o7216 A New Monitoring Tool in Drinking Water Quality Management

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1 o7216 A New Monitoring Tool in Drinking Water Quality Management Christopher Chow, CRC for Water Quality and Treatment, Australian Water Quality Centre, chris.chow@sawater.com.au Rob Dexter, DCM Process Control Ltd Luke Sutherland-Stacey, DCM Process Control Ltd Fiona Fitzgerald, CRC for Water Quality and Treatment, Australian Water Quality Centre Rolando Fabris, CRC for Water Quality and Treatment, Australian Water Quality Centre Mary Drikas, CRC for Water Quality and Treatment, Australian Water Quality Centre Mike Holmes, United Water International EXECUTIVE SUMMARY This paper provides an overview of current applications of UV-Vis spectrometry in the drinking water industry and particularly the use of UV absorbance as a surrogate measure for DOC concentration, chlorine demand and DBP formation. Special reference is made to compare the use of multiple wavelength quantification with the conventional single wavelength (UV 254 ) approach. In addition, the field trial results of a commercially available multiple wavelength detector at one of the water treatment plants (Myponga, South Australia) is presented to evaluate the on-line capability for real time chlorine demand prediction and disinfection by-production formation. INTRODUCTION Natural organic matter (NOM) is an important consideration in drinking water quality management. In recent years, considerable research effort has been expended to understand the impact of various components of NOM on drinking water treatment processes. NOM is a complex matrix of heterogenous organic material which comes from decaying terrestrial and aquatic organisms. Its presence in source water can be problematic for the production of high quality drinking water and it is considered a key factor in the determination of both coagulant and disinfectant doses. Furthermore, NOM can react with disinfectants to produce disinfection by-products (DBPs) and also can act as a carbon food source for bacterial growth in distribution systems (Edwards, 1997; Hwang et al., 2). The concentration and also the character of NOM are important in many instances, including for the estimation of a coagulant dose and coagulation efficiency. A number of characterisation techniques have been developed to enable a better understanding of the impact of organic compounds on treatment processes. NOM is more commonly represented by the measurement of total (TOC) or dissolved organic carbon (DOC) concentration due to the high cost of quantification of the separate components. Generally, TOC and DOC are determined using an organic carbon analyser. Though most of the commercially available organic carbon analysers are automated units, the principle of organic carbon measurement requires multiple analytical steps to be performed in sequence and are therefore not true online instruments. Simple analytical techniques, such as single wavelength UV absorbance measurements (UV 254 ) or colour (456nm) have been used as surrogate parameters to monitor the concentration of NOM and they are widely accepted by water treatment operators as parameters to assess treatment plant performance. Measurement of single wavelength UV absorbance is one of the simplest characterisation methods and spectrophotometers of both laboratory and field instruments are widely available. In contrast to single frequency devices, the full UV/Vis absorption spectrum can be used to obtain far more accurate analytical data including surrogate

2 parameters such as DOC, etc. This allows the direct calculation of the ratio of UV 254 to DOC (specific UV absorbance SUVA) which is recognised as a simple and informative organic characterisation method. The recent developments in instrument portability have resulted in a number of reliable online / in-situ monitoring instruments which are now commercially available. Together with the development of chemometric approaches, a number of quantitative analysis techniques, such as principal component regression (PCR) and partial least-squares (PLS) have been implemented as part of the instrumentation software to improve the reliability of the measurement. This paper provides an overview of current applications of UV-Vis spectrometry in the drinking water industry and particularly the use of UV absorbance as a surrogate measure for dissolved organic carbon (DOC) concentration and chlorine demand. Special reference will be made to compare the use of multiple wavelength quantification with the conventional single wavelength (UV 254 ) approach. In addition, the field trial result of the S::CAN Spectro::lyser at one of the water treatment plants (Myponga, South Australia) will be discussed. This was to evaluate the on-line capability for real time chlorine demand prediction and disinfection by-production formation. MATERIALS AND METHODS To perform UV/Vis absorption measurement, a spectrophotometer is required. A sample is placed in the spectrophotometer and ultraviolet or visible light at a certain wavelength, or range of wavelengths, is transmitted through the sample. The spectrophotometer measures how much of the light is absorbed by the sample. Absorbance of the sample is determined based on the intensity of light and the path length of the measurement cell. There are several types of laboratory UV/Vis spectrophotometers, such as single beam and double beam (reference) designs utilising fixed wavelength or wavelength scanning capabilities. One of the latest generation of on-line UV/Vis spectrophotometers is the S::CAN Spectro::lyser (DCM Process Control, Australia). The S::CAN hardware comprises a robust but sensitive submersible double beam full spectrum UV-Vis-spectrometer (2nm to 75nm) with optical pathlengths in a selectable range of.5 to 1 cm. The instrument was designed upon the principle of the photodiode array (PDA) emitter which has no moving parts. The rapid full spectrum UV absorbance measurement provides concentration information for a number of parameters and calculated equivalents for others such as TOC, DOC, trihalomethane formation potential, nitrate, nitrite, turbidity, total suspended solids, and particle size. It has proved to be a useful tool to provide warning of any sudden changes of water quality. Consequently the S::CAN Spectro::lyser has many diverse applications, from drinking water to waste water as well as other kinds of industrial fluids (Langergraber et al. 22). Water samples for the evaluations were chosen to represent the wide variation in water quality found in Australia and included both surface and ground waters. The laboratory trial was performed using standard laboratory procedure for UV 254, Colour and DOC measurements (Bennett and Drikas, 1993; APHA et al., 1998). For laboratory sample measurement, filtration through a.45 micron filter was performed. The comparison was focussed on using the S::CAN in the on-line / in-situ application without prior sample filtration. Chlorine demand was determined by addition of a known dose of chlorine (as a pure saturated aqueous solution) to 2mL of sample and storage at room temperature (2 C) in the dark. Residual chlorine was determined after 72 hours by the DPD ferrous titrimetric method (45-Cl F., APHA et al., 1998) and the demand calculated by the difference. Trihalomethane formation potential (THMfp) was determined by dosing 2mg/L

3 chlorine to 6mL of sample with no headspace. Samples were stored for 4 hours at 35 C and then quenched. THMs were analysed using purge and trap GC-MS. RESULTS AND DISCUSSION UV 254 as Surrogate of DOC Advantages of Using Multi-wavelength Measurement UV 254 has been identified as a potential surrogate measure for DOC although it tends to represent only the aromatic NOM character. UV 254 is becoming a popular parameter for water treatment operators to assess treatment performance and also as one of the parameters for feed forward prediction of coagulant dose (van Leeuwen et al. 25). This technique requires only very simple instrumentation and can be performed by the operators at the treatment plant. However, care must be taken when using UV 254 as DOC surrogate. Good prediction can only be obtained from well characterised and modelled waters. Figure 1 indicates raw water has higher UV absorbance (UV 254 ) per DOC than treated water. Also when grab sampling; samples should be taken from times when conditions were stable compositionally, rather than at times of change..7.6 UV 254 (cm -1 ) Raw Water R 2 =.94.2 Treated Water.1 R 2 = DOC (mg/l) 15 Figure 1: Correlation between DOC and UV 254 for both raw and treated waters. The analytical results (Figure 2) between standard laboratory methods and S::CAN derived values correlated well (UV 254 : R 2 =.98; Colour: R 2 =.96; DOC: R 2 =.84) demonstrating their applicability as good surrogates for actual laboratory parameter measurement. (a).5 (b) 8 (c) 15 Lab UV254 (cm-1) Lab Colour (HU) Lab DOC (mg/l) SCAN Calibrated UV254 (cm-1) SCAN Calibrated Colour (HU) SCAN Calibrated DOC (mg/l) Figure 2: Comparison of analytical results between laboratory standard procedures and S::CAN (a) UV 254, (b) Colour and (c) DOC. Laboratory samples were filtered <.45μm while S::CAN measurement was performed without filtration.

4 Prediction of Chlorine Demand Conventional methods to determine bulk water chlorine demand are contact time dependent and often require several days to complete in order to match actual water ages found in water distribution systems. Unstable water supplies, having variable water quality, may exert a variable chlorine demand. This requires substantial changes to be made to the applied disinfectant dose if disinfection residuals are to be maintained within the required ranges throughout the distribution system. This presents a challenge to operators when attempting to control secondary disinfection as feed back loops are usually of the order of several days making the process highly reactive. In addition, the necessity for a safety margin in dosing impacts upon the costs of disinfection and customer satisfaction. Based on a 24-month laboratory study using water samples from a range of water quality, UV absorbance measurement was identified as a surrogate parameter for rapid chlorine demand assessment. A linear relationship was determined between chlorine demand and UV 254 from which prediction can be made with an accuracy of better than ± 1 mg/l chlorine for 72 hour demand (Figure 3). (a) (b) day Chlorine Demand (mg/l) R 2 =.93 Predicted Chlorine Demand (mg/l) UV 254 (cm -1 ) Actual Chlorine Demand (mg/l) Figure 3: (a) Relationship between UV 254 and 3-day chlorine demand. (b) The predicted 3-day chlorine demand using UV absorbance at 254nm versus the actual 3-day chlorine demand and the dotted lines represent ± 1 mg/l chlorine demand boundary. The most useful feature of the S::CAN Spectro::lyser is the on-line / in-situ monitoring capability. A 2-week on-line monitoring trial was conducted at the Myponga water treatment plant, SA. With the specifically developed software, on-line chlorine demand prediction based on UV absorbance measurement was monitored in real time on the instrument (Figure 4). The prediction matched well with the laboratory conventional chlorine demand measurements using grab samples collected during the period. Note that grab samples missed several peak and trough events seen on the S::CAN indicating sampling regimes would be much improved using an S::CAN to drive sample collection during various stages of events rather than on a fixed time, flow or random basis.

5 6.5 Chlorine Demand (mg/l) /Nov 13/Nov 15/Nov 17/Nov 19/Nov 21/Nov 23/Nov 25/Nov Figure 4: Comparison of real time chorine demand monitoring using on-line UV absorbance measurement and chlorine demand determined in a laboratory using conventional method. : Laboratory chlorine demand measurement from grab samples. Prediction of THM Formation Using UV 254 It is reasonable to postulate that if a linear relationship can be defined between UV 254 and DOC and also UV 254 and chlorine demand, a generally linear relationship between UV 254 and THM formation may also exist. In evaluation of the data (Figure 5), this was found to hold true, albeit with a lesser regression coefficient (R 2 =.92). This is likely due to the significant impact of other parameters such as ph, temperature and bromide concentrations that also exert influence on THM formation. However, the ability for operators to use simple and single point measurements to predict THM formation would provide great advantage in managing distribution system performance. As previously discussed, care must be taken when using UV 254 for prediction of indirect parameters such as THM formation. Local validation with regular checking, both during stable conditions and during times of water quality changes is generally required in order to provide good estimation. 6 5 THMfp (ug/l) UV 254 (cm -1 ) Figure 5: Relationship between UV 254 and THM formation potential (THMfp) from water samples collected for a range of water quality (R 2 =.92). CONCLUSIONS For water utilities required to manage a dynamic system with reactive water, using UV absorbance measurement as a rapid assessment of water quality is the most convenient

6 and easily achievable option. Using single wavelength (254nm) as a surrogate can only provide a limited accuracy of prediction and care must be taken to ensure reliable measurement. Multiple wavelength instruments improve the accuracy and reliability of measurement. In addition, the option of using it in an on-line / in-situ mode provides an extra dimension and makes it a potentially helpful tool in managing water treatment operation and distribution system performance through automated control. ACKNOWLEDGEMENTS The authors wish to thank Kathryn Clarkson (Power and Water Authority, NT), Ken Turner (Gippsland Water, Vic), Vince Sweet (South Australia Water Corporation, SA), David Smith (Gold Coast Water, QLD), Shane Hayden, Noel Miles, Robert Considine and Caroline Hussey (Melbourne Water Corporation, VIC), Dammika Vitanage, Corinna Doolan, Tony Venturino and Phil Duker (Sydney Water Corporation, NSW), Richard Walker and Kevin Xanthis (Water Corporation, WA) for the coordination of the water sampling programme. REFERENCES APHA, AWWA and WEF 1998 Standard Methods For The Examination of Water and Waste Water, 2 th Edition, American Public Health Association, Washington, DC. Bennett L. E. and Drikas M. (1993) The Evaluation of Colour in natural waters. Water Research 27(7), Edwards M. (1997) Predicting DOC removal during enhanced coagulation. Journal of American Water Works Association 89(5), Hwang C.J., Sclimente M.J. and Krasner S.W. (2) In Natural Organic Matter and Disinfection By-Products; Barret S.E., Krasner S.W. and Amy G.L. Eds.; ACS Symposium Series 761; American Chemical Society: Washington, DC, pp Langergraber G., Fleischmann N., Hofstaedter F (22) A multivariate calibration procedure for UV/VIS spectrometric quantification of organic matter and nitrate in wastewater, Fleischmann N. et al. (Eds.): Proceedings of the International IWA Conference on Automation in Water Quality Monitoring AutMoNet 22, May 21-22, 22, University of Agricultural Sciences Vienna (BOKU); Vienna, Austria, pp van Leeuwen J., Daly R. and Holmes M. (25) Modelling the treatment of drinking water to maximize dissolved organic matter removal and minimize disinfection by-product formation. Desalination 177,