Improving air quality and Health risk forecasts by data-driven modelling of traffic and atmospheric environment

Objective
The objective of the Improving air quality and Health risk forecasts by data-driven modelling of traffic and atmospheric environment (iHorse) project is to increase the accuracy of air pollution and health risk forecasts. The current system relies on deterministic meteorological dispersion modelling to forecast the impacts of emissions on concentrations. One of the main uncertainties is forecasting emissions from road traffic which is a dominant source of air pollution in the urban environment. In this project, emissions are calculated based on detailed information on the vehicle fleet composition and emission factors. In addition, a novel, innovative data-driven deep learning model will be developed and integrated with the air pollution and traffic modelling processes. The aim is to improve the forecast of air pollutants, pollen and AQHI for the Greater Stockholm area.

Background
Air pollution is one of the leading causes of mortality worldwide. Acute effects of air pollution are due to short-term exposures that can lead to reduced lung function, respiratory infections and aggravated asthma. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. Predicting traffic emissions and induced air pollution has been an activity of high priority in the agenda of transport authorities and municipalities The project will also contribute to the use of artificial intelligence, identified as a key priority of the city of Stockholm and the SMart URBan Solutions (SMURBS; http://smurbs.eu/), an EU project involving several European cities with the overall aim to provide solutions based on Earth Observations that make cities more smart and sustainable.

Crossdisciplinary collaboration
The researchers in the team represent the Department of Environmental Science, SU and the School of industrial engineering and management, KTH.

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