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Particulate air pollution at commonly occurring concentrations is associated with daily deaths. Recent attention has focused on the shape of the concentration-response curve, particularly at low doses. Several recent articles have reported that particulate matter with aerodynamic diameter < or = 10 microm (PM(10)) was associated with daily deaths with no evidence of a threshold. These reports have used smoothing or spline methods in individual cities and pooled the results across multiple cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic diameter Less than or equal to 2.5 microm; PM(2.5)), a component of PM(10), has not been examined in this regard. We examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-linear models including smooth functions of PM(2.5) in each city, controlling for season, weather, and day of the week. These smooth functions allowed for nonlinearities in the city-specific associations. We combined the estimated curves across cities using a hierarchical model that allows for heterogeneity. We found an essentially linear relationship down to 2 microg/m(3). The same approach was applied to examine the concentration response to traffic particles, controlling for particles from other sources. Once again, the association showed no sign of a threshold. The magnitude of the association suggests that controlling fine particle pollution would result in thousands of fewer early deaths per year.
Environmental Health Perspectives
VOLUME 110 |NUMBER 10 |October 2002
The Concentration–Response Relation between PM2.5 and Daily Deaths
Joel Schwartz,1,2,3 Francine Laden,1,3 and Antonella Zanobetti1
1Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts,
USA; 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA; 3Channing Laboratory, Brigham and
Women’s Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
In the last decade, a series of studies reported
associations between daily concentrations of
airborne particles and daily deaths (1–3). The
magnitude of the regression coefficients in
those studies indicated that particulate air pol-
lution was associated with between 50 and
100,000 early deaths per year in the United
States, and similar numbers were found in
Europe. More recently, a number of large,
multicity studies (4–7) have reported associa-
tions between airborne particles, measured in
various ways, and daily deaths. The largest
study demonstrated that gaseous air pollutants
did not confound the association, and that
none of the gaseous air pollutants showed an
independent effect on daily deaths (7). These
studies assumed a linear concentration–
response relation between airborne particles
and daily deaths and did not address the ques-
tion of what the association looked like for
particle constituents, characterized by size,
physiochemical composition, or source.
In a recent study of six U.S. cities (5), we
demonstrated that daily mortality was associ-
ated with fine particulate matter (aerody-
namic diameter 2.5 µm; PM
) and not
with coarse particulate matter (aerodynamic
diameter between 2.5 and 10 µm; PM
Each 10 µg/m
increase in the 2-day mean
concentration of PM
was associated with a
1.5% (95% confidence interval, 1.1–1.9%)
increase in daily mortality.
Ambient PM
consists mainly of com-
bustion particles from motor vehicles and the
burning of coal, fuel oil, and wood, but also
contains some crustal particles from finely pul-
verized road dust and soils. These sources pro-
duce particles with different characteristics,
and the relative toxicity of those sources and
characteristics is an area of relative recent but
intense interest. In a follow-up study (8), we
used the elemental composition of size-frac-
tionated particles to identify several distinct
source-related fractions of fine particles. We
then examined the association of these frac-
tions with daily mortality in each of the six
cities and combined the city-specific results in
a meta-analysis to derive overall relative risks
for each fraction. We found positive associa-
tions with particles from traffic, particles from
coal, and particles from residual oil combus-
tion when included jointly in the model pre-
dicting daily deaths (8). The largest effect size
was for residual oil particles, followed by traffic
particles and then coal particles. Only the latter
two associations were statistically significant,
however. Again, as traditional, these analyses
assumed a linear association between the vari-
ous particle constituents and daily deaths.
The shape of the concentration–response
relationship is critical for public health assess-
ment, and in particular, some have speculated
that thresholds might exist.
Recently, three reports have explored this
question for particulate air pollution, using
multicity studies in the United States. In one
study, Daniels et al. (9) used data from 20 U.S.
cities, five of which had daily measurements of
, with the rest having measurements
only one day in six. They used regression
splines to model the concentration–response
curve in each city and combined the results
across cities. They found no evidence for a
threshold. In fact, the concentration–response
relation was quite linear across the entire
range of exposure. In another report,
Schwartz and Zanobetti (10) used data from
10 cities, all of which had daily measurements
of PM
, resulting in slightly more days of
study than in the first report. They used non-
parametric smoothing to model the concen-
tration–response curve between air pollution
and daily deaths in each city and combined
the results across cities. Again, a linear, no-
threshold relationship was seen. Schwartz and
Zanobetti also performed simulations to con-
firm the ability of this approach to detect
thresholds and other types of nonlinearity
(10). Schwartz et al. (11), using data from
eight Spanish cities, similarly reported a linear
association between daily deaths and black
smoke, an optical measure of black particles.
These results held after adjusting for SO
. To
date, no similar examination of the concen-
tration–response curve has been done for
, or for any source components.
Because PM
is now the regulated form of
particulate air pollution in the United States,
we here report results of such an analysis.
Materials and Methods
Air pollution data. As part of the Harvard Six
Cities studies (12), dichotomous virtual
impactor samplers were placed at a central
residential monitoring site in six U.S. metro-
politan areas: Boston, Massachusetts;
Knoxville, Tennessee; St. Louis, Missouri;
Stuebenville, Ohio; Madison, Wisconsin; and
Topeka, Kansas. Separate filter samples were
collected of fine particles (PM
) and of the
coarse mass (PM
) fraction. Integrated
24-hr samples were collected at least every
other day from 1979 until the late 1980s,
with daily sampling during health survey
periods. For fine and coarse particle samples,
mass concentration was determined separately
by beta-attenuation (13). Except for a period
Address correspondence to J. Schwartz, Environmental
Epidemiology Program, Harvard School of Public
Health, 665 Huntington Ave., Boston, MA 02115
USA. Telephone: (617) 384-8752. Fax: (617) 384-
8745. E-mail:
This work was supported by U.S. EPA Grant
R827353 and NIEHS Grant ES 00002.
Received 21 December 2001; accepted 20 March
Particulate air pollution at commonly occurring concentrations is associated with daily deaths.
Recent attention has focused on the shape of the concentration–response curve, particularly at low
doses. Several recent articles have reported that particulate matter with aerodynamic diameter
10 µm (PM10) was associated with daily deaths with no evidence of a threshold. These reports
have used smoothing or spline methods in individual cities and pooled the results across multiple
cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic
diameter 2.5 µm; PM2.5), a component of PM10, has not been examined in this regard. We
examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-lin-
ear models including smooth functions of PM2.5 in each city, controlling for season, weather, and
day of the week. These smooth functions allowed for nonlinearities in the city-specific associa-
tions. We combined the estimated curves across cities using a hierarchical model that allows for
heterogeneity. We found an essentially linear relationship down to 2 µg/m3. The same approach
was applied to examine the concentration response to traffic particles, controlling for particles
from other sources. Once again, the association showed no sign of a threshold. The magnitude of
the association suggests that controlling fine particle pollution would result in thousands fewer
early deaths per year. Key words: meta-analysis, mortality, particulate air pollution, smoothing,
time series, traffic. Environ Health Perspect 110:1025–1029 (2002).
[Online 27 August 2002]
between October 1981 and January 1984 in
all cities, elemental composition of fine and
coarse mass was determined by X-ray fluores-
cence (14). Elemental composition was avail-
able on 97% of these samples. In the fine
fraction, 15 elements were routinely found
above the limit of detection: silicon, sulfur,
chlorine, potassium, calcium, vanadium,
manganese, aluminum, nickel, zinc, sele-
nium, bromine, lead, copper, and iron.
Source identification. In separate analyses
for each city, we used specific rotation factor
analysis to identify up to five common factors
from the 15 specified elements. We specified a
single element as the tracer for each factor and
maximized the projection of these elements
using the Procrustes rotation, a variant of the
oblique rotation method (15). The Procrustes
method allows us to use known tracers for dif-
ferent sources as targets for the different fac-
tors and to maximize their loadings on those
factors instead of having factors defined in an
entirely data-driven manner. To rescale the
factor scores from the normalized scale to the
mass scale (in micrograms per cubic meter),
we regressed the total daily fine particle con-
centrations on the daily factor scores for all of
the factors in separate regression models for
each city and took the product of each factor
score with its regression coefficient (16). Only
sources that were significant predictors of total
fine particle mass (p< 0.10) were considered
in the mortality analyses. Further details have
been published previously (8).
Meteorologic data. We obtained meteo-
rologic data from the National Center for
Atmospheric Research, including hourly
measures of temperature, dew point tem-
perature, and precipitation from the
National Oceanographic and Atmospheric
Administration weather station nearest to
each city (17). We calculated 24-hr mean
values for temperature and dew point
Mortality data. We defined the six metro-
politan areas in this study as the county con-
taining the air pollution monitor and
contiguous counties (5). We extracted daily
deaths from annual detail mortality tapes
(National Center for Health Statistics) (18)
for people who lived and died in the selected
counties for the time periods with fine partic-
ulate measurements. After excluding all
deaths caused by accidents and other external
causes [International Classification of Diseases,
9th Revision (ICD-9)(19), clinical modifica-
tion codes 800–999], we analyzed the
remaining total daily deaths.
Poisson regression of mortality. We inves-
tigated the association of daily deaths with
sources of fine particles separately for each
city using Poisson regression in a generalized
additive model (GAM) (20,21). That is, in
each city we assumed
where Y
is the number of deaths in the city
on day tand X
is the value of covariate ion
day t. GAMs are distinguished by allowing us
to use smooth functions S
instead of linear
terms to control for covariates, such as tem-
perature, that may affect daily deaths in a
nonlinear way. Linear functions may be used
where appropriate. This approach was intro-
duced for time series of counts in 1994 (22)
and is now standard (23,24).
To control for trend and season, we used a
locally weighted linear regression (LOESS)
smooth function of date with a span of 0.05
(25). For the smooth functions of temperature
and dew point temperature, we used LOESS
functions with spans of 0.80. Indicator vari-
ables for day of the week also were included in
the models. This is the identical model used
by Schwartz et al. (5) and Laden et al. (8), and
more details are provided there. To these mod-
els we added a smooth function of the mean
concentration on the day of death and
the previous day, instead of the linear term
previously used by Schwartz et al. (5). The
smoothing window included 50% of the data,
which corresponds to between four and five
degrees of freedom for the air pollution rela-
tion in each city. Alternatively, we added the
estimated mass for each of the source factor
scores (in micrograms per cubic meter ) simul-
taneously in the model. That is, the estimate of
the mobile source factor is in a model control-
ling for coal-derived particles, crustal particles,
and the other source factors, and vice versa.
Because only the particles from traffic showed
a strong linear association, and because the
exposure ranges for the exposures to coal parti-
cles did not overlap sufficiently, we only used a
smooth function for the traffic particles and
followed Laden et al. (8) in treating the particle
mass from the other sources as linear terms.
Hierarchical model. To combine the
smooth curves across cities, we applied the
approach of Schwartz and Zanobetti (10), as
modified by Schwartz et al. (11). In each city,
the predicted log relative risk and its point-
wise standard error was computed for each 2
Log EY S X
Articles Schwartz et al.
VOLUME 110 |NUMBER 10 |October 2002
Environmental Health Perspectives
Table 1. Mean daily deaths in six U.S. cities and mean concentrations of PM2.5 overall, and from the three
source categories showing evidence of an association with daily deaths in Laden et al. (8).
PM2.5 Traffic Coal Residual oil Dates
City Deaths (µg/m3)(µg/m3)(µg/m3)(µg/m3)(month/year)
Boston 59 16.5 4.8 8.3 0.5 5/79–1/86
Knoxville 12 21.1 4.4 6.8 — 1/80–12/87
St. Louis 55 19.2 2.9 5.6 9/79–1/87
Steubenville 3 30.5 1.5 19.2 0.9 4/79–9/87
Madison 11 11.3 3.1 4.9 — 3/79–12/97
Topeka 3 12.2 2.1 7.0 — 9/79–10/88
Figure 1. Overall estimated dose–response relation between total PM2.5 and daily deaths in six U.S. cities.
The estimate is obtained by combining the estimated smoothed curves in each of the cities, after control-
ling for weather, season, and day of the week. The shaded area indicates the pointwise 95% confidence
intervals at each point. The line shown is a least-squares regression line through the estimated points.
Percent increase in deaths
PM2.5 (µg/m3)
increment in exposure. These esti-
mates are provided by the GAM function in
S-plus (MathSoft, Inc., Seattle, WA). To suc-
cessfully combine data across cities, we need
to use a range of exposures that is common to
all cities. Because high concentrations of
were rare, the curves were combined
only in the range of 0–35 µg/m
. The first
phase of the analysis produced estimated
effect sizes (log relative risks) Y
in each city i
for each exposure category j. A pointwise
standard error of the estimate is also esti-
mated by GAM. To produce the combined
curve, we regressed these estimates against
indicator variables for each level, using inverse
variance weighting and allowing for a random
variance component to capture heterogeneity
in the association across cities. That is, we
where d
are dummy variables for the jexpo-
sure levels, V
is the estimated variance in city
iat level j, and δis the estimated random
variance component.
We used the iterative meta-regression
approach of Berkey et al. (26) to obtain a
maximum likelihood estimate of the random
variance component.
The nonparametric smooth functions we
use to estimate the shape of the concentration
response relation use four to five degrees of
freedom, and it is not clear that the source-
specific relations can support so many degrees
of freedom, which would entail a total of 20
degrees of freedom for all the PM
In our previous report (8), the relation
between PM
from traffic and daily deaths
was estimated with considerably greater preci-
sion than for particles from other sources,
most of which were not significant. Further,
the range of overlap in exposures across cities
was lower for coal, crustal, and residual oil
factors. Therefore, in our source-specific
models, we only modeled the traffic source
particles using a nonparametric smooth,
while controlling for PM
from the other
sources using linear terms, as in Laden et al.
(8). We then combined the estimated con-
centration–response relations for traffic parti-
cles similarly to what we did for PM
the other sources.
Table 1 shows the daily deaths, PM
and estimated concentrations of PM
each source. Figure 1 shows the meta-smooth
dose–response relation between PM
daily deaths in the six cities. There is no evi-
dence of a threshold, and the relation occurs
well below the U.S. Environmental Protection
Agency standard of 65 µg/m
(27). The line
shows the least-squares fit of a linear relation
through the estimated points.
The next results come from the source
component models. These models had a
smooth function of PM
from traffic and
linear functions of PM
from the other
sources in each city. Figure 2 shows the
results when we combined the estimated
dose–response curves for traffic particles
across the six cities. Again, there is no evi-
dence of a threshold, and the association is
essentially linear. If anything, the slope is
steeper at lower concentrations. To test the
robustness of the association with traffic par-
ticles to our method of controlling for parti-
cles from other sources, we re-estimated the
relationship controlling for smooth functions
of the estimated particle mass from other
sources, rather than the linear terms. This
association is shown in Figure 3 and differs
little from that shown in Figure 2. We also fit
linear regressions through the points shown
on Figures 1 and 2. We obtained a slope of
1.5% increase in deaths per 10 µg/m
increase in PM
and 3% increase in deaths
per 10 µg/m
increase in particles from traf-
fic, which is the same as the results reported
by Laden et al. (8). These lines are shown on
the figures. This supports the assumption of a
linear relationship.
We have explored the concentration–response
relation between PM
and daily deaths in six
U.S. cities and combined the results to obtain
greater stability, while accounting for hetero-
geneity in response. The population mean
curve shows no evidence of a threshold down
to the lowest levels of PM
. In fact, the curve
is quite linear over the exposure range from 0
to 35 µg/m
. This is consistent with previous
results using a similar methodology but with
(10) and black smoke (11) as the expo-
sure metric. In addition, a different methodol-
ogy, using regression splines, was applied by
Daniels et al. (9) to PM
data in different
cities. They combined these spline models
across 20 cities. Again, the association
appeared to be quite linear without any evi-
dence of a threshold. A spline model had pre-
viously been applied by Schwartz (22) to the
data from Boston, with a similar find-
ing. Indeed, the original study of these data by
Schwartz, Dockery, and Neas (5) found a sig-
nificant association when limited to days
below 30 µg/m
, with a slightly larger slope.
The consistency of the results on two conti-
nents, and using different techniques, suggests
that this finding is robust. The concentra-
tion–response curve seen here for PM
steeper than that previously reported (per
) for PM
(10). This is consistent with
the previous report from this study (5) that
coarse mass (the difference between PM
) is not associated with daily deaths. We
note that Schwartz and Zanobetti (10)
demonstrated in simulation studies that mea-
surement error was not likely to distort the
shape of the association. Similarly, recent
studies of “harvesting” have shown that effect
sizes increase rather than decrease when longer
lags are taken into account; for example, high
ˆ~,,YNd d dV
ij k k ij
ββ β δ
11 2 2
+++ +
Articles PM2.5 and daily deaths
Environmental Health Perspectives
VOLUME 110 |NUMBER 10 |October 2002
Figure 2. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.
cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after
controlling for weather, season, and day of the week and for PM2.5 from crustal sources, coal combustion,
residual oil, salt, and metal processes as linear terms. The line shown is a least-squares regression line
through the estimated points.
Percent increase in deaths
Traffic particles (µg/m3)
days producing harvesting that mutes the
effect on the next high day is unlikely to have
distorted the shape of the association.
These results are also biologically plausi-
ble. Schwartz (28) pointed out that if thresh-
olds exist in individuals, but there is a
distribution of those thresholds among indi-
viduals, and if multiple genetic and predis-
posing illnesses each contributed to the
distribution of those thresholds, then by the
central limit theorem, the distribution of
thresholds should approach a normal distrib-
ution. Hence, the population concentration–
response curve should approach a cumulative
normal curve. But the low-dose end of the
cumulative normal curve is linear. To see this,
consider that typical death rates in U.S. cities
are 8/1,000 per year, or 2 ×10
per day. The
normal range of variation in daily deaths in
U.S. cities is a factor of two or less. Hence,
the normal range of daily death probabilities
in response to all risk factors is from 1 to 3 ×
. Figure 4 shows the cumulative normal
curve in that range of probabilities, which is
quite linear. Because we are clearly in the low-
dose regime, in the sense that the exposures
to particles are well below the threshold for
mortality for most people, this linearity is
exactly what would be expected.
Figure 1 also indicates that the association
reported here has public health significance.
The difference between mean PM
trations of 10 µg/m
and 20 µg/m
, which is
a difference found between U.S. cities, is
associated with about a 1.5% increase in
deaths. In a metropolitan area of a million
inhabitants, this would amount to about 130
additional early deaths per year, and in the
country as a whole, these results indicate that
a reduction of 10 µg/m
would be expected to
result in about 36,000 fewer early deaths per
year. Although this study does not indicate
the extent to which these deaths are brought
forward, other studies of the harvesting issue
(29–32) suggest that they are considerable.
The association of daily deaths with traf-
fic particles also has no threshold and is some-
what steeper than the association with all
. This is consistent with the results of
Laden et al. (8), except that they used linear
terms instead of smooth functions. This study
confirms that this association extends to low
levels. This result has considerable public
policy relevance. Recently, automotive
companies have proposed using diesel engines
to achieve higher fuel economy in the future.
However, diesel engines produce substantially
greater emissions of particles and particle pre-
cursors such as NO
. The present results indi-
cate that such an expansion of diesel engine
use in the United States before diesel engines
can meet the same particle emission levels as
gasoline engines may result in important pub-
lic health problems. A 1 µg/m
increase in the
concentration of traffic particles in the
United States, for example, could be associ-
ated with about 7,000 additional early deaths
per year in the United States.
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VOLUME 110 |NUMBER 10 |October 2002
Environmental Health Perspectives
Figure 4. Cumulative normal curve versus a stan-
dardized predictor (the sum of the effects of all risk
factors) over the range of exposures that corre-
spond to daily death rates of between 1 and 3 per
million, which is the observed range of variation in
U.S. cities. It is quite linear in the predictor.
10 20
Probability of death (×
Scaled linear predictor
Figure 3. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.
cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after
controlling for weather, season, and day of the week. Instead of linear terms for particles from other
sources, in this analysis we controlled for smoothed terms for PM2.5 from crustal sources, coal combus-
tion, residual oil, and salt. The line shown is a least-squares regression line through the estimated points.
Percent increase in deaths
Traffic particles (µg/m3)
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Articles PM2.5 and daily deaths
Environmental Health Perspectives
VOLUME 110 |NUMBER 10 |October 2002
... In this study, the linear functional form of the CRF, which is often used for simplification based on biological evidence, is adopted to assess the expected deaths caused by exposure to PM 2.5 pollution, which is illustrated in Equation (11) (Schwartz et al., 2002). ...
... As discussed above, RR is a key parameter in the CRF model for health impact assessment. However, the value in the GAINS-China model is fixed and mainly derived from American or European studies (Schwartz et al., 2002). Considering the air pollution differences between China and other developed countries, the more reliable RR values of mortality attributable to the average concentration of PM 2.5 for China are needed. ...
Overcapacity is regarded as an inevitable problem for rapid economic developing countries like China, which also causes serious adverse impacts on the environment and public health. However, few studies have quantified the overcapacity feature and corresponding co-benefit from de-capacity policy. To fill such research gaps, this study constructed a comprehensive assessment model by combining the Data Envelopment Analysis (DEA) model, the GAINS-China (Greenhouse gas-Air pollution Interactions and Synergies) model, and a meta-analysis and health impact assessment module, to measure the capacity utilization rate of 41 industrial sectors in 31 Chinese provinces and forecast the environmental and health co-benefits from de-capacity policy in 2050. Results showed that the capacity utilization rate of China's industry is 64.13% in 2018, which is much lower than the threshold value of 75%, indicating serious overcapacity in China's industry. Capacity utilization rates of light industries are higher (around 70%) than heavy industries (50%-60%), and the capacity utilization rate in East and South-Central China is higher (70%-96%) than West China (below 40%). Under a de-capacity scenario in 2050, China's CO 2 and PM 2.5 emissions are reduced by 1.05 billion tons (9.6%) and 57.8 kilotons (5.8%), respectively. This reduction in PM 2.5 emissions results in a substantial health co-benefit, reducing national premature mortality cases by approximately 792,100 (1.6%). Finally, it is recommended that de-capacity priority be given to industries with low capacity utilization rate, as well as regions with intensive heavy industry or high levels of greenhouse gas emissions, severe air pollution, and dense population.
... The health impact functions were calculated following a log-linear model (1). Risk ratios reported in this study were in the range of those reported in the literature [70][71][72][73][74][75][76][77][78]. ...
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Vehicular traffic is one of the major sources of air pollution in European cities. This work aims to understand which characteristics of the urban environment could influence mobility-related air pollution, quantify the health impacts of exposure to traffic-derived PM2.5 and NO2 concentrations, and assess the potential health benefits expected from traffic interventions. The health benefits modeled are intended to provide a set of comparable data to support decision-makers and encourage informed decision-making to design healthier cities. Targeting a large geographical coverage, 12 European cities from 9 countries were comparatively assessed in terms of mean daily traffic volume/area, the number of public transport stops/area, and the percentage of green and outdoor leisure areas, among other urban indicators. This was implemented using an open-source data mining tool, which was seen as a useful engine to identify potential strategies to improve air quality. The comparison of urban indicators in the selected cities evidenced two trends: (a) cities with the most heterogeneous distribution of public transport stops, as an indicator of poor accessibility, are also those with the lowest proportion of km dedicated to cycleways and footways, highlighting the need in these cities for more sustainable mobility management; and (b) the percentage of green and outdoor leisure areas may influence the share of journeys by bicycle, pointing out that promoting the perception of green routes is relevant to enhance the potential of active transport modes. Socioeconomic factors can be key determinants of the urban indicators and would need further consideration. For the health impact assessment (HIA), two baseline scenarios were evaluated and compared. One is based on mean annual traffic contributions to PM2.5 concentrations in each target city (ranging between 1.9 and 13 µg/m3), obtained from the literature, and the second is grounded on mean annual NO2 concentrations at all available traffic and urban background stations within each city (17.2–83.5 µg/m3), obtained from the European Environment Agency database. The intervention scenarios modeled were designed based on traffic mitigation strategies in the literature, and set to ranges of 6–50% in traffic-derived PM2.5 concentrations and of 4–12.5% in NO2 concentrations. These scenarios could result in only a 1.7% (0.6–4%) reduction in premature mortality due to exposure to traffic-derived PM2.5, and 1.0% (0.4–2%) due to exposure to NO2, as the mean for all the cities. This suggests that more ambitious pollution abatement strategies should be targeted.
... It is an inhalable pollutant that causes health problems and contributes to the deterioration of air quality. This pollutant forms in chemical reactions among the compounds of small particles emitted by various human activities, including motorized travel, energy production and consumption, agriculture, and natural disasters such as wildfires and sandstorms (Schwartz et al., 2002). In urban settings, the increase of traffic volume and the higher building density resulting from population agglomeration elevates the concentration of PM2.5. ...
Particulate matter (PM) 2.5 generates a variety of negative effects on health, such as heart and lung disease, asthma, and respiratory symptoms. The pollutants in the atmosphere primarily result from human activities, and, in urban settings, increases in traffic volume and higher building density can elevate the level of PM2.5. Building on previous research, this study primarily focuses on two highly developed urban areas in the Texas Triangle region: Travis County in the Austin Metropolitan Area and Harris County in the Greater Houston Area. It explores different types of urban features, such as urban structures, land use/land cover, traffic volume, and distance from roads, that affect the PM2.5 concentration in urban environments at the local scale. Throughout this study, we use various research methods, including geographically weighted regression, to estimate the PM2.5 concentrations at local scales, 3D city models to derive urban characteristics, and the random forest algorithm to predict the effects of urban features on PM2.5 concentrations. Our findings suggest that developed land use, tall buildings in dense areas, and major traffic networks are the key contributors to PM2.5. However, we also find that tree canopy cover can significantly reduce PM2.5 concentrations.
... (Supplementary Table S.1) For mortality, to avoid double counting, we used reported associations of PM 2.5 adjusted by NO 2 and NO 2 adjusted by PM 2.5 . Following the literature, we assumed a linear concentration-response function for the association of exposure to NO 2 and PM 2.5 with our health endpoints in the range of exposures observed in Seattle [31][32][33]. ...
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Background Climate mitigation policies that focus on the transportation sector yield near-term health co-benefits that could motivate policy action. Objective We quantified CO2 emission reductions as well as the air pollution and health benefits of urban transportation policies promoting electric vehicles (EV) and walking and bicycling in Seattle, Washington. Methods We compared a business-as-usual scenario projected to 2035 with intervention scenarios in which 35% of gasoline vehicles were switched to EV, and 50% of car trips less than 8 kilometers were replaced by walking or bicycling. We modeled changes in primary traffic-generated oxides of nitrogen (NOx) and fine particulate matter (PM2.5) as well as walking and bicycling activity, CO2 emissions from traffic, and fatal traffic injuries due to the transportation policy scenarios. We estimated the impacts of these changes on annual cases of asthma and premature mortality in the Seattle population. Results Increasing the use of EV, walking, and bicycling is estimated to reduce CO2 emissions by 744 tons/year (30%) and lower annual average concentrations of primary traffic-generated NOx and PM2.5 by 0.32 ppb (13%) and 0.08 μg/m³ (19%), respectively. In Seattle, the lower air pollutant concentrations, greater active transportation, and lower fatal traffic injuries would prevent 13 (95% CI: −1, 28), 49 (95% CI: 19, 71), and 5 (95% CI: 0, 14) premature deaths per year, respectively and 20 (95% CI: 8, 27) cases of asthma per year. Significance Moving towards cleaner vehicles and active transportation can reduce CO2 emissions, improve air quality, and population health. The resulting public health benefits provide important motivation for urban climate action plans. Impact statement Using key components of the health impact assessment framework, we quantify the environmental and health benefits of urban transportation policy scenarios that promote electric vehicle use and replace short car trips with walking and bicycling as compared with a business as usual scenario in 2035. Our findings demonstrate that transportation scenarios promoting cleaner vehicles and active transportation can reduce CO2 emissions, improve air quality, and increase physical activity levels, resulting in significant public health benefits.
... Ikeuchi et al. proposed that health risks caused by PM2.5 would increase with the decrease of precipitation duration and incidence [11]. Schwartz et al. believed that particulate air pollution at common concentrations is associated with daily death [12]. Gulia et al. highlighted the problem of high spatiotemporal variation of air pollution levels in urban areas and the methods that can be used to eliminate pollutants. ...
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Air pollution control as the background of a cost allocation method is based on the Shapley value to determine the core stakeholder, so fair pollution control projects and the establishment of the atmospheric pollution of governance cost allocation model are put forward for the solution of air pollution coordinated by the government supervision and the atmospheric pollution control collaborative group. The results show that the cost allocation model of air pollution control based on Shapley value is more reasonable, and the cost of stakeholders is reduced to a certain extent, and the risk of the participants is reduced so that it maximizes social benefits.
... Central South American fires emit smoke plumes that cover hundreds of thousands of hectares every year over central Brazil and western Bolivia and Peru (Freitas et al., 2005). The smoke impacts the radiation budget and cloud formation in the region and leads to deterioration of the air quality in many inland cities and smaller villages, affecting not only human health (Schwartz et al., 2002;Barregard et al., 2006;Ignotti et al., 2007) but also visibility and closure of inland airports for many weeks Andreae et al., 2004;Barregard et al., 2006;Hoelzemann et al., 2009;Longo et al., 2009). The smoke plumes are regularly transported over the continent, first mostly to the south and then over southern and southeastern cities and megacities including São Paulo, Rio de Janeiro, Montevideo, and Buenos Aires. ...
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This commentary paper from the recently formed International Global Atmospheric Chemistry (IGAC) Southern Hemisphere Working Group outlines key issues in atmospheric composition research that particularly impact the Southern Hemisphere. In this article, we present a broad overview of many of the challenges for understanding atmospheric chemistry in the Southern Hemisphere, before focusing in on the most significant factors that differentiate it from the Northern Hemisphere. We present sections on the importance of biogenic emissions and fires in the Southern Hemisphere, showing that these emissions often dominate over anthropogenic emissions in many regions. We then describe how these and other factors influence air quality in different parts of the Southern Hemisphere. Finally, we describe the key role of the Southern Ocean in influencing atmospheric chemistry and conclude with a description of the aims and scope of the newly formed IGAC Southern Hemisphere Working Group.
La qualité de l’air est devenue un enjeu sanitaire majeur dans le monde. Rapportés au voyageur.kilomètre, les transports en commun permettent de diminuer les émissions polluantes mais ils n’en sont pas exempts. Du fait de l’usure de ses composants (roue, rail, ballast, pantographe, caténaire, frein), l’exploitation ferroviaire génère des polluants particulaires. Ceux-ci peuvent s’accumuler dans les enceintes ferroviaires souterraines à cause des effets de confinement. Les études d’impact de cette pollution restant rares, SNCF a engagé dès le début des années 2000 des travaux sur le sujet. En 2016, un programme de caractérisation de la qualité de l’air dans 24 gares souterraines du réseau Transilien a été mis en œuvre. Il est nécessaire de compléter ces connaissances par celle des mécanismes de dispersion des particules émises au freinage en gare souterraine pour implémenter des solutions pérennes. L’objet de cette thèse est donc de caractériser cette dispersion. Pour cela, une approche couplée numérique et expérimentale est proposée. La dispersion est d’abord étudiée grâce à des simulations Euler-Lagrange. La configuration d’étude numérique a ensuite été reproduite en soufflerie. Des campagnes de mesures monophasiques (PIV) et diphasiques permettent ensuite de valider les simulations. De là, les principales régions de l’écoulement responsables de la dispersion des particules de freinage sont identifiées. Les résultats montrent que les structures cohérentes générées par le matériel roulant ont une grande influence sur la dispersion des particules émises lors du freinage. Toutefois elles ne sont pas responsables de la dispersion de ces particules nouvellement émises vers le quai. Enfin, de nombreuses particules se déposent dans la cavité du bogie qui les a émises.
Purpose This study sought to determine if environmental barriers (i.e. air pollution, temperature and precipitation) affect outdoor (i.e. soccer and baseball) and indoor (i.e. basketball) professional sport attendance in South Korea. Design/methodology/approach By including actual air quality, temperature and precipitation data collected from each place where the sporting events take place, this study conducted a regression analysis to examine factors that influenced outdoor and indoor sport attendance. Findings In outdoor sports, the estimated results suggested that soccer and baseball attendance were not affected by air pollution. Indoor sport consumers did not change their consumption behaviors in attending sports despite the presence of air pollution. In addition, there was mixed evidence on the effect of weather-related variables on attendance. Average temperature had a positive effect on baseball (outdoor) and basketball (indoor) sport attendance, indicating that the warmer the temperature, the more likely those fans were to attend the games. Average precipitation was negatively associated with outdoor (soccer) sport spectators. Originality/value The present study contributes to the sport environment literature by examining the impact of environmental barriers on spectators' behaviors in the context of outdoor and indoor professional sports.
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Air pollution, both gaseous and in the form of dust, is a problem that affects numerous densely built-up areas of modern cities. Based on this assumption, the authors of the following paper have examined an exemplary part of urban space with various building developments located in Warsaw downtown. Both experimental and numerical studies were conducted for the two prevailing wind directions observed in this area, that is the west wind and the southwest wind. Experimental research was conducted with the application of two known laboratory techniques, i.e., the oil visualization method and the sand erosion technique. The studies were conducted in an open-circuit wind tunnel. Commercial ANSYS Fluent program was used for numerical simulations. The k-e realizable turbulence model, often applied for this type of tasks , was used in the calculations. As a result, distributions of the velocity amplification coefficient were obtained in the area under consideration, as well as images that present the averaged airflow direction. On basis thereof, potential zones where contamination accumulation may occur were determined. The impact that introduction of a hypothetical high-rise building into the area would exert on wind conditions in its vicinity was also tested. High-rise buildings tend to intensify airflow in their immediate vicinity. Thus, they can improve ventilation conditions of nearby streets. However, in this particular case, the research prompted the conclusion that the proposed building causes turbulence and increased velocity gradients in the majority of elevation planes. On the other hand, in the ground-level zone, the building blocks rather than intensifies the airflow.
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This article summarizes epidemiological evidence of health effects of particulate air pollution. Acute exposure to elevated levels of particulate air pollution has been associated with increased cardiopulmonary mortality, increased hospitalization for respiratory disease, exacerbation of asthma, increased incidence and duration of respiratory symptoms, declines in lung function, and restricted activity. Small deficits in lung function, higher risk of chronic respiratory disease and symptoms, and increased mortality have also been associated with chronic exposure to respirable particulate air pollution. Health effects have been observed at levels common to many U.S. cites and at levels below current US. National Ambient Air Quality Standards. Although the biological mechanisms involved are poorly understood, recent epidemiological evidence supports the hypothesis that respirable particulate air pollution is an important risk factor for respiratory disease and cardiopulmonary mortality.
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Inhalable particulate matter (IP) samples have been collected in six U.S. cities in conjunction with an air pollution health study. The IP were collected using dichotomous virtual impactors in two size ranges: fine particles (FP) having aerodynamic diameter (d(a)) < 2.5 μm, and coarse particles (CP) with 2.5 μm < d(a) < 15 μm. The mass measurements were determined by beta-gauge attenuation. The elemental composition of the FP and CP were determined by X-ray fluorescence. The means and distributions for FP and CP and selected elemental data highlight the similarities and differences that exist among these cities in the health study. Examining the temporal variations gives additional information on the meteorology and sources influencing the FP and CP fractions of inhalable particle mass. Differences in the concentration (and ratios) of selected elements have indicated the varying presence of crustal, steel industry, automotive, oceanic and fuel combustion sources in these cities. The noted variation in the concentrations and character of ambient aerosols in these cities are pertinent to interpreting differences in population exposures.
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Objective: To test the efficacy of a graded aerobic exercise programme in the chronic fatigue syndrome. Design: Randomised controlled trial with control treatment crossover after the first follow up examination. Setting: Chronic fatigue clinic in a general hospital department of psychiatry.
Results of an experimental determination of the precision and the accuracy of a ..beta..-ray attenuation method for measurement of aerosol mass are presented. The instrumental precision for a short-term experiment was 25 for a 6.5-cm² deposit collected on approximately 1 mg/cm² Teflon filters; for a longer-term experiment the precision was 27 The precision of the gravimetric determinations of aerosol deposits was 22 for Teflon filters weighed to 1 Filter reorientation and air density changes that were able adversely to affect the ..beta..-ray attenuation results are discussed. ..beta..-ray attenuation results are in good agreement with gravimetric measurements on the same filter-collected aerosols. Using dichotomous samplers in Durham, NC, we collected 136 aerosol samples on Teflon filters in two size ranges. A regression line was calculated implicitly assuming errors in both measurements of mass. The 90% confidence intervals lay within 21 of the regression line for mean fine fraction aerosol mass loadings of 536 and within 19 of the regression line for mean coarse fraction aerosol mass loadings of 349 Any bias between gravimetric and ..beta..-gauge mass measurements was found to be less than 5%.
In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.One advantage of the PCA source apportionment approach developed is that it allows the estimation of mass and source particle characteristics for an unconventional source category: transported (coal combustion related) aerosols. This particle class is estimated to represent a major portion of the aerosol mass, averaging roughly 40 per cent of the fine mass and 25 per cent of the inhalable particle mass at the Watertown, MA site. About 45 per cent of the fine particle sulfur is ascribed to this one component, with only 20 per cent assigned to pollution from local sources. The composition of the coal related aerosol at this site is found to be quite different from particles measured in the stacks of coal-fired power plants. Sulfates were estimated to comprise a much larger percentage of the ambient coal related aerosol than has been measured in stacks, while crustal element percentages were much reduced. This is thought to be due to primary panicle deposition and secondary aerosol accretion experienced during transport. Overall, the results indicate that the application of further emission controls to local point sources of particles would have less influence on fine aerosol and sulfate concentrations than would the control of more distant emissions causing aerosols transported into the Boston vicinity.
This article describes flexible statistical methods that may be used to identify and characterize nonlinear regression effects. These methods are called "generalized additive models". For example, a commonly used statistical model in medical research is the logistic regression model for binary data. Here we relate the mean of the binary response ¯ = P (y = 1) to the predictors via a linear regression model and the logit link function: log
The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x i , y i ), i = 1, …, n, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i , y i ) is large if x i is close to x k and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.