River temperature exerts a critical control on habitat for aquatic biota. As the climate warms in eastern Canada, threats to habitats of cold-water species will increase, underpinning the necessity to develop an understanding of landscape-scale, thermal regimes of flowing waters. We assessed the performance of spatial statistical network (SSN) models of river temperature using high-resolution thermal infrared imagery (0.6 m) and LiDAR (1 m) compared to NASA's Shuttle Radar Topography Mission (SRTM-30 m) topographic data and interrogate LiDAR derived fine-scale models (3 ha) to describe groundwater connectivity to surface waters in catchments with shallow overburden and varied bedrock geology. LiDAR improved model performance in a catchment underlain by a homogeneous, high hydraulic conductance bedrock (Cains River) but did not improve model performance in a catchment with heterogeneous bedrock and variable hydraulic conductance (North Pole Stream). We hypothesize that differences in bedrock conductance modified topographic controls on subsurface flows and discharge patterns to the rivers and thus produced the mixed performance of the SSN models. At finer scales, river reaches in steep valleys incising high conductance bedrock produced groundwater discharge, which was absent in incised valleys with low conductance bedrock. These findings indicate that while topography exerts an important control on landscape-scale hydrological processes, geologic setting is a similarly important influence on hydrological processes. We suggest the inclusion of a third dimension of spatial autocorrelation, representative of the vertical plane that captures the geologic setting, would broaden the geographic applicability of spatial statistical models for river temperature studies.

Naturally occurring isotopes of radon in indoor air are identified as the second leading cause of lung cancer after tobacco smoking. Radon-222 (radon gas) and radon-220 (thoron gas) are the most common isotopes of radon. While extensive radon surveys have been conducted, indoor thoron data are very limited. To better assess thoron exposure in Canada, radon/thoron discriminating detectors were deployed in 45 homes in Fredericton and 65 homes in Halifax for a period of 3 months. In this study, radon concentrations ranged from 16 to 1374 Bq m(-3) with a geometric mean (GM) of 82 Bq m(-3) and a geometric standard deviation (GSD) of 2.56 in Fredericton, and from 4 to 2341 Bq m(-3) with a GM of 107 Bq m(-3) and a GSD of 3.67 in Halifax. It is estimated that 18 % of Fredericton homes and 32 % of Halifax homes could have radon concentrations above the Canadian indoor radon guideline of 200 Bq m(-3). This conclusion is significantly higher than the previous estimates made 30 y ago with short-term radon measurements. Thoron concentrations were below the detection limit in 62 % of homes in both cities. Among the homes with detectable thoron concentrations, the values varied from 12 to 1977 Bq m(-3) in Fredericton and from 6 to 206 Bq m(-3) in Halifax. The GM and GSD were 86 Bq m(-3) and 3.19 for Fredericton, and 35 Bq m(-3) and 2.35 for Halifax, respectively. On the basis of these results, together with previous measurements in Ottawa, Winnipeg and the Mont-Laurier region of Quebec, it is estimated that thoron contributes ∼8 % of the radiation dose due to indoor radon exposure in Canada.

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Regolith, or unconsolidated materials overlying bedrock, exists as an active zone for many geological, geomorphological, hydrological and ecological processes. This zone and its processes are foundational to wide-ranging human needs and activities such as water supply, mineral exploration, forest harvesting, agriculture, and engineered structures. Regolith thickness, or depth-to-bedrock (DTB), is typically unavailable or restricted to finer scale assessments because of the technical and cost limitations of traditional drilling, seismic, and ground-penetrating radar surveys. The objective of this study was to derive a high-resolution (10 m2) DTB model for the province of New Brunswick, Canada as a case study. This was accomplished by developing a DTB database from publicly available soil profiles, boreholes, drill holes, well logs, and outcrop transects (n = 203,238). A Random Forest model was produced by modeling the relationships between DTB measurements in the database to gridded datasets derived from both a LiDAR-derived digital elevation model and photo-interpreted surficial geology delineations. In developing the Random Forest model, DTB measurements were split 70:30 for model development and validation, respectively. The DTB model produced an R2 = 92.8%, MAE = 0.18 m, and RMSE = 0.61 m for the training, and an R2 = 80.3%, MAE = 0.18 m, and RMSE = 0.66 m for the validation data. This model provides an unprecedented resolution of DTB variance at a landscape scale. Additionally, the presented framework provides a fundamental understanding of regolith thickness across a post-glacial terrain, with potential application at the global scale.