This study explored the sustainable use of treated sugar mill wastewater (SMW) to cultivate the White button (Agaricus bisporus J.E. Lange) mushroom and the attendant risk of trace metals accumulated in the fruiting bodies. The wheat straw substrate was loaded with a normal water supply and different doses of SMW to enhance its moisture and nutrient contents. The impact of the SMW amendment on A. bisporus yield, biological efficiency, and spawn-running time was assessed. Furthermore, the substrate properties (pH, organic matter, total nitrogen, total phosphorus, etc.) based prediction models for trace metal uptake by A. bisporus fruiting bodies were developed using multiple linear regression (MLR) and artificial neural network (ANN) approaches. The results showed that maximum A. bisporus yield (158.42 ± 8.74 g/kg fresh substrate), biological efficiency (105.61 ± 3.97%), and minimum time of spawn-running (15 days) were observed in 75% SMW enrichment. For the prediction of Cd, Cu, Cr, Fe, Mn, and Zn trace metal uptake, the ANN models showed better performance in terms of R2 (> 0.995), root means square error (RMSE < 0.075), model efficiency (ME > 0.99), and model normalized bias (MNB < 0.009), as compared with those of MLR models with R2 (0.972), RMSE (< 0.441), ME (> 0.96), and MNB (< 0.034), respectively. On the other hand, the target hazard quotient (THQ) showed no significant health risk associated with the consumption of trace metal-contaminated A. bisporus in both adult and child groups. Thus, the findings of this study present a novel, safe, and sustainable method of A. bisporus cultivation along with treated agro-based wastewater management.
Keywords: Agaricus bisporus; Artificial neural network; Health risk; Regression model; Sugar mill wastewater; Trace metals.