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Oxygen diffusion in biochar-based mixtures as plant growth media: Experimental and modelling.

Golnoosh BanitalebiMohammad Reza MosaddeghiHossein Shariatmadari
Published in: Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA (2024)
A large amount of agricultural waste is produced annually. Producing biochar is an excellent solution for waste management, resource recovery, emission reduction, energy production, reduction in transportation and enhancing carbon sequestration. This study was done to investigate the aeration status of biochar-based growth media as compared with the commercial soilless medium of cocopeat-perlite. Biochars from oven-dried residues were produced by slow pyrolysis at 300 (B300) and 500°C (B500) with a rate of 2°C min -1 and using a continuous inflow of nitrogen. Sawdust (Sd), wheat straw (WS), rice hull (Rh), palm bunches (Plm) and sugarcane bagasse (SC), their biochars, vermiculite (V) and zeolite (Z) were used to prepare 13 mixed growth media. Oxygen diffusion coefficient ( D p ) of media was measured at six matric potentials ( h ) of -5, -10, -15, -20, -40 and -60 hPa. Troeh et al. (1982) model was fitted to D p / D 0 versus air-filled porosity (AFP) data. Although AFP was more than 0.1 m 3  m -3 for some media, the D p / D 0 was very low. Considering optimum D p / D 0 (i.e. 0.010-0.015) for growth substrates at h  = -8 hPa, aeration status of four media (cocopeat-perlite, Rh-SCB300-Z, Sd-SCB300-Z and WSB500-Rh-V) was optimum. Highest D p / D 0 at h  = -8 hPa was observed for Rh-SCB300-Z. The AFP at h  = -10 hPa was highest for Rh-SCB300-Z, cocopeat-perlite and WSB500-Rh-V. Biochar-based media with good aeration status and water retention can be a suitable substitute for commercial soilless culture in greenhouse production. Overall, WSB500-Rh-V is a suitable substitute for cocopeat-perlite.
Keyphrases
  • sewage sludge
  • heavy metals
  • plant growth
  • anaerobic digestion
  • municipal solid waste
  • risk assessment
  • machine learning
  • magnetic resonance imaging
  • ionic liquid