Bioremediation of Hydrocarbon-Polluted Soil: Evaluation of Different Operative Parameters

The bioremediation of soils polluted with hydrocarbons demonstrated to be a simple and cheap technique, even if it needs a long time. The current paper shows the application of statistical analysis, based on two factors involved in the biological process at several levels. We focus on the Design of...

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Published inApplied sciences Vol. 12; no. 4; p. 2012
Main Authors Castro Rodríguez, David Javier, Gutiérrez Benítez, Omar, Casals Pérez, Enmanuel, Demichela, Micaela, Godio, Alberto, Chiampo, Fulvia
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Agricultural wastes
Biodegradability
Biodegradable wastes
Biodegradation
Biological activity
biopiles
Bioremediation
categorical factors
Crude oil
Design of experiments
Environmental impact
Hydrocarbons
Laboratories
Metabolism
microcosms
Microorganisms
Oil pollution
Oil wastes
Pollutants
Pollution
Refineries
Sediment pollution
Sludge
Soil contamination
Soil pollution
Statistical analysis
Statistics
Sugarcane
Variance analysis
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Summary:The bioremediation of soils polluted with hydrocarbons demonstrated to be a simple and cheap technique, even if it needs a long time. The current paper shows the application of statistical analysis, based on two factors involved in the biological process at several levels. We focus on the Design of Experiments (DOE) to determine the number and kind of experimental runs, whereas the use of the categorical factors has not been widely exploited up to now. This method is especially useful to analyze factors with levels constituted by categories and define the interaction effects. Particularly, we focused on the statistical analysis of (1) experimental runs carried out at laboratory scale (test M, in microcosm), on soil polluted with diesel oil, and (2) bench scale runs (test B, in biopile), on refinery oil sludge mixed with industrial or agricultural biodegradable wastes. Finally, the main purpose was to identify the factor’s significance in both the tests and their potential interactions, by applying the analysis of variance (ANOVA). The results demonstrate the robustness of the statistical method and its quality, especially when at least one of the factors cannot be defined with a numerical value.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12042012