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Field-Based Approaches to Characterizing Long-Term Indoor Environmental Quality in Homes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Field-Based Approaches to Characterizing Long-Term Indoor Environmental Quality in Homes./
Author:	Purgiel, Andrew.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	173 p.
Notes:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
Subject:	Environmental health. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064999
ISBN:	9798438778417

Field-Based Approaches to Characterizing Long-Term Indoor Environmental Quality in Homes.
Purgiel, Andrew.

Field-Based Approaches to Characterizing Long-Term Indoor Environmental Quality in Homes. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 173 p.

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--Colorado State University, 2022.

This item must not be sold to any third party vendors.

The overall goal of this research was to evaluate the performance of energy and indoor environmental quality (IEQ) metrics for future use in impact evaluations of residential energy efficiency upgrades. Analysis focused on the temporal representativeness, spatial representativeness, and spatial specificity of indicators, with an aim to answer the questions:1.To what extent do shorter-duration measurements of indoor environmental quality (IEQ) indicators characterize long-term trends observed in daily IEQ conditions within a sampling location?2.To what extent do single-zone measurements of IEQ indicators in a home characterize multi-zone IEQ conditions within the home?3.What specific information is gained by measuring IEQ in more than one room within a home?The spatial and temporal patterning of indoor environmental quality (IEQ) metrics were observed using commercial-grade IEQ sensors in the living room, bedroom, kitchen, garage, and outdoors for 15 owner- and renter-occupied single-family homes in the City of Fort Collins, Colorado. Indicators of IEQ, including: fine particulate matter (PM2.5), a measure of total volatile organic compounds (TVOC), carbon dioxide (CO2), temperature (T), relative humidity (RH), light, noise, and energy use were monitored continuously in each home for six to ten months. The number of hours for which valid IEQ sensor data were recorded from indoor locations (bedroom, kitchen, and living rooms) ranged from 3,248 hours (136 days) to 7,507 hours (315 days), with a median of 6,589 hours (275 days) across all homes. Time weighted hourly average values of indoor concentrations, pooled across all homes, were calculated over the entire study period for PM2.5 (mean: 8.2 μg/m3, standard deviation: 27.0 μg/m3, coefficient of variation: 3.27), TVOC (mean: 340 ppb, standard deviation: 377 ppb, coefficient of variation: 1.11), and CO2 (mean: 749 ppm, standard deviation: 364 ppm, coefficient of variation: 0.49). Seasons were defined by daily participant heating and cooling behaviors. These behaviors were measured using one-minute resolution energy use data from heating (e.g., furnace) and cooling (e.g., air conditioning) devices within each home. Overall, median PM2.5, TVOC, and CO2 concentrations were lower in the heating season than in the cooling and shoulder seasons. Ranges of indoor PM2.5, TVOC, and CO2 concentrations were comparable between seasons.Hour-of-day average trends of PM2.5 suggested cooking activities in the kitchen were significant sources of PM2.5 in most homes. Average PM2.5 concentrations increased at similar hours of the day between living rooms, kitchens, and bedrooms. Bedroom and living room evening peaks (around 6pm) yielded lower PM2.5 concentrations, on average, compared to kitchen evening peaks. Hour-of-day average TVOC trends in kitchens and living rooms displayed evening peaks that were likely attributed to garage sources or increased indoor participant activity (i.e., cooking and cleaning). Correlations between PM2.5 hourly concentrations recorded in the garage and those recorded in indoor rooms were observed to vary with a predictable pattern throughout the day. If future studies investigated drivers and determinants of this garage-to-indoor relationship, we may expect to discover more on the mechanisms of infiltration of PM2.5 and other pollutants from garages and outdoor areas into living spaces.The extent to which in-home hourly PM2.5, TVOC, and CO2 samples (sampling periods ranging from one day to fourteen days in all three seasons) represented IEQ conditions over a long-term (six- to ten- month) period was evaluated. This evaluation was performed using a measure defined as time-structured temporal representativeness. This measure quantifies how well the average hour-of-day structure for a long-term monitoring period is characterized by data from a shorter sampling period (i.e., how representative the shorter sampling period is). A threshold value was defined to identify when a sample is considered representative. Temporal representativeness of samples increased with sample length. Depending on the season, 80 to 91% of three-day PM2.5 samples and three-day TVOC samples were considered representative. Representativeness of PM2.5 and CO2 samples varied by season. Analysis suggested practitioners sampling IEQ indicators can be confident in the time of day at which PM2.5 or TVOC peaks occur on a "typical" day, based on three-day samples; CO2 samples may require longer lengths. Even if resources are only available to sample for one day, our analysis suggested the time structure of a PM2.5 sample (i.e., the hour(s) when concentration peaks during the day) has a high likelihood of being representative of a "typical" day; however, this likelihood may vary depending on sampling season.The measure of spatial representativeness of IEQ samples was defined in the current study to evaluate how well data gathered from a sampling location (a room) captures trends and magnitudes that characterize average conditions within the larger location of interest (the home). The complementary measure of spatial specificity was used to evaluate how well data recorded in a room captures trends and magnitudes that are not captured by samples recorded in other sampling locations within the home (i.e., how specific or unique the room's data are). In most homes, PM2.5, TVOC, and CO2 data recorded in bedrooms were the most specific of all three indoor rooms (bedroom, living room, and kitchen), but the least representative. These results suggest that if practitioners are aiming to observe the full range of IEQ variability between living spaces, and they are only able to install IEQ sensors in two rooms within a home, the bedroom should be one of the rooms sampled. However, the data gathered from the bedroom may only be applicable if conditions within the bedroom are of interest. Relationships between room, spatial representativeness, spatial specificity, and other variables, such as distance between rooms and HVAC structure, could be explored to discover why between-room variability is higher in certain homes compared to others. Understanding these relationships would help practitioners estimate how many sensors are needed within a home to characterize IEQ conditions within living areas, given building characteristics and the focus of the sampling campaign.This study was conducted in partnership with the Epic Homes program, the purpose of which is to improve the energy efficiency of Fort Collins homes (while also improving the health and well-being of residents) by offering technical and financial assistance for home energy efficiency upgrades. Findings have implications for those aiming to develop best practices when taking short samples of IEQ indicators in homes, whether they be energy efficiency practitioners determining the impacts of residential upgrades or researchers considering IEQ impacts on occupant health.

ISBN: 9798438778417Subjects--Topical Terms:

543032
Environmental health.
Subjects--Index Terms:

Indoor air quality

Field-Based Approaches to Characterizing Long-Term Indoor Environmental Quality in Homes.
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520 $a The overall goal of this research was to evaluate the performance of energy and indoor environmental quality (IEQ) metrics for future use in impact evaluations of residential energy efficiency upgrades. Analysis focused on the temporal representativeness, spatial representativeness, and spatial specificity of indicators, with an aim to answer the questions:1.To what extent do shorter-duration measurements of indoor environmental quality (IEQ) indicators characterize long-term trends observed in daily IEQ conditions within a sampling location?2.To what extent do single-zone measurements of IEQ indicators in a home characterize multi-zone IEQ conditions within the home?3.What specific information is gained by measuring IEQ in more than one room within a home?The spatial and temporal patterning of indoor environmental quality (IEQ) metrics were observed using commercial-grade IEQ sensors in the living room, bedroom, kitchen, garage, and outdoors for 15 owner- and renter-occupied single-family homes in the City of Fort Collins, Colorado. Indicators of IEQ, including: fine particulate matter (PM2.5), a measure of total volatile organic compounds (TVOC), carbon dioxide (CO2), temperature (T), relative humidity (RH), light, noise, and energy use were monitored continuously in each home for six to ten months. The number of hours for which valid IEQ sensor data were recorded from indoor locations (bedroom, kitchen, and living rooms) ranged from 3,248 hours (136 days) to 7,507 hours (315 days), with a median of 6,589 hours (275 days) across all homes. Time weighted hourly average values of indoor concentrations, pooled across all homes, were calculated over the entire study period for PM2.5 (mean: 8.2 μg/m3, standard deviation: 27.0 μg/m3, coefficient of variation: 3.27), TVOC (mean: 340 ppb, standard deviation: 377 ppb, coefficient of variation: 1.11), and CO2 (mean: 749 ppm, standard deviation: 364 ppm, coefficient of variation: 0.49). Seasons were defined by daily participant heating and cooling behaviors. These behaviors were measured using one-minute resolution energy use data from heating (e.g., furnace) and cooling (e.g., air conditioning) devices within each home. Overall, median PM2.5, TVOC, and CO2 concentrations were lower in the heating season than in the cooling and shoulder seasons. Ranges of indoor PM2.5, TVOC, and CO2 concentrations were comparable between seasons.Hour-of-day average trends of PM2.5 suggested cooking activities in the kitchen were significant sources of PM2.5 in most homes. Average PM2.5 concentrations increased at similar hours of the day between living rooms, kitchens, and bedrooms. Bedroom and living room evening peaks (around 6pm) yielded lower PM2.5 concentrations, on average, compared to kitchen evening peaks. Hour-of-day average TVOC trends in kitchens and living rooms displayed evening peaks that were likely attributed to garage sources or increased indoor participant activity (i.e., cooking and cleaning). Correlations between PM2.5 hourly concentrations recorded in the garage and those recorded in indoor rooms were observed to vary with a predictable pattern throughout the day. If future studies investigated drivers and determinants of this garage-to-indoor relationship, we may expect to discover more on the mechanisms of infiltration of PM2.5 and other pollutants from garages and outdoor areas into living spaces.The extent to which in-home hourly PM2.5, TVOC, and CO2 samples (sampling periods ranging from one day to fourteen days in all three seasons) represented IEQ conditions over a long-term (six- to ten- month) period was evaluated. This evaluation was performed using a measure defined as time-structured temporal representativeness. This measure quantifies how well the average hour-of-day structure for a long-term monitoring period is characterized by data from a shorter sampling period (i.e., how representative the shorter sampling period is). A threshold value was defined to identify when a sample is considered representative. Temporal representativeness of samples increased with sample length. Depending on the season, 80 to 91% of three-day PM2.5 samples and three-day TVOC samples were considered representative. Representativeness of PM2.5 and CO2 samples varied by season. Analysis suggested practitioners sampling IEQ indicators can be confident in the time of day at which PM2.5 or TVOC peaks occur on a "typical" day, based on three-day samples; CO2 samples may require longer lengths. Even if resources are only available to sample for one day, our analysis suggested the time structure of a PM2.5 sample (i.e., the hour(s) when concentration peaks during the day) has a high likelihood of being representative of a "typical" day; however, this likelihood may vary depending on sampling season.The measure of spatial representativeness of IEQ samples was defined in the current study to evaluate how well data gathered from a sampling location (a room) captures trends and magnitudes that characterize average conditions within the larger location of interest (the home). The complementary measure of spatial specificity was used to evaluate how well data recorded in a room captures trends and magnitudes that are not captured by samples recorded in other sampling locations within the home (i.e., how specific or unique the room's data are). In most homes, PM2.5, TVOC, and CO2 data recorded in bedrooms were the most specific of all three indoor rooms (bedroom, living room, and kitchen), but the least representative. These results suggest that if practitioners are aiming to observe the full range of IEQ variability between living spaces, and they are only able to install IEQ sensors in two rooms within a home, the bedroom should be one of the rooms sampled. However, the data gathered from the bedroom may only be applicable if conditions within the bedroom are of interest. Relationships between room, spatial representativeness, spatial specificity, and other variables, such as distance between rooms and HVAC structure, could be explored to discover why between-room variability is higher in certain homes compared to others. Understanding these relationships would help practitioners estimate how many sensors are needed within a home to characterize IEQ conditions within living areas, given building characteristics and the focus of the sampling campaign.This study was conducted in partnership with the Epic Homes program, the purpose of which is to improve the energy efficiency of Fort Collins homes (while also improving the health and well-being of residents) by offering technical and financial assistance for home energy efficiency upgrades. Findings have implications for those aiming to develop best practices when taking short samples of IEQ indicators in homes, whether they be energy efficiency practitioners determining the impacts of residential upgrades or researchers considering IEQ impacts on occupant health.
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