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Association between particulate matter and its chemical constituents of urban air pollution and daily mortality or morbidity in Beijing City

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Recent time series studies have indicated that daily mortality and morbidity are associated with particulate matters. However, about the relative effects and its seasonal patterns of fine particulate matter constituents is particularly limited in developing Asian countries. In this study, we examined the role of particulate matters and its key chemical components of fine particles on both mortality and morbidity in Beijing. We applied several overdispersed Poisson generalized nonlinear models, adjusting for time, day of week, holiday, temperature, and relative humidity, to investigate the association between risk of mortality or morbidity and particulate matters and its constituents in Beijing, China, for January 2005 through December 2009. Particles and several constituents were associated with multiple mortality or morbidity categories, especially on respiratory health. For a 3-day lag, the nonaccident mortality increased by 1.52, 0.19, 1.03, 0.56, 0.42, and 0.32 % for particulate matter (PM)2.5, PM10, K+, SO4 2−, Ca2+, and NO3 − based on interquartile ranges of 36.00, 64.00, 0.41, 8.75, 1.43, and 2.24 μg/m3, respectively. The estimates of short-term effects for PM2.5 and its components in the cold season were 1 ~ 6 times higher than that in the full year on these health outcomes. Most of components had stronger adverse effects on human health in the heavy PM2.5 mass concentrations, especially for K+, NO3 −, and SO4 2−. This analysis added to the growing body of evidence linking PM2.5 with mortality or morbidity and indicated that excess risks may vary among specific PM2.5 components. Combustion-related products, traffic sources, vegetative burning, and crustal component and resuspended road dust may play a key role in the associations between air pollution and public health in Beijing.
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1 23
Environmental Science and Pollution
Research
ISSN 0944-1344
Environ Sci Pollut Res
DOI 10.1007/s11356-014-3301-1
Association between particulate matter
and its chemical constituents of urban air
pollution and daily mortality or morbidity
in Beijing City
Pei Li, Jinyuan Xin, Yuesi Wang,
Guoxing Li, Xiaochuan Pan, Shigong
Wang, Mengtian Cheng, Tianxue Wen,
Guangcheng Wang & Zirui Liu
1 23
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RESEARCH ARTICLE
Association between particulate matter and its chemical
constituents of urban air pollution and daily mortality
or morbidity in Beijing City
Pei Li &Jinyuan Xin &Yuesi Wang &Guoxing Li &
Xiaochuan Pan &Shigong Wang &Mengtian Cheng &
Tianxue Wen &Guangcheng Wang &Zirui Liu
Received: 5 January 2014 /Accepted: 6 July 2014
#Springer-Verlag Berlin Heidelberg 2014
Abstract Recent time series studies have indicated that daily
mortality and morbidity are associated with particulate mat-
ters. However, about the relative effects and its seasonal
patterns of fine particulate matter constituents is particularly
limited in developing Asian countries. In this study, we ex-
amined the role of particulate matters and its key chemical
components of fine particles on both mortality and morbidity
in Beijing. We applied several overdispersed Poisson gener-
alized nonlinear models, adjusting for time, day of week,
holiday, temperature, and relative humidity, to investigate
the association between risk of mortality or morbidity and
particulate matters and its constituents in Beijing, China, for
January 2005 through December 2009. Particles and several
constituents were associated with multiple mortality or mor-
bidity categories, especially on respiratory health. For a 3-day
lag, the nonaccident mortality increased by 1.52, 0.19, 1.03,
0.56, 0.42, and 0.32 % for particulate matter (PM)
2.5
,PM
10
,
K
+
,SO
4
2
,Ca
2+
,andNO
3
based on interquartile ranges of
36.00, 64.00, 0.41, 8.75, 1.43, and 2.24 μg/m
3
,respectively.
The estimates of short-term effects for PM
2.5
and its compo-
nents in the cold season were 1~ 6 times higher than that in the
full year on these health outcomes. Most of components had
stronger adverse effects on human health in the heavy PM
2.5
mass concentrations, especially for K
+
,NO
3
,andSO
4
2
.This
analysis added to the growing body of evidence linking PM
2.5
with mortality or morbidity and indicated that excess risks
may vary among specific PM
2.5
components. Combustion-
related products, traffic sources, vegetative burning, and crust-
al component and resuspended road dust may play a key role
in the associations between air pollution and public health in
Beijing.
Keywords Air pollution .Chemical constituents .Mortality .
Morbidity .Particulate matter .PM
2.5
.Time series .Beijing
Introduction
Particulate matter (PM) refers to a complex mixture of
pollutants consisting of smoke, dust, and all kinds of solid
and liquid material generated by many different sources and
that is in suspension in the atmosphere. Many epidemiolog-
ical studies have provided evidence of adverse health effects
of PM, including particles 2.5 μm in aerodynamic diameter
(PM
2.5
) and particles 10 μm in aerodynamic diameter
(PM
10
)(Dockeryetal.1993; Garrett and Casimiro 2011;
Katsouyanni et al. 1997;Pengetal.2005;Petersetal.1997;
Pope and Dockery 2006;Schwartzetal.1996; Styer et al.
1995). However, most of these studies and the regulations
designed to protect public health from airborne particles
have focused on the risk associated with the total mass of
particles, without regard to the characteristics of its
Responsible editor: Philippe Garrigues
P. L i :J. Xin (*):Y. Wa n g :M. Cheng :T. Wen :Z. Liu
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing, China
e-mail: xjy@mail.iap.ac.cn
P. L i
e-mail: lipei@dq.cern.ac.cn
P. L i :S. Wang
Key Laboratory of Semi-Arid Climate Change of Ministry of
Education, College of Atmospheric Science, Lanzhou University,
Lanzhou, Gansu, China
P. L i :G. Wang
Unit 93501 of PLA, Beijing, China
G. Li :X. Pan
Department of Occupational and Environmental Health, School of
Public Health, Peking University Health Science Center, Beijing,
China
Environ Sci Pollut Res
DOI 10.1007/s11356-014-3301-1
Author's personal copy
components and sources. If PM toxicity could be determined
based on specific chemical constituents or source types that
emit such constituents, the regulation of PM may be
implemented more effectively. Also, if a given adverse
health outcome is associated with specific PM components
but not with others, then a specific mechanism may be
postulated for further consideration by toxicological studies.
Recent time series studies of PM have generated important
information regarding the possible role sources but have also
raised some issues that require further investigation. Bell et al.
(2009) found the largestrisk estimates for PM
2.5
mass for both
cardiovascular and respiratory hospitalizations in northeastern
cities and during the winter season. And, Peng et al. (2005)
similarly found the largest PM
10
risk estimates in the north-
eastern cities but during the summer season in USA. In our
previous work, we also demonstrated the season associations
of daily PM
2.5
and PM
10
mass concentration with total mor-
tality and several mortality subcategories in Beijing, the cap-
ital of China (Li et al. 2013a,b). Although both the mortality
and morbiditystudies found the strongerassociations between
PM and health effect in different countries, the levels of risk
estimates and seasonal patterns were inconsistent among these
areas (Bell et al. 2008;Lietal.2013a,b;Pengetal.2005).
Depending on sources, there is a significant heterogeneity in
PM composition, which represented a heterogeneous mixture
of solid and liquid particles generated by many sources.
However, the evidence for which constituents of PM are
associated with the greatest risks is limited all over the
world. Among the available evidence from exposure studies,
Pope et al. (1995) and Dockery et al. (1993)bothindicated
that exposure to PM
2.5
sulfate (SO
4
2
), likely generated from
combusted fossil fuel, was associated with cardiopulmonary
mortality. Some other studies reported mortality associations
for several chemical components of PM
2.5
, including major
particulate components such as elemental carbon (EC), organ-
ic carbon (OC), and nitrate (NO
3
) as well as trace compo-
nents like nickel (Ni) and arsenic (As) (Burnett et al. 2000).
Because the composition and sources of particles vary dra-
matically by location, studies are needed in many regions. In
China, research on the adverse effects of PM
2.5
is very limited,
especially in relation to specific types of particles. To date,
almost all of these studies focused on the total mass of a given
size of particles, but the constituents and specific sources
responsible for the adverse effects of PM
2.5
have not been
investigated in Beijing. Thus, relatively, little is known about
the chemical composition of PM
2.5
and how different types of
particles may impact public health in this region.
Beijing, which is the city faced with some of the worlds
worst smog, has one of the nations highest levels of PM
2.5
mass concentration, population, and the nations highest den-
sity of traffic. The objective of our analysis was toexamine the
role of key PM
2.5
chemical constituents on both mortality and
morbidity. To the best of our knowledge, no previous study
has assessed the independent contribution of specific PM
constituents to various health outcomes in Beijing or else-
where in North China.
Methods
Data We obtained PM data for the 5-year period 2005
through 2009 from the Institute of Atmospheric Physics,
Chinese Academy of Sciences (IAP, CAS). The study site is
located at 39° 58N, 116° 22E, between the north third Ring
Road and the north fourth Ring Road in a high-density resi-
dential area of Beijing. The 24-h average concentrations of
PM
10
and PM
2.5
were measured using the tapered element
oscillating microbalance (TEOM) method (Franklin, MA,
USA). Samples were collected using a modified rapid collec-
tor of fine particle (RCFP) system combined with ion chro-
matography (IC), RCFP-IC,which can continuously ana-
lyze and determine the concentration of the water-soluble ions
in ambient aerosols (Wen et al. 2006; Zhang et al. 2006). The
following constituents of PM
2.5
were measured as hourly
averages: sulfates(SO
4
2
), nitrates(NO
3
,NO
2
),
ammonia(NH
4
+
), calcium(Ca
2+
), sodium(Na
+
),
magnesium(Mg
2+
), potassium(K
+
), chlorine(Cl
), and fluo-
rine (F
). These PM
2.5
components represent multiple sources
of PM
2.5
, including gasoline combustion, diesel exhaust,
wood smoke, crustal material, and secondary pollutants,
among others.
Meteorological data including the 24-h average tempera-
ture (T) and relative humidity (RH) were also used. All data
were defined as nonmissing if at least 75 % of the hourly
values of each variable were available covering the study
period. The data of nonaccidental mortality (NAM), respira-
tory mortality (RM), respiratory disease (RD), circulatory
mortality (CM), and circulatory disease (CD) were obtained
from the China Centers for Disease Control and Prevention
and the Third Hospital of Peking University. The causes of
death were coded according to the International Classification
of Diseases, 10 (ICD-10).
Statistical analysis We estimated percent excess risk (%ER)
for air pollution using Poisson time series models, adjusting
for temporal trends and seasonal cycles, immediate and de-
layed temperature or RH effects, holiday, season, and day of
the week. The nature spline model is a parametric approach
that fits cubic functions joined at knots, which are typically
placed evenly throughout the distribution of the variable of
concern, such as time. Previous analysis has indicated that
different spline models generate relatively similar results,
although increasing the number of knots generally tends to
decrease the estimated effect of pollution (Health Effects
Instiute (HEI) 2003; Ostro et al. 2006; Ostro et al. 2007).
We used natural cubic splines to adjust for potentially
Environ Sci Pollut Res
Author's personal copy
confounding temporal trends. We also used the degrees pro-
vided by smoothness estimated automatically to reduce the
human error (Li et al. 2013a,b). First, we characterized the
chemical composition of PM
2.5
and examined the temporal
variability of PM
2.5
composition by different seasons. We
identified relative contribution of each component to total
PM
2.5
mass and then calculated correlations between PM
2.5
total mass and each component. Second, we employed 09-
day average lags (lag0 to lag9 days) to compute excess risks
for an interauartile range (IQR) increase of PM
10
,PM
2.5
,and
each component. To estimate the relationship between daily
mortality or morbidity and PM mass and chemical constitu-
ents, we applied an overdispersed Poisson generalized non-
linear model with natural cubic splines for time and meteorol-
ogy:
log EY
i
ðÞ½¼βjXj
ilþsT
i;dfðÞþsRH
i;dfðÞþsT
il;dfðÞþsRH
il;dfðÞ
þstime;dfðÞþas:factor DOWðÞþSeason þHoliday þβ
ð1Þ
where E(Yi) refers to the expected count at day i;X
il
j
refers to
the PM mass or particular chemical component jon day iat a
lag of ldays (e.g., l=0 is the same day); β
j
is the regression
coefficient of the PM mass or particular chemical component
j;T
i
refers to the daily average temperature at day i;RH refers
to daily average RH at day i;T
i-l
refers to the average of the l
previous daystemperature; RH
i-l
refers to the average of the l
previous daysRH; s( ) is the cubic smoothing spline; df is the
degree of freedom of the smooth function; Season denotes the
seasons of the year; DOW denotes the days of week on day i;
Holiday denotes the holiday of the year; and αrefers to the
intercept.
Third, multiple models were developed to examine wheth-
er the effect of each component on health outcomes was
heterogeneous across different levels of temperature, humid-
ity, season, and concentration. We categorized season to two
levels (warm JuneSeptember, cold DecemberFebruary) and
investigated whether particle chemical component and weath-
er conditions modified the PM
2.5
and chemical component
effect by replacing the pollution term in Eq. 1, β
j
X
il
j
,with
βwarmIwarm Xj
ijþβcoldIcold Xj
ijð2Þ
where I
warm
,I
cold
=0/1 indicator variables representing warm,
cold days, respectively. β
warm
,β
cold
=regression coefficients
regarding the relation between PM
2.5
and death or emergency
counts.
Besides examining the acute effect of PM and its compo-
nents on mortality and morbidity under different weather
conditions, we also considered the effect under different levels
of PM
2.5
mass concentrations. We characterized PM
massanditscomponentintotwopartsbythelevelof
PM
2.5
. These days whose level of PM
2.5
mass concen-
tration was higher than the National Grade II standard
level for ambient air quality for 3 consecutive days were
defined as heavy pollution episode days. Conversely,
they were called light pollution episode days. The pol-
lutant term in Eq. 1 can be replaced by the multiple term
with
βheavyIheavyXj
ijþβlightIlight Xj
ijð3Þ
where I
heavy
,I
light
=0/1 indicator variables representing heavy
pollution episode days and light pollution episode days, re-
spectively. β
heavy
,β
light
=regression coefficients regarding the
relation between PM
2.5
and death or emergency counts.
Similarly, a categorical variable with five categories
(5 years) is created to investigate the changes of adverse effect
over the recent 5 years. The whole data are classed into five
periods by different years. We then add a product term of the
pollutant concentrations and dummy variables into the core
model to test the possible variations from year-to-year chang-
es. The equation is as follows:
Log E YiðÞ½¼
β2005I2005Xj
ijþβ2006I2006Xj
ij
þβ2007I2007 Xj
ij
þβ2008I2008Xj
ijþβ2009I2009Xj
ij
þstime;dfðÞþsT
il;dfðÞþsRH
il;dfðÞþsT
i;dfðÞþsRH
i;dfðÞ
þas :factor DOWðÞþSeason þHoliday þβ
ð4Þ
where I
2005
,I
2006
,I
2007
,I
2008
,I
2009
=0/1 indicator variables
representing the year of 2005, 2006, 2007, 2008, and 2009,
respectively. β
2005
,β
2006
,β
2007
,β
2008
,β
2009
=regression co-
efficients regarding the relation between PM
2.5
or chemical
components and health outcomes for a given year. The results
are expressed in terms of the percentage increase in daily
NAM, RM, CM, RD, and CD for a 10-μg/m
3
and 10-point
increment of pollutant concentrations, and respective 95 %
confidence interval (95 % CI). All model analyses are con-
ducted in the statistical environment R, version 2.11.1, using
the mgcv package, 1.6-2 (http://www.r-project.org).
Results
Characterization of PM and chemical composition Table 1
shows the descriptive statistics of the ambient PMs, chemical
components, and the contribution of each component to PM
2.5
total mass for the entire period and by season. The total
number of observations for each component was 1,356 days.
Environ Sci Pollut Res
Author's personal copy
Tabl e 1 Summary statistics of PM and chemical component mass concentrations in Beijing, China, 20052009
Components Mass of component (μg/m
3
) Average percentage of PM
2.5
total mass
Average ±SD Minimum Maximum
PM
10
123.64±86.23 6.67 768.13
PM
2.5
74.58±53.93 2.09 434.71
Na
+
1.00±1.48 0.04 28.43 1.35 %
NH
4
+
12.29±8.55 0.11 49.61 16.48 %
K
+
1.08±1.16 0.02 17.06 1.45 %
Mg
2+
0.63±0.78 0.03 10.51 0.84 %
Ca
2+
3.62±3.03 0.03 28.82 4.86 %
F
1.18±0.55 0.26 3.56 1.58 %
Cl
1.55±1.50 0.02 12.54 2.07 %
NO
2
2.26±2.07 0.08 17.83 3.03 %
NO
3
6.51±6.09 0.04 63.16 8.72 %
SO
4
2
23.49±21.12 0.28 143.16 31.50 %
Spring (March through May)
PM
10
144.27±113.27 6.67 768.13
PM
2.5
69.98±49.94 3.36 348.03
Na
+
1.30±2.42 0.11 28.43 1.85 %
NH
4
+
12.61±8.34 0.24 48.24 18.03 %
K
+
0.98±0.84 0.02 5.13 1.40 %
Mg
2+
0.59±0.63 0.04 4.16 0.84 %
Ca
2+
4.49±3.52 0.21 19.29 6.42 %
F
1.23±0.63 0.26 3.56 1.76 %
Cl
1.54±1.30 0.09 9.20 2.20 %
NO
2
2.10±2.09 0.14 17.83 3.00 %
NO
3
7.06±6.69 0.15 41.64 10.09 %
SO
4
2
20.44±17.34 0.51 126.41 29.21 %
Summer (June through August)
PM
10
116.84±54.96 16.38 347.66
PM
2.5
79.44±45.82 3.95 352.47
Na
+
1.05±1.00 0.07 5.68 1.32 %
NH
4
+
16.47±8.48 0.90 39.73 20.73 %
K
+
1.11±1.11 0.03 10.34 1.40 %
Mg
2+
0.83±1.20 0.03 10.51 1.05 %
Ca
2+
4.03±2.96 0.03 28.82 5.08 %
F
1.05±0.48 0.36 2.96 1.32 %
Cl
0.92±0.97 0.02 8.19 1.16 %
NO
2
3.31±2.37 0.08 13.62 4.17 %
NO
3
6.41±4.90 0.16 27.15 8.07 %
SO
4
2
13.98±10.14 0.28 57.83 17.60 %
Autumn (September through November)
PM
10
118.66±72.31 16.18 377.31
PM
2.5
73.18±53.04 6.82 306.33
Na
+
0.78±0.45 0.04 3.99 1.07 %
NH
4
+
10.71±7.72 0.51 39.31 14.64 %
K
+
0.95±0.78 0.05 4.41 1.30 %
Mg
2+
0.65±0.48 0.05 4.38 0.89 %
Ca
2+
3.69±2.69 0.09 17.37 5.04 %
F
1.11±0.45 0.36 3.41 1.51 %
Environ Sci Pollut Res
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The mean±SD PM
2.5
and PM
10
total mass concentration were
74.58±53.93 and 123.64 ±86.23 μg/m
3
for the entire period.
PM
2.5
levels had obviously seasonal patterns, with higher
values in summer (June through August) (79.44±45.82 μg/
m
3
) and lower values in spring (March through May) (69.98±
49.94 μg/m
3
). In Beijing, some components of PM
2.5
also
showed seasonal patterns. For instance, the mean concentra-
tions of NH
4
+
,Mg
2+
,Ca
2+
,andNO
2
were 1.89, 1.86, 1.87,
and 2.94 times higher in summer than those in winter, respec-
tively. In contrast, Cl
and SO
4
2
were 2.26 and 2.96 times
higher in winter than those in summer. Other components
such as K
+
,Na
+
,F
,andNO
3
did not show distinct seasonal
patterns. The largest contributors to PM
2.5
were SO
4
2
(31.50 %), NH
4
+
(16.48 %), and NO
3
(8.72 %) for the entire
period, respectively, and for each season, SO
4
2
,NH
4
+
,NO
3
,
and Ca
2+
comprised the majority (3858 %) of PM
2.5
total
mass. Table 2provides the correlations among the species and
PM
2.5
. Moderate to high correlations (R= 0.4~0.6) were found
between PM
2.5
and NO
3
,NH
4
+
,K
+
,andCl
;moremodest
correlation (R=0.2~0.4) was observed between PM
2.5
and
NO
2
and SO
4
2
.
Associations between PM chemical components and
mortality Figure 1shows the percentage change in risk of
total mortality per IQR increase in PM mass and components
by different lags. It can be seen that the acute effects of each
PM components have similar characteristics on different lags
on no-accident total mortality. An IQR increase in the 10-day
lag of PM
2.5
was associated with the largest relative risk of
1.02 (95 % CI 0.561.47) in total nonaccident total mortality,
which appeared at the lag of 3 days. We identified the lag with
the most certain effect estimates (largest values, smallest SD,
and Pvalue) for use in subsequent analysis of PM
2.5
chemical
components. For convenience and uniformity of comparison,
we adopted the structure with a lag of 3 days to analyze the
effect of PM
2.5
and its components.
Figure 2summarizes the results of distributed lag model
that produced the cumulative effects of lag 3 days on different
health outcomes. For no-accident total mortality, all central
estimates were positive except for F
,Mg
2+
,andNH
4
+
.An
IQR increase in PM
2.5
(36.00 μg/m
3
), PM
10
(64.00 μg/m
3
),
K
+
(0.41 μg/m
3
), Ca
2+
(1.43 μg/m
3
), NO
3
(2.24 μg/m
3
), and
SO
4
2
(8.75 μg/m
3
) was associated with a 1.52 (95 % CI
1.071.99 %), 0.19 (95 % CI 0.280.65 %), 1.03 (95 % CI
0.791.40 %), 0.42 (95 % CI 0.020.83 %), 0.32 (95 % CI
0.090.87 %), and 0.56 % (95 % CI 0.130.98 %) increase in
total mortality of the full year, respectively. The three highest
effects of chemical components which showed the similar
characteristics on NAM and RM all passed the significant test
at 0.01 levels. NH
4
+
was significantly negatively associated
with all-caused mortality and morbidity. We observed no
significantly association between F
or NO
2
and these health
Tabl e 1 (continued)
Components Mass of component (μg/m
3
) Average percentage of PM
2.5
total mass
Average ±SD Minimum Maximum
Cl
1.70±1.67 0.05 8.01 2.32 %
NO
2
2.56±1.78 0.22 13.76 3.49 %
NO
3
6.63±5.35 0.04 28.73 9.06 %
SO
4
2
17.13±14.86 0.29 91.66 23.41 %
Winter (December through February)
PM
10
110.56±82.12 8.29 600.00
PM
2.5
75.90±65.32 2.09 434.71
Na
+
0.78±0.70 0.16 6.99 1.03 %
NH
4
+
8.76±7.50 0.11 49.61 11.54 %
K
+
1.25±1.65 0.08 17.06 1.64 %
Mg
2+
0.45±0.45 0.03 4.03 0.59 %
Ca
2+
2.15±2.02 0.03 15.03 2.84 %
F
1.30±0.54 0.51 3.15 1.72 %
Cl
2.08±1.76 0.06 12.54 2.74 %
NO
2
1.13±1.10 0.09 9.64 1.48 %
NO
3
5.86±6.92 0.16 63.16 7.73 %
SO
4
2
41.61±26.03 3.97 143.16 54.82 %
“–” not applicable
Calculations were based on daily values using hourly observations
SD standard deviation
Environ Sci Pollut Res
Author's personal copy
outcomes. For circulate health, most of the components have
little harmful except K
+
and SO
4
2
.
Figure 2b, c summarizes the season-specific results. During
the cooler months, there were more associations between
pollutants and mortality or morbidity than when the entire
year was included in the analysis. Only SO
4
2
showed the
highest effect in warm season on NAM, RD, and CD. The
estimates of short-term effects for PM
2.5
in the cold season
were 1.63, 1.54, 1.57, 1.67, and 2.52 times than those in the
full year, for NAM, RM, RD, CM, and CD, respectively. As
well as PM
2.5
mass concentration, the most prominent contrast
between cold season and hot season was the similar pattern
but the various level of the relative risk. Generally, some
components showed stronger associations in the cold season,
whereas most of them were weak negatively with people
health in the hot season in Beijing. In comparing the beta
coefficients, the percent change in cold season was much
greater for many of the components relative to the full year
or hot season. For example, Ca
2+
was 3.43 and 5.84 times and
NO
3
was 5.84 and 3.24 times higher in cold season than that
in the full year on NAM and RM, respectively.
We evaluated the associations between chemical compo-
nents and these health outcomes by different PM
2.5
mass
concentrations divided by the National Grade II standard.
Results from the mass-specific analysis using the multiplica-
tive model identified the overall trends of the damage from the
exposure to chemical components, as shown in Fig. 3. These
findings indicated that Na
+
and SO
4
2
were significantly
positively associated with all health outcomes during the
heavy pollution episodes. In contrast, during the light pollu-
tion days, there were higher estimate values of relative risk
from espousing to K
+
in Beijing. Most of components had
stronger adverse effects on human health in the heavy PM
2.5
mass concentrations. Figure 4shows excess risk of total
mortality per IQR of concentrations for some components in
recent years in Beijing. It can be indicated that these estimates
show an increasing tendency in addition to have a low value in
2008, because strict atmospheric pollution control measures
were implemented in BeijingeTianjineHebei region before the
Olympics games (Xin et al. 2010,2012). But, they started a
new uptrend after 2008. The values which show the largest
effect in 2009 rose again after the Beijing Olympics.
Tabl e 2 Correlation coefficients between PM
2.5
and chemical components
PM
2.5
Na
+
NH
4
+
K
+
Mg
2+
Ca
2+
F
Cl
NO
2
NO
3
SO
4
2
PM
2.5
1
Na
+
0.11
**
1
NH
4
+
0.45
**
0.26
**
1
K
+
0.42
**
0.23
**
0.40
**
1
Mg
2+
0.14
**
0.40
**
0.33
**
0.33
**
1
Ca
2+
0.16
**
0.49
**
0.28
**
0.19
**
0.60
**
1
F
0.05 0.06
*
0.08
**
0.09
**
0.14
**
0.14
**
1
Cl
0.42
**
0.12
**
0.31
**
0.50
**
0.13
**
0.02 0.26
**
1
NO
2
0.21
**
0.07
*
0.59
**
0.19
**
0.24
**
0.24
**
0.16
**
0.06
*
1
NO
3
0.54
**
0.05 0.55
**
0.49
**
0.00 0.07
*
0.21
**
0.59
**
0.28
**
1
SO
4
2
0.34
**
0.06
*
0.16
**
0.36
**
0.12
**
0.23
**
0.43
**
0.57
**
0.14
**
0.47
**
1
*Correlation is significant at the 0.05 level (2-tailed)
**Correlation is significant at the 0.01 level (2-tailed)
0.97
0.98
0.99
1.00
1.01
1.02
Relative Risk
Component increase per IQR
lag0
lag1
lag2
lag3
lag4
lag5
lag6
lag7
lag8
lag9
Non-accident total mortality
PM
10
PM
2.5
Na
+
NH
4
+
K
+
Mg
2+
Ca
2+
F
-
Cl
-
NO
2
-
NO
3
-
SO
4
2-
Fig. 1 The percentage change in
risk of total nonaccident mortality
per IQR increase in PM and
component mass concentrations
by different lags in Beijing
Environ Sci Pollut Res
Author's personal copy
-5 0 5 -5 0 5 -15 0 15 -8 0 8 -8 0 8
SO
4
2-
NO
3
-
NO
2
-
Cl
-
F
-
Ca
2+
Mg
2+
K
+
NH
4
+
Na
+
PM
2.5
PM
10
NAM RM
A
Percent excess risk (%)
RD CM CD
-5 0 5 -5 0 5 -15 0 15 -8 0 8 -8 0 8
SO
4
2-
NO
3
-
NO
2
-
Cl
-
F
-
Ca
2+
Mg
2+
K
+
NH
4
+
Na
+
PM
2.5
PM
10
NAM RM
B
Percent excess risk (%)
RD CM CD
-5 0 5 -5 0 5 -15 0 15 -8 0 8 -8 0 8
SO
4
2-
NO
3
-
NO
2
-
Cl
-
F
-
Ca
2+
Mg
2+
K
+
NH
4
+
Na
+
PM
2.5
PM
10
NAM RM
C
Percent excess risk (%)
RD CM CD
Fig. 2 Excess risk (mean (95 %
CI)) of mortality and morbidity
per IQR of PM mass and
chemical components
concentrations in Beijing. aFor
the full year. bCold season
(November, December, January,
and February). cHot season
(May, June, July, and August).
Points represent central estimates,
error bars represent 95 %
confidence intervals (95 % CI),
and dotted lines indicate %= 0.00
Environ Sci Pollut Res
Author's personal copy
Discussion
Efforts to protect human health from ambient PM are limited
by scientific understanding of the toxicity of various compo-
nents of the PM mixture and the sources that contribute
injurious particles (Bell et al. 2008). If the chemical composi-
tion of particles affects toxicity, we would expect to find
evidence of seasonal and regional heterogeneity in the short-
term risks associated with PM
2.5
total mass. Characterization
of spatial and temporal heterogeneity in risks associated with
PM provides an opportunity to test hypotheses regarding the
significance of particle characteristics for human health and to
develop focused hypotheses on variation in risks by time
period and region. Our aim of this study was to investigate
the association between exposure to particles or the compo-
nents of fine particles to them and public health and the
temporal characterization of adverse effect on mortality and
morbidity in Beijing, 20052009. In this time series analysis,
ambient concentrations of several constituents of fine particles
were associated with daily mortality and morbidity. Specifi-
cally, the data suggest consistent associations with SO
4
2
,
NO
3
,K
+
,andCa
2+
,aswellaswithPM
2.5
mass
-4
-2
0
2
4
-8
0
8
16
-10
-5
0
5
10
PM10 PM2.5 Na+ NH4
+ K+ Mg2+ Ca2+ F- Cl- NO2
- NO3
- SO4
2-
RAM-heavy pollution days
RAMlight pollution days
Percent excess risk (%)
RM-heavy pollution days RD-heavy pollution days
RM-light pollution days RD-light pollution days
CM-heavy pollution days CD-heavy pollution days
CM-light pollution days CD-light pollution days
Fig. 3 Percent excess mortality
and morbidity risk under different
levels of PM
2.5
mass
concentration in Beijing. aNAM,
bRM and RD, cCM and CD.
Points represent central estimates,
error bars represent 95 %
confidence intervals (95 % CI),
and dotted lines indicate %= 0.00
2005 2006 2007 2008 2009
-3
-2
-1
0
1
2
3
4
5
Percent increase (%)
year
K
+
Ca
2+
SO
4
2+
NO
3
-
Non-accodent total mortality
Fig. 4 Excess risk of total
mortality per IQR of
concentrations for some
components in recent years in
Beijing. Points represent central
estimates, and dotted lines
indicate %=0.00
Environ Sci Pollut Res
Author's personal copy
concentration. Stronger associations were observed with no-
accident total mortality, RM, and CM in the cold season and
heavy pollution days. We performed, to the best of our knowl-
edge, the first study of the relationship between chemical
composition of particles and mortality or morbidity in Beijing,
China, and observed significant modifying effect by seasons.
We found a higher central estimate of particles for all health
outcomes. Several components that were among the largest
contributors to PM
2.5
total mass (SO
4
2
,NH
4
+
,NO
3
,and
Ca
2+
) were moderately associated with mortality (P<0.01)
except for NH
4
+
. Other components with smaller mass con-
tributions (F
,Na
+
,Mg
2+
,K
+
,NO
2
,andCl
) were weak
associated with mortality and morbidity (P<0.1) except for
K
+
.Fairley(2003) also examined the impacts of SO
4
2
and
NO
3
in Santa Clara Country, and the constituents were asso-
ciated with all cause mortality, whereas NO
3
was associated
with cardiovascular mortality. The similar findings were
found in Netherlands and Seoul (Son et al. 2012). By using
factor analysis, Sun et al. (2004)foundthatSO
4
2
and NO
3
in Beijing were mainly from the coal burning and, in turn,
the chemical transformation that formed the secondary
aerosols,astheywereshownmainlytobeammonium
sulfate and ammonium nitrate. Liu et al. (2013)alsofound
a high positive correlation with NO
X
which suggested a
relationship with on-road traffic emissions, and the compo-
nents such as SO
4
2
might have been transported from the
known coal-fired power plants. Our results are consistent
with previous studiesprimary and secondary products of
fuel combustion and mobile source-related emissions (SO
4
2
,NO
3
) exhibit the strongest and most consistent associa-
tions with mortality or morbidity (Ito et al. 2011;Ostroetal.
2007; Son et al. 2012; Zhou et al. 2011). K
+
is generally
considered a reasonable marker for biomass combustion,
including residential wood burning (Maykut et al. 2003;
Watson et al. 2001), which is an important contributor to
air pollution in Beijing (Liu et al. 2013). It was found that
biomass burning which performed as K
+
could explain
6.2 % of the total variance at Beijing (Liu 2011). Our result
also showed a significant association between K
+
and no-
accident mortality, RM, and CM. It can be indicated that
biomass burning had a great impact on public health in this
city. For no-accidentmortality and RM, Ca
2+
also expressed
positive associations at the P<0.01 level. It can clearly
represent the mineral aerosols that would likely be from
the resuspended road dust and from the long-range
transported dust from outside Beijing (Liu et al. 2013;Sun
et al. 2004). Factor analysis of multiple elements conducted
by Mar et al. (2000) in Phoenix also suggested associations
between cardiovascular mortality and factors relating to
three sources: motor vehicle exhaust and resuspended road
dust, vegetative burning, and regional SO
4
2
. Several other
studies also examined source-oriented combinations of
pollutants, and both motor vehicle exhaust and car factors
were associated with mortality, with the strongest effect
from the former. As mentioned above, in general, daily mor-
tality and morbidity in Beijing were associated with various
PM
2.5
chemical components and source categories, including
secondary aerosols which were transformed from combustion
(SO
4
2
,NO
3
), motor vehicle sources (NO
3
), biomass burn-
ing (K
+
), and crustal component and resuspended road dust
(Ca
2+
). Thus, reduction of health adverse risk in Beijing may
need to focus on combustion-related products, traffic sources,
vegetative burning, and crustal component and resuspended
road dust. Although we found the similar results on mortality
and morbidity in Beijing, the risk levels of particle compo-
nents were less lowly compared with other cities (Bell et al.
2008; Dockery et al. 1993; Ito et al. 2011;Maykutetal.2003;
Ostro et al. 2007; Son et al. 2012;Zhouetal.2011). The
observed regional heterogeneity in the short-term effects of
PM
2.5
may also be explained by differences in mass concen-
tration, population susceptibility, and access to health care and
socioeconomic status. Whether its source came from primary
pollutants or secondary products of fuel combustion, most of
the components exhibited strong associations with mortality
and morbidity. But, it is difficult to identify individual effects
of PM
2.5
components because every component has multiple
and shared sources and effects observed for one component
may be the result of a component with similar sources.
The acute effects exhibited differing seasonal patterns on
these health outcomes in Beijing. We observed stronger and
more frequent associations between mortality or morbidity
and PM
2.5
components during the cooler months, when most
components have higher concentrations. In our previous
study, we also found that the modifying effect of coarse and
fine particles existed in the cool days in Beijing (Li et al.
2013a,b). These differences represent seasonal variation in
sources, particle chemistry, and meteorology (Ostro et al.
2007). For example, the winter and summer averages for
SO
4
2
and K
+
were 41.61 and 13.98 μg/m
3
and 1.25 and
1.11 μg/m
3
, respectively. For NO
3
and Ca
2+
, the average
percentages were 10.09 and 6.42 % in spring, which were
slightly higher than 8.07 and 5.08 % in summer, respectively.
Becker et al. (2005) similarly reported seasonal variation in
the toxicity of particles, which they justified on the basis that
particles in different seasons contain different elemental com-
positions that are affected by changes of local environment
and weather conditions. Besides, the average temperature was
very high in summer in Beijing, and high percentages of air-
conditioned homes and buildings may reduce the average
ability of air pollutants to reach residents for much of the
day, which may diminish the overall population adverse health
implications of outdoor air pollution in summer. However, the
acute effects of most components showed the similar risk
characteristics of PM
2.5
mass concentration, except that
SO
4
2
had stronger impact with few exceptions in warm
season than in cool season. That may be explained by the
Environ Sci Pollut Res
Author's personal copy
difference formation of SO
4
2
in summer: The sulfates for-
matted by the gas phase homogeneous reaction accounted the
highest ratio of the products which transformed from SO
2
.
Due to lack of statistical evidence, the influence of such
potential effect modifiers needs to be further investigated.
Our findings add to the growing body of evidence linking
PM
2.5
with mortality or morbidity and indicate that excess
risks may vary with the specific PM
2.5
constituents. First, in
our study, with 5 years of daily PM
2.5
speciation data for
Beijing, we were able to characterize the short-term health
effects of PM
2.5
more thoroughly and examine the distribution
of mortality or morbidity effect over season. Second, this
study potentially provided insights for better targeted regula-
tion to reduce source emissions and indicated sources of
particle air pollution that could be targeted as part of a com-
prehensive air quality control strategy. Our results showed that
PM
2.5
components associated with mortality and morbidity in
Beijing appear to be associated with secondary aerosols and
traffic markers especially in the cold season, such as coal
burning, motor vehicle emitting, biomass burning, and road
dust.
Conclusions
In this time series study of mortality and morbidity in Beijing,
we obtained that exposure to outdoor particle and its chemical
component was strongly associated with public health. This
study also suggests that K
+
,Ca
2+
,NO
3
,andSO
4
2
showed
the most fearful impact on these outcomes throughout the
year, especially in cold seasons. We conclude that those sec-
ondary aerosols, motor vehicle emitting, biomass burning, and
resuspended road dust may be the most important sources
affecting public adverse effects in Beijing.
Acknowledgments We thank Yang Sun, Dongshen Ji, Guiqian Tang,
Lili Wang, and employees of IAP, CAS for their assistance with various
aspects of data collection and data preparation. This work is partially
supported by the CAS Strategic Priority Research Program Grant No.
XDA05100100, the public project of the Beijing Municipal Science and
Technology Commission (D09040903670902), the National Natural Sci-
ence Foundation of China (41021004, 41075103), the Research Subject
of State Science and Technology Support Program of China
(2012BAJ18B08), the Gong-Yi Program of China Meteorological Ad-
ministration (GYHY201106034), and the Meteorological Environment
and HealthSpecial Service Program of National Population and Health
Science Data Sharing Platform.
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... Besides, differences in climatic conditions, the proportion of susceptible populations, and socio-economic characteristics could be potential sources of the aforementioned seasonal pattern (Chen et al., 2013;Kan et al., 2008). Seasonal modifications of PM 2.5 constituents may explain the controversy in previous studies regarding the different effects of PM 2.5 in cold and warm seasons (Ito et al., 2011;Li et al., 2015). ...
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A growing number of studies associated increased mortality with exposures to specific fine particulate (PM2.5) constituents, while great heterogeneity exists between locations. In China, evidence linking PM2.5 constituents and mortality was extensively sparse. This study primarily aimed to quantify short-term associations between PM2.5 constituents and non-accidental mortality among the Chinese population. We collected daily mortality records from 32 counties in China between January 1, 2011, and December 31, 2013. Daily concentrations of main PM2.5 constituents (organic carbon (OC), elemental carbon (EC), nitrate (NO3−), sulfate (SO42−), and ammonium (NH4+)) were estimated using the modified Community Multiscale Air Quality model. Time-stratified case-crossover design with conditional logistic regression models was adopted to estimate mortality risks associated with short-term exposures to PM2.5 mass and its constituents. Stratification analyses were done by sex, age, and season. A total of 116,959 non-accidental deaths were investigated. PM2.5 concentrations on the day of death were averaged at 75.7 μg m−3 (control day: 75.6 μg m−3), with an interquartile range (IQR) of 65.2 μg m−3. Per IQR rise in PM2.5, EC, OC, NO3−, SO42−, and NH4+ at lag-04 day was associated with an increase in non-accidental mortality of 2.4% (95% confidence interval, (1.0–3.7), 1.7% (0.8–2.7), 2.9% (1.6–4.3), 2.1% (0.4–3.9), 1.0% (0.2–1.9), and 1.6% (0.3–2.9), respectively. Both PM2.5 mass and its constituents were strongly associated with elevated cardiovascular mortality risks, but only PM2.5, EC, and OC were positively associated with respiratory mortality at lag-3 day. PM2.5 mass and its constituents associated effects on mortality varied among sex- and age-specific subpopulations. Differences in the seasonal pattern of associations exist among PM2.5 constituents, with stronger effects related to EC and NO3− in warm months but SO42− and NH4+ in cold months. Short-term exposures to PM2.5 compositions were positively associated with increased risks of mortality, particularly those constituents from combustion-related sources.
... We conducted a cross-sectional study and utilized data based on communities distributed from south to north in Beijing, which experienced the nation's highest levels of PM 2.5 during the last 20 years (38). In 2016, the annual average concentration of PM 2.5 reached 73 µg/m 3 in Beijing according to the Ministry of Environmental Protection of China, while the China National Air Quality Standard for PM 2.5 is 35 µg/m 3 (39). ...
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Background Environmental exposure to toxic elements contributes to the pathogenesis of chronic kidney disease (CKD). Few studies focus on the association of urinary metals and metalloids concentrations with the urinary albumin/creatinine ratio (UACR) among elderly, especially in areas and seasons with severe air pollution. Objective We aimed to evaluate the associations of urinary metals and metalloids concentration with UACR, which is an early and sensitive indicator of CKD. Method We conducted a cross-sectional study among 275 elderly people in Beijing from November to December 2016, which has experienced the most severe air pollution in China. We measured 15 urinary metals and metalloids concentration and estimated their association with UACR using a generalized linear model (GLM). Bayesian kernel machine regression (BKMR) and quantile g-computation (qgcomp) models were also conducted to evaluate the combined effect of metal and metalloid mixtures concentration. Results Of the 275 elderly people included in the analysis, we found that higher urinary Cu concentration was positively associated with UACR using GLM (β = 0.36, 95% CI: 0.25, 0.46). Using the BKMR model, we found that the change in UACR was positively associated with a change in urinary Cu concentration from its 25th to 75th percentile value with all other metals and metalloids concentration fixed at their 25th, 50th, or 75th percentile levels. Urinary Cu concentration had the most significant positive contribution (59.15%) in the qgcomp model. Our finding was largely robust in three mixture modeling approaches: GLM, qgcomp, and BKMR. Conclusion This finding suggests that urinary Cu concentration was strongly positively associated with UACR. Further analyses in cohort studies are required to corroborate this finding.
... A plausible biological mechanism for this might be through the oxidative pathway that has been associated with inhibition uterine contraction and retained placenta [57,58]. Increased air pollution from gas flaring in oil refineries (including illegal refineries) and oil spillage sites resulting in the release of pollutants such as polycyclic aromatic hydrocarbon and volatile organic hydrocarbon into the atmosphere have been associated with oxidative stress [59][60][61]. Also, studies have shown that oxidative stress can result in the inhibition of uterine contraction [57] and retained placenta [58]. ...
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Background: Maternal exposure to oil pollution is an important public health concern. However, there is a dearth of literature on the effects of maternal exposure to oil pollution on maternal outcomes in the Niger Delta region of Nigeria. This study was therefore designed to determine the effect of maternal exposure to oil pollution on maternal outcomes in the Niger Delta region of Nigeria. Methods: Prospective cohort study design involving 1720 pregnant women followed from pregnancy to delivery was conducted. The participants were 18-45 years old at a gestational age of less than 17 weeks, who attended randomly selected health facilities in the areas with high exposure and low exposure to oil pollution in the Niger Delta, Nigeria. Data were collected using an interviewer-administered questionnaire and review of medical records from April 2018 to April 2019. Multivariate log-binomial model was used to examine the effect of maternal exposure to oil pollution on the risk of adverse maternal outcomes adjusting for sociodemographic, maternal and lifestyle characteristics. Results: A total of 1418 women completed the follow-up and were included in the analysis. Women in high exposure areas had a higher incidence of premature rupture of membrane (PROM), caesarean section (CS) and postpartum haemorrhage (PPH) compared to women in areas with low exposure to oil pollution. After adjusting for cofounders, women in high exposure areas also had a higher risk of PROM (ARR = 1.96; 95% CI: 1.24-3.10) and PPH (ARR = 2.12; 95% CI: 1.28-3.36) in Model I-III when compared to women in areas with low exposure to oil pollution. However, pregnancy-induced hypertension and CS had no association with maternal exposure area status to oil pollution. Conclusion: Women in high exposure areas are at a higher risk of PROM and PPH. This calls for policies and intervention toward reducing maternal exposure to oil pollution in the Niger Delta region of Nigeria.
... A plausible biological mechanism for this might be through the oxidative pathway that has been associated with inhibition uterine contraction and retained placenta [57,58]. Increased air pollution from gas flaring in oil refineries (including illegal refineries) and oil spillage sites resulting in the release of pollutants such as polycyclic aromatic hydrocarbon and volatile organic hydrocarbon into the atmosphere have been associated with oxidative stress [59][60][61]. Also, studies have shown that oxidative stress can result in the inhibition of uterine contraction [57] and retained placenta [58]. ...
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Background: Maternal exposure to oil pollution is an important public health concern. However, there is a dearth of literature on the effects of maternal exposure to oil pollution on maternal outcomes in the Niger Delta region of Nigeria. This study was therefore designed to determine the effect of maternal exposure to oil pollution on maternal outcomes in the Niger Delta region of Nigeria. Methods: Prospective cohort study design involving 1720 pregnant women followed from pregnancy to delivery was conducted. The participants were 18-45 years old at a gestational age of less than 17 weeks, who attended randomly selected health facilities in the areas with high exposure and low exposure to oil pollution in the Niger Delta, Nigeria. Data were collected using an interviewer-administered questionnaire and review of medical records from April 2018 to April 2019. Multivariate log-binomial model was used to examine the effect of maternal exposure to oil pollution on the risk of adverse maternal outcomes adjusting for sociodemographic, maternal and lifestyle characteristics. Results: A total of 1418 women completed the follow-up and were included in the analysis. Women in high exposure areas had a higher incidence of premature rupture of membrane (PROM), caesarean section (CS) and postpartum haemorrhage (PPH) compared to women in areas with low exposure to oil pollution. After adjusting for cofounders, women in high exposure areas also had a higher risk of PROM (ARR = 1.96; 95% CI: 1.24-3.10) and PPH (ARR = 2.12; 95% CI: 1.28-3.36) in Model I-III when compared to women in areas with low exposure to oil pollution. However, pregnancy-induced hypertension and CS had no association with maternal exposure area status to oil pollution. Conclusion: Women in high exposure areas are at a higher risk of PROM and PPH. This calls for policies and intervention toward reducing maternal exposure to oil pollution in the Niger Delta region of Nigeria.
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Road silt loading (sL) is an important parameter in the fugitive road dust (FRD) emissions. In this study, the improved Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER) combined with the AP-42 method was firstly developed to quickly measure and estimate the sLs of paved roads in Beijing, China. The annual average sLs in Beijing was 0.59±0.31 g/m² in 2020, and decreased by 22.4% compared with that in 2019. The seasonal variations of sLs followed the order of spring > winter > summer > autumn in the two years. The seasonal mean road sLs on the same type road in the four seasons presented a decline trend from 2019 to 2020, especially on the Express way, decreasing 47.4%-72.7%. The road sLs on the different type roads in the same season followed the order of Major arterial ∼ Minor arterial ∼ Branch road > Express road, and Township road ∼ Country highway > Provincial highway ∼ National highway. The emission intensities of PM10 and PM2.5 from FRD in Beijing in 2020 were lower than those in 2019. The PM10 and PM2.5 emission intensities at the four planning areas in the two years all presented the order of the capital functional core area > the urban functional expansion area > the urban development new area > the ecological conservation and development area. The annual emissions of PM10 and PM2.5 from FRD in Beijing in 2020 were 74,886 ton and 18,118 ton, respectively, decreasing by ∼33.3% compared with those in 2019.
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Background Increasing prevalence of childhood allergic rhinitis(AR) needs a deeper understanding on the potential adverse effects of early life exposure to air pollution. Objectives The main aim was to evaluate the effects of maternal exposure to PM2.5 and chemical constituents during pregnancy on preschool children’s AR, and further to explore the modification effects of regions and exclusive breastfeeding. Methods A multi-center population-based study was performed in 6 cities from 3 regions of China in 2011-2012. Maternal exposure to ambient PM2.5 and main chemical constituents(BC, OM, SO4²⁻, NO3⁻, NH4⁺) during pregnancy was assessed and a longitudinal prospective analysis was applied on preschool children’s AR. The modification effects of regions and exclusive breastfeeding were investigated. Results A total of 8.8% and 9.8% of children reported doctor-diagnosed allergic rhinitis(DDAR) and current hay fever, respectively, and 48.6% had less than 6 months of exclusive breastfeeding. The means of PM2.5 during pregnancy were 52.7μg/m³, 70.3μg/m³ and 76.4μg/m³ in the east, north and central south of China, respectively. Multilevel log-binomial model regression showed that each interquartile range(IQR) increase of PM2.5 during pregnancy was associated with an average increase in prevalence ratio (PR) of DDAR by 1.43(95% confidence interval(CI): 1.11, 1.84) and current hay fever by 1.79(95%CI: 1.26, 2.55), respectively. Among chemical constituents, black carbon (BC) had the strongest associations. Across 3 regions, the eastern cities had the highest associations, followed by those in the central south and the north. For those equal to or longer than 6 months of exclusive breastfeeding, the associations were significantly reduced. Conclusions Children in east of China had the highest risks of developing AR per unit increase of maternal exposure to PM2.5 during pregnancy, especially BC constituent. Remarkable decline was found in association with an increase in breastfeeding for ≥6 months, in particular in east of China.
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Fine particulate matter (PM2.5) is the main pollutant particle of smog pollution. Public PM2.5-reduction behavior is beneficial and important to the reduction of smog emissions. The article aims to investigate the influencing factors of the intention for individuals’ PM2.5-reduction. A conceptual model was proposed from the perspective of the protection behavior decision model and the heuristic-systematic information processing model. A random questionnaire survey was conducted in Hefei City, China. Results suggest that risk perception is a positive determinant of PM2.5-reduction intention, information insufficiency, and information-seeking intention. Information insufficiency positively determines systematic processing and information-seeking intention but fails to influence heuristic processing. Information-seeking intention is positively correlated with systematic processing and heuristic processing. Systematic processing positively determines PM2.5-reduction intention. However, the results suggest that heuristic processing has no significant effect on PM2.5-reduction intention. The findings of this study provide practical implications for enhancing individuals’ PM2.5-reduction intention.
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Continuous particle number concentration and chemical composition data were collected over one month during summertime in Beijing to investigate the source apportionment of ambient fine particles. Particle size distributions from 15 nm to 2.5 μm in diameter and composition data, such as organic matter, sulfate, nitrate, ammonium, chlorine, and gaseous pollutants, were analyzed using positive matrix factorisation (PMF) which indentified eight factors: cooking, solid mode exhaust, nucleation mode exhaust, accumulation mode, secondary nitrate, secondary sulfate, coal-fired power plant and road dust. Nearly two-thirds of particle number concentrations were attributed to cooking (22.8%) and motor vehicle (37.5%), whereas road dust, coal-fired power plant and regional sources contributed 69.0% to particle volume concentrations. Local and remote sources were distinguished using size distributions associated with each factor. Local sources were generally characterised by unimodal or bimodal number distributions, consisting mostly of particles less 0.1 μm in diameter, and regional sources were defined by mostly accumulation mode particles. Nearly one third of secondary nitrate and secondary sulfate was transported from the surrounding areas of Beijing during study period. Overall the introduction of combination of particle number concentration and chemical composition in PMF model is successful at separating the components and quantifying relative contributions to the particle number and volume population in a complex urban atmosphere.
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The Atmospheric Environmental Monitoring Network successfully undertook the task of monitoring the atmospheric quality of Beijing and its surrounding area during the 2008 Olympics. The results of this monitoring show that high concentrations of PM2.5 pollution exhibited a regional pattern during the monitoring period (1 June–30 October 2008). The PM2.5 mass concentrations were 53 μg m−3, 66 μg m−3, and 82 μg m−3 at the background site, in Beijing, and in the Beijing-Tianjin-Hebei urban agglomerations, respectively. The PM2.5 levels were lowest during the 2008 Olympic Games (8-24 August): 35 μg m−3 at the background site, 42 μg m−3 in Beijing and 57 μg m−3 in the region. These levels represent decreases of 49%, 48%, and 56%, respectively, compared to the prophase mean concentration before the Olympic Games. Emission control measures contributed 62%–82% of the declines observed in Beijing, and meteorological conditions represented 18%–38%. The concentration of fine particles met the goals set for a “Green Olympics.”
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Recent epidemiological and toxicological studies have shown associations between particulate matter and human health. However, the estimates of adverse health effects are inconsistent across many countries and areas. The stratification and interaction models were employed within the context of the generalized additive Poisson regression equation to examine the acute effects of fine particles on respiratory health and to explore the possible joint modification of temperature, humidity, and season in Beijing, China, for the period 2004-2009. The results revealed that the respiratory health damage threshold of the PM2.5 concentration was mainly within the range of 20-60 μg/m(3), and the adverse effect of excessively high PM2.5 concentration maintained a stable level. In the most serious case, an increase of 10 μg/m(3) PM2.5 results in an elevation of 4.60 % (95 % CI 3.84-4.60 %) and 4.48 % (95 % CI 3.53-5.41 %) with a lag of 3 days, values far higher than the average level of 0.69 % (95 % CI 0.54-0.85 %) and 1.32 % (95 % CI 1.02-1.61 %) for respiratory mortality and morbidity, respectively. There were strong seasonal patterns of adverse effects with the seasonal variation of temperature and humidity. The growth rates of respiratory mortality and morbidity were highest in winter. And, they increased 1.4 and 1.8 times in winter, greater than in the full year as PM2.5 increased 10 μg/m(3).
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The Beijing-Tianjin-Hebei Atmospheric Environment Monitoring Network was established by the Institute of Atmospheric Physics, Chinese Academy of Sciences. The goals of the network were to monitor and provide warnings of the atmospheric quality in Beijing and its surrounding area during the Beijing 2008 Olympic Games. The results showed that the atmospheric complex pollution exhibited high concentrations of ozone and fine particles and oxidation in summer, with a ubiquitous regional source. The regional mean concentrations of SO2, PM2.5, NO2, and O3_8h max (the maximum daily 8 h mean) and Ox were 22±11, 90±40, 25±5, 136±35 and 112±21 μg/m3 in summer, respectively. During the Olympic Games, the mean concentration of SO2, PM2.5, NO2, O3_8h max, and Ox were 12.5±4, 56±28, 23±4, 114±29, 95±17 μg/m3 in the region, respectively, and fell by 51.0%, 43.7%, 13%, 20.2%, and 18.9%, respectively, compared to the prophase mean before the Olympic Games. The concentration of atmospheric pollutants declined significantly and achieved the “Green Olympics” control goal of air quality. After the Olympic Games, SO2, PM2.5 and NOx increased significantly as the temporary atmospheric pollution control measures were terminated.
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Evidence concerning the health risk of fine and coarse particles is limited in developing Asian countries. The modifying effect between particles and temperature and season also remains unclear. Our study is one of the first to investigate the acute effect of particles size fractions, modifying effects and interannual variations of relative risk in a developing megacity where particulate levels are extraordinarily high compared to other Asian cities. After controlling for potential confounding, the results of a time-series analysis during the period 2005-2009 show that a 10 μg m-3 increase in PM2.5 levels is associated with a 0.65% (95% CI: 0.29-0.80%), 0.63% (95% CI: 0.25-0.83%), and 1.38% (95% CI: 0.51-1.71%) increase in non-accidental mortality, respiratory mortality, and circulatory mortality, respectively, while a 10 μg m-3 increase in PM10 is similarly associated with increases of 0.15% (95% CI: 0.04-0.22%), 0.08% (95% CI: 0.01-0.18%), and 0.44% (95% CI: 0.12-0.63%). We did not find a significant effect of PM2.5-10 on daily mortality outcomes. Our analyses conclude that temperature and particulates, exposures to both of which are expected to increase with climate change, might act together to worsen human health in Beijing, especially in the cool seasons. The level of the estimated percentage increase assume an escalating tendency during the study period, in addition to having a low value in 2008, and after the Olympic Games, the values increased significantly as the temporary atmospheric pollution control measures were terminated mostly.
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To gain an understanding of the characteristics of nitrate, sulfate and ammonium in the urban atmosphere of Beijing, an experiment was conducted in October 2004, using a method involving the rapid collection of particles and analysis using an ion chromatography system. The study shows that the mean concentration of water soluble ions (WSI) increased during heavily polluted weather, and this change in the concentration of pollutants was related to the meteorological background. The concentration of nitrate, sulfate and ammonium increased 7.9, 4.1 and 5.4 times, respectively, during heavily polluted periods. The concentration of nitrate increased most among the WSI in PM10. The diurnal variations of nitrate, sulfate and ammonium in more polluted periods were different from those in less polluted periods. The highest concentration of nitrate (NO3−), sulfate (SO42−), and ammonium (NH4+) appeared at 19:00 during more polluted periods. In contrast, the highest concentrations of these compounds occurred at noon during less polluted periods. A correlation analysis showed that NO3−, SO42−, NH4+, nitrogen oxides (NOx) and sulfur dioxide (SO2) had significant positive correlations in more polluted periods. The transformation ratio from SO2 and NOx to SO42− and NO3− was higher in more polluted than that in less polluted periods.
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Aerosol samples of PM 2.5 and PM 10 were collected in both summer and winter seasons from 2002 to 2003 synchronously at a traffic site, an industrial site, and a residential site in Beijing, which could basically be the representatives over Beijing. Twenty-three elements and 15 ions together with organic carbon and elemental carbon were analyzed systematically for characterization of Beijing aerosol. PM 2.5 was the major part of the inhalable particles (PM 10), as the ratios of PM 2.5 /PM 10 were 0.45–0.48 in summer and 0.52–0.73 in winter. SO 4 2À , NO 3 À , NH 4 + , organic matter, crustal matter, and element carbon were the six dominant species, which totally accounted for 85.8–97.7% of PM 2.5 . Secondary aerosol (SO 4 2À , NO 3 À , and NH 4 +), road dust or/and long-range transported dust from outside Beijing, industry and motor vehicles emission, coal burning were the major contributors to the air-borne particulate pollution in Beijing. Overall, coal burning and the traffic exhausts, plus the dust from the long-range transport, could be the major sources of the aerosol pollution at Beijing. A relatively even spatial distribution of chemical species in PM 2.5 was found while in PM 10 a significant variation with the highest concentrations at the industrial site in summer and at the residential site in winter was observed. The concentrations of PM 10 , PM 2.5 as well as various chemical species were higher in winter than in summer. The contributions of mineral aerosol from outside Beijing were first estimated with a newly developed element tracer technique, which accounted for 79% and 37% of the total mineral in PM 10 and PM 2.5 in winter, and 19% and 20% in summer, respectively. During the dust storm period from 20 to 22 March, it reached up to 97% in TSP, 79% in PM 10 and 76% in PM 2.5 . This is the technique, firstly, developed for estimating the relative contributions of sources from inside and outside Beijing to the total mineral aerosol and it could provide the basic information in controlling the air-borne particulate pollution at Beijing.
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Combining the system of rapid collection of ambient particles and ion chromatography, the system of rapid collection of fine particles and ion chromatography (RCFP-IC) was established to automatically analyze on-line the concentrations of water-soluble ions in ambient particles. Here, the general scheme of RCFP-IC is described and its basic performance is tested. The detection limit of RCFP-IC for SO 42−, NO 3−, NO 2−, Cl− and F− is below 0.3 µg m−3. The collection efficiency of RCFP-IC increases rapidly with increasing sized particles. For particles larger than 300 nm, the collection efficiency approaches 100%. The precision of RCFP-IC is more than 90% over 28 repetitions. The response of RCFP-IC is very sensitive and no obvious cross-pollution is found during measurement. A comparison of RCFP-IC with an integrated filter measurement indicates that the measurement of RCFP-IC is comparable in both laboratory experiments and field observations. The results of the field experiment prove that RCFP-IC is an effective on-line monitoring system and is helpful in source apportionment and pollution episode monitoring.