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Predicting the transpiration stream concentration factor (TSCF) and other concentration factors is essential in understanding the plant uptake of organic contaminants. Traditional mechanistic and numerical modeling methods often fail to reliably predict the TSCF. This study developed a hybrid deep model to predict TSCF by integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN-LSTM model used eight physicochemical properties of organic contaminants to predict TSCF. The training procedure for this hybrid model was successful. The results indicated the training and test losses for predicting TSCF were both in the same order and close to zero. This study showed that the hybrid CNN-LSTM model can outperform mechanistic models and have higher performances compared to classical machine learning models. Feature importance analysis using extreme gradient boosting highlighted the role and importance of lipophilicity in predicting uptake and translocation of organic contaminants. 

Accurate prediction of the uptake and translocation of emerging contaminants in plants has serious implications for assessing impacts on ecosystems and human health. However, traditional modeling approaches are not reliable in the prediction of transpiration stream concentration factor (TSCF) and root concentration factor (RCF). This study applied deep neural networks (DNN), recurrent neural networks (RNN), and long short-term memory (LSTM) to enhance the accuracy of predictive models for TSCF and RCF. The predictions and feature importance analysis were based on nine chemical properties and two plant root macromolecular compositions. The results indicated that deep learning models predict TSCF and RCF with improved accuracy compared to mechanistic models. The coefficient of determination (R^2) for the DNN, RNN, and LSTM models in predicting TSCF was 0.62, 0.67, and 0.56, respectively. The corresponding mean squared error (MSE) on the test set for the models was 0.055, 0.035, and 0.06, respectively. The R^2 for the DNN, RNN, and LSTM models in predicting RCF was 0.90, 0.91, and 0.84, respectively. The corresponding MSE for the models was 0.124, 0.071, and 0.126, respectively. The results of feature extraction using extreme gradient boosting underlined the importance of lipophilicity and root lipid fraction. 

Artificial intelligence (AI) and machine learning (ML) are novel techniques to detect hidden patterns in environmental data. Despite their capabilities, these novel technologies have not been seriously used for real-world problems, such as real-time environmental monitoring. This survey established a framework to advance the novel applications of AI and ML techniques such as Tiny Machine Learning (TinyML) in water environments. The survey covered deep learning models and their advantages over classical ML models. The deep learning algorithms are the heart of TinyML models and are of paramount importance for practical uses in water environments. This survey highlighted the capabilities and discussed the possible applications of the TinyML models in water environments. This study indicated that the TinyML models on microcontrollers are useful for a number of cutting-edge problems in water environments, especially for monitoring purposes. The TinyML models on microcontrollers allow for in situ real-time environmental monitoring without transferring data to the cloud. It is concluded that monitoring systems based on TinyML models offer cheap tools to autonomously track pollutants in water and can replace traditional monitoring methods.