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. 10 0 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000] 0 1 4 13 0 1 2 0 0 0 1 1 28.6.2004 Jouni Tuomisto This is the version 4 of the model calculating risks and benefits of farmed salmon. (c) Copyright KTL (National Public Health Institute, Finland). <ref>[http://ytoswww/yhteiset/Huippuyksikko/Tutkimus/Viljelylohi/Materiaali/Viljelylohi.rmd Reference Manager database</ref> <ref>[http://ytoswww/yhteiset/Huippuyksikko/Tutkimus/Viljelylohi/ Directory for data and models</ref> Jouni Tuomisto 9. tamta 2004 20:14 jtue 31. jouta 2008 15:50 48,24 1,19,28,894,538,17 2,102,90,553,461 Trebuchet MS, 13 0,Model Risks_from_farmed_an,2,2,0,1,C:\DOCUME~1\jtue\LOCALS~1\Temp\Opasnet_base_connection-4.ANA 97,1,1,0,2,1,2794,4312,0 2,25,65,696,600 avoided cases/a Pollutant health risk is calculated assuming additivity between the pollutants. However, dioxin risks are not considered because they were not considered in Hites. After unit conversion, numbers are calculated for Western Europe as cases per year. Note that negative numbers mean increased risk unlike in previous versions of the model. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1900">Wiki variable</a> var a:= -H1906*H1905/1000*H1910; var b:= (if pollutant1='Dioxin' then 0 else a); var c:= sum(b,pollutant1); c 256,336,1 48,24 1,1,1,1,1,1,0,,1, 2,291,123,476,224 2,357,128,628,450,0,MEAN 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] [H1899,H1898] [1,0,0,0] [Salmon1,1,Year3,1,H1898,1,H1899,1] Op_en1900 avoided cases/a Numbers are calculated for Western Europe as avoided deaths per year. Note that positive numbers mean increased benefit unlike in previous versions of the model. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1912">Wiki variable</a> -H1909*min([H1908,benefit_limit])*H1911 384,336,1 48,24 1,1,1,1,1,1,0,,1, 2,80,222,476,224 2,95,7,589,375,0,MEAN [H1898,Salmon1] [1,0,0,0] [H1899,1,Cause_of_death3,1,Year3,1,Salmon1,1,H1898,1] Op_en1912 avoided cases/a Net health effect of pollutant cancer and omega-3 cardiac benefit. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1901">Wiki variable</a> H1912+H1900 320,408,1 48,24 1,1,1,1,1,1,0,,1, 2,102,90,476,420 2,537,56,726,259,0,MIDM [H1898,Salmon1,1,2] [0,0,0,0] [Salmon,1,Reg_poll,1,Recommendation,1,Sys_localindex('PROBABILITY'),1] Op_en1901 ktluser 11. tamta 2004 9:20 48,24 56,208,1 48,24 1,40,0,610,544,17 100,1,1,0,2,9,4744,6798,7 Epa_model 288,40,1 56,32 However, defending his study in an interview with IntraFish yesterday afternoon, David Carpenter of the State University of New York at Albany said that the differences between wild and farmed salmon PCBs levels are not insignificant. ÒI donÕt agree with [Gallo] and I donÕt think others would agree with him either.Ó Carpenter said that the EPA risk assessment model that he and his colleagues used to determine that salmon posed a health risk is a well-respected and sound one. ÒIt is not just something that we made up. It is a time-tested measureÉitÕs a yard stick that we have.Ó <ref>http://www.intrafish.com/articlea.php?articleID=41070&s=1</ref> Epa_model 288,128,1 52,36 2,102,90,630,401 A set of four volumes that provides guidance for assessing health risks associated with the consumption of chemically contaminated non-commercial fish and wildlife. EPA developed the series of documents to help state, local, regional, and tribal environmental health officials who are responsible for developing and managing fish consumption advisories. <ref>http://www.epa.gov/waterscience/fish/guidance.html</ref> Developed_for_spceci 528,192,1 56,28 Developed_for_spceci 360,432,1 68,56 Applies_only_to_non_+Developed_for_spceci 528,296,1 64,56 200,296,1 48,38 [Constant Developed_for_spceci] Point_of_view_is_tha+This_is_not_a_public 528,432,1 64,56 2,102,90,476,373 Epa_model 528,80,1 72,64 Epa_model 360,296,1 84,56 [Constant Precautionary_princi] Epa_model+Fda_model 64,288,1 52,52 5 64,448,1 48,24 2,136,146,416,303,0,MIDM [] <ref>U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1. [http://www.epa.gov/waterscience/fish/guidance.html Open access Internet file] [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/EPAFishAdvisory/ Intranet file]</ref> jtue 28. Junta 2004 18:03 48,24 64,80,1 48,29 1,40,0,517,300,17 2^(Floor(logten(Epa_model)/logten(2))) 56,176,1 48,24 2,48,219,743,303,1,MIDM [Location1,Undefined] meals/month CRmm variable in the U.S.EPA advisory model. <ref>U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1. [http://www.epa.gov/waterscience/fish/guidance.html Open access Internet file] [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/EPAFishAdvisory/ Intranet file]</ref> index Effect:=['Cancer','Non-cancer']; var MS:= 0.227; var Tap:= 365.25/12; var CSF:=Potency[potency='Ca (CSF)']; var RfD:= Potency[Potency='Non-Ca (RfD)']; var CRlimCa:= ARL*BW/sum(CSF*in1*(Poll_salmon_hites/1000),Pollutant1); var a:= In1*RfD/(Poll_salmon_hites/1000); var b:= if isnan(a) then 1 else a; var c:= if b>0 then b else 1; var CRlimNonCa:= min(c,Pollutant1)*BW; var CRlim:= array(Effect,[CRlimCa,CRlimNonCa]); var CRmm:= CRlim*Tap/MS; CRmm 56,104,1 48,29 2,43,74,632,412 2,120,130,416,303,0,MIDM [] 1 56,32,1 48,24 Poll_salmon_hites Table(Pollutant1)( 1,1,0,1) 216,160,1 48,24 2,469,131,476,224 2,242,231,416,303,0,MIDM 1 216,104,1 48,24 Potency probability Acceptable risk level <ref>U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1. [http://www.epa.gov/waterscience/fish/guidance.html Open access Internet file] [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/EPAFishAdvisory/ Intranet file]</ref> 10u 216,32,1 48,24 2,471,122,476,382 ktluser 11. tamta 2004 9:20 48,24 760,96,1 48,24 1,0,0,1,1,1,0,,0, 1,193,72,636,526,17 - ['Dieldrin','Toxaphene','Dioxin','PCB'] 504,64,1 48,12 2,82,4,476,493 Op_en2705 ['Dieldrin','Toxaphene','Dioxin','PCB'] kg 70 504,392,1 48,24 1,1,1,1,1,1,0,0,0,0 - ['Scotland','Faroe Islands','Frankfurt','Edinburgh','Norway','Paris','London','Oslo','East Canada','Boston','Maine','San Francisco','West Canada','Toronto','Los Angeles','Vancouver','Washington DC','Seattle','Chicago','New York','Washington St','Chile','SE AK Chinook','Denver','BC Chinook','BC Sockeye','Oregon Chinook','SE AK Sockeye','New Orleans','BC Coho','Kodiak AK Sockeye','SE AK Coho','Kodiak AK Coho','BC Pink','Kodiak AK Pink','SE AK Pink','SE AK Chum','BC Chum','Kodiak AK Chum'] 504,32,1 48,12 ['Scotland','Faroe Islands','Frankfurt','Edinburgh','Norway','Paris','London','Oslo','East Canada','Boston','Maine','San Francisco','West Canada','Toronto','Los Angeles','Vancouver','Washington DC','Seattle','Chicago','New York','Washington St','Chile','SE AK Chinook','Denver','BC Chinook','BC Sockeye','Oregon Chinook','SE AK Sockeye','New Orleans','BC Coho','Kodiak AK Sockeye','SE AK Coho','Kodiak AK Coho','BC Pink','Kodiak AK Pink','SE AK Pink','SE AK Chum','BC Chum','Kodiak AK Chum'] - ['Farmed salmon','Wild salmon','Market salmon'] 504,96,1 48,13 Op_en2706 ['Farmed salmon','Wild salmon','Market salmon'] 20.1.2004 Jouni Tuomisto En ole ehtinyt aiemmin lokia kirjoittaa, joten nyt yleiskuvaus mallista. Viljelylohi on tehty arvioimaan, onko Hites et al (Science 9.1.2004) riskinarviointi hyvin tehty. Ensikommenttina KTL:ssŠ oli se, ettŠ kalan terveyshyšdyt on unohdettu. NiinpŠ rakensimme mallin, joka 1) kŠyttŠŠ samaa EPAn riskimallia saasteiden terveyshaittojen laskemiseen kuin Hites (PCB:n, dieldriinin ja toksafeenin (mutta ei dioksiinien) aiheuttama yhdistetty syšŠriski olettaen additiivisuuden ja linearised multistage-mallin eli suoraan kŠyttŠmŠllŠ EPAn CSF-arvoja) ja 2) laskee myšs omega-3-rasvahappojen tuoman hyšdyn sydŠnkuolemariskiin. Vertailu tehtiin 1) olettamalla lohensyšntiŠ 0.25 - 32 amerikkalaista annosta kuukaudessa ja laskemalla syšpŠriski ja/tai sydŠnhyšty sekŠ 2) olettamalla jokin lohensyšnti (esim. 20 g/d) ja lisŠksi jotain oletuksia muista omega-3-lŠhteistŠ sekŠ niiden muutoksista jos lohensyšnti muuttuisi. Vaikutuksen lisŠksi tehtiin argumenttianalyysi (oma moduli) jossa katsottiin importance analysis eli rank-korrelaatio lŠhtšmuuttujien ja lopputuleman vŠlille. TŠssŠ oli mukana erilaisia pŠŠtšksiŠ, mm. pitŠisikš katsoa saasteiden syšpŠhaittaa vai nettovaikutusta?, PitŠisikš katsoa terveysvastetta lainkaan vai pelkkŠŠ altistusta? ja MillŠ viljelty lohi pitŠisi korvata? PŠŠtškset otettiin mukaan analyysiin siten, ettŠ kullekin pŠŠtšsvaihtoehdolle oletettiin sama todennŠkšisyys, ja ne otettiin mukaan satunnaismuuttujina (ikŠŠn kuin me yrittŠisimme arvioida, mikŠ on ŠŠnestyksen tulos kun tŠstŠ ŠŠnestetŠŠn). TŠhŠn liittyen jŠin pohtimaan sitŠ, pitŠisikš meidŠn olettaa pienempi todennŠkšisyys huonoille vaihtoehdoille (kuten epŠtodennŠkšisille presidenttiehdokkaille annetaan vŠhemmŠn aikaa televisiossa) mutta en pŠŠtynyt tŠssŠ mihinkŠŠn lopputulokseen, ja niin tasajako jŠi malliin. LisŠksi on tehty VOI-analyysi (oma moduli). TŠssŠ yritin rakentaa VOI-funktiota, joka olisi suoraan laskenut mielenkiinnon kohteena olevan tuloksen (helpottaisi mallinrakennusta jatkossa ja tekisi erilaisten VOIn laskemisen kŠtevŠksi), mutta ongelmaksi muodostui se, ettŠ mean-funktio toimi oikein vain, kun se laskettiin variablesta. Jos yritti laskea sen tilapŠisestŠ, solmun sisŠllŠ olevasta muuttujasta, tuloksena oli yleensŠ mid. NiinpŠ tyydyttiin laskemaan homma kŠsipelillŠ kuten Particle VOI -mallissa. Conclusions from Hites 2004 sisŠltŠŠ sitaatteja ja argumentteja keskustelusta, joka on Hitesin myštŠ kŠynnistynyt. What should be the scope of the assessment oli aikeissa olla moduli, josta eri pŠŠtšsvaihtoehdot olisivat sinne kirjatun argumentoinnin seurauksena nousseet, mutta sitŠ ei ollut aikaa tyšstŠŠ kovin pitkŠlle. Confounder analysis -moduli sisŠltŠŠ pohdintaa siitŠ, millaiset tekijŠt voivat vaikuttaa Hitesin lopputulokseen ja kvalitatiivista argumentointia niiden mahdollisista vaikutuksista tulosten tulkintaan. Help on yksinkertaisesti kopio Help v4 mallista. 20.1. alkoi ongelmaksi tulla se, ettŠ malli ršnsysi liikaa, ja oli hankalaa saada indeksit tŠsmŠŠmŠŠn lŠhtšarvojen ja lopputuloksen kesken. NiinpŠ pŠŠtin tehdŠ uuden version 3, josta kaikki ršnsyt on poistettu ja jonka tarkoituksena on toimia mallina Science-artikkelia varten. Kaikki laajemmat tarkastelut siis sŠŠstetŠŠn mallin seuraaviin versioihin. NiinpŠ versio 2:een jŠtetŠŠn kaikki ršnsyt, josta niitŠ sitten voi tarpeen mukaan kopioida takaisin kŠytšssŠ olevaan malliversioon. NŠin ehkŠ pysyy selvŠnŠ se, mitŠ Science-vastineessa on ja mitŠ ei ole. Fishing and farming, Arguments on fish pollutants, Total pollutant exposure, What should be the scope of risk assessment?, Conclusions from Hites 2004, ja Confounder analysis ovat semmoiset modulit jotka nyt poistetaan versiosta 3. Samoin poistetaan pŠŠmodulista solmut Risk or net health effect?, Acceptable risk ja Health effects or exposures? sekŠ nŠiden input nodet. 0 504,128,1 48,12 2,463,67,476,399 65535,54067,19661 20.1.2003 Jouni Tuomisto Versiosta 3 siis on tehty riisuttu versio Science-juttua varten. Lue tarkemmin Loki v 2:sta. Nyt versiosta 3 poistetaan aiemmin kuvatun lisŠksi Other parts -modulista indeksejŠ, joita ei kŠytetŠ missŠŠn. NŠmŠ ovat Viljelyalue, Kalastusalue, Ostokaupunki, Lohilaji ja Saaste. Argument analysis -modulista poistetaan solmut We should not consider concentrations..., Acceptable exposure, Va2, Health or exposure?, What is salmon replacement?, Va5, Va3, Va3 inputs, Va3 importance eli kaikki solmut. TŠrkeyssolmu luodaan uudelleen, mutta nyt se voidaan tehdŠ suoraan Outcome-solmulle ilman indeksimuunnoksia. NiinpŠ koko Argument analysis -moduli poistetaan ja asia siirretŠŠn VOIs-moduliin, joka nimetŠŠn uudelleen VOI and importance analysis. Food intake -modulista poistetaan Salmon consumption, Salmon replacement, Food change, Salmon amount, Oil increase, Food intake, Wild salmon compensation, Va1, Food_rec, Source1. Eli kaikki solmut keskittyvŠt nyt vain loheen, eikŠ muita omega-3-lŠhteitŠ huomioida. Ne tulevat mukaan malliin raja-arvossa, joka kuvaa hyšdyllisen lisŠsaannin rajaa ja siis sisŠltŠŠ absoluuttisen fysiologisen rajan, josta on vŠhennetty muusta ravinnosta tuleva mŠŠrŠ. TŠmŠ solmu tehdŠŠn Annosvastemoduliin. VOIs-modulista poistetaan Va16, Va12, VOI, VOI1 ja VOI-laskenta tehdŠŠn suoraan Outcome-solmusta. 21.1.2004 Jouni Tuomisto Malli muuttui eilen siten, ettŠ nyt lasketaan VOI kahdelle eri kysymykselle: pitŠisikš suositella viljellyn lohen enimmŠissaanniksi 1 annos/kk ja pitŠisikš rajoittaa enemmŠn kalanrehun saastepitoisuuksia. NŠmŠ kaksi nostetaan esiin, koska edellinen on suora vastine Hitesin ym. argumenttiin, ja jŠlkimmŠinen on korostamassa sitŠ, ettŠ asetettu kysymys mŠŠrŠŠ sen, mikŠ tieto on tŠrkeŠŠ ja mikŠ ei. Other parts -modulista poistetaan solmut Acceptable exposure increase ja Amount or replacement, ja ARL siirretŠŠn Fish advirories -moduliin sekŠ Potency Exposure-response function for pollutant risk -moduliin. NŠistŠ moduleista poistetaan vastaavat aliakset. Unit- ja Description-kentŠt pŠivitetŠŠn koko mallissa, ja viitteitŠ listŠtŠŠn sikŠli kuin ne ovat helposti kŠsillŠ. Kuitenkin viitteet on vielŠ pistettŠvŠ kuntoon, nyt muotoilut eivŠt ole kunnossa. 0 504,152,1 48,12 2,212,144,476,344 65535,54067,19661 g/d H1900/H1909/H1910 504,224,1 48,24 [H1898,Salmon1] var a:= sum(H1900,Salmon1); Probability(a[H1898='BAU']+0.0001>a[H1898='Restrict farmed salmon use']) 504,336,1 48,24 2,115,372,476,291 var a:= sum(H1901,Salmon1); Probability(a[H1898='BAU']>a[H1898='Change farmed to wild salmon']) 504,280,1 48,24 index a:= ['Net effect of salmon recommendation','Net effect of feed regulation','Cancer effect of recommendation']; var b:= array(a,[H1901,0,H1900]); var c:= b[H1898='Restrict farmed salmon use']-b[H1898='BAU']; var d:= (if regulate_pollutants_=1 then c[H1899='More actions'] else c[H1899='BAU']); var e:= H1901[H1899='More actions'] - H1901[H1899='BAU']; var f:= (if recommend= 1 then e[H1898='Restrict farmed salmon use'] else e[H1898='BAU']); var g:= (if a= 'Net effect of feed regulation' then f else d); var h:= sum(g,Salmon1); h 232,264,1 48,24 2,452,264,476,469 2,767,202,367,474,0,MEAN probability A chance node that collapses the decision about whether the proper endpoint metric is pollutant risk or net health effect. Bernoulli( .5 ) 336,56,1 48,29 1,1,1,1,1,1,0,0,0,0 2,585,196,416,303,0,MIDM 2,168,178,416,303,0,SAMP cases/a Net health effect indexed by only consumption recommendation. if Regulate_pollutants_=1 then outcome3[H1899='More actions'] else outcome3[H1899='BAU'] 168,152,1 48,32 2,102,90,476,293 2,499,269,416,303,0,MIDM 2,415,126,518,378,0,MEAN [Salmon1,H1898] probability A chance node that collapses the decision about regulating fish feed. bernoulli(0.5) 56,152,1 48,24 cases/a Net health effect indexed by only fish feed regulation. if recommend = 1 then outcome3[H1898='Restrict farmed salmon use'] else outcome3[H1898='BAU'] 280,152,1 48,32 2,408,141,646,274 2,499,269,416,303,0,MIDM 2,77,202,518,378,0,MEAN [Salmon1,H1898] probability A chance node that collapses the decision about consumption recommendations for farmed salmon. bernoulli(0.5) 384,152,1 48,24 1,1,1,1,1,1,0,0,0,0 2,298,233,476,224 2,80,145,416,303,0,SAMP cases/a Net health effect indexed by the two decisions of concern: a) whether to recommend salmon consumption restrictions and b) whether to apply stricter regulations for fish feed. The definition contained also this row: if a>ARL then 1 else 0 But it was removed when we cut the acceptable concentration concept out of the model. var a:= (if Poll_or_net = 1 then H1900 else H1901); sum(a,Salmon1) 224,56,1 48,46 2,102,90,476,277 2,499,269,416,303,0,MIDM 2,723,592,518,176,0,MEAN [H1899,H1898] [1,0,0,0] 28.6.2004 Jouni Tuomisto This version is an update of the model that was used for calculating the results for Tuomisto paper submitted to Science on January 28, 2004. Only argumentation and comments have been clarified and added. No substantive changes have been made to definitions. The descriptions of the variables described in the Table 1 in 'Supportive online material' have not been changed. Therefore, this version produces the results that were presented in the manuscript. The main conclusions of this study were added as arguments on the top level of the model. The argumentation about the pollutant model selection was clarified (see module Fish advisories). 11.1.2005 Jouni Tuomisto The model was given a unified resource name (URN) and metadata. The only addition since 28.6.2004 is the node Urn and this note. 0 504,184,1 48,12 2,306,93,476,540 [Alias Log_v4] 65535,54067,19661 index benefit_risk: ['Benefits','Risks']; var a:= array(benefit_risk,[H1912,H1900]); a[Salmon1='Farmed salmon'H1899='BAU'] 72,264,1 48,42 2,20,7,551,791,1,MEAN [Sys_localindex('BENEFIT_RISK'),H1898,Undefined,Undefined,1] [0,0,0,0] [Recommendation,1,Sys_localindex('BR'),1,Sys_localindex('STEP'),1] 72,352,1 48,32 var a:= slice(Result_reporting,Result_reporting.decision,4); '"'&3000+run&'";"'&1&'";"'&a&'";"'&run&'"' 400,288,1 48,24 2,102,90,490,397 2,568,87,416,303,0,MIDM 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 1..4000 56,32,1 48,24 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1332,1333,1334,1335,1336,1337,1338,1339,1340,1341,1342,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1362,1363,1364,1365,1366,1367,1368,1369,1370,1371,1372,1373,1374,1375,1376,1377,1378,1379,1380,1381,1382,1383,1384,1385,1386,1387,1388,1389,1390,1391,1392,1393,1394,1395,1396,1397,1398,1399,1400,1401,1402,1403,1404,1405,1406,1407,1408,1409,1410,1411,1412,1413,1414,1415,1416,1417,1418,1419,1420,1421,1422,1423,1424,1425,1426,1427,1428,1429,1430,1431,1432,1433,1434,1435,1436,1437,1438,1439,1440,1441,1442,1443,1444,1445,1446,1447,1448,1449,1450,1451,1452,1453,1454,1455,1456,1457,1458,1459,1460,1461,1462,1463,1464,1465,1466,1467,1468,1469,1470,1471,1472,1473,1474,1475,1476,1477,1478,1479,1480,1481,1482,1483,1484,1485,1486,1487,1488,1489,1490,1491,1492,1493,1494,1495,1496,1497,1498,1499,1500,1501,1502,1503,1504,1505,1506,1507,1508,1509,1510,1511,1512,1513,1514,1515,1516,1517,1518,1519,1520,1521,1522,1523,1524,1525,1526,1527,1528,1529,1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542,1543,1544,1545,1546,1547,1548,1549,1550,1551,1552,1553,1554,1555,1556,1557,1558,1559,1560,1561,1562,1563,1564,1565,1566,1567,1568,1569,1570,1571,1572,1573,1574,1575,1576,1577,1578,1579,1580,1581,1582,1583,1584,1585,1586,1587,1588,1589,1590,1591,1592,1593,1594,1595,1596,1597,1598,1599,1600,1601,1602,1603,1604,1605,1606,1607,1608,1609,1610,1611,1612,1613,1614,1615,1616,1617,1618,1619,1620,1621,1622,1623,1624,1625,1626,1627,1628,1629,1630,1631,1632,1633,1634,1635,1636,1637,1638,1639,1640,1641,1642,1643,1644,1645,1646,1647,1648,1649,1650,1651,1652,1653,1654,1655,1656,1657,1658,1659,1660,1661,1662,1663,1664,1665,1666,1667,1668,1669,1670,1671,1672,1673,1674,1675,1676,1677,1678,1679,1680,1681,1682,1683,1684,1685,1686,1687,1688,1689,1690,1691,1692,1693,1694,1695,1696,1697,1698,1699,1700,1701,1702,1703,1704,1705,1706,1707,1708,1709,1710,1711,1712,1713,1714,1715,1716,1717,1718,1719,1720,1721,1722,1723,1724,1725,1726,1727,1728,1729,1730,1731,1732,1733,1734,1735,1736,1737,1738,1739,1740,1741,1742,1743,1744,1745,1746,1747,1748,1749,1750,1751,1752,1753,1754,1755,1756,1757,1758,1759,1760,1761,1762,1763,1764,1765,1766,1767,1768,1769,1770,1771,1772,1773,1774,1775,1776,1777,1778,1779,1780,1781,1782,1783,1784,1785,1786,1787,1788,1789,1790,1791,1792,1793,1794,1795,1796,1797,1798,1799,1800,1801,1802,1803,1804,1805,1806,1807,1808,1809,1810,1811,1812,1813,1814,1815,1816,1817,1818,1819,1820,1821,1822,1823,1824,1825,1826,1827,1828,1829,1830,1831,1832,1833,1834,1835,1836,1837,1838,1839,1840,1841,1842,1843,1844,1845,1846,1847,1848,1849,1850,1851,1852,1853,1854,1855,1856,1857,1858,1859,1860,1861,1862,1863,1864,1865,1866,1867,1868,1869,1870,1871,1872,1873,1874,1875,1876,1877,1878,1879,1880,1881,1882,1883,1884,1885,1886,1887,1888,1889,1890,1891,1892,1893,1894,1895,1896,1897,1898,1899,1900,1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060,2061,2062,2063,2064,2065,2066,2067,2068,2069,2070,2071,2072,2073,2074,2075,2076,2077,2078,2079,2080,2081,2082,2083,2084,2085,2086,2087,2088,2089,2090,2091,2092,2093,2094,2095,2096,2097,2098,2099,2100,2101,2102,2103,2104,2105,2106,2107,2108,2109,2110,2111,2112,2113,2114,2115,2116,2117,2118,2119,2120,2121,2122,2123,2124,2125,2126,2127,2128,2129,2130,2131,2132,2133,2134,2135,2136,2137,2138,2139,2140,2141,2142,2143,2144,2145,2146,2147,2148,2149,2150,2151,2152,2153,2154,2155,2156,2157,2158,2159,2160,2161,2162,2163,2164,2165,2166,2167,2168,2169,2170,2171,2172,2173,2174,2175,2176,2177,2178,2179,2180,2181,2182,2183,2184,2185,2186,2187,2188,2189,2190,2191,2192,2193,2194,2195,2196,2197,2198,2199,2200,2201,2202,2203,2204,2205,2206,2207,2208,2209,2210,2211,2212,2213,2214,2215,2216,2217,2218,2219,2220,2221,2222,2223,2224,2225,2226,2227,2228,2229,2230,2231,2232,2233,2234,2235,2236,2237,2238,2239,2240,2241,2242,2243,2244,2245,2246,2247,2248,2249,2250,2251,2252,2253,2254,2255,2256,2257,2258,2259,2260,2261,2262,2263,2264,2265,2266,2267,2268,2269,2270,2271,2272,2273,2274,2275,2276,2277,2278,2279,2280,2281,2282,2283,2284,2285,2286,2287,2288,2289,2290,2291,2292,2293,2294,2295,2296,2297,2298,2299,2300,2301,2302,2303,2304,2305,2306,2307,2308,2309,2310,2311,2312,2313,2314,2315,2316,2317,2318,2319,2320,2321,2322,2323,2324,2325,2326,2327,2328,2329,2330,2331,2332,2333,2334,2335,2336,2337,2338,2339,2340,2341,2342,2343,2344,2345,2346,2347,2348,2349,2350,2351,2352,2353,2354,2355,2356,2357,2358,2359,2360,2361,2362,2363,2364,2365,2366,2367,2368,2369,2370,2371,2372,2373,2374,2375,2376,2377,2378,2379,2380,2381,2382,2383,2384,2385,2386,2387,2388,2389,2390,2391,2392,2393,2394,2395,2396,2397,2398,2399,2400,2401,2402,2403,2404,2405,2406,2407,2408,2409,2410,2411,2412,2413,2414,2415,2416,2417,2418,2419,2420,2421,2422,2423,2424,2425,2426,2427,2428,2429,2430,2431,2432,2433,2434,2435,2436,2437,2438,2439,2440,2441,2442,2443,2444,2445,2446,2447,2448,2449,2450,2451,2452,2453,2454,2455,2456,2457,2458,2459,2460,2461,2462,2463,2464,2465,2466,2467,2468,2469,2470,2471,2472,2473,2474,2475,2476,2477,2478,2479,2480,2481,2482,2483,2484,2485,2486,2487,2488,2489,2490,2491,2492,2493,2494,2495,2496,2497,2498,2499,2500,2501,2502,2503,2504,2505,2506,2507,2508,2509,2510,2511,2512,2513,2514,2515,2516,2517,2518,2519,2520,2521,2522,2523,2524,2525,2526,2527,2528,2529,2530,2531,2532,2533,2534,2535,2536,2537,2538,2539,2540,2541,2542,2543,2544,2545,2546,2547,2548,2549,2550,2551,2552,2553,2554,2555,2556,2557,2558,2559,2560,2561,2562,2563,2564,2565,2566,2567,2568,2569,2570,2571,2572,2573,2574,2575,2576,2577,2578,2579,2580,2581,2582,2583,2584,2585,2586,2587,2588,2589,2590,2591,2592,2593,2594,2595,2596,2597,2598,2599,2600,2601,2602,2603,2604,2605,2606,2607,2608,2609,2610,2611,2612,2613,2614,2615,2616,2617,2618,2619,2620,2621,2622,2623,2624,2625,2626,2627,2628,2629,2630,2631,2632,2633,2634,2635,2636,2637,2638,2639,2640,2641,2642,2643,2644,2645,2646,2647,2648,2649,2650,2651,2652,2653,2654,2655,2656,2657,2658,2659,2660,2661,2662,2663,2664,2665,2666,2667,2668,2669,2670,2671,2672,2673,2674,2675,2676,2677,2678,2679,2680,2681,2682,2683,2684,2685,2686,2687,2688,2689,2690,2691,2692,2693,2694,2695,2696,2697,2698,2699,2700,2701,2702,2703,2704,2705,2706,2707,2708,2709,2710,2711,2712,2713,2714,2715,2716,2717,2718,2719,2720,2721,2722,2723,2724,2725,2726,2727,2728,2729,2730,2731,2732,2733,2734,2735,2736,2737,2738,2739,2740,2741,2742,2743,2744,2745,2746,2747,2748,2749,2750,2751,2752,2753,2754,2755,2756,2757,2758,2759,2760,2761,2762,2763,2764,2765,2766,2767,2768,2769,2770,2771,2772,2773,2774,2775,2776,2777,2778,2779,2780,2781,2782,2783,2784,2785,2786,2787,2788,2789,2790,2791,2792,2793,2794,2795,2796,2797,2798,2799,2800,2801,2802,2803,2804,2805,2806,2807,2808,2809,2810,2811,2812,2813,2814,2815,2816,2817,2818,2819,2820,2821,2822,2823,2824,2825,2826,2827,2828,2829,2830,2831,2832,2833,2834,2835,2836,2837,2838,2839,2840,2841,2842,2843,2844,2845,2846,2847,2848,2849,2850,2851,2852,2853,2854,2855,2856,2857,2858,2859,2860,2861,2862,2863,2864,2865,2866,2867,2868,2869,2870,2871,2872,2873,2874,2875,2876,2877,2878,2879,2880,2881,2882,2883,2884,2885,2886,2887,2888,2889,2890,2891,2892,2893,2894,2895,2896,2897,2898,2899,2900,2901,2902,2903,2904,2905,2906,2907,2908,2909,2910,2911,2912,2913,2914,2915,2916,2917,2918,2919,2920,2921,2922,2923,2924,2925,2926,2927,2928,2929,2930,2931,2932,2933,2934,2935,2936,2937,2938,2939,2940,2941,2942,2943,2944,2945,2946,2947,2948,2949,2950,2951,2952,2953,2954,2955,2956,2957,2958,2959,2960,2961,2962,2963,2964,2965,2966,2967,2968,2969,2970,2971,2972,2973,2974,2975,2976,2977,2978,2979,2980,2981,2982,2983,2984,2985,2986,2987,2988,2989,2990,2991,2992,2993,2994,2995,2996,2997,2998,2999,3000,3001,3002,3003,3004,3005,3006,3007,3008,3009,3010,3011,3012,3013,3014,3015,3016,3017,3018,3019,3020,3021,3022,3023,3024,3025,3026,3027,3028,3029,3030,3031,3032,3033,3034,3035,3036,3037,3038,3039,3040,3041,3042,3043,3044,3045,3046,3047,3048,3049,3050,3051,3052,3053,3054,3055,3056,3057,3058,3059,3060,3061,3062,3063,3064,3065,3066,3067,3068,3069,3070,3071,3072,3073,3074,3075,3076,3077,3078,3079,3080,3081,3082,3083,3084,3085,3086,3087,3088,3089,3090,3091,3092,3093,3094,3095,3096,3097,3098,3099,3100,3101,3102,3103,3104,3105,3106,3107,3108,3109,3110,3111,3112,3113,3114,3115,3116,3117,3118,3119,3120,3121,3122,3123,3124,3125,3126,3127,3128,3129,3130,3131,3132,3133,3134,3135,3136,3137,3138,3139,3140,3141,3142,3143,3144,3145,3146,3147,3148,3149,3150,3151,3152,3153,3154,3155,3156,3157,3158,3159,3160,3161,3162,3163,3164,3165,3166,3167,3168,3169,3170,3171,3172,3173,3174,3175,3176,3177,3178,3179,3180,3181,3182,3183,3184,3185,3186,3187,3188,3189,3190,3191,3192,3193,3194,3195,3196,3197,3198,3199,3200,3201,3202,3203,3204,3205,3206,3207,3208,3209,3210,3211,3212,3213,3214,3215,3216,3217,3218,3219,3220,3221,3222,3223,3224,3225,3226,3227,3228,3229,3230,3231,3232,3233,3234,3235,3236,3237,3238,3239,3240,3241,3242,3243,3244,3245,3246,3247,3248,3249,3250,3251,3252,3253,3254,3255,3256,3257,3258,3259,3260,3261,3262,3263,3264,3265,3266,3267,3268,3269,3270,3271,3272,3273,3274,3275,3276,3277,3278,3279,3280,3281,3282,3283,3284,3285,3286,3287,3288,3289,3290,3291,3292,3293,3294,3295,3296,3297,3298,3299,3300,3301,3302,3303,3304,3305,3306,3307,3308,3309,3310,3311,3312,3313,3314,3315,3316,3317,3318,3319,3320,3321,3322,3323,3324,3325,3326,3327,3328,3329,3330,3331,3332,3333,3334,3335,3336,3337,3338,3339,3340,3341,3342,3343,3344,3345,3346,3347,3348,3349,3350,3351,3352,3353,3354,3355,3356,3357,3358,3359,3360,3361,3362,3363,3364,3365,3366,3367,3368,3369,3370,3371,3372,3373,3374,3375,3376,3377,3378,3379,3380,3381,3382,3383,3384,3385,3386,3387,3388,3389,3390,3391,3392,3393,3394,3395,3396,3397,3398,3399,3400,3401,3402,3403,3404,3405,3406,3407,3408,3409,3410,3411,3412,3413,3414,3415,3416,3417,3418,3419,3420,3421,3422,3423,3424,3425,3426,3427,3428,3429,3430,3431,3432,3433,3434,3435,3436,3437,3438,3439,3440,3441,3442,3443,3444,3445,3446,3447,3448,3449,3450,3451,3452,3453,3454,3455,3456,3457,3458,3459,3460,3461,3462,3463,3464,3465,3466,3467,3468,3469,3470,3471,3472,3473,3474,3475,3476,3477,3478,3479,3480,3481,3482,3483,3484,3485,3486,3487,3488,3489,3490,3491,3492,3493,3494,3495,3496,3497,3498,3499,3500,3501,3502,3503,3504,3505,3506,3507,3508,3509,3510,3511,3512,3513,3514,3515,3516,3517,3518,3519,3520,3521,3522,3523,3524,3525,3526,3527,3528,3529,3530,3531,3532,3533,3534,3535,3536,3537,3538,3539,3540,3541,3542,3543,3544,3545,3546,3547,3548,3549,3550,3551,3552,3553,3554,3555,3556,3557,3558,3559,3560,3561,3562,3563,3564,3565,3566,3567,3568,3569,3570,3571,3572,3573,3574,3575,3576,3577,3578,3579,3580,3581,3582,3583,3584,3585,3586,3587,3588,3589,3590,3591,3592,3593,3594,3595,3596,3597,3598,3599,3600,3601,3602,3603,3604,3605,3606,3607,3608,3609,3610,3611,3612,3613,3614,3615,3616,3617,3618,3619,3620,3621,3622,3623,3624,3625,3626,3627,3628,3629,3630,3631,3632,3633,3634,3635,3636,3637,3638,3639,3640,3641,3642,3643,3644,3645,3646,3647,3648,3649,3650,3651,3652,3653,3654,3655,3656,3657,3658,3659,3660,3661,3662,3663,3664,3665,3666,3667,3668,3669,3670,3671,3672,3673,3674,3675,3676,3677,3678,3679,3680,3681,3682,3683,3684,3685,3686,3687,3688,3689,3690,3691,3692,3693,3694,3695,3696,3697,3698,3699,3700,3701,3702,3703,3704,3705,3706,3707,3708,3709,3710,3711,3712,3713,3714,3715,3716,3717,3718,3719,3720,3721,3722,3723,3724,3725,3726,3727,3728,3729,3730,3731,3732,3733,3734,3735,3736,3737,3738,3739,3740,3741,3742,3743,3744,3745,3746,3747,3748,3749,3750,3751,3752,3753,3754,3755,3756,3757,3758,3759,3760,3761,3762,3763,3764,3765,3766,3767,3768,3769,3770,3771,3772,3773,3774,3775,3776,3777,3778,3779,3780,3781,3782,3783,3784,3785,3786,3787,3788,3789,3790,3791,3792,3793,3794,3795,3796,3797,3798,3799,3800,3801,3802,3803,3804,3805,3806,3807,3808,3809,3810,3811,3812,3813,3814,3815,3816,3817,3818,3819,3820,3821,3822,3823,3824,3825,3826,3827,3828,3829,3830,3831,3832,3833,3834,3835,3836,3837,3838,3839,3840,3841,3842,3843,3844,3845,3846,3847,3848,3849,3850,3851,3852,3853,3854,3855,3856,3857,3858,3859,3860,3861,3862,3863,3864,3865,3866,3867,3868,3869,3870,3871,3872,3873,3874,3875,3876,3877,3878,3879,3880,3881,3882,3883,3884,3885,3886,3887,3888,3889,3890,3891,3892,3893,3894,3895,3896,3897,3898,3899,3900,3901,3902,3903,3904,3905,3906,3907,3908,3909,3910,3911,3912,3913,3914,3915,3916,3917,3918,3919,3920,3921,3922,3923,3924,3925,3926,3927,3928,3929,3930,3931,3932,3933,3934,3935,3936,3937,3938,3939,3940,3941,3942,3943,3944,3945,3946,3947,3948,3949,3950,3951,3952,3953,3954,3955,3956,3957,3958,3959,3960,3961,3962,3963,3964,3965,3966,3967,3968,3969,3970,3971,3972,3973,3974,3975,3976,3977,3978,3979,3980,3981,3982,3983,3984,3985,3986,3987,3988,3989,3990,3991,3992,3993,3994,3995,3996,3997,3998,3999,4000] Calculate rows for the SQL result database. It assumes 1000 iterations. var a:= ['Business as usual','Recommend restrictions to salmon consumption','Stricter limits for fish feed pollutants','Restrictions to salmon consumption AND stricter fish feed limits']; var b:= floor((in2-1)/1000); '"'&in2&'";"1";"'&slice(a,a,b+1)&'"' 400,344,1 48,24 (param1) var a:= slice(Net_health_effects_i,net_health_effects_i.decision,4); '"'&3000+run&'";"'&1&'";"'&a&'";"'&run&'"' 56,88,1 48,24 2,102,90,476,224 param1 This node is used to report results from this model. Just replace the variable name on the first row with the variable you want to report and calculate. This node calculates the result only for farmed salmon, and it looks all four possible deicisions along one dimension (not 2*2 table). var a:= sample(H1909); a:= a[Salmon1='Farmed salmon']; index decision:= ['Business as usual','Recommend restrictions','Stricter rules for feed','Both']; a:= array(decision,[ slice(slice(a,H1898,1),H1899,1), slice(slice(a,H1898,2),H1899,1), slice(slice(a,H1898,1),H1899,2), slice(slice(a,H1898,2),H1899,2)]); index statistics:= ['Mean','SD','0.01','0.025','0.05','0.25','0.5 (Median)','0.75','0.95','0.975','0.99']; array(statistics,[mean(a), sdeviation(a), getfract(a,0.01), getfract(a,0.025), getfract(a,0.05), getfract(a,0.25), getfract(a,0.5), getfract(a,0.75), getfract(a,0.95), getfract(a,0.975), getfract(a,0.99)]) 232,328,1 48,24 2,102,90,476,485 2,362,39,568,303,0,MIDM [Sys_localindex('DECISION'),Sys_localindex('STATISTICS'),Undefined,Undefined,Undefined,1] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [Pollutant1,1,Sys_localindex('STATISTICS'),1,Sys_localindex('DECISION'),1] µg/kg/d Pollutant exposure per body weight per day. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1905">Wiki variable</a> H1903*H1904/1000/BW 256,272,1 48,24 1,1,1,1,1,1,0,,1, 2,287,149,476,224 2,72,47,653,399,0,MIDM [Salmon1,Pollutant1] [1,0,0,0] [H1899,1,H1898,1,Pollutant1,1,Salmon1,1] Op_en1905 ktluser 11. Janta 2004 12:08 48,24 168,144,1 48,24 1,472,152,516,377,17 2,40,50,576,600 Rideout said that major feed companies have been able reduce toxin levels in their fish meal over the past several years by using substitute ingredients and less contaminated fish. ÒIt has been an issue that the industry is responding to. Feed companies have been working overtime to use high quality meals that are very low in contaminants and have twice the amount of omega-3sÉThe line is trending downward. We donÕt like having this in our product.Ó <ref>http://www.intrafish.com/articlea.php?articleID=41061&s=1</ref> Feed_backgr 328,48,1 64,36 2,102,90,720,332 fraction <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1902">Wiki variable</a> var a:= (if H1899='More actions' then impr_in_feed else 0); Feed_backgr*(1-a) 192,128,1 48,29 1,1,1,1,1,1,0,,1, 2,102,90,479,462 2,291,326,636,303,0,SAMP [Undefined,H1899,Undefined,Undefined,Undefined,1] [1,0,0,0] Op_en1902 H1902 192,240,1 60,44 Impr_in_feed+H1898+H1899 64,248,1 48,55 [Alias Should_we_change_fi1] - Pollutant concentrations in fish feed can be further reduced from current levels. The estimate of 0-100 % with a gradual decrease in probability density is based on author judgement. It reflects rather a theoretical range of improvement than a realistic estimate. Triangular( 0, 0, 1 ) 64,128,1 48,38 2,102,90,476,409 2,515,277,416,303,0,MIDM 2,216,226,416,303,1,CDFP 52425,39321,65535 - This is a dummy variable only, because the actual concentrations in fish feed are not needed in the current model. 1 192,48,1 48,24 52425,39321,65535 - Two options are assumed for fish feed regulations: 1) business as usual (BAU) with current legislation, and 2) More restrictive regulations for fish feed, resulting in reduction of pollutant levels in feed and consequently in salmon. (This is irrespective of any trends unrelated to the decision). <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1899">Wiki variable</a> ['BAU','More actions'] 280,144,1 48,32 1,1,1,1,1,1,0,,1, 2,590,242,476,224 Op_en1899 ['BAU','More actions'] jtue 12. Janta 2004 8:51 48,24 504,336,1 48,42 1,76,122,586,421,17 probability/(g/d) Exposure-response function where also the uncertainty about the population that benefits from omega-3 is taken into account. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1909">Wiki variable</a> var a:= Erf_hcrude*(if All_or_chd=1 then 1 else F_chd_pati); array(cause_of_death3,[a]) 200,248,1 52,44 1,1,1,1,1,1,0,,1, 2,291,175,476,224 2,136,146,489,288,0,MEAN 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:[0,0.01,0.05,0.25,0.5,0.75,0.95,0.99,1] [0,0,0,0] Op_en1909 Erf_hcrude 336,152,1 52,56 All_or_chd 64,56,1 48,46 probability A large part of omega-3 benefit literature is based on studies on cardiac patients. This node reflects the uncertainty whether there is cardiac health benefit for everyone or only coronary heart disease (CHD) patients. The estimate is not based on data but the aim is to maximise uncertainty. Bernoulli( 0.5 ) 64,160,1 48,38 2,102,90,476,333 52425,39321,65535 fraction Fraction of coronary heart disease patients among the deaths. Current estimate is based on the fraction of cardiac deaths from total deaths in EEA countries, although there are cardiac deaths among non-CHD patients, and there are CHD patients with other causes of death. <ref>[http://www.who.int WHO data]</ref> 1.5717M/3.8664M 64,248,1 48,38 2,102,90,476,434 probability/(g/d) Dose-response function comes from secondary prevention trials reviewed by Din 2004 Table 1. The relative risk reductions are divided by the omega3 exposure in each study. A continuous distribution is used, and each study result is used as a quintile point for the distribution. Another review is Marckmann and Gronbaek 1999 that concluded that 0.6-0.9 g/d of omega-3 results in 40-60 % decrease in coronary heart disease mortality. The low estimate from this result was used (40% per 0.9 g/d). <ref>Din JN, Newby DE, Flapan AD. Science, medicine, and the future - Omega 3 fatty acids and cardiovascular disease - fishing for a natural treatment. British Medical Journal 2004; 328(7430):30-35. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/Din_Omega3andCVD_BMJ2004.pdf Intranet file]</ref> <ref>Marckmann P, Gronbaek M. Fish consumption and coronary heart disease mortality. A systematic review of prospective cohort studies. European Journal of Clinical Nutrition 1999; 53(8):585-590.</ref> -Fractiles( [0/3.5,.325/1.5,.482/1.8,.297/0.85, 0.4/0.9 ]) 200,152,1 52,32 2,102,90,512,527 2,72,82,416,303,1,PDFP 52425,39321,65535 g/d Describes the amount of fish that is still beneficial when added to diet. After this limit, no extra benefit is assumed from omega-3 fatty acids. The value reflects both the physiological need of omega-3 and the current intake of omega-3 from other sources than salmon. Both parts of the estimate are complicated, and the latter varies from country to country. This might have implications to the decision if we could give country-wise recommendations of feed regulations. The estimate is based on author judgement. A rough idea about the magnitude comes from Din 2004, where the trials had 0.85 - 1.8 g/d of omega-3 with benefit but 3.5 g/d showed no benefit in a small trial where the population used reasonable amount of fish anyway. If the physiological limit is lower, the slope of the exposure-response function should be steeper. Another data comes from Albert 1998: the benefit may be limited to omega-3 doses <4.9 g/mo = 0.16 g/d. Markmann and Gronbaek concluded that 0.6-0.9 g/d is beneficial. <ref>Din JN, Newby DE, Flapan AD. Science, medicine, and the future - Omega 3 fatty acids and cardiovascular disease - fishing for a natural treatment. British Medical Journal 2004; 328(7430):30-35. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/Din_Omega3andCVD_BMJ2004.pdf Intranet file]</ref> <ref>Albert CM, Hennekens CH, O'Donnell CJ, Ajani UA, Carey VJ, Willett WC et al. Fish consumption and risk of sudden cardiac death. Jama-Journal of the American Medical Association 1998; 279(1):23-28.</ref> <ref>Marckmann P, Gronbaek M. Fish consumption and coronary heart disease mortality. A systematic review of prospective cohort studies. European Journal of Clinical Nutrition 1999; 53(8):585-590.</ref> Triangular( .2, .5, 1 ) 496,64,1 48,38 2,135,16,476,598 52425,39321,65535 ICD-10 ['Cardiovascular'] 200,304,1 56,12 2,613,92,416,303,0,MIDM Op_en2707 Pieta 16. tamta 2004 1:32 48,24 136,336,1 48,51 1,141,229,418,300,17 (mg/kg/d)^Æ1 Potency of pollutants. Cancer slope factors (CSF) are used for cancer and Reference Dose (RfD) values are used for non-cancer endpoints. The data comes from the U.S.EPA. <ref>U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1. [http://www.epa.gov/waterscience/fish/guidance.html Open access Internet file]</ref> <ref>[http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/EPAFishAdvisory/ Intranet file]</ref> Table(Pollutant1,Self)( 16,50u, 1.1,250u, 156K,0, 2,20u ) ['Ca (CSF)','Non-Ca (RfD)'] 64,40,1 48,24 2,249,11,476,457 2,480,276,416,303,0,MIDM 2,103,144,416,303,0,MIDM [Alias Potency1] 65535,52427,65534 [Self,Pollutant1] [Self,Pollutant1] [1,0,0,0] (mg/kg/d)-1 The response assessment is restricted to cancer endpoints, because it is the more sensitive endpoint. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1906">Wiki variable</a> potency[Potency='Ca (CSF)'] 64,128,1 48,38 1,1,1,1,1,1,0,,1, 2,102,90,476,399 2,499,251,655,303,0,MIDM [0,0,0,0] Op_en1906 H1906 232,128,1 72,72 2,436,19,476,224 jtue 16. Janta 2004 12:54 48,24 320,208,1 48,24 1,81,109,605,297,17 ÒWeÕre telling people that if they want to reduce their risk of cancer, they should not eat more than one meal of farmed salmon a month,Ó Carpenter said. He added that the cancer risk from the toxins effectively cancels out the benefits of omega-3 fatty acids found in farmed salmon, which have not been proven to prevent or reduce the risk of cancer. ÒThere are other places to get omega-3s - wild salmon, other fish, canola oil, flaxseed oil,Ó Carpenter said. <ref>[http://www.intrafish.com/articlea.php?articleID=41061&s=1 Intrafish.com press release 9 Jan 2004]</ref> F 120,56,1 60,44 2,102,90,609,347 g/d Data comes from EPIC study looking at fish consumption in 10 European countries by gender. We take the minimum, the unweighed average and the maximum of these values in the distribution to represent uncertainty in population average fatty fish intake. All fatty fish is assumed to be salmon. <ref>Welch AA, Lund E, Amiano P, Dorronsoro M. Variability in fish consumption in 10 European countries. In: Riboli E, Lambert R, editors. Nutrition and lifestyle: opportunities for cancer prevention. Lyon: International Agency for Research on Cancer, 2002: 221-222. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/RiboliNutritionLifestyle_IARC156_2002.pdf PDF of article] [http://ytoswww/yhteiset/Huippuyksikko/Tutkimus/Viljelylohi/Materiaali/ConsumptionOfFish.xls Data in Excel]</ref> Triangular( 7.5, 15.3, 31 ) 352,48,1 48,38 2,376,70,476,496 2,0,0,793,492,0,MEAN [Chance Welch_et_al_2002] [1,0,0,0] fraction Fraction of farmed salmon of total salmon consumption in Western Europe. The current estimate is based on author judgement after discussions with people from the Finnish Game and Fisheries Research Institute. Uniform( .8, 1 ) 248,48,1 52,44 2,102,90,476,367 g/d Intake of farmed and wild salmon after the two decisions (regulate fish feed pollutants / recommend restrictions for farmed salmon use) has been made. Although market salmon exists in the index, it is not used in this version of the model. Wild salmon use after restricting farmed salmon use has a triangular probability distribution. Min assumes the same relative decrease as in farmed salmon; mode assumes no change; max assumes that wild salmon intake increases so much that it totally compensates the decrease in farmed salmon use. Estimates are based on author judgement. The wild salmon production capacity is probably much than the max used for the variable. This overestimation causes bias towards smaller costs due to salmon use restrictions. <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1904">Wiki variable</a> Table(Salmon1,H1899,H1898)( F,(A*F), (F+Pollutant_scare),((A*F)+Pollutant_scare), W,Triangular((A*W),W,(W+((1-A)*F))), W,Triangular((A*W),W,(W+((1-A)*F))), 0,0, 0,0 ) 248,216,1 48,24 1,1,1,1,1,1,0,,1, 2,516,77,476,224 2,58,276,500,232,0,MIDM 2,248,286,678,354,1,PDFP [H1898,Salmon1] [Undefined,H1898,Undefined,Undefined,1] [Index Salmon1] [1,0,0,0] [Salmon1,1,H1899,1,H1898,1,Sys_localindex('STEP'),1] Op_en1904 g/d Average farmed salmon consumption in Western Europe in the base case. Fraction_farmed*crude_salmon 248,144,1 48,29 2,469,178,476,300 g/d Average wild salmon consumption in Western Europe in the base case. (1-Fraction_farmed)*crude_salmon 352,144,1 48,24 fraction Farmed salmon use per baseline after a recommendation to restrict farmed salmon use. A uniform distribution between 1 American meal/month (227 g) and no change to baseline. If baseline is lower than the recommendation, no change occurs. var a:= 1*227/(365.25/12); var b:= min([a/f,1]); uniform(b,1) 128,144,1 56,36 2,264,94,476,252 2,552,65,424,320,0,MIDM g/d Change in farmed salmon use when fish feed is more strictly regulated. Consumer may consume more salmon, when pollutant problems are handled. However, there is a possibility of bad reputation ('There is a big problem, because authorities have to intervene'). The range overlaps zero to reflect this uncertainty. The expectation is slightly positive. The estimate is based on author judgement. Triangular( -1, 0.5, 1 ) 128,216,1 52,32 2,151,377,416,303,1,PDFP <ref>Welch AA, Lund E, Amiano P, Dorronsoro M. Variability in fish consumption in 10 European countries. In: Riboli E, Lambert R, editors. Nutrition and lifestyle: opportunities for cancer prevention. Lyon: International Agency for Research on Cancer, 2002: 221-222. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/RiboliNutritionLifestyle_IARC156_2002.pdf PDF of article] [http://ytoswww/yhteiset/Huippuyksikko/Tutkimus/Viljelylohi/Materiaali/ConsumptionOfFish.xls Data in Excel]</ref> 0 472,48,1 48,24 2,102,90,476,379 2,40,50,416,303,0,MIDM 65535,52427,65534 [Chance Crude_salmon] - A decision about whether a general recommendation should be given to restrict the consumption of European farmed salmon to one meal (227 g) per month or not (business as usual, BAU). <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1898">Wiki variable</a> ['BAU','Restrict farmed salmon use'] 424,144,1 76,32 1,1,1,1,1,1,0,,1, 2,102,90,476,354 Op_en1898 ['BAU','Restrict farmed salmon use'] g/g Omega-3 fatty acid content in salmon. Estimate is from Din 2004 Table 2. In a previous version, there were other fish types as well: min is the lowest value, max is the highest value and mode is the average of the two. <ref>Din JN, Newby DE, Flapan AD. Science, medicine, and the future - Omega 3 fatty acids and cardiovascular disease - fishing for a natural treatment. British Medical Journal 2004; 328(7430):30-35. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/Din_Omega3andCVD_BMJ2004.pdf Intranet file]</ref> <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1907">Wiki variable</a> array(Year3,[Uniform(0.0128,0.0215)]) 464,216,1 48,32 1,1,1,1,1,1,0,0,1,0 2,102,90,476,445 2,106,70,416,303,0,MIDM 2,472,482,416,303,0,MIDM 52425,39321,65535 [1,0,0,0] Op_en1907 g/d Omega-3 fatty acid intake from salmon. <a href="http://heande.pyrkilo.fi/heande/index.php?curid=1908">Wiki variable</a> H1904*H1907 384,272,1 48,24 1,1,1,1,1,1,0,,1, 2,102,90,476,420 2,57,94,723,303,0,MIDM [H1898,Salmon1] [1,0,0,0] [H1899,1,Year3,1,Salmon1,1,H1898,1] Op_en1908 jtok 16. tamta 2004 22:14 48,24 168,208,1 48,24 1,439,232,556,308,17 µg/kg Pollutant concentration data from Hites et al, Fig 2. <ref>Hites RA, Foran JA, Carpenter DO, Hamilton MC, Knuth BA, Schwager SJ. Global assessment of organic contaminants in farmed salmon. Science 2004; 303(5655):226-229. [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/HitesRA%26al_Science2004.pdf Intranet file]</ref> Table(Location1,Pollutant1)( 5.607,160.008,2.94m,50.904, 5.607,190.0095,2.5725m,48.177, 6.8085,136.6735,2.52m,47.268, 3.738,126.673,2.31m,49.995, 4.539,113.339,2.31m,41.814, 4.9395,150.0075,1.995m,37.269, 3.6045,93.338,2.52m,46.359, 4.4055,153.341,2.1m,34.542, 3.204,66.67,1.7325m,39.087, 2.5365,43.3355,1.89m,34.1784, 3.3375,70.0035,1.4175m,29.997, 2.8035,43.3355,1.449m,34.542, 2.5365,36.6685,1.5225m,34.542, 2.403,36.6685,1.3545m,32.724, 2.0025,30.0015,1.554m,26.361, 1.7355,32.0016,1.1025m,21.816, 2.0025,58.6696,840u,12.726, 1.2015,23.3345,1.344m,22.725, 1.2015,23.3345,945u,18.18, 1.2015,26.668,787.5u,15.453, 0.9345,14.6674,1.344m,21.816, 1.068,18.6676,892.5u,21.816, 1.2015,53.336,1.575m,7.272, 0.801,18.6676,1.155m,14.544, 0.534,20.6677,430.5u,15.453, 0.6675,23.3345,315u,7.272, 0.534,26.668,283.5u,8.181, 0.4806,20.001,315u,6.363, 0.4806,13.334,735u,9.09, 0.4005,16.6675,178.5u,4.545, 0.267,13.334,210u,3.636, 0.4005,14.0007,105u,3.636, 0.4005,15.3341,105u,3.636, 0.4005,12.0006,126u,3.636, 0.4005,10.0005,105u,2.727, 0.4005,10.0005,105u,1.818, 0.267,6.667,105u,1.818, 0.267,6.667,105u,1.818, 0.267,6.667,105u,1.818 ) 56,48,1 48,29 2,354,132,476,475 2,17,12,416,638,0,MIDM [Alias Pollutants_in_salmo2] 65535,52427,65534 [Pollutant1,Location1] [Pollutant1,Location1] , , , [1,0,0,0] µg/kg Pollutant concentrations in salmon <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1903">Wiki variable</a> Poll_i_types*H1902 312,112,1 48,24 1,1,1,1,1,1,0,,1, [Salmon1,Pollutant1] [1,0,0,0] [Reg_poll,2,Pollutant1,1,Salmon1,1] Op_en1903 Data from Hites classified based on salmon type (farmed, wild, market) Table(Location1,Self)( 'Farmed salmon','Europe', 'Farmed salmon','Europe', 'Market salmon','Europe', 'Market salmon','Europe', 'Farmed salmon','Europe', 'Market salmon','Europe', 'Market salmon','Europe', 'Market salmon','Europe', 'Farmed salmon','North America', 'Market salmon','North America', 'Farmed salmon','North America', 'Market salmon','North America', 'Farmed salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Market salmon','North America', 'Farmed salmon','North America', 'Farmed salmon','South America', 'Wild salmon','North America', 'Market salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America', 'Wild salmon','North America' ) ['Type','Region'] 56,112,1 48,24 2,102,90,476,395 2,72,82,416,614,0,MIDM 52425,39321,65535 [Self,Location1] [Self,Location1] µg/kg Dieldrin, toxaphene, PCB, and dioxin concentrations in farmed, wild, and market salmon. Triangular probability distribution is used for each pollutant and salmon type. Estimates are based on data from Hites 2004. Parameters min, mode, and max are the minimum, average, and maximum of Hites' data, respectively. var typ:= salmon_type[salmon_type='Type']; var a:= (if Salmon1=typ then poll_salmon_hites else 0); var b:= (if Salmon1=typ then 1 else 0); var c:= max(a,location1); var d:= sum(a,location1)/sum(b,location1); var e:= (if Salmon1=typ then poll_salmon_hites else 1M); var f:= min(e,location1); triangular(f,d,c) 192,113,1 48,38 2,36,56,476,309 2,489,195,416,303,1,PDFP jtue 28. Junta 2004 18:03 48,24 488,40,1 48,24 1,0,1,1,1,1,0,,0, 1,40,0,517,300,17 µg/kg Triangular probability distribution for concentrations indexed by salmon type AND THE THREE REGIONS based on data from Hites 2004. Min is the minimum of Hites, Max is the maximum of Hites, and Mode is the average of Hites. Currently not used, but probably we should look at European values, because the consumption and population data comes from Europe. var typ:= salmon_type[salmon_type='Type']; var reg:= salmon_type[salmon_type='Region']; var a:= (if typ=Salmon1 and reg=Region then poll_salmon_hites else 0); var b:= sum((if typ=Salmon1 and reg=Region then 1 else 0),location1); var c:= max(a,location1); var d:= if b>0 then sum(a,location1)/b else 0; var e:= (if Salmon1=salmon_type then poll_salmon_hites else 1M); var f:= min(e,location1); d 56,32,1 48,29 2,102,90,476,402 2,72,82,451,390,0,MIDM [Salmon1,Pollutant1] The three regions considered in Hites et al 2004. ['Europe','North America','South America'] 56,72,1 48,12 µg/kg Triangular probability distribution for concentrations indexed by salmon type based on data from Hites et al, 2004. Min is the minimum, Mode is the average, and Max is the maximum calculated for each salmon type (farmed, wild, and market) separately. var typ:= salmon_type[salmon_type='Type']; var a:= (if Salmon1=typ then poll_salmon_hites else 0); var b:= (if Salmon1=typ then 1 else 0); var c:= max(a,location1); var d:= sum(a,location1)/sum(b,location1); var e:= (if Salmon1=typ then poll_salmon_hites else 1M); var f:= min(e,location1); index x:=['Min','Mode','Max']; array(x,[f,d,c]) 56,136,1 48,38 2,102,90,476,365 2,561,214,416,303,0,MIDM 1,D,4,2,0,0 cases/a Total mortality in European Economic Area countries (386.63 million inhabitants) <ref>[http://www.who.int WHO data]</ref> <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1910">Wiki variable</a> array(Year3,[3.8664M]) 136,424,1 48,32 1,1,1,1,1,1,0,,1, 2,102,90,476,330 65535,52427,65534 [1,1,0,1] Op_en1910 cases/a Coronary heart disease mortality in European Economic Area countries (386.63 million inhabitants). The estimate consists of acute myocardial infarction and other ischaemic heart diseases (ICD 10: 270, 279). <ref>[http://www.who.int WHO data]</ref> <a href="http://heande.pyrkilo.fi/heande/index.php?title=rdb&curid=1911">Wiki variable</a> array(Year3,[615.3k]) 504,416,1 48,32 1,1,1,1,1,1,0,,1, 2,102,90,476,224 65535,52427,65534 [1,1,0,1] Op_en1911 1 568,145,1 48,61 Should_we_change_fis 1 760,144,1 52,12 65535,54067,19661 Loki_v4 H1901+H1900 256,488,1 60,51 65535,65532,19661 H1898+V3 424,51,1 64,51 65535,65532,19661 H1899+V1 280,48,1 72,42 65535,65532,19661 DC-attribute with refinement Scheme (if any) Value Title Risk benefit analysis of eating farmed salmon Creator.personalName Tuomisto, Jouni T Creator.personalName Tuomisto, Jouko Creator.personalName Tainio, Marko Creator.personalName Niittynen, Marjo Creator.personalName Verkasalo, Pia Creator.personalName Vartiainen, Terttu Creator.personalName Kiviranta, Hannu Creator.personalName Pekkanen, Juha Subject risk benefit analysis Subject persistent organic pollutants Subject omega-3 fatty acids Subject MeSH polychlorinated biphenyls Subject MeSH salmon Subject MeSH risk assessment Subject MeSH fatty acids, omega-3 Subject UDC 614 Public health and hygiene. Description.abstract In their Report ÒGlobal assessment of organic contaminants in farmed salmon,Ó R. A. Hites and co-workers analyzed wild and farmed salmon samples from North and South America and Europe for organic pollutants (9 Jan. 2004, p. 226). The authors conclude that, because of chemical contaminants, farmed salmon should not be eaten more often than 0.25 to 1 times per month. However, the model used does not take into account any beneficial effects of eating fish. We analyzed both risks and benefits. We also performed a value-of-information analysis to see which uncertainties were relevant for decision-making. This is the version 4 of the model calculating risks and benefits of farmed salmon. (c) Copyright Kansanterveyslaitos (KTL; National Public Health Institute, Finland). Publisher Kansanterveyslaitos Date.issued W3C-DTF 2004-07-23 Type DCMIType Software Format IMT text/xml Format.medium computerFile Format 115 kB Identifier http://www.ktl.fi/risk Identifier URN URN:NBN:fi-fe20042774 Language ISO639-2 en Relation.hasPart URL http://www.sciencemag.org/cgi/content/full/305/5683/476 Rights Copyright Kansanterveyslaitos, 2004 0 744,176,1 80,12 2,102,90,523,439 65535,54067,19661 year [2000] 504,456,1 48,12 Op_en2708 The node Variables_to_be_saved in the module RDB-connection.ANA from Heande should contain the following: Var_name Probabilistic? 'H1898' 0 'H1899' 0 'H1900' 1 'H1901' 1 'H1902' 1 'H1903' 1 'H1904' 1 'H1905' 1 'H1906' 0 'H1907' 1 'H1908' 1 'H1909' 1 'H1910' 0 'H1911' 0 'H1912' 1 760,32,1 48,24 65535,54067,19661 <a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a> HP_Omistaja 9. maata 2008 10:42 ktluser 18. Decta 2008 6:57 48,24 112,64,0 48,32 1,0,0,1,1,1,0,0,0,0 1,662,90,572,442,17 2,102,90,476,224 Arial, 15 100,1,1,1,1,9,2970,2100,1,0 jtue 1. jouta 2008 10:57 48,24 192,112,1 48,24 1,9,11,636,543,17 100,1,1,0,1,9,2970,2100,15,0 jtue 18. heita 2008 10:14 48,24 384,304,1 48,24 1,651,229,564,502,17 100,1,1,1,1,9,2970,2100,15,0 This library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array. David Kendall & Lonnie Chrisman Mon, Jan 26, 2004 8:49 AM Lonnie Wed, Sep 05, 2007 3:23 PM 48,24 456,176,1 68,20 1,0,0,1,1,1,0,0,0,0 1,39,36,798,452,23 (A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType ) Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3 Index I12 := Concat(I1,I2); Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J ) 88,64,1 48,26 2,56,56,986,596 A1,A2,A3,I1,I2,I3,J (A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType ) Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, J); 192,64,1 48,24 2,30,30,986,596 A1,A2,A3,A4,I1,I2,I3,I4,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType) Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, J); 88,232,1 48,24 2,27,120,469,638 A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J 0 (A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType ) Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, J); 88,120,1 48,24 2,160,160,986,596 A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J (A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType ) Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, J); 192,120,1 48,24 2,644,94,602,712 A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J 0 (A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType ) Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, J); 88,176,1 48,24 2,580,98,551,565 A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J (A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType ) Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, J); 192,176,1 48,24 2,12,98,561,737 A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType) Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Index I123456789 := Concat(I12345678, I9); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, I123456789), A10, I123456789, I10, J); 192,232,1 48,24 2,542,93,632,744 A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J 0 (A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType) Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements. index L := [ identifier of RowIndex, identifier of ColIndex, "val"]; slice(Mdarraytotable(A,ResultIndex,L),L,3) 320,64,1 64,24 2,30,320,478,348 A,RowIndex,ColIndex,ResultIndex Lonnie Thu, Sep 11, 1997 2:15 PM Lonnie Tue, Feb 05, 2008 10:03 AM 48,24 440,128,1 52,20 1,1,1,1,1,1,0,0,0,0 1,20,272,499,497,17 Arial, 13 (A:ArrayType;I:IndexType;L:IndexType;row;dbTableName) Generates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it. 29.8.2008 Jouni Tuomisto I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices. (';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[I=row],L)) & ')' 184,32,1 52,24 2,41,136,487,469 A,I,L,row,dbTableName (V:ArrayType;I:IndexType) Takes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted. JoinText( '''' & V & '''', I, ',') 72,32,0 52,24 V,I 1,F,4,14,0,0 (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. 'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,32,1 88,24 2,728,341,510,476 Tabl,RowIndex,LabelIndex,dbTableName (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,88,1 88,24 2,559,127,510,476 Tabl,RowIndex,LabelIndex,dbTableName [] Makes a table to be written to the Loc table. index j:= ['id','Obj_id_i','Location','Description']; Locations2[.j=j] 320,200,1 48,16 2,711,325,476,224 2,513,49,515,278,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] ICD-10 ['Cardiopulmonary','Lung cancer','All others','All causes'] 456,272,1 48,22 2,102,90,476,440 Op_en2665 ['Cardiopulmonary','Lung cancer','All others','All causes'] ['Harjavalta'] 448,304,1 64,12 2,184,219,476,343 kg <a href="http://en.opasnet.org/w/index.php?title=rdb&curid=2693">Wiki description</a> array(Op_en2665,uniform(100,110))+municipality_fin1 456,224,1 48,24 2,102,90,476,392 2,93,226,539,360,0,MIDM [] [0,1,0,1] Op_en2693 (table:texttype) Brings the largest id number from the table defined in the parameter. index i:= DBquery(odbc,' SELECT MAX(id) AS id FROM '&table&' '); index j:= dblabels(i); max(max(DBTable(i, j ),i),j) 440,88,1 48,12 2,102,90,476,331 39325,65535,39321 table List of such tables in Opasnet Base that are being written to by this module. ['Obj','Res','Loc','Locres','Sett','Item','Sam'] 200,64,1 48,13 2,15,594,158,227,0,MIDM ['Obj','Res','Loc','Locres','Sett','Item','Sam'] The largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button. Table(Table1)( 137,1625,688,1606,38,71,4.294967295G ) 200,32,1 48,24 2,634,394,476,332 2,193,270,416,303,0,MIDM 2,472,313,416,303,0,MIDM 39325,65535,39321 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 Makes a list of all locations in all results in all variables. The list is as long as is needed for the Locres table. A subset is taken then for the Res table. 1) Initialises local variables, and slices variables from Object1. 2)-4) Does the process for each variable one at a time. 2) Only the deterministic information about variables are considered (therefore mean). Makes a 2D table of the locres info. 3) Makes a table with fields required by the Locres and Res tables. 4) Reduces one dimension by expanding the length from the length of Res to that of Locres. 5) Makes i the row index. var output=0; var e:= Cardinals[table1='Res']; var f:= Cardinals[table1='Locres']; var o:= if Objects1[.j='Typ_id'] = 1 or Objects1[.j='Typ_id'] = 10 then 1 else 0; index j:= ['id', 'Location', 'Res_id', 'Loc_id', 'Vident', 'Obj_id_v', 'Obj_id_r', 'Mean', 'N']; index k:= subset(o); o:= objects1[Object_all=k]; var x:= 1; while x<= size(k) do ( var c:= slice(o,k,x); var a:= mean(sample(evaluate(c[.j='identifier']))); index h:= indexnames(a); index L:= concat(h,['Value']); index res_id:= (1..size(a))+e; index locres_id= (1..size(a)*size(h))+f; e:= e+size(res_id); f:= f+size(locres_id); a:= mdarraytotable(a,res_id,L); var mean1:= a[L='Value']; a:= a[L=h]&''; var g:= if Loc.j='Obj_id_i' then Loc&'+'&Loc[.j='Location'] else Loc; var p:= h; a:= array(j,[ locres_id, a, res_id, findid(findid((Ident of p), Obj, 'Ident')&'+'&a, g, 'Obj_id_i'), c[.j='Ident'], findid(c[.j='Ident'], Obj, 'Ident'), findid(objects1[.j='Ident', Object_all='Run'], Obj, 'Ident'), mean1, if c[.j='Probabilistic?']=0 then 0 else samplesize]); a:= concatrows(a,h,res_id,locres_id); output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 200,248,1 48,16 2,70,16,526,801 2,479,54,777,552,0,MIDM [Formnode Inp_locres1] [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] [Undefined] [Sys_localindex('H'),1,Sys_localindex('RES_ID'),1,Sys_localindex('LOCRES_ID'),1,Sys_localindex('J'),1] (in, table; cond:texttype) This function gets an id from a table. in: the property for which the id is needed. In MUST be unique in cond. table: the table from where the id is brought. The table MUST have .j as the column index, .i as the row index, and a column named 'id'. cond: the name of the field that is compared with in. Cond must be text. var id:= if (in&' ') = (table[.j=cond]&' ') then table[.j='id'] else 0; sum(id, table.i) 440,56,1 48,12 2,636,101,494,398 in,table,cond Slices fields that are needed in the Locres table from Inp_locres. index j:= ['id','Res_id','Loc_id']; inp_locres[.j=j] 320,248,1 52,16 2,790,83,476,224 2,632,155,416,303,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] [] Slices the fields that are needed in the Res table. Removes duplicate rows. index j:= ['id','Obj_id_v','Obj_id_r','Mean','N']; var a:= inp_locres[.j=j]; a:= if j='id' then inp_locres[.j='Res_id'] else a; index i:= unique(a,a.i); a[.i=i] 320,280,1 48,16 2,807,62,476,224 2,723,178,416,303,0,MIDM [Formnode Write_res1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] [] Names of different wikis used. Table(Self)( 'Op_en','Op_fi','Heande','En','Fi','Erac','Beneris','Intarese','Piltti','Kantiva','Bioher','Heimtsa') [1,2,3,4,5,8,9,10,11,13,14,15] 56,64,1 48,12 65535,52427,65534 [Object2,Self] Types of different objects that may exist in Analytica or Opasnet Base. Types that have the same number are treated equally in these systems. Table(Self)( 'Variable','Dimension','Method','Model','Class','Index','Nugget','Encyclopedia article','Run','Chance','Decision','Objective','Constant','Determ','Module','Library','Form') [1,2,3,4,5,6,7,8,9,1,10,1,1,1,4,4,4] 56,32,1 48,20 2,674,34,416,606,0,MIDM 2,193,235,416,390,0,MIDM 65535,52427,65534 Makes a list of sets for the Sett table. There are three major kinds of sets: Indices belonging to an assessment, variables belonging to an assessment, and variables belonging to a run. Indices belonging to a dimension are NOT created with this node. index i:= ['Assessment','Assessment','Run']; index j:= ['id','Obj_id','Sty_id']; array(j,[ Cardinals[table1='Sett']+@i, findid(Objects1[Object_all=i, .j='Ident'], Obj, 'Ident'), array(i,[3,4,9])]) 320,64,1 48,16 2,59,190,495,444 2,583,221,416,340,0,MIDM [Formnode Write_sett1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] Selects relevant information for the Obj table from Objects1 node. index j:= ['id','Ident','Name','Unit','Typ_id','Page','Wik_id']; index i:= 1..size(Object_all); Objects1[.j=j, @Object_all=@i] 320,32,1 48,16 2,289,70,909,492,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] [] Makes a list of items of sets into the Item table. This node does NOT handle indices of a dimensions, but they must be described elsewhere. For types of sets, see Write_sett. index j:= ['id','Sett_id','Obj_id','Fail']; index k:= types(1); index L:= types(6); var c:= if sett.j='Obj_id' then sett&'+'&sett[.j='Sty_id'] else sett; c:= findid(write_sett[.j='Obj_id']&'+'&write_sett[.j='Sty_id'], c, 'Obj_id'); var a:= array(j,k, [0, slice(c,1), k, 0]); var b:= array(j,L,[0, slice(c,2), L, 0]); index m:= 1..(size(k)+size(L)); a:= concat(a,b,k,l,m); b:= array(j,k, [0, slice(c,3), k, 0]); index i:= 1..(size(m)+size(k)); a:= concat(a,b,m,k,i); if j='id' then cardinals[table1='Item']+@i else a; 320,104,1 48,16 2,80,84,476,473 2,921,13,345,638,0,MIDM [Formnode Write_item2] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] [Self,1,Sys_localindex('J'),1,Sys_localindex('K'),1] If the result is not a number, then the actual result text can be written into the Description field of the Descr table. Makes a list of text values to be written into the Descr table. index j:= ['id','Description']; index i:= subset(sample1[.j='Description']); sample1[.j=j, .i=i] 320,320,1 48,16 2,182,215,416,303,0,MIDM [Formnode Write_descr1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] Makes a list of objects that contains some additional information to be written into the Inf table. index j:= ['id','Begin','End','Who','Url']; var a:= Objects1; var b:= findid(a[.j='Ident'], Obj, 'Ident'); a:= a[.j=j]; a:= if a = null or j='id' or a='' then 0 else a; a:= if sum(a,j) = 0 then 0 else 1; index i:= subset(a); a:= Objects1[Object_all=i, .j=j]; a:= if j='id' then b[Object_all=i] else a; if a=null or a=0 then '' else a 320,136,1 48,16 2,94,102,476,340 2,55,45,483,478,0,MIDM [Formnode Write_inf1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] Table(Op_en2665)( 'Cardiopulmonary deaths, ICD-10 ## ','Lung cancer deaths, ICD-10 ## ','All other non-accidental deaths, ICD-10 ## ','All non-accidental deaths, ICD-10## ') 456,344,1 48,24 2,102,90,476,437 52425,39321,65535 (type) Finds the objects that are of the object type "type" (the only parameter of this function). Based on the information in Objects1. var a:= if Objects1[.j='Typ_id']=type then 1 else 0; Objects1[Object_all=subset(a),.j='id'] 440,24,1 48,12 2,551,191,476,344 type Additional information about the run and the assessment. Table(Self,Info)( 'Op_en1896','Benefit-risk assessment of farmed salmon','Jouni','2004-01-09', 'Op_en2694','Testrun 1','Jouni',0 ) ['Assessment','Run'] 56,104,1 48,13 2,576,173,476,392 2,339,445,416,303,0,MIDM 2,664,117,416,303,0,MIDM [Formnode Run_info1] 52425,39321,65535 [Self,Info] [Self,Info] Op_en2694 Makes a table about object information. 1) Finds information for other parameters based on objects. 2) Adds info from nodes Run_info, Probabilistic_, and Index_info. 3) Makes the final adjustements based on information described above. There is no need to search for existing objects, because the Ident is unique. Thus, all attempts of duplicate additions just are ignored. Null values are ignored in the write procedure. This causes mistmatch between column and values. Therefore, null is replaced by ''. var a:= Object_all; var d:= findintext(Object_types,Class of a); d:= sum(if d=0 then 0 else indexvalue(object_types),object_types); var f:= {findid(Object4,Obj,'Ident'); f:= if f>0 then f else} Cardinals[table1='Obj']+@Object_all; Index j:= ['identifier', 'id','Ident','Name','Unit','Typ_id','Page','Wik_id', 'Who','Begin','Url','Probabilistic?','Description node']; a:= array(j,[ Object_all, f, Ident of a, Title of a, Units of a, if Object_all = 'Run' then 9 else d, '', '', '', '', 0, 0, 0]); var b:= if j='Probabilistic?' then probabilistic_[objects_excl_indices=Object_all] else null; a:= if b=null then a else b; b:= index_info[Add_info=j, Indices=Object_all]; a:= if b=null then a else b; b:= run_info[info=j, run_info=Object_all]; b:= if j= 'Begin' and Object_all='Run' and b=0 then datepart(today(),'Y')&'-'&datepart(today(),'M')&'-'&datepart(today(),'D') else b; a:= if b=null then a else b; b:= if Object_all ='Run' and j='Name' then a&': Analytica '&Analyticaedition&', ('&Analyticaplatform&'), Version: '&Analyticaversion&', Samplesize: '&samplesize else null; a:= if b=null then a else b; b:= findintext(wikis,a[j='Ident']); b:= if b=0 then 0 else b+textlength(Wikis); var c:= sum(if b=0 then 0 else @wikis,wikis); b:= sum(b,wikis); b:= if b = 0 then 0 else selecttext(a[j='Ident'],b); a:= if j='Page' then b else a; a:= if j='Wik_id' then c else a if a = null then '' else a 200,136,1 48,16 2,20,57,581,758 2,41,312,1217,247,0,MIDM [Formnode Objects] [Sys_localindex('J'),Object_all] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [0,1,1,0] ['','','','','','','','','','','','','','','','','','','','','','',''] [Indices,6,Add_info,2,Object_all,1,Sys_localindex('IOBJ'),1] This makes a list of all indices (including decision nodes) that are used by the variables in Object1. index a:= indexnames(evaluate(Objects_excl_indices)); a:= if a='Object1' or a='Objects_excl_indices' then 0 else 1; subset(a) 56,272,1 48,13 2,102,90,476,464 2,32,349,416,303,0,MIDM [Op_en2665,Objects_excl_indices] ['Pollutant1','Salmon1','H1899','Cause_of_death3','H1898','Year3'] Additional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index. Table(Add_info,Indices)( 0,0,0,0,0,0 ) 56,216,1 48,20 2,140,217,476,224 2,605,351,664,303,0,MIDM 2,506,220,684,303,0,MIDM [Formnode Index_info1] 52425,39321,65535 [Add_info,Indices] [Add_info,Indices] Additional pieces of information about indices. Currently, the only piece is a description node. ['Description node'] 56,248,1 48,12 ['Description node'] Contains 1 for all variables that are stored as samples from probability distributions, and 0 for deterministic variables. Table(Objects_excl_indices)( 0,0,1,1,1,1,1,1,0,1,1,1,0,0,1) 56,168,1 48,22 2,197,26,416,371,0,MIDM 2,17,23,416,364,0,MIDM [Formnode Probabilistic_1] 52425,39321,65535 ['Ident','Name','Who','Begin'] 56,128,1 48,12 ['Ident','Name','Who','Begin'] List of variables, indices, assessment, and run to be stored into the Opasnet Base. concat(concat(objects_excl_indices,Indices),indexvalue(Run_info))&'' 200,160,1 48,13 1,1,1,1,1,1,0,0,0,0 2,49,109,558,527 2,200,210,773,264,0,MIDM [Self,Info] ['H1898','H1899','H1900','H1901','H1902','H1903','H1904','H1905','H1906','H1907','H1908','H1909','H1910','H1911','H1912','Pollutant1','Salmon1','H1899','Cause_of_death3','H1898'] The usage of local variables: a: the temporary variable that is being edited. e: cardinal of the Res table. f: cardinal of the Sam table. j: output column headings. i: output row numbers. 1) Several local variables are initiated. Variables and Decisions are included. 2)-5) The process is done for each variable one at a time (this is indexed by x). 3) Several within-loop local variables are initiated. 4) The variable is given index runn which is equal to run if probabilistic and [0] if not. The array is flattened first to 2-D, the value only is kept. 5) Variables are concatenated to each other. 6) Index i is made the index of the implicit index. var output=0; var e:= Cardinals[table1='Res']; var f:= Cardinals[table1='Sam']; index j:= ['id','Res_id','Sample','Result','Description']; var o:= if Objects1[.j='Typ_id'] = 1 or Objects1[.j='Typ_id'] = 10 then 1 else 0; index k:= subset(o); o:= objects1[Object_all=k]; var x:= 1; while x<= size(k) do ( var c:= slice(o,k,x); var a:= c[.j='identifier']; a:= sample(evaluate(a)); index h:= indexnames(max(a,run)); index L:= concat(h,['Value']); index runn:= if c[.j='Probabilistic?']=1 then copyindex(run) else [0]; index res_id:= (1..size(max(a,run)))+e; index sam_id:= (1..size(res_id)*size(runn))+f; e:= e+size(res_id); f:= f+size(sam_id); a:= if c[.j='Probabilistic?']=1 then a[run=runn] else (if runn=0 then a else a); a:= mdarraytotable(a,res_id,L)[.L='Value']; a:= array(j,[0, res_id, runn, a, a]); a:= concatrows(a,res_id,runn,sam_id); a:= if j='id' then sam_id else a; a:= if j='Description' then ( if istext(a) then a else 0) else a; output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 200,320,1 48,16 2,19,11,585,772 2,133,242,753,337,0,MIDM [Formnode Sample2] [Sys_localindex('J'),Sys_localindex('I')] Makes a list of all locations of all indices used in variables listed in Object1. 1) Initialises local variables. 2) Takes one index at a time, calculates the values and concatenates them to the previous values. 3) All parameters are lumped into a single array, with some fields calculated based on others. var a:= if objects1[.j='Typ_id']= 6 or objects1[.j='Typ_id']= 10 then 1 else 0; index k:= subset(a); a:= objects1[Object_all=k]; var b:= [0]; var c:= [0]; var e:= [0]; var f:= [0]; var x:= 1; while x<=size(k) do ( var d:= evaluate(slice(k,x)); b:= concat(b,d); c:= concat(c,(if d=0 then slice(k,x) else slice(k,x))); e:= concat(e,1..size(d)); var g:= evaluate(a[@k=x, .j='Description node']); g:= {if size(g) = size(d) then g else} (if d=0 then g else g); f:= concat(f, g); x:= x+1); index i:= 1..size(b)-1; c:= slice(c,i+1); index j:= ['id','Obj_id_i', 'Ind_identifier', 'Location', 'Roww', 'Description']; array(j,[cardinals[table1='Loc']+i, findid(Ident of c,Obj,'Ident'), c, slice(b,i+1)&'', slice(e,i+1), slice(f,i+1)]) 200,200,1 48,16 2,40,23,521,628 2,521,153,727,401,0,MIDM [Formnode Locations1] [Sys_localindex('J'),Sys_localindex('I')] ['','','','','','','','','',''] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] 384,400,1 48,12 2,781,100,476,721 This module saves model results into the Opasnet Base. You need a password for that. Note that the necessary variable, index, dimension, and run information will be asked. You must fill in all tables before the process is completed. 472,76,-1 136,68 Note! You can insert several variables at the same time. Each variable MUST have at least one index. 472,176,-1 136,32 Fill in the data below if needed (in this order). 168,256,-5 160,248 1,0,0,1,0,1,0,,0, 0 156,44,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Username 0 156,68,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Password 0 156,93,1 140,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Objects_excl_indices ['H1898','H1899','H1900','H1901','H1902','H1903','H1904','H1905','H1906','H1907','H1908','H1909','H1910','H1911','H1912'] 384,240,1 48,24 2,958,152,321,481 2,328,338,416,361,0,MIDM [Formnode Object2] 52425,39321,65535 ['H1898','H1899','H1900','H1901','H1902','H1903','H1904','H1905','H1906','H1907','H1908','H1909','H1910','H1911','H1912'] 0 156,164,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Run_info 224,236,4 48,12 224,188,1 48,12 384,352,1 48,12 384,376,1 48,12 2,102,90,473,465 224,333,3 48,13 224,477,1 48,13 Check all the nodes before running buttons (from top to bottom)! 60,274,-1 44,74 1 200,381,1 96,13 1,0,0,1,0,0,0,72,0,1 Inp_locres 1 200,405,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_res 1 200,285,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_sett 1 200,357,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_item 1 200,453,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_descr 1 200,261,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_inf 1 200,212,1 96,12 1,0,0,1,0,0,0,72,0,1 Objects1 1 200,428,1 96,12 1,0,0,1,0,0,0,72,0,1 Sample1 dbwrite(odbc_write, appendtablesql(write_obj,write_obj.i, write_obj.j, 'Obj ')) 488,244,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_inf,write_inf.i, write_inf.j, 'Inf ')) 488,268,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_sett,write_sett.i, write_sett.j, 'Sett ')) 488,292,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_loc,write_loc.i, write_loc.j, 'Loc ')) 488,316,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_item,write_item.i, write_item.j, 'Item ')) 488,340,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_locres,write_locres.i, write_locres.j,'Locres ')) 488,364,1 48,12 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_res,write_res.i, write_res.j,'Res ')) 488,388,1 48,12 65535,45873,39321 [] index j:= ['id','Res_id','Sample','Result']; var write_sam:= sample1[.j=j]; dbwrite(odbc_write, appendtablesql(write_sam,write_sam.i, write_sam.j,'Sam ')); 'done' 488,412,1 48,12 2,102,90,476,333 2,642,255,416,303,0,MIDM 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(write_descr,write_descr.i, write_descr.j,'Descr ')) 488,436,1 48,12 65535,45873,39321 [] 384,424,1 48,12 1 200,309,1 96,13 1,0,0,1,0,0,0,72,0,1 Locations2 ktluser 29. Decta 2008 21:51 48,24 384,472,1 48,24 1,279,39,902,527,17 92,1,1,0,2,9,2970,2100,15,0 192,56,1 48,24 39325,65535,39321 Cardinals__all_table 320,120,1 48,76 Objects__ 192,184,1 48,67 65535,45873,39321 Obj__id_ident_name_u 192,328,1 48,40 65535,45873,39321 Sett__id_obj_id_typ_ 56,329,1 48,49 65535,45873,39321 Obj__id_ident_name_u 56,186,1 48,58 65535,45873,39321 Obj__id_ident_name_u 320,336,1 48,52 65535,45873,39321 Loc__id_obj_id_d_loc 456,336,1 48,92 Inp_locres__locres_i 592,424,1 48,40 65535,45873,39321 Inp_locres__locres_i 592,312,1 48,58 65535,45873,39321 Sample__id_res_id_sa 592,120,1 48,52 65535,45873,39321 Sample__id_res_id_sa 592,216,1 48,31 65535,45873,39321 The arrows only show sequential dependencies. This means that e.g. Cardinals is a parent to many other nodes as well, but the critical values in Cardinals only change before Objects is defined, and there is no need to update Cardinals during the writing process. Orange nodes are actual Tables in Opasnet Base. Green nodes are SQL queries from Opasnet Base. Blue nodes are computed in Analytica. 752,168,-1 112,152 Objects__ 456,121,1 48,58 R Objects 192,176,-1 56,80 1,0,0,1,0,1,0,,0, R Structure 256,332,-1 120,68 1,0,0,1,0,1,0,,0, R Cardinals 192,48,-1 56,40 1,0,0,1,0,1,0,,0, 0 156,140,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Index_info 0 156,116,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Probabilistic_ ktluser 3. Augta 2008 18:31 jtue 9. lokta 2008 14:01 48,24 192,64,1 48,24 1,1,1,1,1,1,0,0,0,0 1,15,17,593,446,17 Arial, 15 (vident:text, run:optional) Reads the data about the var_name variable from the Opasnet Base. Uses the run_id run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Var_name: the name of the variable in the result database. * Run_id: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(run) then run:= newestrun(vident); index i:= DBquery(Odbc,' SELECT Var.Ident as Vident, Var.Name as Vname, Var.Unit as Vunit, Res.id, Ind.Ident as Iident, Location, Mean, N, Run.Name as Rname, Run.id AS Runid FROM Obj as Var, Res, Locres, Loc, Obj as Ind, Obj as Run WHERE Res.Obj_id_r = Run.id AND Res.Obj_id_v = Var.id AND Locres.Res_id = Res.id AND Locres.Loc_id = Loc.id AND Loc.Obj_id_i = Ind.id AND Var.Ident = "'&vident&'" '); index j:= dblabels(i); dbtable(i,j) 56,40,1 48,12 2,740,74,516,589 39325,65535,39321 vident,run (vident:text) This function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Do_first and Readdata. PARAMETERS: * Var_name: the name of the variable in the result database. * Run_id: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. index i:= DBquery(Odbc,' SELECT Obj_id_r FROM Res, Obj as Var WHERE Var.id = Res.Obj_id_v AND Var.Ident = "'&vident&'" GROUP BY Var.id, Obj_id_r '); index j:= dblabels(i); max(max(dbtable(i,j),i),j) 56,16,1 48,12 2,678,59,476,566 39325,65535,39321 vident (vident:text; run, textornot:optional) Brings the data from the result database and transforms it into variables of the correct form. NOTE! All necessary indices must be created before running this function. The necessary indices can be viewed by calling the function Do_first with the same parameters as this function. PARAMETERS: * Vident: the Ident of the variable in the result database. * Run: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. * Textornot: Tells whether the result is numerical or text. If parameter is omitted or false, numerical is assumed, otherwise text. 1) Brings the data and makes indices for index list, locations, and result_id's. 2) Makes an array containing result_id, indexed by all indices and all locations. 3) Makes an array containing result_id, indexed by the indices of the variable itself. 4) Brings the results into the structure created in 3). Makes the sample fo along the index Run. var data:= Read_sample(vident, run); var a:= data[.j='Iident']; index ind_name:= a[.i=unique(a,a.i)]; a:= data[.j='Loct']; index location:= a[.i=unique(a,a.i)]; a:= data[.j='Res.id']; index result_id:= a[.i=unique(a,a.i)]; var x:= 1; a:= null; while x<= size(data.i) do ( var b:= data[@.i=x]; a:= if location = b[.j='Loct'] and ind_name = b[.j='Iident'] and result_id = b[.j='Res.id'] then b[.j='Res.id'] else a; x:= x+1); var c:= result_id; x:= 1; while x<=size(ind_name) do ( c:= if c= a[location=evaluate(ind_name[@ind_name=x]), @ind_name=x] then result_id else 0; x:=x+1); c:= sum(c,result_id); a:= data[.j='sample']; index sample:= a[.i=unique(a,a.i)]; x:= 1; a:= null; while x<= size(data.i) do ( var b:= data[@.i=x]; a:= if c = b[.j='result_id'] and sample = b[.j='sample'] then b[.j='result'] else a; x:= x+1); a:= if max(sample)>0 then a[sample=run] else a[@sample=1]; if isnotspecified(textornot) or textornot=false then evaluate(a) else a 56,200,1 48,12 2,415,23,476,728 vident,run,textornot (vident:text; run:optional) This function brings the variable from the Result Database and analyses its structure. Each index used will be shown as a column along '.Ind_name', and each location of that index will be shown along '.K'. The last row of '.K' shows the samplesize of the variable. Use this information to create the necessary indices for your model and to adjust the samplesize of the model. If the samplesize of the model is smaller than in the result database, the remaining samples are omitted; if larger, the cells with no results in the database are replaced with null. NOTE! The indices created should be lists of labels (not lists of numbers). PARAMETERS: * Var_name: the name of the variable in the result database. * Run_id: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(run) then run:= newestrun(vident); var data:= Read_mean(vident, run); index j:= ['Iident','Location']; var a:= data[.j=j]; index i:= unique(a,a.i); a:= a[.i=i]; index ind_name:= a[i=unique(a[j='Iident'], i), j='Iident']; a:= if ind_name=a[j='Iident'] then a[j='Location']; index b:= ['Sample size']; var d:= array(b,[max(data[.j='N'])]); concat(a,d,i,b) 56,64,1 48,12 2,701,48,476,579 vident,run do_first(Enter_variable) 288,112,1 48,12 2,680,114,476,224 2,23,420,665,335,0,MIDM [Sys_localindex('IND_NAME'),Sys_localindex('K')] Var_sample(Enter_variable) 288,400,1 48,12 2,612,23,639,490,0,MIDM [] (index1:texttype) This node brings descriptions (if any) of each location of an index from the result database. The only parameter for function Descr is the name of an index (as text). var a:= sum((if indices[.j='Iident'] = index1 then indices[.j='Dim.id'] else 0), indices.i); a:= if locations[.j='Dim.id'] = a and locations[.j='Loct'] = evaluate(index1) then locations[.j='Description'] else ''; jointext(a,a.i) 56,368,1 48,12 2,281,63,476,368 index1 This node brings descriptions (if any) of each location of an index from the result database. The only parameter for function Descr is the name of an index (as text). Descr(Enter_variable) 56,344,1 52,12 2,630,132,476,224 2,610,365,331,391,0,MIDM [Sys_localindex('I')] ['ang','bou','brw','caw','cho','dcc','eas','ess','fol','har','mik','nor','nww','por','sea','sev','sos','sou','sww','teh','tha','thr','wes','wrx','yor'] 464,72,1 48,24 ['item 1','item 2','item 3','item 4','item 5','item 6','item 7','item 8','item 9','item 10','item 11','item 12','item 13','item 14','item 15','item 16','item 17','item 18','item 19','item 20','item 21','item 22','item 23','item 24','item 25'] read_sample(Enter_variable) 288,304,1 48,24 2,399,395,416,303,0,MIDM [Sys_localindex('I'),Sys_localindex('J')] var_mean(Enter_variable) 288,360,1 48,24 2,56,66,1169,303,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] (vident:text; run, textornot:optional) Brings the data from the result database and transforms it into variables of the correct form. NOTE! All necessary indices must be created before running this function. The necessary indices can be viewed by calling the function Do_first with the same parameters as this function. PARAMETERS: * Vident: the Ident of the variable in the result database. * Run: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. * Textornot: Tells whether the result is numerical or text. If parameter is omitted or false, numerical is assumed, otherwise text. 1) Brings the data and makes indices for index list, locations, and result_id's. 2) Makes an array containing result_id, indexed by all indices and all locations. 3) Makes an array containing result_id, indexed by the indices of the variable itself. 4) Brings the results into the structure created in 3). Makes the sample fo along the index Run. var data:= Read_mean(vident, run); var a:= data[.j='Iident']; index ind_name:= a[.i=unique(a,a.i)]; a:= data[.j='Location']; index location:= a[.i=unique(a,a.i)]; a:= data[.j='Res.id']; index result_id:= a[.i=unique(a,a.i)]; var x:= 1; a:= null; while x<= size(data.i) do ( var b:= data[@.i=x]; a:= if location = b[.j='Location'] and ind_name = b[.j='Iident'] and result_id = b[.j='Res.id'] then b[.j='Res.id'] else a; x:= x+1); var c:= result_id; x:= 1; while x<=size(ind_name) do ( c:= if c= a[location=evaluate(ind_name[@ind_name=x]), @ind_name=x] then result_id else 0; x:=x+1); c:= sum(c,result_id); a:= data[.j='N']; index sample:= a[.i=unique(a,a.i)]; x:= 1; a:= null; while x<= size(data.i) do ( var b:= data[@.i=x]; a:= if c = b[.j='id'] and sample = b[.j='N'] then b[.j='Mean'] else a; x:= x+1); a:= if max(sample)>0 then a[sample=run] else a[@sample=1]; if isnotspecified(textornot) or textornot=false then evaluate(a) else a 56,136,1 48,12 2,431,23,557,770 vident,run,textornot (vident:text, run:optional) Reads the data about the var_name variable from the result database. Uses the run_id run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Var_name: the name of the variable in the result database. * Run_id: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(run) then run:= newestrun(vident); index i:= DBquery(Odbc,' SELECT Res.id, Sample, Result FROM Res, Sam WHERE Res.Obj_id_v = "'&vident&'" AND Res.Obj_id_r ='&Run&' AND Sam.Res_id = Res.id '); index j:= dblabels(i); dbtable(i,j) 56,168,1 48,22 2,7,60,516,428 39325,65535,39321 vident,run 'Op_en1912' 168,128,1 48,31 [Formnode Enter_variable1] 52425,39321,65535 0 304,24,1 176,13 1,0,0,1,0,0,0,170,0,1 52425,39321,65535 Enter_variable (vident:text, run:optional) Reads the data about the var_name variable from the Opasnet Base. Uses the run_id run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Var_name: the name of the variable in the result database. * Run_id: the identifier of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(run) then run:= newestrun(vident); index i:= DBquery(Odbc,' SELECT Var.Ident as Vident, Var.Name as Vname, Var.Unit as Vunit, Res.id, Ind.Ident as Iident, Loct, Mean, N, Run.Name as Rname FROM Obj as Var, Res, Locres, Loc, Obj as Ind, Obj as Run, Roww WHERE Res.Obj_id_r = Run.id AND Res.Obj_id_v = Var.id AND Locres.Res_id = Res.id AND Locres.Roww_id = Roww.id AND Roww.Obj_id_i = Ind.id AND Roww.Loc_id = Loc.id '); index j:= dblabels(i); dbtable(i,j) 48,304,1 48,16 2,714,55,516,428 39325,65535,39321 vident,run newestrun(Enter_variable) 288,176,1 48,24 read_mean(Enter_variable) 288,224,1 48,24 2,56,66,1205,308,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] Status of nodes and functions: * Newestrun: OK * Read mean: OK * Do_first: works reasonably well * Var_sample ? * Var_mean ? * Descr is this needed? * Read_sample ? 456,208,-1 104,92 Instructions for uploading results to the Opasnet Base: * Make sure that you have created an object page in the Opasnet wiki for each object you want to upload. (This applies also to assessments and runs!) * Use the wiki identifier as the Identifier for the object in Analytica. * Use the wiki page name as the Title for the object. * Make sure that you have defined the Units. * If an object with the same Ident (Analytica identifier) already exists, metadata about that object will NOT be updated. However, the actual results will be uploaded normally. 208,360,-1 196,192 ktluser 8. Decta 2008 3:01 48,24 64,64,1 48,24 1,15,216,495,296,17 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User='&username&'; Password='&password&';Option=3' 168,200,1 48,12 1,1,0,1,1,1,0,,0, 2,102,90,495,302 2,168,178,833,303,0,MIDM [] 'add username' 168,152,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Username1] 52425,39321,65535 'add password' 168,176,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Password1] 52425,39321,65535 Contains the parameters for the open database connectivity (ODBC). 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User=result_reader; Password=ora4ever;Option=3' 168,128,1 48,12 1,1,0,1,1,1,0,,0, 2,102,90,476,224 index i:= copyindex(D_i); index j:= copyindex(D_j); Dim1[d_i=i, d_j=j] 400,160,1 48,13 1,1,0,1,1,1,0,0,0,0 2,89,98,476,224 2,635,328,556,489,0,MIDM 19661,54073,65535 [D_i,D_j] [Sys_localindex('J'),Sys_localindex('I')] index i:= copyindex(I_i); index j:= copyindex(I_j); Ind1[I_i=i, I_j=j] 400,184,1 48,13 1,1,0,1,1,1,0,0,0,0 2,380,47,476,296 2,490,110,649,655,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] index i:= copyindex(L_i); index j:= copyindex(L_j); Loc1[L_i=i, L_j=j] 400,96,1 48,13 1,1,0,1,1,1,0,0,0,0 2,370,45,476,445 2,43,42,1147,516,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(O_i); index j:= copyindex(O_j); Obj2[O_i=i, O_j=j] 400,48,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,21,103,889,421,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] Standard versions 400,112,-1 72,100 1,0,0,1,0,1,0,,0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] 168,24,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] ['id','Ident','Name'] 168,48,1 48,12 ['id','Ident','Name'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] 168,72,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] ['id','Iident','Iname','Did','Dident','Dname'] 168,96,1 48,12 ['id','Iident','Iname','Did','Dident','Dname'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466] 56,120,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466] ['id','Obj_id_i','Location','Roww','Locn','Num','Description','id','Ident','Name','Unit','Typ_id','Page','Wik_id'] 56,144,1 48,12 ['id','Obj_id_i','Location','Roww','Locn','Num','Description','id','Ident','Name','Unit','Typ_id','Page','Wik_id'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112] 56,24,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112] ['id','Ident','Name','Unit','Typ_id','Page','Wik_id'] 56,48,1 48,13 ['id','Ident','Name','Unit','Typ_id','Page','Wik_id'] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(S_i); index j:= copyindex(S_j); Sett1[S_i=i, S_j=j] 400,72,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,227,134,319,448,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(It_i); index j:= copyindex(It_j); Item1[it_i=i, it_j=j] 400,120,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,298,216,382,519,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] 56,168,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] ['id','Sett_id','Obj_id','Fail'] 56,192,1 48,13 ['id','Sett_id','Obj_id','Fail'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] 56,72,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] ['id','Obj_id','Sty_id'] 56,96,1 48,13 ['id','Obj_id','Sty_id'] Table(D_i,D_j)( 43,'Vehicle_type','Vehicle type', 45,'Transport_mode','Transport mode', 46,'Cost_type','Cost type', 47,'Composite_fraction','Composite fraction', 51,'Food_source','The method for food production', 52,'Feed_pollutant','Decision about fish feed', 53,'Salmon_recomm','Decision about samon consumption recommendation', 32,'0','No dimension has been identified', 54,'Parameter','Statistical and other parameters of a variable', 42,'Environ_compartment','Environmental compartment', 41,'Emission_source','Emission source', 36,'Pollutant','Pollutant', 34,'Health_impact','Health impact', 33,'Decision','Possible range of decisions for a single decision-maker', 35,'Time','Time', 40,'Period','Period', 48,'Age','Age', 37,'Spatial_location','Spatial location', 38,'Length','Length', 49,'Municipality_fin','Municipalities in Finland', 44,'Person_or_group','Person or group', 39,'Non_health_impact','Non-health impact' ) 280,160,1 48,13 1,1,1,1,1,1,0,0,0,0 2,89,98,476,224 2,604,56,556,489,0,MIDM 39325,65535,39321 [D_i,D_j] [D_j,D_i] Table(I_i,I_j)( 55,'Salmon_decision','',33,'Decision','Possible range of decisions for a single decision-maker', 80,'Reg_poll','',33,'Decision','Possible range of decisions for a single decision-maker', 81,'Recommendation1','',33,'Decision','Possible range of decisions for a single decision-maker', 83,'H1899','',33,'Decision','Possible range of decisions for a single decision-maker', 84,'H1898','',33,'Decision','Possible range of decisions for a single decision-maker', 56,'Hma_area','',37,'Spatial_location','Spatial location', 57,'Hma_region','',37,'Spatial_location','Spatial location', 58,'Hma_zone','',37,'Spatial_location','Spatial location', 88,'Condb_location1','',37,'Spatial_location','Spatial location', 93,'Op_en2672','',37,'Spatial_location','Spatial location', 59,'Year_1','',35,'Time','Time', 61,'Year_2','',35,'Time','Time', 82,'Year3','',35,'Time','Time', 60,'Op_en2665','Cause of death 1',34,'Health_impact','Health impact', 62,'Cause_of_death_2','',34,'Health_impact','Health impact', 85,'Cause_of_death3','',34,'Health_impact','Health impact', 63,'Length_1','',38,'Length','Length', 70,'Output_1','',39,'Non_health_impact','Non-health impact', 65,'Period_1','',40,'Period','Period', 86,'Run','',32,'0','No dimension has been identified', 71,'Vehicle_noch','',43,'Vehicle_type','Vehicle type', 92,'Vehicle_1','',43,'Vehicle_type','Vehicle type', 72,'Stakeholder_1','',44,'Person_or_group','Person or group', 73,'Mode1','',45,'Transport_mode','Transport mode', 74,'Cost_structure_1','',46,'Cost_type','Cost type', 75,'Comp_fr_1','',47,'Composite_fraction','Composite fraction', 76,'Age1','',48,'Age','Age', 77,'Municipality_fin1','',49,'Municipality_fin','Municipalities in Finland', 79,'Salmon1','',51,'Food_source','The method for food production', 78,'Pollutant1','',36,'Pollutant','Pollutant', 89,'Condb_agent1','',36,'Pollutant','Pollutant', 91,'Condb_agent2','',36,'Pollutant','Pollutant', 87,'Condb_compartment1','',42,'Environ_compartment','Environmental compartment', 90,'Condb_param1','',54,'Parameter','Statistical and other parameters of a variable' ) 280,184,1 48,13 1,1,1,1,1,1,0,0,0,0 2,380,47,476,296 2,232,242,874,303,0,MIDM 2,12,22,876,493,0,MIDM 39325,65535,39321 [I_j,I_i] [I_j,I_i] Table(L_i,L_j)( 1,1,'Business as usual',0,0,0,'',1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 2,1,'Recommend restrictions to salmon consumption',0,0,0,'',2,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 3,1,'Stricter limits for fish feed pollutants',0,0,0,'',3,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 4,1,'Restrictions to salmon consumption AND stricter fish feed limits',0,0,0,'',4,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 26,2,'All causes',0,0,0,'',26,'Op_en2693','Testvariable','kg',1,2693,1, 197,6,'>= 5 km',0,0,0,'',197,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 196,6,'< 5 km',0,0,0,'',196,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 8,3,'2020',0,0,0,'',8,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 7,3,'1997',0,0,0,'',7,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 10,2,'Cardiopulmonary',0,0,0,'',10,'Op_en2693','Testvariable','kg',1,2693,1, 11,2,'Lung cancer',0,0,0,'',11,'Op_en2693','Testvariable','kg',1,2693,1, 12,2,'All others',0,0,0,'',12,'Op_en2693','Testvariable','kg',1,2693,1, 27,5,'Downtown',0,0,0,'',27,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 28,5,'Centre',0,0,0,'',28,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 29,5,'Suburb',0,0,0,'',29,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 30,5,'Länsi-Espoo',0,0,0,'',30,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 31,5,'Pohjois-Espoo',0,0,0,'',31,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 32,5,'Etelä-Espoo',0,0,0,'',32,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 33,5,'Keski-Espoo',0,0,0,'',33,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 34,5,'Länsi-Vantaa',0,0,0,'',34,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 35,5,'Keski-Vantaa',0,0,0,'',35,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 36,5,'Pohjois-Vantaa',0,0,0,'',36,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 37,5,'Itä-Vantaa',0,0,0,'',37,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 38,5,'Kanta-Helsinki',0,0,0,'',38,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 39,5,'Länsi-Helsinki',0,0,0,'',39,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 40,5,'Vanha-Helsinki',0,0,0,'',40,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 41,5,'Konalanseutu',0,0,0,'',41,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 42,5,'Pakilanseutu',0,0,0,'',42,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 43,5,'Malminseutu',0,0,0,'',43,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 44,5,'Itä-Helsinki',0,0,0,'',44,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 45,5,'1001',0,0,0,'',45,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 46,5,'1002',0,0,0,'',46,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 47,5,'1003',0,0,0,'',47,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 48,5,'1004',0,0,0,'',48,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 49,5,'1005',0,0,0,'',49,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 50,5,'1006',0,0,0,'',50,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 51,5,'1007',0,0,0,'',51,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 52,5,'1008',0,0,0,'',52,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 53,5,'1009',0,0,0,'',53,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 54,5,'1010',0,0,0,'',54,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 55,5,'1011',0,0,0,'',55,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 56,5,'1012',0,0,0,'',56,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 57,5,'1013',0,0,0,'',57,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 58,5,'1014',0,0,0,'',58,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 59,5,'1015',0,0,0,'',59,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 60,5,'1016',0,0,0,'',60,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 61,5,'1017',0,0,0,'',61,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 62,5,'1018',0,0,0,'',62,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 63,5,'1019',0,0,0,'',63,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 64,5,'1020',0,0,0,'',64,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 65,5,'1021',0,0,0,'',65,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 66,5,'1022',0,0,0,'',66,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 67,5,'1023',0,0,0,'',67,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 68,5,'1024',0,0,0,'',68,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 69,5,'1025',0,0,0,'',69,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 70,5,'1026',0,0,0,'',70,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 71,5,'1027',0,0,0,'',71,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 72,5,'1028',0,0,0,'',72,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 73,5,'1029',0,0,0,'',73,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 74,5,'1030',0,0,0,'',74,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 75,5,'1031',0,0,0,'',75,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 76,5,'1032',0,0,0,'',76,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 77,5,'1033',0,0,0,'',77,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 78,5,'1034',0,0,0,'',78,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 79,5,'1035',0,0,0,'',79,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 80,5,'1036',0,0,0,'',80,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 81,5,'1037',0,0,0,'',81,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 82,5,'1038',0,0,0,'',82,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 83,5,'1039',0,0,0,'',83,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 84,5,'1040',0,0,0,'',84,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 85,5,'1041',0,0,0,'',85,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 86,5,'1042',0,0,0,'',86,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 87,5,'1043',0,0,0,'',87,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 88,5,'1044',0,0,0,'',88,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 89,5,'1045',0,0,0,'',89,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 90,5,'1046',0,0,0,'',90,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 91,5,'1047',0,0,0,'',91,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 92,5,'1048',0,0,0,'',92,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 93,5,'1049',0,0,0,'',93,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 94,5,'1050',0,0,0,'',94,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 95,5,'1051',0,0,0,'',95,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 96,5,'1052',0,0,0,'',96,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 97,5,'1053',0,0,0,'',97,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 98,5,'1054',0,0,0,'',98,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 99,5,'1055',0,0,0,'',99,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 100,5,'1056',0,0,0,'',100,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 101,5,'1057',0,0,0,'',101,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 102,5,'1058',0,0,0,'',102,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 103,5,'1059',0,0,0,'',103,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 104,5,'1060',0,0,0,'',104,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 105,5,'1061',0,0,0,'',105,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 106,5,'1062',0,0,0,'',106,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 107,5,'1063',0,0,0,'',107,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 108,5,'1064',0,0,0,'',108,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 109,5,'1065',0,0,0,'',109,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 110,5,'1066',0,0,0,'',110,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 111,5,'1067',0,0,0,'',111,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 112,5,'1068',0,0,0,'',112,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 113,5,'1069',0,0,0,'',113,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 114,5,'1070',0,0,0,'',114,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 115,5,'1071',0,0,0,'',115,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 116,5,'1072',0,0,0,'',116,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 117,5,'1073',0,0,0,'',117,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 118,5,'1074',0,0,0,'',118,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 119,5,'1075',0,0,0,'',119,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 120,5,'1076',0,0,0,'',120,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 121,5,'1077',0,0,0,'',121,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 122,5,'1078',0,0,0,'',122,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 123,5,'1079',0,0,0,'',123,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 124,5,'1080',0,0,0,'',124,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 125,5,'1081',0,0,0,'',125,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 126,5,'1082',0,0,0,'',126,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 127,5,'1083',0,0,0,'',127,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 128,5,'1084',0,0,0,'',128,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 129,5,'1085',0,0,0,'',129,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 130,5,'1086',0,0,0,'',130,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 131,5,'1087',0,0,0,'',131,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 132,5,'1088',0,0,0,'',132,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 133,5,'1089',0,0,0,'',133,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 134,5,'1090',0,0,0,'',134,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 135,5,'1091',0,0,0,'',135,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 136,5,'1092',0,0,0,'',136,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 137,5,'1093',0,0,0,'',137,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 138,5,'1094',0,0,0,'',138,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 139,5,'1095',0,0,0,'',139,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 140,5,'1096',0,0,0,'',140,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 141,5,'1097',0,0,0,'',141,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 142,5,'1098',0,0,0,'',142,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 143,5,'1099',0,0,0,'',143,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 144,5,'1100',0,0,0,'',144,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 145,5,'1101',0,0,0,'',145,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 146,5,'1102',0,0,0,'',146,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 147,5,'1103',0,0,0,'',147,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 148,5,'1104',0,0,0,'',148,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 149,5,'1105',0,0,0,'',149,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 150,5,'1106',0,0,0,'',150,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 151,5,'1107',0,0,0,'',151,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 152,5,'1108',0,0,0,'',152,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 153,5,'1109',0,0,0,'',153,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 154,5,'1110',0,0,0,'',154,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 155,5,'1111',0,0,0,'',155,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 156,5,'1112',0,0,0,'',156,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 157,5,'1113',0,0,0,'',157,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 158,5,'1114',0,0,0,'',158,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 159,5,'1115',0,0,0,'',159,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 160,5,'1116',0,0,0,'',160,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 161,5,'1117',0,0,0,'',161,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 162,5,'1118',0,0,0,'',162,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 163,5,'1119',0,0,0,'',163,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 164,5,'1120',0,0,0,'',164,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 165,5,'1121',0,0,0,'',165,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 166,5,'1122',0,0,0,'',166,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 167,5,'1123',0,0,0,'',167,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 168,5,'1124',0,0,0,'',168,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 169,5,'1125',0,0,0,'',169,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 170,5,'1126',0,0,0,'',170,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 171,5,'1127',0,0,0,'',171,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 172,5,'1128',0,0,0,'',172,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 173,5,'1129',0,0,0,'',173,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 174,5,'1130',0,0,0,'',174,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 175,35,'2000',0,0,0,'',175,'Time','Time','s or date',2,2497,1, 176,3,'2001',0,0,0,'',176,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 177,3,'2002',0,0,0,'',177,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 178,3,'2003',0,0,0,'',178,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 179,3,'2004',0,0,0,'',179,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 180,3,'2005',0,0,0,'',180,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 181,3,'2006',0,0,0,'',181,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 182,3,'2007',0,0,0,'',182,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 183,3,'2008',0,0,0,'',183,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 184,3,'2009',0,0,0,'',184,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 185,3,'2010',0,0,0,'',185,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 186,3,'2011',0,0,0,'',186,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 187,3,'2012',0,0,0,'',187,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 188,3,'2013',0,0,0,'',188,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 189,3,'2014',0,0,0,'',189,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 190,3,'2015',0,0,0,'',190,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 191,3,'2016',0,0,0,'',191,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 192,3,'2017',0,0,0,'',192,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 193,3,'2018',0,0,0,'',193,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 194,3,'2019',0,0,0,'',194,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 418,1,'BAU3',0,0,0,'',418,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 198,8,' 6.00-20.00',0,0,0,'',198,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 199,8,'20.00-24.00',0,0,0,'',199,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 200,8,' 0.00- 6.00',0,0,0,'',200,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 364,7,'Trips',0,0,0,'',364,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 365,7,'Trips by vehicle',0,0,0,'',365,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 366,7,'Vehicle km',0,0,0,'',366,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 367,7,'Parking lot',0,0,0,'',367,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 368,7,'Link intensity',0,0,0,'',368,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 369,7,'Vehicles',0,0,0,'',369,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 370,7,'Waiting',0,0,0,'',370,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 371,11,'Bus no change',0,0,0,'',371,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 372,11,'Bus one change',0,0,0,'',372,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 373,11,'Cab no change',0,0,0,'',373,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 374,11,'Cab one change',0,0,0,'',374,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 375,11,'Cab non-full',0,0,0,'',375,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 376,11,'Car',0,0,0,'',376,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 377,11,'No-change',0,0,0,'',377,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 378,12,'Passenger',0,0,0,'',378,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 379,12,'Society',0,0,0,'',379,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 380,13,'Car',0,0,0,'',380,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 381,13,'Composite',0,0,0,'',381,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 382,14,'Vehicle',0,0,0,'',382,'Testvariable2','Another variable for testing','kg',1,0,0, 383,14,'Driver',0,0,0,'',383,'Testvariable2','Another variable for testing','kg',1,0,0, 384,14,'Driving',0,0,0,'',384,'Testvariable2','Another variable for testing','kg',1,0,0, 385,14,'Parking',0,0,0,'',385,'Testvariable2','Another variable for testing','kg',1,0,0, 386,14,'Parking land',0,0,0,'',386,'Testvariable2','Another variable for testing','kg',1,0,0, 387,14,'Emissions',0,0,0,'',387,'Testvariable2','Another variable for testing','kg',1,0,0, 388,14,'Time',0,0,0,'',388,'Testvariable2','Another variable for testing','kg',1,0,0, 389,14,'Accidents',0,0,0,'',389,'Testvariable2','Another variable for testing','kg',1,0,0, 390,14,'Ticket',0,0,0,'',390,'Testvariable2','Another variable for testing','kg',1,0,0, 391,15,'0',0,0,0,'',391,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 392,15,'0.02',0,0,0,'',392,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 393,15,'0.05',0,0,0,'',393,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 394,15,'0.1',0,0,0,'',394,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 395,15,'0.25',0,0,0,'',395,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 396,15,'0.4',0,0,0,'',396,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 397,15,'0.45',0,0,0,'',397,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 398,15,'0.5',0,0,0,'',398,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 399,15,'0.55',0,0,0,'',399,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 400,15,'0.65',0,0,0,'',400,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 401,15,'0.75',0,0,0,'',401,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 402,15,'0.9',0,0,0,'',402,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 403,15,'1',0,0,0,'',403,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 404,16,'18-65',0,0,0,'',404,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 405,16,'3',0,0,0,'',405,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 406,17,'Harjavalta',0,0,0,'',406,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1, 407,36,'Dieldrin',0,0,0,'',407,'Pollutant','Pollutant','-',2,2493,1, 408,36,'Toxaphene',0,0,0,'',408,'Pollutant','Pollutant','-',2,2493,1, 409,36,'Dioxin',0,0,0,'',409,'Pollutant','Pollutant','-',2,2493,1, 410,36,'PCB',0,0,0,'',410,'Pollutant','Pollutant','-',2,2493,1, 411,42,'Farmed salmon',0,0,0,'',411,'Environ_compartment','Environmental compartment','-',2,2490,1, 412,42,'Wild salmon',0,0,0,'',412,'Environ_compartment','Environmental compartment','-',2,2490,1, 413,42,'Market salmon',0,0,0,'',413,'Environ_compartment','Environmental compartment','-',2,2490,1, 414,33,'BAU',0,0,0,'',414,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 415,33,'More actions',0,0,0,'',415,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 416,33,'BAU2',0,0,0,'',416,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 417,33,'Restrict farmed salmon use',0,0,0,'',417,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 419,1,'More actions',0,0,0,'',419,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 421,1,'Restrict farmed salmon use2',0,0,0,'',421,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 422,34,'Cardiovascular',0,0,0,'',422,'Health_impact','Health impact','',2,2495,1, 423,10,'Home indoor',0,0,0,'Abbreviation in the Concentration database: I',423,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 424,10,'(Home) outdoor',0,0,0,'Abbreviation in the Concentration database: O',424,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 425,10,'(Personal) Work',0,0,0,'Abbreviation in the Concentration database: W',425,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 426,10,'Personal',0,0,0,'Abbreviation in the Concentration database: P',426,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 427,10,'Drinking water',0,0,0,'Abbreviation in the Concentration database: DW',427,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 428,10,'Indoor dust',0,0,0,'Abbreviation in the Concentration database: ID',428,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 429,10,'Human',0,0,0,'Abbreviation in the Concentration database: H',429,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 430,10,'Soil',0,0,0,'Abbreviation in the Concentration database: S',430,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 431,10,'Beverage',0,0,0,'Abbreviation in the Concentration database: B',431,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 432,10,'Food',0,0,0,'Abbreviation in the Concentration database: F',432,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 433,10,'In-Vehicle',0,0,0,'Abbreviation in the Concentration database: IV',433,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 434,10,'School',0,0,0,'Abbreviation in the Concentration database: SC',434,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 435,5,'Athens',0,0,0,'Country: Greece. Abbreviation in the Concentration Database: A',435,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 436,5,'Antwerp',0,0,0,'Country: Belgium. Abbreviation in the Concentration Database: ANT',436,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 437,5,'Antioch-Pittsburg',0,0,0,'Country: USA. Abbreviation in the Concentration Database: AP',437,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 438,5,'Antioch-Pittsburg A-P',0,0,0,'Country: USA. Abbreviation in the Concentration Database: A-P',438,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 439,5,'Arizona',0,0,0,'Country: USA. Abbreviation in the Concentration Database: AZ',439,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 440,5,'Basel',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: B',440,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 441,5,'Baltimore',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BAL',441,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 442,5,'Bayonne',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BAY',442,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 443,5,'Bayonne-Ellizabeth',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BE',443,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 444,5,'Copenhagen',0,0,0,'Country: Denmark. Abbreviation in the Concentration Database: C',444,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 445,5,'California',0,0,0,'Country: USA. Abbreviation in the Concentration Database: CA',445,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 446,5,'Columbus',0,0,0,'Country: USA. Abbreviation in the Concentration Database: CO',446,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 447,5,'Daegu',0,0,0,'Country: South Korea. Abbreviation in the Concentration Database: D',447,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 448,5,'Devils Lake',0,0,0,'Country: USA. Abbreviation in the Concentration Database: DLA',448,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 449,5,'Dublin',0,0,0,'Country: Ireland. Abbreviation in the Concentration Database: DU',449,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 450,5,'Elizabeth',0,0,0,'Country: USA. Abbreviation in the Concentration Database: ELI',450,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 451,5,'EPA Region 5.',0,0,0,'Country: USA. Abbreviation in the Concentration Database: EPA5',451,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 452,5,'Flanders',0,0,0,'Country: Belgium. Abbreviation in the Concentration Database: FLA',452,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 453,5,'Florence',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: FL',453,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 454,5,'Grenoble',0,0,0,'Country: France. Abbreviation in the Concentration Database: G',454,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 455,5,'Germany',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: GE',455,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 456,5,'Genoa',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: GEN',456,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 457,5,'Greensboro GNC',0,0,0,'Country: USA. Abbreviation in the Concentration Database: GNC',457,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 458,5,'Greensboro',0,0,0,'Country: USA. Abbreviation in the Concentration Database: GRB',458,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 460,5,'Helsinki',0,0,0,'Country: Finland. Abbreviation in the Concentration Database: H',460,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 461,5,'Hannover',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: HA',461,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 462,5,'Ile de France',0,0,0,'Country: France. Abbreviation in the Concentration Database: IDF',462,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 463,5,'Los Angeles',0,0,0,'Country: USA. Abbreviation in the Concentration Database: LA',463,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 464,5,'Milan',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: M',464,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 465,5,'Minneapolis',0,0,0,'Country: USA. Abbreviation in the Concentration Database: MP',465,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 466,5,'Minnesota',0,0,0,'Country: USA. Abbreviation in the Concentration Database: MS',466,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 467,5,'Murcia',0,0,0,'Country: Spain. Abbreviation in the Concentration Database: MU',467,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 468,5,'Mexico City',0,0,0,'Country: Mexico. Abbreviation in the Concentration Database: MXC',468,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 469,5,'Oxford',0,0,0,'Country: England. Abbreviation in the Concentration Database: O',469,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 470,5,'Prague',0,0,0,'Country: Czech. Abbreviation in the Concentration Database: P',470,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 471,5,'Padua',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: PA',471,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 472,5,'Puebla',0,0,0,'Country: Mexico. Abbreviation in the Concentration Database: PB',472,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 473,5,'Rouen',0,0,0,'Country: France. Abbreviation in the Concentration Database: R',473,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 475,5,'Strasbourg',0,0,0,'Country: France. Abbreviation in the Concentration Database: STR',475,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 476,5,'Umbria region',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: UMB',476,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 477,5,'United States',0,0,0,'Country: USA. Abbreviation in the Concentration Database: USA',477,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 478,5,'Valdez',0,0,0,'Country: USA. Abbreviation in the Concentration Database: VAL',478,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 479,5,'Woodland',0,0,0,'Country: USA. Abbreviation in the Concentration Database: WDL',479,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 480,4,'66-25-1',0,0,0,'hexanal',480,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 481,4,'71-36-3',0,0,0,'1-butanol',481,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 482,4,'71-43-2',0,0,0,'benzene',482,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 483,4,'78-83-1',0,0,0,'2-methyl-1-propanol',483,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 484,4,'79-00-5',0,0,0,'1,1,2-trichloroethane',484,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 485,4,'79-01-6',0,0,0,'trichloroethene',485,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 486,4,'80-56-8',0,0,0,'alfa-pinene',486,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 487,4,'91-20-3',0,0,0,'naphtalene',487,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 488,4,'95-47-6',0,0,0,'o-xylene',488,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 489,4,'95-63-6',0,0,0,'trimethylbenzenes',489,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 490,4,'100-41-4',0,0,0,'ethylbenzene',490,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 491,4,'100-42-5',0,0,0,'styrene',491,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 492,4,'100-52-7',0,0,0,'benzaldehyde',492,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 493,4,'103-65-1',0,0,0,'propylbenzene',493,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 494,4,'104-76-7',0,0,0,'2-ethylhexanol',494,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 495,4,'108-38-3',0,0,0,'m(&p)-xylene',495,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 496,4,'108-88-3',0,0,0,'toluene',496,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 497,4,'108-95-2',0,0,0,'phenol',497,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 498,4,'110-54-3',0,0,0,'hexane',498,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 499,4,'110-82-7',0,0,0,'cyclohexane',499,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 500,4,'111-76-2',0,0,0,'ethanol, 2-butoxy-',500,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 501,4,'111-84-2',0,0,0,'nonane',501,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 502,4,'111-87-5',0,0,0,'1-octanol',502,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 503,4,'124-13-0',0,0,0,'octanal',503,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 504,4,'124-18-5',0,0,0,'decane',504,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 505,4,'127-18-4',0,0,0,'tetrachloroethene',505,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 506,4,'138-86-3',0,0,0,'d-limonene',506,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 507,4,'872-50-4',0,0,0,'2-pyrrolidinone, 1-methyl-',507,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 508,4,'1120-21-4',0,0,0,'undecane',508,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 509,4,'13466-78-9',0,0,0,'3-caren',509,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 510,4,'TVOC',0,0,0,'Toluene based total VOC',510,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 511,4,'67-66-3',0,0,0,'chloroform',511,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 512,4,'106-46-7',0,0,0,'1,4-dichlorobenzene',512,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 514,4,'56-23-5',0,0,0,'carbon tetrachloride',514,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 515,4,'75-09-2',0,0,0,'methylene chloride',515,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 517,4,'127-91-3',0,0,0,'b-pinene',517,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 520,4,'142-82-5',0,0,0,'n-heptane',520,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 521,4,'111-65-9',0,0,0,'n-octane',521,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 525,4,'112-40-3',0,0,0,'n-dodecane',525,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 526,4,'629-50-5',0,0,0,'n-tridecane',526,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 527,4,'629-59-4',0,0,0,'n-tetradecane',527,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 528,4,'629-62-9',0,0,0,'n-pentadecane',528,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 529,4,'107-83-5',0,0,0,'2-methylpentane',529,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 530,4,'96-14-0',0,0,0,'3-methylpentane',530,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 531,4,'565-59-3',0,0,0,'2,3-dimethylpentane',531,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 532,4,'591-76-4',0,0,0,'2-methylhexane',532,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 533,4,'589-34-4',0,0,0,'3-methylhexane',533,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 534,4,'592-27-8',0,0,0,'2-methylheptane',534,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 535,4,'589-81-1',0,0,0,'3-methylheptane',535,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 536,4,'96-37-7',0,0,0,'methylcyclopentane',536,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 537,4,'108-87-2',0,0,0,'methylcyclohexane',537,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 538,4,'526-73-8',0,0,0,'1,2,3-trimethylbenzene',538,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 540,4,'108-67-8',0,0,0,'1,3,5 trimethylbenzene',540,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 541,4,'4994-16-5',0,0,0,'4-phenylcyclohexene',541,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 542,4,'1,1,1-trichloroethane',0,0,0,'1,1,1-trichloroethane',542,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 545,4,'141-78-6',0,0,0,'ethylacetate',545,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 546,4,'123-86-4',0,0,0,'n-butylacetate',546,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 547,4,'78-93-3',0,0,0,'methyl ethyl ketone',547,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 548,4,'106-35-4',0,0,0,'3-heptatone',548,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 549,4,'93-58-3',0,0,0,'methyl benzoate',549,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 552,4,'123-51-3',0,0,0,'iso-amyl alcohol^a',552,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 554,4,'67-63-0',0,0,0,'2-propanol^a',554,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 555,4,'1634-04-4',0,0,0,'t-butyl methylether',555,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 556,4,'7439-92-1',0,0,0,'lead',556,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 557,4,'7440-38-2',0,0,0,'arsenic',557,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 558,4,'7440-43-9',0,0,0,'cadmium',558,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 559,4,'7440-39-3',0,0,0,'barium',559,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 560,4,'7440-47-3',0,0,0,'chrome',560,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 561,4,'7440-50-8',0,0,0,'copper',561,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 562,4,'7439-96-5',0,0,0,'manganese',562,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 563,4,'7440-02-0',0,0,0,'nickel',563,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 564,4,'7782-49-2',0,0,0,'selenium',564,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 565,4,'7440-62-2',0,0,0,'vanadium',565,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 566,4,'7440-66-6',0,0,0,'zinc',566,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 567,4,'71-55-6',0,0,0,'1,1,1-trichloroethane',567,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 568,4,'7439-97-6',0,0,0,'mercury',568,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 570,4,'60-27-5',0,0,0,'creatinine',570,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 571,4,'7429-90-5',0,0,0,'aluminium',571,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 572,4,'7440-70-2',0,0,0,'calcium',572,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 573,4,'7439-95-4',0,0,0,'magnesium',573,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 574,4,'7723-14-0',0,0,0,'phosphorus',574,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 575,4,'7440-24-6',0,0,0,'strontium',575,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 576,4,'7439-89-6',0,0,0,'iron',576,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 577,4,'7440-09-7',0,0,0,'potassium',577,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 578,4,'7440-23-5',0,0,0,'sodium',578,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 579,4,'58-89-9',0,0,0,'lindane',579,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 580,4,'52645-53-1',0,0,0,'permenthrine',580,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 581,4,'107-13-1',0,0,0,'acrylonitrile',581,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 582,4,'79-06-1',0,0,0,'acrylamide',582,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 589,4,'611-14-3',0,0,0,'1-ethyl 2methyl benzene',589,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 592,4,'109-66-0',0,0,0,'n-pentane',592,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 593,4,'7785-26-4',0,0,0,'alpha-pinene',593,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 594,4,'5989-27-5',0,0,0,'d-limonene',594,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 596,4,'106-99-0',0,0,0,'butadiene',596,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 597,4,'74-84-0',0,0,0,'ethane',597,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 598,4,'74-85-1',0,0,0,'ethylene',598,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 599,4,'74-86-2',0,0,0,'acetylene',599,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 600,4,'107-06-2',0,0,0,'1,2-dichloroethane',600,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 601,4,'106-42-3',0,0,0,'p-xylene',601,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 603,4,'98-82-8',0,0,0,'isopropylbenzene',603,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 604,4,'110-86-1',0,0,0,'pyridine',604,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 606,4,'109-06-8',0,0,0,'2-picoline',606,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 608,4,'108-99-6',0,0,0,'3-picoline',608,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 609,4,'108-89-4',0,0,0,'4-picoline',609,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 610,4,'104-51-8',0,0,0,'n-butylbenzene',610,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 611,4,'536-78-7',0,0,0,'3-ethylpyridine',611,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 613,4,'25551-13-7',0,0,0,'trimethylbenzene',613,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 618,4,'1336-36-3',0,0,0,'PCBs',618,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 619,4,'3547-04-4',0,0,0,'DDE',619,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 620,4,'118-74-1',0,0,0,'HCB',620,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 621,4,'5315-79-7',0,0,0,'1-hydroxypyrene',621,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 623,4,'1330-20-7',0,0,0,'xylenes',623,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 624,4,'37210-16-5',0,0,0,'CO2',624,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 625,4,'630-08-0',0,0,0,'CO',625,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 626,4,'54-11-5',0,0,0,'nicotine',626,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 628,4,'3588-17-8',0,0,0,'trans,trans-Muconic acid',628,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 629,4,'50-32-8',0,0,0,'benzo(a)pyrene',629,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 631,4,'590-86-3',0,0,0,'isovaleraldehyde',631,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 632,4,'123-38-6',0,0,0,'propionaldehyde',632,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 633,4,'123-72-8',0,0,0,'n-butyraldehyde',633,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 634,4,'75-07-0',0,0,0,'acetaldehyde',634,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 636,4,'50-00-0',0,0,0,'formaldehyde',636,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 637,4,'110-62-3',0,0,0,'valeraldehyde',637,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 638,4,'4170-30-3',0,0,0,'crotonaldehyde',638,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 639,22,'n',0,0,0,'Number of observations',639,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 640,22,'n_lt_LOQ',0,0,0,'Number of observations below level of quantitation',640,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 641,22,'F0.10',0,0,0,'Fractile 0.1',641,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 642,22,'F0.50',0,0,0,'Fractile 0.5',642,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 643,22,'F0.90',0,0,0,'Fractile 0.9',643,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 644,22,'F0.95',0,0,0,'Fractile 0.95',644,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 645,22,'Mean',0,0,0,'Arithmetic mean',645,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 646,22,'GeoMean',0,0,0,'Geometric mean',646,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 647,5,'ang',0,0,0,'Anglian Water ',647,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 648,5,'bou',0,0,0,'Bristol Water ',648,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 649,5,'brw',0,0,0,'Bournemouth & West hants ',649,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 650,5,'caw',0,0,0,'Cambridge Water ',650,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 651,5,'cho',0,0,0,'Cholderton Water ',651,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 652,5,'dcc',0,0,0,'Dee Valley Water ',652,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 653,5,'eas',0,0,0,'Welsh Water ',653,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 654,5,'ess',0,0,0,'Essex and Suffolk Water ',654,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 655,5,'fol',0,0,0,'Folkestone & Dover Water ',655,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 656,5,'har',0,0,0,'Hartlepool Water ',656,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 657,5,'mik',0,0,0,'Mid Kent Water ',657,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 658,5,'nor',0,0,0,'Northumbrian Water ',658,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 659,5,'nww',0,0,0,'Portsmouth Water ',659,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 660,5,'por',0,0,0,'Sutton & East Surrey Water ',660,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 661,5,'sea',0,0,0,'South East Water ',661,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 662,5,'sev',0,0,0,'Southern Water ',662,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 663,5,'sos',0,0,0,'South Staffordshire Water ',663,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 664,5,'sou',0,0,0,'Severn Trent Water ',664,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 665,5,'sww',0,0,0,'South West Water ',665,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 666,5,'teh',0,0,0,'Tendring Hundred Water ',666,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 667,5,'tha',0,0,0,'Thames Water ',667,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 668,5,'thr',0,0,0,'Three Valleys Water ',668,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 669,5,'wes',0,0,0,'United Utilties (North West Water) ',669,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 670,5,'wrx',0,0,0,'Wessex Water ',670,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 671,5,'yor',0,0,0,'Yorkshire Water',671,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 672,25,'BAU',0,0,0,'0.00000000000000',672,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 673,25,'Restrict farmed salmon use',0,0,0,'0.00000000000000',673,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 674,26,'BAU',0,0,0,'0.00000000000000',674,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 675,26,'More actions',0,0,0,'0.00000000000000',675,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 676,130,'Dieldrin',0,0,0,'0.00000000000000',676,'Op_en2705','Pollutant','-',6,2705,1, 677,130,'Toxaphene',0,0,0,'0.00000000000000',677,'Op_en2705','Pollutant','-',6,2705,1, 678,130,'Dioxin',0,0,0,'0.00000000000000',678,'Op_en2705','Pollutant','-',6,2705,1, 679,130,'PCB',0,0,0,'0.00000000000000',679,'Op_en2705','Pollutant','-',6,2705,1, 680,131,'Farmed salmon',0,0,0,'0.00000000000000',680,'Op_en2706','Salmon type','-',6,2706,1, 681,131,'Wild salmon',0,0,0,'0.00000000000000',681,'Op_en2706','Salmon type','-',6,2706,1, 682,131,'Market salmon',0,0,0,'0.00000000000000',682,'Op_en2706','Salmon type','-',6,2706,1, 685,133,'Cardiovascular',0,0,0,'0.00000000000000',685,'Op_en2707','Cause of death3','ICD-10',6,2707,1, 688,135,'2000',0,0,0,'0.00000000000000',688,'Op_en2708','Year3','year',6,2708,1 ) 280,96,1 48,13 1,1,1,1,1,1,0,0,0,0 2,370,45,476,445 2,518,523,725,303,0,MIDM 2,404,34,750,516,0,MIDM 39325,65535,39321 [L_j,L_i] [L_j,L_i] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(O_i,O_j)( 1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 2,'Op_en2693','Testvariable','kg',1,2693,1, 3,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 4,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 5,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 6,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 7,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 8,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 9,'Fig_3_cost_by_source','Cost by source','e/trip',1,0,0, 10,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 11,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 12,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 13,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 14,'Testvariable2','Another variable for testing','kg',1,0,0, 15,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 16,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 17,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1, 18,'Op_en1905','Exposure to persistent pollutants due to salmon in the population of the Western Europe','µg/kg/d',1,1905,1, 19,'Op_en1906','Dose-response function of persistent pollutants','(mg/kg/d)-1',1,1906,1, 20,'Op_en1907','Omega-3 content in salmon','g/g',1,1907,1, 21,'Op_en1908','Omega-3 intake due to salmon in the population of the Western Europe','g/d',1,1908,1, 22,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 23,'Op_en1911','Cardiovascular mortality in the Western Europe','cases/a',1,1911,1, 24,'Op_en1912','Cardiovascular effects of omega-3 in salmon in teh Western Europe','avoided cases/a',1,1912,1, 25,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 26,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 27,'Op_en1902','Persistent pollutant concentrations in fish feed','fraction',1,1902,1, 28,'Op_en1904','Salmon intake in the population of the Western Europe','g/d',1,1904,1, 29,'Op_en1909','ERF of omega-3 fatty acids on cardiovascular effects','1/(g/d)',1,1909,1, 30,'Op_en2556','Personal exposures to volatile organic compounds in Germany','ug/m^3',1,2556,1, 31,'Op_en2406','Excess cases of iMetHb in England and Wales','number',1,2406,1, 33,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 34,'Health_impact','Health impact','',2,2495,1, 35,'Time','Time','s or date',2,2497,1, 36,'Pollutant','Pollutant','-',2,2493,1, 37,'Spatial_location','Spatial location',' km or °',2,2498,1, 38,'Length','Length','km',2,2498,1, 39,'Non_health_impact','Non-health impact','-',2,2500,1, 40,'Period','Period','s',2,2497,1, 41,'Emission_source','Emission source','-',2,2492,1, 42,'Environ_compartment','Environmental compartment','-',2,2490,1, 43,'Vehicle_type','Vehicle type','-',2,0,0, 44,'Person_or_group','Person or group','-',2,2499,1, 45,'Transport_mode','Transport mode','-',2,0,0, 46,'Cost_type','Cost type','-',2,0,0, 47,'Composite_fraction','Composite fraction','fraction',2,0,0, 48,'Age','Age','a',2,2497,1, 49,'Municipality_fin','Municipalities in Finland','-',2,2498,1, 51,'Food_source','The method for food production','-',2,0,0, 52,'Feed_pollutant','Decision about fish feed','-',2,0,0, 53,'Salmon_recomm','Decision about samon consumption recommendation','-',2,0,0, 32,'0','No dimension has been identified','-',2,0,0, 54,'Parameter','Statistical and other parameters of a variable','-',2,0,0, 55,'Salmon_decision','','',6,0,0, 56,'Hma_area','','',6,0,0, 57,'Hma_region','','',6,0,0, 58,'Hma_zone','','',6,0,0, 59,'Year_1','','',6,0,0, 60,'Op_en2665','Cause of death 1','ICD-10',6,2665,1, 61,'Year_2','','',6,0,0, 62,'Cause_of_death_2','','',6,0,0, 63,'Length_1','','',6,0,0, 70,'Output_1','','',6,0,0, 65,'Period_1','','',6,0,0, 86,'Run','','',6,0,0, 71,'Vehicle_noch','','',6,0,0, 72,'Stakeholder_1','','',6,0,0, 73,'Mode1','','',6,0,0, 74,'Cost_structure_1','','',6,0,0, 75,'Comp_fr_1','','',6,0,0, 76,'Age1','','',6,0,0, 77,'Municipality_fin1','','',6,0,0, 82,'Year3','','',6,0,0, 81,'Recommendation1','','',6,0,0, 80,'Reg_poll','','',6,0,0, 79,'Salmon1','','',6,0,0, 78,'Pollutant1','','',6,0,0, 83,'H1899','','',6,0,0, 84,'H1898','','',6,0,0, 85,'Cause_of_death3','','',6,0,0, 87,'Condb_compartment1','','',6,0,0, 88,'Condb_location1','','',6,0,0, 89,'Condb_agent1','','',6,0,0, 90,'Condb_param1','','',6,0,0, 91,'Condb_agent2','','',6,0,0, 92,'Vehicle_1','','',6,0,0, 93,'Op_en2672','','',6,0,0, 94,'94','Analytica','',9,0,0, 95,'95','Analytica 4.1.0.9','',9,0,0, 97,'97','Analytica 4.1.0.9, CompositeTraffic_1_0_6.ana v. 11:47, 1000 iterations','',9,0,0, 99,'99','Analytica 4.1.0.9, RDB connection.ANA, 100 iterations','',9,0,0, 100,'100','RDB connection.ANA v. 1.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 101,'101','RDB connection.ANA v. 2.9.2008. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 102,'102','RDB connection.ANA v. 3.9.2008 b. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 103,'103','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 104,'104','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 105,'105','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 106,'106','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 107,'107','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 108,'108','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 98,'98','Test','',9,0,0, 109,'109',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 110,'110',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 111,'111','RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 112,'112','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 113,'113','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 114,'Op_en1896','Benefit-risk assessment on farmed salmon','',4,1896,1, 130,'Op_en2705','Pollutant','-',6,2705,1, 131,'Op_en2706','Salmon type','-',6,2706,1, 133,'Op_en2707','Cause of death3','ICD-10',6,2707,1, 135,'Op_en2708','Year3','year',6,2708,1, 137,'Op_en2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,2694,1 ) 280,48,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,152,162,1057,343,0,MIDM 2,573,21,700,421,0,MIDM 39325,65535,39321 [O_j,O_i] [O_j,O_i] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(S_i,S_j)( 1,33,1, 2,37,1, 5,35,1, 6,34,1, 9,38,1, 10,39,1, 11,40,1, 12,32,1, 13,43,1, 14,44,1, 15,45,1, 16,46,1, 17,47,1, 18,48,1, 19,49,1, 23,51,1, 24,36,1, 28,42,1, 31,54,1, 35,114,3, 38,137,9, 37,114,4 ) 280,72,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,529,143,700,421,0,MIDM 39325,65535,39321 [S_j,S_i] [S_i,S_j] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(It_i,It_j)( 1,1,55,0, 2,2,56,0, 3,2,57,0, 4,2,58,0, 5,5,59,0, 6,6,60,0, 7,5,61,0, 8,6,62,0, 9,9,63,0, 10,11,65,0, 11,10,70,0, 12,13,71,0, 13,14,72,0, 14,15,73,0, 15,16,74,0, 16,17,75,0, 17,18,76,0, 18,19,77,0, 19,24,78,0, 20,23,79,0, 21,1,80,0, 22,1,81,0, 23,5,82,0, 24,1,83,0, 25,1,84,0, 26,6,85,0, 27,12,86,0, 28,28,87,0, 29,2,88,0, 30,24,89,0, 31,31,90,0, 32,24,91,0, 33,13,92,0, 34,2,93,0, 35,35,28,0 ) 280,120,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,529,143,700,421,0,MIDM 39325,65535,39321 [It_j,It_i] [It_i,It_j] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] DO NOT REMOVE THIS NODE. It is needed for computing the Objects node. ktluser 29. Decta 2008 21:51 48,24 168,224,1 52,12