Assessing landscape and contaminant point-sources as spatial determinants of water quality in the Vermilion River System, Ontario, Canada

Abstract

The Vermilion River and major tributaries (VRMT) are located in the Vermilion watershed (4272 km) in north-central Ontario, Canada. This watershed not only is dominated by natural land-cover but also has a legacy of mining and other development activities. The VRMT receive various point (e.g., sewage effluent) and non-point (e.g., mining activity runoff) inputs, in addition to flow regulation features. Further development in the Vermilion watershed has been proposed, raising concerns about cumulative impacts to ecosystem health in the VRMT. Due to the lack of historical assessments on riverine-health in the VRMT, a comprehensive suite of water quality parameters was collected monthly at 28 sites during the ice-free period of 2013 and 2014. Canadian water quality guidelines and objectives were not met by an assortment of water quality parameters, including nutrients and metals. This demonstrates that the VRMT is an impacted system with several pollution hotspots, particularly downstream of wastewater treatment facilities. Water quality throughout the river system appeared to be influenced by three distinct land-cover categories: forest, barren, and agriculture. Three spatial pathway models (geographical, topographical, and river network) were employed to assess the complex interactions between spatial pathways, stressors, and water quality condition. Topographical landscape analyses were performed at five different scales, where the strongest relationships between water quality and land-use occurred at the catchment scale. Sites on the main stem of Junction Creek, a tributary impacted by industrial and urban development, had above average concentrations for the majority of water quality parameters measured, including metals and nitrogen. The river network pathway (i.e., asymmetric eigenvector map (AEM)) and topographical feature (i.e., catchment land-use) models explained most of the variation in water quality (62.2%), indicating that they may be useful tools in assessing the spatial determinants of water quality decline.

A review of spatial statistical approaches to modeling water quality

“…Multi-scale datasets can be treated with principal component analysis to reduce the dimension of the data and include the variability of different scale processes (Miralha and Kim, 2018). Redundancy analysis can identify which variables at what scale can explain variation in water quality, and use them as a predictor in the spatial regression (Strangway et al, 2017). To avoid overfitting the data that identify the best subset of the covariates, a “Best Subset Regression” can be used (Scown et al, 2017).…”

“…Like any other natural processes, the factors affecting water quality operate at different scales. These factors have to be identified based on the understanding of the scale related to the source, mobilization, delivery, and instream processes related to these parameters (Lintern (Yang and Jin, 2010), a buffer of a certain distance (Chang, 2008), circular upstream buffer, multi-scale (Chang, 2008;Su et al, 2013 (Mainali and Chang, 2018;Strangway et al, 2017). Scale information derived from eigenvectors is also used (Vrebos et al, 2017).…”

“…Some modify it based on the distance classes (De Oliveira Marcionilio et al, 2016). The majority of papers report RSAC of the model, except Strangway et al (2017). RSACs are removed when this modeling approach is used.…”

A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: a case study in Eagle Creek Watershed, USA

“…Numerous studies recorded the negative impacts of some agricultural practices on water quality (Dupas et al 2015; Fournier et al 2017). Likewise, urbanization affects the water quality through sediment, oils, and solid wastes washed from hard surfaces, bacteria, and input of nutrients from failing septic systems and wastewater (USEPA 2008; Walters et al 2011; Zhao et al 2015; Strangway et al 2017).…”