eAppendix 1: PM2.5 mass and black carbon exposure assessment
Personal PM2.5 exposure
We measured 24-hr personal integrated gravimetric exposure to fine PM (particles <2.5 microns in
aerodynamic diameter; PM2.5) in all children using a personal pump (Apex Pro, Casella CEL,
Kempston, Bedford, UK) placed inside a small backpack. The pump was connected to a 37 mm
Slaton aluminum cassette (BGI Inc., Waltham, MA, USA) holding a 37 mm Teflon filter (SKC Inc.,
Eighty Four, PA, USA, 2.0 μm pore size) and attached downstream from a GK2.05 (KTL) cyclone
with a 2.5 µm aerodynamic-diameter cut point (BGI Inc., Waltham, MA, USA). We attached the
cyclone to backpack’s shoulder strap so that it was within the child’s breathing zone. We conducted
2 days of repeat 24-hr exposure measurements in a 10% random sample of study children. Field staff
monitored compliance through daily visits to participants’ homes.
Children were instructed by field staff to perform routine daily activities and wear the backpack at
all times. However, they could place the backpack within 1 meter while sitting or sleeping and
within 2 meters during activities when wearing it was impossible (i.e., washing). The Teflon filters
were weighed pre- and post-sampling at the Wisconsin State Hygiene Laboratory after being
conditioned in a temperature- and humidity-controlled environment (23°C and 40%) for at least 24hr and statically discharged using a polonium source. Filters were weighed in triplicate using a
microbalance (MX-5; Mettler-Toledo, Columbus, OH, USA) with a readability of 1 μg. The average
of the three closest weights was deemed the final weight for statistical analysis. Detailed information
on the study design, data collection and quality control has been previously published.1
Personal black carbon exposure
Following gravimetric analysis, we estimated personal 24-hr BC exposure based on reflectance
analysis on the same Teflon filters using an optical transmissometer data acquisition system (Model
OT21, Magee Scientific, Berkeley, CA, USA). This system provides measurements that are highly
correlated with concentrations measured using thermal/optical methods.2 The optical method
measures and compares the transmission intensity of light at 880 nm passing through an exposed
Teflon filter with that of a blank filter. The resulting light absorption coefficient (ATN) was
computed based on the amount of light transmitted through the exposed filter (I) and the amount
transmitted through the blank filter (I0), where ATN = 100ln(I0/I). We determined BC density
(μg/cm2) by dividing the ATN by the specific attenuation coefficient σ ATN, using the relationship,
BC = ATN/σATN.3 We converted BC density to BC exposure concentration based on the total volume
of air (m3) that passed through the exposed filter during 24-hr pollution measurement period. The
instrument was re-zeroed with a blank filter between measurements.
Empirical correction for BC at higher concentrations
BC is defined as the fraction of carbonaceous aerosol absorbing light over a broad region of the
visible spectrum, and is measured by determining the attenuation of light (ATN) transmitted through
a sample filter. Previous studies have found that the relationship between ATN change and BC
concentration is not linear.4-6 There are several reasons for this non-linear relationship, including that
both scattering and absorbing particles collected on the filter alter the internal reflection of the filter
such that it that changes the absorption of the aerosol/filter combination.5,7 As a result, an
underestimation of measured aethalometer signals (ATN or BC mass concentrations) increases as the
sample filter becomes darker and more loaded (i.e., the BC concentrations increase).
To account for this underestimation of the ATN at higher BC concentrations, we applied an
empirical correction using a previously derived formula. This formula is based on measured 24-hr
ambient air pollution concentrations (n=334) in downtown Los Angeles, CA. To empirically derive
the correction factor, we first estimated the empirical relationship between EC and BC using ambient
air pollution samples in Los Angeles, for which a quadratic relationship was the best fit (R2=0.90).
Knowing that the values of measured BC are underestimated at higher levels, we started with the
estimated value of EC based on the observed quadratic relationship between EC and BC. Then, for
any given level of BC, we estimated a corrected value of BC based on the linear relationship
between EC and BC for BC values less than 4.07 μg/cm2 (Appendix Figure 1; data not published).
We applied the correction factor to the original value of BC in our data set using the equation:
BC=1.3*((0.1*BC02)+(0.5*BC0))+0.7
Eq. (1)
Here, BC is the corrected BC exposure level and BC0 is the measured BC exposure level. We
applied Eq. (1) to all measured BC concentrations in our study. Our results with the analysis using
the corrected BC values were not different from the analysis using the uncorrected BC values
(results not reported).
Figure A1. The measured (blue diamond) and corrected (purple cross) values of BC using data from
Los Angeles. The grey line is the predicted linear relationship between measured BC and EC based
on the BC/EC relationship for BC values less than 4.07 μg/cm2 (n=287) and the black line is the
predicted quadratic relationship between BC and EC based on all data (n=334).
eAppendix 2: Measurement of blood pressure and other covariates
Blood pressure measurement
Children’s BP measurements were conducted at the village primary school between 8 and 10 AM
using an automated device (Omron-705, Omron Corp) that has been validated against a mercury
sphygmomanometer in normotensive children and adolescents according to the revised protocol of
the British Hypertension Society.8 Following ≥5 min of quiet rest, three consecutive resting BP
measures were taken, one minute apart, on the supported right arm of the seated child using an
appropriate cuff size.9 We recorded the date, time and room air temperature during BP measurement.
The average of the last two measures was used as the child’s average BP for that day.
Questionnaires and other measurements
We interviewed an adult in the child’s home and collected socio-demographic and health
information for each child including age, gender, mother’s education, current medication use and
physician-diagnosed diabetes. Passive smoking status was assessed by asking about the number of
tobacco smokers who lived for 9 or more months per year in the child’s household. We used
principal component analysis to derive a composite socioeconomic score for each household using
data on the ownership of 17 household assets.10
We measured each child’s height (cm) and weight (kg) and calculated body mass index (BMI) as
weight (kg) divided by squared-height (m2). Physical activity was assessed using 24-hr pedometer
measurements, which may be a more valid and accurate measure of physical activity than selfreported activity patterns for young children.11 We estimated 24-hr salt and monosodium glutamate
(MSG) intake by weighing the child's household salt and MSG containers at the beginning and end
of a 24-hr period, taking the difference in weights and dividing it by the number of household
residents. In a sub-sample of the study children (n=94; 40%), BP measurement was also performed
on their mothers who were enrolled in a separate study.12
eAppendix 3: Statistical analysis
Statistical analysis was conducted using Stata 11.0. We examined the distributions of all variables
and calculated summary measures accordingly. Both PM2.5 and BC exposure concentrations were
transformed to the natural log scale in order to account for their skewedness to high values.13 For
children with more than one day of PM2.5 and BC exposure measurement, we used the mean
exposure for all measurement days in the analysis. Multivariate linear regression models were used
to quantify the association between PM2.5 and BC with SBP and DBP, while adjusting for potential
confounders. Specifically, we controlled for child’s age, gender, height, BMI, physical activity (24hr pedometer/1000), grams of salt intake, whether or not MSG was consumed in the household,
passive smoking (number of smokers in the household), mother’s years of education, household
socioeconomic status, day of the week (weekday/weekend day), and the 24-hr average ambient air
temperature during pollution monitoring. We conducted our regression models using the BP
measurement taken after the 24-hr pollution measurement.
We retained potential confounders in the final model if they were associated with BP at p<0.10 or if
they changed the estimated effect of PM2.5 or BC exposure on BP by ≥10%.14 We used a robust
variance estimation for cluster-correlated data in the regresion models to account for the fact that
observations from children from the same household cannot be considered independent.15
eAppendix 4: Results and Sensitivity Analyses
In univariate regression models, age (SBP: 1.4 mm Hg, 95% CI: 0.9, 1.8; DBP: 0.4 mm Hg, 95% CI:
0.1, 0.7), height (SBP: 0.2 mm Hg, 95% CI: 0.2, 0.3; DBP: 0.1 mm Hg, 95% CI: 0.0, 0.2), BMI
(SBP: 1.8 mm Hg, 95% CI: 1.3, 2.2; DBP: 0.6 mm Hg, 95% CI: 0.2, 0.9) and gender (SBP: -2.1 mm
Hg, 95% CI: -4.2, -0.1) were significantly associated with higher BP (P<0.05). Physical activity (i.e.,
number of steps/1000) was borderline significantly associated with lower SBP (-0.3 mm Hg, 95% CI:
-0.6, 0.0) (results not presented). A 1-ln(µg/m3) increase in both PM2.5 and BC was associated with a
decrease in SBP and DBP in the univariate models and after adjusting for known risk factors for
higher BP in children, including age, gender, height and BMI (Appendix Table 2).
Sensitivity analyses
In an analysis restricted to the sub-sample of children with maternal BP information, we found that
maternal DBP was significantly associated with a small but significant change in children’s SBP (0.2
mm Hg, 95% CI: 0.1, 0.3) and DBP (0.2 mm Hg, 95% CI: 0.1, 0.3) in the multivariate analysis.
However, inclusion of maternal BP did not change the estimated effects of PM2.5 or BC exposure on
BP and, therefore, we excluded maternal BP from our final models (results not presented). None of
our pollution-BP associations were modified by gender or passive smoking status (all interaction pvalues>0.50).
Also, while we adjusted for age and height, we did not have information on the children’s hormonal
levels or age of puberty onset. BP is known to rise progressively with age and height, and may
increase more rapidly during puberty.16 We examined this source of potential bias by limiting our
analysis to girls ages 10-14 years old (n=73) and boys ages 12-14 years old (n=51). The effects of
PM2.5 and BC in these groups were similar to those observed when all children were included in the
analysis (data not shown). Therefore, our findings cannot be explained by different level of sexual
maturity in exposed and non-exposed children. Further, it suggests that residual confounding by age
does not explain our results when all study children (ages 5-14 years old) are included in the same
analysis.
As a final sensitivity analysis, we transformed SBP and DBP values into quantiles (10th, 25th, 50th,
75th and 90th), and re-conducted our single-pollutant multivariate regression models using (Appendix
Table 3). Though the results with linear regression (i.e., continuous SBP or DBP) were an under- or
over-estimate for some quantiles, the qualitative interpretation of our results using continuous SBP
and DBP did not change.
eAppendix 4: Discussion of possible mechanisms
Although changes in BP in response to acute exposure to ambient PM could be explained by
autonomic nervous system responses, persistent changes may take years to occur. The association
between sodium intake and higher BP has been shown to strengthen with increasing age in a large
meta-analysis, suggesting a possible cumulative burden over time.17 It is possible that BP effects of
PM2.5 and BC exposure were not observed in our study because exposure for prolonged periods of
time is required for persistent changes in the physiologic mechanisms involved in BP control, such
as neurohormonal regulation of arterial tone and control of body fluids by the kidney. We have
recently reported that the effect of PM2.5 exposure from biomass smoke on BP among adult women
is dependent on age, with the strongest effects observed among women over 50 years old.12 Further,
mechanisms that potentially mediate an effect of PM inhalation on prolonged increases in BP may
need to occur over long periods of exposure.18 A persistent increase in BP among older adults occurs
after a resetting to higher values of the level of BP that normally results in renal-pressure natriuresis,
a process that is likely to occur only as a consequence of repeated acute increases in BP after PM
inhalation. In contrast, children in our study were probably too young for developing an abnormal
renal-pressure natriuresis despite persistent PM2.5 exposure.
It is also a possible that the concentrations of PM2.5 and BC exposure among children were too low
to induce a significant change in BP. The average personal PM exposure observed among children in
our study (meanstandard deviation-SD: 5847 µg/m3) was lower than the personal exposures
observed among adult women living in the same villages and during the same time period
(meanSD: 8081 µg/m3).1 Nevertheless, personal PM2.5 exposure concentrations for children in our
study were higher than those observed among ETS-exposed children in an urban setting (mean=37
µg/m3; range: 21-60)19, and ETS exposure has been associated with small increases in BP in
preschool children.20 Though, the chemical composition of emissions from burning tobacco and
wood differs, and studies in adult populations indicate that the composition of PM may affect its
cardiovascular impacts.21
eTable 1. Socio-demographic characteristics of the study children by exposure
Variable
Age (years)
5-8
9-11
12-14
Gender
Male
Female
BMI-for-age a
Severe thinness
(<-3 SD)
Thinness
(≥-3 SD – <-2 SD)
Normal
(≥-2 SD – ≤+1 SD)
Overweight
(>+1 SD – ≤+2 SD)
Socioeconomic status
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Passive smoking status
None
1 smoker
2+ smokers
Salt intake
(mean, SD; grams)
Height
(mean, SD; meters)
Maternal education
(mean, SD; years)
Systolic blood pressure
(mean, SD; mm Hg)
Diastolic blood pressure
(mean, SD; mm Hg)
Below median
PM2.5 exposure
No. (%)
(n=120)
At or above median
PM2.5 exposure
No. (%)
(n=120)
All children
14 (12)
50 (42)
56 (47)
47 (39)
37 (31)
36 (30)
61 (25)
87 (36)
92 (38)
58 (48)
62 (52)
67 (56)
53 (44)
125 (52)
115 (48)
8 (7)
4 (3)
12 (5)
21 (18)
22 (18)
43 (18)
89 (74)
89 (74)
178 (74)
2 (2)
5 (4)
7 (3)
26 (22)
36 (30)
32 (27)
26 (22)
34 (28)
25 (21)
27 (23)
34 (28)
60 (25)
61 (25)
59 (25)
60 (25)
23 (19)
81 (68)
16 (13)
16 (13)
80 (67)
24 (20)
39 (16)
161 (67)
40 (17)
5.0 (1.8)
5.2 (1.9)
5.1 (1.9)
1.3 (0.1)
1.4 (0.1)
1.4 (0.1)
6.7 (2.7)
7.1 (2.6)
6.9 (2.7)
103.7 (8.2)
100.5 (8.2)
102.1 (8.4)
64.6 (6.5)
62.6 (6.5)
63.6 (6.5)
No. (%)
(n=240)
BMI; body mass index as calculated by weight (kg) divided squared height (meters)
No; number of children
SD; standard deviation
a
Based on the WHO’s BMI-for-age growth charts for girls and boys ages 5-19 years.22
eTable 2. Effects of personal PM2.5 and BC exposure (ln(µg/m3)) on blood pressure in univariate single pollutant
models (I, III), single pollutant models adjusted for age, height, BMI and gender (II, IV) and two pollutant models
adjusted for age, height, BMI, gender (V)a
I
Systolic blood pressure
PM2.5; ln(µg/m3)
-3.5 (-5.5, -1.5)
3
BC; ln(µg/m )
Age; years
Height; cm
BMI; kg/m2
Female gender
Difference in mm Hg, (95% CI)
II
III
IV
-2.1 (-3.9, -0.3)
-4.4 (-8.2, -0.7)
-0.6 (-1.5, 0.2)
0.2 (0.1, 0.4)
1.4 (0.8, 2.0)
-1.3 (-3.1, 0.6)
-2.6 (-5.8, 0.6)
0.5 (0.1, 0.4)
0.2 (0.1, 0.4)
1.3 (0.8, 1.9)
-1.3 (-3.1, 0.6)
V
-1.7
-1.0
-0.6
0.2
1.4
-1.4
(-3.8, 0.3)
(-4.6, 2.5)
(-1.5, 0.3)
(0.1, 0.4)
(0.8, 1.9)
(-3.2, 0.5)
Diastolic blood pressure
PM2.5; ln(µg/m3)
-1.8 (-3.3, -0.3)
-1.3 (-2.9, 0.2)
-1.2 (-2.9, 0.7)
3
BC; ln(µg/m )
-2.2 (-4.9, 0.6) -1.7 (-4.5, 1.1)
-0.6 (-3.9, 2.6)
Age; years
-0.6 (-1.3, 0.2)
-0.6 (-1.4, 0.1)
-0.6 (-1.4, 0.1)
Height; cm
0.1 (0.0, 0.3)
0.1 (0.0, 0.2)
0.1 (0.0, 0.2)
2
BMI; kg/m
0.6 (0.1, 1.0)
0.5 (0.2, 1.0)
0.6 (0.2, 1.0)
Female gender
1.6 (-0.1, 3.3)
1.6 (-0.1, 3.3)
1.6 (-0.2, 3.3)
BC; black carbon
BMI; body mass index
CI; confidence interval
PM; particulate matter
a
The “effect” is the estimated difference in blood pressure associated with a one-unit increase in the natural log of
PM2.5 or BC.
eTable 3. Comparison of the effects of personal PM2.5 and black carbon exposures (ln(µg/m3)) on blood pressure using
linear and quantile regression (10th, 25th, 50th and 90th blood pressure quantiles)a
Difference in mm Hg, (95% CI)
Linear
regression
Systolic blood pressure
PM2.5
Black carbon
Diastolic blood pressure
PM2.5
Black carbon
10th
25th
50th
75th
90th
-1.8 (-3.6, 0.1) -0.7 (-2.9, 1.4) -0.5 (-2.3, 2.1) -1.8 (-4.0, 0.4) -1.3 (-4.2, 1.6) -2.9 (-6.3, 1.6)
-2.2 (-5.4, 1.0) -1.1 (-4.9, 2.7) -2.7 (-7.3, 1.9) -2.5 (-6.7, 1.8) -1.5 (-7.6, 4.6) -2.3 (-10, 5.5)
-0.5 (-2.1, 1.2) -0.1 (-1.8, 1.6)
-0.4 (-3.3, 2.6) 0.9 (-2.8, 4.6)
-0.5 (-2.0, 1.1)
0.8 (-2.4, 4.0)
0.2 (-2.2, 2.6) -0.1 (-2.1, 1.8) -1.2 (-3.8, 1.3)
0.8 (-4.1, 5.8) -0.4 (-5.9, 5.0) -0.4 (-5.8, 5.0)
CI; confidence interval
PM; particulate matter
a
The “effect” is the estimated difference in blood pressure associated with a one-unit increase in the natural log of PM2.5
or black carbon for each blood pressure quantile using single-pollutant models.
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