Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

A novel data-driven screening method of antidepressants stability in wastewater and the guidance of environmental regulations

Wastewater-based epidemiology (WBE) represents a powerful technique for quantifying the attenuation characteristics and consumption of pharmaceuticals. In addition to WBE, no further methods have been developed to assess the wastewater stability related to antidepressants (ADs). In this study, the biodegradability, solubility, and adsorption or partition of 66 ADs were objectively scored according to the relevant guidelines of the Organisation for Economic Cooperation and Development. An assessment framework and the MSSL-RealFormer classification model of ADs wastewater stability were constructed based on physicochemical properties to predict the ADs wastewater stability and the quantitative structure-activity relationship. The constructed MSSL-RealFormer classification model exhibited a markedly higher prediction accuracy than traditional methods. Furthermore, 15 high-stable ADs in wastewater with low biodegradability, high solubility, and low adsorption or partition were identified. SHapley Additive exPlanation method demonstrated that group hydrophobicity, electrostatic and van der Waals forces exerted a significant influence on the ADs wastewater stability. And molecular stability was found to be significantly correlated with the ADs wastewater stability. A combination of density functional theory and MSSL-RealFormer classification model was employed to identify 17 high-stable transformation products of nine medium- and low-stable ADs in wastewater. The Ecological Structure Activity Relationships model demonstrated that bupropion, tapentadol and chlorpheniramine exhibited significant acute toxicity to the aquatic food chain. In this study, a novel deep learning model was constructed to rapidly screen the correlation between the ADs wastewater stability and their molecular structures. It is anticipated to prove a favorable tool for optimizing the wastewater stability screening of pharmaceuticals.

Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox

The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a "CombinedConfidence" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.

Research on predicting the diffusion of toxic heavy gas sulfur dioxide by applying a hybrid deep learning model to real case data

Toxic heavy gas sulfur dioxide (SO2) is a specific life and environmental hazard. Predicting the diffusion of SO2 has become a research focus in fields such as environmental and safety studies. However, traditional methods, such as kinetic models, cannot balance precision and time. Thus, they do not meet the needs of emergency decision-making. Deep learning (DL) models are emerging as a highly regarded solution, providing faster and more accurate predictions of gas concentrations. To this end, this study proposes an innovative hybrid DL model, the parallel-connected convolutional neural network-gated recurrent unit (PC CNN-GRU). This model utilizes two CNNs connected in parallel to process gas release and meteorological datasets, enabling the automatic extraction of high-dimensional data features and handling of long-term temporal dependencies through the GRU. The proposed model demonstrates good performance (RMSE, MAE, and R2 of 20.1658, 10.9158, and 0.9288, respectively) with real data from the Project Prairie Grass (PPG) case. Meanwhile, to address the issue of limited availability of raw data, in this study, time series generative adversarial network (TimeGAN) are introduced for SO2 diffusion studies for the first time, and their effectiveness is verified. To enhance the practicality of the research, the contribution of drivers to SO2 diffusion is quantified through the utilization of the permutation importance (PIMP) and Sobol' method. Additionally, the maximum safe distance downwind under various conditions is visualized based on the SO2 toxicity endpoint concentration. The results of the analyses can provide a scientific basis for relevant decisions and measures.

Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends

Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful tool for exploring and managing wastewater treatment systems. This study provides a summary of the current status and trends in AI research as applied to wastewater treatment, based on published papers and patents. Our results indicate that, at present, AI is primarily used to evaluate removal of pollutants (conventional, typical, and emerging contaminants), optimize models and process parameters, and control membrane fouling. Future research will likely continue to focus on removal of phosphorus, organic pollutants, and emerging contaminants. Moreover, analyzing microbial community dynamics and achieving multi-objective optimization are promising directions of research. The knowledge map shows that there may be future technological innovation related to predicting water quality under specific conditions, integrating AI with other information technologies and utilizing image-based AI and other algorithms in wastewater treatment. In addition, we briefly review development of artificial neural networks (ANNs) and explore the evolutionary path of AI in wastewater treatment. Our findings provide valuable insights into potential opportunities and challenges for researchers applying AI to wastewater treatment.