Fast-developing machine learning support complex system research in environmental chemistry

Abstract

Complex systems and their various effects are always of concern in environmental chemistry (EC). With the increasing number of research dimensions, traditional paradigms based on methodological reductionism have failed to help us determine the accurate effects or behaviors of chemical pollutants in multi-media environments. As an adept means of handling complicated objects, artificial intelligence (AI) supported by various machine learning (ML) algorithms is one of the best ways to cope with this problem. In this perspective, we try to explain some similarities between the complex matter in EC and the AI networks and provide some suggestions for combining the two networks for EC data mining.