Journal of Environmental Protection, 2013, 4, 1476-1487
Published Online December 2013 (http://www.scirp.org/journal/jep)
http://dx.doi.org/10.4236/jep.2013.412169
Open Access JEP
Risk Assessment Capacity Building Program in
Zaporizhzhia Ukraine: Emissions Inventory Construction,
Ambient Modeling, and Hazard Results
Jane C. Caldwell1, Andrei Serdyuk2, Olena Turos2, Arina Petrosian2, Oleg Kartavtsev2,
Simon Avaliani3, Alexander Golub4, Elena Strukova5, Michael Brody6,7
1National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency,
Washington DC, USA; 2O. M. Marzeiev Institute for Hygiene and Medical Ecology, National Academy of Medical Sciences, Kyiv,
Ukraine; 3Russian Academy of Advanced Medical Studies, Center for Risk Assessment, Moscow, Russia; 4American University,
Washington DC, USA; 5World Bank, Washington DC, USA; 6Office of the Chief Financial Officer, US Environmental Protection
Agency, Washington DC, USA; 7American University, Washington DC, USA.
Received August 16th, 2013; revised September 18th, 2013; accepted October 15th, 2013
Copyright © 2013 Jane C. Caldwell et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-
dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual
property Jane C. Caldwell et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
ABSTRACT
Historically, Ukraine has been a major source of industrial production for the former Soviet Union and the source of
pollution associated with an aging industrial infrastructure. The US Environmental Protection Agency (US EPA) and
the Ukrainian Ministry of Environment and Natural Resources (MENR) entered into partnership to develop Ukrainian
expertise and capacity in risk assessment so that Ukraine could more effectively use its National and Regional Envi-
ronmental Protection Funds and set priorities for cleanup and regulation. Ukrainian scientists, local officials, and EPA
consultants conducted a pilot study in the heavily industrialized Zaporizhzhia Oblast so that the process, analytical tools,
and approach for a risk assessment could be developed for and tailored to Ukrainian needs. As a first step, site-specific
information was obtained from multiple sources of air pollution and an emissions inventory of air pollution developed.
Efforts by local officials were critical for emissions inventory construction. After refinements were made to the inven-
tory, Ukrainian scientists then performed exposure modeling using this information so that ambient concentrations of
pollutants could be estimated. 11 industry types (i.e., enterprises) were identified as a major emission source. Results of
the modeling effort demonstrated that emissions estimates of particulate matter (as measured by particles of less than 10
micron diameter or “PM10”) and a number of carcinogens were consistent with those from other cities with high con-
centrations of metallurgical industries in former Soviet Union countries, and were above safety standards. Hazard in-
formation was gathered from international databases for each of the estimated pollutants. Using such data, prioritization
and identification of potential health concerns can be made, but most importantly, the expertise and experience gained
from the pilot allowed for continued support of risk assessment capacity building in the Ukraine and support by the
World Bank.
Keywords: Air Pollution; Exposure Modeling; Ukraine; Risk Assessment
1. Introduction
Although constituting a small percentage of the overall
landmass of the former Soviet Union, Ukraine was re-
sponsible for a significant amount of its overall industrial
production. After the breakup of the Soviet Union and
especially in years not as affected by worldwide reces-
sions, the aging industrial infrastructure of Ukraine con-
tinued to emit large volumes of air and water pollution,
and wastes. The Ukrainian Ministry of Environment and
Natural Resources (MENR) has reported that these emis-
sions contain a number of pollutants [1] also described in
a number of international databases to be associated with
developmental effects, chronic long-term health effects,
and cancer (e.g., US EPA IRIS assessments and IARC

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Monographs on carcinogenic risks to humans). Ukraine
also has been identified as a major source of transbound-
ary air pollution for the eastern Mediterranean region and
a significant source of greenhouse gases emissions [2].
After independence, Ukraine had set up a limited fund to
begin to address its environmental problems. However,
the system of pollution management in Ukraine was
based on the Soviet System in which pollution limits
were set to very low levels and generally not complied
with [3].
Despite setting standards for numerous individual
compounds, no system to prioritize the control of pollu-
tion and its sources were in place; nor had expertise been
developed to perform those evaluations. The choices of
which sources and pollutants to address and control were
also made difficult by the magnitude of pollution, num-
ber of the pollutants emitted, and number of significant
pollution sources. By contrast, the US EPA has been
tasked through a number of laws (e.g., 1990 Clean Air
Act Amendments) to use risk assessment to rank the
relative risk of different industrial emission and sources,
and to aid in the development of decision criteria for ef-
ficient and effective regulatory actions. The US system
and methodologies have been modified and adopted by
Russia for similar applications. The US EPA has pro-
vided training to develop risk assessment capabilities in
Russia for a number of years [3].
To help address some of the problems cited above and
strengthen Ukraine’s capacity to set environmental pri-
orities, the US EPA set up a partnership with Ukraine’s
Ministry of Environment and Natural Resources (MENR)
to develop expertise in environmental risk assessment
and economic analyses. This Capacity Building Project
(CBP) was funded through an US EPA Cooperative
Agreement (CX4-831993) with the Environmental De-
fense Fund (EDF); support also came from US EPA’s
Offices of Research and Development, International Af-
fairs, and the Chief Financial Officer. The CBP was ini-
tiated in 2002 and has been described previously [3]. In
order to introduce the US system and provide risk as-
sessment, management, and environmental finance in-
formation, the project began with a series of workshops
and consultations. Ukrainian representatives at the na-
tional and oblast level scientists from Ukrainian research
institutes, EPA consultants, representatives of the World
Bank (Washington DC and Kyiv), specialists from US
non-governmental organizations (NGOs) (i.e., Counter-
part International and the Environmental Defense Fund),
and environmental finance specialists from the State of
Pennsylvania water infrastructure management agency
were involved.
So that a template for data development and analyses
could be implemented at the local level and then be
adapted on a broader scale, a model case study was de-
veloped with the assistance of municipal officials of the
Zaporizhzhia Oblast. An Oblast is most analogous to a
county in the US Resting on both sides of the Dnipro
River with relatively flat topography, Zaporizhzhia is
comprised of five administrative municipal zones on the
left bank and two others on the right. The Dnipropet-
rovsk water reserve is situated on the north from the city,
the Kakhovske water reserve on the south. Data from the
Statistics Administration in Zaporizhzhia Oblast (2007)
indicate a population of ~800,000 for the year 2001 in a
city area of 330 km2 [4]. The choice was ideal as model
of significant Ukrainian air pollution sources as the
Oblast is the country’s largest producer of high quality
steel, nonferrous metals, ferrous-alloys, power transfor-
mers, various equipment, and automobiles.
This paper describes some of the key results of the
Ukrainian pilot project included in the “Final Report on
the project “Environmental Capacity Building in the
NIS” US EPA grant registration # X4-83199301 (US
Environmental Protection Agency (EPA), Environmental
Defense Fund (EDF), Marzeev Institute of Hygiene and
Medical Ecology AMSU (IHME), Center of Environ-
mental Health and Risk Assessment (CEHRA) [4]. EPA
collaborators had access to the final report, which formed
the basis of this paper, but not the original data. Specifi-
cally, this paper focuses on the development of the emis-
sions inventory and dispersion modeling for derivation of
ambient concentrations of pollutants at various popula-
tion receptor points at the Zaporizhzhia oblast level.
More recent hazard data from international sources is
also presented for modeled pollutants.
2. Methods
2.1. Exposure Data Collection
Because of Ukraine’s system of legally binding monitor-
ing systems and related information used for permitting
and fees, local emissions data from stationary sources
were generally available for Zaporizhzhia. An emission
inventory was assembled that is analogous to those of
EPA (i.e., the Toxic Release Inventory and the National
Air Toxics Assessment) [5]. The inventory information
included: 1) volumes of air emissions from standard
State form “2-TP” (“AIR”); 2) emission permits for at-
mospheric air pollutants; 3) stationary source location
information on industrial sites; and 4) source and emis-
sion characteristics through Ukrainian inventory reports
(i.e., “Instructions on the Content and Order of the Re-
port on the Pollutant Inventories on the Enterprise”, ap-
proved by the Decree of the Ministry of Environment
Protection and Nuclear safety from 10.02.95 No. 7 regis-
tered in the Ministry of Legal Affairs of Ukraine

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(15.03.95 # 61/597). The “2-TP” (“AIR”) form was in-
troduced in the Soviet Union in the 1980s with reporting
required by law in Ukraine. Inventory information for
particulate matter (PM) was given in the form of total
suspended particles (TSP). The list of 30 major industrial
Zaporizhzhia enterprises in the 2007 emissions inventory
were those used to model emissions. Some of the specific
enterprises have since been renamed or are no longer
functioning. Unlike the EPA emission inventories, Ukra-
inian plant emissions data are not public so that further
examination or update of emissions in the inventory can-
not occur. The 2007 inventory includes a number of in-
dustries that include not only steel-associated facilities
but also silicon, asphalt, car repair, transformer, and a
number of public corporations. The top source types that
contributed 63% of emissions are shown in Table 1.
2.2. Dispersion Modeling
Pollutant dispersion is dependent on terrain characteris-
tics, land use type, and meteorological data. Dispersion
modeling methods officially certified in Ukraine are
adopted from the official risk assessment methods of
Russia (Human Health Risk Assessment from Environ-
mental Pollutants) [6] that were, in turn, modeled on
EPA approaches [3]. Although the official Ukrainian air
pollutant dispersion model is the EOL model (i.e., an
interface based on the OND-86 methods, see Onischenko
et al. as an example [7]), the ISC-AERMOD program (a
more modern model) was used for the Zaporizhzhia pro-
ject instead [8]. That model (ISC short term stack model)
uses the steady-state Gaussian plume equation for a con-
tinuous elevated source. For each source and each hour,
the origin of the source’s coordinate system is placed at
Table 1. List of the major source types of industrial Za-
porizhzhia enterprises in the emissions inventory.
No Types of industry Contribution of emissions, %
1 Coke industry 2
2 Steel-rolling industry 1
3 Silicon industry 1
4 Steel production 41
5 Alluminium industry 6
6 Abrasive industry 3
7 Transformer industry 1
8 Graphite industry 2
9 Titano-magnesium industry 1
10 Ferro-alloy industry 4
11 Glass factory 1
the ground surface at the base of the stack. Model pa-
rameters included: digital elevation models (i.e., relief of
the territory), meteorology, land-use data (i. e. , residential
building density, surrounding “greenness”, industrial areas,
presence of surface waters), stationary source parameters,
and emission-specific data.
The input data for model preprocessing included me-
teorological data (i.e., 1 hour interval measurements) and
specific territorial factors that characterize vertical mix-
ing in ground atmospheric layers. Meteorological data
for the entire year of 2005 were provided by the Za-
porizhzhia Hydro-Meteorological Service (HydroMet).
Dominant wind directions were to the southwest and
west. Southwestern wind with speed starting from 3 - 4
m/s dominated during the greatest number of hours (14.9
%) with almost equal number of hours dominating West-
ern and Southeastern directions. Zaporizhzhia belongs to
the zone with continental type of climate with hot sum-
mer and moderate cold winter. The coldest month of the
year is January (i.e., average monthly temperature −4.3˚C,
absolute minimum −34˚C) and the warmest month is July
(i.e., average monthly temperature +22.3˚C with absolute
maximum +41˚C). The yearly precipitation rate is 469
mm and average snow cover is 14 cm with a maximum
of 35cm. Land use was not accurately recorded in the
stationary source inventory. Therefore, land-use data were
provided by remote sensing images of high resolution
(i.e., Quick Bird Standard Imagery PAN+MSI, 05/04/
2005, product for Zaporizhzhia territory, “Grandproject”
Co. Zaporizhzhia) and processed by ArcGIS software to
pinpoint 5000 emission points using US Geological Sur-
vey methods [4].
Based on information in air pollution modeling soft-
ware and “2-TP” (“AIR”) form, 76 pollutants were iden-
tified in the inventory. As a first step, initial ground level
calculations of annual concentrations for 34 pollutants
were estimated for 6 population-based receptor points.
However, more refined modeling was conducted for the
emissions of 51 priority pollutants (that included the ini-
tial 34) at the 6 population-based receptor points after: 1)
conducting a more detailed emission analyses of 12 ma-
jor Zaporizhzhia sources; 2) prioritization by potential
risk using volume and hazard information; and 3) taking
into account difference between “2-TP” and permitted
emissions through consideration of operating mode, emis-
sion source specification, and physicochemical condi-
tions (wet and dry concretion of the substances). Addi-
tional calculations were done for total suspended parti-
cles (TSP), using a specialized model of calculation TSP
in the ISC-Aermod program. The accurate locations of
5000 stationary sources of emissions from the 30 enter-
prises in the Zaporizhzhia industrial sites were identified
for the 52 priority pollutants (i.e., 51 priority pollutants

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plus TSP) with a spatial accuracy of several meters. Input
information for each of these emission points within the
enterprises were used in the modeling calculations. Emis-
sion point source parameters, wind speed profile adjust-
ments, and pollutant removal by physical or chemical
processes methods were given in the report [4] and are
not shown here.
The gender and age structure of the population in
Zaporizhzhia, the number of residents in each neighbor-
hood, and density of residents was collected from the
Zaporizhzhia Regional Statistical Administration. Popu-
lation data were geocoded and linked to the residential
living places in the “ArcGIS environment”. Population-
based receptor points were linked to population density
so that all of the population in each receptor point was
similar with respect to the impact of ambient air pollution
impacts. Dispersion model outputs were hourly concen-
trations produced at each receptor by combined source
emissions: they were summed to obtain total 1-hour, 24-
hour, month, and annual concentrations. The land use
classifications and population receptor points are shown
in Figure 1 and the wind speeds are demonstrated by a
wind rose in Figure 2.
2.3. Hazard Characterization
Of the 52 priority pollutants modeled for ambient air
concentration estimates, a number of pollutants were
identified as at least possible human carcinogens. The
weight of evidence for human carcinogenicity was de-
termined by either the US EPA [9] or the International
Agency for Research on Cancer (IARC) [10]. Others
have been regulated primarily on noncancer effects (e.g.,
particulate matter and other “criteria” pollutants that are
subject to National Ambient Air Quality Standards by
US EPA). Table 2 shows the hazard information and the
initial emission inventory information derived from the
“EOL” air pollution software and the “2-TP AIR” data
for the 52 priority pollutants identified by CAS number.
In some cases the specific identities of the pollutants in
the inventory is not clear and more than one CAS num-
ber is given.
3. Results
The 52 priority pollutants cited in the refined Zapori-
zhzhia emissions inventory are presented in Table 2.
Those pollutants identified by either US EPA or IARC as
at least possible carcinogens and the estimates of their
ambient concentrations at the 6 receptor points are shown
in Table 3. A number of the priority pollutants also be-
long to chemical groups with potential toxicity variations
between members within those groupings. The specific-
ity of the inventory information for such pollutants is
dependent on information provided in the 2-TP (AIR)
(a) (b)
(c) (d)
Figure 1. Zaporizhzhia Population Receptor Points (a);
Composite for Receptor Modeling (b); Housing Zone (c);
and Industrial Zone (d). The map of Zaporizhzhia shows 6
receptor points in areas of significant population for esti-
mation of exposure to priority air pollution emissions with
1010, 3292, 6197, and 17,744 people/km2 grids from no color
to dark brown (a). The composite overview of land use
along the Dnipro River basin and Zaporizhzhia includes:
Grey as an industrial zone, Brown as low-rise housing zone,
Orange as high-rise housing zone, Blue as the Dnipro River,
and Green as flora. For the housing zone (c); light brown
indicates high-rise housing and darker brown low-rise
housing. The industrial zone is indicated as grey (d). Each
part of the figure is drawn to the same scale.
forms. As shown for chromium, valence state has a sig-
nificant impact on toxicity. The lack of specificity in the
inventory makes assignment of appropriate hazard in-
formation for these modeled emissions difficult.
One of the pollutants whose ambient concentrations
were estimated in the Zaporizhzhia case study was
TSP/PM10 (see Table 4). American regulatory standards
apply to PM10 and to PM2.5 (a more respirable and poten-
tially toxic particle size) [11,12]. Over the last 15 years
those standards have been modified with an increasing
emphasis on the health effects of PM2.5. In 1997 the US
EPA National Ambient Air Quality Standards for PM2.5
were 65 μg/m3 (24-hour) and 15 μg/m3 (Annual) and for
PM10 were 150 μg/m3 (24 hour) and 50 μg/m3 (Annual)
[13]. In the 2006 the 24-hour PM2.5 standard was lowered
to 35 μg/m3 and the annual PM10 standard dropped. In
December 2012 the annual PM2.5 standard was lowered
to 12 μg/m3 [13]. The three primary sources of PM10
were aluminum production, abrasive materials industry
and steel production; they were also major sources of
other pollutants. Emission inventory-based estimates of

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Figure 2. Annual wind rose in year 2005 for Zaporizhzhia. The annual wind rose for Zaporizhia is shown for the year 2005.
The dominant wind directions were Southwestern and Western.
annual ambient TSP concentrations were subsequently
converted to estimates of PM10 and corresponding popu-
lation estimates are shown in Table 4 for the 6 receptor
points. These annual estimates exceed the older annual
and more recent 24-hour US EPA national standards for
particulate matter.
After TSP inventories were modeled, the results were
extrapolated to estimates of PM10 and PM2.5 for com-
parative purposes to US health standards and other am-
bient estimates. Avaliani and Revich [14] proposed a
0.55 conversion coefficient to convert TSP into PM10 for
Russia. This value is slightly below the 0.6 conversion
coefficient suggested in Larson et al. [15] for Russia and
Strukova et al. [16] proposed for Ukraine. Because many
former Soviet regions have more combustion-related
activities than average, a higher conversion coefficient
was used than that for the world average 0.5 [17]. The
conversion ratio used in the final report [4] was 0.55.
Further conversions of PM10 estimates to PM2.5 include
greater uncertainty in relation to the original data in the
emissions inventory (i.e., TSP). In Russia, the PM2.5/
PM10 ratio has been estimated to range from 0.55 to 0.75
[15,17]. For this article we have chosen a conversion
ratio of 0.65 (i.e., a ratio in the middle of that range) for
estimates of PM10 to PM2.5 with the resulting modeled
estimates for TSP and all conversions to smaller particle
sizes shown in Table 4.
4. Discussion
Often, former Soviet countries (including Ukraine) have
used a retrospective rather than prospective approach for
assessment of health effects from pollution. Epidemiol-
ogical methods have been used to try to identify risk after

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Table 2. List of priority pollutants from Zaporizhzhia emissions inventory used for more refined dispersion modeling.
CAS# Pollutant IARC WOE for Cancer** ЕРА WOE for Cancer**
Emissions
Inventory***
(Tons/year)
106-99-0 1,3-Butadiene Carcinogenic to humans Carcinogenic to humans 0.039
10102-44-0 Nitrogen dioxide Not assessed Not assessed 8937.296
10102-43-9 Nitrous oxide Not assessed Not assessed 3.276
107-13-1 Acrylonitrile Possibly carcinogenic to humansProbable human carcinogen (UR) 0.422
107-02-8 Acrolein Not classifiable as to
human carcinogenicity Cannot be determined 7.882
1344-28-1 Aluminium oxide Not assessed Not assessed 3359.415
7664-41-7 Ammonia Not assessed Cannot be determined 134.209
75-07-0 Acetaldehyde Possibly carcinogenic to humansProbable human carcinogen (UR) 0.173
67-64-1 Acetone Not assessed Cannot be determined 35.51
50-32-8 Benzo[a]pyrene Carcinogenic to humans Probable human carcinogen (UR) 0.422
100-44-7 Benzyl chloride Not assessed Probable human carcinogen
****
8006-61-9 Automotive gasoline Not assessed Not assessed ****
71-43-2 Benzene Carcinogenic to humans Human carcinogen 52.058
123-86-4 Butyl acetate Not assessed Not assessed 151.458
7440-62-2 Vanadium as dust and fumes
Vanadium dust and fumes—Not
assessed Vanadium pentoxide
(CAS 1314-61-1)-Possibly
carcinogenic to humans
Vanadium dust and fumes—Not
assessed Vanadium pentoxide
(CAS 1314-61-1)—Not assessed
3.885
75-01-4 Vinyl chloride Carcinogenic to humans Human carcinogen 0.796
7647-01-0 Hydrogen chloride Not classifiable as to
human carcinogenicity Cannot be determined 131.408
630-08-0 Carbon monoxide Not assessed Not assessed 103662.5
106-89-8 Epichlorohydrin Possibly carcinogenic to humansProbable human carcinogen ****
141-78-6 Ethyl acetate Not assessed Cannot be determined 16.675
100-41-4 Ethylbenzene Possibly carcinogenic to humansCannot be determined (UR) 3.622
1332-37-2 Iron oxide Not assessed Not assessed
7720-78-7 Ferous sulfate Not assessed Not assessed
1897.246 Ferum and
its compounds
7440-43-9
13477-23-1
Cadmium metal
Cadmium sulfite
Carcinogenic to humans
Not assessed
Probable human carcinogen (UR)
Not assessed
0.19 Cadmium and its
compounds
1330-20-7 Xylene Not classifiable as to
human carcinogenicity Cannot be determined 15.944
7439-96-5 Manganese and its compounds Not assessed Cannot be determined 477.276
74-82-8 Methane (gas) Not assessed Not assessed 736.649
78-93-3 Methyl ethyl ketone Not assessed Cannot be determined 61.548
7440-50-8
7758-98-7
Copper metal
Copper sulfate
Not assessed
Not assessed
Cannot be determined (UR)
Not assessed
13814-81-8 Copper (1+) disulfide dihydrate Not assessed Not assessed
1317-39-1 Cuprous oxide Not assessed Not assessed
3.405
Copper and its
compounds

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Continued
91-20-3 Naphthalene Possibly carcinogenic to humansPossible human carcinogen (UR) 16.859
7440-02-0 Nickel refinery dust Possibly carcinogenic to humans
Human carcinogen
Nickel Carbonyl (CAS 13463-39-3)—
Probable human carcinogen
9.814
Nickel and its
compounds
TSP (PM10) Not assessed Not assessed
17009.138
(Substances featured
as suspended solid
particles)
10045-94-0 Mercury nitrate hydrate Not assessed
Not assessed
Mercuric chloride (CAS 7487-94-7)
—Possible human carcinogen
7439-97-6 Mercury (elemental) and
inorganic mercury
Not classifiable as to
human carcinogenicity Cannot be determined
0.029
Mercury and its
compounds
8007-45-2 Soot* (coke oven emissions)
IARC lists as coal tars (distillation) Carcinogenic to humans Human carcinogen 207.579
7439-92-1 Lead and its compounds Possibly carcinogenic to humansProbable human carcinogen 8.91
7446-095 Sulfur dioxide Not classifiable as to
human carcinogenicity Not assessed 10647.656
7783-064 Hydrogen sulfide Not assessed Cannot be determined 72.964
75-15-0 Carbon disulfide Not assessed Cannot be determined ****
100-42-5 Styrene Possibly carcinogenic to humansAssessment not available (UR) 4.564
7664-93-9
Sulphuric acid Listed by IARC as
strong inorganic acid mists
containing sulfuric acid
Carcinogenic to humans Not assessed 49.747
108-88-3 Toluene Not classifiable as to
human carcinogenicity Cannot be determined 27.755
8030-30-6 Petroleum ether or naphtha Not assessed Not assessed ****
108-95-2 Phenol Not classifiable as to human
carcinogenicity Cannot be determined 13.379
50-00-0 Formaldehyde Carcinogenic to humans Probable human carcinogen (UR) 1.971
7664-39-3 Hydrogen Fluoride Not assessed Not assessed 5.529
7782-50-5 Chlorine and its compounds Not assessed Not assessed 193.873
16065-83-1
18540-29-9 Chromium compounds
Chromium (III) (CAS 16065-83-1)
—Not classifiable as to human
carcinogenicity
Chromium (VI) (CAS 18540-29-9)
—Carcinogenic to humans
Chromium (III)
(CAS 16065-83-1—Cannot
be determined
Chromium (VI) (CAS 18540-29-9)—
Human carcinogen
55.056
108-93-0
108-94-1
Cyclohexanol
Cyclohexanone
Not assessed
Not classifiable as to
human carcinogenicity
Not assessed
1.059 CAS# in
inventory is for
cyclohexanol but
listed as
cyclohexanone
1314-13-2
7440-66-6
Zinc oxide
Zinc and its compounds
Not assessed
Not assessed Not assessed 14.988 Zinc and its compounds
*Soot is identified in the emissions inventory with the EPA equivalent of coke oven emissions. **For IARC and EPA chemicals that are “not assessed” have not
been examined for carcinogenicity in the IARC or IRIS databases. For EPA and IARC “Cannot be determined” and “Not classifiable as to human carcinogenic-
ity” mean that the chemicals have been assessed but a determination has been made that the available data do not support a classification. This is not the same
as the determination that the chemicals are probably not carcinogenic. For EPA UR designates under review. ***The emissions in tons/year are derived from the
initial data from “EOL” (air pollution modeling software) data and the available report “2-TP” (“AIR”). ****Added after additional consideration, not present on
initial reporting forms.
exposure has occurred [4]. However, such approaches
have little ability to ascertain the environmental sources
of pollution that may affect health. For an endpoint such
as cancer, the 15 - 20 year lag time from exposure to
manifestation of disease makes epidemiological ap-
proaches for prevention of this health effect using current

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Table 3. Annual estimated concentrations for priority pollutants with a WOE of at least possibly carcinogenic to humans at 6
receptor points in Zaporizhzhia.
Estimated Average Annual Concentration at 6 Population Receptors (Concentration in μg/m3)
CAS Pollutants
1 2 3 4 5 6 Mean
106-99-0 1,3-Butadiene 0.002 0.016 0.043 0.091 0.178 0.402 0.122
75-07-0 Acetaldehyde 0.0001 0.0026 0.0091 0.023 0.0459 0.0789 0.0266
107-13-1 Acrylonitrile 1.5E−5 0.025 0.06 0.125 0.235 0.425 0.1452
50-32-8 Benzo[a]pyrene 7E−07 0.0015 0.0029 0.0052 0.0089 0.0197 0.0064
71-43-2 Benzene 0.634 2.311 5.875 13.003 25.582 54.094 16.917
8006-61-9 Automobile gasoline
(Benzine) 0.115 0.706 1.711 3.426 6.973 15.19 4.6868
100-44-7 Benzyl chloride 0.0064 0.0492 0.1584 0.3579 0.6096 1.2176 0.3998
141-78-6 Epichlorohydrin 0.0011 0.0087 0.0239 0.0529 0.0979 0.195 0.0633
1332-37-2 Ethylbenzene 2.989 20.261 53.08 113.54 210.26 443.45 140.6
50-00-0 Formaldehyde 0.137 1.052 2.982 7.045 12.632 26.042 8.315
7439-92-1 Lead and its
compounds 0.0009 0.0065 0.02 0.044 0.0789 0.145 0.0497
630-08-0 Nickel and its
compounds* 0.02 0.07 0.192 0.471 0.895 1.949 0.5828
1330-2-7 Cadmium
Sulfite 1.6E−8 1.1E−5 2.3E−5 4.5E−8 8.2E−5 0.0002 5.8E−5
See Table 1 Chromium and
its compounds 0.028 0.18 0.55 1.18 2.17 4.51 1.4363
See Table 1 Soot* 5.962 15.067 26.244 45.311 76.871 173.52 57.146
100-42-5 Styrene* 0.029 0.093 0.181 0.338 0.573 1.279 0.4255
75-01-4 Vinyl Chloride 0.002 0.0167 0.052 0.121 0.214 0.419 0.1375
*Two tables appear in the original report with slight difference in estimates for one receptor point. Values from the table used to identify cancer risk were shown
as the default value.
exposures problematic. One of the strengths of the ap-
proach used in the Zaporizhzhia case-study is that it al-
lows for identification of risk and the opportunity to
change risk before detection of disease through retro-
spective epidemiological approaches. Ambient monitor-
ing also cannot identify specific sources for control but
the approach used in the EPA Cumulative Exposure Pro-
ject (CEP) [18-23], National Air Toxics Assessment [24],
adaptations of the CEP applied for a more localized level
in the State of California [25], and taken here (i.e., that
uses modeling of emission inventories to predict ambient
concentrations) can. This approach also does not wait for
harm to occur such as an epidemiology retrospective
study would.
A number of studies in the region conducted between
1996 and 2008 have estimated health risks from air pol-
lution in Russia and Ukraine [3,4,6,7,16,26] or Kazakh-
stan [27] and have generally concluded that there are
significant health risks from inhalation of pollutants, par-
ticularly particulate matter. Ambient air pollution stan-
dards in the former Soviet Union required short-term
pollutant estimates for all major pollution sources. Risk
assessment methodologies have evolved to fit advances
in the science that supports them. More recently, former
Soviet Union countries have started to use more specific
meteorological data in dispersion modeling, similar to
the approach used in this Zaporizhzhia case-study. For
example, Larson et al. [15] recalculated dispersion model
outputs to obtain annual average pollutant concentrations
in Volgograd Russia. In comparison to the EOL model,
the ISC-Aermod has the advantage of a more state of the
art design and is capable of greater utilization of the
Zaporizhzhia HydroMet data. This modeling tool esti-
mates atmospheric stability classes rather than the “worst
weather conditions” used by the EOL. In addition, annual
average pollutant concentrations are estimated rather
than maximum 20-minute concentrations. Consequently,
because of dependence on short term higher estimates,

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Table 4. Estimation of the annual average TSP and РМ10
concentrations and population at receptors (i.e., receptor
points, RP) in Zaporizhzhia.
RP TSP (modeled)
(µg/m3)
PM10 (ext.)
(µg/m3)
PM2.5 (ext.)
(µg/m3) Population
1 330 180 120 52,958
2 420 230 150 62,146
3 510 280 180 323,963
4 580 320 210 144,292
5 640 350 230 61,695
6 690 380 250 78,978
*Total population at all listed receptors (9) was 83,480; ext. = extrapolated.
EOL estimates tend to be much higher. Thus, the Zapho-
rizhzhia study uses a more accurate state-of-the art air
dispersion methodology than previously practiced in
Ukraine. Through the successful development of the Za-
porizhzhia pilot, not only has Ukrainian risk assessment
expertise been further developed, but centers of risk as-
sessment expertise have also been established for con-
tinuing applications across Ukraine.
The 2007 emissions inventory and the subsequent dis-
persion modeling developed for the Zaporizhzhia pilot
study show a range of pollutants to which significant
segments of the population are exposed; these exposures
include particulate matter and a number of carcinogens.
The results of the pilot study identified major sources of
pollution, what types of pollutants were expected to be
emitted, and areas in the Zaporizhzhia with the greatest
exposure. Such information is critical for the placement
of monitors to both confirm the distribution of the pollu-
tion geographically and identify pollutants in the plume
that should be monitored. Although monitoring data of
the Sanitary and Epidemiologic stations of Zaporizhzhia
were briefly mentioned in the final Ukrainian report, no
monitoring data for any pollutants were provided or
compared to the modeled ambient concentration esti-
mates. The report also noted that the content of respirable
fine particles (РМ10 and РМ2.5) was not monitored and
accounted for (i.e., they were not monitored). The UN
Economic Commission for Europe [28] stated that in
specific areas, such as Zaporizhzhia Oblast (in the highly
polluted Donetsk-Dnieper area), a regional monitoring
system and observation network was created to bring
together all active monitoring entities. The most recent
UN report for Ukraine [29] was published in 2007 and
notes that:
Self-monitoring by enterprises is not properly carried
out and related data are not closely analyzed. Last but not
least, findings from inspections end up in statistical da-
tabases and are not followed up with in-depth analysis
and appropriate actions. Even though a monitoring pro-
gramme was adopted in 2004, the related budget streng-
thened and the monitoring network developed, there are
still significant gaps in the monitoring coverage; priori-
ties are often absent or contradictory; the treatment of
data is inappropriate; and the data are practically un-
available. Moreover, there is no process for reconciling
the data collected by different ministries, which results in
different sets of values being issued for the same indica-
tor. Some oblast environmental authorities have recently
established online databases linking all monitoring insti-
tutions and polluting enterprises in their regions, an effort
that needs to be replicated in other oblasts and at the na-
tional level.
Descriptions of chemical classes such as nickel and
chromium compounds in the existing emission invento-
ries lack speciation of the emissions; the appropriate ap-
portionment of emissions cannot be done between mem-
bers of the group that have differing carcinogenic poten-
cies. For example in the case of Chromium compounds,
assignment of the highest carcinogenic potency estimate
for all chromium emissions can lead to an overestimation
of risk.
The ratios of PM2.5/PM10 vary for emission sources
with different types of technologies, industrial sectors,
fuels, and by distance from emission sources to monitor-
ing locations, etc. Cities that are not located in arid/
semi-arid or agricultural zones, but have high traffic
emissions and relatively low fugitive road dust, will tend
to have very high PM2.5/PM10 ratios [27]. Because the
conditions in Zaporizhzhia closely resemble the general
case in Russia and Ukraine where coal-fired power con-
tributes a significant portion of air pollution, the esti-
mates of PM2.5 have less variation due to sandstorms and
confounding by agricultural dust generation. The Zapo-
rizhzia emission inventory is for major stationary sources
of PM and not mobile sources. Therefore even with only
capturing a portion of the total particulate load through
modeling large stationary sources, the modeled estimates
may be underestimates of fine particle loads. The emis-
sion inventory for particulate matter would also be im-
proved by the inclusion of speciation between larger and
smaller particles (i.e., PM10 vs. PM2.5) that would allow
for a more accurate prediction of risk from mortality.
Clearly, the accuracy of a risk assessment of the Za-
porizhzhia air pollution is limited by uncertainty in the
emissions inventory. Improvement of emissions accuracy
would in turn provide the basis of a more accurate as-
sessment of hazard and health risk.
Given the limitations the emissions inventory to dis-
cern PM10 and PM2.5, ambient monitoring would help to
verify the modeling results. Ground-level measurements
of air pollution, especially those of PM2.5 are not avail-

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able for much of the world [30]. Ambient monitoring in
Ukraine is even more limited and the work by Brauer et
al. using satellite estimates based on population density
and assumptions for PM2.5 generation without considera-
tion of the local and high industrial PM sources does not
give an accurate assessment for comparative purposes.
Their study notes that there is no Eastern European (i.e.,
Russia and Ukraine) monitoring to validate their results.
Ambient monitoring estimates for the same period were
taken from Brauer et al. [30]. Their estimates were de-
rived from global estimates of PM2.5 using satellite ob-
servations, a global atmospheric model, an econometrics
model, and airport observations of visual range. Briefly,
satellite-derived and TM5 global atmospheric model es-
timates were averaged at a 0.1˚ × 0.1˚ grid cell resolution
(equivalent to approximately 11 km × 11 km at the
equator). In this process, population density is used as a
proxy to identify high emission (“urban”) areas within
each 1˚ × 1˚ grid cell. The outputs of the Brauer et al. [30]
model are population-weighted averages and not ambient
concentrations and the model assumed that urban pri-
mary PM2.5 should not exceed the rural concentration by
a factor 5. All secondary components (SO4, NO3) and
primary natural PM (mineral dust, sea salt) are assumed
to be distributed uniformly over the native grid cell and
hence are not incremented.
Without monitoring data, how realistic are the reported
values? The World Health Organization (WHO) Regional
publications noted Monitoring in Prague was reported to
show average PM10 in the city center to be 94 μg/m3 with
daily concentrations as high as 225 μg/m3 during a 3
month period (January-March) in 1997 [31]. For Ukraine,
National as well as WHO standards for specific pollut-
ants were reported to be exceeded in almost all major
Ukrainian cities with the values for nitrogen dioxide and
particulate matter exceeded at almost all of the country’s
national measurement stations (i.e., National Ukrainian
standard of 150 μg/m3 of particulate matter and WHO
standard i.e., 40 μg/m3 for PM10) [29].
A question arises as to whether the conditions de-
scribed in the pilot still exist. When economic conditions
force the shutdown of these industries and thereby limit
emissions from these sources, emission estimates from
this case-study can result in an overestimation of risk.
According to World Bank estimates of GDP [32], 2005
was $86B (current USD), up from about $65B in 2003,
the first year in this study. GDP peaked in 2008 at $117B
and is still recovering from the recession of 2008. The
WHO 2007 [28] report states that the steel industry still
dominated the Ukrainian economy and that in 2004, the
capacity utilization of Ukraine’s steel industry was at a
high of 89%, with Ukraine being the seventh biggest
metal producer in the world. Donetsk oblast alone ac-
counted for about 40 per cent of total air emissions in
Ukraine, followed by Dnipropetrovsk (21%) and Zapor-
izhzhia (6%) oblasts [28]. Ukraine remains one of the top
producers of steel in the world with 2.3% of the world
production as of 2011; 2011 levels are similar to the av-
erage production from 2001-2005 [33,34]. Therefore, the
emissions inventory estimates in this study have not been
reduced by the shutdown of these industries.
As described above, this emission inventory contains
uncertainty and the estimation of individual risk to the
population living in Zaporizhzhia based upon it is be-
yond the scope of this paper. Nonetheless, the develop-
ment of the emissions inventory and subsequent applica-
tion of more current dispersion modeling should be
viewed as a success and did fulfill the goals of the CPB.
An important aspect of the project was that not only did
local officials and health experts help in providing expo-
sure information, but outside experts in various aspects
of risk assessment (e.g., exposure and toxicology) par-
ticipated from several countries. The results from the
case-study provided a useful tool for risk management
and environmental policy. They have helped aid further
development of risk assessment expertise and capacity.
Under the auspices of the CBP, outside experts have also
been able to contribute to risk management and policy
development. However, the integrity of the process has
been maintained as one developed by Ukraine for its
specific needs and situation. It is important to note that
the types of exposure information needed for the case-
study have not been easy to access as the Ukrainian sys-
tem did not have a tradition of public emissions data-
bases. A great deal of credit is due to the local officials
and industrial facilities for providing this information.
Hazard information for the pollutant emissions can be
obtained from a number of international sources, how-
ever exposure information cannot. In addition and as a
result of this effort, a center has been established that still
is in operation in Kyiv (i.e., Center of Environmental
Health and Risk Assessment) within the O. M. Marzeiev
Institute of Hygiene and Medical Ecology.
5. Disclaimer
This manuscript has been reviewed by the US Envi-
ronmental Protection Agency and approved for publica-
tion. The views expressed in this manuscript are those of
the authors and do not necessarily reflect the views or
policies of the US Environmental Protection Agency.
6. Acknowledgements
The authors would like to thank the late Dr. William E.
Freeman (US EPA Office of International Affairs) for his
support and vision of cooperation between the US EPA
and its environmental counterparts in countries of the
former Soviet Union. We would also like to thank Ok-

Risk Assessment Capacity Building Program in Zaporizhzhia Ukraine: Emissions
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sana Voznyuk for her efforts in data processing, report
formulation, and translation. Funding for this project was
provided by the US Department of State (Freedom Sup-
port Act).
REFERENCES
[1] Ukrainian Ministry of Environment and Natural Re-
sources (MENR), Ukraine Ministry of Environmental
Protection, “National Report on the State of Environment
in Ukraine 1999,” Rayevsky Scientific Publishers, Kyiv,
2000. http://enrin.grida.no/htmls/ukraina/soe98
[2] United Nations Economic Commission for Europe’s Con-
vention on Long-Range Transboundary Air Pollution (UNEC),
“EMEP Status Report 3/2011; Persistant Organic Pollut-
ants in the Environment,” Meterological Synthesizing
Centre-East. A. Gusev, S. Dutchak, O. Rozovskaya, V.
Shatalov, V. Sokovykh and N. Vulykh (authors) Chemi-
cal Coordinating Centre, 2011. http://www.msceast.org
[3] M. Brody, J. Caldwell and A. Golub, “Developing Risk-
Based Priorities for Reducing Air Pollution in Urban Set-
tings in Ukraine,” In: L. Craig, D. Krewski, J. Shortreed,
J. Samet, Eds., Strategies for Clean Air and Health, Pro-
ceedings of the AIRNET Annual Conference/NERAM In-
ternational Colloquium, Rome, 5-7 November 2003, p.
268.
[4] A. Serdyuk, O. Turos, O. Kartavtsev, A. Petrosian and O.
Voznyuk, “Final Report on the Project ‘Environmental
Capacity Building in the NIS’,” Marzeev Institute of Hy-
giene and Medical Ecology, Center of Environmental
Health and Risk Assessment. US EPA Cooperative Agree-
ment #X4-83199301: “Environmental Capacity Building
in the Newly Independent States,” Environmental De-
fense Fund, Contributions from M. Brody, J. C. Caldwell,
A. Golub, S. Avaliani, G. Safonov and E. Strukova, 2008.
[5] “EPA Toxic Release Inventory and the National Air Tox-
ics Assessment,” 2013.
www.epa.gov
http://www.epa.gov/nata
http://www2.epa.gov/toxics-release-inventory-tri-program
[6] “Human Health Risk Assessment from Environmental
Pollutants,” Руководство По Оценке Риска Для
Здоровья Населения При Воздействии Химических
Веществ, Загрязняющих Окружающую Среду, Руко-
водство, 2004.
[7] 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.
http://dx.doi.org/10.1016/S0305-750X(99)00086-8
[8] ISC-AERMOD Program.
http://www.epa.gov/scram001/dispersion_prefrec.htm#ae
rmod
[9] “US Environmental Protection Agency’s Integrated Risk
Information System,” 2013.
http://cfpub.epa.gov/ncea/iris/index.cfm
[10] International Agency for Cancer Research (IARC), 2013.
http://monographs.iarc.fr
[11] K. L. Shumake, J. D. Sacks, J. S. Lee and D. O. Johns,
“Susceptibility of Older Adults to Health Effects Induced
by Ambient Air Pollutants Regulated by the European
Union and the United States,” Aging Clinical and Ex-
perimental Research, Vol. 25, No. 1, 2013, pp. 3-8.
http://dx.doi.org/10.1007/s40520-013-0001-5
[12] J. S. Brown, T. Gordon, O. Price and B. Asgharian,
“Thoracic and Respirable Particle Definitions for Human
Health Risk Assessment,” Particle and Fibre Toxicology,
2013.
[13] US EPA, “Summary of the History of the Particulate
Matter National Ambient Air Quality Standards,” 2012.
http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_history
.html
http://www.epa.gov/pm/2012/decfstandards.pdf
[14] S. Avaliani and B. Revich, “Авалиани С.Л., Ревич Б.М.
Оценка риска загрязнения окружающей среды для
здоровья населения как инструмент муниципальной
экологической политики в Московской области,”
2010.
[15] 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, No. 10, 1999,
pp. 1803-1819.
http://dx.doi.org/10.1016/S0305-750X(99)00086-8
[16] E. Strukova, A. Golub and A. Markandya, “Air Pollution
Costs in Ukraine,” Fondazione Eni Enrico Mattei, Nota
Di Lavoro, Milano, 2006.
[17] 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 Quan-
tification of Health Risks: Global and Regional Burden of
Disease Due to Selected Major Risk Factors, Vol. 2,
World Health Organization, Geneva, 2004, pp. 1353-
1433.
[18] D. Axelrad, R. Morello-Frosch, T. Woodruff and J. Cald-
well, “Assessment of Estimated 1990 Air Toxics Concen-
trations in Urban Areas in the United States,” Environ-
mental Science & Policy, Vol. 2, No. 4-5, 1999, pp. 397-
411. http://dx.doi.org/10.1016/S1462-9011(99)00036-2
[19] J. Caldwell, T. Woodruff, R. Morella-Frosch and D. Ax-
elrad, “Application of Health Information to Hazardous
Air Pollutants Modeled in EPA’s Cumulative Exposure
Project,” Toxicology and Industrial Health, Vol. 14, No.
3, 1998. pp. 429-454.
http://dx.doi.org/10.1177/074823379801400304
[20] A. Rosenbaum, D. Axelrad, T. Woodruff, Y. Wei, M.
Ligocki and J. Cohen, “National Estimates of Outdoor
Air Toxics Concentrations,” Journal of the Air & Waste
Management Association, Vol. 49, No. 10, 1999, pp.
1138-1152.
http://dx.doi.org/10.1080/10473289.1999.10463919
[21] A. Rosenbaum, M. Ligocki and Y. Wei, “Modeling Cu-
mulative Outdoor Concentrations of Hazardous Air Pol-
lutants: Revised Final Report,” Systems Applications In-
ternational, Inc., San Rafael, 1999.

Risk Assessment Capacity Building Program in Zaporizhzhia Ukraine: Emissions
Inventory Construction, Ambient Modeling, and Hazard Results
Open Access JEP
1487
[22] T. Woodruff, D. Axelrad, J. Caldwell, R. Morello-Frosch
and A. Rosenbaum, “Public Health Implications of 1990
Air Toxics Concentrations across the United States,” En-
vironmental Health Perspectives, Vol. 106, No. 5, 1998,
pp. 245-251. http://dx.doi.org/10.1289/ehp.98106245
[23] T. Woodruff, J. Caldwell, V. Cogliano and D. Axelrad,
“Estimating Cancer Risk from Outdoor Concentrations of
Hazardous Air Pollutants in 1990,” Environmental Re-
search, Vol. 82, 2000, pp. 194-206.
http://dx.doi.org/10.1006/enrs.1999.4021
[24] US Environmental Protection Agency, Science Advisory
Board, “Summary of July 2000 Peer Review of the Draft
Document Planning and Scoping the Initial National-
Scale Assessment: An Element of the EPA National Air
Toxics Program,” 2003.
www.epa.gov/ttn/atw/nata/peer.html
[25] Morello-Frosch, T. Woodruff, D. Axelrad, and J. Cald-
well, “Air Toxics and Health Risks in California: The
Public Health Implications of Outdoor Concentrations,”
Risk Analysis, Vol. 20, No. 2, 2000, pp. 273-291.
http://dx.doi.org/10.1111/0272-4332.202026
[26] 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.
http://dx.doi.org/10.1080/15287390701558238
[27] U. Kenessariyev, A. Golub, M. Brody, A. Dosmukhame-
tov, M. Amrin, A. Erzhanova and D. Kenessary, “Human
Health Cost of Air Pollution in Kazakhstan,” Journal of
Environmental Protection, Vol. 4, No. 11, 2013, pp. 1-8.
http://www.scirp.org/journal/jep
http://dx.doi.org/10.4236/jep.2013.48101
[28] Economic Commission for Europe, “Environmental Mo-
nitoring and Reporting: Eastern Europe, the Caucasus and
Central Asia,” Published by the United Nations, 2003, 67 p.
http://www.unece.org/env/europe/monitoring/EnvMonRe
p/en/chapter1.pdf Chapter1
[29] UNECE, “Environmental Performance Reviews: Ukraine,
Second Review. ISBN 978-92-1-116958-4. ISSN 1020-
4563,” United Nations Economic Commission for Europe,
Committee on Environmental Policy, United Nations
Publication, 2007.
http://www.unece.org/fileadmin/DAM/env/epr/epr_studie
s/Ukraine%20II.pdf
[30] 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. Thurs-
ton, “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.
http://dx.doi.org/10.1021/es2025752
[31] WHO Europe, “Monitoring Ambient Air Quality for
Health Impact Assessment,” WHO Regional Publications,
European Series No. 85, 1999, p. 102.
[32] World Bank Estimates of Ukraine GDP, “World Data-
Bank; World Development Indicators, GDP 2000-2012
(Current US$),” The World Bank, Washington DC, 2013.
http://databank.worldbank.org/Data/Views/Reports/Table
View.aspx?IsShared=true&IsPopular=series
[33] V. S. Vlasjuk, “Global Market and Ukrainian Steel In-
dustry in 2011-2012,” Ministry of Economic Develop-
ment and Trade of Ukraine SE UPE Co. Research &
Consulting, 71st Session of the OECD Steel Committee
Meeting, Paris, 5-6 December 2011.
http://www.oecd.org/sti/ind/49206658.pdf
[34] World Steel Association, “World Steel Figures 2012,” E
Basson Director General, World Steel in Figures © World
Steel Association, 2012.