L
Lidia Morawska
Researcher at Queensland University of Technology
Publications - 777
Citations - 132997
Lidia Morawska is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Particle number & Ultrafine particle. The author has an hindex of 100, co-authored 746 publications receiving 95412 citations. Previous affiliations of Lidia Morawska include University of Surrey & Jinan University.
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Using the Generalised Additive Model to model the particle number count of ultrafine particles
TL;DR: In this paper, the authors compared the Generalized Linear Model (GLM) and Generalised Additive Model (GAM) for modeling the particle number concentration (PNC) of outdoor, airborne ultrafine particles in Helsinki, Finland.
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Design approaches for promoting beneficial indoor environments in healthcare facilities: a review
Heidi Salonen,Marjaana Lahtinen,Sanna Lappalainen,Nina Nevala,Luke D. Knibbs,Lidia Morawska,Kari Reijula +6 more
TL;DR: In this paper, the implications of key indoor physical design parameters, in relation to their potential impact on human health and wellbeing, were reviewed and discussed within the context of relevant guidelines and standards for the design of healthcare facilities.
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Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates
Alma Lorelei de Jesus,Helen Thompson,Luke D. Knibbs,Michał Kowalski,Josef Cyrys,Jarkko V. Niemi,Anu Kousa,Hilkka Timonen,Krista Luoma,Tuukka Petäjä,David C. S. Beddows,Roy M. Harrison,Philip K. Hopke,Lidia Morawska +13 more
TL;DR: It is shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management.
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A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques
TL;DR: In this paper, the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms were used to estimate the ground-level PM2.5 concentration.
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Variations in coil temperature/power and e‐liquid constituents change size and lung deposition of particles emitted by an electronic cigarette
Ariane Lechasseur,Simon Altmejd,Natalie Turgeon,Giorgio Buonanno,Giorgio Buonanno,Lidia Morawska,David Brunet,Caroline Duchaine,Mathieu C. Morissette +8 more
TL;DR: It is shown that coil temperature, propylene glycol and glycerol concentrations, presence of nicotine, and flavors affect the size of particles emitted by an electronic cigarette, directly affecting predicted lung deposition of these particles.