ES&T and ES&T Letters seek papers on machine learning and artificial intelligence research studies in environmental areas that demonstrate the potential to improve our understanding of natural and engineered environmental systems, towards maintaining a healthy ecosystem, and/or building a circular economy.
There are many environmentally relevant research areas where advances in data science including machine learning (ML) and artificial intelligence (AI) have been applied to large datasets to better decipher the complex relationships between system variables and system behaviors, leading to new insights on solution development.
This joint call for papers by Environmental Science & Technology and Environmental Science & Technology Letters seeks contributions on ML and AI research studies in environmental areas that demonstrate the great potential of these approaches to improve, for example, our understanding of natural and engineered environmental systems, towards maintaining a healthy ecosystem, and/or building a circular economy.
Papers are desired that include either novel applications of data science/ML methodologies and approaches adapted for use in environmental datasets, or address knowledge gaps in an important environmental science and technology that were not approachable using standard analysis tools.
Submissions to the Special Issue should demonstrate the “value added” of taking a ML or AI approach over existing approaches. Submissions should also ensure that the datasets are large and complex enough that ML approaches are necessary and robust, and researchers must go beyond the “black box” of simple agnostic applications of existing algorithms to determine the “best one”. Papers should ideally also allow insights into mechanistic underpinnings of the system being investigated.
To serve as model examples of ML and AI analyses on complex environmental datasets, papers must facilitate reproducibility by adhering to FAIR data principles and demonstrate computational rigor (e.g., discuss model assumptions/limitations, data considerations, cross validation, model performance), and provide ML and AI models and datasets to readers through publicly available data repositories.
Editors:
Greg Lowry, Executive Editor, Environmental Science & Technology
Alexandria Boehm, Associate Editor, Environmental Science & Technology and Environmental Science & Technology Letters
Bryan W. Brooks, Editor-in-Chief, Environmental Science & Technology Letters
Pablo Gago-Ferrero, Topic Editor, Environmental Science & Technology
Guibin Jiang, Associate Editor, Environmental Science & Technology
Gerrad Jones, Topic Editor, Environmental Science & Technology
Qian Liu, Guest Editor
Z. Jason Ren, Topic Editor, Environmental Science & Technology and Environmental Science & Technology Letters
Shuxiao Wang, Associate Editor, Environmental Science & Technology and Environmental Science & Technology Letters
Julie Zimmerman, Editor-in-Chief, Environmental Science & Technology
Author Instructions:
To submit your manuscript, please visit the Environmental Science & Technology or Environmental Science & Technology Letters website. Please follow the normal procedures for manuscript submission and when in the ACS Paragon Plus submission site, select the special issue of Data Science for Advancing Environmental Science, Engineering, and Technology.” All manuscripts will undergo rigorous peer review. For additional submission instructions, please see the Environmental Science & Technology Author Guidelines or the Environmental Science & Technology Letters Author Guidelines.
The deadline for submissions is January 12, 2023.
