Indoor Exposure to "Outdoor PM10" Assessing Its Influence on the Relationship Between PM10 and Short-term Mortality in US Cities

From the aDepartment of Building Science, School of Architecture, Tsinghua University, Beijing, China
Epidemiology (Cambridge, Mass.) (Impact Factor: 6.2). 09/2012; 23(6):870-8. DOI: 10.1097/EDE.0b013e31826b800e
Source: PubMed


: Seasonal and regional differences have been reported for the increase in short-term mortality associated with a given increase in the concentration of outdoor particulate matter with an aerodynamic diameter smaller than 10 μm (PM10 mortality coefficient). Some of this difference may be because of seasonal and regional differences in indoor exposure to PM10 of outdoor origin.
: From a previous study, we obtained PM10 mortality coefficients for each season in seven U.S. regions. We then estimated the change in the sum of indoor and outdoor PM10 exposure per unit change in outdoor PM10 exposure (PM10 exposure coefficient) for each season in each region. This was originally accomplished by estimating PM10 exposure coefficients for 19 cities within the regions for which we had modeled building infiltration rates. We subsequently expanded the analysis to include 64 additional cities with less well-characterized building infiltration rates.
: The correlation (r = 0.71 [95% confidence interval = 0.46 to 0.86]) between PM10 mortality coefficients and PM10 exposure coefficients (28 data pairs; four seasons in each of seven regions) was strong using exposure coefficients derived from the originally targeted 19 National Morbidity, Mortality, and Air Pollutions Study cities within the regions. The correlation remained strong (r = 0.67 [0.40 to 0.84]) when PM10 exposure coefficients were derived using 83 cities within the regions (the original 19 plus the additional 64).
: Seasonal and regional differences in PM10 mortality coefficients appear to partially reflect seasonal and regional differences in total PM10 exposure per unit change in outdoor exposure.

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Available from: Bin Zhao, Mar 13, 2015
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    • "It is a critical aspect of air pollution exposure assessment as it influences the import of outdoor-generated air pollutants and the export of indoorgenerated air pollutants. Chen et al. [6] [7] showed that variance in the air change rates of different regions could partially explain the inter-regional variance in health risks to both ozone and particulate matter. Considering the fact that people spend majority of their time in residences [8] [9], an evaluation of the air change rate of residences in Beijing is required to assess its population's exposure to air pollution. "
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    ABSTRACT: Air change rate is a very important parameter for indoor air quality estimation as it influences the exchange of air pollutants between indoor and outdoor environments. Consequently, determining air change rate distribution is indispensable for assessment of a population’s exposure to air pollutants. In this study, the annual and seasonal average infiltration rates (air change rate for window close conditions) of 180 representative residences were simulated using the multi-zone network airflow model (CONTAM) to understand the residential infiltration rate distributions in Beijing. The representative residences were selected by probability sampling based on building characteristics, including building type, floor area, number of rooms, construction year, number of floors, and building orientation. The results show that the annual average infiltration rates in Beijing range from 0.02 to 0.82 h-1 with a median value of 0.16 h-1. The empirical distributions of the annual and seasonal average residential infiltration rates in Beijing were provided. The annual average infiltration rates were also found to well fit a two-parameter lognormal distribution, the median and standard deviation of which is -1.79 and 0.62. Infiltration rates of 34 residences in Beijing were measured via the CO2 decay method, and the measured infiltration rates of residences matched the simulated distributions well. The differences between the simulated and measured infiltration rates are discussed.
    Building and Environment 05/2015; 92. DOI:10.1016/j.buildenv.2015.05.027 · 3.34 Impact Factor
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    • "Moreover, World Health Organization (WHO) reported that hospital admission increments due to respiratory problems are associated with a rapidly increase of PM mass concentrations in a short period (WHO, 2005). For example, several studies have also shown the relations between increased PM2.5 concentrations and adverse health effects in U.S. cities (Schwartz and Neas, 2000; Pope et al., 2009; Chen et al., 2012). "
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    ABSTRACT: Possible source regions of the PM2.5 mass concentrations in Beijing, China have been identified using the Potential Source Contribution Function (PSCF) analysis based on the backward trajectory modeling. Five–day backward trajectories for the arrival of air masses to the ground observation station at Chaoyang Olympic Sports Center were calculated, using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model in 2013. The PSCF results demonstrated that regional sources in different seasons could be one of the crucial contributors to PM2.5 mass concentrations in Beijing, especially in the winter season. Compared PSCF results with the MODIS fine–mode aerosol optical thickness (AOTfine), the regions showing high AOTfine especially the Southern Hebei, Northern Henan and Southwest Shandong are the significant potential PM2.5 contributors to Beijing. Environmental authorities may use the derived models and results for pinpointing the source areas, for improving air quality in Beijing City.
    Atmospheric Pollution Research 01/2015; 6(1):173-177. DOI:10.5094/APR.2015.020 · 1.23 Impact Factor
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    • "Seasonal variations maybe partly be explained by variation in the chemical composition (Krall et al., 2013), in exposure (Chen et al., 2012), or by interaction with other parameters such as ozone and temperature. Using a case-crossover temperature-stratified design, we found that the PM effects were only significant for the warm temperatures . "
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    ABSTRACT: Background Multicentric studies in Europe are required to gain knowledge on the short-term impacts of PM2.5 and PM10–2.5. We present an analysis of the short-term associations between particulate matters (PM10, PM10–2.5 and PM2.5) and mortality by causes, age-groups and seasons in nine French cities. Methods The associations between PM and daily mortality were investigated in each city using a generalized additive Poisson regression model for the 2000–2006 period. The percent increases in the mortality rate were estimated for a 10 μg/m3 increase and for an interquartile range increase in PM levels in each city, for the whole year and by season. The models also compared the PM effect observed on “non-warm” days and on “warm” days. Results A significant effect of PM10 (+0.8% CI 95% [0.2; 1.5] for a 10 μg/m3 increase) and PM2.5 (+0.7% [−0.1; 1.6]) on all-ages non-accidental mortality whole year was observed. The largest impacts were observed on all-ages cardiovascular mortality during summer for PM2.5 (+5.1% [1.8; 8.4]) and PM10–2.5 (+7.2% [2.8; 11.7]). These estimates were lowered when the model included PM2.5 and PM10–2.5. We also report a significant interaction between warm days and PM. Adjusting PM on ozone did not modify the results for the whole year, but decreased the estimates for summer, when a high correlation is observed between these pollutants. Conclusions Our results confirm the short-term impacts of PM10 on mortality, even at concentrations complying with the European annual regulation. They underline the short-term impacts of PM2.5 and PM10–2.5 and call for the setting of regulation values for these PM indicators.
    Atmospheric Environment 10/2014; 95:175–184. DOI:10.1016/j.atmosenv.2014.06.030 · 3.28 Impact Factor
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