Michelle Bellas Romariz Gaudie Ley, Ricardo Abranches Felix Cardoso Júnior, Henrique Vieira de Mendonça, Alexandre Lioi Nascentes, Leonardo Breno Pessoa DA Silva
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Firstly, climate data were collected with Brazil’s National Institute of Meteorology, and landfill structural and operational data were assembled with the company responsible for CTR-SG’ management, Foxx-Haztec, from 2014 to 2018. Subsequently, simulations were conducted on both tools, which indicated that: Swiss Method and HELP do not consider relevant variables for leachate prediction, such as: waste composition and moisture content, organic matter decomposition and dumping methods; thus, these techniques results vary mainly according to annual precipitation, landfill surface area and, for HELP, covering layer thickness. In addition, it was verified that the models forecasted approximately half of the actual volume of generated leachate. In this bias, despite the requirement of few and generally known parameters, these are not reliable tools for assertive prediction. Furthermore, it was found that the Swiss Method employs an imprecise variable, the compaction coefficient, which made pertinent the proposal of a new coefficient, suitable to tropical regions, which should be validated in other landfill scenarios.","PeriodicalId":14063,"journal":{"name":"International Journal of Hydrology","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison between prediction models and monitored data on leachate generation from a sanitary landfill in the metropolitan region of rio de janeiro, Brazil\",\"authors\":\"Michelle Bellas Romariz Gaudie Ley, Ricardo Abranches Felix Cardoso Júnior, Henrique Vieira de Mendonça, Alexandre Lioi Nascentes, Leonardo Breno Pessoa DA Silva\",\"doi\":\"10.15406/IJH.2021.05.00266\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Estimating leachate generation during a landfill lifespan is a key issue in reducing its potential risk. 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引用次数: 1
摘要
估计垃圾填埋场生命周期内产生的渗滤液是降低其潜在风险的关键问题。因此,一个有用的工具是由经验和计算模型来表示的。为了验证在巴西最常用的瑞士方法和在美国最常用的填埋性能水文评价(HELP)的适用性,本文在位于湿润亚热带气候的s o gonalo废物处理中心(cr - sg)进行了案例研究。首先,从2014年到2018年,巴西国家气象研究所收集了气候数据,并与负责cr - sg管理的fox - haztec公司收集了垃圾填埋场结构和运营数据。随后,对这两种工具进行了模拟,结果表明:Swiss Method和HELP在预测渗滤液时没有考虑相关变量,如:废物组成和含水率、有机物分解和倾倒方式;因此,这些技术的结果主要取决于年降水量、填埋面积和HELP覆盖层厚度。此外,还验证了模型预测的实际渗滤液量约为实际渗滤液量的一半。在这种偏差中,尽管需要很少的和已知的参数,但这些不是自信预测的可靠工具。此外,还发现瑞士方法采用了一个不精确的变量,即压实系数,这使得提出一个适用于热带地区的新系数的建议成为可能,该建议应在其他填埋方案中得到验证。
Comparison between prediction models and monitored data on leachate generation from a sanitary landfill in the metropolitan region of rio de janeiro, Brazil
Estimating leachate generation during a landfill lifespan is a key issue in reducing its potential risk. Hence, a useful tool is represented by empirical and computational models. In order to ratify the applicability of the Swiss Method, most applied tool in Brazil, and the Hydrologic Evaluation of Landfill Performance (HELP), most utilized in USA, the present article carried out a case study in the São Gonçalo Waste Treatment Center (CTR-SG), located in a humid subtropical climate. Firstly, climate data were collected with Brazil’s National Institute of Meteorology, and landfill structural and operational data were assembled with the company responsible for CTR-SG’ management, Foxx-Haztec, from 2014 to 2018. Subsequently, simulations were conducted on both tools, which indicated that: Swiss Method and HELP do not consider relevant variables for leachate prediction, such as: waste composition and moisture content, organic matter decomposition and dumping methods; thus, these techniques results vary mainly according to annual precipitation, landfill surface area and, for HELP, covering layer thickness. In addition, it was verified that the models forecasted approximately half of the actual volume of generated leachate. In this bias, despite the requirement of few and generally known parameters, these are not reliable tools for assertive prediction. Furthermore, it was found that the Swiss Method employs an imprecise variable, the compaction coefficient, which made pertinent the proposal of a new coefficient, suitable to tropical regions, which should be validated in other landfill scenarios.