Themed collection Artificial Intelligence and Machine Learning in Environmental Science
Introduction to artificial intelligence and machine learning in environmental science
Hemi Luan and Zongwei Cai introduce the Environmental Science: Advances themed issue on artificial intelligence and machine learning in environmental science.
Machine learning for screening active metabolites with metabolomics in environmental science
The current challenges and opportunities for machine learning in the interdisciplinary fields of metabolomics and environmental science.
Application of neural network in metal adsorption using biomaterials (BMs): a review
ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.
Multi-class machine learning classification of PFAS in environmental water samples: a blinded test of performance on unknowns
A multi-class method was developed to identify PFAS origin based on chemical composition, and performance of the method was evaluated in a blinded test against unknowns. The method showed great promise in its ability to recognize sample origin.
Intersections between materials science and marine plastics to address environmental degradation drivers: a machine learning approach
This article uses natural language processing and expert knowledge to bridge the marine plastics community to polymer science.
Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes
The extraction of multiple attributes from past hours in univariate trends of hourly oxides of nitrogen (NOx) recorded at ground-level sites substantially improves NOx hourly forecasts for at least four hours ahead without exogenous-variable inputs.
Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling
Machine learning of the mid-infrared spectra of postconsumer plastics will help prevent, separate, and purify wastestreams contributing to global pollution.
Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research
A CNN was developed to classify extracted features from nontarget mass spectrometry workflows. The CNN accuracy ranged from 85% to 100%. These tools will be important in data-driven research enabling rapid processing of large volume and complex datasets.
Climate change and population aging may impact the benefits of improved air quality on cardiovascular mortality in Guangzhou: epidemiological evidence and policy implications
Dynamic changes in the contribution of air pollution, meteorological conditions and aging to cardiovascular mortality.
About this collection
This collection showcases some of the latest research in Environmental Science: Advances on utilising artificial intelligence and machine learning technology for environmental applications.
Guest-edited by Hemi Luan (South University of Science and Technology), this broad collection highlights these powerful tools for improving our understanding of the environment to build a cleaner, safer, more sustainable and equitable planet.