Outdoor heat stress assessment using an integrated multi-scale numerical weather prediction system: A case study of a heatwave in Montreal.

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 10 km 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.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools.

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 CO2 meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO2 generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO2 readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO2 levels. The occupancy schedule becomes critical when the CO2 data are limited, whereas a 15-min measurement interval could capture dynamic CO2 profiles well even without the occupancy information. Hourly CO2 recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m3 and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m3 and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m3 and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m3 and 8-9 occupancies) for three consecutive days.

A real-time web tool for monitoring and mitigating indoor airborne COVID-19 transmission risks at city scale.

Airborne transmission of aerosols contributes to a large portion of the SARS-CoV-2 spread indoors. This study develops a real-time interactive web-based platform for the public to compare various strategies to curb indoor airborne transmission of COVID-19 in different archetype buildings at a city scale. Although many countries have started vaccination and a gradual re-opening, because of emerging new variants of the virus and the possibility of future pandemics, a lively updated tool for monitoring and mitigation of infection risk is essential. As a demonstration, we evaluated the impacts of six mitigation measures on the infection risks in various building types in a city. It shows that the same strategy could perform quite differently, depending on building types and properties. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the minimum required outdoor air ventilation rate is not as effective as other strategies to reduce the risk. It also causes considerable penalties on energy consumption. Therefore, new building ventilation standards, control actions, and design criteria should be considered to mitigate the infection risk and save energy.

Isolating the impact of COVID-19 lockdown measures on urban air quality in Canada.

We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada's four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO2, PM2.5, and O3 during a "pre-lockdown" period (15 February-14 March 2020) and a "lockdown" period (22 March-2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010-2019). Higher-than-usual seasonal declines in mean daily NO2 were observed for the pre-lockdown to lockdown periods in 2020. For PM2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O3 all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO2 surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM2.5. O3 surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but Ox (odd oxygen), like NO2 and PM2.5, decreased as expected over these cities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-021-01039-1.