Comparative assessment on ammonia nitrogen adsorption onto a saline soil-groundwater environment: distribution, multi-factor interaction, and optimization using response surface methodology and artificial neural network.

Abstract

The adsorption of soil can reduce the leaching of NH4+-N from the external environment into groundwater. The adsorption of NH4+-N is affected by many factors. It is critical to use statistical model to quantitatively describe the effects of interaction between two or more factors on the system response. In this study, HJ-Biplot was used to analyze the correlation characteristics of soil water, salt, and nitrogen, and the response surface methodology and artificial neural network were used to statistically visualize the interaction between factors, including concentration, total dissolved solids (TDS), temperature, and pH. The results showed that the study soil was a typical saline soil, with maximum soil NH4+-N content of 85.45mg/kg. For the adsorption experiments of NH4+-N on saline soils, the effects of factors on the adsorption capacity were assessed using the RSM model. The RSM model was coupled with an ANN to predict the adsorption of NH4+-N by saline soils. The NH4+-N concentration and water pH were both significant at a linear level (p < 0.0001). The interaction between NH4+-N concentration and pH was also more significant (p < 0.01). Under optimal conditions (concentration: 800mg/L; temperature: 24°C; TDS: 637mg/L; pH: 7.83), the NH4+-N adsorption capacity was 1650.2 ug/g, which was in general agreement with the calculated values from the Box-Behnken and RSM model. In addition, a statistical error criterion for the model showed that the RSM-ANN model had greater predictive ability than RSM model.