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 “Dynamic()” function in your variables to perform dynamic simulation. 1000 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] 1 1 4 22 1 2 0 2 0 -1460 PM 14. jouta 2006 16:17 pmea 30. heita 2008 16:20 48,24 1,0,0,557,277,17 2,102,90,476,224 Arial Narrow, 13 0,Model Intarese_wp3_4,2,2,0,1,C:\Documents and Settings\pmea\Desktop\Nitrate.ANA Päivi 11. tamta 2008 10:45 48,24 176,48,1 48,24 1,30,22,1432,791,17 ['Finland','UK','Spain','Hungary','Romania'] 264,144,1 48,24 2,102,90,476,224 ['Finland','UK','Spain','Hungary','Romania'] ktluser 28. maata 2008 10:20 48,24 72,144,1 48,24 1,0,744,1432,791,17 Table(Water_source)( 1,1) 96,184,1 40,32 2,0,-23,1440,799 [] [Variable Total_exposure_due] l/day Truncate( Normal (0.240, 0.247), 0, 0.800 ) 320,168,1 104,32 2,0,-23,1433,792 2,0,-23,1072,555,1,STAT [Objective Total_ingestion] http://rais.ornl.gov/homepage/DRAFT_EXPOSURE_HANDBOOK.pdf mg/kg/d ’((No3_tap*Total_daily_consump*Total_exposure_due)’/weight)*Percentage_of_bottle’ 96,616,1 52,36 2,0,-23,1432,791 2,0,-23,1440,799,0,MIDM 2,94,226,1077,303,0,MIDM [Water_source,Country] [Water_source,Country] [Chance Total_daily_consump, Variable Total_exposure_due, Chance Weight, Chance Percentage_of_bottle] [] [Water_source,1,Age_group,1,Country,1,Data,1] [Data,1,Country,1,Water_source,1] 0.1 96,264,1 48,40 2,102,90,476,224 [Variable Total_exposure_due] 0.5 96,344,1 48,31 [Variable Total_exposure_due] {Rate_of_ingestion*{Proportion_of_inges*Proportion_of_boile} 96,448,1 48,49 2,0,-23,1440,799 [] [Variable Proportion_of_boile, Variable Proportion_of_inges1, Variable Rate_of_ingestion] [Objective Total_ingestion] kg Distribution of weight for infants < 6 months of age Normal( 7, 4 ) 264,360,1 48,24 2,392,402,416,303,1,PDFP [Objective Total_ingestion] [1,0,0,0] Uniform( 8, 50 ) 288,272,1 48,24 2,40,50,416,303,1,PDFP [Objective Total_ingestion] [1,0,0,0] 328,80,1 48,24 96,80,1 48,29 l/day cold water ingestion rate in infants up to 3 months 0.64 504,88,1 48,24 Table(Bottled_water_scena1)( 12.7,254) 704,176,1 52,40 2,0,-23,1432,791 2,253,198,416,303,0,MIDM [Variable Daily_per_capita_bo1] [36] {Annual_per_capita_b/365.25 840,176,1 52,40 [Variable Annual_per_capita_b1] [Chance Total_tap_water_con1] 2 840,280,1 52,39 [Chance Total_tap_water_con1] (Triangular(0.5,{Daily_per_capita_to,4))*(1-Triangular(0,({Daily_per_capita_bo/Daily_per_capita_tot),1)) 704,288,1 60,40 2,0,-23,1432,791 2,152,162,416,303,1,PDFP Diststeps:0 [Undefined,Bottled_water_scena1,Undefined,Undefined,1] [Variable Daily_per_capita_bo1, Variable Daily_per_capita_to1] [Variable Exposure_adjustment1] [1,0,0,0] Table({Bottled_water_scena)( {Total_tap_water_con[Bottled_water_scena1="baseline"]/Total_tap_water_cons[{Bottled_water_scena="baseline"],{Total_tap_water_con[Bottled_water_scena1="increased bottled"]/Total_tap_water_cons[{Bottled_water_scena="baseline"]) 704,400,1 48,31 2,0,-23,1432,791 2,564,578,573,303,0,MIDM [Chance Total_tap_water_con1] 696,80,1 48,24 {Monitoring_data_dis*Exposure_adjustment_ 472,352,1 72,28 2,662,307,416,303,0,MIDM [Bottled_water_scena1,England___wales_wate] [Index England___wales_wate] [Bottled_water_scenar,1,Water_supply_zones,1,Sys_localindex('STEP'),1] Table(Country)( 5.194901M,0,0,0,0) 176,424,1 48,24 2,0,-23,1440,799 Drinking water sources: Treated tap water or untreated water from private well ['Tap water','Well water'] 264,256,1 48,24 ['Tap water','Well water'] ktluser 28. maata 2008 10:20 48,24 72,312,1 48,24 1,0,-23,1432,791,17 Lognormal( , , 0.025, 0.01 ) 792,136,1 76,28 2,0,-23,1152,763 [Variable Infection_status_of_] [] 792,48,1 48,24 [Chance Background_prevalen1] ['Distribution','Average','Max'] 456,112,1 48,24 mg NO3-/l Nitrate concentration in drinking water. EU Directive 80/778/ETY for waterworks >5000 people of production >1000m3/d 50 1016,40,1 68,32 [Variable Standard_for_nitrat2] mg NO3-/l Table(Country)( 50,50,50,50,50) 1016,120,1 48,24 2,0,-23,1440,799 [Decision Drinking_water_legi1] [Variable Risk_based_on_legist] Table(Country,Water_source)( 0,0, 0,0, 0,0, 0,0, 0,0 ) 1016,200,1 48,24 2,0,-23,1440,799 [Water_source,Country] [Data,Country] [Variable Standard_for_nitrat2] '1-exp(Exposure_response*Total_ingestion)' 112,336,1 48,31 2,0,-23,1440,799 2,0,-23,1440,799,0,MIDM 2,384,316,479,481,0,MIDM [Water_source,Country] [] [Object Variable, Chance A1] [Scenarios,1,Water_companies,1,Sys_localindex('STEP'),1] Excess_rate_of_imeth*Infants__6_months 272,336,1 48,40 2,0,0,1280,922,1,PDFP {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Graph_pdf_valdim:1,0,1,1,0} [] [Index Age_categories1] [Sex,0,Age_categories,0,Scenarios,1,Water_companies,0,Sys_localindex('STEP'),1] Hazard quotient (HQ) = Ingestion exposure = Daily NO3 intake / Reference NO3 intake Reference intake = A level of daily metal intake at or below which adverse health effects are unlikely to occur, includes a safety factor TDI = Tolerable daily intake (RIVM) RfD = Reference dose (U.S.EPA) ADI = Acceptable daily intake (U.S.EPA) Margin of safety (MOS) MOS for ingestion exposure = NOAEL (or LOAEL) / Daily NO3 intake NOAEL = No observed adverse effect level (no safety factors included) LOAEL = Lowest observed adverse effect level (no safety factors included) Table(Risk_characterisatio)( '’’’Total_ingestion[Age_categories=’0-4’,Data=’Distribution’]/TDI’’’','’Noael/Total_ingestion[Age_categories=’0-4’’,Data=’Distribution’])’','(A[Age_categories=’0-4’]/Environmental_risk, Data=’Distribution)') 104,104,1 48,29 2,0,-23,1432,791 2,230,484,779,255,0,MIDM [Risk_characterisatio,Country] [Water_source,1,Country,1,Risk_characterisatio,1] ['HQ calculation: TCA/TDI','MOS calculation: NOAEC/LOAEC/NOAEL/LOAEL','EQC'] 464,48,1 48,24 ['HQ calculation: TCA/TDI','MOS calculation: NOAEC/LOAEC/NOAEL/LOAEL','EQC'] 15-20 % clinical cyanosis 20-45 % symptons of hypoxia result 792,224,1 48,24 2,0,-23,1440,799 Number of infants < 6 months in each country Table(Country)( 28.6792K,'Population_age[Populaition_age="T0_4")/8','Population_age[Populaition_age="T0_4")/8','Population_age[Populaition_age="T0_4")/8','Population_age[Populaition_age="T0_4")/8') 272,248,1 48,24 2,0,-23,1440,799 2,207,422,506,265,0,MIDM 2,0,-23,1440,799,0,MIDM [Variable Population_age] Table(Country,Age_categories1)( 287K,322K,323K,329K,326K,312K,335K,377K,378K,399K,421K,326K,274K,225K,212K,166K,104K,79.7K, 3.49M,3.74M,3.88M,3.67M,3.55M,3.87M,4.49M,4.63M,4.15M,3.74M,4.04M,3.34M,2.88M,2.6M,2.34M,1.97M,1.31M,1.12M, 1.92M,1.91M,2.1M,2.46M,3.18M,3.5M,3.38M,3.29M,3.03M,2.61M,2.43M,2.21M,1.85M,2.09M,1.85M,1.44M,875K,705K, 488K,582K,624K,669K,809K,787K,701K,609K,708K,824K,705K,609K,535K,490K,437K,339K,154K,126K, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ) 104,248,1 48,29 2,0,-23,1440,799 2,0,-23,1440,799,0,MIDM 2,296,306,419,484,0,MIDM [Country,Age_categories1] [Country,Age_categories1] [Variable Infants__6_months] ......... 1 856,144,-3 252,140 1,0,0,0,1,1,0,,0, 65535,65535,65535 Unused nodes 656,32,-1 48,24 Risk estimate 1 80,32,-1 48,24 Risk estimate 2 80,176,-1 48,24 Which risk estimates are relevant and possible to calculate for nitrate? 488,280,-1 48,53 ktluser 28. maata 2008 10:20 48,24 72,232,1 48,24 1,0,-23,1432,791,17 mg/kg/d Nitrate toxicity value for infants U.S. EPA has evaluated the noncancer oral data for nitrate and derived a reference dose (RfD) of 1.6 mg/kg-day with 10 % of risk to MetHb. Normal( 0.168576 ) 112,88,1 68,36 2,0,-95,1432,791 2,0,-23,1440,799,0,MIDM [Water_source,Country] [Data,Country] [] mg/l Risk level for nitrate Truncate( Normal(100,50), 100, 1000 ) 104,176,1 48,29 2,0,-23,1433,792 mg/l LOAEL: 11-20 mg nitrate- nitrogen/L (1.8-3.2 mg/kg/day) Uniform( 11, 20 ) 104,248,1 48,24 2,0,-23,1433,792 mg/kg/d No observed adverded effect level (NOEL) for nitrate in drinking water NOAEL: 10 mg nitrate-nitrogen/L (1.6 mg/kg/day) Early clinical signs of methemoglobinemia in excess of 10% (0-3 months old infants formula) Human Epidemiological Surveys Bosch et al., 1950; Walton, 1951 USEPA Iris http://www.epa.gov/NCEA/iris/subst/0076.htm Normal( 1.6, 16 ) 104,328,1 48,24 2,0,-23,1433,792 2,0,-23,1440,799,1,PDFP [1,0,0,0] mg/kg/day US EPA reference oral dose for nitrate 7 96,424,1 48,24 2,0,-23,1433,792 mg/kg/day Exposure response function (acceptable daily intake according to FAO/WHO 3.7 96,504,1 48,24 Check exposure response! 256,91,-1 48,27 ktluser 28. maata 2008 10:20 48,24 72,64,1 48,24 1,0,-23,1432,791,17 mg/l If Local_data_well="Na" Then General_well Else Local_data_well 80,280,1 60,40 2,0,-23,1432,791 2,0,-23,1433,792,0,MIDM [Object Variable] [, Variable Clipboard_total_dail, Variable Adjustment_factor] mg/l If Local_data_tap="Na" then General_tap else Local_data_tap 248,280,1 60,40 2,0,-23,1432,791 2,0,-119,1432,791,1,MEAN [Water_source,Country] [Objective Excess_rate_of_imeth] [0,1,1,0] mg/l Table(Country,Water_companies_in_e)( 'normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)','normal(1.73255814, 1.374642461)', 'Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)','Normal (22.77, 39.53570237)', '’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)','’normal (3.3, 4.74751E-16)', 'Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na', 'Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na','Na' ) 248,104,1 48,29 2,583,108,476,224 2,800,744,1432,791,0,MIDM 2,0,-23,1432,789,1,MIDM [Undefined,Country] [Country] [Constant Source_of_drinking_w] [Rc,2,Finland_areas_nuts2,1,Country,1] mg/l Table(Country,Water_source)( Normal (1,0),Normal (1,0), Normal (1,0),Normal (1,0), Normal (1,0),Normal (1,0), Normal (1,0),Normal (1,0), Normal (1,0),Normal (1,0) ) 248,184,1 48,38 2,0,0,1436,799 2,0,-23,1440,799,0,MIDM 2,0,-23,1440,799,0,MIDM [Water_source,Country] [Water_source,Country] mg/l Table(Country)( 'Na','Na','Na','Na','Na') 80,104,1 48,29 2,0,-23,1432,791 2,264,274,416,303,0,MIDM 2,312,322,416,303,1,MIDM [Country] [Constant Source_of_drinking_w] mg/l Table(Country)( Normal (0,0),Normal (0,0),Normal (0,0),Normal (0,0),Normal (0,0)) 80,184,1 48,29 2,0,-23,1432,791 % Percentage of population using tap and well water Table(Country,Water_source)( 0.06,0.94, 0.06,0.94, 0.06,0.94, 0.06,0.94, 0.06,0.94 ) 160,32,1 48,29 2,640,744,1440,799 2,0,-23,1072,555,0,MIDM [Water_source,Country] [Undefined,Undefined] [Variable Local_data_tap, Variable Local_data_well] 464,56,1 48,24 456,112,1 48,24 448,176,1 48,24 Add concentration and general distributions with spatial distribution! 448,293,-1 48,53 ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] 264,200,1 48,24 2,0,-23,1440,799 [] ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] jgrellie 22 May 2008 17:19 jgrellie 29 Jul 2008 16:05 48,24 456,64,1 48,24 1,1,1,1,1,1,0,0,0,0 1,160,744,1432,791,17 Arial, 15 0.37 256,128,1 92,40 2,102,90,476,224 2,40,50,416,303,0,MIDM 0 256,384,1 92,40 2,102,92,472,222 Lognormal( , , {Mean_concentrations, Standard_deviations1 ) 256,256,1 80,60 2,102,90,476,224 2,171,312,484,376,0,MIDM [England___wales_wate,England___wales_wate,Undefined,Undefined,2] ['ALB','ANG','BRL','BWH','CAW','CHO','DVW','DWR','ESK','FLK','HRT','MKT','NWL','PRT','SES','SEW','SRN','SST','SVT','SWT','THD','TMS','TVW','UUT','WSX','YKS'] 104,40,1 88,20 Normal( 2m, 1.8m ) 640,128,1 76,40 2,0,-23,1152,753 2,0,-23,1152,753,0,MIDM {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Exposure_response_fu:1,,,,0} [Sex1] Table(Age_categories2)( 68.6K,73.7K,76.4K,71.9K,67.9K,72.9K,84.6K,87.6K,79.1K,71.2K,77K,63.5K,54.2K,47.7K,40.7K,31.5K,18.6K,11.9K) 768,512,1 48,49 1,1,1,1,1,1,0,0,0,0 2,0,-23,1152,753 2,216,226,732,401,0,MIDM 2,120,130,703,493,0,MIDM {!40000|Att_graphindexrange Sex1:1,,,,,,10} {!40000|Att_graphvaluerange Population_structure:1,,,,,,10} Baroverlap:0 {!40000|Flip:0} {!40000|Att_catlinestyle Graph_primary_valdim:9} {!40000|Att_stackedbar Graph_primary_valdim:0} {!40000|Graph_pagebrush: } [Sex1,Age_categories2] [Sex1,Age_categories2] [Index England___wales_wate, Index Sex1, Index Age_categories2] ['item 1'] [Water_supply_zones,0,Age_categories,1,Sex,1] [Water_supply_zones,1,Age_categories,2,Sex,1] ['male','female'] 256,40,1 44,20 ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] 368,40,1 48,20 2,102,90,476,224 ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] Table(Age_categories2)( 0.2,0,0.1,0.1,0.3,0.4,0.7,1,2.8,5.6,13.9,28.8,55,86.7,132.5,189.5,217.1,302.1) 640,512,1 48,49 2,0,0,1152,753 [Sex1,Age_categories2] [Sex1,Age_categories2] ((exp({Exposure_response_f*Modelled_exposure_of)-1)/exp(Exposure_response_fu*Modelled_exposure_of))*Background_mortality/100000 640,257,1 48,67 2,357,59,476,224 2,707,52,416,303,0,MIDM [Age_categories2,England___wales_wate] [Index England___wales_wate, Index Sex1, Index Age_categories2] [Sex,0,Bottled_water_scenar,1,Water_supply_zones,1,Age_categories,1] {Attributable_risk_}*{Population_structur 768,257,1 48,76 2,414,66,579,323 2,237,58,747,529,1,PDFP Probindex:[0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99 ] Diststeps:0 [Bottled_water_scena1,Age_categories2,1,Undefined,0] [Sysfunction Probability, Index England___wales_wate, Index Sex1, Index Age_categories2] [Sex,0,Water_supply_zones,0,Bottled_water_scenar,1,Age_categories,0,Sys_localindex('STEP'),1] Table(Bottled_water_scena1)( 36,720) 192,640,1 52,40 2,0,-23,1432,791 2,253,198,416,303,0,MIDM [36] (Triangular(0.5,{Daily_per_capita_to,4))*(1-Triangular(0,({Daily_per_capita_bo/Daily_per_capita_tot),1)) 448,512,1 60,40 2,0,-23,1432,791 2,152,162,416,303,1,PDFP Diststeps:0 [Undefined,Bottled_water_scena1,Undefined,Undefined,1] [0,0,0,0] {Monitoring_data_dis*Exposure_adjustment_ 448,256,1 72,52 2,662,307,416,303,0,MIDM [Bottled_water_scena1,England___wales_wate] [Index England___wales_wate] [Bottled_water_scenar,1,Water_supply_zones,1,Sys_localindex('STEP'),1] ['baseline','increased bottled'] 496,40,1 56,20 ['baseline','increased bottled'] Table({Bottled_water_scena)( {Total_tap_water_con[Bottled_water_scena1="baseline"]/Total_tap_water_cons[{Bottled_water_scena="baseline"],{Total_tap_water_con[Bottled_water_scena1="increased bottled"]/Total_tap_water_cons[{Bottled_water_scena="baseline"]) 448,384,1 48,31 2,625,68,476,224 2,564,578,573,303,0,MIDM {Annual_per_capita_b/365.25 320,640,1 52,40 2 448,640,1 52,39 Years_of_life_disabl+Years_of_life_lost_d 896,512,1 60,60 [Age_categories2,England___wales_wate] jgrellie 29 Jul 2008 15:47 48,24 896,256,1 48,32 1,1,2,314,479,17 224,159,1 48,40 [Objective Years_of_life_disabl] 160,263,1 48,40 [Objective Years_of_life_disabl] Average_duration_of_*Excess_deaths_from_b 96,384,1 48,40 Average_duration_of_*Disability_weight_fo*{Attributable_burden 224,384,1 48,49 [Variable Disability_weight_fo, Variable Average_duration_of_] {Attributable_burden*Survival_for_bladder 96,160,1 48,58 96,55,1 48,31 jgrellie 22 May 2008 17:19 jgrellie 04 Jun 2008 12:49 48,24 456,128,1 48,24 1,1,1,1,1,1,0,0,0,0 1,1,0,448,245,17 Arial, 15 Table(Water_companies)( 2.515616105,3.462306609,1.061904662,1.891604804,3.508230042,1.968248375,3.812996075,1.127094735,1.152390134,0,3.587048158,3.684155296,2.543052225,2.567033836,3.201538972,3.385417133,2.784197439,3.636112187,3.210850627,2.7069758,1.593463434,2.336630105,3.134370357,3.397089518) 128,576,1 48,49 2,102,90,476,224 2,392,402,546,493,0,MIDM Table(Water_companies)( 0.850161316,0.496615774,0.9944016610000001,0,0.735875544,1.43615411,0.351172012,1.92591779,0.373289121,2.314687324,0.5910945560000001,0.726992581,0.764971871,1.08916286,0.700487414,0.856400759,0.754094272,0.618670419,0.7633092,0.887639414,1.911632371,0.708324068,1.1219691,0.546802236) 256,577,1 48,58 2,102,92,472,222 Lognormal(,, exp(Mean_), exp(Standard_deviations_) ) 192,448,1 76,36 2,365,323,690,303,1,PDFP [Water_companies,Water_companies,Undefined,Undefined,2] ['ang','brw','caw','cho','dcc','eas','ess','fol','har','mik','nor','nww','por','sea','sev','sos','sou','sww','teh','tha','thr','wes','wrx','yor'] 832,136,1 44,24 2,0,744,1432,791 ['ang','brw','caw','cho','dcc','eas','ess','fol','har','mik','nor','nww','por','sea','sev','sos','sou','sww','teh','tha','thr','wes','wrx','yor'] Note that this is an approximation... Need to work out how to make it robust... should it be log or normal?? etc... Normal( 1.0132, 2m ) 512,256,1 104,36 2,568,93,476,224 2,486,357,779,339,1,PDFP Distresol:8 Diststeps:1 [Sex] [Sex,Sex,Undefined,Undefined,2] Table(Water_companies,Sex,Age_categories)( 329,306,314,277,225,270,340,318,290,238,250,180,161,153,155,149,95,47, 304,402,263,276,247,335,408,350,314,248,231,221,176,202,251,225,141,145, 410,397,351,283,313,323,379,386,326,250,289,198,196,163,132,108,116,41, 403,383,302,326,394,380,444,415,293,231,267,230,192,195,166,203,129,99, 373,375,370,288,299,276,428,394,351,294,266,242,185,174,157,119,74,46, 342,313,295,304,292,379,459,449,360,247,325,230,176,220,183,184,109,81, 430,579,480,450,386,428,505,627,556,496,488,380,336,289,244,181,109,90, 424,459,455,465,431,449,613,687,603,496,518,423,365,305,286,234,176,180, 599,688,626,566,467,581,591,582,529,429,367,265,207,218,188,130,84,43, 590,642,552,496,542,628,751,754,587,496,426,297,260,260,240,212,156,186, 537,550,470,469,753,983,722,640,508,441,451,371,296,278,256,197,114,181, 493,502,452,469,894,1105,791,652,552,470,519,392,344,301,308,288,258,349, 552,497,468,500,568,589,547,551,437,408,346,329,230,224,172,114,55,35, 579,524,406,464,648,711,734,596,530,412,412,284,300,208,194,155,107,113, 470,442,427,427,500,662,785,667,536,455,410,310,219,195,196,107,94,57, 438,470,406,383,471,768,784,684,617,473,502,298,262,238,217,177,138,212, 498,507,516,446,399,559,698,651,580,524,508,400,303,267,238,174,97,68, 491,514,462,453,436,661,686,732,576,513,545,403,311,274,263,284,168,158, 403,438,385,302,490,744,773,604,464,383,410,324,269,236,206,172,91,55, 458,399,364,329,632,915,729,679,533,436,432,384,291,277,233,242,178,280, 488,465,573,515,453,416,451,484,497,536,523,370,312,262,256,186,140,95, 406,483,539,553,514,476,508,554,562,553,589,388,366,279,325,249,186,215, 367,419,361,340,532,691,584,556,403,375,431,345,290,250,267,180,144,119, 369,366,362,382,567,699,543,514,400,452,431,401,349,285,327,292,206,240, 470,410,390,326,416,621,579,509,485,425,490,383,285,278,216,212,143,127, 480,457,352,336,524,644,617,572,521,465,558,439,355,294,289,297,260,406, 678,686,624,499,664,797,563,493,469,427,386,283,253,250,235,187,124,140, 597,629,574,534,787,841,580,539,538,440,441,303,308,261,293,277,230,301, 565,556,568,464,434,523,573,618,543,489,482,388,303,273,264,204,116,108, 578,528,511,559,446,573,632,709,603,514,535,425,333,317,304,261,202,236, 465,497,475,435,770,768,622,550,457,393,400,330,277,277,254,176,124,133, 536,451,405,421,849,809,640,529,479,437,474,334,298,291,278,258,207,327, 476,422,400,394,346,427,567,600,551,477,492,360,285,242,248,199,127,86, 369,435,358,353,354,485,564,614,573,501,515,387,308,335,302,299,234,282, 549,475,500,534,393,545,572,604,541,461,499,400,323,277,229,200,114,114, 515,522,516,442,431,603,630,698,568,463,548,413,344,299,318,299,200,252, 420,458,466,399,446,484,474,546,555,511,487,455,328,315,259,210,138,117, 365,445,523,432,409,487,561,572,538,502,583,463,347,351,347,288,239,294, 492,448,403,384,365,510,647,622,498,453,469,354,275,313,238,206,111,94, 484,466,416,369,365,563,637,653,568,400,512,410,326,308,342,252,215,258, 552,589,559,504,386,426,541,580,552,457,474,397,346,325,224,214,141,74, 502,549,599,567,425,527,655,745,585,544,530,454,360,339,326,311,184,178, 392,362,317,297,479,840,762,681,490,390,386,297,247,248,217,161,101,85, 412,374,326,346,613,796,765,669,536,404,431,321,296,278,311,238,199,246, 445,508,378,442,557,662,629,657,495,391,356,294,272,287,290,179,98,63, 454,439,420,409,641,726,680,609,537,390,427,312,357,314,305,221,161,165, 448,479,463,410,486,698,717,679,558,489,427,350,307,278,250,193,136,115, 429,453,423,392,498,754,735,693,582,474,508,358,342,319,323,274,195,255 ) 640,576,1 44,48 1,1,1,1,1,1,0,0,0,0 2,0,-23,1280,922 2,216,226,732,401,0,MIDM 2,0,-23,1280,922,0,MIDM {!40000|Att_graphindexrange Sex1:1,,,,,,10} {!40000|Att_graphvaluerange Uk_population_data_f:1,,,,,,10} Baroverlap:0 {!40000|Flip:0} {!40000|Att_catlinestyle Graph_primary_valdim:9} {!40000|Att_stackedbar Graph_primary_valdim:0} {!40000|Graph_pagebrush: } [Age_categories,Water_companies] [Water_companies,Age_categories] [Index Water_companies, Index Age_categories] ['item 1'] [Sex,1,Water_companies,1,Age_categories,1] ['male','female'] 832,456,1 48,24 2,102,90,476,224 ['male','female'] ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] 832,392,1 48,24 ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','>85'] Note: this model uses data for 2001 (where relevant) 832,576,-1 64,38 Lognormal( , , 2500, 100 ) 512,576,1 76,36 2,0,-23,1280,922 2,472,482,416,303,1,PDFP [Sex,Age_categories] ((exp(Exposure_response_sl*Modelled_exposure_to)-1)/exp(Exposure_response_sl*Modelled_exposure_to))*Background_prevalenc/100000 512,448,1 40,36 2,439,111,476,224 2,384,316,479,481,0,MIDM [Bottled_water_scenar,Water_companies] [Scenarios,1,Water_companies,1,Sys_localindex('STEP'),1] Excess_rate_of_iugr*Uk_population_data_f 640,449,1 48,49 2,0,-23,1280,922,0,MIDM {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Graph_pdf_valdim:1,0,1,1,0} [Bottled_water_scenar,Water_companies] [Index Water_companies, Index Sex, Index Age_categories] [Sex,0,Age_categories,0,Scenarios,1,Water_companies,0,Sys_localindex('STEP'),1] ['shower','bath','swimming'] 832,264,1 48,24 2,0,-23,1281,923 ['shower','bath','swimming'] Indexless_total_exp2+Indexless_total_expo+Total_exposure_due_2+Total_exposure_due_t 384,192,1 48,31 2,102,90,476,224 2,424,434,602,303,0,MIDM [Exposure_pathways,Bottled_water_scenar] [Scenarios,1,Types_of_drink,1,Exposure_pathways,1] ['Cold drinks','Hot drinks'] 832,200,1 48,24 Table(Exposure_pathways)( 0.9,0.3,0.3) 128,192,1 80,48 ['Scenario 0 (BAU)','Scenario 1 (Increased bottled water consumption)'] 832,329,1 48,31 ['Scenario 0 (BAU)','Scenario 1 (Increased bottled water consumption)'] Table(Bottled_water_scenar)( Overall_exposure_to_[Bottled_water_scenar="Scenario 0 (BAU)"]/Overall_exposure_to_[Bottled_water_scenar="Scenario 0 (BAU)"],Overall_exposure_to_[Bottled_water_scenar="Scenario 1 (Increased bottled water consumption)"]/Overall_exposure_to_[Bottled_water_scenar="Scenario 0 (BAU)"]) 384,321,1 48,40 2,102,90,476,224 2,168,178,1093,489,0,MIDM 2,488,498,416,303,0,MIDM [Types_of_drink,Exposure_pathways] jgrellie 30 May 2008 18:52 48,24 384,64,1 48,42 1,960,744,1432,791,17 0.1 192,384,1 48,40 Table(Bottled_water_scenar)( 1,1) 320,512,1 48,40 2,774,619,476,224 2,812,243,416,303,0,MIDM 2 2 56,192,1 52,40 2,440,339,476,224 Daily_total_water_co[Types_of_drink="Cold drinks"]*Proportion_of_ingest*Proportion_of_tap_w1 192,256,1 48,49 2,437,90,581,409 [Types_of_drink,Bottled_water_scenar] 0.1 192,640,1 48,31 2,0,-23,1280,922 0.5 64,512,1 48,31 Rate_of_ingestion_of*Proportion_of_ingest*Proportion_of_tthm_e*Proportion_of_tap_wa 192,512,1 48,49 2,0,0,1281,923 [Bottled_water_scenar,Types_of_drink] Daily_total_water_co-Daily_bottled_water_ 328,192,1 52,32 2,310,465,476,224 Table(Bottled_water_scenar)( 0.3,0.6) 448,192,1 56,32 Daily_tap_water_cons/Daily_total_water_co 192,128,1 48,40 jgrellie 30 May 2008 18:52 48,24 256,64,1 48,42 1,1,2,593,487,17 1.81 224,73,1 48,31 Table(Exposure_pathways)( 0.1,0.01,0.01) 352,72,1 48,58 Possible____*Dermal_absorption_ra*Body_surface_area*Duration_of_exposure*Proportion_of_popula 224,201,1 48,49 2,545,545,476,224 2,248,258,416,303,0,MIDM Table(Exposure_pathways)( 1,1,1) 352,201,1 48,24 0.002 352,280,1 48,40 sum(Total_exposure_due_6,Exposure_pathways) 96,265,1 48,49 jgrellie 30 May 2008 18:52 48,24 256,320,1 48,42 1,1,2,593,557,17 Water_air_partition_ 352,247,1 48,24 2,226,113,476,224 Table(Exposure_pathways)( 2,2,1) 352,376,1 48,40 0.83333 224,55,1 48,24 Table(Exposure_pathways)( 0.2,0.01,0.01) 352,55,1 48,31 2,536,546,416,303,0,MIDM Possible_*A*Breathing_rate*Duration_of_inhalati*Proportion_of_popula 224,184,1 48,49 2,232,242,416,303,0,MIDM Table(Exposure_pathways)( 1,1,1) 352,183,1 48,24 sum(Total_exposure_due_1, Exposure_pathways) 96,119,1 48,49 Indexes 832,288,-1 64,204 Tthm_exposure_adjust*Distributions_of_ 384,448,1 48,31 [Bottled_water_scenar,Water_companies] Sex is irrelevant for this model, since it only looks at exposure of mothers 960,506,-1 48,74 ['baseline','increased bottled'] 256,312,1 56,20 ['baseline','increased bottled'] ['ang','brw','caw','cho','dcc','eas','ess','fol','har','mik','nor','nww','por','sea','sev','sos','sou','sww','teh','tha','thr','wes','wrx','yor'] 272,88,1 48,24 2,0,744,1432,791 ['ang','brw','caw','cho','dcc','eas','ess','fol','har','mik','nor','nww','por','sea','sev','sos','sou','sww','teh','tha','thr','wes','wrx','yor'] Unify model with arsenic and THM models when possible! 456,204,-1 48,36 ktluser 10. huhta 2008 12:14 48,24 56,48,1 48,24 1,160,744,1440,799,17 {Daly_valuation}; {Concentration_of_ni 976,256,1 60,28 [Variable Background_prevalen2, Objective Total_met_hb_mortal3] [Objective Dalys_and_or_financ2] [1] 976,320,1 64,20 2,0,-23,1152,763 [Objective Attributable_burden2] 0 376,264,1 60,32 [Variable Concentration_of_ni7, Variable Removal_of_nitrates1] [Variable Total_daily_consump7] [1,Raw_water_quality1,Concentration_of_ni6,Concentration_of_ni7,Removal_of_nitrates1] 536,104,1 72,28 [Variable Total_daily_consump6] Lognormal( 1 ) 224,104,1 48,32 2,166,180,476,224 [Constant Usage_of_nitrate_fe3] [1,Usage_of_nitrate_fe3] 224,264,1 48,32 [Variable Concentration_of_ni4] [1,Daily_sales_of_bott2] 776,176,1 48,32 [Variable Total_daily_consump6, Variable Total_daily_consump7] 880,432,1 48,28 {Dbp_dalys}; {Mirobes_dalys} 1104,256,1 48,28 [Objective Attributable_burden2, Variable Daly_and_or_cost_we2] 0 1104,104,1 48,32 [Objective Dalys_and_or_financ2] 5 88,264,1 68,32 {Chlorination}; {Dose_response_of_mi; Water_treatment_meth 984,104,1 64,28 [Objective Total_met_hb_mortal3] 728,432,1 48,29 [Variable Total_daily_consump7] 0 88,104,1 68,32 0 672,176,1 40,32 chemical and microbial quality of raw water [1] 376,432,1 52,28 {Dbp_exposure_via_dr; Dbp_exposure_via_oth; Dose_response_of_dbp 536,368,1 72,24 [Variable Standard_for_nitrat3, Constant Water_treatment_met3] [Variable Concentration_of_ni4] [1,Drinking_water_legi2] 88,368,1 64,24 2,0,0,1025,668 [Variable Removal_of_nitrates1] Actions 80,40,-1 48,24 Sources 224,40,-1 48,24 Media 360,40,-1 48,24 Exposure 696,40,-1 48,24 Impacts 984,40,-1 44,24 Valuation 1096,40,-1 48,24 0 720,104,1 92,28 [Variable Concentration_of_ni5, Variable Daily_consumption_o2] 1 728,368,1 100,24 2,102,90,476,224 [Variable Concentration_of_ni4, Variable Daily_consumption_o2, Constant Population_using_pr2] [1,Total_daily_consump6,Total_daily_consump7,Proportion_of_boile2,Population_demandin2] 720,256,1 92,24 2,102,90,476,224 [Objective Total_met_hb_mortal3] {Daly_valuation}; {Dbp_health_effects} 976,176,1 64,32 [Variable Exposure_response_f1, Variable Total_ingestion_of_3] [Objective Attributable_burden2] 208,432,1 48,29 [Chance Concentration_of_ni6] none water treatment methods [1] 536,432,1 48,28 2,0,0,1025,668 [Variable Removal_of_nitrates1] ktluser 11. kesta 2008 12:21 48,24 296,48,1 48,24 1,0,-23,1440,799,1 Arial Narrow, 13 This model is built for INTARESE project, WP3.4. The risk assessment behind the model is on INTARESE wiki http:// www.pyrkilo.fi/intarese. This model uses 2001 data when appropriate and possible 248,88,-1 184,68 Contains data that comes from a referrable source. The reference must be mentioned in the Reference attribute. Colour 3R1B. 1 224,496,1 48,24 2,102,90,476,516 65535,52427,65534 Contains data that comes from a non-referrable source, i.e. some general knowledge or author judgement. Colour 4R2B. 1 224,552,1 48,24 52425,39321,65535 Contains information about general issues related to the structure and content of a model. Text is written to Description. Each addition is started with the date and the name of the user. The title of the node is Loki n or Log n (n=version number of the model). You should not write information related to a particular node, that should be written in the node itself so that the information will be inherited with the node. Colour 4L3B. 1 112,624,1 48,24 65535,54067,19661 Argument about a node, data, or relationship in a model; or a description of its importance. Colour: automatic (2L3B). 1 336,440,1 48,24 65535,31131,19661 This is the basic building block of an Analytica model. It is a variable that defines a (typically) measurable entity. Usually it is calculated based on data on and relationships about its causes. 1 224,440,1 48,24 Modules are used to create a hierarchical structure. Modules may contain nodes and other modules inside them. mtad 16. Aprta 2003 12:56 48,24 112,576,1 48,24 1,40,0,505,406,17 A conclusion is basically an argument. The colour is used to enhance the fact that the data for this argument originates from the results of the model. Colour 6L3B. 1 336,552,1 48,24 2,44,90,476,224 65535,65532,19661 Index related to the node beside it. Indexes should be as close as possible to the place where they are used. Otherwise there is the risk of a connection brake. Colour: automatic (5R2B). [0] 112,476,1 48,24 2,341,157,476,224 Colour description: xLyT describes the coordinates in the colour palette, xth cell from left and yth cell from top. Directions are L left, R right, T top, B bottom, e.g. 1R1B is the right bottom cell. 552,472,-1 112,44 Decision mode defines a decision under analysis. Other decisions (such as those decided by someone else) can be defined as uncertain variables instead of decisions. 0 112,440,1 48,24 Outcome of interest. The optimisation of this variable is often defined as the criteria for choosing between decision options. 0 112,520,1 48,24 An uncertain variable that is defined as a probability distribution. 0 224,608,1 48,24 A value or preference. Colour 8L4B. 0 336,608,1 48,24 5,65535,1 Pyrkilo diagram method (or structured deliberation as it is sometimes called) has been developed to facilitate the Science-Policy Interface. There is a need for methods facilitating the flow of information and understanding between science and policy. The principle is to describe a risk situation in a formal manner. Pyrkilo is an enhanced causal diagram that contains items along a causal pathway (or network) from e.g. abatement strategies to emissions to dispersion to exposure to effects. It has been designed to describe also other than causal connections such as non-causal reasoning, values, preferences, and arguments. These diagrams use Analytica(TM) platform, a graphical Monte Carlo simulation program. It is based on nodes (or variables or objects). They are used to describe and define all the pieces needed for a description of the situation under scrutiny. Many nodes are used as described in Analytica manuals. However, there are also special colours and shapes representing features that are important for pyrkilo diagrams. See Description of each node for more details. You can see the definitions and descriptions by clicking or double-clicking the nodes. 464,223,-1 400,87 2,402,92,530,558 A scope node is basically an argument. The bevel is used to enhance the fact that the argument is about the scope of the model, (i.e. about the existence of a node or module). Colour: automatic (2L3B). 1 336,496,1 48,24 1,1,1,1,1,1,0,,1, 65535,31131,19661