Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA

Abstract

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM_2.5) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor's data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM_2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60 % of test addresses. Exposure to infiltrated ambient PM_2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM_2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM_2.5 exposure, by 26 % and 47 % respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM_2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment.

Keywords: CONTAM; Energy-Plus; Indoor air quality; Particulate matter, building simulation modeling; Public datasets.

Figure 1.

Datasets and methodologies used to create personalized residential fine particulate matter (PM_2.5) exposure profiles for children in house types common for pediatric asthma patients seen at Boston Medical Center (BMC) from 2000 to 2017. Acronyms used, from left to right, top to bottom: MAPC = Metropolitan Area Planning Committee; HUD LIHTC = Department of Housing and Urban Development Low-income Housing Tax Credit database; NIST = National Institute of Standards and Technology; US EPA = US Environmental Protection Agency; ESRI ArcGIS = a software program for analyzing Geographic Information Systems; CONTAM = a multi-zone contaminant transport model, maintained by NIST; EnergyPlus = a multi-zone whole building energy model, maintained by the US Department of Energy.

Figure 2.

Flowchart for matching patient addresses to housing templates in the National Institute of Standards and Technology (NIST) residential floorplan template database. MAPC = Metropolitan Area Planning Council.

Figure 3.

Distributions of daily average PM_2.5 exposure, varying by season (box fill), housing type (x-axis), and PM_2.5 source – ambient (panel A), cooking (panel B) and tobacco smoke (panel C). The middle bar of each box shows the 50^th percentile of exposure, and the lower and upper box hinges correspond to the 25^th and 75^th percentiles.

Figure 4.

Distributions of daily average PM_2.5 exposure, varying by model resolution, HVAC type, housing type (x-axis), and PM_2.5 source – ambient (panel A), cooking (panel B) and tobacco smoke (panel C). Box fill represents model resolution: lower resolution floor-level modeling (white fill) versus higher resolution room-level modeling (grey fill). Box shading displays HVAC type: central HVAC with a MERV4 filter (diagonal stripes) versus dedicated HVAC of window A/C and baseboard heating (no stripes). The middle bar of each box shows the 50^th percentile of exposure, and the lower and upper box hinges correspond to the 25^th and 75^th percentiles.