Predicting the effect of silver nanoparticles on soil enzyme activity using the machine learning method: type, size, dose and exposure time

Abstract

In this study, machine learning models predicted the impact of silver nanoparticles (AgNPs) on soil enzymes. Artificial neural network (ANN) optimized with genetic algorithm (GA) (MAE = 0.1174) was more suitable for simulating overall trends, while the gradient boosting machine (GBM) and random forest (RF) were ideal for small-scale analysis. According to partial dependency profile (PDP) analysis, polyvinylpyrrolidone coated AgNPs (PVP-AgNPs) had the most inhibitory effect (average of 49.5%) on soil enzyme activity among the three types of AgNPs at the same dose (0.02-50 mg/kg). The ANN model predicted that enzyme activity first declined and then rose when AgNPs increased in size. Based on predictions from the ANN and RF models, when exposed to uncoated AgNPs, soil enzyme activities continued to decrease before 30 d, but gradually rose from 30 to 90 d, and fell slightly after 90 d. The ANN model indicated the importance order of four factors: dose > type > size > exposure time. The RF model suggested the enzyme was more sensitive when experiments were conducted at doses, sizes, and exposure times of 0.01-1 mg/kg, 50-100 nm, and 30-90 d, respectively. This study presents new insights on the regularity of soil enzyme responses to AgNPs.

Keywords: Genetic algorithm; Machine learning; Model interpretability; Silver nanoparticles; Soil enzymes.