title: Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills.
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.
authors.affiliation: Institute for Water Quality, Resource and Waste Management, Vienna University of Technology, Karlsplatz 13/226-2, 1040 Vienna, Austria. Electronic address: bran.chri@gmail.com.
authors.forename: Christian
authors.initials: C
authors.lastname: Brandstätter
authors.affiliation: Institute for Water Quality, Resource and Waste Management, Vienna University of Technology, Karlsplatz 13/226-2, 1040 Vienna, Austria. Electronic address: david.laner@tuwien.ac.at.
authors.forename: David
authors.initials: D
authors.lastname: Laner
authors.affiliation: blp GeoServices gmbh, Felberstrasse 24/1, 1150 Vienna, Austria. Electronic address: r.prantl@blpgeo.at.
authors.forename: Roman
authors.initials: R
authors.lastname: Prantl
authors.affiliation: Institute for Water Quality, Resource and Waste Management, Vienna University of Technology, Karlsplatz 13/226-2, 1040 Vienna, Austria. Electronic address: johann.fellner@tuwien.ac.at.
authors.forename: Johann
authors.initials: J
authors.lastname: Fellner
mesh_terms: D002712:Chlorides / Q000032:analysis; D004553:Electric Conductivity; D004784:Environmental Monitoring / Q000379:methods*; D006863:Hydrogen-Ion Concentration; D017745:Incineration; D015999:Multivariate Analysis; D012044:Regression Analysis; D062611:Solid Waste / Q000032:analysis*; D064091:Waste Disposal Facilities
chemical_list: D002712:Chlorides; D062611:Solid Waste
keywords: Bootstrapping; Landfill emission; MSW landfill; Multivariate linear model; Waste characterization