Optimization of energy-efficient multi-pollutant air purification for healthy residential buildings

High-level exposure to indoor air pollutants (IAPs) poses significant health risks. Using air purifiers can effectively reduce exposure to IAPs, but it also leads to increased energy consumption. To investigate a multi-objective optimization strategy, this study developed a dynamic control model for multi-pollutant concentrations. Through year-round simulations of six IAPs (PM_2.5, formaldehyde, ozone, benzene, sulfur dioxide, and nitrogen dioxide) in residential buildings in Beijing, the energy-health nexus under different intervention scenarios was quantified. PM_2.5 was the dominant IAP. Implementation of intervention scenario P1 (PM_2.5 control threshold range of 1-5 μg/m³) resulted in average percentage reductions of 84.5 % in annual average concentrations across six IAPs and 36.7 % in associated disease burdens. By integrating non-dominated sorting multi-objective optimization method, we established pareto-optimal solutions for multi-pollutant control. The intervention scenario P4 + B1 (PM_2.5 control threshold range of 25-35 μg/m³ and benzene control threshold range of 0.01-0.02 mg/m³) is the most cost-effective intervention scenario for Beijing. Under this scenario, 108.9 thousand disability-adjusted life years (DALYs) can be avoided, with avoidable DALY rates of 873.4 per 100,000. This study can provide methodological support for formulating collaborative multi-pollutant governance strategies aimed at protecting human health in other countries and regions.