Drinking water chlorination efficiency: Difference between revisions

Revision as of 04:29, 9 July 2019

This page is a knowledge crystal of subtype variable. The page identifier is Op_en7956
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Question

How does chlorination affect the concentrations of pathogens in drinking water, reported in log-decrese?

Answer

Pathogen	Log-dercease
Campylobacter	8.837981871
E.coli O157:H7	7.182699561
Rotavirus	11.97117474
Norovirus	13.55252482
Cryptosporidium	0
Giardia	0.095329311

Rationale

Chloriantion efficiency, or chlorine's capacity to destroy microbes, depends on many factors: the form of the chlorine, temperature, retention period, pH and concentration as well as other chemicals in the water. In some circumstances it might efficiently kill all indicator organisms, but some active viruses, protists or their cysts may remain in the water. The meter to measure the efficiency of chlorination is kloorikokema ⇤--arg5411: . Someone else has to translate this --Heta (talk) 14:31, 4 July 2019 (UTC) (type: truth; paradigms: science: attack), which is the concentration multiplied by retention period, so called CT-value. The required CT-value depends on the temperature: the lower the temperature, the higher the CT-value has to be.

^[1]

Data

Pathogen sensitivity to chlorine:

The rows tell which pathogen the ct-values on that row are for.

The columns tell the ct-value required to decrease the amount of each pathogen in the drinking water to a certain level on the log-scale. Column 1 means pathogen concentration will drop to 10^-1 of the original, column 2 means the concentration will drop to 10^-2 and so on.

Drinking water chlorination efficiency: Difference between revisions((mg/l)*min)
Obs	Pathogen	1	2	3	4
1	Campylobacter	0.152	0.294	0.436	0
2	E.coli O157:H7	0.17	0.34	0.52	1.06
3	Rotavirus	0.12	0.16	0.2	0.3
4	Norovirus	0.09	0.18	0.245	0.314
5	Cryptosporidium	0	0	0	0
6	Giardia	75	150	216	0

Pathogen	Reference
Campylobacter	^[2]; ^[3]
E.coli O157:H7	^[4]; ^[5]
Rotavirus	^[6]
Norovirus	^[7]
Cryptosporidium	^[8]
Giardia	^[9]

Causality

Drinking water disinfection efficiency

Unit

logarithmic decrease

Calculations

CT-value = Chlorine residue concentration (mg/l)* time (min)

+ Show code - Hide code

  #This is code "Op_en7956/dose" on page [[Drinking water chlorination efficiency]]
  library(OpasnetUtils)

ChlorineDose = data.frame(ChlorineDoseResult = 1.5)

ChlorineDose = Ovariable("ChlorineDose", data = ChlorineDose, save = TRUE)

oprint(ChlorineDose)

+ Show code - Hide code

#This is code "Op_en7956/seisitivity" on page [[Drinking water chlorination efficiency]]
library(OpasnetUtils)

# Get the data from OpasnetBase. The data is in long format, each log decrease for each microbe one row.
CLsensitivity <- tidy(
  opbase.data("Op_en7956"), 
  objname = "CLsensitivity"
)

# At this point the data is identical to the t2b on the page. Stick it into an ovariable
CLsensitivity <- Ovariable("CLsensitivity", data = CLsensitivity)
objects.store(CLsensitivity)

oprint(CLsensitivity)

+ Show code - Hide code

# This is code "Op_en7956/efficiency" on page [[Drinking water chlorination efficiency]]
library(OpasnetUtils)
library(reshape2)

riippuvuudet = data.frame(
	Name = c("ChlorineDose", "CLMysteeri", "CLsensitivity", "ChlorineEfficiencyF"),
	Ident = c("Op_fi2667/dose")
)

ChlorineEfficiencyF <- function(ChlorineDose, Ctvalues, logDecreases) {
  if(sum(Ctvalues) == 0) {return(0) # if all the ct:s are 0, return 0
    } else {
      # this next bit calculates the ct, given Chlorine dose. I don't know what it does.
  Mrt <- 12
  Ncstr <- 6
  Ttimes <- c(0.001,0.01,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,2,2.5,4)
  Times <- Ttimes * Mrt
  Times.inc <- Times - c(0,Times[1:20])
  Probability <- (Times^(Ncstr - 1) * exp(-Ncstr * Times / Mrt))/(factorial(Ncstr - 1) * (Mrt / Ncstr) ^ Ncstr) # If we calculate for each CSTR separately, we could integrate analytically and get rid of Times and Times.inc altogether.
  ChlorineDose  <- ChlorineDose * exp(-0.13 * Times) # Instead of this, we could integrate analytically because it is easy with exponential functions.
# Where does 0.13 come from? Can it vary in different situations, so should it be an ovariable as well?
  ChlorineDose  <- ChlorineDose  * Times.inc
  ChlorineDoseCumsum <- cumsum(ChlorineDose)
  ClConcDistr <- sum(ChlorineDoseCumsum * Probability) / length(Probability)
  # given the ct:s known to be required for different log-decreases of concentration, what's the log-decrease with the ct calculated above
  # simple linear interpolation. Rule: if CTConc exceeds data, use the highest logDecrease
  logD <- approx(x=Ctvalues, y=logDecreases, xout=ClConcDistr, rule=1:2)$y # The line must start from point (0,0). Add that to data
  return(logD) # return the log-decrease
    }
}

ChlorineEfficiency <- Ovariable("ChlorineEfficiency", dependencies = riippuvuudet, formula = function(...){
  ChlorineDose <- ChlorineDose$ChlorineDoseResult[1] # Take the chlorine dose value out of the df and save as a value
  temp <- data.frame(Pathogen=(unique(CLsensitivity$Pathogen))) # make a df with  the names of pathogens in one column
  for (j in 1:nrow(temp)) { # for each pathogen
# This for loop is problematic because all inputs are ovariables and that meant that a) they may be probabilistic and have e.g. 1000 rows of iterations (numbered in Iter column), and b) they may have other, unknown index columns.
# So, the code must reflect situations where the sizes and widths of the output dataframes of ovariables are not know.
# One way to do this is to merge the ovariables
# tmp <- ChlorineDose + CLsensitivity
# and then use their
    temp$Result[j] <- ChlorineEfficiencyF( #run Chlorine efficiency, and put the resulting log decrease in temp$Result for that pathogen
      ChlorineDose, 
      CLsensitivity$CLsensitivityResult[CLsensitivity$Pathogen == unique(CLsensitivity$Pathogen)[j]],
      # the ct:s for different log decreases for the pathogen in question
      CLsensitivity$`Log decrease`[CLsensitivity$Pathogen == unique(CLsensitivity$Pathogen)[j]]
      # the log decreases resulting from those ct:s for the pathogen in question
    )
  }
  temp$TreatmentMethod <- "Chlorination"
  # By returning as ovariable marginals can be manually defined
  temp <- Ovariable(output = temp, marginal = colnames(temp) %in% c("TreatmentMethod", "Pathogen", "ChlorineDoseSource"))
  
  return(temp)
}
)

objects.store(ChlorineEfficiency, ChlorineEfficiencyF)

oprint(ChlorineEfficiency)

References

↑ Valve, M ja Isomäki, E. 2007. Klooraus - Tuttu ja turvallinen? Vesitalous 4/2007.
↑ Blaser, M. J., Smith, P. F., Wang, W.‐L. L. and Hoff, J. C. (1986). "Inactivation of Campylobacter jejuni by Chlorine and Monochloramine." Applied and Environmental Microbiology 51(2): 307‐311.
↑ Lund, V. (1996). "Evaluation of E. coli as an indicator for the presence of Campylobacter jejuni and Yersinia enterocolitica in chlorinated and untreated oligotrophic lake water." Water Research 30(6): 1528‐ 1534.
↑ Blaser, M. J., Smith, P. F., Wang, W.‐L. L. and Hoff, J. C. (1986). "Inactivation of Campylobacter jejuni by Chlorine and Monochloramine." Applied and Environmental Microbiology 51(2): 307‐311.
↑ Lund, V. (1996). "Evaluation of E. coli as an indicator for the presence of Campylobacter jejuni and Yersinia enterocolitica in chlorinated and untreated oligotrophic lake water." Water Research 30(6): 1528‐ 1534.
↑ Rice, E. W., Hoff, J. C. and III, F. W. S. (1982). "Inactivation of Giardia cysts by chlorine." Applied and Environmental Microbiology 43(1): 250‐251
↑ Keswick, B. H., Satterwhite, T. K., Johnson, P. C., DuPont, H. L., Secor, S. L., Bitsura, J. A., Gary, G. W. and Hoff, J. C. (1985). Inactivation of norwalk virus in drinking water by chlorine. Applied and Environmental Microbiology 50(2): 261-264.
↑ Benito Corona-Vasquez, Amy Samuelson, Jason L. Rennecker and Benito J. Mariñas (2002): Inactivation of Cryptosporidium parvum oocysts with ozone and free chlorine. Water Research 36, 4053-4063
↑ Rice, E. W., Hoff, J. C. and III, F. W. S. (1982). "Inactivation of Giardia cysts by chlorine." Applied and Environmental Microbiology 43(1): 250‐251

[1] Valve, M ja Isomäki, E. 2007. Klooraus - Tuttu ja turvallinen? Vesitalous 4/2007.

[2] Blaser, M. J., Smith, P. F., Wang, W.‐L. L. and Hoff, J. C. (1986). "Inactivation of Campylobacter jejuni by Chlorine and Monochloramine." Applied and Environmental Microbiology 51(2): 307‐311.

[3] Lund, V. (1996). "Evaluation of E. coli as an indicator for the presence of Campylobacter jejuni and Yersinia enterocolitica in chlorinated and untreated oligotrophic lake water." Water Research 30(6): 1528‐ 1534.

[4] Blaser, M. J., Smith, P. F., Wang, W.‐L. L. and Hoff, J. C. (1986). "Inactivation of Campylobacter jejuni by Chlorine and Monochloramine." Applied and Environmental Microbiology 51(2): 307‐311.

[5] Lund, V. (1996). "Evaluation of E. coli as an indicator for the presence of Campylobacter jejuni and Yersinia enterocolitica in chlorinated and untreated oligotrophic lake water." Water Research 30(6): 1528‐ 1534.

[6] Rice, E. W., Hoff, J. C. and III, F. W. S. (1982). "Inactivation of Giardia cysts by chlorine." Applied and Environmental Microbiology 43(1): 250‐251

[7] Keswick, B. H., Satterwhite, T. K., Johnson, P. C., DuPont, H. L., Secor, S. L., Bitsura, J. A., Gary, G. W. and Hoff, J. C. (1985). Inactivation of norwalk virus in drinking water by chlorine. Applied and Environmental Microbiology 50(2): 261-264.

[8] Benito Corona-Vasquez, Amy Samuelson, Jason L. Rennecker and Benito J. Mariñas (2002): Inactivation of Cryptosporidium parvum oocysts with ozone and free chlorine. Water Research 36, 4053-4063

[9] Rice, E. W., Hoff, J. C. and III, F. W. S. (1982). "Inactivation of Giardia cysts by chlorine." Applied and Environmental Microbiology 43(1): 250‐251

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Drinking water chlorination efficiency: Difference between revisions

Revision as of 04:29, 9 July 2019

Contents

Question

Answer

Rationale

Data

Causality

Unit

Calculations

See also

References

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