Supplemental Digital Content 1
Does Ambient Temperature Interact with Air Pollution to Alter Blood Pressure?
A Repeated-Measure Study in Healthy Adults
Shaowei Wu, Furong Deng, Jing Huang, Xin Wang, Yu Qin, Chanjuan Zheng,
Hongying Wei, Masayuki Shima, Xinbiao Guo
Tables of contents
Supplementary Methods: Environmental Measurement and Analysis
References
Table S1. Spearman correlation matrix for daily levels of major environmental
exposure variables during the study in Beijing, China in 2010-2011.
Table S2. Estimated changes (95% confidence intervals) in blood pressure related to a
10 °C decrease in temperature variables at lag 0 stratified by low/high levels of PM10
and CO.
Figure S1. Trends of the environmental exposure and blood pressure variables over
the study measurements in Beijing, China in 2010-2011.
Figure S2. Exposure-response relationships between daily ambient temperature and
systolic and diastolic blood pressures at lag 0.
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Supplementary Methods: Environmental Measurement and Analysis
Beijing City has over 20 million inhabitants and over 5 million motor vehicles by the
end of 2011. Traffic emission is one of the major sources for air pollution in Beijing
urban area, and also contributes to air pollution in Beijing suburban area to a lesser
extent. The BIT suburban campus is about 2 kilometers from the nearest freeway, and
the BIT urban campus is located in the downtown area of Beijing City along the
northwest inner side of the 3rd ring road that circles the city (1). The air monitoring
site in the BIT suburban campus was set on the rooftop of a three-storey building
(about 10 meters high) and the monitoring site in the BIT urban campus was installed
on the rooftop of a five-storey building (about 15 meters high) within 200 meters from
the 3rd ring road. Participants lived in school dormitories closely around the air
monitors during the study.
We used the following professional instruments and materials for environmental
measurements: a HOBO Pro V2 logger (Onset Corp., Pocasset, MA, USA) for
real-time temperature and relative humidity measurements in 1-min intervals; a digital
dust monitor for real-time PM2.5 concentration measurement (LD-3K; Sibata
Scientific Technology Inc., Tokyo, Japan); SKC sampling systems for PM2.5 mass
collection on quartz-fiber filters and polytetrafluoroethylene filters (SKC Inc., Eighty
Four, PA, USA); a model T15n enhanced CO measurer for real-time CO
concentration measurement (Langan Products Inc., San Francisco, CA, USA); Ogawa
passive samplers for NOX and NO2 collection on cellulose fiber filters (Ogawa Air Inc.,
Osaka, Japan); and a HOBO Pro V2 logger for real-time temperature and relative
humidity measurements (Onset Corp., Pocasset, MA, USA). Instruments were
calibrated according to manufacturer’s specifications before field work began in each
study period.
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Daily mass concentrations of PM2.5 samples were determined by standard
weighing procedures before and after the sample collection using a XS105 Dual
Range Scale (Mettler Toledo, Columbus, OH, USA) and PM2.5 filter samples were
analyzed in the laboratory for the following chemical constituents using professional
techniques: OC and EC by thermo/optical transmission method(2); negative ions by
ion chromatography(3); and metals/metalloids by inductively coupled plasma atomic
emission spectrophotometry or inductively coupled plasma mass spectrometry(4, 5).
Proper quality control and quality assurance are essential for prevention of
contamination and for ensuring chemical quantification accuracy. Field blank filters
were collected on 20% of the sampling days, and were extracted and analyzed
following the same procedures used for the main sampling filters to assess potential
contamination. The field blanks were subtracted from the daily measurements to
adjust for sampling artifacts, including contamination from the filter substrates,
handling or contaminated solvents and supplies.
NOX and NO2 contents collected on cellulose fiber filters were determined by a
spectrophotometer at a wavelength of 545 nm in the laboratory following
manufacturer’s specification(6).
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References
1.
Wang X, Westerdahl D, Chen LC, Wu Y, Hao J, Pan X, Guo X, Zhang KM. Evaluating the air
quality impacts of the 2008 Beijing Olympic Games: on-road emission factors and black carbon
profiles. Atmos Environ 2009; 43(30):4535-4543.
2.
Birch ME. Analysis of carbonaceous aerosols: interlaboratory comparison. The Analyst 1998;
123(5):851-857.
3.
Pathak RK, Wu WS, Wang T. Summertime PM2.5 ionic species in four major cities of China:
nitrate formation in an ammonia-deficient atmosphere. Atmos Chem Phys 2009; 9:1711-1722.
4.
Zhuang G, Guo J, Yuan H, Zhao C. The compositions, sources, and size distribution of the dust
storm from China in spring of 2000 and its impact on the global environment. China Science Bulletin
2001; 46(12):895-901.
5.
Health Effects Institute USA. Improved source apportionment and speciation of low-volume
particulate matter samples. Appendix D. Determination of elemental constituent by HR-ICP-MS:
Health Effects Institute; 2010.
6.
Ogawa & Company USAI. NO, NO2, NOX and SO2 Sampling protocol using the Ogawa sampler
Version 6.06. http://www.ogawausa.com/pdfs/prono-noxno2so206.pdf (04/01/2010.
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Table S1. Spearman correlation matrix for daily levels of major environmental exposure variables during the study in Beijing, China in 2010-2011
Average
Minimum
Maximum
PM10
PM2.5
EC
OC
NO2
CO
temperature temperature temperature
Average temperature
1.00
Minimum temperature
0.96
1.00
Maximum temperature
0.96
0.88
1.00
PM10
0.24
0.20
0.24
1.00
PM2.5
0.19
0.22
0.19
0.79
1.00
EC
0.08
0.10
0.05
0.59
0.68
1.00
OC
0.16
0.14
0.15
0.74
0.70
0.78
1.00
NO2
-0.14
-0.14
-0.16
0.41
0.37
0.70
0.69
1.00
CO
0.39
0.39
0.35
0.58
0.59
0.47
0.58
0.37
1.00
Abbreviations: CO, carbon monoxide; EC, elemental carbon; NO2, nitrogen dioxide; OC, organic carbon; PM10, particulate matter with an aerodynamic diameter ≤10
µm; PM2.5, particulate matter with an aerodynamic diameter ≤2.5 µm.
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Table S2. Estimated changes (95% confidence intervals) in blood pressure related to a
10 °C decrease in temperature variables at lag 0 stratified by low/high levels of PM10 and
CO.
Air pollutant level*
Daily average†
Daily minimum
Daily maximum
Analysis stratified by PM10
SBP
Low
2.2 (0.5, 4.0)
2.7 (0.7, 4.7)
0.9 (-0.6, 2.5)
High
3.8 (2.3, 5.3)
4.1 (2.4, 5.8)
3.0 (1.5, 4.5)
0.06
0.02
0.03
Low
2.2 (0.9, 3.4)
3.0 (1.6, 4.4)
1.5 (0.4, 2.6)
High
3.3 (2.1, 4.5)
3.8 (2.4, 5.2)
2.9 (1.8, 4.1)
0.01
0.004
<0.001
Low
3.5 (1.3, 5.6)
3.7 (1.0, 6.4)
1.6 (-0.4, 3.6)
High
2.9 (1.3, 4.5)
4.4 (2.5, 6.3)
3.2 (1.6, 4.7)
0.81
0.21
0.21
Low
2.3 (0.9, 3.8)
2.7 (1.0, 4.5)
1.4 (0.0, 2.7)
High
3.0 (1.9, 4.2)
4.2 (2.8, 5.6)
3.2 (2.1, 4.4)
0.08
0.02
0.03
P value for interaction
DBP
P value for interaction
Analysis stratified by CO
SBP
P value for interaction
DBP
P value for interaction
Abbreviations: CO, carbon monoxide; DBP, diastolic blood pressure; PM10, particulate matter
with an aerodynamic diameter ≤10 µm; SBP, systolic blood pressure.
*
Low and high air pollutant levels were divided by the median air pollutant values at lag 0.
† Estimates were adjusted for age, body mass index, study period, day count over the study,
day of week, hour of measurement and average relative humidity at lag 0 as fixed-effect terms,
and participant and day count over the study as random-effect terms.
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Figure S1. Trends of the environmental exposure and blood pressure variables over
the study measurements in Beijing, China in 2010-2011. Blood pressure levels were
medians of systolic and diastolic blood pressures (SBP/DBP) in each measurement,
and exposure levels were medians of daily air pollutants and temperature levels at lag
0 before the blood pressure measurements.
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Figure S2. Exposure-response relationships between daily ambient temperature and
systolic and diastolic blood pressures at lag 0. Results are based on generalized linear
mixed models with adjustment for age, body mass index, study period, day count over
the study, day of week, hour of measurement average relative humidity and average
PM2.5 at lag 0 as fixed-effect terms, and participant and day count over the study as
random-effect terms.
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