Control Action Demand for Major Cities Pollutants Using Bayesian Belief Networks

Abstract

Air pollutants in large cities are an overwhelming problem and have been responsible for many premature deaths all around the world. Risk perception maps how people evaluate a hazard in a subjective manner using different statistical tools. In this paper, we use of Bayesian belief network (BBN) to estimate the likelihood of control action demand from people towards authorities based on a proposed framework relating risk perception, risk judgment, and demand. The results showed that it is possible to model control action demand based on BBN structure, given an observed scenario for risk perception and judgment. Different pollutants were compared and the method distinguished the most feared from the lesser feared.