Kopra

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Kopra was a research project about health impacts of fine particles in Finland.

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Rationale

Data

←# : See N:\YMAL\Projects\R83_piltti filelist_kopra.txt for data list. --Jouni (talk) 14:12, 18 April 2017 (UTC)

Calculations

Marko's iF calculations

The codes are from U:\arkisto_kuopio\huippuyksikko\Tutkimus\R19_kopra\Mallit\IntakeFraction2. This section is for documentation only. The codes do not run.

Tainio_iF_emis_eur_pop_all_01.R

+ Show code- Hide code #7.3.2008, Marko Tainio # This model calculates intake fractions (iF) for European # primary PM2.5 emissions. Description of model: # - Emission source: European primary PM2.5 emissions from different countries # - Dispersion: Dispersion over the Europe # - Population: European population divided to different countries (38) # (Note: the population data does not include Finnish population!!) #Work folders - the location of data and where the output is recorded wrk_input = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Data 2/" wrk_output = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/Result/" #Breathing rate, 20 m^3/day -> unit g/s BR = 20/(24 * 60 * 60) # Calculation for different countries. The iF's are calculated separately # for all emission sources (countires). There are 48 sources in data. for (k in 1:48) { # This reads from file the titles of countries to be used in calculations maa = read.table("N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/EUR_country.txt", sep="") maa = maa[,k] #Emission volume - this part estimates emission volume in unit g/s Q = read.csv("N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/Eur_emission_05.csv", header=T) #Concentration data #The order in list debends on the prepared emission data rm(conc) conc = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Data 2/Concentration_primary_Europe_2000/EMEP_monthly_PM2_5_2000/" conc = paste(conc,"EMEP_2000_PM_2_5_hirlam_v3_8_1_",maa,"_200012.txt", sep="") conc = read.table(conc, header=T, sep="") #Population data: rm(pop) pop = paste(wrk_input, "Population_Combined/Pop_eur00_all3.txt", sep="") pop = read.table(pop, header=T, sep="") #Co-ordination check-in - this check that co-ordinate system is same #in population and concentration data. check = (conc["lon"] - pop["Lo"]) + (conc["lat"] - pop["La"]) sum(check) #This part slice the concentration data from the data file #(that is, LO and LA data is sliced oof) month = c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec", "annual") conc = data.frame(conc[month]) #Unit change in concentration data: kg/m^3 -> g/m^3 conc = conc * 1000 #This part slice the population data from the data file #(that is, LO and LA data is sliced off) rm(country_eur) country_eur = c("Sum_Austri", "Sum_Belgiu", "Sum_Cyprus", "Sum_Czech_", "Sum_Denmar", "Sum_Estoni", "Sum_France", "Sum_German", "Sum_Greece", "Sum_Hungar", "Sum_Irish_", "Sum_Italy", "Sum_Latvia", "Sum_Lithua", "Sum_Luxemb", "Sum_Malta", "Sum_Nether", "Sum_Poland", "Sum_Portug", "Sum_Romani", "Sum_Slovak", "Sum_Sloven", "Sum_Spain", "Sum_UK", "Sum_Sweden", "Sum_Bulgar", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Sum_Switze", "Sum_Ukrain", "Sum_Moldov", "Sum_Croati", "Sum_Serbia", "Sum_Albani", "Sum_Macedo", "Sum_Turkey", "Sum_bosni", "Sum_Finl", "Sum_all") pop = data.frame(pop[country_eur]) #X must be defined before for -loop x = NULL #Month/concentration - month 13 in annual mean for (i in 1:13) { #Removal of huge values #There are some weird very high values in some concentration datasets. #This code replace those values with value 0. y = conc[,i] y = ifelse(y > 100, 0, y) #Country/population - there are 40 different population data files for (j in 1:40) { x[j] = sum(y * pop[,j]) } #Calculation of iF x = (x * BR) / Q[k,i] #Names for the data names(x) = c("Sum_Austri", "Sum_Belgiu", "Sum_Cyprus", "Sum_Czech_", "Sum_Denmar", "Sum_Estoni", "Sum_France", "Sum_German", "Sum_Greece", "Sum_Hungar", "Sum_Irish_", "Sum_Italy", "Sum_Latvia", "Sum_Lithua", "Sum_Luxemb", "Sum_Malta", "Sum_Nether", "Sum_Poland", "Sum_Portug", "Sum_Romani", "Sum_Slovak", "Sum_Sloven", "Sum_Spain", "Sum_UK", "Sum_Sweden", "Sum_Bulgar", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Sum_Switze", "Sum_Ukrain", "Sum_Moldov", "Sum_Croati", "Sum_Serbia", "Sum_Albani", "Sum_Macedo", "Sum_Turkey", "Sum_bosni", "Sum_Finl", "Sum_all") #Recording of results write.table(x, paste(wrk_output, file = "European_emission/2000/if_emis_eur_year_2000_country_",maa,"_month_",i,"_pop_all.txt", sep=""), row.names = TRUE, col.names = FALSE) } } #Remove of all data from memory rm(wrk_input, wrk_output, BR, k, maa, Q, pop, check, conc, month, country_eur, x, i, y, j)

Tainio_iF_emis_eur_pop_all_data-conbination.R

+ Show code- Hide code # 18.4.2008 Marko Tainio # This model change the calculated 1-dimensional data to 2-dimensional so # that data can be downloaded e.g. to Analytica more easily. # Description of data: # - Intake fraction data: iF for European population due to European emissions #The location of data files folder="N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/Result/European_emission/" #Month/concentration - month 13 in annual mean for (i in 1:13) { #Y must be defined before for -loop y = NULL # Calculation for different countries. There are 48 sources in data. for (j in 1:48) { # This reads from file the titles of countries to be used in calculations maa = read.table("N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/EUR_country.txt", sep="") maa = maa[,j] # This read the intake fraction data files a = paste(folder,"2000/if_emis_eur_year_2000_country_",maa,"_month_",i,"_pop_all.txt", sep="") a = read.table(a, sep="") #This combines all the data to 1-dimensional data y = c(y, a[,2]) } #This change the 1-dimensional data to 2-dimensional # 40 is the number of populations used, 48 is number of emission sources (countries) dim(y) = c(40,48) #Recording of results write.csv(y, paste(folder, file="comb_if_year_2000_country_all_month_",i,".csv", sep="")) } #Remove of all data from memory rm(folder, i, y, maa, a)

Tainio_iF_emis_fin_pop_all_02.R

+ Show code- Hide code #21.4.2008, Marko Tainio # This model calculates intake fractions (iF) for Finnish # primary PM2.5 emissions. Description of model: # - Emission source: Finnish primary PM2.5 emissions from different sectors # - Dispersion: Dispersion over the Europe - small scale (11 countries) # - Population: European population (11 countries) #Work folders - the location of data and where the output is recorded wrk_input = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Data 2/" wrk_output = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/Result/" wrk_emis = "N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/" #Breathing rate, 20 m^3/day -> unit g/s BR = 20/(24 * 60 * 60) #Calculation of different years (2000, 2001, 2002) for (l in 0:2) { #The calcultion of sectors - seven sectors are [DOM OTH TRA LPC LPP AGR ALL] for (k in 1:7) { #Emission volume - this part copy emission data in unit g/s Q = paste(wrk_emis,"fin_emission_200",l,"_01.csv", sep="") Q = read.csv(Q, header=T) #Concentration data #The data is read separately for different years because #year 2000 and 2001,2002 data has different dimensions rm(conc) if (l == 0) (conc = paste(wrk_input,"Concentration_primary_sector_200",l,"/monthly_PM_2_5/Fin_200",l,"_PM_hirlam_v3_8_1_", sep="")) if (l == 1) (conc = paste(wrk_input,"Concentration_primary_sector_200",l,"/monthly_200",l,"_PM_2_5/Fin_200",l,"_PM_EC_v3_8_1_", sep="")) if (l == 2) (conc = paste(wrk_input,"Concentration_primary_sector_200",l,"/monthly_200",l,"_PM_2_5/Fin_200",l,"_PM_EC_v3_8_1_", sep="")) #The reading order for the data files if (k == 1) (conc = paste(conc,"_DOM_200",l,"12.txt", sep="")) if (k == 2) (conc = paste(conc,"_OTH_200",l,"12.txt", sep="")) if (k == 3) (conc = paste(conc,"_TRA_200",l,"12.txt", sep="")) if (k == 4) (conc = paste(conc,"_LPC_200",l,"12.txt", sep="")) if (k == 5) (conc = paste(conc,"_LPP_200",l,"12.txt", sep="")) if (k == 6) (conc = paste(conc,"_AGR_200",l,"12.txt", sep="")) if (k == 7) (conc = paste(conc,"all_srcs.txt", sep="")) conc = read.table(conc, header=T, sep="") #Population data #The data is read separately for different years because #year 2000 and 2001,2002 data has different dimensions rm(pop) if (l == 0) (pop = paste(wrk_input, "Population_Combined/Pop_fin00_all.txt", sep="")) if (l == 1) (pop = paste(wrk_input, "Population_Combined/Pop_fin01_all.txt", sep="")) if (l == 2) (pop = paste(wrk_input, "Population_Combined/Pop_fin01_all.txt", sep="")) pop = read.table(pop, header=T, sep="") #Co-ordination check-in - this check that co-ordinate system is same #in population and concentration data. check = (conc["lon"] - pop["Lo"]) + (conc["lat"] - pop["La"]) sum(check) #This part slice the concentration data from the data file #(that is, LO and LA data is sliced off) month = c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec", "annual") conc = data.frame(conc[month]) #Unit change in concentration data: kg/m^3 -> g/m^3 conc = conc * 1000 #This part slice the population data from the data file #(that is, LO and LA data is sliced off) #The data is read separately for different years because #year 2000 and 2001,2002 data has different dimensions rm(country_fin) if (l == 0) (country_fin = c("Sum_ASUKKA", "Sum_Estoni", "Sum_Latvia", "Sum_Lithua", "Sum_Sweden", "Sum_Poland", "Sum_Denmar", "Sum_German", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Pop_all")) if (l == 1) (country_fin = c("Finland", "Sum_Estoni", "Sum_Latvia", "Sum_Lithua", "Sum_Poland", "Sum_Sweden", "Sum_Denmar", "Sum_German", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Pop_all")) if (l == 2) (country_fin = c("Finland", "Sum_Estoni", "Sum_Latvia", "Sum_Lithua", "Sum_Poland", "Sum_Sweden", "Sum_Denmar", "Sum_German", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Pop_all")) pop = data.frame(pop[country_fin]) #X must be defined before for -loop x = NULL #Month/concentration - month 13 in annual mean for (i in 1:13) { #Removal of huge values #There are some weird very high values in some concentration datasets. #This code replace those values with value 0. y = conc[,i] y = ifelse(y > 100, 0, y) #Country/population - iF's are calculated separately for 12 different population for (j in 1:12) { x[j] = sum(y * pop[,j]) } #Calculation of iF x = (x * BR) / Q[k,i] #Names for the data names(x) = c("FIN", "EST", "LAT", "LIT", "SWE", "POL", "DEN", "GER", "NOR", "BEL", "RUS", "All") #Recording of results write.table(x, paste(wrk_output, file = "Finnish_emission/200",l,"/if_emis_fin_year_200",l,"_sector_",k,"_month_",i,".txt", sep=""), row.names = TRUE, col.names = FALSE) } } } rm(wrk_input, wrk_output, wrk_emis, BR, l, k, Q, conc, pop, check, month, country_fin, x, i, y, j)

Tainio_iF_emis_fin_pop_all_data_conbination.R

+ Show code- Hide code # 21.4.2008 Marko Tainio # This model change the calculated 1-dimensional data to 2-dimensional so # that data can be downloaded e.g. to Analytica more easily. # Description of data: # - Intake fraction data: iF for European population due to Finnish emissions #The location of data files folder="N:/Huippuyksikko/Tutkimus/R19_Kopra/Mallit/IntakeFraction2/Result/Finnish_emission/" #Year (2000, 2001, 2002) for (k in 0:2) { #The calcultion of sectors - seven sectors are [DOM OTH TRA LPC LPP AGR ALL] for (j in 1:7) { #Y must be defined before for -loop y = NULL #Month/concentration - month 13 in annual mean for (i in 1:13) { # This read the intake fraction data files a = paste(folder,"200",k,"/if_emis_fin_year_200",k,"_sector_",j,"_month_",i,".txt", sep="") a = read.table(a, sep="") #This combines all the data to 1-dimensional data y = c(y, a[,2]) } #This change the 1-dimensional data to 2-dimensional # 12 is the number of populations used, 13 is month (13 in annual mean) dim(y) = c(12,13) #Recording of results write.csv(y, paste(folder, file="comb_if_year_200",k,"_sector_",j,"_month_all.csv", sep="")) } } rm(folder, k, j, y, i, a)

Tainio_population_01.R

+ Show code- Hide code #7.3.2008 Marko Tainio #This model summarise the population from different population datasets used #in intake fraction calculation ####### #Data location for all datasets data_input = ("C:/Varsova/Kopra/Data 2/") data_output = ("C:/Varsova/Muutokset/Result/") ####### #Finnish emissions, year 2000 population data pop_fin00_all = paste(data_input,"Population_Combined/Pop_fin00_all.txt", sep="") a_fin00 = read.table(pop_fin00_all, sep="", header=T) title_fin00 = c("Sum_ASUKKA", "Sum_Estoni", "Sum_Latvia", "Sum_Lithua", "Sum_Sweden", "Sum_Poland", "Sum_Denmar", "Sum_German", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Pop_all") a_fin00 = data.frame(a_fin00[title_fin00]) a = NULL for (i in 1:12) { a[i] = sum(a_fin00[,i]) } names(a) = c("FIN", "EST", "LAT", "LIT", "SWE", "POL", "DEN", "GER", "NOR", "BEL", "RUS", "All") write.csv(a, paste(data_output, file="pop_fin00_sum.csv", sep="")) rm(pop_fin00_all, a_fin00, title_fin00, a) ####### #Finnish emissions, year 2001 population data pop_fin01_all = paste(data_input,"Population_Combined/Pop_fin01_all.txt", sep="") a_fin01 = read.table(pop_fin01_all, sep="", header=T) title_fin01 = c("Finland", "Sum_Estoni", "Sum_Latvia", "Sum_Lithua", "Sum_Sweden", "Sum_Poland", "Sum_Denmar", "Sum_German", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Pop_all") a_fin01 = data.frame(a_fin01[title_fin01]) a = NULL for (i in 1:12) { a[i] = sum(a_fin01[,i]) } names(a) = c("FIN", "EST", "LAT", "LIT", "SWE", "POL", "DEN", "GER", "NOR", "BEL", "RUS", "All") write.csv(a, paste(data_output, file="pop_fin01_sum.csv", sep="")) rm(pop_fin01_all, a_fin01, title_fin01, a) ####### #European emissions, year 2000 population data pop_eur00_all = paste(data_input,"Population_Combined/Pop_Eur00_all.txt", sep="") a_eur00 = read.table(pop_eur00_all, sep="", header=T) title_eur00 = c("Sum_Austri", "Sum_Belgiu", "Sum_Cyprus", "Sum_Czech_", "Sum_Denmar", "Sum_Estoni", "Sum_France", "Sum_German", "Sum_Greece", "Sum_Hungar", "Sum_Irish_", "Sum_Italy", "Sum_Latvia", "Sum_Lithua", "Sum_Luxemb", "Sum_Malta", "Sum_Nether", "Sum_Poland", "Sum_Portug", "Sum_Romani", "Sum_Slovak", "Sum_Sloven", "Sum_Spain", "Sum_UK", "Sum_Sweden", "Sum_Bulgar", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Sum_Switze", "Sum_Ukrain", "Sum_Moldov", "Sum_Croati", "Sum_Serbia", "Sum_Albani", "Sum_Macedo", "Sum_Turkey", "pop_all") a_eur00 = data.frame(a_eur00[title_eur00]) a = NULL for (i in 1:38) { a[i] = sum(a_eur00[,i]) } names(a) = c("Sum_Austri", "Sum_Belgiu", "Sum_Cyprus", "Sum_Czech_", "Sum_Denmar", "Sum_Estoni", "Sum_France", "Sum_German", "Sum_Greece", "Sum_Hungar", "Sum_Irish_", "Sum_Italy", "Sum_Latvia", "Sum_Lithua", "Sum_Luxemb", "Sum_Malta", "Sum_Nether", "Sum_Poland", "Sum_Portug", "Sum_Romani", "Sum_Slovak", "Sum_Sloven", "Sum_Spain", "Sum_UK", "Sum_Sweden", "Sum_Bulgar", "Sum_Norway", "Sum_Belaru", "Sum_Russia", "Sum_Switze", "Sum_Ukrain", "Sum_Moldov", "Sum_Croati", "Sum_Serbia", "Sum_Albani", "Sum_Macedo", "Sum_Turkey", "pop_all") write.csv(a, paste(data_output, file="pop_eur00_sum.csv", sep="")) rm(pop_eur00_all, a_eur00, title_eur00, a) #European emissions, year 2000 population data, Finnish pop_eur00_fin = paste(data_input,"Population_Combined/Pop_Eur00_fin.txt", sep="") a_eur00 = read.table(pop_eur00_fin, sep="", header=T) a_eur00 = sum(a_eur00[,3]) write.csv(a_eur00, paste(data_output, file="pop_eur00_sum_fin.csv", sep="")) rm(pop_eur00_fin, a_eur00) ####### rm(data_input, data_output)