Chapter 42
Can Assimilation of Ground Particulate
Matter Observations Improve Air
Pollution Forecasts for Highly Polluted
Area of Europe?
Małgorzata Werner, Maciej Kryza and Jakub Guzikowski
Abstract In this study we present the influence of assimilation of ground PM2.5
measurements on forecasted concentrations of particulate matters for low air quality
episode observed in the year 2017 over Poland. The episode was not reproduced by
a standard forecasting system, based on the Weather Research and Forecasting with
Chemistry model (WRF-Chem), working operationally without data assimilation.
Here, we used Grid point Statistical Interpolation (GSI) system to assimilate ground
observations from 42 stations measuring PM2.5 concentrations. The results show
that the assimilation of PM2.5 concentrations has a positive impact on modelled
concentration of PM2.5 and PM10. The greatest positive impact is noticed for the
period with the high measured concentrations of pollutants. The results also show
that for some stations the assimilation of PM2.5 and PM10 may lead to overestimation of concentrations at the warmer period characterised by lower overall PM
concentrations. Further study with an application of degree-day factors for residential
emissions and GSI assimilation is planned as the next step.
42.1 Introduction
Central and Eastern European countries suffer from high concentrations of particulate
matter (PM) in winter periods, which is mainly related to high emissions from SNAP
sector 2 (residential combustion). Meteorological conditions i.e. low temperatures
and anticyclone systems make favourable conditions to accumulation of air pollutants
M. Werner (B) · M. Kryza · J. Guzikowski
Department of Climatology and Atmosphere Protection,
University of Wroclaw, Wroclaw, Poland
e-mail: malgorzata.werner@uwr.edu.pl
M. Kryza
e-mail: maciej.kryza@uwr.edu.pl
J. Guzikowski
e-mail: jakub.guzikowski@uwr.edu.pl
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_42
267
Can Assimilation of Ground Particulate
Matter Observations Improve Air
Pollution Forecasts for Highly Polluted
Area of Europe?
Małgorzata Werner, Maciej Kryza and Jakub Guzikowski
Abstract In this study we present the influence of assimilation of ground PM2.5
measurements on forecasted concentrations of particulate matters for low air quality
episode observed in the year 2017 over Poland. The episode was not reproduced by
a standard forecasting system, based on the Weather Research and Forecasting with
Chemistry model (WRF-Chem), working operationally without data assimilation.
Here, we used Grid point Statistical Interpolation (GSI) system to assimilate ground
observations from 42 stations measuring PM2.5 concentrations. The results show
that the assimilation of PM2.5 concentrations has a positive impact on modelled
concentration of PM2.5 and PM10. The greatest positive impact is noticed for the
period with the high measured concentrations of pollutants. The results also show
that for some stations the assimilation of PM2.5 and PM10 may lead to overestimation of concentrations at the warmer period characterised by lower overall PM
concentrations. Further study with an application of degree-day factors for residential
emissions and GSI assimilation is planned as the next step.
42.1 Introduction
Central and Eastern European countries suffer from high concentrations of particulate
matter (PM) in winter periods, which is mainly related to high emissions from SNAP
sector 2 (residential combustion). Meteorological conditions i.e. low temperatures
and anticyclone systems make favourable conditions to accumulation of air pollutants
M. Werner (B) · M. Kryza · J. Guzikowski
Department of Climatology and Atmosphere Protection,
University of Wroclaw, Wroclaw, Poland
e-mail: malgorzata.werner@uwr.edu.pl
M. Kryza
e-mail: maciej.kryza@uwr.edu.pl
J. Guzikowski
e-mail: jakub.guzikowski@uwr.edu.pl
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_42
267