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Title: Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments

Abstract

PM 2.5 exposure is associated with significant health risk. Exposures in homes derive from both outdoor and indoor sources, with emissions occurring primarily in discrete events. Data on emission event magnitudes and schedules are needed to support simulation-based studies of exposures and mitigations. For this study, we applied an identification and characterization algorithm to quantify time-resolved PM 2.5 emission events from data collected during 224 days of monitoring in 18 California apartments with low-income residents. We identified and characterized 836 distinct events with median and mean values of 12 and 30 mg emitted mass, 16 and 23 minutes emission duration, 37 and 103 mg/h emission rates, and pseudo-first–order decay rates of 1.3 and 2.0/h. Mean event-averaged concentrations calculated using the determined event characteristics agreed to within 6% of measured values for 14 of the apartments. There were variations in event schedules and emitted mass across homes, with few events overnight and most emissions occurring during late afternoons and evenings. Event characteristics were similar during weekdays and weekends. Emitted mass was positively correlated with number of residents (Spearman coefficient, ρ=.10), bedrooms (ρ=.08), house volume (ρ=.29), and indoor-outdoor CO 2 difference (ρ=.27). The event schedules can be used in probabilistic modeling of PM 2.5 in low-incomemore » apartments.« less

Authors:
 [1];  [1];  [2];  [3]; ORCiD logo [4];  [5]; ORCiD logo [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Indoor Environment Group, Sustainable Energy and Environmental Systems Dept., Energy Analysis and Environmental Impacts Division, Whole Building Systems Dept. and Building Technologies and Urban Systems Division
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Whole Building Systems Dept., Building Technologies and Urban Systems Division
  3. San Diego State Univ., CA (United States). Center for Behavioral Epidemiology and Community Health (C-BEACH) and Graduate School of Public Health
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Indoor Environment Group, Sustainable Energy and Environmental Systems Dept. and Energy Analysis and Environmental Impacts Division
  5. R2M Solution Srl, Pavia (Italy)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B); US Environmental Protection Agency (EPA); US Dept. of Housing and Urban Development (HUD); California Energy Commission
OSTI Identifier:
1432240
Alternate Identifier(s):
OSTI ID: 1396420
Grant/Contract Number:
AC02-05CH11231; I‐PHI‐01070; DW‐89‐9232201‐7; 500‐09‐022
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Indoor Air
Additional Journal Information:
Journal Volume: 28; Journal Issue: 1; Related Information: © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd; Journal ID: ISSN 0905-6947
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; cooking; exposure; multifamily; PM2.5; residential

Citation Formats

Chan, W. R., Logue, J. M., Wu, X., Klepeis, N. E., Fisk, W. J., Noris, F., and Singer, B. C.. Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments. United States: N. p., 2017. Web. doi:10.1111/ina.12425.
Chan, W. R., Logue, J. M., Wu, X., Klepeis, N. E., Fisk, W. J., Noris, F., & Singer, B. C.. Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments. United States. doi:10.1111/ina.12425.
Chan, W. R., Logue, J. M., Wu, X., Klepeis, N. E., Fisk, W. J., Noris, F., and Singer, B. C.. Wed . "Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments". United States. doi:10.1111/ina.12425.
@article{osti_1432240,
title = {Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments},
author = {Chan, W. R. and Logue, J. M. and Wu, X. and Klepeis, N. E. and Fisk, W. J. and Noris, F. and Singer, B. C.},
abstractNote = {PM2.5 exposure is associated with significant health risk. Exposures in homes derive from both outdoor and indoor sources, with emissions occurring primarily in discrete events. Data on emission event magnitudes and schedules are needed to support simulation-based studies of exposures and mitigations. For this study, we applied an identification and characterization algorithm to quantify time-resolved PM2.5 emission events from data collected during 224 days of monitoring in 18 California apartments with low-income residents. We identified and characterized 836 distinct events with median and mean values of 12 and 30 mg emitted mass, 16 and 23 minutes emission duration, 37 and 103 mg/h emission rates, and pseudo-first–order decay rates of 1.3 and 2.0/h. Mean event-averaged concentrations calculated using the determined event characteristics agreed to within 6% of measured values for 14 of the apartments. There were variations in event schedules and emitted mass across homes, with few events overnight and most emissions occurring during late afternoons and evenings. Event characteristics were similar during weekdays and weekends. Emitted mass was positively correlated with number of residents (Spearman coefficient, ρ=.10), bedrooms (ρ=.08), house volume (ρ=.29), and indoor-outdoor CO2 difference (ρ=.27). The event schedules can be used in probabilistic modeling of PM2.5 in low-income apartments.},
doi = {10.1111/ina.12425},
journal = {Indoor Air},
number = 1,
volume = 28,
place = {United States},
year = {Wed Oct 04 00:00:00 EDT 2017},
month = {Wed Oct 04 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 4, 2018
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