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.
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.
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.
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.
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.
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.
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.
In this project funded by the Ministère de l'Environnement et de la Lutte contre les Changements Climatiques (Québec), we strive to understand the impact of greenhouse gas (GHG) emission mitigation strategies on urban air quality and population exposure and health. We develop scenarios of land use, transport policies and demographics and model the subsequent changes in air quality using a Chemical Transport Model. Detailed emission inventories for critical sectors such as transport and wood burning are developed by combining analyses of satellite and drone images with statistical and traffic assignment models.
In this project in collaboration with Environmental Defence and Ontario Public Health Association, we studied how electric vehicles and cleaner trucks can help reduce pollution, improve health and save lives in the Greater Toronto and Hamilton Area. We have modelled five scenarios (plus a base case of current conditions) to compare the health benefits of reducing traffic pollution from cars and SUVs, trucks, and buses. Each scenario explores what the GTHA can look like with a specific mix of cleaner vehicles, and how this shift impacts air pollution, improves health, and reduces greenhouse gas emissions in the region.
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.
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.
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)
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)
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.
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)
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)
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)
- MASc, CLAIR lab, Civil Engineering, University of Victoria (2022-)
- BSc, Civil Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Ghana (2018-2021)
- PhD student, 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)
- PhD 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)
- PhD student, Transportation and Air Quality (TRAQ) research group, Civil and Mineral Engineering, University of Toronto (2020-)
- MASc, Atmospheric Composition Analysis group, Physics and Atmospheric Science, Dalhousie University (2010-2013)
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.