Report No FR0436

JAN 1994



Identifying associations between the presence of coliforms and fluctuations in external parameters will provide information on how samples with coliforms differ chemically, physically and biologically compared to those without coliforms.

This information will form the foundation for developing a model to predict (from fluctuations in controllable or known parameters) the increased probability of coliform failures within distribution networks.

It will also assist in the identification of root causes and origins of failures so that operational factors and remedial treatments may be developed to minimise the problem.


To identify statistical associations between the occurrences of coliforms in water samples and fluctuations in chemical, physical and biological factors within the water distribution supply using statutory water quality monitoring data provided by the water companies.


Coliform failures in 100 ml volume water samples collected from distribution systems under statutory monitoring (water quality) legislation may occur for a variety of reasons such as problems at the treatment works, infiltration, release from biofilms and sediments, and regrowth. Coliform failures tend to occur intermittently. Before this contract, little work had ever been performed using the statutory monitoring water quality data to identify associations between the presence of coliform organisms in such samples and fluctuations in other physical, chemical and biological factors within the distribution. This would provide information on whether intermittent coliform failures reflected real changes in the local environment within the distribution system or, alternatively, were mainly due to the chance `capture' of a coliform from a homogeneously distributed population during sampling. Identifying such associations would also facilitate implementation of operational processes and remedial treatments to minimise coliform failures.

Furthermore, the associations identified may form the foundations for further work to develop a model to predict coliform concentrations within particular parts of the distribution supply and hence impending coliform failure. At present the only precautionary method available is to monitor large volume water samples (up to 10 litres) for coliforms; the presence of one coliform in a litre volume suggesting a 10% risk of coliform failure in a 100 ml statutory monitoring sample. A model to predict coliform concentrations of above one in a litre volume from chlorine, temperature and plate counts (perhaps measured from 10 ml samples) would be beneficial to the water supply companies and could be integrated into chlorine concentration modelling packages such as WATNET.


Statutory water quality monitoring data, representing some 36 000 water samples (number of total chlorine records), have been analysed statistically. These data were supplied by nine water companies, A to I, located across the UK. The main conclusions are:

  1. Water samples which contain at least one coliform organism per 100 ml differ in many ways from samples without coliforms. The differences may be chemical, physical or biological. Thus, water samples with coliforms may have higher plate count concentrations, higher temperatures, lower nitrate ion concentrations, higher nitrite ion concentrations, lower total chlorine concentrations, and higher iron (III) or phosphate ion concentrations compared with samples without coliforms.

  2. These associations suggest that coliforms are not homogeneously distributed within the distribution supply. Indeed, the distribution supply system would appear to be heterogeneous with respect to chemical, physical and microbiological factors. Coliform failures are linked to real changes within the distribution system and are not merely the result of random capture during sampling. Registering a coliform failure in a 100 ml volume water sample is not a random process such as Russian roulette and it may be possible to predict increased probabilities of coliform failures using fluctuations in values of measurable or controllable parameters, such as those listed above.

  3. Association of coliforms with higher three day (or seven day) plate count concentrations appears to be strong and universal throughout the distribution supply systems of all the water companies so far analysed.

  4. Associations of coliforms with external factors vary between different water companies suggesting that there is not one single mechanism for the occurrence of coliforms within the UK distribution supply. Three day plate count concentrations were on average between 1.5x to 25.5x higher for samples with coliforms compared to those without coliforms depending on the water company. For some companies, total chlorine and temperature appear to be important factors. However, total chlorine concentration does not appear to be an important factor for Water Company C and temperature may not important for Water Company H. In the distributions of Water Companies C, G, and I, coliforms are linked to higher iron (III) ion concentrations, while in Water Company A operational hydrants the opposite relationship may exist. Furthermore, while Water Companies A and possibly B and I, show associations of coliforms with lower nitrate ion concentrations, Water Companies C and G do not. Perhaps there are two mechanisms for coliform failures operating which are related to iron (III) and nitrate in opposite ways. There is evidence that higher nitrite concentrations may be associated with coliform failures in Water Company A fixed point taps, but not in random taps from Water Companies B, C and G.

  5. Different mechanisms for coliform failure may operate within different tap types. Thus for Water Company A, temperature was not an important factor for random statutory tap failures but lower chlorine concentration was. In contrast for the operational hydrant samples , in which total chlorine concentrations were considerably higher, total chlorine was not an important factor in coliform failure but temperature was. Temperature was also important for fixed point taps in Water Company A. On average, however, three day plate count concentrations were 1.5x higher for samples with coliforms in fixed and random statutory taps and operational hydrants within Water Company A.

  6. Different mechanisms for coliform failure may operate within different zones or groups of zones in the same water company. Analysing pooled data from all the zones from a water company may fail to detect important associations localised to small numbers of zones or to individual coliform positive samples. Thus, coliforms in operational hydrant samples from a group of 11 zones in Water Company A in 1991 showed strong associations with lower total chlorine concentrations. This association was lost using pooled total chlorine data (1990 to 1992 inclusive) from operational hydrant samples in 55 zones.

  7. Pooling data from different parts of distribution supply within the same water company, could also conceivably demonstrate associations which were an artefact. The strong associations identified between coliforms and certain external parameters in Water Company I could be artefact arising from natural differences (e.g. pH, metal ion concentration, and colour) between the source waters (e.g. spring or surface), with coliform failures arising more in a particular type of source water because of less efficient treatment. Thus the associations identified between the presence of coliforms and lower free or total chlorine concentrations may be the dominant factor in Water Company I. Regression analysis, however, performed using coliform positive data would suggest that this is not the case, at least for colour measurements. Thus regression analysis showed that coliform concentrations in Water Company I not only increase with lower total chlorine concentrations, but also with higher colour measurements.

  8. Associations between coliforms and other factors are best detected from statutory water quality monitoring data by comparing statistically the means from samples with and without coliforms. This method cannot unfortunately be used per se to predict an increased probability of coliform failures. The associations detected, however, will not only be useful in addressing operational factors and remedial treatments to minimise coliform failures but also serve as a preliminary screen for developing models to predict increased probability of coliform failures. A linear regression approach using coliform positive statutory monitoring data is less powerful at detecting associations because it uses only a small proportion of the data, although it provides the possibility for a predictive model. A more powerful predictive model could be developed using data from 10 litre samples.

  9. From the statistical distributions of total coliform concentrations it is predicted that samples registering 0 coliforms per 100 ml may contain between 10-1 and 10-9 coliforms per 100 ml. Samples registering 0 per 100 ml may therefore give a false sense of security. A predicative model is needed to estimate when and where in the distribution supply, coliform concentrations are approaching one per ten litres or one per litre. Although 100 ml samples may be registering zero coliforms, the risk of a coliform failure at a coliform concentration of one per litre is 10% and precautionary action is required.

  10. Developing models to predict coliform failures from other measurable or controllable parameters (e. g. chlorine concentration) may be limited with the statutory water quality monitoring data available. There are too few samples with coliform concentrations of one or more per 100 ml, which are applicable to modelling. Furthermore they represent samples with the highest coliform concentrations, providing information only on samples which have failed, and thus may not be representative of the distribution as a whole. A more pertinent model could be developed by using data from samples identified as being of high risk of coliform failure, i.e. samples with 1 coliform per 10 litres or 1 coliform per litre. Such data would also enable the model to be evaluated at this critical coliform concentration range.


The present FWR contract (F-1702) ends in March 1994. Work which relates to coliform failures in the distribution and which is to be completed and reported by then includes:

  1. Linear regression analysis to assess feasibility of predicting log coliform concentrations from values of other factors. This is to be performed using water quality monitoring data from 100 ml samples with coliforms as already supplied by the water companies A to I.

  2. Assessing feasibility of developing a predictive model for coliform failures using multiple regression.

  3. Identification (from the associations) of operational factors and remedial treatments to minimise coliform failures

  4. Comparing statistical distributions of coliform concentrations with reported failure rates and commenting on present system of compliance monitoring based on presence/absence detection of coliforms.

    After the current contract F-1702 has finished the following work should be undertaken :

  5. Further identification, using 1993 statutory monitoring water quality data, of statistical associations between the presence of coliform organisms and fluctuations in other factors.

  6. Identification of factors associated with zones which show no coliform failures. To date associations have been investigated by comparing results from samples with and without coliforms from the same zone. To identify conditions specific to zones with no failures, comparison of external parameters between zones with and without coliforms is required.

  7. Development of a model for predicting coliform failures in the distributing from fluctuations in controllable and measurable parameters. The existing data bases from statutory monitoring may not be sufficient to achieve this because samples with 1 or more coliforms are few in number and do not provide enough information. It may therefore be necessary to perform detailed analysis of 10 litre volumes to obtain coliform concentrations between 1 and 50 000 per 10 litre. This would increase the data base on which to develop and evaluate the model and provide data over the range of coliform concentrations where there is increased risk of detecting a coliform in a 100 ml volume.

  8. The present system of monitoring compliance based on the presence or absence of coliforms in a 100 ml volume is not satisfactory. First it provides very little information on water quality and second it falsely exaggerates differences in microbiological quality between different companies and zones. It is proposed to design and test an alternative standard based on coliform statistical distributions which provides a more realistic assessment of water quality.


    This Final Report details statistical analyses performed between April 1992 and November 1993 to identify associations between the presence of coliforms and fluctuations in external parameters within the distribution supply. This work is funded by the Foundation for Water Research and is part of a larger project, 'Water Quality in the Distribution', (F-1702).

    Section 2 describes the graphical presentation and statistical treatment of data, z"scussing the use of probability plots in determining the most appropriate and best estimates for the statistical parameters, namely the mean and standard deviation. In Section 3, results are presented from statistical analyses for associations between coliform failures and fluctuations in a variety of parameters of chemical, biological and physical nature. The parameters of particular interest include temperature, conc entrations of plate count organisms (one, two, three and seven day), and concentrations of total chlorine, nitrate and nitrite, iron (III) and phosphate.

    The findings are put into context for application in the Water Industry in Section 4, which presents initial studies to develop a model to predict coliform concentrations within the distribution supply. Section 4 also introduces the concepts of describing coliform concentrations in the distribution supply as this is critical not only for correctly applying regression analysis but also for defining risks of coliform failures. The concept that samples registering 0 coliforms per 100 ml give a false sense of security is developed, Preliminary linear regression relationships between coliform concentrations and values for external parameters are also presented in Section 4, which concludes by considering further information needed to develop the model.

    Conclusions are made in Section 5, and recommendations for further work summarised in Section 6. Details of statistical analyses which have not previously been reported are presented in Appendix A.

    Copies of the Report are available from FWR, price 35.00 less 20% to FWR Members