Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy

Area of Science:

• Environmental Engineering
• Environmental Science
• Data Science

Background:

• Effective air quality monitoring network (AQMN) design is crucial for environmental management.
• Optimal AQMN design requires consideration of station interdependencies and system uncertainties.

Purpose of the Study:

• To develop a novel optimization model for designing an effective AQMN.
• To maximize spatial coverage while minimizing information uncertainty and the probability of selecting redundant stations.

Main Methods:

• Utilized a non-dominated sorting genetic algorithm II (NSGA-II) for optimization.
• Employed Bayesian maximum entropy (BME) and transinformation entropy (TE) methods for station generation and information assessment.
• Incorporated fuzzy degree of membership and nonlinear interval number programming (NINP) to handle joint information uncertainty.
• Applied the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) for Pareto optimal solution selection.

Main Results:

• The methodology was applied to Los Angeles, Long Beach, and Anaheim, California.
• Recommended 4, 4, and 5 stations for CO, NO2, and ozone, respectively.
• Achieved substantial reductions in transinformation entropy (TE) for CO (8.25x), NO2 (5.86x), and ozone (4.75x).

Conclusions:

• The proposed optimization model provides an effective framework for AQMN design.
• The approach balances monitoring coverage with information redundancy, leading to more efficient air quality monitoring.
• The study demonstrates a significant decrease in information uncertainty with a reduced number of monitoring stations.