iHorse – improving air quality and Health risk forecasts by data-driven modelling of traffic and atmospheric environment
Title: iHorse – improving air quality and Health risk forecasts by data-driven modelling of traffic and atmospheric environment
SNIC Project: Berzelius-2021-31
Project Type: LiU Berzelius
Principal Investigator: Xiaoliang Ma <liang@kth.se>
Affiliation: Kungliga Tekniska högskolan
Duration: 2021-08-24 – 2022-03-01
Classification: 10299
Keywords:

Abstract

The objective of the 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 to forecast emissions from road traffic that 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 both the forecast of air pollutants, pollen and AQHI for the whole Greater Stockholm area.