The transportation industry has led efforts to fight climate change and reduce air pollution. Autonomous electric vehicles (A-EVs) that use artificial intelligence, next-generation batteries, etc., are predicted to replace conventional internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) in the coming years. In this study, we performed a life cycle assessment to analyze A-EVs and compare their impacts with those from EV and ICEV systems. The scope of the analysis consists of the manufacturing and use phases, and a functional unit of 150,000 miles·passenger was chosen for the assessment. Our results on the impacts from the manufacturing phase of the analyzed systems show that the A-EV systems have higher impacts than other transportation systems in the majority of the impacts categories analyzed (e.g., global warming potential, ozone depletion, human toxicity-cancer) and, on average, EV systems were found to be the slightly more environmentally friendly than ICEV systems. The high impacts in A-EV are due to additional components such as cameras, sonar, and radar. In comparing the impacts from the use phase, we also analyzed the impact of automation and found that the use phase impacts of A-EVs outperform EV and ICEV in many aspects, including global warming potential, acidification, and smog formation. To interpret the results better, we also investigated the impacts of electricity grids on the use phase impact of alternative transportation options for three representative countries with different combinations of renewable and conventional primary energy resources such as hydroelectric, nuclear, and coal. The results revealed that A-EVs used in regions that have hydropower-based electric mix become the most environmentally friendly transportation option than others.

The alarming increase in the use of chemically driven pesticides for enhanced crop productivity has severely affected soil fertility, ecosystem balance, and consumer health. Inadequate handling protocols and ineffective remediation strategies have led to elevated pesticide concentrations, contributing to human respiratory and metabolic disorders in humans. In the current context, where agricultural activities and pesticide applications are intertwined, strong and sustainable remediation strategies are essential for environmental protection without sacrificing crop productivity. Various bio-inspired methods have been reported, such as phytoremediation, bioremediation, and in situ remediation; however, limited success has been observed with either single or combined approaches. Consequently, biopolymer biomanufacturing, nanoparticle-based bioengineering, and computational biology for improved understanding of mechanisms have been revisited to incorporate updated methodologies that detail the fate and action of harmful chemical pesticides in agriculture. An in silico mechanistic approach has been emphasized to understand the molecular mechanisms involved in agricultural pesticides’ degradation using nanomaterials. A roadmap has been created by integrating cutting-edge machine learning techniques to develop nature-inspired sustainable agricultural practices and contaminant disposal methods. This review represents a pioneering effort to explore the roles of wet-lab chemistry and in silico methods in mitigating the effects of agricultural pesticides, providing a comprehensive strategy for balancing environmental sustainability and agricultural practices.