Journal of Environmental Protection, 2013, 4, 869-876 Published Online August 2013 (
Human Health Cost of Air Pollution in Kazakhstan
Ussen Kenessariyev1, Alexander Golub2, Michael Brody2, Askhat Dosmukhametov1, Meiram Amrin1,
Aya Erzhanova1, Dinara Kenessary1
1Department of Public Health, Kazakh National Medical University Named after S.D. Asfendiyarov, Almaty, Kazakhstan; 2De-
partment of Environmental Science, American University, Washington DC, USA.
Received December 11th, 2012; revised February 5th, 2013; accepted April 9th, 2013
Copyright © 2013 Ussen Kenessariyev et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Kazakhstan, like other former Soviet Republics, inherited a number of serious environmental problems. Air pollution is
one of these serious problems, leading to significant environmental health effects on the population of Kazakhstan. This
study provides a baseline analysis of health damages from air pollution, based on readily available information. Mean
estimates of mortality risk attributable to air pollution are about 16,000 cases per year with a 95% confidence level of
the risk not exceeding 25,500. Even taking into account all the uncertainties related to the collection and processing of
primary data, as well as the application of risk analysis methodology, we conclude that air pollution in Kazakhstan con-
stitutes a significant contribution to the environmental burden of diseases. In relative terms, the impact of air pollution
on premature mortality in Kazakhstan is notably higher than in Russia and the Ukraine.
Keywords: Air Pollution; PM; Kazakhstan; Health Risk; Uncertainty
1. Introduction
Kazakhstan, geographically the largest of the former So-
viet republics, excluding Russia, possesses enormous
fossil fuel reserves and plentiful supplies of other
minerals and metals. It also has large energy and agri-
cultural sectors which account for a significant fraction
of GDP. On post-Soviet territory, Kazakhstan is the most
rapidly growing economy. Kazakhstan’s economy has
largely recovered from the global financial crisis of 2008,
and consequently, GDP increased 7% per year until 2011.
Extractive industries have been and will continue to be
the engine of this growth. These industries have an
adverse effect on the environment. The combination of
large coal and energy sectors results in high levels of air
pollution. Recently, Kazakhstan has embarked on an
ambitious diversification program aimed at developing
targeted sectors like transportation, pharmaceuticals, tele-
communications, petrochemicals and food processing. In
2013, the government expects to join the World Trade
Organization. This move should further help Kazakhstan
develop its manufacturing and service sector base. Inte-
gration into the world economy will additionally boost
economic growth and create new opportunities for di-
versification of economic growth. However, it also may
create additional burdens on the environment.
Like other former soviet republics, Kazakhstan in-
herited significant environmental problems, but relatively
little analysis of their effects. However, a number of
studies in the region conducted in 1996-2008 have
estimated health risks from air pollution in Russia and
Ukraine [1-6]. These studies generally conclude that
there are significant health risks attributable to environ-
mental pollution. According to these studies, up to 90%
of adverse health effects could be attributed to air
pollutants [5], and primarily to PM 2.5. The similarity of
Kazakhstan’s industrial profile and pollution levels with
Russia and Ukraine, suggests that these conclusions will
likely hold true for Kazakhstan. The observed similarities
between the countries prompted the undertaking of the
current study. This is the first time that a human health
risk analysis was conducted in Kazakhstan. This study
also provides a baseline analysis of health and economic
costs of environmental pollution in Kazakhstan, using
currently available information.
The goal of this study is to demonstrate the general
magnitude of the adverse effects of environmental pollu-
tion on human health and to examine the level of uncer-
tainty for the major conclusions. Finally, this study should
draw attention to the problem of air pollution in Central
Asia and provide some guidance for future research.
Copyright © 2013 SciRes. JEP
Human Health Cost of Air Pollution in Kazakhstan
Description of Study Area
Kazakhstan is the largest landlocked country in the world.
It has a relatively dry climate with hot Central Asian
summers and can have extremely cold winters. Parts of
the year can be marked by sand storms, which are typical
for the south and center of the country. The two largest
cities are the former capitol, Almaty and the new capitol,
Astana. Almaty is located in the southeast of the country,
is in a broad valley, closed on three sides by the mountains.
Astana is on the steppes in north central Kazakhstan has
hot and dry summers, and a Siberian winter.
As of 2010, the overall population of Kazakhstan was
16,200,000. The average age of the population is about 30
years, with an average life expectancy of 63.51 for men
and 73.32 for women. Unemployment rate is just under
6%. (official national statistics,
2. Methods
This study was conducted primarily utilizing reported data
that is readily available in Kazakhstan and by applying the
risk analysis methodology described in [7]. The analysis
includes reported ambient concentrations and baseline
mortality in cities across Kazakhstan.
This study is similar to the one conducted for Russia [5]
and Ukraine [4]. These studies demonstrated how health
risk analysis could be adapted and applied in the former
Soviet Union, taking into account important factors such
as data availability, demographics, and the composition of
industrial emissions.
This study required the following steps and the methods
section is organized by these steps:
1) Acquisition and analysis of official monitoring data
of Total Suspended Particles (TSP).
2) Conversion of TSP concentration to PM10 concen-
3) Conversion to PM 2.5 concentration from PM10.
4) Application of a PM2.5 log-linear concentration-
response function.
5) Collection of the mortality and morbidity data, and
6) Monte Carlo analysis to better account for uncer-
tainties in the data and model.
2.1. Particulate Data in Kazakhstan
Systematic, country-wide monitoring of PM2.5 or PM10
is not available in Kazakhstan. As in most other post-
Soviet countries, particulate monitoring and reporting is
still based only on total suspended particles. Ambient TSP
concentration is usually monitored and reported only for
larger cities, and not in smaller cities, towns or rural areas.
However, big cities are “hot spots” in terms of concen-
tration of TSP. Due to these information constraints, we
focused on bigger cities with high reported levels of pol-
lution. As a result, total risk attributed to air pollution is
likely underestimated as the study does not cover the
entire country. We address this and other shortcomings of
the study in the section where we present the results of the
uncertainty analysis.
Concentrations of TSP are reported in the official
statistics of the Kazakh HydroMeteorological Agency
( Monitoring stations are a com-
bination of automatic and manual samplers. Automatic
monitors take TSP samples every 6 hours. The number of
monitors for each city in the study is shown in Table 1
We calculated an average monthly TSP concentration
over 3 years (2008, 2009 and 2010) for each city in the
study. As an example Table 2 shows the data for Astana,
the capital of Kazakhstan.
The ambient concentration data in Table 2 exhibit sig-
nificant variability within years as well as within monthly
observations. One important aspect in understanding TSP
in Kazakhstan, however, is the occurrence of sandstorms
that complicate the conversion of TSP to PM. The abso-
lute maximum TSP concentration of 1260 µg/m3 was
observed in April 2008, and the minimum of 250 µg/m3
was observed twice during the winter, once in February
2008 and a second time in December 2009. Such a dif-
ference in concentrations is likely due to the effect of
sandstorms. The highest TSP concentrations are usually
reported from April to October. This is consistent with the
hypothesis about the effects of sandstorms.
2.2. Conversion of TSP to PM10
Avaliani and Revich [8] proposed a 0.55 conversion co-
efficient to convert TSP into PM10 for Russia, slightly
Table 1. Location and number of TSP monitoring stations.
City Number of Monitoring Stations
Almaty 16
Astana 7
Shimkent 4
Zhezkazgan 2
Taraz 5
Pavlodar 2
Ust-Komenogorsk 5
Semey 2
Atyrau 7
Temirtau 3
Aktau 2
Source: National statistics, KazHydroMet, 2011.
Copyright © 2013 SciRes. JEP
Human Health Cost of Air Pollution in Kazakhstan
Copyright © 2013 SciRes. JEP
Table 2. Average monthly TSP concentration in Astana.
Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
2008 290 250 420 1260 900 900 480 600 n.a. 640 530 700
2009 290 530 380 500 400 450 450 450 420 540 510 250
2010 260 410 460 820 720 530 490 550 800 580 280 350
Source: Informational Bulletin on Environmental Conditions, Ministry of the Environment, RSE KazHydroMet, 2011.
below the 0.6 conversion coefficient suggested in Larson
et al. [1] for Russia and in Strukova et al. [4] for Ukraine.
Many fo rmer Soviet regions have more combustion-
related activities than average, so using a higher coe-
fficient to convert TSP to PM10 than the world average
coefficient of 0.5 would be appropriate [5]. However, the
slightly more conservative coefficient of 0.5 was used in
this study.
2.3. Conversion of PM10 to PM2.5
In Russia, the PM 2.5/PM10 ratio have estimated ranges
of 0.55 - 0.75 [1] and in Cohen et al, [9]. The ratios of PM
2.5/PM10 vary for emission sources with different types
of technologies, industrial sectors, fuels, and by distance
from emission sources to monitoring locations, etc.
Therefore, it appears that conversion coefficients calcu-
lated for Russia and Ukraine should be applicable in Ka-
zakhstan. However, with the difference in climatic con-
ditions, particularly sandstorms, it is not possible to use
this conversion range in Kazakhstan.
As Shahsavani et al. [10] report, the PM 2.5/PM10 ra-
tio during sandstorms is close to 0.2. However, the ratio is
at the 0.4 level when there are no sandstorms. For ex-
ample, in the United States, the conversion ratio in arid/
semi-arid states (Arizona, Nevada, New Mexico, and
Utah) generally falls in the range of 0.2 - 0.4, and in the
range of 0.4 - 0.5 in agricultural states like Iowa, Kansas,
and Missouri. The range is 0.55 - 0.75 in more forested
states with less agriculture (Pennsylvania, Virginia, West
Virginia; http://www.epa .gov/air/data/reports.html).
Cities that are not located in arid/semi-arid or agricul-
tural zones, but have high traffic emissions and relatively
low fugitive road dust, will tend to have very high
PM2.5/PM10 ratios. It is plausible that the PM ratio in
Kazakhstan falls in the range of 0.2 - 0.5. The exact ratio
depends on fugitive road dust, the influence of industrial
emissions on urban PM ambient concentrations, and on
the size distribution of particulates from industrial sources.
In Kazakhstan, as in Russia and Ukraine, coal-fired
power contributes a significant portion of air pollution.
Based on these observations, a ratio of PM 2.5/PM 10 of
0.4 has been applied to areas of Kazakhstan that have
significantly fewer sandstorms. This includes Almaty, the
largest city in Kazakhstan and is located in a mountainous
area. For areas more prone to sandstorms, such as in a
steppe area like the capital, Astana, we used lower con-
version coefficients of either 0.2 or 0.3 depending upon
the known local severity of sandstorms. Finally, to help
bound these estimates; we compared them to estimates
from remote sensing analysis [11].
2.4. Concentration-Response Coefficient
The WHO in the World Health Report [12] provided a
global estimate of the health effects of environmental risk
factors. The estimation of air pollution-related mortality
was based on a concentration-response coefficient, and a
log-linear model that links ambient pollutant concentra-
tions with cardiopulmonary mortality. This approach
based on Pope et al. [13] makes the best use of available
concentration data and the evidence of the mortality ef-
fects of ambient particulate pollution (PM 2.5). Dockery
[14] in his review of the health effects of PM discusses
this approach at length. Alternative approaches would
require building a local time-series database as in Zmirou
et al. [15] but without the corresponding high quality PM
monitoring database.
This particular analysis was based on [7], who sug-
gested using this log-linear approximation of health risk
function as in expression (1) whereas other authors sug-
gest linear approximations. For instance, one of the most
recent studies conducted for the US [16] suggest a linear
approximation. Additional relative risk would be calcu-
lated per 10 µg/m3 of PM with a diameter less than 2.5
microns in [7]. Linear approximation implies an equal
weight of each incremental increase in concentration
while log-linear function implies decreasing return with
additional increase in concentration. Accordingly, ap-
proximation (1) mimics this property of concentration-
response function as long as β < 1 holds true. The selec-
tion of (1) instead of a simple linear approximation is
important for risk analysis when ambient concentration is
high, and leads to overall more conservative estimates of
where RR stands for relative risk;
β—is a concentration-response coefficient;
C denotes concentration of PM2.5;
C0 represents concentration threshold (usually 7.5 µg/m3
Human Health Cost of Air Pollution in Kazakhstan
like a background concentration as in [13]);
In order to translate relative risk into population risk,
we multiply the baseline mortality by the calculated rela-
tive risk and an exposed population (2):
 (2)
M0—a baseline mortality net of adverse health effect of
pop—represents the exposed population.
Unfortunately for this study on the current level of the
negative health effects of pollution, there is no study of the
“net baseline mortality”. Reported mortality statistics do
not separate out effects attributable to air pollution. In
other words:
M denotes a reported mortality (cardio-pulmonary in case
of PM2.5);
Then baseline mortality could be expressed as a func-
tion of the reported mortality:
which leads to (3):
RR op
The concentration-response coefficient (β) represents
the change in health outcomes per unit of pollution. In the
linearized model, this coefficient is the slope of the linear
concentration-response function.
Pope et al. [13] provides a comprehensive and detailed
study to date on the relationship between air pollution and
mortality. The study confirms and strengthens the evi-
dence of the long-term mortality effects of particulate
pollution found earlier. Pope et al. [13] utilized ambient
air quality data from metropolitan areas across the United
States for the two periods 1979-1983 and 1999-2000, and
information on certified causes of mortality of adults in
the American Cancer Society (ACS) database over a pe-
riod of 16 years. The ACS database contained specific
information obtained through questionnaires surveying
more than 1 million adult individuals. The study could
therefore control for a large set of factors that may also
affect variations in mortality rates such as age, smoking
behavior, education, marital status, body weight, occupa-
tional risk factors, and dietary indices across metropolitan
The study found a statistically significant relationship
between levels of PM2.5 and mortality rates, controlling
for all the factors discussed above. Pope et al. [13] es-
timated relative risk for the linear function for cardio-
pulmonary mortality:
expRRX X
RR stands for relative risk for cardiopulmonary mortality,
X is the observed PM2.5 concentration and X0 is a back-
ground PM 2.5 concentration, which is equal to 7.5 ug/m3
as in WHO [12].
In the underlying Pope et al. [13] study, the estimated
increase in cardiopulmonary mortality was 6 - 9 percent,
and 8 - 14 percent for lung cancer per 10 ug/m3 of PM2.5.
The former two risk ratios could be applied in this study.
However, as we can see from Table 3, the average annual
PM2.5 concentration in Kazakhstan is well above the
range of the original Pope et al. (2002) study1.
For higher PM2.5 concentrations than what Pope con-
sidered in his analysis, Ostro [7] proposed using log-
linear relative risk function from cardiopulmonary mor-
tality to reflect the uncertainty about the health impact
with higher PM2.5 concentration. The log-linear relative
risk function for cardiopulmonary mortality has the form
described above: see expression (1). The concentration
response coefficient β for cardio-pulmonary mortality is
equal to 0.15515 [7].
2.5. Monte Carlo Analysis
Monte-Carlo simulations were used to estimate the com-
bined uncertainties of two types, the monitoring data and
the model of additional mortality attributed to air pollu-
tion. Using TSP data from a monitoring network based
on old standards and converting these data to PM2.5 has
certain, but unknown uncertainty. Even with the possibi-
lity of unknown systematic errors in this conversion
process, our estimates all fell within empirically derived
bounds from the remotely sensed data [11]. Additionally
there are always underlying parameter uncertainties in
the risk model, despite the many years of use and general
Table 3. Population and cardiovascular mort ality in selected
cities of Kazakhstan.
City Total population
mortality per
Almaty 1.39 446
Astana 0.63 205
Shimkent 0.45 260
Zhezkazgan 0.35 426
Taraz 0.30 335
Pavlodar 0.25 577
Ust-Komenogorsk 0.31 609
Semey 0.30 491
Atyrau 0.17 213
Temirtau 0.14 704
Aktau 0.17 223
Source: official national statistics.
(4) 1Under 20 ug/m3.
Copyright © 2013 SciRes. JEP
Human Health Cost of Air Pollution in Kazakhstan 873
verification of these models. Thus, even with these un-
certainties in data and models we can offer an educated
guess regarding distributions of the underlying parame-
ters in the formulas for calculation of mortality risk. To
carry out the Monte-Carlo simulation we used Crystal
Ball 11.11.
We thus assumed that there is no correlation among
independent variables [in this case estimated PM2.5 con-
centrations in different cities, and the risk mortality func-
tion]. Thus we were able to run Monte-Carlo simulations
in order to calculate combined uncertainty of additional
mortality we attribute to air pollution. The Beta PERT
distribution (with parameters: min = 5, max = 10 and
likelihood = 7.5) was applied for the threshold case, and
parameters (min = 0.06, max = 0.25 and likelihood =
0.16) for the case to preclude generation of negative PM
2.5 values Additional reasons for the use of this probabi-
lity distribution include important but unknown levels of
various confounding factors specific to Kazakhstan such
as smoking, drinking, and indoor air pollution levels as
examples. Nonetheless, the Monte-Carlo simulations
generated a wide range of different states and the distri-
bution of outputs covers all plausible range of potential
outcomes. Results of Monte-Carlo simulations are shown
in Table 5.
2.6. Mortality Data and Population
Background mortality plays a critical role in population
risk calculation. The total population of the highly pol-
luted cities with reported pollution data listed in Table 3 is
about 4.5 million. It is about 50% of the urban population
in Kazakhstan and 30% of the total Kazakhstan popula-
tion in 2010 [17]. The latest available data on mortality is
presented in Table 3.
There is a significant difference in cardiovascular
mortality per 100,000 of population across the cities. This
difference could be partially explained by the different age
structures of each city’s population, climatic conditions,
but also potentially by the difference in air quality.
3. Results
The concentrations of the 3 classifications of particulate
matter for each of the cities in this study are shown in
Table 4. The mortality effects attributable to PM2.5
concentrations are then shown in Table 5.
By far, the highest number of deaths attributable to air
pollution is in Almaty. The high mortality could be ex-
plained by the fact that Almaty has a relatively high
PM2.5 concentration and one of the highest crude mor-
tality rates as well as having the largest population (more
than double that of Astana, the next largest city). These
results with their associated probabilities are shown in
Figure 1 for the selected cities of the study and in Figure
2 for the rest of Kazakhstan.
4. Discussion
This study used official monitoring data, and went
through a series of established calculations to address
health risks from particulate matter. Although the input
data, assumptions and results of the study exhibit sig-
nificant uncertainty, one can conclude that even a con-
servative interpretation of the results of the study suggest
that industrial air pollution constitutes a substantial
problem in Kazakhstan. Our mortality estimates are sev-
eral times higher than the reported 2200 cases of mortality
attributed to outdoor air pollution by WHO in their series
“Country Profile for Environmental Burden of Disease”
leseuro.pdf). One important difference is the annual av-
erage ambient concentration used in the analysis. While
WHO reports 25 µg/m3 ambient PM10 concentrations, we
found that the actual numbers are far higher, depending
upon the city (see Table 4). The data from Brauer et al.
[10] also support the use of higher estimates than from the
WHO analysis.
Although absolute numbers in Kazakhstan are lower
than in Russia and the Ukraine, in relative terms, air pol-
lution constitutes a more severe environmental prob- lem
in Kazakhstan than in other post-Soviet countries. Ka-
zakhstan’s problem could be partly explained by the fact
that it has the relatively highest share of coal in its energy
mix. Coal combustion is one of the leading sources of PM
2.5 emission in post-Soviet countries [3,18]. Table 6
demonstrates the magnitude of health risk attributed to air
pollution in Kazakhstan relative to Russia and the
From the mortality data in Table 6, one can conclude
that the contribution of air pollution to total mortality in
Kazakhstan is higher than in Russia and Ukraine.
Table 4. Annual average concentrations (mg/m3, 2008-2010)
of monitored TSP (column 2) estimated PM-10 (column 3)
and PM-2.5 (column 4). PM-2.5 concentration range based
on remote sensing (column 5).
City TSP РМ10 РМ2.5 PM2.5
Almaty 284.6142.3 56.9 40 - 60
Astana* 529.2264.6 52.9 40 - 60
Shimkent 203.3101.65 40.7 40 - 60
Zhezkazgan** 345.5172.75 51.8 40 - 60
Taraz 133.666.8 26.7 20 - 40
Pavlodar 141.970.95 28.4 20 - 40
Ust-Kamenogorsk160.880.4 32.2 20 - 40
Semey 152.676.3 30.5 20 - 40
Atyrau** 432.7216.35 64.9 60 - 80
Temirtau 217.3108.65 43.5 40 - 60
Aktau 237.5118.75 47.5 40 - 60
*Conversion factor РМ10 to РМ2.5 - 0.2. **Conversion factor РМ10 to РМ2.5
- 0.3.
Copyright © 2013 SciRes. JEP
Human Health Cost of Air Pollution in Kazakhstan
Copyright © 2013 SciRes. JEP
Table 5. Additional mortality attributable to PM2.5 conce ntrations in se lec t ed cities of the study and the rest of Kazakhsta n .
Selected Cities of Study &
Rest of Kazakhstan
Base Case
Calculated PM2.5 value Mean Standard
Deviation Minimum Maximum
Almaty 1666 1638 197 985 2352
Astana 322 324 39 200 467
Shimkent 259 285 35 171 424
Zhezkazgan 373 377 45 234 534
Taraz 170 186 29 92 306
Pavlodar 249 264 41 134 430
Ust-Komehogorsk 354 355 54 169 558
Semey 271 278 43 134 451
Atyrau 99 103 12 65 141
Temirtau 222 238 29 146 346
Aktau 90 94 11 57 133
Sub-Total Selected Cities 4075 4140 491 2590 5973
Rest of Kazakhstan 12042 12372 2098 4638 19445
Total for Kazakhstan 16117 16512 2452 7504 25419
Figure 1. Mortality attributed to PM2.5 pollution in selected cities of study.
Human Health Cost of Air Pollution in Kazakhstan 875
Figure 2. Mortality attributed to PM2.5 pollution for the rest of Kazakhstan.
Table 6. Comparison of air pollution attributed mortality
with other mortality causes in Kazakhstan, Russia and
Ukraine per 100,000 of population.
Kazakhstan Russia Ukraine
All internal causes of
death 940 1228 1500
Air pollution 48.5 - 84.8 59 55
Source: authors calculations; official national statistics, [4,5].
Based on the sensitivity analyses, we can formulate
priorities for the improvement of mortality risk estimates.
The concentration-response coefficient that is accountable
for more than 40% of the sensitivity could be tailored to
the specifics of confounding factors in Kazakhstan. For
example, this coefficient could be adjusted based on the
actual proportion of smokers. A field study of TSP and
PM would be another important step towards improving
risk estimates. Also, more precise mapping of the popu-
lation relative to ambient concentration will reduce un-
Nevertheless, even with all the uncertainties mentioned
above, we can conclude that air pollution in Kazakhstan
constitutes a significant contribution to the environmental
burden of diseases. And as it was beyond the scope, this
study did not look at levels of more general cardio-pul-
monary diseases, or rates of lung cancer. And in addition
to the uncertainties addressed in this paper, no other im-
pacts of air pollution were addressed at all. This includes
widespread compounds like ozone, but also would include
other facility specific emissions such as heavy metals or
VOCs. In relative terms, the impact of air pollution to
premature mortality in Kazakhstan is notably higher than
in Russia and the Ukraine.
The GDP of Kazakhstan is within the top 50 of the
world. As its economy continues to grow, as it further
integrates into the world’s economy and political organi-
zations it should also develop a science-based approach to
air pollution control to improve the health of the popula-
tion. In the US, control of particulates was shown to be the
most cost-effective health regulation of the Federal Gov-
ernment [13]. And studies continue to corroborate and
elaborate on the health damages from fine particulates
[19]. This analysis is a start on the scientific foundation of
the severe and significant air pollution effects in Kazakh-
stan and for the eventual control of this pollution.
[1] B. Larson, S. Avaliani, A. Golub, et al., “The Economics
of Air Pollution Health Risks in Russia: A Case Study of
Volgograd,” World Development, Vol. 27, 10, No. 1999,
Copyright © 2013 SciRes. JEP
Human Health Cost of Air Pollution in Kazakhstan
pp. 1803-1819. doi:10.1016/S0305-750X(99)00086-8
[2] G. Oniszhenko, S. Avaliani, S. Novikov, U. Rakhmanin
and K. Bushtueva, “Basis for human Health Risk As-
sessment Resulting from Chemical Pollutants,” NII ECH
& GOS, Moscow City, 2002.
[3] S. Avaliani, D. Dudek, A. Golub and E. Strukova, “An-
cillary Benefits of Climate Change Mitigation in Russia.
Mitigation and Adaptation Strategies for Global Change,”
Mitigation and Adaptation Strategies for Global Change,
2006. doi:10.1007/s11027-006-2948-4
[4] E. Strukova, A. Golub and A. Markandya, “Air Pollution
Costs in Ukraine,” Fondazione Eni Enrico Mattei, Nota
Di Lavoro, Milano, 2006, p. 120.
[5] A. Golub and E. Strukova, “Evaluation and Identification
of Priority Air Pollutants for Environmental Management
on the Basis of Risk Analysis in Russia,” Journal of Toxi-
cology and Environmental Health, Part A, Vol. 71, No. 1,
2008, pp. 86-91. doi:10.1080/15287390701558238
[6] V. Reshetin and V. Kazazyan, “Public-Health Impact of
Outdoor Air Pollution in Russia,” Environmental Model-
ling and Assessment, Vol. 9, No. 1, 2004, pp. 43-50.
[7] B. Ostro, “Outdoor Air Pollution: Assessing the Envi-
ronmental Burden of Disease at National and Local Lev-
els,” World Health Organization, Geneva, WHO Envi-
ronmental Burden of Disease Series, No 5, 2004.
[8] S. Avaliani and B. Revich,Human Health Environmen-
tal Pollution Risk Assessment as a Tool for Moscow Re-
gion Municipal Environmental Policy,” News Library,
Moscow City, 2010, p. 311.
[9] A. J. Cohen, H. R. Anderson, B. Ostro, K. D. Pandey, M.
Krzyzanowski, N. Kuenzli, K. Gutschmidt, C. A. Pope, I.
Romieu, J. M. Samet and K. R. Smith, “Mortality impacts
of urban air pollution,” In: M. Ezzati, A. D. Lopez, A.
Rodgers and C. U. J. L. Murray, Eds., Comparative
Quantification of Health Risks: Global and Regional Bur-
den of Disease Due to Selected Major Risk Factors, Vol.
2, World Health Organization, Geneva, 2004, pp. 1353-
[10] A. Shahsavani,, K. Naddafi, N. J. Haghighifard, M. Mes-
daghinia, M. Yunesian, R. Nabizadeh, M. Arahami, M.
Sowlat, M. Yarahmadi, H. Saki, M. Alimohamadi, S.
Nazmara, S. Motevalian and G. Goudarzi, “The Evalua-
tion of PM10, PM2.5, and PM1 Concentrations during the
Middle Eastern Dust (MED) Events in Ahvaz, Iran, from
April through September 2010,” Journal of Arid Envi-
ronments, Vol. 77, 2012, pp. 72-83.
[11] M. Brauer, M. Amann, R. T. Burnett, A. Cohen, F. Den-
tener, M. Ezzati, S. B. Henderson, M. Kryzanowski, R. V.
Vartin, R. V. Dingenen, A. van Donkelaar and G. D.
Thurston, “Exposure Assessment for Estimation of the
Global Burden of Disease Attributable to Outdoor Air
Pollution,” Environmental Science & Technology, Vol.
46, No. 2, 2012, pp. 652-660. doi:10.1021/es2025752
[12] WHO, “The World Health Report 2002—Reducing Risks,
Promoting Healthy Life,” 2002.
[13] C. A. Pope III, R. T. Burnett, M. J. Thun, et al., “Lung
cancer, Cardiopulmonary mortality, and Long-term Ex-
posure to Fine Particulate Air Pollution,” Journal of the
American Medical Association, Vol. 287, No. 9, 2002, pp.
1132-1141. doi:10.1001/jama.287.9.1132
[14] D. W. Dockery, “Health Effects of Particulate Air Pollu-
tion,” Annals of Epidemiology, Vol. 19, No. 4, 2009, pp.
257-263. doi:10.1016/j.annepidem.2009.01.018
[15] D. Zmirou, J. Schwartz, M. Saez, A. Zanobetti, B. Wo-
jtyniak, G. Touloumi, C. Spix, A. Ponce de Leon, Y. Le
Moullec, L. Bacharova, J. Schouten, A. Ponka and K.
Katsouyanru, “Time-Series Analysis of Air Pollution and
Cause-Specific Mortality,” Epidemiology, Vol. 9, No. 5,
1998, pp. 495-503.
[16] N. Fann, A. Lamson, S. Anenberg, K. Wesson, D. Risley
and B. Hubbell, “Estimating the National Public Health
Burden Associated with Exposure to Ambient PM2.5 and
Ozone,” Risk Analysis, Vol. 32, No. 1, 2012, pp. 81-95.
[17] WDI, “World Development Indicators,” World Bank,
[18] E. Strukova, J. Balbus and A. Golub, “Saving Lives,
Saving Money: Regulating Particulate Matter Air Pollu-
tion in Russia,” Environmental Defense Fund, 2007.
[19] J. O. Anderson, J. G. Thundiyil and A. Stolbach, “Clear-
ing the Air: A Review of the Effects of Particulate Matter
Air Pollution on Human Health,” Journal of Medical
Toxicology, Vol. 8, No. 2, 2012, pp. 166-175.
Copyright © 2013 SciRes. JEP