Development and Application of a Physics-Based Simulation Model to Investigate Residential PM2.5 Composition and Size Distribution Across the US
INTRODUCTION: Reducing PM2.5 exposure in homes could substantially improve health.
METHODS: We have modified the Lawrence Berkeley National Lab Population Impact Assessment Modeling Framework to determine indoor PM2.5 concentrations and exposures in a set of 50,000 homes representing the US housing stock. A mass-balance model calculated time-dependent pollutant concentrations within each home. The model included size- and species-dependent removal mechanisms and age-based occupancy patterns. We conducted an initial analysis of the impact of increasing central HVAC MERV rating on homes with central ducting on indoor concentrations of outdoor PM2.5.
RESULTS: On average, compared to homes with no filter, MERV6 reduced indoor concentrations by 6%, MERV11 by 19% and MERV14 by 39%. The impact varied by climate zone based on system run time and outdoor conditions that drive infiltration (temperature and wind) and outdoor aerosol composition.
CONCLUSIONS: The modeling framework will allow for the identifications of cost effective methods to control PM2.5 indoors.
IMPLICATIONS: PM2.5 exposures have a substantial impact on the health of occupants of the US housing stock. The impact of filtration on home energy use could have a sizeable net impact on the energy use of the housing stock. It is critical to provide guidance for energy efficient filtration to maximize the benefits and minimize the energy impacts. The correct filtration strategy for a given home will be a function of home location, characteristics, and occupant behavior. Costs and benefits of a given strategy may vary widely within a given location. LBNL has developed a tool to model the expected variations in costs and benefits associated with filtration across the US housing stock. This tool will aid us in providing guidance for filtration in US homes.