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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.

The widespread availability of nano-enabled products in the global market may lead to the release of a substantial amount of engineered nanoparticles in the environment, which frequently display drastically different physiochemical properties than their bulk counterparts. The purpose of the study was to evaluate the impact of citrate-stabilised silver nanoparticles (AgNPs) on the plant Arabidopsis thaliana at three levels, physiological phytotoxicity, cellular accumulation and subcellular transport of AgNPs. The monodisperse AgNPs of three different sizes (20, 40 and 80 nm) aggregated into much larger sizes after mixing with quarter-strength Hoagland solution and became polydisperse. Immersion in AgNP suspension inhibited seedling root elongation and demonstrated a linear dose-response relationship within the tested concentration range. The phytotoxic effect of AgNPs could not be fully explained by the released silver ions. Plants exposed to AgNP suspensions bioaccumulated higher silver content than plants exposed to AgNO3 solutions (Ag(+) representative), indicating AgNP uptake by plants. AgNP toxicity was size and concentration dependent. AgNPs accumulated progressively in this sequence: border cells, root cap, columella and columella initials. AgNPs were apoplastically transported in the cell wall and found aggregated at plasmodesmata. In all the three levels studied, AgNP impacts differed from equivalent dosages of AgNO3.

Sustainable development of nanotechnology requires an understanding of the long term ecotoxicological impact of engineered nanomaterials on the environment. Cerium oxide nanoparticles (CeO₂-NPs) have great potential to accumulate and adversely affect the environment owing to their widespread applications in commercial products. This study documented the chronic phenotypic response of tomato plants to CeO₂-NPs (0.1-10 mg L⁻¹) and determined the effect of CeO₂-NPs on tomato yield. The results indicated that CeO₂-NPs at the concentrations applied in this study had either an inconsequential or a slightly positive effect on plant growth and tomato production. However, elevated cerium content was detected in plant tissues exposed to CeO₂-NPs, suggesting that CeO₂-NPs were taken up by tomato roots and translocated to shoots and edible tissues. In particular, substantially higher Ce concentrations were detected in the fruits exposed to 10 mg L⁻¹ CeO₂-NPs, compared with controls. This study sheds light on the long term impact of CeO₂-NPs on plant health and its implications for our food safety and security.