Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada
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Optical sensing of chlorophyll-a (chl-a), turbidity, and fluorescent dissolved organic matter (fDOM) is often used to characterize the quality of water. There are many site-specific factors and environmental conditions that can affect optically sensed readings; notwithstanding the comparative implication of different procedures used to measure these properties in the laboratory. In this study, we measured these water quality properties using standard laboratory methods, and in the field using optical sensors (sonde-based) at water quality monitoring sites located in four watersheds in Canada. The overall objective of this work was to explore the relationships among sonde-based and standard laboratory measurements of the aforementioned water properties, and evaluate associations among these eco-hydrological properties and land use, environmental, and ancillary water quality variables such as dissolved organic carbon (DOC) and total suspended solids (TSS). Differences among sonde versus laboratory relationships for chl-a suggest such relationships are impacted by laboratory methods and/or site specific conditions. Data mining analysis indicated that interactive site-specific factors predominately impacting chl-a values across sites were specific conductivity and turbidity (variables with positive global associations with chl-a). The overall linear regression predicting DOC from fDOM was relatively strong (R2 = 0.77). However, slope differences in the watershed-specific models suggest laboratory DOC versus fDOM relationships could be impacted by unknown localized water quality properties affecting fDOM readings, and/or the different standard laboratory methods used to estimate DOC. Artificial neural network analyses (ANN) indicated that higher relative chl-a concentrations were associated with low to no tree cover around sample sites and higher daily rainfall in the watersheds examined. Response surfaces derived from ANN indicated that chl-a concentrations were higher where combined agricultural and urban land uses were relatively higher.
KeywordsChlorophyll-a Optical probes Sonde Fluorescent dissolved organic matter Dissolved organic carbon turbidity Watershed Water quality
We would like to thank the Ausable-Bayfield and the South Nation Conservation Authorities for field support and collaboration. We would also like to thank Lyne Sabourin (Agriculture and Agri-Food Canada (AAFC)) for supporting coordination of field activities in Southern Ontario, and Weifan Lu, Yigit Keskinler, and Mike Ballard of Algonquin College, Ottawa for GIS analytical support. Contributions to support this work were provided by AAFC and the Build in Canada Innovation Program and Fluvial Systems Research Inc.
- APHA. (1998). 10200H chlorophyll. In: standard methods for the examination of water and wastewater. In L. S. Clesceri, A. E. Greenberg, and A. D. Eaton (Eds.) (20th ed.). Washington, D.C.: American Public Health Association, American Water Works Association, Water Environment Federation.Google Scholar
- APHA (2012). Section 1060, Collection and Preservation of Samples. In: Standard Methods for the Examination of Water and Wastewater. In Eugene W. Rice, Rodger B. Baird, Andrew D. Eaton, and L. S. Clesceri (Eds.), (22nd ed., pp. 1360). Washington, D.C.: American Public Health Association, American Water Works Association, Water Environment Federation.Google Scholar
- Arar, E. J. (1997a). Method 446.0: In vitro determination of chlorophylls a, b, c + c and pheopigments in 1 2 marine and freshwater algae by. In Visible spectrophotometry. Washington, DC: U.S. Environmental Protection Agency.Google Scholar
- Arar, E. J. (1997b). Method 447.0 - determination of chlorophylls a and b and identification of other pigments of interest in marine and freshwater algae using high performance liquid chromatography with visible wavelength detection. Washington, Dc: U.S. Environmental Protection Agency.Google Scholar
- Arar, E. J., & Collins, G. B. (1997). Method 445.0 in vitro determination of chlorophyll a and pheophytin Ain marine and freshwater algae by fluorescence. Washington, DC: U.S. Environmental Protection Agency.Google Scholar
- Breiman, L., Freidman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Pacific Grove: Wadsworth.Google Scholar
- Chang, N. B., Imen, S., & Vannah, B. (2015). Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Critical Reviews in Environmental Science and Technology, 45(2), 101–166. https://doi.org/10.1080/10643389.2013.829981.CrossRefGoogle Scholar
- Chen, W., Wilkes, G., Khan, I. U., Pintar, K. D., Thomas, J. L., Lévesque, C. A., Chapados, J. T., Topp, E., & Lapen, D. R. (2018). Aquatic bacterial communities associated with land use and environmental factors in agricultural landscapes using a metabarcoding approach. Frontiers in Microbiology, 9.Google Scholar
- Dos Santos, A. C. A., Calijuri, M. C., Moraes, E. M., Adorno, M. A. T., Falco, P. B., Carvalho, D. P., et al. (2003). Comparison of three methods for chlorophyll determination: spectrophotometry and fluorimetry in samples containing pigment mixtures and spectrophotometry in samples with separate pigments through high performance liquid chromatography. Acta Limnologica Brasiliensia, (12), 15.Google Scholar
- Environment and Climate Change Canada (1998). Fraser River action plan. http://publications.gc.ca/site/eng/9.805610/publication.html
- Environment and Climate Change Canada (2018). Historical climate data. http://climate.weather.gc.ca/historical_data/search_historic_data_e.html Accessed summer fall 2018.
- Exo Sonde User Manual. Advanced water quality monitoring platform. Item# 603789REF. Revision G. https://www.ysi.com/File%20Library/Documents/Manuals/EXO-User-Manual-Web.pdf.
- Frey, S. K., Gottschall, N., Wilkes, G., Grégoire, D. S., Topp, E., Pintar, K. D. M., Sunohara, M., Marti, R., & Lapen, D. R. (2015). Rainfall-induced runoff from exposed streambed sediments: an important source of water pollution. Journal of Environmental Quality, 44(1), 236–247.CrossRefGoogle Scholar
- Government of Canada. (2015). FoodNet Canada: an overview. Available at: http://www.phac-aspc.gc.ca/foodnetcanada/overview-apercu-eng.php. Accessed on December, 2018.
- Hall, K. J., & Schreier, H. (1996). Urbanization and agricultural intensification in the lower Fraser River valley: Impacts on water use and quality. [conference paper]. GeoJournal, 40(1–2), 135–146.Google Scholar
- Hambrook Berkman, J. A., and Canova, M. G. (2007). Algal biomass indicators (version 1.0): U.S. Geological Survey techniques of water-resources investigations, book 9, chap. A7, sec. 7.4, August, accessed [7 June 2017], from http://pubs.water.usgs.gov/twri9A/.
- Isoyama, R., Taie, M., Kageyama, T., Miura, M., Maeda, A., Mori, A., & Lee, S. S. (2017). A feasibility study on the simultaneous sensing of turbidity and chlorophyll a concentration using a simple optical measurement method. Micromachines, 8(4), 112. https://doi.org/10.3390/mi8040112.CrossRefGoogle Scholar
- Khamis, K., Bradley, C., Stevens, R., & Hannah, D. M. (2017). Continuous field estimation of dissolved organic carbon concentration and biochemical oxygen demand using dual-wavelength fluorescence, turbidity and temperature. Hydrological Processes, 31(3), 540–555. https://doi.org/10.1002/hyp.11040.CrossRefGoogle Scholar
- Kovács, J., Tanos, P., Várbíró, G., Anda, A., Molnár, S., & Hatvani, I. G. (2017). The role of annual periodic behavior of water quality parameters in primary production – chlorophyll-a estimation. Ecological Indicators, 78, 311–321. https://doi.org/10.1016/j.ecolind.2017.03.002. CrossRefGoogle Scholar
- Lapen, D. R., Schmidt, P. J., Thomas, J. L., Edge, T. A., Flemming, C., Keithlin, J., Neumann, N., Pollari, F., Ruecker, N., Simhon, A., Topp, E., Wilkes, G., & Pintar, K. D. M. (2016). Towards a more accurate quantitative assessment of seasonal cryptosporidium infection risks in surface waters using species and genotype information. Water Research, 105, 625–637. https://doi.org/10.1016/j.watres.2016.08.023.CrossRefGoogle Scholar
- Liu, X., Zhang, Y., Shi, K., Zhu, G., Xu, H., & Zhu, M. (2014). Absorption and fluorescence properties of chromophoric dissolved organic matter: Implications for the monitoring of water quality in a large subtropical reservoir. Environmental Science and Pollution Research, 21(24), 14078–14090. https://doi.org/10.1007/s11356-014-3319-4.CrossRefGoogle Scholar
- Murphy, K. R., Butler, K. D., Spencer, R. G. M., Stedmon, C. A., Boehme, J. R., & Aiken, G. R. (2010). Measurement of dissolved organic matter fluorescence in aquatic environments: an interlaboratory comparison. Environmental Science and Technology, 44(24), 9405–9412. https://doi.org/10.1021/es102362t.CrossRefGoogle Scholar
- Niu, C., Zhang, Y., Zhou, Y., Shi, K., Liu, X., & Qin, B. (2014). The potential applications of real-time monitoring of water quality in a large shallow lake (Lake Taihu, China) using a chromophoric dissolved organic matter fluorescence sensor. Sensors, 14(7), 11580–11594. https://doi.org/10.3390/s140711580.CrossRefGoogle Scholar
- Pintar, K. D. M., Fazil, A., Pollari, F., Waltner-Toews, D., Charron, D. F., McEwen, S. A., & Walton, T. (2012). Considering the risk of infection by cryptosporidium via consumption of municipally treated drinking water from a surface water source in a southwestern Ontario community. Risk Analysis, 32(7), 1122–1138. https://doi.org/10.1111/j.1539-6924.2011.01742.x.CrossRefGoogle Scholar
- Rice, E. W., Baird, R., Eaton, A. D., Clesceri, L. S., & Bridgewater, L. (2012). Standard methods for the examination of water and wastewater. Washington, D.C.: American Public Health Association, American Water Works Association, Water Environment Federation.Google Scholar
- Rymszewicz, A., O'Sullivan, J. J., Bruen, M., Turner, J. N., Lawler, D. M., Conroy, E., & Kelly-Quinn, M. (2017). Measurement differences between turbidity instruments, and their implications for suspended sediment concentration and load calculations: a sensor inter-comparison study. Journal of Environmental Management, 199, 99–108. https://doi.org/10.1016/j.jenvman.2017.05.017.CrossRefGoogle Scholar
- Song, K., Li, L., Tedesco, L., Clercin, N., Hall, B., Li, S., Shi, K., Liu, D., & Sun, Y. (2013). Remote estimation of phycocyanin (PC) for inland waters coupled with YSI PC fluorescence probe. Environmental Science and Pollution Research, 20(8), 5330–5340. https://doi.org/10.1007/s11356-013-1527-y.CrossRefGoogle Scholar
- Statistica, 2018. Statistica Documentation. STATISTICA Automated neural networks (SANN) - neural networks: an overview. (i) performing regression with 4-Bar linkage data. i). http://documentation.statsoft.com/STATISTICAHelp.aspx?path=SANN/Overview/SANNNeuralNetworksAnOverview ; ii) http://documentation.statsoft.com/STATISTICAHelp.aspx?path=SANN/Examples/SANNExample1PerformingRegressionwith4BarLinkageData Accessed Fall 2018.
- Steinberg, D., & Colla, P. (1995). CART: tree-structured non parametric data analysis. San Diego, CA: Salford Systems.Google Scholar
- Thomas, J., Pintar, K., Wallis, P., & Neumann, N. (2016). Using host-specificity of cryptosporidium to understand contaminant sources, seasonality, and human health risk in three watersheds of differing land-use. Journal of Environmental Protection, 7, 372–381. https://doi.org/10.4236/jep.2016.73033.CrossRefGoogle Scholar
- Veliz, M., Brock, H., & Neary, J. (2006). Ausable Bayfield Conservation Authority Watershed Report Card 2007 (p. 104). Exeter, Ontario: Ausable Bayfield Conservation Authority.Google Scholar
- Wade, A. J., Palmer-Felgate, E. J., Halliday, S. J., Skeffington, R. A., Loewenthal, M., Jarvie, H. P., Bowes, M. J., Greenway, G. M., Haswell, S. J., Bell, I. M., Joly, E., Fallatah, A., Neal, C., Williams, R. J., Gozzard, E., & Newman, J. R. (2012). Hydrochemical processes in lowland rivers: insights from in situ, high-resolution monitoring. Hydrology and Earth System Sciences, 16(11), 4323–4342. https://doi.org/10.5194/hess-16-4323-2012.CrossRefGoogle Scholar
- Waller, M. E., Bramburger, A. J., & Cumming, B. F. (2016). Bi-weekly changes in phytoplankton abundance in 25 tributaries of Lake St. Francis, Canada: evaluating the occurrence of nuisance and harmful algae. [article]. Journal of Great Lakes Research, 42(5), 1049–1059. https://doi.org/10.1016/j.jglr.2016.07.003.CrossRefGoogle Scholar
- Wetzel, R. G. (1983). Limnology. Philadelphia, PA: Saunders College Publishing.Google Scholar
- Wilkes, G., Edge, T. A., Gannon, V. P. J., Jokinen, C., Lyautey, E., Medeiros, D., et al. (2009). Seasonal relationships among indicator bacteria, pathogenic bacteria, cryptosporidium oocysts, giardia cysts, and hydrological indices for surface waters within an agricultural landscape. Water Research, 43(8), 2209–2223. https://doi.org/10.1016/j.watres.2009.01.033.CrossRefGoogle Scholar
- Wilkes, G., Edge, T. A., Gannon, V. P. J., Jokinen, C., Lyautey, E., Neumann, N. F., Ruecker, N., Scott, A., Sunohara, M., Topp, E., & Lapen, D. R. (2011). Associations among pathogenic bacteria, parasites, and environmental and land use factors in multiple mixed-use watersheds. Water Research, 45(18), 5807–5825. https://doi.org/10.1016/j.watres.2011.06.021.CrossRefGoogle Scholar
- Wilkes, G., Ruecker, N., Neumann, N., Gannon, V., Jokinen, C., Sunohara, M., Topp, E., Pintar, K., Edge, T., & Lapen, D. (2013). Spatiotemporal analysis of cryptosporidium species/genotypes and relationships with other zoonotic pathogens in surface water from mixed-use watersheds. Applied and Environmental Microbiology, 79(2), 434–448.CrossRefGoogle Scholar
- YSI (2010). Calibration, maintenance and troubleshooting tips for YSI 6-series sondes and sensors. (pp. 39): YSI.Google Scholar
- Zamyadi, A., Choo, F., Newcombe, G., Stuetz, R., & Henderson, R. K. (2016). A review of monitoring technologies for real-time management of cyanobacteria: recent advances and future direction. TrAC Trends in Analytical Chemistry, 85(Part A), 83–96. https://doi.org/10.1016/j.trac.2016.06.023.CrossRefGoogle Scholar