Wastewater irrigation: An opportunity for improving soil phosphorus availability; PHREEQC modeling and adsorption studies

Wastewater, an alternative supply of water and nutrients, is being allocated as a priority for human population sustainability in arid and semi-arid regions. This work proposes phosphorus (P), a vital growth-limiting nutrient, adsorption behavior in wastewater irrigated agricultural soils in comparison to non-irrigated soils using laboratory batch experiments. The adsorption mechanism was assessed using different adsorption isotherm models. Saturation indices were modeled, using the hydro-geochemical transport code PHREEQC and MINTEQ geochemical software. Phosphorus buffering parameters were also calculated based on the standard equations. The equilibrium data were well fitted with the Freundlich isotherm model. The physical adsorption mechanism was found based on the calculated isotherm parameters. The maximum adsorption capacity was two times more in non-wastewater irrigated soils than irrigated. Results highlighted the effectiveness of wastewater irrigation in P availability in soil. Based on the PHREEQC modeling data, precipitation of Pb and Zn mineral phases was probable in soils by wastewater influence. Meanwhile, the precipitation of stable calcium phases, that affect the P sorption and/or co-precipitation, in non-wastewater irrigated soils was highlighted in the PHREEQC calculations. The standard buffer capacity (SBC) was 43 and 64 L kg^-1 in wastewater irrigated soils and non-irrigated soils, respectively. Findings of the present study demonstrate the importance of wastewater reuse opportunities for agricultural application, especially soil P availability, and are helpful to minimize the environmental impacts of wastewater and solid waste.

Digital mapping of soil erodibility factor in northwestern Iran using machine learning models

Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1 km spacing. To calculate the K-factor, the distribution of particle size and organic carbon (OC) were determined. In addition, 21 terrain attributes were calculated by Digital Elevation Model (DEM) to add value to the soil data. Then, K-factor was modeled using Random Forest (RF) and Artificial Neural Network (ANN) models. In the next step, a non-linear Multiple Logistic Regression (NMLR) was used to obtain low and high classes of K-factor. The results showed that the performance of RF is superior to ANN with a high coefficient of determination [R² = 0.85] and good accuracy [RMSE = 0.003 (Mg ha h/ha MJ mm)]. Therefore, the RF was employed for predicting the K-factor spatial distribution. Finally, using the NMLR model, the study area was divided into low and high classes of K-factor with good correlation [R² Cox and Snell = 0.78, R² Nagelkerke = 0.65]. The areas of these two classes were 60.4% for low class and 39.6% for the high class of K-factor. Based on these results, it was concluded that the resultant map of low and high classes of K-factor could be used by farmers and managers for managing soil water erosion risks in the study area.