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Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm

Author

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  • Ewa Wilk

    (Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, Chair of Geomatics and Information Systems, University of Warsaw, 00-927 Warsaw, Poland)

  • Małgorzata Krówczyńska

    (Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, Chair of Geomatics and Information Systems, University of Warsaw, 00-927 Warsaw, Poland)

  • Bogdan Zagajewski

    (Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, Chair of Geomatics and Information Systems, University of Warsaw, 00-927 Warsaw, Poland)

Abstract

The unique set of physical and chemical properties of asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used asbestos-containing products. Since asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of asbestos–cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of asbestos–cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on asbestos–cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of asbestos–cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of asbestos–cement products used in Poland. The total amount was estimated at 710,278,645 m 2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of asbestos exposure to humans.

Suggested Citation

  • Ewa Wilk & Małgorzata Krówczyńska & Bogdan Zagajewski, 2019. "Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4355-:d:256915
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    1. Vincenzi, Simone & Zucchetta, Matteo & Franzoi, Piero & Pellizzato, Michele & Pranovi, Fabio & De Leo, Giulio A. & Torricelli, Patrizia, 2011. "Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy," Ecological Modelling, Elsevier, vol. 222(8), pages 1471-1478.
    2. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    3. Veall, Michael R & Zimmermann, Klaus F, 1996. "Pseudo-R-[superscript 2] Measures for Some Common Limited Dependent Variable Models," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 241-259, September.
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    1. Pei-Yu Wu & Kristina Mjörnell & Mikael Mangold & Claes Sandels & Tim Johansson, 2021. "A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    2. Seunghon Ham & Sungho Hwang & Chungsik Yoon, 2019. "Comparison of Methods for Pretreatment and Quantification of Bulk Asbestos Samples for Polarized Light Microscopy Analysis to Evaluate Asbestos-Containing Waste," Sustainability, MDPI, vol. 11(22), pages 1-13, November.

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