Quantifying the effectiveness of early warning systems for heavy air pollution based on public responses

Abstract

Air pollution is a major environmental and public health issue in China. Air pollution warnings are issued with the aim of allowing individuals to take protective measures and mitigate risks. Because these warnings are only reminders and not mandatory instructions, public responses play a vital role in the effectiveness of Early Warning Systems (EWSs) for heavy air pollution. However, public responses have never been considered in the evaluation of EWS effectiveness. To address this knowledge gap, a method is proposed to incorporate public responses in the assessment of the effectiveness of EWSs. Warning effectiveness was based upon costs associated with public responses and non-responses, and the minimization of total costs. Health harm was assessed based on an exposure-response relationship, and health effect terms were used to determine the cost of non-responses. In addition, willingness-to-pay values for health protection measures that reduce the risk of dying or getting sick from air pollution were used to determine response investment costs. A Monte Carlo simulation model was then designed to simulate the uncertainty of the warning issuance and public behavior. In addition, numerical experiments were performed to evaluate the model. Experimental parameters were based on the air quality index and warning response surveys from individuals in Beijing, and the effects of air pollution warning issuance were evaluated using model parameters based on several specific scenarios. The results indicated that the current warning threshold used in China is acceptable for optimizing public response. The results also suggested that positive actions taken by people to reduce health risks can improve the effectiveness of EWSs. The model proposed herein can be used by policy makers and governments to monitor and improve air pollution EWSs. In addition, the model and the results presented here are of use for investigating the improvement of global air quality EWSs.