EMISSION INVENTORY FOR ISRAEL
E. Weinrotha, M. Luriaa, A. Ben-Nunb, J. Kaplanc, M. Pelega, and I. Mahrerd
a
Environmental Sciences, The Hebrew University of Jerusalem,
GIS Center, The Hebrew University of Jerusalem
c Department of Geography, The Hebrew University of Jerusalem
d Faculty of Agriculture, Food and Environmental Quality Sciences, Department of Soil and Water
b
Abstract- High ozone levels are regularly reached during summer months over inland areas
of Israel and the West Bank. Studies have been made which analyzed the back trajectories of
air mass and identified the source of most of the precursors of ozone as originating from the
densely populated coastline of Israel. In order to better understand the contribution of the
ozone precursor emission sources, it is essential to have an accurate emission inventory,
which can be used as input for a photochemical model. In the present paper, the methods
used in preparing an emission inventory for Israel (for year 1998) are clarified and the results
obtained are presented. From the emission inventory it is clear, that the major source of SO 2
and NOx is industrial, the major source of CO are vehicles, and that VOC (Volatile Organic
Compounds) originate from solvents, vehicles, industry and also vegetation. While there are
many improvements that can still be made to this emission inventory, its spatial distribution
and collection of most of the sources can provide a corner stone to anyone interested in
running a photochemical model for the East Mediterranean area.
1. Introduction
For more than a decade elevated ozone levels, above the Israeli ambient standards
have been observed during the early summer months at inland rural sites (Peleg et al., 1994;
Peleg, 1997; Alper Siman Tov et al., 1997; Ranmar et al., 2001). Air mass back trajectories
calculations have shown that air masses originating over Tel Aviv Metropolis caused, a few
hours later the elevated mixing ratios at rural inland sites in central Israel. The precursors,
carbon monoxide (CO), nitrogen oxides (NO2 and NO) and volatile organic compounds
(VOC) undergo chemical and photochemical reactions in the presence of solar radiation
(Seinfeld and Pendis, 1998; Finlayson-Pitts and Pitts, 1997) to create ozone, while
undergoing significant
mixing and transportation (Kely,
1997;
Seinfeld,
1989).
Photochemical models have been applied in an attempt to better understand ozone formation.
These models need to take into account the variables effecting ozone formation, such as
atmospheric dynamics, solar radiation, chemical and photochemical reactions and the
emission inventory of primary pollutants that undergo transformation (Ziomas et al., 1994;
Naresh et al., 1994; Giovannoni et al., 1995, Svensson, 1996; Sillman et al., 1997; Jacobson
et al., 1997). An emission Inventory is therefore a prerequisite in order to run a
photochemical model.
A study of the quantitative influence of the different air pollution sources (stationary,
mobile) on ozone formation is presently under investigation in our laboratory (Weinroth et al,
2003). This study includes a simulation of the effects of the various air pollution sources
(including industrial, biogenic and transportation pollutants) by use of a series of computer
models which including the CAMx photochemical model (CAMx, 1998). The preparation of
an extensive database for emissions from the different pollution sources, including
geographical information, is a cornerstone of the study as it is a prerequisite for running the
photochemical model. In the present paper methods for preparing the first overall spatial
emission inventory of pollutants in Israel will be presented.
An emission inventory of greenhouse gases in Israel based on the IPCC regulations has been
prepared (May-Marom and Koch, 1998). The yearly emission inventory is available on the
website of the Israel central bureau of statistics (http://www.cbs.gov.il). The above emission
inventories of Israel were not spatially connected.
2. Methodology
2.1 Data assimilation of the different pollution sources
The present paper summarizes the process of data collection with respect to emission
rates from the various different pollution sources including, large (point) stationary sources,
medium (point) stationary sources, small (area) stationary sources, biogenic (area) stationary
sources, mobile (area) sources (both surface and elevated), and VOC (volatile organic
compounds) solvents (line and area) sources. In those cases where emission data rates were
directly available, they could be readily incorporated into the model. However in many
cases, raw data was only available, and certain calculations and assumptions were required in
order to convert the information available into a format suitable for incorporation into the
model.
The present paper outlines such calculations and the assumptions used in the
preparation of the database. The data presented was updated to the year 1998.
Data regarding the exact geographical location of each emission source were also collected
and integrated into the database for use within the model. This data is critical for obtaining
optimal results for input into the three dimensional photochemical model. The collection and
processing of the geographical data with respect to all the pollution sources is described in
the following section.
2.2 Geographical location
In conjunction with collecting data relating to the emission factors, an attempt was
made to ascertain the exact geographical location in three-dimensional space of the emission
source.
For each stationary source, the plant’s address, if available, together with the Central Bureau
of Statistics’ G.I.S. address database was used to determine the precise two-dimensional
coordinates for each plant site position. In cases where the site address was unavailable, a
1:50,000-scale map was employed to identify the plant’s exact location. The emission height
for each source was calculated from data available regarding output rate, emission velocity
and temperature, stack diameter and height, which enabled calculating the effective stack
height emission (Turner et al., 1986). The data required to calculate the effective stack
heights were available for all the large stationary sources and part of the medium stationary
sources.
The geographic point of emission for the aircraft emission sources was obtained by referring
to the flight corridor between Ben-Gurion Airport and the Mediterranean coastline as a
diagonal line, and calculating the emissions as being released at the airport itself and from
three points along this line, in ratios based on information provided by El-Al.
2.3 National fuel consumption - 1998
The total annual fuel consumption in Israel in 1998 was reported to be as follows:
coal 1 x 1010 kg; fuel oil 4.4 x 109 kg; distillate fuel 2.2 x 109 kg; diesel 2.2 x 109 kg; kerosene
and naphtha 1.1 x 109 kg; LPG and natural gas 4.3 x 108 kg and oil fragments and petcock 6.7
x 108 kg.
3. Stationary Sources
3.1Major pollution sources
The principal large stationary sources in Israel are the five electrical company power
plants, the Haifa and Ashdod oil refineries and the three “Nesher” cement processing plants.
These sources together account for over 71% of the fuel consumption by stationary sources in
Israel and 58% of its overall fuel consumption. Each of these facilities has monitors installed
that record, on a per-hour per-stack basis, the quantity of the different pollutants emitted
and/or the plant capacity. The locations of the large stationary point sources are indicated in
figure 1.
3.1.1 Electrical Company Facilities
Equations (1) and (2), were employed to calculate the hourly SO2 and NOx emissions from
the electricity producing power plants.
E SO2  P  D  FSO2
(1)
E NOx  P  D  FNOx (2)
Where (P) is the hourly output of electricity generated, (D) the average hourly fuel
consumption per output (watt), and (F) the emission rate (gm) per fuel (Kg) consumed to (F
was calculated separately for SO2 and NOx).
In order to calculate CO and VOC (Volatile Organic Compounds) emissions, the amounts of
fuel consumed (P*D) was multiplied by the EPA stationary sources emission factors (α)
(henceforth referred to as “EPA factors”) (EPA, 1995) according to equations (3) and (4).
ECO  P  D CO
(3)
EVOC  P  D VOC (4)
This necessitated the collection of data regarding the types of fuel involved since the EPA
factors are fuel type sensitive. Unless otherwise stipulated, fuel type, fuel consumption, CO,
SO2 and NOx emission data were collected with respect to stationary sources, were
applicable, and the EPA factors were then used to calculate VOC emission factors (from fuel
combustion) and also missing data for CO, SO2 and NOx emissions.
3.1.2 Haifa and Ashdod Oil Refineries
For the oil refineries, data was available with regard to the SO2 emissions and also
hourly fuel consumption. Equations (1)-(4) were then employed to calculate the hourly
emissions of CO, NOx and VOC as described in section 3.1.1 above.
It should be emphasized that the data available both from the Israeli Electrical
Company and the Haifa and Ashdod refineries were the most accurate data available,
enabling the calculation of exact hourly emission factors, whereas for all the other emission
sources, hourly emissions factors could only be calculated from the available average annual
data and assumptions regarding the daily and seasonal variations.
3.1.3 The “Nesher” Cement Factories Emissions
Average hourly CO, SO2 and NOx emissions were available from a limited number of
measurements performed at the source by an independent company. The accuracy of this
data is reasonable. However, the hourly averages calculated for Nesher, did not take into
account the variations between day and night and the variations between workdays and
weekends/holidays.
3.2 Medium stationary (point) sources
3.2.1 Point sources with emission reports
These sources, taken together, account for over 8.1 % of the total fuel consumption
for stationary sources and 6.6% of overall fuel consumption. There are over 100 plants in this
category. These plants are required by law to have their emissions monitored by an
independent company once a year. The data is then reported to the Ministry of the
Environment or to the Municipal Associations, who supplied this information for the present
emission inventory. The data in the emission reports usually included hourly emission rates
for SO2, NOx, CO and particulates. It was further assumed that during the emission
measurements the plants were operating at only at 70% of full capacity. However it is
reasonable to assume that these measured emission rates represent the average hourly
pollution levels when taking into account the differences expected between day/night and
weekdays/holidays work routines. In those cases when not all of the emissions were reported,
hourly fuel consumption and fuel type data were used as a basis to calculate the missing
pollutant emissions.
3.2.2 Point sources with fuel consumption reports
There are hundreds of medium plants that do not monitor their emission rates. The
average hourly emissions had therefore to be calculated using the annual fuel consumption
data. The fuel consumption information was obtained from The National Fuel Authority,
which collects information regarding annual fuel consumption in Israel, and receives data
regarding individual plant fuel consumption. The data allowed the calculation of the annual
pollutant (SO2, CO, NOx and VOC species) rate emissions and hence the hourly values are
therefore only an approximation, since the plant work routine is not always available. For
example, some plants work at maximum capacity for 12-14 hours a day while others work at
full capacity 24 hours a day but only 5.5 days a week. The plant locations are shown in
Figure 1.
3.3 Small stationary sources
After taking into account the large and medium stationary sources, a myriad of small
plants remain that cannot be treated as separate stationary sources, sine information regarding
their individual fuel consumptions and exact geographic locations is unavailable. The small
stationary sources were therefore treated as area sources. Such sources, including bakeries,
electronic plants etc, use small amounts of fuel for their various manufacturing tasks. These
sources account for over 15% of the fuel consumption by stationary sources and 12.2% of the
overall fuel consumption.
The aggregate fuel consumption for the small stationary sources was calculated by
subtracting the fuel consumption from the large and medium stationary sources from the total
stationary source fuel consumption. The aggregate emissions for the above sources were then
calculated using the EPA emission factors (taking into account the different fuel types). The
total aggregate emission rates thus calculated were then divided uniformly over the
geographical locations of the industrial areas in Israel. These industrial zones can also be
seen in Figure 1.
3.4 Volatile Organic Compounds (line, area) sources
Three different sources need to be considered.
The VOC emissions from road asphalt were calculated by multiplying the length of
roads in Israel (approximately 6000 km according to http://www.cleanaircount.org), by the
EPA ‘s emission factors of VOC emitted daily per kilometer of road.
Estimating the emissions of VOC from solvent evaporation in urban and industrial
areas: paint, aerosol products, household products, adhesives (industrial and non industrial),
pesticide control, space deodorant was done using the EPA factors and distributing the
emissions spatially by population density.
VOC emissions also occur due to evaporation losses during transporting and
marketing of petroleum liquids and gases (LPG). For LPG, this can reach up to 4% of the
total amount of gas transported (http://www.inrets.fr/infos/cost319/MEETdeliverable20.pdf).
For petroleum liquids EPA emission factors (EPA, 1995) were employed. These factors refer
to the VOC loss at each filling stage. This occurs three times, loading of tanker from main
vessel, down loading to distribution point and finally automobile fill up. The total fuel
consumed was assumed to be the basis for calculating the emission rates. The calculated
emission rates were distributed spatially over both urban and industrial areas.
3.4. Additional Sources
The present emission inventory does not include sources such as wood burning
livestock and poultry operations, cattle feedlots, poultry farms, dairy farms, fertilizer
application, and pesticide application (EPA, 1995). Based on previous studies (May-Marom
and Koch, 1998), it can be assumed that the contribution of these sources to the emissions of
NMHC (Non Methane Hydro Carbons) is negligible.
Household and other major heating were not included in the present study since the
main purpose of the emission inventory was for ozone simulation studies. Since ozone events
take place during summer time therefore the emissions from heating sources are not required.
4. Mobile sources
4.1 Ground Based Transportation
The emissions from mobile sources were evaluated as point sources using the
EMME/2 Mobile5 (MOBILE5, 1994) and EMFAC7 (ARB, 1993) transportation models,
and then converted into area sources (grid cells), using the GIS , which could then be inputted
into the photochemical model. The above models provided emission data at three points
along each transportation route – the entry and exit points of such route, and a point midway
in-between. The spatial vehicular density is concentrated essentially in the Tel Aviv
metropolitan area, Jerusalem and Haifa as can be seen in figure 2.
The transportation models were based on several assumptions with respect to travel time,
type of travel (to work, pleasure, from work etc.), geo-economic structure of the country
studied and other variables. The models also utilize an emission-vehicle speed curve (Fig. 3),
derived from measurements from three different studies, Fort Mc-Henry (Pierson, 1996,
Haifa (Tratakovsky, 1997) and Jerusalem (Yavin, 1998). The emission rate results obtained
from the transportation models referred to CO, NOx and VOC. The various specific organic
compounds comprising the VOC group were calculated using the percentages of VOC
vehicle emission out of total VOC based on a study conducted by Kleindienst (1992).
Although Kleindienst evaluated only 82% of the individual compounds, it was assumed that
the organic compounds in their respective ratios also constituted the remaining 18% of the
total emitted pollutants.
It should be noted that the emissions results obtained from the transportation model all related
to peak morning transportation loads. In order to calculate the emissions for all hours of the
day, a vehicle load versus hour of day curve for a medium sized city (Netanya 1995, Fig 4.)
was employed. It is reasonable to assume that such a medium sized city represents the
average between rural areas and large urban areas.
4.2 Trains
The data concerning train and railway statistics was collected from The Israel Rail
Corporation (http://www.israrail.org.il/english/index.html).
The EPA emission factors
(http://www.epa.gov/orcdizux/regs/nonroad/locomotv/frm/42097051.htm)
were
used
to
calculate the emissions assuming average fuel consumption per kilometer traveled.
4.3 Aviation Transportation
In order to calculate the NOx, VOC and CO emissions from civilian aviation
transportation sources, information was obtained from the Israeli Aviation Administration
concerning the number of daily incoming and outgoing flights from the Ben Gurion
International Airport, The daily flights were divided into heavy (Boeing 757, 767 and 747 or
equivalents) and medium plane categories assuming an average fleet distribution (EL-AL
fleet: 65% medium sized planes 35% heavy planes). The number of flights in each category
was multiplied by the appropriate fuel consumption factor, (supplied by El-Al). The fuel
consumption factors relate to the fuel consumed starting from engine ignition, idling, taxing
and in flight between Israel’s Ben-Gurion airport and the Israeli Mediterranean coastline.
The fuel consumption thus obtained was then multiplied by the EPA emission factors in order
to obtain the relevant emission rates.
5. Natural sources
5.1 Vegetation
Biogenic sources that influence the results obtained from photochemical model,
include emissions from trees and vegetation, which emit isoprene and monoterpene
(Chameides et al., 1988).
While each different plant type has its specific VOC emission
makeup, in order to reduce the size of the data base the vegetation was reduced to eight
categories of plant types; Coniferous (Pines), Acer, Acacia, Quercus, Rhamnus, Artemisia,
Populus and Salix. Since no extensive and reliable study on vegetation VOC emissions has
been performed for Israel, data available for emission factors from California (Winer et al.,
1992; Benjamin et al., 1996,1997,1998) were employed, since California has a climate
similar to the Mediterranean climate. Further, the taxonomy and genome of some of the
vegetation in California is similar to that for Israel. An additional category of plant types was
defined (giving nine categories in all) to include all plant types that grow in Israel but not
found in California. Since no emission factor was available for this category, an arbitrarily
emission factor was assigned, based on an average of all the emission factors for the other
eight categories.
The emissions from each category of plant type in each grid block were calculated using
equation (5) as follows:
Veh  Ba  Vc * Veb
(5)
Where, Ba is the biomass amount [per hectare], Vc is the vegetation coverage [percentage],
Veb is the VOC emissions [per biomass amount] and Veh is the resulting VOC emissions
[per hectare], (10 dunam equals 1 hectare)
To calculate the biomass amount for each plant type, biomass tables (Benjamin et al 1997)
were utilized.
The vegetation coverage percentage was obtained by assuming a linear
connection between the annual rainfall in each particular geographic area and vegetation
coverage (Kadmon and Danin 1997,1999). Figure 5 shows the vegetation coverage and
annual rainfall for Israel.
Data regarding plant types was collected from several sources. With respect to certain
geographical areas, data was available from the Reservation and National Parks Authority.
Such data were relatively detailed, and it was possible to single out the dominant plant type in
each defined area, and assign that plant type as the emission category for that area. Missing
data was filled in using more general information provided by the relevant government
ministries regarding the vegetation types in other areas.
5.2 Soils
Emission of NOx from soils is estimated to be 16% of the global budget of NOx in the
troposphere (EPA, 1995). The amount of NOx emitted from soils in Israel is estimated as 78% of the total global budget (Mey-Marom and Koch 1998).
Variations in soil
characteristics such as temperature, pH, organic carbon content, oxygen supply, effect the
emitted amounts of NOx and N2O emitted, so regional differences in soil types may have
great impact on the contribution of soil to NOx emissions. The soil in Israel was divided into
two major categories, natural and cultivated. As no extensive and reliable study of soil
nitrous oxides emissions has been determined for soil characteristics in Israel, the EPA
factors (which are USA oriented) were employed together with the detailed land use of Israel.
6. Data Summary
Table 1 below, summarizes the total emissions from the various sources divided into the
different pollutant species, (the units are in mol/hour). The results as reported are based on
the data sources, calculations, assumptions etc., as described above. Inspection of the
emission inventory table leads to the following conclusions regarding the spatial distribution
of the sources and their individual contribution to the emission of primary pollutants.
The table clearly shows that transportation, although only accountable for a fifth of total fuel
consumption, is responsible for almost the entire CO and 30% of the VOC emitted. Most of
the NOx emitted can be attributed to industrial sources. Figures 6 to 9 show, in graphic
format, the contribution of the different sources to the emissions of CO, SO2. NOx and
alkanes.
The spatial distribution from the different pollutant sources to the total pollutant load is
shown in figures 10 to 14. A number of interesting deductions can be inferred from these
figures. Not of all of the pollution sources contribute to each overall pollutant loads, as in the
case of SO2, which is only emitted by stationary sources and vehicles. Figure 13 shows that
stationary sources and mobile sources contribute almost equally to the total NOx emission.
This, despite the fact, that the emission of NOx from stationary sources is three times greater
than its emission from vehicle sources. Since the spatial distribution from vehicle sources is
greater (approximately 3500 points of vehicular emission as compared to 500 points for
industrial emission, this could have a great influence on the proportional contribution to the
creation of ozone.
Emission
Sources\pollutant
SO2
NOx
CO
ALK
ETH
OLE
ISOP
TOL
XYL
FORM
ALD2
Stationary Total
8.64E+05 8.56E+05 2.83E+04 1.10E+04 8.50E+03 3.43E+02 0.00E+00 5.20E+02 5.10E+02 2.85E+02 1.76E+02
Refineries
3.42E+04 2.67E+04 3.26E+03 1.10E+04 8.50E+03 3.40E+02 0.00E+00 5.10E+02 5.10E+02 1.76E+02 1.70E+02
Israeli Electricity
Comp
5.70E+05 6.38E+05 1.51E+04 3.60E+01 0.00E+00 2.81E+00 0.00E+00 6.08E+00 2.30E-01 4.55E+01 5.43E+00
Cement Factories
2.81E+04 2.66E+04 9.52E+02 1.24E+00 0.00E+00 9.21E-02 0.00E+00 3.64E-01 1.10E-02 4.29E+00 1.78E-01
Industry (R)
2.32E+05 1.65E+05 8.99E+03 5.86E+00 0.00E+00 3.68E-01 0.00E+00 3.96E+00 9.60E-02 5.92E+01 6.92E-01
Transportation
5.27E+03 2.89E+05 1.68E+06 1.09E+05 4.48E+04 2.77E+03 0.00E+00 5.29E+03 5.18E+03 0.00E+00 2.48E+03
Vegetation
0.00E+00 0.00E+00 0.00E+00 1.24E+03 0.00E+00 0.00E+00 9.55E+02 0.00E+00 0.00E+00 0.00E+00 1.27E+02
VOC-solvents
0.00E+00 0.00E+00 0.00E+00 9.64E+05 2.69E+05 1.67E+04 0.00E+00 2.10E+04 2.01E+04 5.37E+03 1.56E+04
Aviation
2.33E+02 5.28E+03 8.97E+03 5.50E+02 5.40E+02 2.95E+01 0.00E+00 0.00E+00 0.00E+00 8.34E+02 9.24E+01
Table 1. Contribution of Emission Sources to the various Pollutants (mol/hour)
ALK = Saturated hydrocarbons
ETH = Ethylene
OLE = Unsaturated hydrocarbons
ISOP = Isoprene
TOL = Toluene
XYL = Xylene
FORM = Formaldehyde
ALD2 = All aldehydes
To aid the viewer in interpreting figures 10 through 14, it should be noted that the legends in
each figure were divided into following groups:
Pollutant mol/hour
0
0 to median
Median to two standard deviations
> Two standard deviations
7. Future work and conclusions
The following actions could significantly improve the inventory database:
1.
Collect data regarding all small stationary (point) emission sources, rather than
treating some of these sources as a general area emission source.
2.
Collect data regarding the various plants’ actual operation hours.
3.
Differentiate between trucks and smaller vehicles, when running the
transportation models, with respect to the following variables: differences
between weekday and weekend use, differences in use during the various hours
of the day, and different transportation routes usage.
4.
Measure VOC emissions from vegetation in Israel, rather than reliance upon
California measurements.
5.
Calculation of vegetation cover through use of satellite photography and area
measurements rather than reliance upon the connection between rainfall and
vegetation cover.
6.
Addition of emission data from flights out of military airports.
Acknowledgments
We would like to thank all the great helpers that made our task easier and better: Ms Hagar
Leshner Hebrew University Jerusalem Israel (HUJI), Prof. Avinoam Danin (HUJI), Dr.
Ronen Kadmon (HUJI), Yonat Magal Israel Nature and National Parks Protection
Authority(INNPPA), Yisrael Taober(INNPPA), Shahar Katz Central Bureau of Statistics
(CBS), Orit Stone (CBS), Eran Tas (HUJI), Dr. Dafna Alper – Simantov (HUJI). Regional
and Local Authorities for Environmental Protection at Ashdod, Ashkelon, Haifa, Hadera,
Yavne, Jerusalem.
8. References
ARB (California Air Resources Board).(1993). EMFAC7 Computer Model, Technical Support
Division.
Benjamin Mt.,Sudol M., Vorsatz D. and Winer MW. (1997). A spatially and temporally resolved
biogenic hydrocarbon emissions inventory for the California South coast air basin. Atmos.
Environ.,31, 18, 3078-3100
Benjamin Mt.,Sudol M., Bloch L. and Winer MW. (1996).Low emitting urban forests: A
taxonomic methodology for assigning Isoprene and Monoterpene emission rates. Atmos.
Environ.,30, 9, 1437-1452
Benjamin Mt. and Winer MW.(1998) Estimating the Ozone-forming potential of urban trees and
shrubs. Atmos. Environ.,32, 1, 53-68
Chameides, W., R. Lindsay, J. Richardson, and C. Kiang (1988) The role of biogenic hydrocarbons
in urban photochemical smog: Atlanta as a case study, Science, vol. 241, pp. 1473-1475.
Kaplan J., (1997). Model Structure and Data Requirements. Working Paper for the Trilateral
Research Project on GIS Tools for Sustainable Transport in Palestine and Israel.(Personal
communication).
Kadmon R., Danin A. (1997) Floristic variation in Israel: a GIS analysis Flora
192 (4): 341-345.
Kadmon R., Danin A. (1999) Distribution of plant species in Israel in relation to spatial variation in
rainfall Journal of Vegetation Science 10 (3): 421-432.
Kleindienst TE., Smith DF., Hudgens EE., Snow RF., Perry E., Claxton LD., Bufalini JJ., Black F.
and Cupitt LT.(1992). The Photooxidation of automobile emissions - measurements of the
transformation products and their mutagenic activity., Atmos. Environ.,26, 16, 3039-3053
May-Marom A. and Koch J. (1998) Inventory of emissions and removals of greenhouse gases in
Israel ; Part B: Nitrous oxides and precursors of ozone and aerosols ; Part C: Reporting the
national Inventory, Soreq Nuclear Research Center.
Pierson w., et al.,(1996).Real world automobile emissions – Summery of studies in Fort Mc-Henry
tunnel. , Atmos. Environ., 30, 2233-2256.
Tratakovsky L., Gotman M., Zvirin Y., Golgotio I. and Alinikov Y.,(1997). Estimation of vehicles
emission factors in Israel, Research report –Technion Israel Institute of Technology,
Transportation research Institute, 87 pp.
US-EPA (ed.) (1995), Compilation of the Pollutant Emission Factors; Volume 1: Stationary Point
and Area Sources fifth edition. Office of Air Quality Planning and Standards Research
Triangle Park NC.
User’s guide to MOBILE5(mobile source emission factor model)(1994)
U.S.EPA Office of air and radiation office of mobile sources emission planning and strategies
division air quality analysis branch 2565 Plymouth road Ann Arbor, Michigan 48105
Winer MW., Arey J., Atkinson R., Aschmann SM., Long WD., Morrison L. and Olszyk
DM.(1992) Emission rates of organics from vegetation in California’s central valley., Atmos.
Environ., 26A, 14, 2647-2659
Figure 1. Geographical distribution of large, medium stationary point sources and industrial areas
(PP power plant , CF cement factory, OR oil refinery )
Figure 2. Peak hour vehicular spatial distribution percents
Emission Factors
TOG (g/km)
20
L D Leaded
L D Unleaded
L D Diesel
H D Diesel
15
10
5
0
(g
/km
)
Emission Factor
25
8 16 24 32 40 48 56 64 72 80 88
Speed (km/hr)
35
30
25
20
15
10
5
0
L D Leaded
L D Unleaded
L D Diesel
H D Diesel
(g
/km
)
Emission Factor
Emission Factors
NOx (g/km)
8 16 24 32 40 48 56 64 72 80 88
Speed (km/hr)
350
300
250
200
150
100
50
0
L D Leaded
L D Unleaded
L D Diesel
H D Diesel
(g
/km
)
Emission Factor
Emission Factors
CO (g/km)
8 16 24 32 40 48 56 64 72 80 88
Speed (km/hr)
Figure 3. Emission rates for CO/NOx/VOC versus vehicle speed
Averge Daily Distribution of motor vehicle emissions in Israel
12
Presentage of pollution
10
8
6
4
2
0
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
Time
Figure 4. Average daily distribution of motor vehicle emissions
(Natanya, Israel 1995)
Figure 5. Land –use coverage and Israeli annual rainfall
VOCsolvents
0%
Vegetation
0%
CO mol/hour
Aviation
1%
Refineries
0%
IEC
1%
Cement
factories
0%
Industry (R)
1%
Vehicles
97%
Vegetation
0%
Vehicles
1%
Industry (R)
27%
Cement
factories
3%
SO2 mol/hour
Aviation
0%
VOCsolvents
0%
Refineries
4%
IEC
65%
VOCsolvents
0%
Vegetation
0%
Vehicles
26%
Industry (R)
14%
NOx mol/hour
Aviation
0%
Cement
factories
2%
IEC
0%
Refineries
2%
IEC
56%
Alkane mol/hour Cement
Refineries
1%
Aviation
0%
Industry (R)
0%
VOCsolvents
89%
Figures 6 - 9 Contribution of Emission Sources to total Pollutant Loads
factories
0%
Vehicles
10%
Vegetation
0%
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14