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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 ~10). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM) and ozone (O) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM (< 35 μg m) and O (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NO (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM goals, SO reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.
This article was published in the following journal.
Name: The Science of the total environment
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A scale comprising 18 symptom constructs chosen to represent relatively independent dimensions of manifest psychopathology. The initial intended use was to provide more efficient assessment of treatment response in clinical psychopharmacology research; however, the scale was readily adapted to other uses. (From Hersen, M. and Bellack, A.S., Dictionary of Behavioral Assessment Techniques, p. 87)
The large scale production of pharmaceutically important and commercially valuable RECOMBINANT PROTEINS.
A subjective psychometric response scale used to measure distinct behavioral or physiological phenomena based on linear numerical gradient or yes/no alternatives.