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Sustainable Use of Sewage Sludge for Marigold (Tagetes erecta L.) Cultivation: Experimental and Predictive Modeling Studies on Heavy Metal Accumulation

  • Arwa A. AL-Huqail
  • , Pankaj Kumar
  • , Sami Abou Fayssal
  • , Bashir Adelodun
  • , Ivan Širić
  • , Madhumita Goala
  • , Kyung Sook Choi
  • , Mostafa A. Taher
  • , Aziza S. El-Kholy
  • , Ebrahem M. Eid
  • Princess Nourah Bint Abdulrahman University
  • Gurukula Kangri Vishwavidyalaya
  • University of Forestry
  • Lebanese University
  • University of Ilorin
  • Kyungpook National University
  • University of Zagreb
  • Graphic Era
  • King Khalid University
  • Aswan University
  • Kafrelsheikh University

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

The present study aimed to investigate the impact of sewage sludge (SS) amendment on the growth, yield, and biochemical attributes of the marigold (Tagetes erecta L. var. Pusa Basanti Gainda) crop. For this purpose, marigold flowers were cultivated using three different treatments of SS, i.e., 0% (control with no SS), 5%, and 10%. Multiple linear regression (MLR) modeling was performed to develop prediction models for the impact of soil properties on heavy metals uptake by marigold plants. The results showed that the growth, yield, and biochemical attributes of marigold plants significantly (p < 0.05) increased with an increase in SS dose from 0 to 10%. The most feasible SS treatment was found to be 10%, which achieved a maximum flower yield of 318.42 g/plant. On the other hand, the bioaccumulation factor (BAF) values (>1) showed that the marigold plant was capable of uptaking significant contents of six heavy metals in the order of Cd < Cr < Cu < Zn < Mn < Fe. The MLR-based predictive models were capable of precisely predicting the contents of most heavy metal uptake by marigold plants as indicated by the coefficient of determination (R2 > 0.73), model efficiency (ME > 0.49), root mean square error (RMSE < 3.25), and analysis of variance (ANOVA; p < 0.05) results. Overall, this study presented a novel approach to floriculture by sustainable management of SS while reducing public health and environmental impacts.

Original languageEnglish
Article number447
JournalHorticulturae
Volume9
Issue number4
DOIs
StatePublished - Apr 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • biochemical components
  • floriculture
  • mathematical modeling
  • multiple linear regression
  • waste management

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