Water guide

Obs! Water guide will be updated during summer 2019. Currently the results can only be seen by updating the result page approximately a minute after beginning to run the code.

Water guide - An assessment of the health impacts of water quality addresses the potential microbiological health risks of drinking water. These microbiological risks are due to the contamination of raw water with microbes that cause potential health problems for people using tap water, as well as the efficiency of drinking water treatment, which can be insufficient for removing harmful microbes from drinking water. The assessment will be used to determine, how high the health risk is from certain microbes in raw water. The assessment is based on a mathematical Water guide -model, which can be found below.

Question

How to assess the microbiological risks of drinking water as well as their health effects? Information of water treatment plants must be possible to use as raw data.

Answer

New version

----arg9228: . This model is based on simple assumption of 100000 population and exposure to 100 CFU per person in a single incident. The logic of the model should be checked, as well as whether the results are reasonable. --Jouni (talk) 00:18, 13 July 2019 (UTC) (type: truth; paradigms: science: comment)

⇤--arg8586: . This model now uses the input data from the form below, and the exposure from the Exposure ovariable calculated from raw water concentrations and cleaning methods. --Heta (talk) 10:36, 17 July 2019 (UTC) (type: truth; paradigms: science: attack)

• Model run 12.7.2019 [1]

Situation Classification of raw water: Surface water - low contamination Surface water - medium contamination Surface water - high contamination Ground water - clean Ground water - surface water contamination Size of the exposed population: Microbe concentrations of raw water Campylobacter concentration (microbes/l): E.coli O157:H7 concentration (microbes/l): Rotavirus concentration (microbes/l): Norovirus concentration (microbes/l): Cryptosporidium concentration (microbes/l): Giardia concentration (microbes/l): Water cleaning: Cleaning process and chlorination Water cleaning methods in use: Traditional cleaning Well working cleaning Enhanced cleaning Slow sand filtration Limestone filtration Active carbon filtration None of the above cleaning methods UV Ozonization None of the above disinfection methods (UV, ozone) Chlorine dose (mg/l): + Show code- Hide code # This is code Op_en6177/ on page [[Water guide]] library(OpasnetUtils) library(ggplot2) # Create ovariables from user input data RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = rawclass, Result=1)) population <- Ovariable("population", data=data.frame(Result=Population)) Treatment <- Ovariable("Treatment", data=data.frame(TreatmentMethod=treatment, Result=1)) ChlorineDose <- Ovariable("ChlorineDose", data=data.frame(Result=chlorinedose)) objects.latest("Op_en7957", code_name="exposure") Exposure <- EvalOutput(Exposure) objects.latest("Op_en7947", code_name="caseburden") case_burden <- EvalOutput(case_burden) incidence <- 1 # Number per 1py # Potentially everyone can get the disease. PAF is the fraction of this that actually occurs ERF <- Ovariable( "ERF", dependencies=data.frame(Name="ERF_micr",Ident="Op_en7948/ERF_micr2"), formula = function(...) return(ERF_micr) ) objects.latest("Op_en2261", code_name="BoDattr2") # [[Health impact assessment]] BoDattr BoDattr <- EvalOutput(BoDattr, verbose=TRUE) oprint(summary(BoDattr, marginals=c("Response","Illness"))) ggplot(BoDattr@output, aes(x=Exposure_agent, weight=BoDattrResult, fill=Illness)) + geom_bar() + coord_flip() + labs( title="Disease burden of waterborne microbes", subtitle=paste("in a population of", Population), y = "Disease burden (DALY/incident)" ) if(FALSE) { ######################## Diagnostics objects.latest("Op_en6007", code_name="diagnostics") oprint(showLoctable()) oprint(showind()) ################ Insight network gr <- scrape(type="assessment") objects.latest("Op_en3861", "makeGraph") # [[Insight network]] gr <- makeGraph(gr) #export_graph(gr, "ruori.svg") #render_graph(gr) }

Old version

Situation Classification of raw water: Surface water - low contamination Surface water - medium contamination Surface water - high contamination Ground water - clean Gound water - surface water contamination Size of the exposed population: Water cleaning: Cleaning process and chlorination Water cleaning methods in use: Traditional cleaning Well working cleaning Enhanced cleaning Slow sand filtration Lime stone filtration Active carbon filtration UV filtration Ozone treatment Chlorine dose (mg/l): + Show code- Hide code library(OpasnetUtils) library(reshape2) # Nämä arvot kysytään käyttäjältä täysverisessä mallissa Kampylo <- 'Luokituksen mukainen' Ecoli <- 'Luokituksen mukainen' Rota <- 'Luokituksen mukainen' Noro <- 'Luokituksen mukainen' Crypto <- 'Luokituksen mukainen' Giardia <- 'Luokituksen mukainen' Kaupunki <- 'Custom' IVedenkulutus <- 1153 IRaakaPitoisuus <- list(Kampylo, Ecoli, Rota, Noro, Crypto, Giardia) # Vedenkulutus if (!is.na(suppressWarnings(as.numeric(IVedenkulutus)))) { InpVedenkulutus = data.frame(VedenkulutusResult = IVedenkulutus) InpVedenkulutus = Ovariable("Vedenkulutus", output = InpVedenkulutus) } # Patogeenien pitoisuudet objects.latest("Op_fi2655", "muuttuja") # RaakaPitoisuus, customoitia varten RaakaPitoisuus <- EvalOutput(RaakaPitoisuus) temp <- RaakaPitoisuus@output temp <- temp[as.character(temp$Raakavesilähde) == RaakaLuokka, ] temp <- merge( temp, data.frame( Patogeeni = c("Kampylobakteeri","E.coli O157:H7","Rotavirus","Norovirus","Cryptosporidium","Giardia"), TempResult = suppressWarnings(as.numeric(IRaakaPitoisuus)) ) ) temp$RaakaPitoisuusResult <- ifelse( is.na(temp$TempResult), temp$RaakaPitoisuusResult, temp$TempResult ) RaakaPitoisuus@output <- temp[, !colnames(temp) %in% c("TempResult", "Raakavesilähde")] temp <- rep(FALSE,9) # vaihtoehdot + klooraus joka on mallin rakenteesta johtuen aina epätosi temp[Puhdistus] <- TRUE Puhdistus <- temp if (!is.na(suppressWarnings(as.numeric(IKlooriAnnos)))) if (IKlooriAnnos > 0) { InpKlooriAnnos <- Ovariable("KlooriAnnos", output = data.frame(KlooriAnnosResult = IKlooriAnnos)) } # Käsitellään puhdistusmenetelmät käsin tätä mallia varten riippuvuudet <- data.frame( Name = c("RaakaPitoisuus", "Desinfiointi", "PuhdistusTeho", "KloorausTeho"), Ident = c("Op_fi2655/muuttuja", "Op_fi2993/muuttuja", "Op_fi2656/muuttuja", "Op_fi2667/tehokkuus") ) objects.latest("Op_fi2993", "muuttuja") # Desinfiointi objects.latest("Op_fi2656", "muuttuja") # PuhdistusTeho PuhdistusTeho <- EvalOutput(PuhdistusTeho) Desinfiointi <- EvalOutput(Desinfiointi) Puhdistus <- data.frame( Käytössä = Puhdistus, Puhdistusmenetelmä = c("Perinteinen puhdistus", "Hyvin toimva puhdistus", "Tehostettu puhdistus" ,"Hidas hiekkasuodatus", "Kalkkikivisuodatus", "Aktiivihiilisuodatus", "UV", "Otsonointi", "Klooraus") ) PuhdistusTeho@output <- merge(PuhdistusTeho@output, Puhdistus[1:6,]) # Vedenkäsittelymenetelmät Desinfiointi@output <- merge(Desinfiointi@output, Puhdistus[7:9,]) # Desinfiointimenetelmät PuhdistusTeho@output$PuhdistusTehoResult <- ifelse( PuhdistusTeho@output$Käytössä, PuhdistusTeho@output$PuhdistusTehoResult, 0 ) Desinfiointi@output$DesinfiointiResult <- ifelse( Desinfiointi@output$Käytössä, Desinfiointi@output$DesinfiointiResult, 0 ) # Poistetaan "Käytössä" kolumin PuhdistusTeho@output <- PuhdistusTeho@output[, !colnames(PuhdistusTeho@output) == "Käytössä"] Desinfiointi@output <- Desinfiointi@output[, !colnames(Desinfiointi@output) == "Käytössä"] objects.latest("Op_fi1759", "muuttuja") # Vaste Vaste <- EvalOutput(Vaste, substitute = TRUE) ################################################################################# dose.response = Vaste@output Pathogen <- c("Kampylobakteeri","E.coli O157:H7","Rotavirus","Norovirus","Cryptosporidium","Giardia") vaesto <- opbase.data("Op_fi2652")[,c("Ikä","Result")] colnames(vaesto) <- c("Age", "Osuus") vaesto$Populaatio <- vaesto$Osuus * Vaestonkoko odotettu.elinika <- 81 colnames(dose.response)[colnames(dose.response) == "Patogeeni"] <- "Pathogen" colnames(dose.response)[colnames(dose.response) == "VasteResult"] <- "P.inf" colnames(dose.response)[colnames(dose.response) == "AltistuminenResult"] <- "Exp.pat" P.ill.g.inf <- data.frame(Pathogen, P.ill.g.inf = c(0.33, 1 - (270 / 1540), 0.9, 0.7, 0.71, 1)) # todennäköisyys sairastua kun saa infektion # Kampylobakteeri, DALYt per infektio P.treat.g.ill.Kamp.Gastr <- data.frame(Pathogen = Pathogen[c(1,1,1)], Outcome = "Gastroenteritis", ill.treat = c("Untreated", "General practitioner", "Hospitalised", "Unspecified")[c(1,2,3)], P.treat.g.ill = c(0.7627, 0.2373, 0.0097)) P.treat.ill.g.inf.Kamp.Gastr <- merge(P.treat.g.ill.Kamp.Gastr, P.ill.g.inf) P.treat.ill.g.inf.Kamp.Gastr$P.treat.ill.g.inf <- P.treat.ill.g.inf.Kamp.Gastr$P.ill.g.inf * P.treat.ill.g.inf.Kamp.Gastr$P.treat.g.ill duration.ill.treat.Kamp.Gastr <- data.frame(Outcome = c("Gastroenteritis"), ill.treat = c("Untreated", "General practitioner", "Hospitalised", "Unspecified")[c(1,2,3)], dur.ill = c(5.1 / 365, 8.4 / 365, 14.39 / 365)) severity.ill.treat.Kamp.Gastr <- data.frame(Outcome = c("Gastroenteritis"), ill.treat = c("Untreated", "General practitioner", "Hospitalised", "Unspecified")[c(1,2,3)], sev.ill = c(0.067, 0.393, 0.393)) daly.ill.treat.Kamp.Gastr <- merge(P.treat.ill.g.inf.Kamp.Gastr, duration.ill.treat.Kamp.Gastr) daly.ill.treat.Kamp.Gastr <- merge(daly.ill.treat.Kamp.Gastr, severity.ill.treat.Kamp.Gastr) daly.ill.treat.Kamp.Gastr$dalys <- daly.ill.treat.Kamp.Gastr$P.treat.ill.g.inf * daly.ill.treat.Kamp.Gastr$dur.ill * daly.ill.treat.Kamp.Gastr$sev.ill P.death.g.ill.Gastr <- 0.0004 P.death.g.inf.Gastr <- P.death.g.ill.Gastr * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"] death.Gastr.life.lost <- 13.2 daly.death.Kamp.Gastr <- P.death.g.inf.Gastr * death.Gastr.life.lost ## GBS Kamp. P.gbs.g.ill <- 2e-004 P.gbs.g.inf <- P.gbs.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"] dur.sev.factor.gbs <- data.frame(Outcome = c("Clinical GBS", "Residual GBS"), dur.sev.factor = c(0.29, 5.8)) # duration * severity * fraction? daly.Kamp.gbs <- data.frame(dur.sev.factor.gbs$Outcome, dalys = dur.sev.factor.gbs$dur.sev.factor * P.gbs.g.inf) P.death.g.gbs <- 0.08 / 3 # triangular 0.01, 0.02, 0.05 P.death.g.inf.gbs <- P.death.g.gbs * P.gbs.g.inf death.gbs.life.lost <- 18.7 daly.death.Kamp.gbs <- P.death.g.inf.gbs * death.gbs.life.lost ## reactive arthritis Kamp. P.arth.g.ill <- 0.02 # triangluar 0.01, 0.02, 0.03 P.arth.g.inf <- P.arth.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Kampylobakteeri"] duration.arth <- 6 / 52 severity.arth <- 0.21 daly.Kamp.arth <- P.arth.g.inf * duration.arth * severity.arth # E.coli P.wd.g.ill <- 0.53 # watery diarrhea P.wd.g.inf <- P.wd.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"] severity.wd <- 0.067 duration.wd <- 3.4 / 365 daly.wd.Ecoli <- P.wd.g.inf * severity.wd * duration.wd P.hc.g.ill <- 0.47 P.hc.g.inf <- P.hc.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"] severity.hc <- 0.39 duration.hc <- 5.6 / 365 daly.hc.Ecoli <- P.hc.g.inf * severity.hc * duration.hc P.death.g.ill.Ecoli <- 0.00027 P.death.g.inf.Ecoli <- P.death.g.ill.Ecoli * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"] age.death.Ecoli <- 81 - 13.2 daly.death.Ecoli <- P.death.g.inf.Ecoli * (odotettu.elinika - age.death.Ecoli) ## Haemolytic uraemic syndrome (HUS) P.hus.g.ill <- 0.01 P.hus.g.inf <- P.hus.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "E.coli O157:H7"] severity.hus <- 0.93 duration.hus <- 21 / 365 daly.hus.Ecoli <- P.hus.g.inf * severity.hus * duration.hus P.death.g.hus <- 0.04 P.death.hus.g.inf <- P.death.g.hus * P.hus.g.inf age.death.hus.Ecoli <- 81 - 26.2 daly.death.hus.Ecoli <- P.death.hus.g.inf * (odotettu.elinika - age.death.hus.Ecoli) ## End Stage Renal Disease (ESRD) P.esrd.g.hus <- 0.118 P.esrd.g.inf <- P.hus.g.inf * P.esrd.g.hus severity.duration.hus <- 8.7 # severity * duration daly.esrd.Ecoli <- P.esrd.g.inf * severity.duration.hus P.death.g.esrd <- 0.0252 P.death.esrd.g.inf <- P.esrd.g.inf * P.death.g.esrd age.death.esrd.Ecoli <- 81 - 34 daly.death.esrd.Ecoli <- P.death.esrd.g.inf * (odotettu.elinika - age.death.esrd.Ecoli) # Rotavirus P.treat.g.ill.Rotavirus <- data.frame(Pathogen = "Rotavirus", ill.treat = c("Untreated", "General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.82,5), rep(0.95, 10), rep(0.99, 50), rep(0.97, 17), rep(0.137, 5), rep(0.0244, 5), rep(0.0511, 5), rep(0.0127, 50), rep(0.0299, 17), rep(0.0416, 5), rep(0.0213, 5), rep(0, 72))) P.treat.ill.g.inf.Rotavirus <- merge(P.treat.g.ill.Rotavirus, P.ill.g.inf) P.treat.ill.g.inf.Rotavirus$P.treat.g.inf <- P.treat.ill.g.inf.Rotavirus$P.ill.g.inf * P.treat.ill.g.inf.Rotavirus$P.treat.g.ill duration.ill.treat.Rotavirus <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(4.9 / 365, 7.1 / 365, 7.7 / 365)) severity.ill.treat.Rotavirus <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067, 0.393, 0.393)) daly.ill.treat.Rotavirus <- merge(P.treat.ill.g.inf.Rotavirus, duration.ill.treat.Rotavirus) daly.ill.treat.Rotavirus <- merge(daly.ill.treat.Rotavirus, severity.ill.treat.Rotavirus) daly.ill.treat.Rotavirus$dalys <- daly.ill.treat.Rotavirus$P.treat.g.inf * daly.ill.treat.Rotavirus$dur.ill * daly.ill.treat.Rotavirus$sev.ill P.death.Rotavirus <- data.frame(Age = 0:81, P.death.g.ill = c(rep(2.13e-005, 5), rep(0, 77))) P.death.Rotavirus$P.death.g.inf <- P.death.Rotavirus$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Rotavirus"] P.death.Rotavirus$Life.lost <- odotettu.elinika - P.death.Rotavirus$Age daly.death.Rotavirus <- data.frame(Age = P.death.Rotavirus$Age, dalys = P.death.Rotavirus$P.death.g.inf * P.death.Rotavirus$Life.lost) # Norovirus P.treat.g.ill.Norovirus <- data.frame(Pathogen = "Norovirus", ill.treat = c("Untreated", "General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.94876706,5), rep(0.9902, 5), rep(0.98239, 5), rep(0.98434, 51), rep(0.992741, 16), rep(0.0448,5), rep(8.6e-003, 5), rep(0.0154, 5), rep(0.0137, 51), rep(6.17e-003, 16), rep(6.43e-003,5), rep(1.2e-003, 5), rep(2.21e-003, 5), rep(1.96e-003, 51), rep(8.85e-004, 16))) P.treat.ill.g.inf.Norovirus <- merge(P.treat.g.ill.Norovirus, P.ill.g.inf) P.treat.ill.g.inf.Norovirus$P.treat.g.inf <- P.treat.ill.g.inf.Norovirus$P.ill.g.inf * P.treat.ill.g.inf.Norovirus$P.treat.g.ill duration.ill.treat.Norovirus <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(3.8 / 365, 5.73 / 365, 7.23 / 365)) severity.ill.treat.Norovirus <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067, 0.393, 0.393)) daly.ill.treat.Norovirus <- merge(P.treat.ill.g.inf.Norovirus, duration.ill.treat.Norovirus) daly.ill.treat.Norovirus <- merge(daly.ill.treat.Norovirus, severity.ill.treat.Norovirus) daly.ill.treat.Norovirus$dalys <- daly.ill.treat.Norovirus$P.treat.g.inf * daly.ill.treat.Norovirus$dur.ill * daly.ill.treat.Norovirus$sev.ill P.death.Norovirus <- data.frame(Age = 0:81, P.death.g.ill = c(rep(2.94e-006, 5), rep(0, 61), rep(2.04e-004, 16))) P.death.Norovirus$P.death.g.inf <- P.death.Norovirus$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Norovirus"] P.death.Norovirus$Life.lost <- odotettu.elinika - P.death.Norovirus$Age daly.death.Norovirus <- data.frame(Age = P.death.Norovirus$Age, dalys = P.death.Norovirus$P.death.g.inf * P.death.Norovirus$Life.lost) # Cryptosporidium P.treat.g.ill.Crypt <- data.frame(Pathogen = "Cryptosporidium", ill.treat = c("Untreated", "General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.9175730049999999,5), rep(0.80937, 5), rep(0.6810499999999999, 5), rep(0.9774191, 50), rep(0.94706, 17), rep(0.082,5), rep(0.188, 5), rep(0.316, 5), rep(0.0209, 50), rep(0.0367, 17), rep(4.26e-004,5), rep(2.63e-003, 5), rep(2.95e-003, 5), rep(1.66e-003, 50), rep(0.0146, 17))) P.treat.ill.g.inf.Crypt <- merge(P.treat.g.ill.Crypt, P.ill.g.inf) P.treat.ill.g.inf.Crypt$P.treat.g.inf <- P.treat.ill.g.inf.Crypt$P.ill.g.inf * P.treat.ill.g.inf.Crypt$P.treat.g.ill duration.ill.treat.Crypt <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(3.5 / 365, 7 /365, 18.4 / 365)) severity.ill.treat.Crypt <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067, 0.393, 0.393)) daly.ill.treat.Crypt <- merge(P.treat.ill.g.inf.Crypt, duration.ill.treat.Crypt) daly.ill.treat.Crypt <- merge(daly.ill.treat.Crypt, severity.ill.treat.Crypt) daly.ill.treat.Crypt$dalys <- daly.ill.treat.Crypt$P.treat.g.inf * daly.ill.treat.Crypt$dur.ill * daly.ill.treat.Crypt$sev.ill P.death.Crypt <- data.frame(Age = 0:81, P.death.g.ill = c(rep(9.95e-007, 5), rep(0, 10), rep(2.09e-005, 50), rep(1.64e-003, 17))) P.death.Crypt$P.death.g.inf <- P.death.Crypt$P.death.g.ill * P.ill.g.inf$P.ill.g.inf[P.ill.g.inf$Pathogen == "Cryptosporidium"] P.death.Crypt$Life.lost <- odotettu.elinika - P.death.Crypt$Age daly.death.Crypt <- data.frame(Age = P.death.Crypt$Age, dalys = P.death.Crypt$P.death.g.inf * P.death.Crypt$Life.lost) # Giardia P.treat.g.ill.Giardia <- data.frame(Pathogen = "Giardia", ill.treat = c("Untreated", "General practitioner", "Hospitalised")[rep(1:3, each = 82)], Age = rep(0:81, 3), P.treat.g.ill = c(rep(0.9376,5), rep(0.91034, 5), rep(0.72642, 5), rep(0.92486, 50), 0.54596, rep(0.5365, 16), rep(0.0609,5), rep(0.0852, 5), rep(0.272, 5), rep(0.0721, 50), rep(0.451, 17), rep(1.5e-003,5), rep(4.46e-003, 5), rep(1.58e-003, 5), rep(3.04e-003, 51), rep(0.0125, 16))) P.treat.ill.g.inf.Giardia <- merge(P.treat.g.ill.Giardia, P.ill.g.inf) P.treat.ill.g.inf.Giardia$P.treat.g.inf <- P.treat.ill.g.inf.Giardia$P.ill.g.inf * P.treat.ill.g.inf.Giardia$P.treat.g.ill duration.ill.treat.Giardia <- data.frame(ill.treat = c("Untreated", "General practitioner","Hospitalised"), dur.ill = c(10 / 365, 10 /365, 30 / 365)) severity.ill.treat.Giardia <- data.frame(ill.treat = c("Untreated", "General practitioner", "Hospitalised"), sev.ill = c(0.067, 0.393, 0.393)) daly.ill.treat.Giardia <- merge(P.treat.ill.g.inf.Giardia, duration.ill.treat.Giardia) daly.ill.treat.Giardia <- merge(daly.ill.treat.Giardia, severity.ill.treat.Giardia) daly.ill.treat.Giardia$dalys <- daly.ill.treat.Giardia$P.treat.g.inf * daly.ill.treat.Giardia$dur.ill * daly.ill.treat.Giardia$sev.ill # yhteenveto DALYistä Health.effects <- vaesto[,c("Age","Populaatio")] Health.effects$Untreated.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "Untreated", c("dalys")] Health.effects$GP.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "General practitioner", c("dalys")] Health.effects$Hospitalised.Gastr.Kamp <- daly.ill.treat.Kamp.Gastr[daly.ill.treat.Kamp.Gastr$ill.treat == "Hospitalised", c("dalys")] Health.effects$Death.Gastr.Kamp <- daly.death.Kamp.Gastr Health.effects$Clinical.GBS.Kamp <- daly.Kamp.gbs$dalys[1] Health.effects$Residual.GBS.Kamp <- daly.Kamp.gbs$dalys[2] Health.effects$Death.GBS.Kamp <- daly.death.Kamp.gbs Health.effects$Arth.Kamp <- daly.Kamp.arth Health.effects$WD.Ecoli <- daly.wd.Ecoli Health.effects$HC.Ecoli <- daly.hc.Ecoli Health.effects$Death.Ecoli <- daly.death.Ecoli Health.effects$HUS.Ecoli <- daly.hus.Ecoli Health.effects$Death.HUS.Ecoli <- daly.death.hus.Ecoli Health.effects$ESRD.Ecoli <- daly.esrd.Ecoli Health.effects$Death.ESRD.Ecoli <- daly.death.esrd.Ecoli Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "Untreated", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Rotavirus" Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "General practitioner", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "GP.Rotavirus" Health.effects <- merge(Health.effects, daly.ill.treat.Rotavirus[daly.ill.treat.Rotavirus$ill.treat == "Hospitalised", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Rotavirus" Health.effects <- merge(Health.effects, daly.death.Rotavirus) colnames(Health.effects)[ncol(Health.effects)] <- "Death.Rotavirus" Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "Untreated", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Norovirus" Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "General practitioner", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "GP.Norovirus" Health.effects <- merge(Health.effects, daly.ill.treat.Norovirus[daly.ill.treat.Norovirus$ill.treat == "Hospitalised", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Norovirus" Health.effects <- merge(Health.effects, daly.death.Norovirus) colnames(Health.effects)[ncol(Health.effects)] <- "Death.Norovirus" Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "Untreated", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Crypt" Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "General practitioner", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "GP.Crypt" Health.effects <- merge(Health.effects, daly.ill.treat.Crypt[daly.ill.treat.Crypt$ill.treat == "Hospitalised", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Crypt" Health.effects <- merge(Health.effects, daly.death.Crypt) colnames(Health.effects)[ncol(Health.effects)] <- "Death.Crypt" Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "Untreated", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Untreated.Giardia" Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "General practitioner", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "GP.Giardia" Health.effects <- merge(Health.effects, daly.ill.treat.Giardia[daly.ill.treat.Giardia$ill.treat == "Hospitalised", c("Age", "dalys")]) colnames(Health.effects)[ncol(Health.effects)] <- "Hospitalised.Giardia" Health.effects <- reshape(Health.effects, idvar = c("Age"), times = colnames(Health.effects)[-c(1,2)], timevar = "Outcome", varying = list(colnames(Health.effects)[-c(1,2)]), direction = "long") colnames(Health.effects)[4] <- "P.daly.g.inf" Health.effects$Pathogen <- NA Health.effects$Pathogen[grep(".Kamp", Health.effects$Outcome)] <- Pathogen[1] Health.effects$Pathogen[grep(".Ecoli", Health.effects$Outcome)] <- Pathogen[2] Health.effects$Pathogen[grep(".Rotavirus", Health.effects$Outcome)] <- Pathogen[3] Health.effects$Pathogen[grep(".Norovirus", Health.effects$Outcome)] <- Pathogen[4] Health.effects$Pathogen[grep(".Crypt", Health.effects$Outcome)] <- Pathogen[5] Health.effects$Pathogen[grep(".Giardia", Health.effects$Outcome)] <- Pathogen[6] Health.effects <- merge(Health.effects, dose.response[,c("Pathogen", "P.inf")]) Health.effects$DALYs <- (1 - (1 - Health.effects$P.inf * Health.effects$P.daly.g.inf)^365) * Health.effects$Populaatio temp <- merge(dose.response, P.ill.g.inf) ############# EXTRA ########################################### MikrobienLogVähenemä <- combine(Desinfiointi, PuhdistusTeho, KloorausTeho, name = "MikrobienLogVähenemä") MikrobienLogVähenemä@output[["Puhdistusmenetelmä"]][ MikrobienLogVähenemä@output[["MikrobienLogVähenemäSource"]] == "KloorausTehoFormula" ] <- "Klooraus" #MikrobienLogVähenemä <- oapply(MikrobienLogVähenemä, cols = c("Puhdistusmenetelmä", "KlooriAnnosSource"), FUN = sum, na.rm = TRUE) #MikrobienLogVähenemä@output <- fillna(MikrobienLogVähenemä@output, colnames(MikrobienLogVähenemä@output)[MikrobienLogVähenemä@marginal]) ############# TULOKSET ######################################################################################################### #cat("Pathogen concentration in the raw water\n") #oprint(RaakaPitoisuus) # Patogeenien konsentraatio raakavedessä #cat("Pathogen log reduction in the treatment\n") #oprint(MikrobienLogVähenemä) temp2 <- Vaste@output temp2$VasteResult <- temp2$VasteResult * 1000 cat("Probability of infection caused by water pathogens for an individual (per mille per day)\n") oprint(temp2[,c("Patogeeni", "VasteResult")], digits = 4) # Patogeeneille altistuminen ja infektion todennäköisyys (vaste) cat("Estimated health impact\n") cat(sum((1 - (1 - temp$P.ill.g.inf * temp$P.inf)^365) * Vaestonkoko, na.rm = TRUE), " gastroenterisis cases per year\n") cat(sum(Health.effects$DALYs, na.rm = TRUE), " DALYs because of gastroenteritis \n")

The result page opens in a new tab by pressing the run code -box. The results will be visible after updating that page approximately a minute after clicking the run code.

User instructions for the Water guide -model

1. Choose classification of raw water

• Ground water - Clean: clean ground water
• Ground water - Surface water load: f.ex. shore infiltration
• Surface water - Low load: relatively clean surface water
• Surface water - Medium load: f.ex. low waste water load
• Surface water - High load: f.ex. waste water load

OR

Choose 'I will determine microbe concentration by hand'. Write pathogen concentrations in their respective boxes.

2. State the concumption of drinking water per 24 h in liters per day per person (default 1.153 l/d).

3. State the population size of target area. (default 100000)

4. Choose treatment processes used. Obs! You can choose multiple options. If none of the first six treatment processes are used, choose "None of the above cleaning methods". If neither UV nor ozone is used, choose "None of the above disinfection methods".

5. Choose whether chlorination is used or not by inputing the chlorine dose used (mg/l). If there is no chlorination, the dose will be 0 mg/l (default 1.5 mg/l).

6. You can see the results in a new tab by pressing 'Run code'

The first two tables and one Figure at the end of the page you can see the starting values inputed into the model. The health effects are reported as two measures:

• How many cases of gastroenteritis there are in the area per year.
• How many DALYs or disability adjusted life years are lost due to those cases of gastroenteritis each year in the area.

Rationale

The model has been translated from Analytica to R. You can find the original model here (page in Finnish, link to the model code at the top): op_fi:Tiedosto:Vesiopas.ANA. If the user does not give concentrations for the pathogens, the values on page Pathogen concentrations in raw water will be used.

Health-based quality requirements and recommendations have been implanted on the quality of household water. In Finland, the ministry of social affairs and health is responsible for the legislation of the quality, and monitoring it is the responsibility of municipal health protection officials. Upholding and developing good water quality require high-standard research and cooperation between different parties. Ground waters and artificial ground waters are usually not disinfected. The chemicals and microbes in raw water and byproducts of disinfection produced in household water production can present health risks to the people using the household water. Water leaving the treatment plant can stay in the pipes for a long time. If the circumstances are favourable for microbial growth, the water can become low in quality and even detrimental to health while in the pipe network.

Household water is produced from either ground or surface water. The problems of these types of waters as raw water are different. Using surface water always requires establishing an actual treatment plant and the associated knowledge, skills, technologies and educated monitoring personnel. Ground water on the other hand doesn't require very complex treatment, so not many treatment plant personnel are needed, and there is less monitoring. Additionally today there are many treatment plants that produce artificial ground water. The qualities of artificial ground water can be similar to good ground waters, but the quality of the water is often only be equal to medium quality surface water.

Treatment of ground water usually includes increasing the pH and and hardness of water. This can be done using different techniques, of which the use of calcium hydroxide and carbon dioxide to regulate pH are the most common. Sometimes also precipitation of iron or manganese is required using strong oxidisers such as permanganate or chlorine. Low quality ground water is also disinfected.

In order to make surface water drinkable, it has to be heavily treated. The hardest thing is to precipitate the humus. This is done by using not only different precipitation chemicals, also for example adjust pH and breaking down the humus with strong oxidisers such as ozone. On top of that, the precipitated chemicals (such as salts of aluminium and iron) need to be removed and water hardness raised so that the pipelines don't disintegrate over time. Also, the population has to be protected from the microbes always present in surface waters, so the water needs to be disinfected to remove pathogens.

The quality of water is also affected by the condition of the pipelines and the water spends in them. If there is a lot of coagulation in the pipelines, these dregs include not only different inorganic compounds but also a large amout of bacterial mass. The coagulates in the pipelines and their qualities have started to be researched in Finland only recently.

The purpose of risk assessment is to produce information of the true size of the risk for decision-making. Risk assessment is a scientific process in which experts hold a key role. Because there are any kinds of risks to study, risk assessment processes can be very different. In environmental health risk assessment consists of four steps:

1. Identifying the danger (does the variable researched pose a danger to humans?)
2. Assessing the exposure-response function (how big a dose results in how big a response?)
3. Assessing exposure (how much are humans exposed?)
4. Characterising the risk (how high is the health risk?)

All water treatment plants should periodically do risk assessment on on the household water they produce. It is done to identify actions to be taken in different crisis situations and prepare plans for different problematic situations and possible damage. Different treatment plants and for example vacation centers need different kinds of plans and implementations.

Dependencies

• Op_en2949 Population of Finland
• Op_en2261 HIA
  • Op_en7948 ERF of waterborne microbes
  • Op_fi2994 op_fi:Paikkakuntakohtaiset tiedot talousveden käsittelyyn (in Finnish) Water treatment in Finland
  • Op_en7957 Health impacts of waterborne microbes
    • Op_en7947 Case burden of waterborne microbes
    • Op_en7953 Pathogen concentrations in raw water
    • Op_en7954 Drinking water treatment efficiency (Filtration etc.)
    • Op_en7955 Drinking water disinfection efficiency (UV, ozone)
    • Op_en7956 Drinking water chlorination efficiency

See also

This page is a knowledge crystal of subtype method. The page identifier is Op_en6177
Moderator:Päivi Meriläinen (see all)
Upload data {{#opasnet_base_link:Op_en6177}}

• op_fi:Juomaveden patogeenit
• op_fi:Juomaveden patogeenien terveysvaikutukset

• op_fi:Arviointi pohjavesilaitoksen mikrobiologisista riskeistä
• op_fi:Arviointi juomaveden kloorifenolien terveysvaikutuksista
• heande:Kuovesi
• op_fi:Mikrobien määrä juomavedessä
• op_fi:Mikrobien määrä pohjavedessä

• http://www.fao.org/docrep/006/y4666e/y4666e06.htm#TopOfPage
• http://wiki.camra.msu.edu/index.php?title=Main_Page
• http://www.medicina.fi/index.php?option=com_content&view=article&id=48&Itemid=56#I

Municipality-specific data

• Op_fi2603 op_fi:Arviointi juomaveden laadun terveysvaikutuksista/Kuopio (vedenpuhdistamon tietoja)
• heande:Polaris:Raakaveden patogeenipitoisuudet/Lahti
• heande:Polaris:Raakaveden patogeenipitoisuudet/Mikkeli
• heande:Polaris:Raakaveden patogeenipitoisuudet/Kirkkonummi
• heande:Polaris:Paikkakuntakohtaiset tiedot talousveden käsittelyyn/Lahti
• heande:Polaris:Paikkakuntakohtaiset tiedot talousveden käsittelyyn/Mikkeli
• heande:Polaris:Paikkakuntakohtaiset tiedot talousveden käsittelyyn/Kirkkonummi
• heande:Polaris:Juomaveden desinfiointi/Lahti
• heande:Polaris:Juomaveden desinfiointi/Mikkeli
• heande:Polaris:Juomaveden desinfiointi/Kirkkonummi
• heande:Polaris:Vedenkäsittelyn tehokkuus/Lahti
• heande:Polaris:Vedenkäsittelyn tehokkuus/Mikkeli
• heande:Polaris:Vedenkäsittelyn tehokkuus/Kirkkonummi

References