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Airborne transmission of SARS-CoV-2 mostly occurs indoors, and effective mitigation strategies for specific building types are needed. Most guidance provided during the pandemic focused on general strategies that may not be applicable for all buildings. A systematic evaluation of infection risk mitigation strategies for different public and commercial buildings would facilitate their reopening process as well as post-pandemic operation. This study evaluates engineering mitigation strategies for five selected US Department of Energy prototype commercial buildings (i.e., Medium Office, Large Office, Small Hotel, Stand-Alone Retail, and Secondary School). The evaluation applied the multizone airflow and contaminant simulation software, CONTAM, with a newly developed CONTAM-quanta approach for infection risk assessment. The zone-to-zone quanta transmission and quanta fate were analyzed. The effectiveness of mechanical ventilation, and in-duct and in-room air treatment mitigation strategies were evaluated and compared. The efficacy of mitigation strategies was evaluated for full, 75%, 50% and 25% of design occupancy of these buildings under no-mask and mask-wearing conditions. Results suggested that for small spaces, in-duct air treatment would be insufficient for mitigating infection risks and additional in-room treatment devices would be needed. To avoid assessing mitigation strategies by simulating every building configuration, correlations of individual infection risk as a function of building mitigation parameters were developed upon extensive parametric studies.

Aim: To determine the impact of chronic kidney disease on corneal nerve measures and dendritic cell counts in type 2 diabetes. Methods: In vivo corneal confocal microscopy images were used to estimate corneal nerve parameters and compared in people with type 2 diabetes with chronic kidney disease (T2DM-CKD) (n = 29) and those with type 2 diabetes without chronic kidney disease (T2DM-no CKD) (n = 29), along with 30 healthy controls. Corneal dendritic cell densities were compared between people with T2DM-CKD and those with T2DM-no CKD. The groups were matched for neuropathy status. Results: There was a significant difference in corneal nerve fiber density (p < 0.01) and corneal nerve fiber length (p = 0.04) between T2DM-CKD and T2DM-no CKD groups. The two diabetes groups had reduced corneal nerve parameters compared to healthy controls (all parameters: p < 0.01). Immature central dendritic cell density was significantly higher in the T2DM-CKD group compared to the T2DM-no CKD group ((7.0 (3.8–12.8) and 3.5 (1.4–13.4) cells/mm2, respectively, p < 0.05). Likewise, central mature dendritic cell density was significantly higher in the T2DM-CKD group compared to the T2DM-no CKD group (0.8 (0.4–2.2) and 0.4 (0.6–1.1) cells/mm2, respectively, p = 0.02). Additionally, total central dendritic cell density was increased in the T2DM-CKD group compared to T2DM-no CKD group (10.4 (4.3–16.1) and 3.9 (2.1–21.0) cells/mm2, respectively, p = 0.03). Conclusion: The study showed that central corneal dendritic cell density is increased in T2DM-CKD compared to T2DM-no CKD, with groups matched for peripheral neuropathy severity. This is accompanied by a loss of central corneal nerve fibers. The findings raise the possibility of additional local factors exacerbating central corneal nerve injury in people with diabetic chronic kidney disease.

Climate change estimates are critical in developing long-term solutions to the dwelling problems that we currently face. This study combines the impact of climate change and the urban heat island effect to study the outcomes of future weather conditions on the cooling of tall residential buildings in hot and humid climates. For the year 2050, we calculate the impact of urban characteristics through the urban weather generator and climate change through the world weather gen tool on the micro-climatic condition of a district in a newly constructed city near Doha, Qatar, the Lusail City. A total of four weather files are compared to the weather data gathered from the established weather station in the city (two for the year 2020 and three for the year 2050). Results reveal that once the open weather map file has been processed through the urban weather generator (UWG) first and then the climate change model, the MAE increases to 3.30, and the RMSE goes to 3.8 with a maximum deviation of 11.4°c occurring. If the process is done the other way around, the climate change model is applied first, and then the UWG file is applied, the MAE of 3.46 is with RMSE of 3.94 with a maximum deviation of 11.3°c occurring. The impact of these weather files is then assessed on a tall residential building in Lusail. A significant increase of 777197 kwh or 20% is seen in the openweather map file that has been processed first through the climate change model and then through the urban weather generator (as compared to the rural weather file); an increase of 739983 kwh or 19% is seen in the openweather map file that has been processed first through the UWG and then through the climate change model; finally close to 22.6 percent increase or 874088 kwh is seen in the openweather map file that has been processed first through the climate change model and then through the climate change model.

More frequent and severe extreme weather events such as heatwaves are among the most serious challenges to society in coping with the changing climate. To evaluate the impacts of the heatwave on large-scale urban areas, a multi-scale weather forecasting system is designed by integrating different resolutions of the Canadian urbanized version of the Global Environmental Multiscale (GEM) Numerical Weather Prediction (NWP) model, cascading from 10km to 2.5 km, and 250 m. The multi-scale model is implemented in Montreal, Canada, for modeling the 2018 heatwave. Simulation results are well-validated against measurement data, including Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and ten weather stations in the city. The Universal Thermal Climate Index (UTCI) map was calculated to identify vulnerable regions in the city against the heatwave. Land-use types in hotspots and coldspots are analyzed to find dominant factors in the formation of hot and cold areas. It is found that natural landscapes such as vegetation, trees, and water bodies are the dominant features of most coldspots. On the other hand, roads, parking lots, less tree covers, and industrial activities are the common land use features in the hotspots. A weak correlation is found between heat-related death locations and the outdoor UTCI map, implying that the assessment of an outdoor heatwave may not address overheated buildings and communities. This paper shows the importance of built environments - their properties and occupants' socio-demographic factors in the study of heat-related mortalities in cities.

Like in other industries, financial institutions are facing a new era of big data, which plays the leading role in transformation of financial and economic environment. This article dives into why big data is important in the financial field, and how the information it provides is useful and helpful to corporations and companies. Applying big data analytics provides financial firms with insight into how they can detect fraud, conduct real-time analytics, deal with risk, and direct customer portfolio. Specific personalized services can be offered for their customers via customer portfolio management, customer data management, and analytics, even with real-time analytics. Corporations can better forecast financial markets, trends, patters, and irregularities. Therefore, financial analytics has become a fundamental instrument for finance firms, professional, and individual investors, as well as financial researchers.

Road tunnels consume a large amount of energy, especially in the Canadian cold climate, where the roads are heated electrically or deicing during the winter. For a more sustainable and resilient road tunnel energy system, we conducted an exploratory study on installing a semi-transparent photovoltaic (STPV) canopy at the entrances and exits of a tunnel under a river. The proposed system generates solar-powered electricity, improves thermal and visual conditions, and reduces energy loads. In this study, field measurements of road surface temperature and air temperature were conducted, and numerical simulations with and without STPV were performed to study air and road surface temperatures under different traffic speeds. The field measurements show the road surface temperatures are higher than the air temperature on average. The interior air and road surface temperature were measured to be above 0 °C, even though the outdoor temperature is far below 0 °C, thus significantly reducing the need for deicing in winter using salts. The simulations show that the air and surface temperatures elevate due to the solar transmission heat through the STPV canopy, thus reducing deicing energy consumption significantly. The fire safety analysis also showed that the proposed system’s top opening should be located near the tunnel entrance instead of the canopy entrance for better smoke exhaust during a fire.

As the installation of solar roofs increases, so has the concern over fires. Smoke from a solar roof fire could spread into a building through roof openings and presents a challenge for existing fire protection strategies. To date, there have been insufficient studies on solar roof fire-induced smoke spread. In this study, we conducted computational fluid dynamics (CDF) simulations using Fire Dynamics Simulator (FDS) to better understand the mechanisms of solar roof fire-induced smoke spread and help with solar roof designs. First, the photovoltaic (PV) combustion model was created in FDS and validated by experimental data. A parametric study was then simulated to investigate the impacts of roof slopes and vent sizes on the smoke spread of the solar roofs. It was found that the roof slope has a significant effect on the fire smoke spread. As the roof slope increases, the region of separation, where the smoke and air are mixed, can extend from the leeward side of the building to the roof ridge. As a result, smoke could fill the attic and room more slowly, leading to a lower soot density and lower indoor temperature. When design a solar roof, both fire smoke protection and PV energy performance should be considered, especially for the low latitude regions where the PV optimal title angle regarding energy performance is small and leads to a higher risk of smoke infiltration.

The building sector is responsible for nearly 40% of the total global direct and indirect CO2 emissions. Urban green infrastructure, which includes features such as urban trees, vegetation, green roofs, and green facades, are examples of nature-based solutions often employed as municipal climate mitigation and adaptation strategies. This approach offers a range of cost-effective strategies for reducing municipal CO2 emissions and presents compelling public policy co-benefits such as improved urban livability and enhanced environmental conditions. For municipalities to confidently deploy these solutions at a scale necessary to achieve climate benefits, acquiring knowledge of quantifiable and demonstrated outcomes is an essential requirement. The objectives of this paper are to (1) provide a comprehensive analysis of the advantages and limitations of nature-based solutions (NBS) to address the challenge of reducing CO2 emissions; (2) evaluate existing design guidelines and policies as may be available across Canada, and that that support the of implementation of NBS in urban agglomerations; (3) identify knowledge gaps and research needs to address challenges to the implementation of NBS. In this review, suggestions and requirements as presented in these documents are examined while giving due consideration to the scientific evidence available in research papers. It was found that the adoption of NBS can contribute to carbon neutral communities through reduced building energy consumption and carbon sequestration. Supportive guidelines and policies have been developed, or are in development, to promote the implementation of NBS at the city scale, despite challenges in assessing, quantitatively, their impact due to uncertainties in data, methods, and scale. Nonetheless, existing research provides sufficient evidence to qualify the measures and suggestions outlined in the guidelines and policies described in this paper.

In the Canadian north, the cost of space heating is very high due to the harsh weather, its remoteness, lack of transportation, and dependency on the high cost of fossil fuel imported from the South. Since the North has an abundance of solar energy, significant energy savings with some added construction cost in houses could be achieved by applying high-performance building envelopes and solar design strategies. The objective of this paper is to investigate the potential of both passive and active solar design strategies in improving the energy efficiency of northern housing. Firstly, a reference house representing a typical single-family home in the North is modeled using EnergyPlus, and the key passive design parameters are optimized to minimize life-cycle cost. Then, the air-based building integrated photovoltaic/thermal (BIPV/T) system is applied to the optimized house and integrated with HVAC systems. It is found that optimal passive solar design can reduce the heating energy demand by 42% with an incremental cost of 8% for Yellowknife and by 27% without incurring an incremental cost for Kuujjuaq. Integrating BIPV/T with HVAC systems can reduce the defrost time of heat recovery ventilator (HRV), extend the working hours and improve the COP of air source heat pump (ASHP). The reduction in the total energy consumption is in the range of 1.4%–3.0% by integrating HRV and 0.3%–0.6% by integrating ASHP due to the mis-match of solar availability and heating energy demand. To maximize the utilization of solar energy available, the optimal use of thermal energy recovered from BIPV/T system in northern housing requires further investigation.

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO

Understanding and quantifying the interactions between urban microclimate and urban buildings is essential to improve the urban environment and building performance, especially during heatwaves. Most previous studies used tool or application specific data exchange mechanisms that cannot be generalized for other tools or applications. In this paper, we introduce a new flexible and tool-agnostic data schema to facilitate the exchange of data between urban building energy models and urban microclimate models. The JSON schema was tested using a district of 97 buildings in San Francisco and running simulations with CityBES and CityFFD as the urban building energy and microclimate modeling platforms, respectively. Compared with simulation results using the historical weather data, simulation results considering interactions between two models over a two-day heatwave event showed a 5.3°C higher average peak building facade temperature, an 8.9 °C higher average peak air node temperature, and a 19.5% higher peak cooling energy use.

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships withenvironmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E: Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E: Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks.