Logistic regression for prediction and diagnosis of bacterial regrowth in water distribution system

Abstract

This paper focuses on the quantitative expression of bacterial regrowth in water distribution system. Considering public health risks of bacterial regrowth, the experiment was performed on a distribution system of selected area. Physical, chemical, and microbiological parameters such as turbidity, temperature, residual chlorine and pH were measured over a three-month period and correlation analysis was carried out. Combined with principal components analysis (PCA), a logistic regression model is developed to predict and diagnose bacterial regrowth and locate the zones with high risks of microbiology in the distribution system. The model gives the probability of bacterial regrowth with the number of heterotrophic plate counts as the binary response variable and three new principal components variables as the explanatory variables. The veracity of the logistic regression model was 90%, which meets the precision requirement of the model.

Keywords

bacterial regrowth / water distribution system / heterotrophic plate counts / logistic regression / principal components analysis / odds ratio / veracity

References

[1] Yang Y. L., Li X., Li G. B., et al. Study on key guidelines of biological stabilization in drinking water distribution system[J]. Water and Wastewater Engineering, 2005, 31(2): 12-16.

[2] US Environmental Protection Agency. National Recommended Water Quality Criteria [S]. 2006, Washington, DC: Office of Water. Office of Science and Technology, US Environmental Protection Agency.

[3] LeChevallier M. W., Olson B. H., McFecters G. A. Assessing and controlling bacterial regrowth in distribution systems[J]. JAWWA, 1990, 82(7): 87-99.

[4] van der Kooij D., van Lieverloo J. H. M., Schellart J., et al. Maintaining quality without a disinfectant residual[J]. JAWWA, 1999, 91(1): 55-64.

[5] Liu W., Wu H., Wang Z., et al. Investigation of assimilable organic carbon (AOC) and bacterial regrowth in drinking water distribution system[J]. Water Research, 2002, 36(4): 891-898.

[6] Alain K., FrederiQue N., Alain D., et al. The effects of water residence time on the biological quality in a distributing network[J]. Water Research, 1995, 29(7): 1719-1727.

[7] Chu C., Lu C., Lee C., et al. Effects of chlorine level on the growth of biofilm in water pipes[J]. Environ Sci Health-Part A, 2003, 38(7): 1377-1388.

[8] Power K., Nagy L. A. Relationships between levels of heterotrophic bacteria and water quality parameters in a drinking water distribution system[J]. Water Research, 2000, 34(5): 1495-1502.

[9] Wu Q., Zhao X. H. Study on HPC and some physical and chemical parameters in drinking water[J]. Chin J Public Health, 2006, 22(3): 280-281.

[10] Chen G., Chen J. W. The function of prediction and diagnosis of Logistic regression and application[J]. Journal of Mathematica1 Medicine, 2007, 20(3): 280-281.

[11] Johnson R. A., Wichern D. W. Applied Multivariate Statistical Analysis[M]. 2008, Beijing: Tsinghua University Press.

[12] Zhao S. Y., Dong L. H., Wu Q., et al. Prediction of bacterial regrowth in water distribution system[J]. China Water and Wastewater, 2006, 22(5): 48-51.

[13] Zhang M., Chen Y. C. Optimization Design and Data Analysis of Environmental Examination[M]. 2008, Beijing: Chemical Industry Press.