PlumeCityNet: Multi-Resolution Air Quality Forecasting
- URL: http://arxiv.org/abs/2110.02661v1
- Date: Wed, 6 Oct 2021 11:28:39 GMT
- Title: PlumeCityNet: Multi-Resolution Air Quality Forecasting
- Authors: Thibaut Cassard, Gr\'egoire Jauvion, Antoine All\'eon, Boris
Quennehen, David Lissmyr
- Abstract summary: This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health.
The engine is fed with air quality monitoring stations' measurements, weather forecasts, physical models' outputs and traffic estimates.
We have implemented and evaluated the engine on the largest cities in Europe and the United States, and it clearly outperforms other prediction methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents an engine able to forecast jointly the concentrations of
the main pollutants harming people's health: nitrogen dioxide (NO2), ozone (O3)
and particulate matter (PM2.5 and PM10, which are respectively the particles
whose diameters are below 2.5um and 10um respectively). The engine is fed with
air quality monitoring stations' measurements, weather forecasts, physical
models' outputs and traffic estimates to produce forecasts up to 24 hours. The
forecasts are produced with several spatial resolutions, from a few dozens of
meters to dozens of kilometers, fitting several use-cases needing air quality
data.
We introduce the Scale-Unit block, which enables to integrate seamlessly all
available inputs at a given resolution to return forecasts at the same
resolution. Then, the engine is based on a U-Net architecture built with
several of those blocks, giving it the ability to process inputs and to output
predictions at different resolutions.
We have implemented and evaluated the engine on the largest cities in Europe
and the United States, and it clearly outperforms other prediction methods. In
particular, the out-of-sample accuracy remains high, meaning that the engine
can be used in cities which are not included in the training dataset. A
valuable advantage of the engine is that it does not need much computing power:
the forecasts can be built in a few minutes on a standard CPU. Thus, they can
be updated very frequently, as soon as new air quality monitoring stations'
measurements are available (generally every hour), which is not the case of
physical models traditionally used for air quality forecasting.
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