<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<ana user="HP_Omistaja" project="Gasbus_model" generated="12. lokta 2007 23:34 " softwareversion="3.1.1" software="Analytica">
<sysvar name="Samplesize">
<definition>10K</definition>
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<sysvar name="Windows">
<definition>2</definition>
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<sysvar name="Typechecking">
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<sysvar name="Checking">
<definition>1</definition>
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<sysvar name="Graphwindows">
<definition>4</definition>
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<sysvar name="Randomseed">
<definition>22</definition>
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<sysvar name="Showhier">
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<sysvar name="Saveoptions">
<definition>2</definition>
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<sysvar name="Savevalues">
<definition>0</definition>
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<sysvar name="Diststeps">
<definition>1</definition>
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<sysvar name="Naming">
<definition>2</definition>
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<sysvar name="Webhelper">
<definition>-1</definition>
</sysvar>
<sysvar name="Allwarnings">
<definition>0</definition>
</sysvar>
<sysvar name="Time">
<title>Time</title>
<description>Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the ÒDynamic()Ó function in your variables to perform dynamic simulation.</description>
<definition>[0,1,2]</definition>
</sysvar>
<attribute name="Reference"/>
<attribute name="Url"/>
<attribute name="Premises"/>
<attribute name="Deduction"/>
<askattribute name="Reference" function="Function" enable="yes"/>
<askattribute name="Url" function="Function" enable="yes"/>
<askattribute name="Reference" function="Module" enable="yes"/>
<askattribute name="Url" function="Module" enable="yes"/>
<askattribute name="Value" function="Variable" enable="yes"/>
<askattribute name="Reference" function="Variable" enable="yes"/>
<askattribute name="Url" function="Variable" enable="yes"/>
<model name="Gasbus_model">
<title>GASBUS MODEL</title>
<author>Marko Tainio, Jouni T. Tuomisto, Otto HŠnninen, PŠivi Aarnio, Kimmo J. Koistinen, Matti J. Jantunen and Juha Pekkanen.</author>
<date>Mon, Mar 26, 2001 14:02</date>
<saveauthor>HP_Omistaja</saveauthor>
<savedate>12. lokta 2007 23:34</savedate>
<defaultsize>48,24</defaultsize>
<diagstate>1,565,86,590,501,17</diagstate>
<fontstyle>Trebuchet MS, 13</fontstyle>
<fileinfo>0,Model Gasbus_model,2,2,0,1,C:\Temp\gasbus_model2.ana</fileinfo>
<att__diagramprintsca>100,17,17,7,1,9,6533,8533,1,0</att__diagramprintsca>
<reference>Marko Tainio, Jouni T. Tuomisto, Otto HŠnninen, PŠivi Aarnio, Kimmo J. Koistinen, Matti J. Jantunen and Juha Pekkanen.
Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland
Risk Analysis, Vol. 25, No.1, 2005. pp.151-160.</reference>
<url>http://www.ktl.fi/risk/</url>
<text name="Te2">
<description>Model identifier:
URN:NBN:fi-fe20051170</description>
<nodelocation>96,328,-1</nodelocation>
<nodesize>84,24</nodesize>
</text>
<module name="Model1">
<title>Model</title>
<author>mtad</author>
<date>21. Marta 2005 11:58</date>
<defaultsize>48,24</defaultsize>
<nodelocation>328,32,1</nodelocation>
<nodesize>48,24</nodesize>
<diagstate>1,433,75,559,510,17</diagstate>
<module name="Emission_model">
<title>Emission model</title>
<author>mtad</author>
<date>21. Marta 2005 11:58</date>
<defaultsize>48,24</defaultsize>
<nodelocation>240,104,1</nodelocation>
<nodesize>48,24</nodesize>
<diagstate>1,36,82,514,473,17</diagstate>
<index name="Strategy">
<title>Strategy</title>
<definition>['BAU A','EURO 2 D2','EURO 3 D3','CRT filter C','Biodiesel B','Alcohol E','Propane G23','Natural gas G22','Propane G32','Natural gas G31']</definition>
<nodelocation>344,104,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,593</windstate>
<valuestate>1,280,290,416,303,0,MIDM</valuestate>
<displayinputs>[Object Constant]</displayinputs>
</index>
<variable name="Total_emissions_from">
<title>Total emissions from busses</title>
<units>kg/a</units>
<definition>Table(Pollutant,Strategy)(
24.291K,15.049K,12.039K,6131,11.147K,3943,Undefined,Undefined,3549,3549,
1.373542M,1.373645M,1.070116M,1.226174M,1.560585M,662.458K,Undefined,Undefined,504.252K,504.252K,
76.355K,64.832K,51.866K,11.886K,36.018K,24.844K,Undefined,Undefined,91.558K,261.594K,
449.679K,407.443K,325.954K,41.499K,301.809K,38.536K,Undefined,Undefined,642.272K,642.272K,
112.614452M,110.991732M,113.211566M,115.027795M,114.49346M,102.371073M,Undefined,Undefined,134.124551M,115.574569M
)</definition>
<nodelocation>344,40,1</nodelocation>
<nodesize>48,29</nodesize>
<windstate>2,294,138,728,569</windstate>
<defnstate>1,72,82,551,296,0,MIDM</defnstate>
<valuestate>2,39,51,625,303,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<reformdef>[Pollutant,Strategy]</reformdef>
<reformval>[Pollutant,Strategy]</reformval>
<reference>11. YTV, Helsinki Metropolitan Area Council. (1999). Vaihtoehtoisten polttoaineiden kŠyttšmahdollisuudet joukkoliikenteessŠ pŠŠkaupunkiseudulla [The possibilities to use alternative fuels in public transport in the Helsinki metropolitan area]. Helsinki Metropolitan Area Council Publication Series B 1999:5 (in Finnish).</reference>
</variable>
<index name="Pollutant">
<title>Pollutant</title>
<description>Air pollutants, classification by YTV.</description>
<definition>['Primary PM','NOx','HC','CO','CO2']</definition>
<nodelocation>344,80,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,478,44,476,476</windstate>
</index>
<constant name="Relative_emission_f2">
<title>Relative emission factor uncertainty</title>
<units>-</units>
<definition>Table(Strategy)(
1,1,1,Triangular(0.6,1,1.4),1,1,1,1,1,Triangular(0.8,1,1.2))</definition>
<nodelocation>224,232,1</nodelocation>
<nodesize>52,40</nodesize>
<windstate>2,102,90,545,344</windstate>
<defnstate>2,252,40,416,303,0,MIDM</defnstate>
<valuestate>2,502,206,416,303,0,MEAN</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<reformdef>[Self,Strategy]</reformdef>
<reformval>[Self,Strategy]</reformval>
</constant>
<variable name="Emission_factor">
<title>Emission factor</title>
<units>g/km</units>
<definition>0.321</definition>
<nodelocation>224,304,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,241,207,731,479</windstate>
<nodecolor>65535,52427,65534</nodecolor>
<reference>11. YTV, Helsinki Metropolitan Area Council. (1999). Vaihtoehtoisten polttoaineiden kŠyttšmahdollisuudet joukkoliikenteessŠ pŠŠkaupunkiseudulla [The possibilities to use alternative fuels in public transport in the Helsinki metropolitan area]. Helsinki Metropolitan Area Council Publication Series B 1999:5 (in Finnish).</reference>
</variable>
<constant name="Relative_emission_fa">
<title>Relative emission factor (Ref)</title>
<units>-</units>
<definition>Using a := Total_emissions_from[Pollutant='Primary PM'] do
a/a[Strategy='BAU A']</definition>
<nodelocation>344,160,1</nodelocation>
<nodesize>48,29</nodesize>
<windstate>2,293,94,611,443</windstate>
</constant>
<variable name="Bus_pm_emission">
<title>Bus PM emission</title>
<units>kg/a</units>
<definition>Relative_emission_fa*Relative_emission_f2*bus_km*(emission_factor/1000)*Rel_bus_activity</definition>
<nodelocation>344,232,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,369,96,530,410</windstate>
<valuestate>2,173,156,953,303,0,MEAN</valuestate>
<reformval>[Scenario,Strategy]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</variable>
<decision name="Transportation_devel">
<title>Transportation development scenarios</title>
<definition>['PLJ current 1994','PLJ BAU 2020','PLJ public 2020','PLJ car 2020','PLJ own']</definition>
<nodelocation>104,64,1</nodelocation>
<nodesize>52,36</nodesize>
<windstate>2,102,90,545,365</windstate>
</decision>
<variable name="Bus_activity">
<title>Bus activity</title>
<definition>Table(Transportation_devel,Self)(
631K,0,
0,0,
1.007M,0,
0,0,
0,0
)</definition>
<indexvals>['trips per day','bus-km/a']</indexvals>
<nodelocation>224,64,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,54,563,362</windstate>
<defnstate>1,88,98,416,303,0,MIDM</defnstate>
<valuestate>2,51,208,485,285,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<reformdef>[Self,Transportation_devel]</reformdef>
<reformval>[Self,Transportation_devel]</reformval>
<numberformat>1,I,4,2,0,0</numberformat>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
<reference>14. YTV, Helsinki Metropolitan Area Council. (1999). Helsinki Metropolitan Area Transport System Plan PLJ 1998. Helsinki Metropolitan Area Council Publication Series A 1999:4.</reference>
</variable>
<constant name="Rel_bus_activity">
<title>Relative bus activity</title>
<units>-</units>
<definition>using a:= bus_activity[bus_activity='trips per day'] do
using b:= a[Transportation_devel='PLJ public 2020']/a[Transportation_devel='PLJ current 1994'] do
array(Scenario,[1,b])</definition>
<nodelocation>224,128,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,269,119,596,371</windstate>
<displayoutputs></displayoutputs>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<index name="Scenario">
<title>Scenario</title>
<definition>['Current 1997','PLJ 2020']</definition>
<nodelocation>224,160,1</nodelocation>
<nodesize>52,12</nodesize>
<windstate>1,361,163</windstate>
</index>
<variable name="Bus_km">
<title>Bus km</title>
<definition>77000000</definition>
<nodelocation>344,304,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,548,518</windstate>
<nodecolor>65535,52427,65534</nodecolor>
<reference>11. YTV, Helsinki Metropolitan Area Council. (1999). Vaihtoehtoisten polttoaineiden kŠyttšmahdollisuudet joukkoliikenteessŠ pŠŠkaupunkiseudulla [The possibilities to use alternative fuels in public transport in the Helsinki metropolitan area]. Helsinki Metropolitan Area Council Publication Series B 1999:5 (in Finnish).</reference>
</variable>
<constant name="We_selected_the_publ">
<title>We selected the public-transportation-intensive scenario</title>
<definition>Rel_bus_activity</definition>
<nodelocation>80,152,1</nodelocation>
<nodesize>68,36</nodesize>
<aliases>[Alias We_selected_the_pub1]</aliases>
<nodecolor>52427,56425,65535</nodecolor>
</constant>
</module>
<module name="Exposure_model">
<title>Exposure model</title>
<author>mtad</author>
<date>21. Marta 2005 11:58</date>
<defaultsize>48,24</defaultsize>
<nodelocation>240,192,1</nodelocation>
<nodesize>48,24</nodesize>
<diagstate>1,277,125,928,468,17</diagstate>
<constant name="Road_traffic_a">
<title>Road traffic PM a</title>
<units>µg/m^3</units>
<definition>Var a := (Pers_out[PM_class='Secondary']*Long_range_transport);
a:= (Expolis__helsinki[Pm_class='CoPM', Environment='Personal']-a);
a:=a*Pm_exposure_fraction[Pm_source='Road traffic'];
a</definition>
<nodelocation>288,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,231,121,506,470</windstate>
<defnstate>1,216,226,695,303,0,MIDM</defnstate>
<valuestate>2,114,163,697,275,0,STAT</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<reformval>[Long_range_transport,Weight_factors_]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<variable name="Pm_em_road">
<title>PM em road</title>
<units>kg/a</units>
<description>PM-emissions from road traffic in Helsinki Metropolitan area 1997.</description>
<definition>Table(Traffic_source)(
169K,250K,83K)</definition>
<nodelocation>616,320,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,608,449</windstate>
<defnstate>1,313,77,416,303,0,MIDM</defnstate>
<valuestate>1,392,172,416,303,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<displayinputs>,</displayinputs>
<displayoutputs></displayoutputs>
<att__discretenessinf>[0,0,0,1]</att__discretenessinf>
<reference>24. MŠkelŠ, K. (2002). Personal communication, Senior Research Scientist, VTT (Technical research Centre of Finland), Building and transport.</reference>
</variable>
<constant name="Pers_out">
<title>Personal/ outdoor</title>
<units>-</units>
<definition>(Expolis__helsinki[Environment='Personal']/Expolis__helsinki[Environment='Ambient'])</definition>
<nodelocation>400,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,111,117,476,224</windstate>
<valuestate>2,150,105,416,303,0,MIDM</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<reformval>[Environment,Pm_class]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<constant name="Bus_traffic">
<title>Bus traffic PM</title>
<units>µg/m^3</units>
<definition>Road_traffic*Bus_fraction_</definition>
<indexvals>['Expolis, central','Vallius, high']</indexvals>
<nodelocation>384,320,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,298,181,476,443</windstate>
<defnstate>1,150,469,905,303,0,MIDM</defnstate>
<valuestate>2,151,101,416,303,0,MIDM</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<reformval>[Self,Long_range_transport]</reformval>
<att__totalsindex>[Index Traffic_source]</att__totalsindex>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<variable name="Bus_pm_scenarios">
<title>Bus PM scenarios</title>
<units>µg/m^3</units>
<definition>using a:= Bus_pm_emission/Bus_pm_emission[Strategy='BAU A', Scenario='Current 1997'] do
a*Bus_traffic</definition>
<nodelocation>384,384,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,528,418</windstate>
<valuestate>2,251,132,353,300,0,MIDM</valuestate>
<reformval>[Scenario,Strategy]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</variable>
<chance name="Long_range_transport">
<title>Long-range transport (Elrt)</title>
<units>µg/m^3</units>
<definition>triangular(1,2,2.5)</definition>
<indexvals>['Transport, Low','Transport, Central','Transport, High']</indexvals>
<nodelocation>288,240,1</nodelocation>
<nodesize>48,29</nodesize>
<windstate>2,102,90,578,407</windstate>
<defnstate>1,306,109,416,303,0,MIDM</defnstate>
<nodecolor>65535,52427,65534</nodecolor>
<reference>21. ApSimon, H. M., Gonzales del Campo, M. T., & Adams H.S. (2001). Modelling long-range transport of primary particulate material over Europe. Atmospheric Environment, 35, 343-352.</reference>
</chance>
<constant name="Ultra___traffic">
<title>ULTRA - traffic</title>
<units>µg/m^3</units>
<definition>Var a:=Ultra[Ultra='Local traffic']*Ultra[Ultra='PM2.5 total'];
a:=average(a)
{2.45}</definition>
<nodelocation>624,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,612,168,476,224</windstate>
<valuestate>2,392,402,416,303,0,MIDM</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
<reference>Vallius, M., Lanki, T., Tiittanen, P., Koistinen, K., Ruuskanen, J., and Pekkanen, J. (2003). Source
apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland. Atmospheric Environment, 37(5), 615-623.</reference>
</constant>
<constant name="Road_traffic_b">
<title>Road traffic PM b</title>
<units>µg/m^3</units>
<definition>Pers_out[Pm_class='CoPM']*Ultra___traffic</definition>
<nodelocation>512,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,500,389</windstate>
<nodecolor>65535,31131,19661</nodecolor>
</constant>
<constant name="Bus_fraction_">
<title>Bus fraction (Fbus)</title>
<units>-</units>
<definition>triangular(0.1,(Pm_em_road[Traffic_source='Busses']/Sum(Pm_em_road)),0.25)</definition>
<indexvals>['Bussisuhde, low','Bussisuhde, central','Bussisuhde, hight']</indexvals>
<nodelocation>504,320,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,565,404</windstate>
<defnstate>2,134,201,829,303,0,MIDM</defnstate>
<valuestate>2,136,146,416,303,0,MEAN</valuestate>
</constant>
<index name="Traffic_source">
<title>Traffic source</title>
<definition>['Cars','Trucks/vans','Busses']</definition>
<nodelocation>616,352,1</nodelocation>
<nodesize>52,12</nodesize>
<windstate>2,102,90,476,446</windstate>
</index>
<index name="Pm_class">
<title>PM class</title>
<definition>['CoPM','Secondary','Soil','Detergents','Sea salt']</definition>
<nodelocation>288,88,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,458</windstate>
</index>
<index name="Environment">
<title>Environment</title>
<definition>['Ambient','Indoor','Work','Personal']</definition>
<nodelocation>288,64,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,524,323</windstate>
</index>
<chance name="Road_traffic">
<title>Road traffic PM</title>
<units>µg/m^3</units>
<definition>using a:= bernoulli(0.7) do
using b:= a*Road_traffic_a + (1-a)*Road_traffic_b do
b</definition>
<nodelocation>384,256,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,479</windstate>
<defnstate>1,150,469,905,303,0,MIDM</defnstate>
<valuestate>2,151,101,416,303,0,MIDM</valuestate>
<reformval>[Self,Long_range_transport]</reformval>
<displayoutputs></displayoutputs>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</chance>
<variable name="Ultra">
<title>ULTRA</title>
<units>µg/m^3 or fraction</units>
<description>Table 1. Descriptive statistics for 29 October 1996Ð28 April 1997 (total n = 83) and 2 November 1998Ð30 April 1999 (total n = 164) (median PM2.5 in µg/m^3).
Fig.3. Contributions (%) of the identifed source components to average PM2.5 concentration.</description>
<definition>Table(Self,Ultra_years)(
8.3,10.6,
0.3,0.23,
0.51,0.5,
0.12,0.05,
0.03,0.13,
0.02,0.07,
0.02,0.02
)</definition>
<indexvals>['PM2.5 total','Local traffic','LRTAP','Crustal source','Oil combustion','Salt / Salt & Pb','Unidentified']</indexvals>
<nodelocation>736,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,258,33,599,581</windstate>
<defnstate>2,17,52,416,303,0,MIDM</defnstate>
<valuestate>2,604,264,416,303,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<reformdef>[Ultra_years,Self]</reformdef>
<reformval>[Ultra_years,Self]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
<reference>23. Vallius, M., Lanki, T., Tiittanen, P., Koistinen, K., Ruuskanen, J., and Pekkanen, J. (2003). Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland. Atmospheric Environment, 37(5), 615-623.</reference>
</variable>
<index name="Ultra_years">
<title>ULTRA years</title>
<definition>['1996-97','1998-99']</definition>
<nodelocation>736,184,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,359</windstate>
</index>
<constant name="Pm_exposure_fraction">
<title>PM exposure fractions</title>
<units>-</units>
<definition>Var a := (Weight_factors_*Primary_pm_emission);
Var b := Sum(a,Pm_source);
a/b</definition>
<nodelocation>176,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,524</windstate>
<valuestate>2,41,224,416,303,0,MIDM</valuestate>
<nodecolor>65535,31131,19661</nodecolor>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<variable name="Primary_pm_emission">
<title>Primary PM emission</title>
<units>kg/a</units>
<definition>Table(Pm_source)(
1.064M,50K,60K,502K,40K)</definition>
<nodelocation>72,152,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,608,449</windstate>
<defnstate>2,543,8,339,264,0,MIDM</defnstate>
<valuestate>1,232,242,416,303,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<displayinputs>,</displayinputs>
<att__discretenessinf>[0,0,0,1]</att__discretenessinf>
<reference>MŠkelŠ, K. (2002). Personal communication, Senior Research Scientist, VTT (Technical research Centre of Finland), Building and transport.
YTV, Helsinki Metropolitan Area Council. (1998). Ilmanlaatu pŠŠkaupunkiseudulla vuonna 1997 [Air Quality in the Helsinki Metropolitan Area in 1997]. Helsinki Metropolitan Area Council Publication Series 1999:1 (in Finnish).</reference>
</variable>
<constant name="Weight_factors_">
<title>Weight factors (wfi) (Table III)</title>
<units>-</units>
<definition>Table(Pm_source)(
0.1,1,1,Triangular(1,2,3),1)</definition>
<nodelocation>176,240,1</nodelocation>
<nodesize>52,28</nodesize>
<windstate>2,241,99,614,575</windstate>
<defnstate>2,345,60,339,287,0,MIDM</defnstate>
<valuestate>2,265,156,416,299,0,MIDM</valuestate>
<nodecolor>52425,39321,65535</nodecolor>
<reformdef>[Self,Pm_source]</reformdef>
<reformval>[Self,Pm_source]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<index name="Pm_source">
<title>PM source</title>
<definition>['Energy production','Other point sources','Surface sources','Road traffic','Harbor']</definition>
<nodelocation>72,184,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,567,60,437,464</windstate>
<valuestate>2,487,140,416,321,0,MIDM</valuestate>
</index>
<variable name="Expolis__helsinki">
<title>EXPOLIS-
Helsinki</title>
<definition>Table(Pm_class,Environment)(
3.547,2.6,2.95,3.506032,
4.668,3.31,3.38,3.35008,
1.6,2.49,2.42,2.8960864,
0,0.54,0.22,0.670016,
0.3,0.23,0.13,0.23311936
)</definition>
<nodelocation>288,32,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,521,467</windstate>
<defnstate>2,574,53,416,303,0,MIDM</defnstate>
<valuestate>2,442,98,416,300,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<reformdef>[Environment,Pm_class]</reformdef>
<reformval>[Environment,Pm_class]</reformval>
<att__totalsindex>[Index Pm_class]</att__totalsindex>
<reference>22. Koistinen, K., Edwards, R. D., Mathys, P., Ruuskanen, J., KŸnzli, N., & Jantunen, M. (2004). Sources of fine particulate matter in personal exposures and residential indoor, residential outdoor and workplace microenvironments in the Helsinki phase of the EXPOLIS study. Scandinavian Journal of Work, Environment & Health, 30 suppl. 2, 36-46.</reference>
</variable>
<constant name="Emissions_from_buses">
<title>Emissions from buses is only small fraction of total traffic emissions</title>
<definition>Pm_em_road</definition>
<nodelocation>752,320,1</nodelocation>
<nodesize>60,48</nodesize>
<aliases>[Alias Emissions_from_buse1]</aliases>
<nodecolor>65535,65532,19661</nodecolor>
</constant>
<constant name="The_exposure_was_est">
<title>The exposure was estimated by using two models</title>
<definition>Road_traffic</definition>
<nodelocation>520,256,1</nodelocation>
<nodesize>56,36</nodesize>
<aliases>[Alias The_exposure_was_es1]</aliases>
<nodecolor>65535,65532,19661</nodecolor>
</constant>
</module>
<module name="Dose_response_model">
<title>Dose-response model</title>
<author>mtad</author>
<date>21. Marta 2005 11:58</date>
<defaultsize>48,24</defaultsize>
<nodelocation>168,272,1</nodelocation>
<nodesize>56,24</nodesize>
<diagstate>1,358,87,574,369,17</diagstate>
<index name="Causes">
<title>Causes</title>
<definition>['Cardiopulmonary','Lung ca','All others','All causes']</definition>
<nodelocation>200,200,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,425</windstate>
</index>
<constant name="Mortality_rate1">
<title>Mortality rate</title>
<units>m^3/µg</units>
<definition>Crude_mortality_rat2*plausibility</definition>
<nodelocation>440,64,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,470</windstate>
<valuestate>2,264,274,591,328,1,CDFP</valuestate>
<reformval>[M_step_2001,Causes]</reformval>
<att__totalsindex>Index M_step_202</att__totalsindex>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<chance name="Plausibility">
<title>Plausibility</title>
<units>-</units>
<description>Includes mechanistic plausibility (coming from toxicological and epidemiological evidence). This plausibility is especially for traffic exhaust primary particles. Does not include aspects related to differential potencies of different particle types.</description>
<definition>Probtable(Causes,Self)(
0.3,0.7,
0.1,0.9,
0.9,0.1,
0.2,0.8
)</definition>
<nodelocation>440,128,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,509</windstate>
<defnstate>2,28,305,416,303,0,MIDM</defnstate>
<aliases>Formnode Plausibility2</aliases>
<nodecolor>52425,39321,65535</nodecolor>
<reformdef>[Self,Causes]</reformdef>
<domain>[0,1]</domain>
<displayinputs>[Object Constant]</displayinputs>
<displayoutputs></displayoutputs>
</chance>
<variable name="Chronic_mortality_rr">
<title>Chronic mortality RR</title>
<units>RR per PM contrast</units>
<description>Original results from PM cohort studies.</description>
<definition>Table(Causes,Ci,Study)(
1.37,1.09,
1.11,1.03,
1.68,1.16,
1.37,1.14,
0.81,1.04,
2.31,1.23,
1.01,1.01,
0.79,0.95,
1.3,1.06,
1.26,1.06,
1.08,1.02,
1.47,1.11
)</definition>
<nodelocation>200,64,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,569,548</windstate>
<defnstate>2,28,215,567,291,0,MIDM</defnstate>
<valuestate>2,88,98,659,277,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<reformdef>[Ci,Causes]</reformdef>
<reformval>[Causes,Study]</reformval>
<displayinputs>,</displayinputs>
<reference>16. Dockery, D. W., Pope, C. A., III, Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., Jr., & Speizer F. E. (1993). An association between air pollution and mortality in six U.S. cities. The New England Journal Oof Medicine, 329(24), 1753-1759.
17. Pope, C. A. III, Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung Cancer, Cardiopulmory Mortality, and Long-term Exposure to Fine Particulate Air Pollution. The Journal of the American Medical Association, 287(9), 1132-1141.</reference>
</variable>
<variable name="Pm_contrast">
<title>PM contrast</title>
<units>µg/m^3</units>
<definition>Table(Study)(
18.6,10)</definition>
<nodelocation>200,144,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,422</windstate>
<defnstate>1,28,274,416,303,0,MIDM</defnstate>
<nodecolor>65535,52427,65534</nodecolor>
<displayinputs>,</displayinputs>
<reference>16. Dockery, D. W., Pope, C. A., III, Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., Jr., & Speizer F. E. (1993). An association between air pollution and mortality in six U.S. cities. The New England Journal Of Medicine, 329(24), 1753-1759.
17. Pope, C. A. III, Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung Cancer, Cardiopulmory Mortality, and Long-term Exposure to Fine Particulate Air Pollution. The Journal of the American Medical Association, 287(9), 1132-1141.</reference>
</variable>
<index name="Coefficient_space">
<title>Coefficient space</title>
<definition>Sequence( -0.02, 0.05, 2m )</definition>
<nodelocation>320,112,1</nodelocation>
<nodesize>48,20</nodesize>
<windstate>2,102,90,476,401</windstate>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</index>
<index name="Ci">
<title>CI</title>
<definition>['Central','0.025 fractile','0.975 fractile']</definition>
<nodelocation>200,96,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,407</windstate>
</index>
<index name="Study">
<title>Study</title>
<definition>['Harvard six cities','ACS 2002']</definition>
<nodelocation>200,176,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,434</windstate>
</index>
<constant name="Crude_mortality_rat2">
<title>Crude mortality rate random</title>
<units>m^3/µg</units>
<definition>using a:= ln(Chronic_mortality_rr)/Pm_contrast
do using au:= a[Ci='0.975 fractile']
do using al:= a[Ci='0.025 fractile']
do using ac:= a[Ci='Central']
do using b:= (au-al)/2/1.96
do using c:= Cumnormal(Coefficient_space,ac,b)
do using d:= Uncumulate(c,Coefficient_space)
do using e:= Probdist(d, coefficient_space )
do using f:= (if bernoulli(0.5)=1 then e[study='Harvard six cities'] else e[study='ACS 2002'])
do f</definition>
<nodelocation>320,64,1</nodelocation>
<nodesize>48,29</nodesize>
<windstate>2,102,90,476,578</windstate>
<valuestate>2,245,357,530,212,0,STAT</valuestate>
<reformval>[Causes,Statistics1]</reformval>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<constant name="Plausibility_was_def">
<title>Plausibility was defined as the probability that the observed dose-response relationship actually represents a causal association</title>
<definition>Plausibility</definition>
<nodelocation>440,240,1</nodelocation>
<nodesize>80,68</nodesize>
<aliases>[Alias Plausibility_was_de1]</aliases>
<nodecolor>52427,56425,65535</nodecolor>
</constant>
</module>
<module name="Mortality_assessment">
<title>Mortality assessment</title>
<author>mtad</author>
<date>21. Marta 2005 11:58</date>
<defaultsize>48,24</defaultsize>
<nodelocation>240,360,1</nodelocation>
<nodesize>48,24</nodesize>
<diagstate>1,608,340,658,380,17</diagstate>
<index name="Cause">
<title>Cause</title>
<definition>['Cardiopulmonary','Lung ca','All others']</definition>
<nodelocation>184,144,-3</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,410</windstate>
</index>
<variable name="Strategies">
<title>Strategies</title>
<definition>Table(Self)(
1,3,4,10)</definition>
<indexvals>['Current fleet','Modern diesel','Diesel with particle trap','Natural gas bus']</indexvals>
<nodelocation>296,40,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,363</windstate>
<defnstate>2,611,131,416,303,0,MIDM</defnstate>
<displayoutputs></displayoutputs>
</variable>
<constant name="Health_effects">
<title>Health effects</title>
<definition>var a:= sum(Health_effect,Cause);
a:= slice(a,Strategy,Strategies);
a[Scenario='PLJ 2020']</definition>
<nodelocation>296,112,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,449</windstate>
<valuestate>2,214,139,538,246,0,CONF</valuestate>
<nodecolor>65535,65532,19661</nodecolor>
<graphsetup>Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:10
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:0
Showkey:1
Xminimum:-5
Xmaximum:35
Yminimum:0
Ymaximum:1
Zminimum:1
Zmaximum:5
Xintervals:8
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1, 1, 1, 1, 1, 0, 0, 0]
Probindex:[5%, 25%, 50%, 75%, 95%]
</graphsetup>
<fontstyle>Arial, 2</fontstyle>
<reformval>[Strategies,Probability2]</reformval>
<att__graphprintscali>98,1,1,0,2,9,4744,6798,7</att__graphprintscali>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<variable name="Mortality_data">
<title>Mortality data</title>
<units>deaths/a</units>
<definition>Table(Causes)(
3338,317,2886,6541)</definition>
<nodelocation>56,112,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,240,66,476,539</windstate>
<defnstate>2,325,209,416,303,0,MIDM</defnstate>
<valuestate>2,504,514,416,303,0,MIDM</valuestate>
<nodecolor>65535,52427,65534</nodecolor>
<att__discretenessinf>[0,0,0,1]</att__discretenessinf>
<reference>30. Statistics Finland (2004). Mortality in Helsinki Metropolitan Area 1996. Helsinki: Statistics Finland.</reference>
</variable>
<objective name="Health_effect">
<title>Health effect</title>
<units>deaths/a</units>
<definition>using a:= ((Exp((Mortality_rate1*Bus_pm_scenarios))-1)*Mortality_data) do
a[Causes=Cause]</definition>
<nodelocation>184,112,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,216,130,476,431</windstate>
<valuestate>2,134,235,405,329,0,MEAN</valuestate>
<graphsetup>Graphtool:0
Distresol:10
Diststeps:1
Cdfresol:5
Cdfsteps:1
Symbolsize:6
Baroverlap:0
Linestyle:1
Frame:1
Grid:1
Ticks:1
Mesh:1
Scales:1
Rotation:45
Tilt:0
Depth:70
Frameauto:1
Showkey:1
Xminimum:0
Xmaximum:1
Yminimum:0
Ymaximum:1
Zminimum:0
Zmaximum:1
Xintervals:0
Yintervals:0
Includexzero:0
Includeyzero:0
Includezzero:0
Statsselect:[1,1,1,1,1,0,0,0]
Probindex:[0.05,0.25,0.5,0.75,0.95]
</graphsetup>
<reformval>[Scenario,Strategy]</reformval>
<att__totalsindex>[Index Causes, Index Cause, Objective Health_effect]</att__totalsindex>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</objective>
<constant name="Table_iv">
<title>Table IV</title>
<definition>Var a:= slice(Health_effect,Strategy,Strategies);
a[Scenario='PLJ 2020']</definition>
<nodelocation>296,176,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,422</windstate>
<valuestate>2,141,178,560,234,0,CONF</valuestate>
<aliases>[Alias Table_iv1]</aliases>
<nodecolor>65535,65532,19661</nodecolor>
<reformval>[Strategies,Probability2]</reformval>
<displayoutputs></displayoutputs>
<att__totalsindex>[Index Cause]</att__totalsindex>
<att__discretenessinf>[0,0,0,0]</att__discretenessinf>
</constant>
<constant name="Only_four_strategies">
<title>Only four strategies were defined</title>
<definition>Strategies</definition>
<nodelocation>456,40,1</nodelocation>
<nodesize>64,29</nodesize>
<aliases>[Alias Only_four_strategie1]</aliases>
<nodecolor>52427,56425,65535</nodecolor>
</constant>
</module>
<alias name="We_selected_the_pub1">
<title>We selected the public-transportation-intensive scenario</title>
<definition>1</definition>
<nodelocation>424,104,1</nodelocation>
<nodesize>68,36</nodesize>
<nodecolor>52427,56425,65535</nodecolor>
<original>We_selected_the_publ</original>
</alias>
<alias name="Emissions_from_buse1">
<title>Emissions from buses is only small fraction of total traffic emissions</title>
<definition>1</definition>
<nodelocation>432,192,1</nodelocation>
<nodesize>60,48</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
<original>Emissions_from_buses</original>
</alias>
<alias name="The_exposure_was_es1">
<title>The exposure was estimated by using two models</title>
<definition>1</definition>
<nodelocation>104,192,1</nodelocation>
<nodesize>56,36</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
<original>The_exposure_was_est</original>
</alias>
<alias name="Plausibility_was_de1">
<title>Plausibility was defined as the probability that the observed dose-response relationship actually represents a causal association</title>
<definition>1</definition>
<nodelocation>424,320,1</nodelocation>
<nodesize>80,68</nodesize>
<nodecolor>52427,56425,65535</nodecolor>
<original>Plausibility_was_def</original>
</alias>
<alias name="Only_four_strategie1">
<title>Only four strategies were defined</title>
<definition>1</definition>
<nodelocation>104,360,1</nodelocation>
<nodesize>64,29</nodesize>
<nodecolor>52427,56425,65535</nodecolor>
<original>Only_four_strategies</original>
</alias>
</module>
<module name="Importance_analyses">
<title>Importance analyses</title>
<author>mtad</author>
<date>22. Marta 2005 9:49</date>
<saveauthor>mtad</saveauthor>
<savedate>22. Marta 2005 9:48</savedate>
<defaultsize>48,24</defaultsize>
<nodelocation>488,32,1</nodelocation>
<nodesize>48,24</nodesize>
<nodeinfo>1,0,1,1,1,1,0,0,0,0</nodeinfo>
<diagstate>1,520,81,517,417,17</diagstate>
<index name="Co1">
<title>Co</title>
<definition>['Hei_index','Cause_of_death','Data']</definition>
<nodelocation>216,184,1</nodelocation>
<nodesize>48,12</nodesize>
<nodeinfo>1,1,1,1,1,1,0,0,0,0</nodeinfo>
<windstate>2,102,90,476,353</windstate>
</index>
<constant name="Importance_1">
<title>Importance (figure 1)</title>
<definition>subscript(Sort_of_the_data,Hei_output3,Variable3)</definition>
<nodelocation>216,304,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,422</windstate>
<valuestate>2,485,195,497,291,0,MIDM</valuestate>
<aliases>[Alias Importance_2]</aliases>
<nodecolor>65535,65532,19661</nodecolor>
<displayoutputs></displayoutputs>
</constant>
<constant name="Health_comparison_i1">
<title>Health comparison importance</title>
<units>rank correlation</units>
<definition>using b:= Abs( RankCorrel( Health_effects_input, Health_comparison ) ) do
using c:= b[Strategy='BAU A', Scenario='PLJ 2020'] do
b</definition>
<nodelocation>216,56,1</nodelocation>
<nodesize>48,29</nodesize>
<nodeinfo>1,1,1,1,1,1,0,0,0,0</nodeinfo>
<windstate>2,102,90,476,485</windstate>
<valuestate>2,304,384,824,314,0,MIDM</valuestate>
<reformval>[Hei_index,Strategy]</reformval>
</constant>
<index name="Hei_output3">
<title>Hei output</title>
<definition>['Plausibility of Cardiopulmonary mortality','Plausibility of Lung cancer mortality','Plausibility of All other mortality','Dose-response coefficient for cardiopulmonary mortality','Dose-response coefficient for Lung cancer mortality','Dose-response coefficient for All other mortality','Relative emission factor uncertainty','Relative weight factor for road traffic emissions','Exposure to road traffic PM2.5','Concentration of combustion-based long-range transported PM2.5','The fraction of bus exposure of total traffic exposure']</definition>
<nodelocation>352,160,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,375,108,476,536</windstate>
<valuestate>2,532,47,416,303,0,MIDM</valuestate>
</index>
<index name="Ro1">
<title>Ro</title>
<definition>sequence(1,size(Health_comparison_i1)/size(Strategy))</definition>
<nodelocation>216,160,1</nodelocation>
<nodesize>48,12</nodesize>
<nodeinfo>1,1,1,1,1,1,0,0,0,0</nodeinfo>
<windstate>2,102,90,476,449</windstate>
<valuestate>2,168,178,416,303,0,MIDM</valuestate>
</index>
<constant name="Sort_of_the_data">
<title>Sort of the data</title>
<definition>using a:=Health_comparison_i1[Strategy='CRT filter C'] do
using b:= mdarraytotable(a,Ro1,Co1) do
using c:= slice(b,Ro1,Hei_table) do
c[Co1='Data']</definition>
<nodelocation>216,128,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,473</windstate>
<valuestate>2,567,466,598,330,0,MIDM</valuestate>
</constant>
<constant name="Variable3">
<title>Variable</title>
<definition>Sortindex( 1-Sort_of_the_data)</definition>
<nodelocation>216,240,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,461</windstate>
<valuestate>2,473,153,722,427,0,MIDM</valuestate>
</constant>
<index name="Hei_index">
<title>Hei index</title>
<definition>['Plausibility','Mortality','Emission factor','Traffic iF','Road traffic PM','Long range transport','Bus relation']</definition>
<nodelocation>88,88,1</nodelocation>
<nodesize>48,12</nodesize>
<windstate>2,102,90,476,404</windstate>
</index>
<constant name="Health_effects_input">
<title>Health effects input</title>
<definition>Table(Hei_index)(
Plausibility,Crude_mortality_rat2,Relative_emission_f2,Weight_factors_[Pm_source='Road traffic'],Road_traffic,Long_range_transport,Bus_fraction_)</definition>
<nodelocation>88,56,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,428</windstate>
<defnstate>2,20,150,606,303,0,MIDM</defnstate>
<valuestate>2,259,89,734,426,0,MIDM</valuestate>
<reformval>[Hei_index,Strategy]</reformval>
</constant>
<constant name="Health_comparison">
<title>Health comparison</title>
<definition>using a:= health_effect[Scenario='PLJ 2020', Causes=Cause] do
using b:= sum(a,Cause) do
using c:= b-b[Strategy='Natural gas G31']
do c</definition>
<nodelocation>352,56,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,458</windstate>
<valuestate>2,124,74,416,303,0,MIDM</valuestate>
</constant>
<constant name="Hei_table">
<title>Hei table</title>
<definition>Table(Hei_output3)(
1,2,3,5,6,7,9,13,17,21,25)</definition>
<nodelocation>352,128,1</nodelocation>
<nodesize>48,24</nodesize>
<windstate>2,102,90,476,473</windstate>
<valuestate>2,40,50,674,303,0,MIDM</valuestate>
</constant>
</module>
<alias name="Table_iv1">
<title>Table IV</title>
<definition>1</definition>
<nodelocation>328,104,1</nodelocation>
<nodesize>48,24</nodesize>
<original>Table_iv</original>
</alias>
<text name="Te5">
<description>Reference:
Marko Tainio, Jouni T. Tuomisto, Otto HŠnninen, PŠivi Aarnio, Kimmo J. Koistinen, Matti J. Jantunen and Juha Pekkanen.
Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland
Risk Analysis, Vol. 25, No.1, 2005. pp.151-160.</description>
<nodelocation>120,152,-1</nodelocation>
<nodesize>108,144</nodesize>
</text>
<constant name="Levels_of_pm_and_the">
<title>Levels of PM and the corresponding health effects can be affected to some extent by changing bus types</title>
<definition>Table_iv</definition>
<nodelocation>416,360,1</nodelocation>
<nodesize>76,51</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
</constant>
<constant name="The_difference_in_th">
<title>The difference in the excess mortality between natural gas buses and the present diesel engines with proper trapping system is not large</title>
<definition>Table_iv</definition>
<nodelocation>312,224,1</nodelocation>
<nodesize>76,70</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
</constant>
<alias name="Importance_2">
<title>Importance (figure 1)</title>
<definition>1</definition>
<nodelocation>488,104,1</nodelocation>
<nodesize>48,24</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
<original>Importance_1</original>
</alias>
<constant name="The_dose_response_re">
<title>The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model</title>
<definition>Importance_1</definition>
<nodelocation>488,224,1</nodelocation>
<nodesize>72,70</nodesize>
<nodecolor>65535,65532,19661</nodecolor>
</constant>
</model>
</ana>