Several research topics related to environmental pollution and change are being rapidly advanced by modeling technologies including machine learning (ML), artificial intelligence (AI), molecular simulation, computational fluid dynamics (CFD) and other types of computational techniques. On the one hand, this promotion is driven by the flourish of high-performance computing in the past few years. More importantly, the intrinsic complexity of emerging environmental issues provides the modeling/computation with a huge stage. Modeling and computing technologies facilitate assessing the toxicity and environmental/ecological risks of countless compounds, identifying and characterizing sources of pollutants, revealing the behavior, fate, and transformation of contaminants in multiple environmental matrixes, as well as many other investigations regarding environmental pollution and change.
Relevant research is emerging, but the current application of more advanced and powerful computational and data analytical approaches than traditional statistical tools, such as ML and AI in the field of environmental science and engineering is mainly based on the direct use of existing functions or commands. Very few models have been developed for environmental issues, which ignore the complexity and specificity of environmental problems. We hope that researchers try to construct environment-specific models including ready-to-use tools or source code to make predictions, which truly integrate the computational and data science methods into traditional environmental modeling to reveal hidden patterns or correlations, thereby promoting environmental management and pollution control. In addition, the complexity of environmental problems leads to an added challenge regarding the interpretation of the modeling results due to the complicated or black-box relationships between input and output variables.
This Research Topic aims to feature Original Research articles and Reviews on the developments and applications of modeling and computing technologies in scientific studies on the sources, environmental behavior, fate, transformation, toxicity, risk and removal of pollutants. Potential topics for this collection include, but are not limited to:
• Prediction and identification of pollutants such as endocrine-disrupting chemicals, dioxin-like compounds, etc.
• Toxicity prediction modeling for pollutants
• Molecular simulation for revealing toxic mechanisms/ formation pathways/environmental behaviors, etc.
• Studying the transportation of pollutants by CFD
• Earth system modeling
• Models and Software development for governance strategies of pollutants
• Development and application of bioinformatics methods and tools to improve data interpretation and facilitate new discoveries
• Risk quantification and management of pollutants
• Predict and optimize treatment efficiencies in various treatment and remediation processes
• Prediction and design of green chemicals
Several research topics related to environmental pollution and change are being rapidly advanced by modeling technologies including machine learning (ML), artificial intelligence (AI), molecular simulation, computational fluid dynamics (CFD) and other types of computational techniques. On the one hand, this promotion is driven by the flourish of high-performance computing in the past few years. More importantly, the intrinsic complexity of emerging environmental issues provides the modeling/computation with a huge stage. Modeling and computing technologies facilitate assessing the toxicity and environmental/ecological risks of countless compounds, identifying and characterizing sources of pollutants, revealing the behavior, fate, and transformation of contaminants in multiple environmental matrixes, as well as many other investigations regarding environmental pollution and change.
Relevant research is emerging, but the current application of more advanced and powerful computational and data analytical approaches than traditional statistical tools, such as ML and AI in the field of environmental science and engineering is mainly based on the direct use of existing functions or commands. Very few models have been developed for environmental issues, which ignore the complexity and specificity of environmental problems. We hope that researchers try to construct environment-specific models including ready-to-use tools or source code to make predictions, which truly integrate the computational and data science methods into traditional environmental modeling to reveal hidden patterns or correlations, thereby promoting environmental management and pollution control. In addition, the complexity of environmental problems leads to an added challenge regarding the interpretation of the modeling results due to the complicated or black-box relationships between input and output variables.
This Research Topic aims to feature Original Research articles and Reviews on the developments and applications of modeling and computing technologies in scientific studies on the sources, environmental behavior, fate, transformation, toxicity, risk and removal of pollutants. Potential topics for this collection include, but are not limited to:
• Prediction and identification of pollutants such as endocrine-disrupting chemicals, dioxin-like compounds, etc.
• Toxicity prediction modeling for pollutants
• Molecular simulation for revealing toxic mechanisms/ formation pathways/environmental behaviors, etc.
• Studying the transportation of pollutants by CFD
• Earth system modeling
• Models and Software development for governance strategies of pollutants
• Development and application of bioinformatics methods and tools to improve data interpretation and facilitate new discoveries
• Risk quantification and management of pollutants
• Predict and optimize treatment efficiencies in various treatment and remediation processes
• Prediction and design of green chemicals
