Study of bacterial transport and retention in soil is important for various environmental applications such as groundwater contamination and bioremediation of soil and water. The main objective of this research was to quantitatively assess bacterial transport and deposition under saturated conditions in calcareous soil. A series of leaching experiments was conducted on two undisturbed soil columns. Breakthrough curves of Pseudomonas fluorescens and Cl were measured. After the leaching experiment, spatial distribution of bacteria retention in the soil columns was determined. The HYDRUS-1D one- and two-site kinetic models were used to predict the transport and deposition of bacteria in soil. The results indicated that the two-site model fits the observed data better than one-site kinetic model. Bacteria interaction with the soil of kinetic site 1 revealed relatively fast attachment and slow detachment, whereas attachment to and detachment of bacteria from kinetic site 2 was fast. Fast attachment and slow detachment of site 1 can be attributed to soil calcium carbonate that has favorable attachment sites for bacteria. The detachment rate was less than 0.02 of the attachment rate, indicating irreversible attachment of bacteria. High reduction rate of bacteria was also attributed to soil calcium carbonate.
Sensing and classification of drought stress levels are very important to agricultural production. In this work, rice drought stress levels were classified based on the commonly used chlorophyll a fluorescence (ChlF) parameter (Fv/Fm), feature data (induction features), and the whole OJIP induction (induction curve) by using a Support Vector Machine (SVM). The classification accuracies were compared with those obtained by the K-Nearest Neighbors (KNN) and the Ensemble model (Ensemble) correspondingly. The results show that the SVM can be used to classify drought stress levels of rice more accurately compared to the KNN and the Ensemble and the classification accuracy (86.7%) for the induction curve as input is higher than the accuracy (43.9%) with Fv/Fm as input and the accuracy (72.7%) with induction features as input. The results imply that the induction curve carries important information on plant physiology. This work provides a method of determining rice drought stress levels based on ChlF.