AI and Big Data in Water Environments

Abstract

Biography Dr. Huichun (Judy) Zhang is the Frank H. Neff professor in the Department of Civil and Environmental Engineering at Case Western Reserve University (Cleveland, OH). She earned her Ph.D. from the Georgia Institute of Technology (Atlanta, GA) and her B.S. and M.S. from Nanjing University (Nanjing, China). Her research focuses on the fate and transformation of environmental contaminants in natural and engineered aquatic environments and the removal of organic contaminants from contaminated water. Her recent research areas also include predictive modeling for contaminant reactivity and sorption using both classical models and machine learning tools. Dr. Zhang has published in numerous journals, such as Chemical Reviews, Environmental Science & Technology, Water Research, and Applied Catalysis B. She has received six competitive research grants from the U.S. National Science Foundation as the PI and directed research projects for many other federal and state agencies and industry. Dr. Zhang is a Topic Editor for ACS ES&T Water and associate editor for Frontiers of Environmental Science & Engineering (FESE).

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

“…With rapid development of artificial intelligence (AI), machine learning (ML) has become one of the most widely employed tools to address broadly scientific problems. 1 For the water environment, AI and ML approaches exhibited practicability, reliability, and high efficiency in solving conventional and emerging concerns in both natural and engineered systems. 2 Among these, wastewater treatment (WWT) has been gaining more and more attention for its connotation toward sustainability.…”

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

“…The important role of artificial intelligence (AI) theory in studying its impact on the ecological economics of management is emphasized in the works of Bartmann (2022), Bolton et al (2021), Ghermandi et al (2022), Gooroochurn et al (2022), Hernandez et al (2022), Ligozat et al (2022), Mao et al (2022), Nost and Colven (2022), Tuia et al (2021), and Zhang (2022).…”

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

“…Acknowledging this trend, we released a virtual issue in ACS ES&T Water in 2022 that pulled together existing papers published since 2021, exploring the latest advancements, research, and obstacles in utilizing AI, ML, and data analytics to address environmental issues within the context of water. 1 The topics covered in that virtual issue spanned various domains, encompassing drinking water and wastewater treatment, energy consumption, quantification of microplastics, prediction of chemical reactivity, and analysis of water usage data in U.S. manufacturing.Building upon this initiative, in 2022 we issued a public call for papers to curate a special issue in ACS ES&T Water dedicated to applications of AI, ML, and data analytics in water environments. We invited submissions that would showcase the latest research activities in this domain, be of broad interest to the water community, and highlight methods, applications, and key insights.…”

“…There has been a remarkable surge in the number of journal articles detailing the application of artificial intelligence (AI), machine learning (ML), and data analytics tools across a wide array of environmental applications. Acknowledging this trend, we released a virtual issue in ACS ES&T Water in 2022 that pulled together existing papers published since 2021, exploring the latest advancements, research, and obstacles in utilizing AI, ML, and data analytics to address environmental issues within the context of water . The topics covered in that virtual issue spanned various domains, encompassing drinking water and wastewater treatment, energy consumption, quantification of microplastics, prediction of chemical reactivity, and analysis of water usage data in U.S. manufacturing.…”

“…Overall, this special issue showcases diverse applications of AI, ML, and data analytics in addressing environmental issues within the broad context of water. As previously highlighted in the editorial for the virtual issue, ensuring the quality of training data, avoiding excessive reliance on complex algorithms, accurately interpreting trained models, and creating user-friendly Web sites or smartphone apps for seamless model accessibility continue to pose challenges and offer opportunities for the future. In addition, while generative AI is still in its infancy, embracing it in future model development and applications presents abundant opportunities to collectively propel the environmental field forward.…”