leachate, the soluble water mixture that filters through solid waste landfills, can potentially migrate into the soil and affect living organisms by making harmful biological changes in the ecosystem. Due to well-documented landfill problems involving contamination, it is necessary to investigate the long-term influence of discharged leachate on the consistency of the soil beds beneath MSW landfills. To do so, the current study collected vertical deep core samples from different locations in the same unlined landfill. The impacts of effluent leachate on physical and chemical properties of the soil and its propagation depth were studied, and the leachate-transport pattern between successive boreholes was predicted by a developed mathematical model using an adaptive neuro-fuzzy inference system (ANFIS). The decomposition of organic leachate admixtures in the landfill yield is to produce organic acids as well as carbon dioxide, which diminishes the pH level of the landfill soil. The chemical analysis of discharged leachate in the soil samples showed that the concentrations of heavy metals are much lower than those of chloride, COD, BOD, and bicarbonate. Using linear regression and mean square errors between the measured and predicted data, the accuracy of the proposed ANFIS model has been validated. Results show a high correlation between observed and predicated data.
在当代世界，城市化进程和不断发展的工业活动使得许多发达国家产生的废料数量增加。城市固体废弃物填埋场（MSW）旨在处理城市地区产生的垃圾。然而，排放的填埋场渗滤液，即通过固体废弃物填埋场过滤的可溶性水混合物，可能会渗入土壤，并通过在生态系统中产生有害的生物变化来影响生物体。由于有大量关于填埋场污染问题的记录，有必要研究排放的渗滤液对城市固体废弃物填埋场下方土壤层稠度的长期影响。为此，本研究从同一个无衬里填埋场的不同位置采集了垂直深芯样本。研究了渗滤液对土壤物理和化学性质及其传播深度的影响，并使用自适应神经模糊推理系统（ANFIS）开发的数学模型预测了连续钻孔之间的渗滤液传输模式。填埋场中有机渗滤液混合物的分解会产生有机酸和二氧化碳，这会降低填埋场土壤的pH值。对土壤样本中排放的渗滤液进行化学分析表明，重金属浓度远低于氯化物、化学需氧量（COD）、生化需氧量（BOD）和碳酸氢盐的浓度。利用测量数据和预测数据之间的线性回归和均方误差，验证了所提出的ANFIS模型的准确性。结果表明观测数据和预测数据之间具有高度相关性。
