Ambient air pollution is an urgent health problem linked with as many as 4.2 million premature deaths worldwide, and about 15,300 in Canada. With more than half of the global population living in cities, a sustainable future involves a better understanding of the factors influencing its air quality and requires a deep assessment of its population exposure to air pollution to optimally mitigate the health burden of air pollution, in the present and the future.
In this context, the Clean Air (CLAIR) Lab's research is motivated by the need to improve urban air quality and minimize population exposure to air pollution to reduce the burden of air pollution on our health.
Pillars
How will climate change and future air pollutant emissions impact urban air quality and population exposure?
The changing climate will impact air pollutant emissions, local weather patterns and dispersion processes.
We strive to analyze the air quality under different climate change scenarios to anticipate changes in urban air quality resulting from factors external to the city operation.
This helps identify the most affected communities and outline urban planning strategies prioritizing the health of vulnerable populations.
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How can we use new capabilities enabled by novel technologies to accurately assess urban air pollution?
By conducting fixed and mobile measurements of urban air pollutants such as nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), black carbon (BC) and ultrafine particles (UFP), we aim to understand the relationship between the built environment and air pollution. Our goal is to unveil potential air pollution exposure and health inequities and propose avenues for environmental justice improvements.
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How can we best adapt our cities to minimize population exposure and mitigate socio-economic inequities in terms of air pollution exposure?
We aspire to unveil practical solutions for cities to tackle recurrent and future ambient air pollution issues to help planners and policy-makers identify the best air pollution exposure mitigation strategies. This requires designing novel methods for assessing population exposure to air pollution and evaluating urban planning scenarios using state-of-the-art air quality models.
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Research Approaches
Using low cost sensors and more complex instruments, we conduct stationary and mobile measurements in the city and build maps of air quality using advanced statistical modelling techniques. We can then analyze changes in air quality over time at a spatial resolution of a few dozen metres and understand the influence of urban features on air quality.
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To evaluate the impact of urban planning policies and changing meteorological conditions, we run dispersion models and chemical transport models to simulate the dispersion and chemical reactions of air pollutants under different scenarios of emissions, land use and meteorology. We combine the results with health impact assessment tools to understand the influence of the scenarios on population exposure and health.
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Aerial and street-view images are analyzed to extract land use information and to accurately identify the location of sources of air pollution and develop refined emission inventories for crucial sectors such as on-road traffic and wood burning.
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Projects
The objective of this project co-funded by NSERC and the Canadian Association of Physicians for the Environment (CAPE) is to provide a framework for assessing the impact of Liquefied Natural Gas (LNG) export facilities on local air quality and population health by incorporating observations from existing export facilities and realistic non-ideal scenarios of flaring. We aim to conduct the first-of-its-kind review of global LNG export facilities to estimate emissions from flaring and to develop a method to assess the cumulative impact of actual flaring on ambient air pollution and population health. Woodfibre LNG, scheduled to open in 2027 near Squamish, BC, will be taken as a test case for the application of our methodology.
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The objective of this project funded by the National Research Council (NRC) of Canada is to provide recommendations and guidelines for interventions in buildings during wildfire smoke events to protect the population from exposure to wildfire smoke. Measurements of outdoor and indoor air quality will be conducted over the summers 2024 and 2025 in the regions of Victoria’s Capital Regional District, BC, and Edmonton, AB. Fine particulate matter (PM2.5) will be measured outside and inside buildings that fall under two major categories: infrastructure buildings, such as libraries, university buildings, schools, hospitals, clean air shelters, and proposed resilience hubs; and residential houses and condo apartments. The impact of different building characteristics (e.g., type of windows, envelope material, cooling systems) and interventions (e.g., portable air cleaners, heating ventilation and air conditioning – HVAC – parametrization) on indoor air quality will be studied.
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In this project funded by NSERC, using meteorological and air quality models, we look at the impact of climate change on urban heat islands and ambient air pollution in Victoria's Capital Regional District and in Metro Vancouver, and investigate the effectiveness of heat mitigation strategies on exposed populations.
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This project investigates the impact of cruise ship activity in Victoria on traffic and associated air pollutant emissions in the neighbourhood of James Bay. We conducted automated and manual traffic counts in May and August/September 2023 at strategic locations in the area to estimate the traffic induced by cruise ship activities and its composition, and deployed air quality sensors for fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) to capture the impact of this traffic on local air quality.
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This study develops a model to accurately predict air pollutant concentrations by extracting traffic variables (such as vehicle count, speed, type) solely from traffic videos. Fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) concentrations are recorded by air quality sensors placed alongside a traffic camera. An object detection algorithm coupled with a tracker extract traffic variables from recorded videos. A prediction model is developed using machine learning algorithms with traffic variables, air pollutant measurements and meteorological data as input.
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Publications
Health and climate incentives for the deployment of cleaner on-road vehicle technologies
Highlights:
Burden of each vehicle fleet on population health in the Greater Toronto and Hamilton Area;
Deploying electric passenger vehicles and replacing old trucks could prevent about 300 premature deaths per year;
Electrifying trust buses and half of the passenger fleet would lead to similar health benefits;
GHG emissions are mainly reduced with passenger fleet electrification.
Highlights:
Algorithms to extract built environment features from Google aerial and street view images;
Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality;
Models based on feature extraction exhibited higher predictive power.
Highlights:
UFP and BC measurements in Toronto using mobile and stationary short-term data collections;
Four land-use regression (LUR) models and exposure surfaces;
Various small-scale traffic variables included in the mobile LUR;
Using two different surfaces can lead to different values for daily exposure.
Should traffic-related air pollution and noise be considered when designing urban bicycle networks?
Minet, L., Stokes, J., Scott, J., Weichenthal, S., Hatzopoulou, M.
Transportation Research Part D: Transport and Environment 65: 736-749 (2018)
Highlights:
Mean UFP and BC exposures measured along biking routes were 18,900 part/cm3 and 1,130 ng/m3;
Exposures along bike routes were higher than average levels measured at a fixed sidewalk station;
The highest UFP and BC concentrations were measured on cycle tracks;
The highest noise levels were experienced along major roads;
Generally, higher levels are expected along planned extensions to the bicycle network in Toronto.
Quantifying the air quality and health benefits of greening freight movements
Minet, L., Chowdhury, T., Wang, A., Gai, Y., Posen, I.D., Roorda, M., Hatzopoulou, M.
Environmental Research 183: 109193 (2020)
Highlights:
Toronto region's diesel exhaust represents 77% and 55% of the BC and NOx emissions;
Diesel exhaust contributes to 6 and 22% of the region's NO2 and BC population exposure;
More than 9,810 Years of Life Lost (YLL) per year are due to diesel exhaust in the region;
Diesel trucks represent $3.2 billion in annual social costs for the region;
Lowering diesel emissions along Highway 401 could bring substantial benefits.
Highlights:
Climate change constrained Urban passenger Transport Integrated Life cycle assessment (CURTAIL) model;
CURTAIL estimates GHG emission budgets that are consistent with global warming below 2 and 1.5°C;
CURTAIL seeks mitigation strategies to remain within the budgets;
Focusing on one mitigation technology or one mode of transport alone will not be sufficient to meet the target.
Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors
Minet, L., Gehr, R., Hatzopoulou, M.
Environmental Pollution 230: 280-290 (2017)
Highlights:
Selection of sampling routes in mobile monitoring campaigns is a complex task;
LUR models are sensitive to the visits per road segment and to their location;
Mobile measurement protocols can lead to different exposure surfaces in the same city;
We found a large sensitivity of the LUR model to the segment selection.
Cyclists’ personal exposure to traffic-related air pollution and its influence on bikeability
Tran, P.T.M., Zhao, M., Yamamoto, K., Minet, L., Nguyen, T., Balasubramanian, R.
Transportation Research Part D: Transport and Environment 88: 102563 (2020)
Highlights:
Influence of air quality on bikeability index was evaluated;
The proposed air quality sub-index involves cyclists’ exposure to PM2.5 and BC;
Open-source data, land-use regression models, and deep neural network were utilized;
Cyclists’ exposure to TRAP is a significant component of the bikeability index;
The proposed framework is useful for recommending cycling routes in cities.
- Postdoctoral Fellow, Diamond Environmental research group, University of Toronto (2020-2021)
- PhD, Transportation and Air Quality (TRAQ) research group, Civil and Mineral Engineering, University of Toronto (2016-2020)
- MASc, Civil and Mechanical Engineering, McGill University (2014-2016)
- MEng, Ecole Centrale de Nantes (2014-2016)
Forood Azargoshasbi
PhD Candidate
- PhD candidate, CLAIR lab, Civil Engineering, University of Victoria (2022-)
- MASc, Air Pollution, Environmental Engineering, University of Tehran, Iran (2019-2022)
- BSc, Civil Engineering, K.N.Toosi University, Iran (2012-2017)
Amirhossein Shojaei Baghini
PhD Student
- PhD student, CLAIR lab, Civil Engineering, University of Victoria (2024-)
- MASc, Mechanical Engineering, University of Tehran, Iran (2019-2022)
- BSc, Mechanical Engineering, Shahid Bahonar University of Kerman, Iran (2015-2019)
Laura Deveer
MASc Student
- MASc, CLAIR lab, Civil Engineering, University of Victoria (2022-)
- BSc, Civil Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Ghana (2018-2021)
Veronica Lenz
Research Assistant
- BEng, Civil Engineering, University of Victoria (2022-)
Muhammad Qasim
MASc Student
- MASc student, CLAIR lab, Civil Engineering, University of Victoria (2022-)
- MASc, Transportation Engineering, Budapest University of Technology & Economics, Hungary (2017-2019)
- BSc, Transportation Engineering, University of Engineering & Technology, Pakistan (2012-2016)
Tanatswa Saira
MASc Student
- MASc, CLAIR lab, Civil Engineering, University of Victoria (2024-)
- BSc, Civil Engineering, University of Johannesburg, South Africa (2018-2022)
Md Rufsun Rahman Shoshe
Research Assistant
- BSc, Civil Engineering, University of Alberta (2020-)
Opportunities
We are recruiting enthusiastic and talented student researchers at the undergraduate, graduate, and postdoc levels.
In order to hear, see, and act on different perspectives, and leverage the opportunities a diverse research group can offer for the development of inclusive projects aligned with communities interests, CLAIR Lab strives to be a diverse and inclusive research group. We encourage applications and partnerships from underrepresented groups, including women, Indigenous peoples, persons with disabilities, members of visible minorities, and persons of any sexual orientation or gender identity.
While physical mobility is useful for some of our field-based research, they are by no means requirements to be a good researcher or a valuable member of our team. We encourage all interested people, regardless of physical ability, to consider a career in research and to get in touch.
To express your interest in joining the research group, please fill in the following form: Expression of interest in the CLAIR lab
If your application is a great fit for the group and I am recruiting, I will get back to you. In general, I will only reply to unsolicited requests from those who have thoughtfully completed the form.
We acknowledge and respect the lək̓ʷəŋən peoples on whose traditional territory the university stands and the Songhees, Esquimalt and W̱SÁNEĆ peoples whose historical relationships with the land continue to this day.
