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Different methods for averaging indoor fine particulate matter (PM2.5) concentrations significantly impact exposure estimates. Simple household averages often underestimate actual individual exposure, highlighting the need for detailed occupancy data for accurate assessment.

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Area of Science:

  • Environmental Health
  • Indoor Air Quality
  • Exposure Science

Background:

  • Indoor fine particulate matter (PM2.5) poses health risks.
  • Accurate estimation of occupant exposure is crucial for health assessments.
  • Existing methods for aggregating indoor PM2.5 data vary, potentially leading to different exposure conclusions.

Purpose of the Study:

  • To investigate the impact of different aggregation methods on indoor PM2.5 exposure.
  • To compare household-level metrics with individual exposure estimates.
  • To evaluate the accuracy of various averaging approaches in a case study home.

Main Methods:

  • Modelling indoor and outdoor PM2.5 concentrations using CONTAM-EnergyPlus.
  • Simulations conducted for a 1950s bungalow before and after energy retrofit.
  • PM2.5 data modelled at 5-min resolution and aggregated using four distinct metrics.

Main Results:

  • Household arithmetic and time-weighted mean concentrations differed up to 50% from individual exposure.
  • Aggregation methods consistently underpredicted individual exposure.
  • Differences between methods were significant regardless of energy efficiency levels.

Conclusions:

  • Simple household averaging methods (arithmetic and time-weighted means) are insufficient for accurately representing individual PM2.5 exposure.
  • Accurate exposure assessment requires detailed data on occupant presence and activity within different rooms.
  • Future research should incorporate real-time occupancy data for improved indoor air quality exposure modelling.