Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada

被引:13
作者
Zolfaghari, K. [1 ]
Wilkes, G. [1 ]
Bird, S. [2 ]
Ellis, D. [1 ]
Pintar, K. D. M. [3 ]
Gottschall, N. [1 ]
McNairn, H. [1 ]
Lapen, D. R. [1 ]
机构
[1] Agr & Agri Food Canada, Ottawa, ON, Canada
[2] Fluvial Syst Res Inc, White Rock, BC, Canada
[3] Nat Resources Canada, Ottawa, ON, Canada
关键词
Chlorophyll-a; Optical probes; Sonde; Fluorescent dissolved organic matter; Dissolved organic carbon; turbidity; Watershed; Water quality; WATER-QUALITY PARAMETERS; MATTER FLUORESCENCE; SURFACE-WATER; IN-SITU; PATHOGENIC BACTERIA; OPTICAL SENSORS; CRYPTOSPORIDIUM; ASSOCIATIONS; MANAGEMENT; VEGETATION;
D O I
10.1007/s10661-019-7800-x
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 (R-2 = 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.
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页数:16
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