Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments - ACS Axial | ACS Publications

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

ACS ES&T Water welcomes submissions for the upcoming Special Issue “Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments”

The past few years have witnessed the transformative impact of artificial intelligence (AI), machine learning (ML), and data analytics in a wide range of applications, such as speech and image recognition, consumer behavior prediction, and self-driving cars. These applications are primarily driven by the tremendous growth in data collection and storage capabilities as well as in computing power.

These powerful tools have also been increasingly applied in the environmental field to assess contaminant toxicity and environmental risks, evaluate the health of water and wastewater infrastructure, examine the fate and transformation of contaminants in different environments, optimize treatment technologies, identify and characterize pollution sources, model water/wastewater treatment processes, predict contaminant activity in treatment systems and the environment, and perform life cycle analysis, to name a few.

This Special Issue Call for Papers from ACS ES&T Water seeks rigorous research articles, reviews, and perspectives on the current progress, research, opportunities and challenges in applying AI/ML and data analytics to solving environmental problems related to water, and to identify research priorities our community should focus on in the near future.

Examples of topics to be covered include, but are not limited to:

  • Big data-informed water/wastewater infrastructure management
  • Characterize sources of pollution and model emissions of various contaminants in different water-involved environments
  • Data mining from various environmental and biological “omics” data to improve data interpretation and facilitate new discoveries
  • Develop quantitative structure-activity relationships (QSARs) for biotic/abiotic reactivity, adsorption, uptake, treatment, and toxicity of organic and inorganic compounds
  • Model and predict contaminant levels and conduct risk assessment in natural and engineered water systems
  • Monitor and predict nutrients and contaminants levels in different environmental compartments
  • Predict and optimize treatment efficiencies in various treatment and remediation processes, such as in drinking water, wastewater, and groundwater treatment and site remediation

Submit your manuscript for inclusion

Submit your manuscript for inclusion now

Guest Editors

Jacqueline MacDonald Gibson, Head of the Department of Civil, Construction, and Environmental Engineering, North Carolina State University, USA

Carla Ng, Department of Civil & Environmental Engineering, University of Pittsburgh, USA

Xu Wang, Harbin Institute of Technology, Shenzhen, China. Editorial Advisory Board of ACS ES&T Water

Associate and Topic Editors

Ching-Hua Huang, Georgia Institute of Technology, USA

Huichun (Judy) Zhang, Case Western Reserve University, USA

Author Instructions:

To submit your manuscript, please visit the ACS ES&T Water website. Please follow the normal procedures for manuscript submission and when in the ACS Paragon Plus submission site, select the special issue of “Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments.” All manuscripts will undergo rigorous peer review. For additional submission instructions, please see the ACS ES&T Water Author Guidelines.

The deadline for submissions is March 31, 2023.

Submit your manuscript now

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