New approach to predict wastewater quality for irrigation utilizing integrated indexical approaches and hyperspectral reflectance measurements supported with multivariate analysis.

Irrigation water quality is critical to maintaining agricultural output. Reusing wastewater is a global activity that serves as an alternative water resource in agriculture. This study integrates water quality indices and hyperspectral reflectance measurements to assess and predict the drain water quality for irrigation in Egypt. For that, 50 drain water samples were collected surrounding Rosette Branch in Egypt. Four major findings emerge from this Nile Delta wastewater irrigation study: First, the integrated index approach revealed significant spatial variability, with 4% of drains (IWQI < 60) requiring pretreatment and 94% showing low metal contamination (PI < 1), except for Zn hotspots near industrial areas. Second, the newly developed spectral indices such RSI and RSI were strongly related to Total Chlorophyll with R = 0.73, and RSI was strongly related to irrigation water quality index (IWQI) with R = 0.67. As well as RSI had good relationship with Biochemical Oxygen Demand (BOD) with R = 0.75. Third, optimized PLSR models demonstrated higher accuracy in estimating WQIs. For instance, the PLSR model produced reliable estimates of T Chl., achieving R = 0.87 and 0.77 for the calibration and validation dataset. Similarly, the model provided accurate predictions for BOD, with R = 0.96 and 0.81 for calibration and validation. Finally, hydrochemical analysis established evaporation dominance (Gibbs ratio > 0.8) in 72% of samples, explaining the Ca-Mg-SO4 facies prevalence. While currently validated for Nile Delta conditions, the methodology's 89% cross-region accuracy in preliminary tests suggests broad applicability to wastewater irrigation schemes. Future implementation should focus on: (1) farmer-adoptable spectral sensors for the identified optimal bands (566-570 nm, 1140 nm), (2) targeted filtration for Zn/Mn reduction in high-PI drains, and (3) seasonal model calibration to account for Nile flow variations. This work establishes a new paradigm for combining precision spectroscopy with traditional water quality assessment in water-scarce agricultural systems.