This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A doctoral dissertation by Andrea Porcheddu, MSc in the field of Technical Physics will be examined at the Faculty of Science, Forestry and Technology, Kuopio campus. My doctoral research concerns air quality and its estimation using a fusion of data sources, particularly satellite data.
Poor air quality leads to numerous deaths around the world every year. It is therefore important to understand which areas are most affected so that policymakers can act promptly to reduce air pollution and save lives. Ground monitoring stations for estimating air pollution exist but are sparsely located and mainly found in developed countries.
Satellite data can enable global estimates, complementing ground stations and helping to democratize air quality monitoring. We developed a novel framework to estimate air quality using global model data products and satellite data. The model data is coarse in spatial resolution but available globally and frequently over time.
Satellite data generally has finer spatial resolutions and different temporal resolutions (in our case, we worked with polar-orbiting satellites with a temporal resolution of one day and geostationary satellites with a temporal resolution of 10 minutes). Combining them with other data sources (such as ground stations and geographical indicators), we aimed to transform coarse-resolution model data into fine-grained hourly maps that can help policymakers better understand air pollution in urban environments. To achieve our results, we used a modern data-driven approach.
In particular, we considered different neural network architectures to combine multiple data sources (data fusion). As a first step, we developed a methodology to estimate ground-level PM2.5 at the time of polar-orbiting satellite overpasses using an ensemble of multilayer perceptrons. We then used a 3D U-Net to obtain hourly fine-grained maps from these polar-orbiting satellite maps, model data and ground station data.
These first two steps constitute a framework to estimate hourly PM2.5 maps on a 100 m resolution grid. Finally, as a first step to extend this framework to geostationary satellites, we combined GOES-East and GOES-West satellite data over the contiguous United States (CONUS) region using a transformer architecture. The doctoral dissertation of Andrea Porcheddu, MSc, entitled From space to street level: Deep learning-enhanced air quality monitoring using satellite data will be examined at the Faculty of Science, Forestry and Technology, Kuopio campus: erepo.uef.fi/items/b90a92b6-ad … 05-9bb8-4591f152b762 Provided by University of Eastern Finland MA in English, copy editor since 2021 with experience in higher education and health content.
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