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Combining nanofiltration and electrooxidation for complete removal of nanoplastics from water.
No full textNanoplastics (NPs) have emerged as significant water contaminants, attracting increasing attention due to their potential impacts on aquatic ecosystems and human health. In addressing the environmental and health hazards posed by NPs in water, this new study explores a combined nanofiltration (NF) and electrooxidation (EO) approach. The proposed process begins with NF to concentrate the NPs in the water, followed by EO to degrade the NPs in the NF rejection. The results indicated that the employed NF system could completely eliminate NPs at different transmembrane pressures and times. The study also highlighted the influence of NP concentrations on recovery rates, showing a reduction in recovery at higher concentrations. Moreover, following the NF process, the EO process was examined for its efficiency in removing NPs over time and at various initial NP concentrations. The results revealed that the most effective durations were 20, 30, and 40 min for NP concentrations of 10, 22.5, and 35 mg/L, respectively. As a kinetic study, the rate of NPs degradation by the EO process was modeled using Langmuir–Hinshelwood (L-H) as well as power law models. The comparison between the models' predictions and the experimental data demonstrated that the power law and L–H models had good predictability for NP concentrations exceeding 10 mg/L and 2 mg/L, respectively. At concentrations below the 2 mg/L, deviations from the model were observed, likely due to changes in the reaction mechanism. It can be concluded from these results that, at low concentrations, the surface reactions were no longer the rate-determining step
Training Super-Resolution deep learning algorithms for high resolution aeromagnetic maps generation from low resolution aeromagnetic maps.
No full textThe province of Quebec (Canada) is regarded as the principal mining Province of Canada due to its substantial exploitable reserves and the significant contribution of its mineral production to the national GDP. Nevertheless, Vast areas, such as northern Quebec, remain insufficiently covered in terms of geoscientific data, limiting the understanding of their mineral exploration potential.
Aeromagnetic data are widely employed for large-scale reconnaissance to map geological structures and guide geologists in identifying exploration targets or defining new prospects. However, the only data that covers the entire area are low-resolution aeromagnetic data, with high-resolution datasets being sporadically available. This low resolution restricts the interpretability of regional data, as certain geological structures remain hidden by coarse sampling intervals. To enhance geological mapping, it is imperative to improve the resolution of aeromagnetic data to reveal structures such as faults, lineaments, and lithological boundaries that are otherwise undetectable in low-resolution geophysical signatures. While acquiring high-resolution data is an ideal solution, the high costs and vast territorial coverage required render this approach challenging in the short term. As an alternative, the advent of artificial intelligence (AI), particularly deep learning, offers promising avenues for exploration. In this study, we adapted and retrained 4 super-resolution deep learning algorithms to generate high resolution aeromagnetic maps from low resolution ones. To avoid bias due to spatial correlation, we split the data sets into a training set covering the southern part of Québec and validation being the Northern part. Each of the AI codes were trained on the same datasets leading to optimal hyperparameters for each algorithm. The AI-generated results for all the 4 algorithms successfully reconstruct high-resolution regional aeromagnetic maps in the training sets compared to measured high resolution data providing reliable high resolution maps for geological mapping. Finally, we generated four high resolution aeromagnetic maps for entire Province including the northern part. This innovative approach holds the potential to revolutionize geophysical exploration, facilitating the discovery of untapped natural resources in underexplored areas
Ancient mantle plume trail beneath the North American Midcontinent Rift revealed from Magnetotelluric data.
No full textThe Midcontinent Rift (MCR) system formed ~1.1 Ga is a failed continental rift within the Superior Province of the Archean Laurentian continent. It is one of the important Precambrian geological features in the North American midcontinent. The abundance of igneous rocks exposed in the vicinity of Lake Superior contemporaneous to MCR is thought to be related to the upwelling of a Keweenaw mantle plume or anomalously hot/enriched mantle. However, in contrast to the classic three-arm model of continental rifting above a mantle plume, the lack of a northward-trending third rift branch or aulacogen in the MCR and the ~300 km deviation of the main rift arms from the inferred center of the mantle plume have not yet been well explained. To investigate this unique mantle plume-rift relationship and better constrain the influence range of the Keweenaw mantle plume, this study builds a three-dimensional electrical resistivity crust-upper mantle model that extends northward from the MCR to the Archean Superior Province using magnetotelluric (MT) data from the United States EarthScope and Canadian Lithoprobe project. The model reveals a prominent high conductivity anomaly near the base of the Western Superior Craton's lithospheric mantle, which is northwest-southeast trending, crosses the western branch of the MCR, and extends more than 300 km to both sides. It is inferred that the anomaly reflects an ancient mantle plume trail and is caused by the metasomatism and/or partial melting of the sulfide-rich basal lithospheric mantle during the Keweenaw mantle plume impingement
Sustainable Heating Analysis and Energy Model Development of a Community Building in Kuujjuaq, Nunavik.
Get PDFEnergy transition is a challenge for remote northern communities mainly relying on diesel for electricity generation and space heating. Solar-assisted ground-coupled heat pump (SAGCHP) systems represent an alternative that was investigated in this study for the Kuujjuaq Forum, a multi-activity facility in Nunavik, Canada. The energy requirements of community buildings facing a subarctic climate are poorly known. Based on energy bills, technical documents, and site visits, this study provided an opportunity to better document the energy consumption of such building, especially considering the recent solar photovoltaic (PV) system installed on part of the roof. A comprehensive model was developed to analyze the building’s heating demand and simulate the performance of a ground-source heat pump (GSHP) coupled with PV panels. The air preheating load, accounting for 268,200 kWh and 47% of the total heating demand, was identified as an interesting and realistic load that could be met by SAGCHP. The GSHP system would require a total length of at least 8000 m, with boreholes at depths between 170 and 200 m to meet this demand. Additional PV panels covering the entire roof could supply 30% of the heat pump’s annual energy demand on average, with seasonal variations from 22% in winter to 53% in spring. Economic and environmental analysis suggest potential annual savings of CAD 164,960 and 176.7 tCO2eq emissions reduction, including benefits from exporting solar energy surplus to the local grid. This study provides valuable insights on non-residential building energy consumption in subarctic conditions and demonstrates the technical viability of SAGCHP systems for large-scale applications in remote communities
Geothermal favourability in data-scarce regions: incorporating physical and socio-economic factors into a modified Play fairway approach, southwestern Yukon, Canada.
Get PDFGeothermal energy could be used to reduce or replace diesel for heating in remote northern communities. Geothermal development has primarily focused on shallow, high-temperature resources, but interest in low-temperature and deep geothermal resource exploration has increased as energy costs and climate change policy have evolved. Here, we evaluated the low-temperature geothermal favourability in southwestern Yukon by adapting Play fairway analysis to data-scarce regions. Play fairway analysis is a spatial statistical tool that uses a layered data approach to model favourability and risk assessments for resource exploration. Previous Play fairway analyses concentrate on the physical aspects of geothermal favourability: heat, permeability, and fluid availability. This study presents an overview of potential direct and indirect physical parameters that could be used in a geothermal Play fairway analysis in data-scarce regions and introduces the importance of considering socio-economic data in the exploration phase. The socio-economic controls are grouped into quantitative and qualitative parameters that describe population trends and community interests. The framework presented is then applied to a Play fairway analysis for southwestern Yukon. Based on the physical and socio-economic analysis, there is interest in exploring geothermal potential along the Denali fault near Duke River to support the community of Burwash Landing
River salinity mapping through machine learning and statistical modeling using Landsat 8 OLI imagery.
No full textThis study uses Landsat 8 OLI imagery and 102 in situ salinity data points to investigate salinity mapping in the Karun River, southwestern Iran. A total of 24 features, including salinity indices and Landsat 8 OLI spectral bands, were assessed using the Random Forest Feature Importance Score (RFFIS), Sobol’ sensitivity analysis, and correlation with salinity to identify the most sensitive features for salinity estimation. These included the Red and Green bands, Salinity index 2–6, Normalized Suspended Material Index (NSMI), and Enhanced Green Ratio Index (EGRI). A total of 24 regression models, including statistical, kernel-based, Neural Network (NN)-based, and Decision Tree (DT)-based models, were evaluated using statistical error metrics and global, as well as local, Moran’s I measures of residual spatial autocorrelation. The DT-based models, specifically Gradient Boosted DT (GBDT), outperformed other models, demonstrating low errors, bias, and non-significant residual spatial autocorrelation. Kernel-based models performed better than conventional linear models, while NN models tended to underfit. Residual spatial autocorrelation analysis indicated that models incorporating spatial information reduced residual autocorrelation. Landsat 8 OLI imagery effectively mapped salinity dynamics, revealing increased salinity from Gotvand to Ahvaz city due to agricultural activities and the Gachsaran formation within the reservoir
A Numerical Approach to Evaluate the Geothermal Potential of a Flooded Open-Pit Mine: Example from the Carey Canadian Mine (Canada).
Get PDFAbandoned mines represent an innovative and under-exploited resource to meet current energy challenges, particularly because of their geothermal potential. Flooded open-pits, such as those located in the Thetford Mines region (Eastern Canada), provide large, thermally stable water reservoirs, ideal for the use of geothermal cooling systems. Thermal short-circuiting that can impact the system performance affected by both free and forced convective heat transfer is hard to evaluate in these large water reservoirs subject to various heat sink and sources. Thus, this study’s objective was to evaluate the impact of natural heat transfer mechanisms on the performance of an open-loop geothermal system that could be installed in a flooded open-pit mine. Energy needs of an industrial plant using water from the flooded Carey Canadian mine were considered to develop a 3D numerical finite element model to evaluate the thermal impact associated with the operation of the system considering free and forced convection in the flooded open-pit, the natural flow of water into the pit, climatic variations at the surface and the terrestrial heat flux. The results indicate that the configuration of the proposed system meets the plant cooling needs over a period of 50 years and can provide a cooling power of approximately 2.3 MW. The simulations also demonstrated the importance of understanding the hydrological and hydrogeological systems impacting the performance of the geothermal operations expected in a flooded open-pit mine
Evaluation of Industrial Wastewaters as Low-Cost Resources for Sustainable Enzyme Production by <i>Bacillus</i> Species.
Get PDFThe increasing demand for industrial enzymes calls for cost-effective and sustainable production strategies. This study investigates the potential of industrial wastewater as an alternative fermentation medium for enzyme synthesis, aligning with the principles of the circular bioeconomy. Four wastewater types from Québec, Canada—beverage wastewater (BW), pulp and paper mill activated sludge (PPMS), food industry wastewater (FIW), and starch industry wastewater (SIW)—were evaluated for their potential to support protease, amylase, and lipase production using Bacillus licheniformis, Bacillus amyloliquefaciens, and Bacillus megaterium. Initial screening identified SIW as optimal for amylase production with B. amyloliquefaciens, and PPMS for protease production with B. megaterium. Optimization using the Box–Behnken design was then performed, followed by scale-up experiments in 5 L bioreactors. B. amyloliquefaciens achieved 5.73 ± 0.01 U/mL of amylase at 48 h under 40 g/L total solids, 30 °C, and a 2% inoculum size, while B. megaterium produced the highest protease of 55.41 ± 3.54 U/mL at 24 h. Lipase production remained negligible across all media and strains. These findings demonstrate the feasibility of the potential of wastewater-based enzyme production, reducing reliance on expensive synthetic substrates, mitigating environmental burdens, and contributing to the transition to a circular bioeconomy
In vitro study on the inhibitory effect of various essential oils against murine coronavirus mouse hepatitis virus A-59 replication
Get PDFBackground: The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide has become one of the biggest health problems due to the lack of knowledge about effective therapy. Purpose: Some scientific studies have shown that essential oils (EOs) have anti-inflammatory, immunomodulatory, and antiviral properties. Study design: This study demonstrated the potential antiviral activity of EOs in emulsion and in vapor forms to reduce the replication of MHV-A59, a murine surrogate of SARS-CoV-2. Methods: In the present study, 32 EOs were screened in vitro against MHV-A59 on DBT cells using plaque assay. EOs in emulsions were applied at their maximum noncytotoxic concentrations to MHV-A59 after penetration of the viruses into the host cells for 1 h during intracellular virus replication. Results: Monarda didyma at 5000 µg/ml showed a reduction of 100% of viral plaque. Tanacetum annuum at 10000 µg/ml inhibited 5.03-log MHV-A59. Beyond 4-log, the drug can be qualified as an antiviral according to the guidelines of ICH. In vapor form, none of the EOs showed potential inhibitory effects against MHV-A59. Conclusion: Results demonstrated that all 32 undiluted EOs, incubated with MHV-A59 for 30 min, had a ≤1.09-log inactivation compared to an untreated virus. The findings of this study may provide proof-of-concept and insight into related trials. </br
RFC1 regulates the expansion of neural progenitors in the developing zebrafish cerebellum
No full textDNA replication and repair are basic yet essential molecular processes for all cells. RFC1 encodes the largest subunit of the Replication Factor C, an essential clamp-loader for DNA replication and repair. Intronic repeat expansion in RFC1 has recently been associated with so-called RFC1-related disorders, which mainly encompass late-onset cerebellar ataxias. However, the mechanisms making certain tissues more susceptible to defects in these universal pathways remain mysterious. Here, we provide the first investigation of RFC1 gene function in vivo using zebrafish. We showed that RFC1 is expressed in neural progenitor cells within the developing cerebellum, where it maintains their genomic integrity during neurogenic maturation. Accordingly, RFC1 loss-of-function leads to a severe cerebellar phenotype due to impaired neurogenesis of both Purkinje and granule cells. Our data point to a specific role of RFC1 in the developing cerebellum, paving the way for a better understanding of the pathogenic mechanisms underlying RFC1-related disorders.</br