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Journal of Environmental Quality Abstract - Waste Management

Different Indices to Express Biodegradability in Organic Solid Wastes


This article in JEQ

  1. Vol. 39 No. 2, p. 706-712
    Received: July 30, 2009

    * Corresponding author(s): teresa.gea@uab.cat
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  1. Sergio Ponsá,
  2. Teresa Gea * and
  3. Antoni Sánchez
  1. Composting Research Group, Dep. of Chemical Engineering, Escola Tècnica Superior d'Enginyeria, Universitat Autònoma de Barcelona, 08913-Bellaterra (Barcelona), Spain


Respiration indices are suggested in literature as the most suitable stability determination and are proposed as a biodegradability measure in this work. An improved dynamic respiration index methodology is described in this work. This methodology was applied to 58 samples of different types of waste including municipal solid wastes and wastewater sludge, both raw materials and samples collected in a mechanical-biological treatment plant at different stages of biodegradation. The information obtained allowed to establish a qualitative classification of wastes in three categories: highly biodegradable, moderately biodegradable, and wastes of low biodegradability. Results were analyzed in terms of long and short-term indices and index expression: dynamic respiration indices expressed as average oxygen uptake rate (mg O2 g−1 dry matter [DM] h−1) at 1 and 24 h of maximum activity (DRI1h, DRI24h); and cumulative oxygen consumption in 24 h of maximum activity and 4 d (AT24h, AT4). The statistical comparison of indices and wastes is also presented. Raw sludge presented the highest biodegradability followed by the organic fraction of municipal solid waste and anaerobically digested sludge. All indices correlated well but different correlations were found for the different wastes analyzed. The information in the dynamic respiration profile allows for the calculation of different indices that provide complementary information. The combined analysis of DRI24h and AT4 is presented here as the best tool for biodegradable organic matter content characterization and process requirements estimation.

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