(375ai) Data-Driven Multi-Objective Optimization for Canister Sampling Site Selection

This presentation focuses on leveraging the methodologies of data-driven modeling and multi-objective optimization to make decisions of canister sampling site selection to measure background volatile organic compound (VOC) levels in the community under diverse environmental conditions. By strategically selecting sampling sites and segments, accounting for spatial and temporal variations in population distribution, wind direction, wind speeds, and seasonal patterns, this study aims to establish a modeling methodology for the further understanding of baseline VOC concentrations through canister sampling and measurement. The protocol encompasses meticulous dataset collection, with revolutionized decision-making processes across various domains. Statistical analyses will elucidate trends and correlations, facilitating informed environmental monitoring and regulatory decisions. The outcomes are anticipated to provide valuable insights into VOC dynamics, thereby enhancing the ability to evaluate environmental risks effectively.