Predicting Residential Air Exchange Rates from Questionnaires and Meteorology: Model Evaluation in Central North Carolina

被引:47
作者
Breen, Michael S. [1 ]
Breen, Miyuki [2 ,3 ]
Williams, Ronald W. [1 ]
Schultz, Bradley D. [1 ]
机构
[1] US EPA, Natl Exposure Res Lab, Res Triangle Pk, NC 27711 USA
[2] US EPA, Natl Hlth & Environm Effects Res Lab, Res Triangle Pk, NC 27711 USA
[3] N Carolina State Univ, Dept Math, Biomath Grad Program, Raleigh, NC 27695 USA
关键词
PARTICULATE MATTER PANEL; UNITED-STATES; EXPOSURE; INDOOR; DISTRIBUTIONS; OUTDOOR; PM2.5; WIND;
D O I
10.1021/es101800k
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time. The AER, which is the airflow into and out of a building, is a primary mechanism for entry of outdoor air pollutants and removal of indoor source emissions. The mechanistic Lawrence Berkeley Laboratory (LBL) AER model was linked to a leakage area model to predict AER from questionnaires and meteorology. The LBL model was also extended to include natural ventilation (LBLX). Using literature-reported parameter values, AER predictions from LBL and LBLX models were compared to data from 642 daily AER measurements across 31 detached homes in central North Carolina, with corresponding questionnaires and meteorological observations. Data was collected on seven consecutive days during each of four consecutive seasons. For the individual model-predicted and measured AER, the median absolute difference was 43% (0.17 h(-1)) and 40% (0.17 h(-1)) for the LBL and LBLX models, respectively. Additionally, a literature-reported empirical scale factor (SF) AER model was evaluated, which showed a median absolute difference of 50% (0.25 h(-1)). The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.
引用
收藏
页码:9349 / 9356
页数:8
相关论文
共 32 条
[1]  
[Anonymous], 2009, 2009 ASHRAE HDB FUND
[2]  
Breen M, 2008, EPIDEMIOLOGY, V19, pS193
[3]   Analyzing a database of residential air leakage in the United States [J].
Chan, WYR ;
Nazaroff, WW ;
Price, PN ;
Sohn, MD ;
Gadgil, AJ .
ATMOSPHERIC ENVIRONMENT, 2005, 39 (19) :3445-3455
[4]  
Dietz R.N., 1982, ENVIRON INT, P419, DOI [DOI 10.1016/0160-4120(82)90060-5, 10.1016/0160-4120(82)90060-5]
[5]  
Dietz R.N., 1986, Measured Air Leakage of Buildings, P203
[6]   The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants [J].
Klepeis, NE ;
Nelson, WC ;
Ott, WR ;
Robinson, JP ;
Tsang, AM ;
Switzer, P ;
Behar, JV ;
Hern, SC ;
Engelmann, WH .
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY, 2001, 11 (03) :231-252
[7]   It's about time: A comparison of Canadian and American time-activity patterns [J].
Leech, JA ;
Nelson, WC ;
Burnett, RT ;
Aaron, S ;
Raizenne, ME .
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY, 2002, 12 (06) :427-432
[8]  
Mcwilliams J., 2006, LBNL59041
[9]   RESIDENTIAL AIR EXCHANGE-RATES IN THE UNITED-STATES - EMPIRICAL AND ESTIMATED PARAMETRIC DISTRIBUTIONS BY SEASON AND CLIMATIC REGION [J].
MURRAY, DM ;
BURMASTER, DE .
RISK ANALYSIS, 1995, 15 (04) :459-465
[10]   Distributions of PM2.5 source strengths for cooking from the research triangle park particulate matter panel study [J].
Olson, DA ;
Burke, JM .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2006, 40 (01) :163-169