The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

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Highly porous polypyrrole (PPy)-coated TiO 2 /ZnO nanofibrous mat has been successfully synthesized. The core TiO 2 / ZnO nanofibers have an average diameter of ca. 100 nm and the shell of ultrathin PPy layer has a thickness of ca. 7 nm. The NH 3 gas sensor using the as-prepared material exhibited a fast response over a wide dynamic range and high sensitivity with a detection limit of 60 ppb (S/N ¼ 3). Compared to conventional pristine PPy film, the improved performance in NH 3 detection can be attributed to the free access of NH 3 to PPy and a minimized gas diffusion resistance through the ultrathin PPy layer.

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