The response of freshwater invertebrates following accidental releases of oil is not well understood. This knowledge gap is more substantial for unconventional oils such as diluted bitumen (dilbit). We evaluated the effects of dilbit on insect emergence and benthic invertebrates by conducting experimental spills in limnocorrals (10-m diameter; ~100-m³) deployed in a boreal lake at the IISD-Experimental Lakes Area, Canada. The study included seven dilbit treatments (spill volumes ranged from 1.5 L [1:66,000, oil:water, v/v] to 180 L [1:590, oil:water, v/v]), two controls, and additional lake reference sites, monitored for 11 weeks. Invertebrate emergence declined at the community level following oil addition in a significantly volume-dependent manner, and by 93–100 % over the 11 weeks following the spill in the highest treatment. Dilbit altered community structure of benthic invertebrates, but not abundance. One-year post-spill and following oil removal using traditional skimming and absorption techniques, benthic richness and abundance were greater among all treatments than the previous year. These results indicate that recovery in community composition is possible following oil removal from a lake ecosystem. Research is needed concerning the mechanisms by which surface oil directly affect adult invertebrates, whether through limiting oviposition, limiting emergence, or both. The response of benthic communities to sediment tar mats is also warranted.

Discarded micro/nano-plastic inputs into the environment are emerging global concerns. Yet the quantification of micro/nanoplastics in complex environmental matrices is still a major challenge, notably for soluble ones. We herein develop in-laboratory built nanostructures (zinc oxide, titanium oxide and cobalt) coupled to mass spectrometry techniques, for picogram quantification of micro/nanoplastics in water and snow matrices, without sample pre-treatment. In parallel, an ultra-trace quantification method for micro/nanoplastics based on nanostructured laser desorption/ionization time-of-flight mass spectrometry (NALDI-TOF-MS) is developed. The detection limit is ∼5 pg for ambient snow. Soluble polyethylene glycol and insoluble polyethylene fragments were observed and quantified in fresh falling snow in Montreal, Canada. Complementary physicochemical studies of the snow matrices and reference plastics using laser-based particle sizers, inductively coupled plasma tandem mass spectrometry, and high-resolution scanning/transmission electron microscopy, produced consistent results with NALDI, and further provided information on morphology and composition of the micro/nano-plastic particles. This work is promising as it demonstrates that a wide range of recyclable nanostructures, in-laboratory built or commercial, can provide ultra-trace capability for quantification for both soluble polymers and insoluble plastics in air, water and soil. It may thereby produce key missing information to determine the fate of micro/nanoplastics in the environment, and their impacts on human health.

Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R² with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.

Twenty-five years after the first look at polycyclic aromatic compounds (PACs) in Canada, this article presents current knowledge on Canadian PAC emission sources. The analysis is based on national inventories (the National Pollutant Release Inventory (NPRI) and the Air Pollutant Emissions Inventory (APEI)), an analysis of Canadian forest fires, and several air quality model-ready emissions inventories. Nationally, forest fires continue to dominate PAC emissions in Canada, however there is uncertainty in these estimates. Though forest fire data show a steady average in the total annual area burned historically, an upward trend has developed recently. Non-industrial sources (home firewood burning, mobile sources) are estimated to be the second largest contributor (∼6-8 times lower than forest fires) and show moderate decreases (25%–65%) in the last decades. Industrial point sources (aluminum production, iron/steel manufacturing) are yet a smaller contributor and have seen considerable reductions (90% +) in recent decades. Fugitive emissions from other industrial sources (e.g. disposals by the non-conventional oil extraction and wastewater sectors, respectively) remain a gap in our understanding of total PAC emissions in Canada. Emerging concerns about previously unrecognized sources such as coal tar-sealed pavement run-off, climate change are discussed elsewhere in this special issue. Results affirm that observations at the annual/national scale are not always reflective of regional/local or finer temporal scales. When determining which sources contribute most to human and ecosystem exposure in various contexts, examination at regional and local scales is needed. There is uncertainty overall in emissions data stemming in part from various accuracy issues, limitations in the scope of the various inventories, and inventory gaps, among others.

Ecological risk assessments (ERAs) of polycyclic aromatic compounds (PACs), as single congeners or in mixtures, present technical challenges that raise concerns about their accuracy and validity for Canadian environments. Of more than 100,000 possible PAC structures, the toxicity of fewer than 1% have been tested as individual compounds, limiting the assessment of complex mixtures. Because of the diversity in modes of PAC action, the additivity of mixtures cannot be assumed, and mixture compositions change rapidly with weathering. In vertebrates, PACs are rapidly oxygenated by cytochrome P450 enzymes, often to metabolites that are more toxic than the parent compound. The ability to predict the ecological fate, distribution and effects of PACs is limited by toxicity data derived from tests of a few responses with a limited array of test species, under optimal laboratory conditions. Although several models are available to predict PAC toxicity and rank species sensitivity, they were developed with data biased by test methods, and the reported toxicities of many PACs exceed their solubility limits. As a result, Canadian Environmental Quality Guidelines for a few individual PACs provide little support for ERAs of complex mixtures in emissions and at contaminated sites. These issues are illustrated by reviews of three case studies of PAC-contaminated sites relevant to Canadian ecosystems. Interactions among ecosystem characteristics, the behaviour, fate and distribution of PACs, and non-chemical stresses on PAC-exposed species prevented clear associations between cause and effect. The uncertainties of ERAs can only be reduced by estimating the toxicity of a wider array of PACs to species typical of Canada’s diverse geography and environmental conditions. Improvements are needed to models that predict toxicity, and more field studies of contaminated sites in Canada are needed to understand the ecological effects of PAC mixtures.

Sediment quality monitoring is commonly used to assess for river pollution by industrial activities, but requires knowledge of pre-disturbance conditions. This has long been a critical knowledge gap for assessing pollution of the Lower Athabasca River within the Athabasca Oil Sands Region (AOSR) because sediment quality monitoring started 30 years after mining operations began in 1967. Here, we analyze oil-sands pollution indicator metals vanadium (V) and nickel (Ni) in sediment cores from five Athabasca River floodplain lakes spanning from 17 km upstream to 58 km downstream of central oil sands operations. These data are used to define pre-development baseline (i.e., reference) concentrations and assess for enrichment in sediment deposited after 1967. Measurements of organic and inorganic matter content were used to differentiate periods of strong and weaker Athabasca River influence in the sediment records, as needed to discern pathways of metal deposition. Numerical analyses reveal that post-1967 V and Ni enrichment factors have remained below the 1.5 threshold for ‘minimal enrichment’ (sensu Birch, 2017) in stratigraphic intervals of strong river influence in the floodplain lakes. Thus, concentrations of V and Ni carried by Athabasca River sediment have not become measurably enriched since onset of oil sands development, as demonstrated by our before-after study design with >99.99% power to detect a 10% increase above pre-development baselines. At the closest lake (<1 km) to oil sands operations, however, enrichment factors for V and Ni increased to 2.1 and 1.5, respectively, in the mid-1980s and have remained at this level when river influence was weaker, indicating contamination via atmospheric transport. Localized enrichment within the oil sands region via atmospheric pathways is a greater concern for ecosystems and society than local and far-field transport by fluvial pathways.