Integrating machine learning with environmental chemistry to forecast pollutant releases in coatings production

Coating and paint manufacturing is a large generator of toxic emissions and intricate waste streams, which consists of volatile organic compounds (VOCs), hazardous air pollutants (HAPs), heavy metals and strong wastes. These contaminants are highly dangerous to the health of the environment and human health and also cause issues with regulation under the regime of the Clean Air Act and the National Emission Standards for Hazardous Air Pollutants (NESHAP). The review studies models that can be used in predicting emissions, composition of waste streams, and process improvement by combining environmental chemistry with artificial intelligence (AI) and machine learning (ML) methods. Environmental chemistry has insight into the mechanistic understanding of the pollutant sources, transformation pathways and its analytical detection whereas AI can be used to enhance the predictive ability through multi-output modeling, deep learning architecture, and physics-informed frameworks. Examples of applications are VOC and particle emission modelling, heavy metals indoor wastewater residue forecasting, on-line estimation of process parameters to optimise the process to control emissions. This combination of AI and environmental chemistry has high promise in terms of proactive regulatory compliance, enhanced occupational health and sustainable manufacturing. Nevertheless, there are still issues of data quality, poor interpretability, scalability, and regulatory acceptability. The results highlight the change-making nature of AI-augmented environmental surveillance as a means of reducing the environmental impact of the coating and paint manufacturing industry.