Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning.

ABSTRACT: A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~10³⁵). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM_2.5) and ozone (O₃) over the

SUBMITTER: Huang J 

PROVIDER: S-EPMC7190429 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature