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Franco MANAI
Franco MANAI
Faculty of Arts, University of Auckland, New Zealand
Faculty of Arts, University of Auckland, New Zealand
Manfredo Manfredini
Associate Professor, School of Architecture and Planning, University of Auckland, New Zealand
Plastique Fantastique, Berlin, Germany
Speaking at Day 2
Cities and Health - Creating Healthy Neighborhoods
Kim Dirks
Associate Professor, Faculty of Engineering, University of Auckland, New Zealand
Kim completed a BSc degree in Physics and Meteorology at McGill University in Canada before moving to New Zealand to complete both an MSc and PhD in Physics and Environmental Science at the University of Auckland.
Her research focusses on the ways in which urban civil infrastructure impact on the health and wellbeing of urban residents, in particular on air pollution and noise from road transport. In air pollution, her work includes the measurement of human exposure using portable sensors, the modelling of air pollution levels in response to changes in local meteorology and transport infrastructure, and the uptake of pollutants in the body through analysis of biomarkers. In the area of noise, her research focusses on the impact of road traffic and community annoyance, as well as the role of urban greenspace in promoting community health and wellbeing.
Insight from "Towards Healthy Cities in the Age of Pandemics"
Kim started her presentation by sharing international examples of significant improvements in air quality during the COVID lockdown period, as shown in satellite images and surface measurements. Similarly in New Zealand, which went through six weeks of lockdown, as residents stayed at homes and were only allowed to walk, bike and exercise around local neighbourhoods, there was a huge drop in vehicle usage and air quality was quoted to have improved by up to 90%. Some also said that there was an improvement in soundscape with audible wildlife.
Kim and her team developed the Site-Specific Semi-Empirical (SOSE) Model several years ago. The model could predict air quality and pollution levels, given traffic levels corresponded to historical levels, and taking wind speed, wind direction, and historical data into account. The model was able to tell what the pollution levels could have been given the traffic level was normal during pandemic. The comparison graphs of air pollution levels before and during lockdown clearly showed a significant drop of observed pollution levels after lockdown, when compared to the predictions made by the model, and could thus give reliable figures of percentage drop of pollution levels.