2022-06-16, 15:30–15:50, Conference room - C202
Air quality monitoring is one of the most important fields when it comes to the individual’s health. Ground monitors provide accurate measurements, but we cannot count only on them to analyze air quality in a large scale. Utilizing satellite remote sensing data and other modelled data with the help of artificial intelligence can increase the limited spatial coverage provided by ground monitors. In this study, we used various sources of open data such as PM2.5 ground observations, Topography, meteorological variables, and remote sensing data to generate daily full coverage PM2.5 maps over Europe with 1 km spatial resolution for a three-year period 2018-2020. An extremely randomized trees model was used to estimate the pollutants concentration. Results will be discussed later during the presentation.
Keywords: PM2.5, AOD, Machine learning.
PhD student at the department of Geomatics, faculty of civil engineering, Czech technical university in Prague.