Opracowanie i implementacja efektywnej prognozy i monitoringu zanieczyszczeń powietrza, w oparciu o techniki AI przy użyciu danych z rozległej sieci pomiarowej
The subject of this project is the development and implementation of innovative calculation methods in the field of artificial intelligence and machine learning regarding analysis, correction, processing and forecasting of information on air pollution.
Data for analysis will be provided through a network of low-cost pollution sensors. A densely distributed network of such measuring devices (ultimately 2-3 sensors/km^2 of the area covered by the measurement) solves three problems: data quality (thanks to the continuous determination of the level of pollution using data from many sensors, which reduces the error of the measured value and allows data correction in the event of damage to any sensor), immediate identification of local sources of pollution and sharing of data from places not yet covered by the measurement (e.g. from single-family housing areas, generating low emissions and areas distant from precise but expensive measuring stations). The above three aspects are critical to the quality of the forecast and for the first time enable an assessment of the effectiveness of measures to protect air quality.
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