Data Analytics for Environmental Science and Engineering Research

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

Automated summary

The advancement of new data acquisition and handling techniques has enabled researchers to use machine learning techniques for analyzing complex environmental systems, leading to more comprehensive approaches in environmental monitoring. The current applications of ML algorithms in Environmental Science and Engineering include metagenomic data analysis for antimicrobial resistance, nontarget analysis for environmental pollutant profiling, and anomaly detection in continuous data from engineered water systems.