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Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada.
Zolfaghari, K; Wilkes, G; Bird, S; Ellis, D; Pintar, K D M; Gottschall, N; McNairn, H; Lapen, D R.
Afiliação
  • Zolfaghari K; Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
  • Wilkes G; Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
  • Bird S; Fluvial Systems Research Inc., White Rock, BC, Canada.
  • Ellis D; Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
  • Pintar KDM; Natural Resources Canada, Ottawa, ON, Canada.
  • Gottschall N; Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
  • McNairn H; Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
  • Lapen DR; Agriculture and Agri-Food Canada, Ottawa, ON, Canada. David.Lapen@canada.ca.
Environ Monit Assess ; 192(1): 67, 2019 Dec 26.
Article em En | MEDLINE | ID: mdl-31879802
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Qualidade da Água / Monitoramento Ambiental / Rios / Hidrodinâmica / Clorofila A / Substâncias Húmicas Tipo de estudo: Prognostic_studies País/Região como assunto: America do norte Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Qualidade da Água / Monitoramento Ambiental / Rios / Hidrodinâmica / Clorofila A / Substâncias Húmicas Tipo de estudo: Prognostic_studies País/Região como assunto: America do norte Idioma: En Ano de publicação: 2019 Tipo de documento: Article