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Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills
Authors:Christian Brandstätter  David Laner  Roman Prantl  Johann Fellner
Affiliation:1. Institute for Water Quality, Resource and Waste Management, Vienna University of Technology, Karlsplatz 13/226-2, 1040 Vienna, Austria;2. blp GeoServices gmbh, Felberstrasse 24/1, 1150 Vienna, Austria
Abstract:Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.
Keywords:Bootstrapping  Landfill emission  Multivariate linear model  MSW landfill  Waste characterization  COD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0045"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  chemical oxygen demand  DW"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0055"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  dry weight  EC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0065"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  electrical conductivity  ICP-AES"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0075"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  inductively coupled plasma atomic emission spectroscopy  Loi"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0085"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  loss on ignition  MSW"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0095"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  municipal solid waste  RMdSPE"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0115"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  root median square percentage error  SD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0125"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  standard deviation  sqrt"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0135"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  square root  TN"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0145"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  total nitrogen  TOC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0155"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  total organic carbon  WC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0165"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  water content
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