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Modelling geosmin concentrations in three sources of raw water in Quebec, Canada

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Abstract

The presence of off-flavour compounds such as geosmin, often found in raw water, significantly reduces the organoleptic quality of distributed water and diverts the consumer from its use. To adapt water treatment processes to eliminate these compounds, it is necessary to be able to identify them quickly. Routine analysis could be considered a solution, but it is expensive and delays associated with obtaining the results of analysis are often important, thereby constituting a serious disadvantage. The development of decision-making tools such as predictive models seems to be an economic and feasible solution to counterbalance the limitations of analytical methods. Among these tools, multi-linear regression and principal component regression are easy to implement. However, due to certain disadvantages inherent in these methods (multicollinearity or non-linearity of the processes), the use of emergent models involving artificial neurons networks such as multi-layer perceptron could prove to be an interesting alternative. In a previous paper (Parinet et al., Water Res 44: 5847-5856, 2010), the possible parameters that affect the variability of taste and odour compounds were investigated using principal component analysis. In the present study, we expand the research by comparing the performance of three tools using different modelling scenarios (multi-linear regression, principal component regression and multi-layer perceptron) to model geosmin in drinking water sources using 38 microbiological and physicochemical parameters. Three very different sources of water, in terms of quality, were selected for the study. These sources supply drinking water to the Québec City area (Canada) and its vicinity, and were monitored three times per month over a 1-year period. Seven different modelling methods were tested for predicting geosmin in these sources. The comparison of the seven different models showed that simple models based on multi-linear regression provide sufficient predictive capacity with performance levels comparable to those obtained with artificial neural networks. The multi-linear regression model (R 2 = 0.657, <0.001) used only four variables (phaeophytin, sum of green algae, chlorophyll-a and potential Redox) in comparison with ten variables (potassium, heterotrophic bacteria, organic nitrogen, total nitrogen, phaeophytin, total organic carbon, sum of green algae, potential Redox, UV absorbance at 254 nm and atypical bacteria) for the best model obtained with artificial neural networks (R 2 = 0.843).

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Notes

  1. Geos = 3.136 + 1.88 (Phaeo) + 0.051(SGA) + 0.674(Chl-a) − 0.007(E).

Abbreviations

Geos:

Geosmin

OFC:

Off-flavour compound

Chl-a:

Chlorophyll-a

MLR:

Multi-linear regression

ANN:

Artificial neural network

MLP:

Multi-layer perceptron

PCR:

Principal component regression

T:

Temperature

pH:

Potential hydrogen

Cond:

Conductivity

DO:

Dissolve oxygen

E:

RedOx potential

NH4 :

Ammonia

Col:

Colour

SS:

Suspended solid

Turb:

Turbidity

Alk:

Alkalinity

Hard:

Hardness

UV:

Ultraviolet absorbance

TN:

Total nitrogen

Norg:

Organic nitrogen

TOC:

Total organic carbon

Phaeo:

Phaeophytin-a

AB:

Atypical bacteria

HB:

Heterotrophic bacteria

FC:

Faecal coliform

TC:

Total coliform

SGA:

Sum of green algae

SD:

Sum of diatoms

SC:

Sum of cyanobacteria

HS:

Headspace

SPME:

Solid-phase microextraction

GC:

Gas chromatography

PDMS\DVB\CAR:

Polydimethylsiloxane\divinylbenzen\carboxene

MDL:

Method detection limit

PCA:

Principal component analysis

PC:

Principal component

r :

Coefficient of correlation

KMO:

Kaiser–Meyer–Olkin

VIF:

Variation inflation factor

R²:

Coefficient of determination

R error:

Relative error

SA:

Sensitivity analysis

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Acknowledgements

The authors would like to thank the National Sciences and Engineering Research Council of Canada (NSERC) and the partners of the Drinking Water Research Chair of Université Laval (Quebec City, City of Lévis, Dessau and ITF Labs-Avensys) for their support.

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Authors and Affiliations

  1. Laboratoire Chimie Environnement, Université Aix-Marseille, Université Aix-Marseille, 3 place Victor Hugo - case 29, Marseille, Canada, 13331

    Julien Parinet

  2. Civil and Water Engineering Department, Université Laval, Québec City, Canada, G1K 7P4

    Jean-Baptiste Sérodes

  3. Department of Urban and Regional Planning, Université Laval, Québec City, Canada, G1K 7P4

    Manuel J. Rodriguez

  4. Laval University, 1628 Pavillon Félix-Antoine-Savard, Québec City, QC, Canada, G1K 7P4

    Manuel J. Rodriguez

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  1. Julien Parinet
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Correspondence to Manuel J. Rodriguez.

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Parinet, J., Rodriguez, M.J. & Sérodes, JB. Modelling geosmin concentrations in three sources of raw water in Quebec, Canada. Environ Monit Assess 185, 95–111 (2013). https://doi.org/10.1007/s10661-012-2536-x

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