Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r² = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems.

Selenomethionine (SeMet) and selenocysteine (SeCys) are the most common forms of organic selenium, which is often found in the effluent of industrial wastewater. These organic selenium compounds are toxic, bioavailable and most likely to bioaccumulate in aquatic organisms. This study investigated the use of two adsorbent candidates (granular activated carbon (GAC) and nano zerovalent iron (nZVI)) as treatment technologies for SeMet and SeCys removal. Batch experiments were performed and inductively coupled plasma optical emission spectrometer (ICP-OES) was used for sample analysis. Experimental data showed GAC demonstrated a higher affinity towards the removal of SeMet and SeCys compared to nZVI. The removal efficiency of SeCys and SeMet by GAC was 96.1% and 86.7%, respectively. NZVI adsorption capacity for SeCys was 39.4% and SeMet < 1.1%. Irrespective of the adsorbent, SeMet is more refractory to be adsorbed compared to SeCys. Kinetics data of GAC and nZVI agreed well with the pseudo-second-order model (R² > 0.990). The experimental data of SeCys was characterized by Langmuir model, indicating monolayer adsorption. The adsorption capacity of nZVI for SeCys increased significantly by about 35%, with a decrease in pH from 9.0 to 4.0, indicating that SeCy removal by nZVI is pH dependent. While electrostatic attraction is considered the driving mechanism for nZVI adsorption, GAC uptake capacity is controlled by weak van der Waal forces. The adsorption of binary adsorbates (SeMet and SeCys) exhibited an inhibitory effect due to the competitive interaction between contaminant molecules.

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (E_NS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

The Athabasca Oil Sands Area (AOSA) in Alberta, Canada, is considered to have a high density of weather stations. Therefore, our objective was to determine an optimal network for the wind data measurement that could sufficiently represent the wind variability in the area. We used available historical data records of the weather stations in the three networks in AOSA, i.e., oil sands monitoring (OSM) water quantity program (WQP) and Wood Buffalo Environmental Association (WBEA) edge sites (ES) and meteorological towers (MT) of the air program. Both graphical and quantitative methods were implemented to find the correlations and similarities in the measurements between weather stations in each network. The graphical method (wind rose diagram) was found as a functional tool to understand the patterns of wind directions, but it was not appropriate to quantify and compare between wind speed data of weather stations. Therefore, we applied the quantitative method of the Pearson correlation coefficient (r) and absolute average error (AAE) in finding a relationship between the wind data of station pairs and the percentage of similarity (PS) method in quantifying the closeness/similarity. In the correlation analyses, we found weak to strong correlations in the wind data of OSM WQP (r = 0.04–0.69) and WBEA ES (r = 0.32–0.77), and a strong correlation (r = 0.33–0.86) in most of the station pairs of the WBEA MT network. In the case of AAE, we did not find any acceptable value within the standard operating procedure (SOP) threshold when logically combining the values of the u and v components together. In the similarity analysis, minor similarities were identified between the stations in the three networks. Hence, we presumed that all weather stations would be required to measure wind data in the AOSA.

This study investigated structural changes in microbial community of biological nutrient removal (BNR) in response to changes in substrate composition (ammonium and phosphate), redox condition, and morphological characteristics (flocs to granules), with a focus on nitrification and phosphate removal. Analyzing treatment performance and 16S rRNA phylogenetic gene sequencing data suggested that heterotrophic nitrification (HN) and autotrophic nitrification (AN) potentially happened in aerobic organic-rich (HN_AS) and aerobic organic-deficient (AN_AS) activated sludge batch reactors, respectively. However, phosphate release and uptake were not observed under alternating anaerobic/aerobic regime. Phosphate release could not be induced even when anaerobic phase was extended, although Accumulibacter existed in the inoculum (5.1% of total bacteria). Some potential HN (e.g., Thauera, Acinetobacter, Flavobacterium), AN (e.g., Nitrosomonas (3.2%) and Nitrospira), and unconventional phosphate-accumulating organisms (PAOs) were identified. Putative HN bacteria (i.e., Thauera (29–36%) and Flavobacterium (18–25%)) were enriched in aerobic granular sludge (AGS) regardless of the granular reactor operation mode. Enrichment of HN organisms in the AGS was suspected to be mainly due to granulation, possibly due to the floc-forming ability of HN species. Thus, HN is likely to play a role in nitrogen removal in AGS reactors. This study is supposed to serve as a starting point for the investigation of the microbial communities of AS- and AGS-based BNR processes. It is recommended that the identified roles for the isolated bacteria are further investigated in future works.