International Science Index


10008923

Multivariate Analytical Insights into Spatial and Temporal Variation in Water Quality of a Major Drinking Water Reservoir

Abstract:

22 physicochemical variables have been determined in water samples collected weekly from January to December in 2013 from three sampling stations located within a major drinking water reservoir. Classical Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis was used to investigate the environmental factors associated with the physico-chemical variability of the water samples at each of the sampling stations. Matrix augmentation MCR-ALS (MA-MCR-ALS) was also applied, and the two sets of results were compared for interpretative clarity. Links between these factors, reservoir inflows and catchment land-uses were investigated and interpreted in relation to chemical composition of the water and their resolved geographical distribution profiles. The results suggested that the major factors affecting reservoir water quality were those associated with agricultural runoff, with evidence of influence on algal photosynthesis within the water column. Water quality variability within the reservoir was also found to be strongly linked to physical parameters such as water temperature and the occurrence of thermal stratification. The two methods applied (MCR-ALS and MA-MCR-ALS) led to similar conclusions; however, MA-MCR-ALS appeared to provide results more amenable to interpretation of temporal and geological variation than those obtained through classical MCR-ALS.

References:
[1] Reghunath, R., T. S. Murthy, and B. Raghavan, The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Research, 2002. 36(10): p. 2437-2442.
[2] Simeonov, V., et al., Lake water monitoring data assessment by multivariate statistics. Journal of Water Resource and Protection, 2010. 2(04): p. 353.
[3] Tauler, R., Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems, 1995. 30(1): p. 133-146.
[4] Tauler, R., A. Smilde, and B. Kowalski, Selectivity, local rank, three‐way data analysis and ambiguity in multivariate curve resolution. Journal of Chemometrics, 1995. 9(1): p. 31-58.
[5] Tauler, R., B. Kowalski, and S. Fleming, Multivariate curve resolution applied to spectral data from multiple runs of an industrial process. Analytical Chemistry, 1993. 65(15): p. 2040-2047.
[6] Salau, J. S. I., et al., Input characterization of sedimentary organic contaminants and molecular markers in the Northwestern Mediterranean Sea by exploratory data analysis. Environmental Science & Technology, 1997. 31(12): p. 3482-3490.
[7] Tauler, R., D. Barcelo, and E. M. Thurman, Multivariate correlation between concentrations of selected herbicides and derivatives in outflows from selected US Midwestern reservoirs. Environmental Science & Technology, 2000. 34(16): p. 3307-3314.
[8] Jolliffe, I. T., Principal Component Analysis and Factor Analysis, in Principal component analysis. 1986, Springer. p. 115-128.
[9] Massart, D., et al., Straight line regression and calibration. Handbook of Chemometrics and Qualimetrics. Part A, 1998: p. 171-230.
[10] Wold, S., K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1987. 2(1-3): p. 37-52.
[11] Lawton, W. H. and E. A. Sylvestre, Self modeling curve resolution. Technometrics, 1971. 13(3): p. 617-633.
[12] Paatero, P., et al., Understanding and controlling rotations in factor analytic models. Chemometrics and Intelligent Laboratory Systems, 2002. 60(1): p. 253-264.
[13] Ruckebusch, C. and L. Blanchet, Multivariate curve resolution: A review of advanced and tailored applications and challenges. Analytica Chimica Acta, 2013. 765(Supplement C): p. 28-36.
[14] Mouton, N., et al., Hybrid hard- and soft-modeling approach for the resolution of convoluted femtosecond spectrokinetic data. Chemometrics and Intelligent Laboratory Systems, 2011. 105(1): p. 74-82.
[15] Giussani, B., et al., A chemometric approach to the investigation of major and minor ion chemistry in lake Como (Lombardia, Northern Italy). Annali Di Chimica, 2006. 96(5‐6): p. 339-346.
[16] Terrado, M., D. Barceló, and R. Tauler, Identification and distribution of contamination sources in the Ebro river basin by chemometrics modelling coupled to geographical information systems. Talanta, 2006. 70(4): p. 691-704.
[17] Tauler, R., et al., Chemometric modeling of main contamination sources in surface waters of Portugal. Environmental Toxicology and Chemistry, 2004. 23(3): p. 565-575.
[18] Tauler, R., I. Marqués, and E. Casassas, Multivariate curve resolution applied to three‐way trilinear data: Study of a spectrofluorimetric acid–base titration of salicylic acid at three excitation wavelengths. Journal of Chemometrics, 1998. 12(1): p. 55-75.
[19] Alier, M., et al., Trilinearity and component interaction constraints in the multivariate curve resolution investigation of NO and O3 pollution in Barcelona. Analytical and Bioanalytical Chemistry, 2011. 399(6): p. 2015-2029.
[20] Smilde, A., R. Bro, and P. Geladi, Multi-way analysis: applications in the chemical sciences. 2005: John Wiley & Sons.
[21] Bosco, M., M. Callao, and M. Larrechi, Resolution of phenol, and its di-hydroxyderivative mixtures by excitation–emission fluorescence using MCR-ALS: Application to the quantitative monitoring of phenol photodegradation. Talanta, 2007. 72(2): p. 800-807.
[22] De Juan, A. and R. Tauler, Comparison of three‐way resolution methods for non‐trilinear chemical data sets. Journal of Chemometrics, 2001. 15(10): p. 749-771.
[23] Galera, M. M., M. G. García, and H. Goicoechea, The application to wastewaters of chemometric approaches to handling problems of highly complex matrices. TrAC Trends in Analytical Chemistry, 2007. 26(11): p. 1032-1042.
[24] Jaumot, J., et al., A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemometrics and Intelligent Laboratory Systems, 2005. 76(1): p. 101-110.
[25] Cole, B. and B. Williams, Grahamstown DamWater Quality & Aquatic Ecological Functioning, A compilation of Scientific and Adaptive Management Studies. 2011 (unpublished report).
[26] van den Berg, R.A., et al., Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics, 2006. 7(1): p. 142.
[27] Smilde, A. K., et al., Fusion of Mass Spectrometry-Based Metabolomics Data. Analytical Chemistry, 2005. 77(20): p. 6729-6736.
[28] Wilhelm, S. and R. Adrian, Impact of summer warming on the thermal characteristics of a polymictic lake and consequences for oxygen, nutrients and phytoplankton. Freshwater Biology, 2008. 53(2): p. 226-237.
[29] Verspagen, J. M., et al., Rising CO2 levels will intensify phytoplankton blooms in eutrophic and hypertrophic lakes. PLoS One, 2014. 9(8): p. e104325.
[30] Müller, S., S. M. Mitrovic, and D. S. Baldwin, Oxygen and dissolved organic carbon control release of N, P and Fe from the sediments of a shallow, polymictic lake. Journal of Soils and Sediments, 2016. 16(3): p. 1109-1120.