Kawka, M.; Struzewska, J.; Kaminski, J.W. Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression. Atmosphere2023, 14, 1171.
Kawka, M.; Struzewska, J.; Kaminski, J.W. Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression. Atmosphere 2023, 14, 1171.
Kawka, M.; Struzewska, J.; Kaminski, J.W. Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression. Atmosphere2023, 14, 1171.
Kawka, M.; Struzewska, J.; Kaminski, J.W. Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression. Atmosphere 2023, 14, 1171.
Abstract
High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50μg/m3 is the permissible threshold for a daily averaged PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive a detailed resolution PM10 concentration maps, we propose a technique which utilizes the results of regional CTM (GEM-AQ, with 2.5km resolution) and local Gaussian plume model. As a result, we receive a detailed, 250-meter resolution PM10 distribution, which resembles the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentration. We also discuss different strategies of data training random forest - using additional features and selecting target variables.
Keywords
random forest; gaussian plume; GEM-AQ; downscalling; PM10
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
